- Email: [email protected]
Current utilities of imaging in grading musculoskeletal soft tissue sarcomas
Accepted Manuscript Title: Current Utilities of Imaging in Grading Musculoskeletal Soft Tissue Sarcomas Author: Stephen M. Fisher Robert Joodi Ananth J. ¨ Rohit Sharma Avneesh Madhuranthakam Orhan K. Oz Chhabra PII: DOI: Reference:
S0720-048X(16)30143-7 http://dx.doi.org/doi:10.1016/j.ejrad.2016.05.003 EURR 7469
To appear in:
European Journal of Radiology
Received date: Revised date: Accepted date:
22-3-2016 2-5-2016 9-5-2016
Please cite this article as: Fisher Stephen M, Joodi Robert, Madhuranthakam Ananth ¨ Orhan K, Sharma Rohit, Chhabra Avneesh.Current Utilities of Imaging J, Oz in Grading Musculoskeletal Soft Tissue Sarcomas.European Journal of Radiology http://dx.doi.org/10.1016/j.ejrad.2016.05.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Title: Current Utilities of Imaging in Grading Musculoskeletal Soft Tissue Sarcomas Authors: Stephen M. Fisher, M.D.a Robert Joodi, M.D.a Ananth J. Madhuranthakam, PhDa Orhan K. Öz, M.D., PhDa Rohit Sharma, M.D.b Avneesh Chhabra, M.D.a,c Author Affiliations: 1. Radiology, University of Texas Southwestern Medical Center, Dallas, TX, United States 2. Surgical Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States 3. Orthopaedic Surgery, University of Texas Southwestern Medical Center, Dallas, TX, United States
Corresponding Author: Dr. Stephen Fisher Department of Radiology University of Texas Southwestern Medical Center 5323 Harry Hines Blvd Dallas, TX 75390 United States 1 (214) 648-0522 [email protected]
Highlights: 1. Non-invasive grading of soft tissue sarcomas is a desirable goal. 2. PET measurements currently correlate most closely with soft tissue sarcoma grade. 3. Emerging MRI techniques hold promise for development of better prognosis. 4. Molecular imaging probes beyond FDG provide functional tumor biology information.
Abstract: The care of patients with musculoskeletal malignancies has increasingly become a multidisciplinary function. Radiologists play an important role in many areas of these patients’ care including initial diagnosis, staging, in many cases guiding therapy, and monitoring treatment response. However, the gold standard for the final diagnosis of these diseases remains the histopathologic proof. Intense efforts have been made to develop non-invasive methods of determining the tumor grade, or a surrogate, in order to predict biologic behavior, aid early treatment decisions, and provide prognostic information. Multiple imaging modalities have been employed in this domain– including computed tomography (CT); anatomic magnetic resonance (MR) imaging techniques; functional MR imaging sequences such as dynamic contrast enhancement (DCE), diffusion weighted imaging (DWI), MR spectroscopy (MRS); and positron emission tomography (PET). This article reviews current available literature in this realm and highlights future directions towards the potential of non-invasive imaging in grading of soft tissue sarcomas. Keywords: MRI; CT; PET; Sarcoma; Grading
Introduction Soft tissue sarcomas are a rare, heterogeneous group of over fifty distinct malignancies that represent less than 1% of all malignant tumors with an estimated 11,280 new diagnoses annually in the United States [1, 2]. The 5-year mortality for high-grade lesions (grade II or III) has been reported to be 30-60% and 10% for low-grade lesions [3, 4]. Histologic grade is widely regarded as the most important independent predictor of metastasis-free and overall survival [5]. The gold standard for the final diagnosis is histopathology, with samples usually acquired by fine needle aspiration (FNA), core biopsy and/or excisional biopsy. A pathologist then determines the apparent cellular origin, amount of tissue differentiation, as well as degree of necrosis and mitosis. Accurate grading is important for patients with soft tissue sarcoma as those with high-grade tumors may benefit from neoadjuvant chemotherapy and/or radiotherapy, whereas those with lower grade can be spared of potential harmful toxicities related to such treatments. Two main pathologic grading methods are popular and commonly used in the treatment of sarcomas: the National Cancer Institute (NCI) system for sarcoma grading, (Table 1), and the Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC) or French Federation of Cancer Centers grading system, (Table 2). Both take into account the cellular differentiation and degree of necrosis, but only the FNCLCC system incorporates the mitotic activity directly. These systems have been shown to be nearly equivalent in predicting metastasis-free and overall survival [6, 7]. Other schemes also exist with unique factors for consideration such as the Scandinavian Sarcoma Group prognostic system [8], which incorporates peripheral growth pattern, and the Memorial Sloan Kettering Cancer Center (MSKCC) sarcoma-specific mortality nomogram [4], a tool that incorporates FNCLCC grade with tumor size, depth, site, and patient age to predict a 10-year probability of death from sarcoma. Molecular and genetic approaches to understanding sarcoma tumor biology beyond histologic subtyping and other morphologic features have been developed in the last decade, which take advantage of gene expression
profiles[9, 10] to differentiate specific tumors as high or low risk, although none have been yet validated in clinical trials. Thus, as it stands currently, the differentiation between high-grade and low-grade lesions drives therapeutic decision-making. The question remains what and how much role imagers can take in aiding the clinicians and pathologists towards an accurate, non-invasive evaluation of tumor grade. Multiple imaging modalities have been employed for non-invasive imaging biomarkers and tumor grading– including computed tomography (CT); anatomic magnetic resonance (MR) imaging techniques; functional MR imaging sequences such as dynamic contrast enhancement (DCE), diffusion weighted imaging (DWI), MR spectroscopy (MRS); and positron emission tomography (PET). This article reviews current available literature in this realm and illustrates future directions towards the potential of non-invasive imaging in grading of soft tissue sarcomas.
Imaging Sarcomas General markers of high-grade sarcomas Most features of sarcomas associated with aggressiveness are histologic and therefore determined by pathology, with the exception of size and depth of invasion. However, many microscopic features have correlative findings on imaging studies and require evaluation. These include extent of necrosis, peripheral growth pattern, neurovascular invasion, cellularity, expression of GLUT-1, cell turnover rates, and neovascularity[5, 8, 11]. These are discussed in a modality-specific fashion below. Radiography Radiographs of the involved extremity are crucial for initial screening due to the ability to detect and define cortical bone involvement and abnormal soft tissue calcifications that otherwise could be missed on MRI [12]. Certain findings may help characterize a lesion as non-malignant, such as with mature peripheral calcification
and/or heterotopic ossification; fat cleft around a lesion, suggesting slow growth, local steal phenomenon in a slow flow vascular malformation or benign peripheral nerve sheath tumor, ossified lipoma; or phleboliths in a hemangioma. Development of
calcifications
in
certain
known
tumors,
such
as
neurofibroma,
pheochromocytoma, leiomyoma and neuroendocrine tumors may serve as indirect marker of developing or progressing necrosis and malignancy. Certain soft tissue sarcomas have a propensity to calcify more than others—such as synovial sarcoma, leiomyosarcoma
and
undifferentiated
pleomorphic
sarcoma.
Beyond this,
radiographs provide little additional information regarding the tumor grade over cross-sectional modalities. In many cases, patients present with trivial trauma or no trauma history; however, there is a sarcoma mimicking a tender mass. If radiographs are not obtained immediately, biopsy may be performed and the lesion may be overdiagnosed as aggressive (Figure 1). Ultrasonography and Computed Tomography The utility of ultrasound (US) is usually limited to differentiating cystic from solid lesions. A subcutaneous echogenic lesion with thin capsule is usually diagnostic of benign lipoma. Other lesions that can be diagnosed as benign using Doppler imaging include hematoma, abscess, glomus tumor and vascular malformations, due to their classic locations, history, clinical findings and appearances [13, 14]. Connection to nerves can aid in the diagnosis of peripheral nerve sheath tumors (PNST) or intraand perineural ganglion cysts but detection of malignancy in PNST is challenging unless central necrosis or local invasion is identified in the setting of neurofibroma. Tumor grading of solid lesions is otherwise limited on US. On the other hand, detection of echogenic foci with shadowing in a lesion which is tender could suggest heterotopic ossification rather than sarcoma. Computed tomography is limited for grading sarcomas; and the lesions are usually incidentally noted on studies performed for other reasons. Estimates can be made of the degree of necrosis, tumor extent including bony involvement, and mass effect on surrounding structures [15] (Figure 2). It is also the initial modality of choice for the
evaluation of extent of abdominal and thoracic soft tissue masses due to widespread availability, ability to characterize macroscopic fat, the type and degree of calcification, evaluation of cortical lesions with soft tissue edema such as osteoid osteoma, stress fractures, cortical metastasis and infections, and the speed with which urgent complications can be evaluated [15]. Evaluation of bone and calcium distribution is enhanced with CT over radiography, allowing more detailed description of calcification patterns – central, peripheral, random. Dynamic contrast enhanced images on wide detector CT or 4D CT have been used to detect not only soft tissue lesions but also, similar to MRI, differentiate osteoid osteomas from other lytic osseous lesions as well as to identify bone marrow edema patterns in patients with lytic bone lesions. In addition, one can differentiate high flow and high blood volume lesions from slow flow lesions [16, 17]. Magnetic Resonance Imaging MR imaging provides the best soft tissue contrast among all imaging modalities with excellent spatial resolution on 1.5 Tesla and higher field strength scanners employing multi-array coils and parallel imaging. Anatomic MR imaging Most institutes perform tumor imaging using 1-2 plane T1-weighted (T1W) images and 3 plane fluid sensitive, fat suppressed imaging. Administration of gadolinium agents varies but typical protocols include pre- and post-contrast fat saturated, T1W images. Three-dimensional imaging is also possible on most current scanners providing both T1W and T2W contrasts. Despite these advantages in the anatomic imaging, the confidence with which a soft tissue tumor can be accurately graded compared to histology is still relatively low [18]. While MR imaging excels at determining cystic versus solid components in the lesion, and for characterizing specific tumor subtypes (such as lipoma, vascular malformation, peripheral nerve sheath tumors and tendosynovial giant cell tumor of tendon sheath) (Figure 3), poor accuracies of 22-58% have been reported for the prediction of histological subtype
in malignant lesions [19, 20]. Recently, illustrating the utility of expert opinion, higher sensitivities and specificities have been reported for the categorical determination of benign or malignant, up to 100% and 89 % respectively [21]. One area in which MR imaging demonstrates added value in evaluation of tumor grade is in determination of the peripheral growth pattern, suggested as the most important prognostic factor in recent work [22]. Fernebro et al. [11] demonstrated that peripheral tumor infiltration characterization as focal or diffuse on MR imaging, correlated well with histology after resection, and that patients with diffuse infiltration fared worse than those with focal infiltration. Similar findings were recently reported by Zhao et. al. [23], with the additional discovery that the presence of peri-tumoral contrast enhancement was predictive of high grade sarcomas. In the series of 156 patients, the sensitivity of this finding was 91% and specificity was 57% (Figure 4). Functional MR imaging The positive correlation between increased cellularity and tumors of higher grade [6] provides an opportunity to exploit this relationship by employing diffusion weighted imaging (DWI). The tumors with high cellularity will generally restrict diffusion to a greater degree than those with lower cellularity [24]. Additionally, if the lesions are of peripheral neural origin, such as in patients with neurocutaneous syndromes, decreasing ADC values can suggest malignant transformation [25-27] (Figure 5). Reports have also been published regarding the use of DWI in the posttreatment setting to monitor for residual or recurrent disease, due to its ability to discriminate necrosis from viable tumor. Dudeck et al. [28] have reported close correlation between the increase in ADC after chemotherapy with the decrease in tumor size. Pitfalls in the use of quantitative DWI for characterization of soft tissue tumors exist, and are related to the effects of perfusion at lower b values (up to 300 s/mm2). This can be partly rectified by using higher b-values; e.g. in the range 500-800 s/mm2 [24]. However, accurate determination of quantitative ADC values needs increased
number of b-values (preferably more than 4) and the use of advanced fitting algorithms such as maximum likelihood estimators (MLE) [29]. Other pitfalls include tumors with excessive fatty components or fibrosis leading to low ADC values, displacement of lesion position due to ghosting artifacts, degradation of images due to poor fat suppression, motion, susceptibility artifacts, and superficial location of the lesion. Presently, it is unclear which measurements of diffusion are most representative of local tumor behavior, i.e. mean ADC, minimum ADC, kurtosis, etc. Tumors are heterogeneous and these images should be evaluated in conjunction with anatomic images. Magnetic resonance spectroscopy (MRS) has the potential to offer prognostic information for patients with STS. This functional imaging method provides information about the microscopic metabolic environment within a region of interest. There is particular interest in choline metabolism as elevated levels of choline have been found in malignant soft tissue lesions due to excessive cellular membrane turnover. Fayad et al. [30] reported significant differences in the signal to noise ratio of the choline peak of malignant lesions compared to benign lesions. Although not directly related to soft tissue sarcomas, the same group found that detection of trimethylamine peaks within peripheral nerve sheath tumors was 100% sensitive but only 50% specific for malignancy in a small study of 18 cases [30, 31]. Moreover, the same paper demonstrated that quantification of the choline fraction increases the specificity of MRS for discrimination of benign and malignant peripheral nerve sheath tumors, particularly of interest in neurofibromatosis due to increased risk of malignant transformation. This type of study has yet to be published with data from sarcomas. Dynamic contrast enhanced sequences are useful for the evaluation of tumor perfusion and are well-established in the musculoskeletal literature. The operating principle is that qualitative and quantitative methods will demonstrate rapid arterial enhancement due to increased perfusion at the microscopic level. Early arterial enhancement on temporally resolved MR angiography technique or multi-
phase dynamic contrast enhanced imaging is useful to detect highly vascular lesions (Figure 6)[32, 33]. However, little is known of its ability to discriminate high and low grade sarcomas. Arterial spin labeling (ASL) is another technique for measuring tissue perfusion characteristics, which have been used in brain imaging. However, performing this technique in the extremities is time and resource intensive and not yet ready for routine clinical practice. Positron Emission Tomography By exploiting the increased metabolic rate of tumors, fluorodeoxyglucose (18F-FDG) is used to detect and evaluate the glucose consumption of a variety of lesions. Early studies of the correlation between FDG avidity and grading of soft tissue sarcomas were published nearly thirty years ago. The first study in the literature was published in 1988 by Kern et al. [34] who showed in five patients that the apparent glucose utilization rate correlated with tumor grade. The sample included three stage 1 lesions, one stage 2 lesion and one stage 3 lesions, of different histologic origins. During the following years, additional studies with small patient numbers employed 18F-FTG PET to show that high-grade soft tissue neoplasms display higher glucose utilization."[35-39] (Figure 7). In the study by Nieweg et al. [36] regional glucose metabolic rate (RMRgl) was shown to correlate with tumor grade, but no significant correlation between standardized uptake values and tumor grade was found. Eary et al. [37] published one of the larger studies, including 70 patients, which showed that there were significant differences between high, intermediate and low grade lesions when using FDG metabolic rate (MRFDG) and dose uptake ratio (DUR) compared to NCI tumor grade. These findings were significant with both parameters of glucose utilization, with MRFDG showing a higher sensitivity. In 2003, Ioannidis et al. [40] published results of a meta-analysis, which included all prior published reports or studies of patients where 18F-FDG and biopsy results were available. In 8 of the included studies, SUV and tumor grade data were
available. These showed that for intermediate-high grade lesions, sensitivity of SUV cutoff of >2.0 and >3.0 were 89.4% and 68.2%, respectively. There were no significant differences between low-grade and benign lesions. Similar correlations between SUV and tumor grade were reported in the systematic review by Bastiaannet et al. [41].to. Evaluation of pulmonary metastatic disease is a limitation of FDG-PET in the setting of soft tissue and bone osteosarcomas. Franzius et al. published initial results indicating that CT was superior to FDG-PET in detecting pulmonary metastases [42]. Their findings were subsequently confirmed in 2 other studies by Igarau et al. [43, 44]. Small lesions (<1 cm) may not retain sufficient FDG for enough contrast to noise ratio but these can be seen on the CT portion of the examination. It is not uncommon for the pulmonary metastases to be lower in FDG avidity than the primary tumor itself, which might be related to the factors, such as heterogeneity of the primary lesion or impact of the lung microenvironment on the tumor metastases. Additional prognostic information can be obtained with FDG-PET/CT by evaluating the extent of tumor necrosis. PET/CT demonstrates necrosis accurately, and it has been shown that the degree or quantification of necrosis correlates with survival outcomes. Rakheja et al[45] found significant differences in 48-month overall and progression free survival among patient groups when they were divided based on the degree of necrosis (0%, 0.1-66%, and >66%), even when adjusted for SUVmax. These findings are echoed in the work performed by other groups as well where it is shown that FDG-PET/CT can demonstrate necrosis reliably and that the presence of these findings correlates with the tumor grade [37, 46]. With the proliferation of new PET radiopharmaceuticals, physiologic evaluation of tumor biology will be enhanced. Thymidine analogs such as 18F-Fluorothymidine (FLT) correlate well with tumor cell proliferation and tumor grade, and in a small study, 18F-FLT has been shown to be superior to 18F-FDG for the discrimination of higher from lower grade soft tissue and bone malignancies [47]. In addition to radiolabeled nucleotide analogs, amino acid derivatives have shown positive results in many small studies. 18F-fluoromethyltyrosine (FMT) is one such analog, which is
brought into tumor cells exclusively by the amino acid transport system [48]. In a study of 75 patients, FMT-PET was found to be 81.3% accurate when used to discriminate between malignant and benign lesions, compared to FDG-PET which was only 68% accurate [49]. Little has been published on the added value of hybrid PET/MRI imaging in the setting of pre-treatment soft tissue sarcomas; however, one study examined the value in four different cases. The latter showed important clinical information regarding local treatment response, chemotherapy timing, as well as some indication towards the tumor grade in the setting of recurrence [50]. Future directions To summarize, one can see that functional techniques, such as MR imaging (DWI, MRS, DCE) and PET imaging (especially FLT and FMT) hold the most promise in generating biomarkers for non-invasive differentiation of lower grade from higher grade lesions. Other techniques, such as elastography[51, 52], arterial spin labeling[53-55] and endogenous CEST imaging [56-59]have been used in other tumors in the kidneys, breast, prostate and brain, but not in soft tissue sarcomas. These techniques also require sequence optimization and complicated postprocessing algorithms. With the availability of PET-MR scanners, it would be possible to compare the utility of different imaging pulse sequences and functional techniques in the same setting. Once more data from newer techniques are available, one could envision a decision model that would take into account the clinical features, qualitative and quantitate information from various imaging techniques and predict the probability of the low grade from intermediate and highgrade lesions. Conclusions With current data, functional MR imaging techniques and new PET tracers hold the most promise in evaluating tumor grade. Established methods for evaluating tumor grade are primarily within the FDG-PET/CT domain; however, recent publications have shown features on standard MR sequences that correlate with high grade soft tissue sarcomas. Imaging can play an important role in the pre-operative work up of patients with soft tissue sarcomas and affect the decision for neoadjuvant chemotherapy or radiation treatment; however, histopathology is still thought to be the gold standard for final diagnosis at present time, despite its limitations in sampling error allowing accurate assessment of tumor necrosis. Conflicts of Interest: None
References [1] K.L. Sherman, J.D. Wayne, J. Chung, M. Agulnik, S. Attar, J.P. Hayes, W.B. Laskin, T.D. Peabody, D.J. Bentrem, R.E. Pollock, K.Y. Bilimoria, Assessment of multimodality therapy use for extremity sarcoma in the United States, Journal of surgical oncology 109(5) (2014) 395-404. [2] R. Siegel, Cancer statistics, CA-A CANCER JOURNAL FOR CLINICIANS 62(1) (2012) 10-29. [3] J.N. Cormier, R.E. Pollock, Soft tissue sarcomas, CA Cancer J Clin 54(2) (2004) 94109. [4] L. Mariani, R. Miceli, M.W. Kattan, M.F. Brennan, M. Colecchia, M. Fiore, P.G. Casali, A. Gronchi, Validation and adaptation of a nomogram for predicting the survival of patients with extremity soft tissue sarcoma using a three-grade system, Cancer 103(2) (2005) 402-8. [5] E.A. Levine, Prognostic factors in soft tissue sarcoma, Semin Surg Oncol 17(1) (1999) 23-32. [6] A.M. Oliveira, A.G. Nascimento, Grading in soft tissue tumors: principles and problems, Skeletal radiology 30(10) (2001) 543-59. [7] L. Guillou, J.M. Coindre, F. Bonichon, B.B. Nguyen, P. Terrier, F. Collin, M.O. Vilain, A.M. Mandard, V. Le Doussal, A. Leroux, J. Jacquemier, H. Duplay, X. Sastre-Garau, J. Costa, Comparative study of the National Cancer Institute and French Federation of Cancer Centers Sarcoma Group grading systems in a population of 410 adult patients with soft tissue sarcoma, Journal of clinical oncology : official journal of the American Society of Clinical Oncology 15(1) (1997) 350-62. [8] A. Carneiro, P.O. Bendahl, J. Engellau, H.A. Domanski, C.D. Fletcher, P. Rissler, A. Rydholm, M. Nilbert, A prognostic model for soft tissue sarcoma of the extremities and trunk wall based on size, vascular invasion, necrosis, and growth pattern, Cancer 117(6) (2011) 1279-87. [9] A. Neuville, F. Chibon, J.M. Coindre, Grading of soft tissue sarcomas: from histological to molecular assessment, Pathology 46(2) (2014) 113-20. [10] T.O. Nielsen, R.B. West, Translating gene expression into clinical care: sarcomas as a paradigm, Journal of clinical oncology : official journal of the American Society of Clinical Oncology 28(10) (2010) 1796-805. [11] J. Fernebro, M. Wiklund, K. Jonsson, P.O. Bendahl, A. Rydholm, M. Nilbert, J. Engellau, Focus on the tumour periphery in MRI evaluation of soft tissue sarcoma: infiltrative growth signifies poor prognosis, Sarcoma 2006 (2006) 21251. [12] B.J. Manaster, Soft-tissue masses: optimal imaging protocol and reporting, AJR. American journal of roentgenology 201(3) (2013) 505-14. [13] H.J. Baek, S.J. Lee, K.H. Cho, H.J. Choo, S.M. Lee, Y.H. Lee, K.J. Suh, T.Y. Moon, J.G. Cha, J.H. Yi, M.H. Kim, S.J. Jung, J.H. Choi, Subungual tumors: clinicopathologic correlation with US and MR imaging findings, Radiographics 30(6) (2010) 1621-36. [14] A.T. Ahuja, P. Richards, K.T. Wong, E.H.Y. Yuen, A.D. King, Accuracy of Highresolution Sonography Compared with Magnetic Resonance Imaging in the Diagnosis of Head and Neck Venous Vascular Malformations, Clinical Radiology 58(11) (2003) 869-875.
[15] B. Amini, A.C. Jessop, D.M. Ganeshan, W.W. Tseng, J.E. Madewell, Contemporary imaging of soft tissue sarcomas, Journal of surgical oncology (2014). [16] P.A. Gondim Teixeira, G. Hossu, S. Lecocq, M. Razeto, M. Louis, A. Blum, Bone marrow edema pattern identification in patients with lytic bone lesions using digital subtraction angiography-like bone subtraction on large-area detector computed tomography, Invest Radiol 49(3) (2014) 156-64. [17] P.A. Gondim Teixeira, S. Lecocq, M. Louis, S. Aptel, A. Raymond, F. Sirveaux, A. Blum, Wide area detector CT perfusion: can it differentiate osteoid osteomas from other lytic bone lesions?, Diagn Interv Imaging 95(6) (2014) 587-94. [18] L.M. Fayad, M.A. Jacobs, X. Wang, J.A. Carrino, D.A. Bluemke, Musculoskeletal tumors: how to use anatomic, functional, and metabolic MR techniques, Radiology 265(2) (2012) 340-56. [19] J.L. Gielen, A.M. De Schepper, F. Vanhoenacker, P.M. Parizel, X.L. Wang, R. Sciot, J. Weyler, Accuracy of MRI in characterization of soft tissue tumors and tumor-like lesions. A prospective study in 548 patients, European radiology 14(12) (2004) 2320-30. [20] T.H. Berquist, R.L. Ehman, B.F. King, C.G. Hodgman, D.M. Ilstrup, Value of MR imaging in differentiating benign from malignant soft-tissue masses: study of 95 lesions, AJR. American journal of roentgenology 155(6) (1990) 1251-5. [21] F.M. Vanhoenacker, K. Van Looveren, K. Trap, J. Desimpelaere, K. Wouters, P. Van Dyck, P.M. Parizel, A.M. De Schepper, Grading and characterization of soft tissue tumors on magnetic resonance imaging: the value of an expert second opinion report, Insights into imaging 3(2) (2012) 131-8. [22] J. Engellau, P.O. Bendahl, A. Persson, H.A. Domanski, M. Akerman, P. Gustafson, T.A. Alvegard, M. Nilbert, A. Rydholm, Improved prognostication in soft tissue sarcoma: independent information from vascular invasion, necrosis, growth pattern, and immunostaining using whole-tumor sections and tissue microarrays, Human pathology 36(9) (2005) 994-1002. [23] F. Zhao, S. Ahlawat, S.J. Farahani, K.L. Weber, E.A. Montgomery, J.A. Carrino, L.M. Fayad, Can MR imaging be used to predict tumor grade in soft-tissue sarcoma?, Radiology 272(1) (2014) 192-201. [24] C.S. van Rijswijk, P. Kunz, P.C. Hogendoorn, A.H. Taminiau, J. Doornbos, J.L. Bloem, Diffusion-weighted MRI in the characterization of soft-tissue tumors, Journal of magnetic resonance imaging : JMRI 15(3) (2002) 302-7. [25] A. Chhabra, T. Soldatos, Soft-tissue lesions: when can we exclude sarcoma?, AJR. American journal of roentgenology 199(6) (2012) 1345-57. [26] A. Chhabra, R.S. Thakkar, G. Andreisek, M. Chalian, A.J. Belzberg, J. Blakeley, A. Hoke, G.K. Thawait, J. Eng, J.A. Carrino, Anatomic MR imaging and functional diffusion tensor imaging of peripheral nerve tumors and tumorlike conditions, AJNR. American journal of neuroradiology 34(4) (2013) 802-7. [27] S. Demehri, A. Belzberg, J. Blakeley, L.M. Fayad, Conventional and functional MR imaging of peripheral nerve sheath tumors: initial experience, AJNR. American journal of neuroradiology 35(8) (2014) 1615-20. [28] O. Dudeck, M. Zeile, D. Pink, M. Pech, P.U. Tunn, P. Reichardt, W.D. Ludwig, B. Hamm, Diffusion-weighted magnetic resonance imaging allows monitoring of
anticancer treatment effects in patients with soft-tissue sarcomas, Journal of magnetic resonance imaging : JMRI 27(5) (2008) 1109-13. [29] Y. Peng, Y. Jiang, C. Yang, J.B. Brown, T. Antic, I. Sethi, C. Schmid-Tannwald, M.L. Giger, S.E. Eggener, A. Oto, Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score--a computer-aided diagnosis development study, Radiology 267(3) (2013) 787-796. [30] L.M. Fayad, P.B. Barker, M.A. Jacobs, J. Eng, K.L. Weber, P. Kulesza, D.A. Bluemke, Characterization of musculoskeletal lesions on 3-T proton MR spectroscopy, AJR. American journal of roentgenology 188(6) (2007) 1513-20. [31] L.M. Fayad, X. Wang, J.O. Blakeley, D.J. Durand, M.A. Jacobs, S. Demehri, T.K. Subhawong, T. Soldatos, P.B. Barker, Characterization of peripheral nerve sheath tumors with 3T proton MR spectroscopy, AJNR. American journal of neuroradiology 35(5) (2014) 1035-41. [32] L.M. Fayad, C. Mugera, T. Soldatos, A. Flammang, F. del Grande, Technical innovation in dynamic contrast-enhanced magnetic resonance imaging of musculoskeletal tumors: an MR angiographic sequence using a sparse k-space sampling strategy, Skeletal radiology 42(7) (2013) 993-1000. [33] C.S. van Rijswijk, M.J. Geirnaerdt, P.C. Hogendoorn, A.H. Taminiau, F. van Coevorden, A.H. Zwinderman, T.L. Pope, J.L. Bloem, Soft-tissue tumors: value of static and dynamic gadopentetate dimeglumine-enhanced MR imaging in prediction of malignancy, Radiology 233(2) (2004) 493-502. [34] K.A. Kern, A. Brunetti, J.A. Norton, A.E. Chang, M. Malawer, E. Lack, R.D. Finn, S.A. Rosenberg, S.M. Larson, Metabolic imaging of human extremity musculoskeletal tumors by PET, Journal of nuclear medicine : official publication, Society of Nuclear Medicine 29(2) (1988) 181-6. [35] L.P. Adler, H.F. Blair, J.T. Makley, R.P. Williams, M.J. Joyce, G. Leisure, N. Alkaisi, F. Miraldi, Noninvasive Grading of Musculoskeletal Tumors Using Pet, Journal of Nuclear Medicine 32(8) (1991) 1508-1512. [36] O.E. Nieweg, J. Pruim, R.J. van Ginkel, H.J. Hoekstra, A.M. Paans, W.M. Molenaar, H.S. Koops, W. Vaalburg, Fluorine-18-fluorodeoxyglucose PET imaging of soft-tissue sarcoma, Journal of nuclear medicine : official publication, Society of Nuclear Medicine 37(2) (1996) 257-61. [37] J.F. Eary, E.U. Conrad, J.D. Bruckner, A. Folpe, K.J. Hunt, D.A. Mankoff, A.T. Howlett, Quantitative [F-18]fluorodeoxyglucose positron emission tomography in pretreatment and grading of sarcoma, Clin Cancer Res 4(5) (1998) 1215-20. [38] M. Schulte, D. Brecht-Krauss, B. Heymer, A. Guhlmann, E. Hartwig, M.R. Sarkar, C.G. Diederichs, M. Schultheiss, J. Kotzerke, S.N. Reske, Fluorodeoxyglucose positron emission tomography of soft tissue tumours: is a non-invasive determination of biological activity possible?, European Journal of Nuclear Medicine 26(6) (1999) 599-605. [39] M. Schulte, D. Brecht-Krauss, B. Heymer, A. Guhlmann, E. Hartwig, M.R. Sarkar, C.G. Diederichs, A. Von Baer, J. Kotzerke, S.N. Reske, Grading of tumors and tumorlike lesions of bone: evaluation by FDG PET, Journal of nuclear medicine : official publication, Society of Nuclear Medicine 41(10) (2000) 1695-701.
[40] J.P. Ioannidis, J. Lau, 18F-FDG PET for the diagnosis and grading of soft-tissue sarcoma: a meta-analysis, Journal of nuclear medicine : official publication, Society of Nuclear Medicine 44(5) (2003) 717-24. [41] E. Bastiaannet, H. Groen, P.L. Jager, D.C. Cobben, W.T. van der Graaf, W. Vaalburg, H.J. Hoekstra, The value of FDG-PET in the detection, grading and response to therapy of soft tissue and bone sarcomas; a systematic review and meta-analysis, Cancer treatment reviews 30(1) (2004) 83-101. [42] C. Franzius, H.E. Daldrup-Link, J. Sciuk, E.J. Rummeny, S. Bielack, H. Jurgens, O. Schober, FDG-PET for detection of pulmonary metastases from malignant primary bone tumors: comparison with spiral CT, Ann Oncol 12(4) (2001) 479-86. [43] A. Iagaru, S. Chawla, L. Menendez, P.S. Conti, 18F-FDG PET and PET/CT for detection of pulmonary metastases from musculoskeletal sarcomas, Nuclear medicine communications 27(10) (2006) 795-802. [44] A. Iagaru, A. Quon, I.R. McDougall, S.S. Gambhir, F-18 FDG PET/CT evaluation of osseous and soft tissue sarcomas, Clin Nucl Med 31(12) (2006) 754-60. [45] R. Rakheja, W. Makis, R. Tulbah, S. Skamene, C. Holcroft, A. Nahal, R. Turcotte, M. Hickeson, Necrosis on FDG PET/CT correlates with prognosis and mortality in sarcomas, AJR. American journal of roentgenology 201(1) (2013) 170-7. [46] M.H. Schwarzbach, A. Dimitrakopoulou-Strauss, F. Willeke, U. Hinz, L.G. Strauss, Y.M. Zhang, G. Mechtersheimer, N. Attigah, T. Lehnert, C. Herfarth, Clinical value of [18-F]] fluorodeoxyglucose positron emission tomography imaging in soft tissue sarcomas, Ann Surg 231(3) (2000) 380-6. [47] A.K. Buck, K. Herrmann, C.M. Buschenfelde, M.E. Juweid, M. Bischoff, G. Glatting, G. Weirich, P. Moller, H.J. Wester, K. Scheidhauer, T. Dechow, C. Peschel, M. Schwaiger, S.N. Reske, Imaging bone and soft tissue tumors with the proliferation marker [18F]fluorodeoxythymidine, Clin Cancer Res 14(10) (2008) 2970-7. [48] H.A. Wieder, K.L. Pomykala, M.R. Benz, A.K. Buck, K. Herrmann, PET Tracers in Musculoskeletal Disease beyond FDG, Semin. Musculoskelet. Radiol. 18(2) (2014) 123-132. [49] H. Watanabe, T. Inoue, T. Shinozaki, T. Yanagawa, A.R. Ahmed, K. Tomiyoshi, N. Oriuchi, M. Tokunaga, J. Aoki, K. Endo, K. Takagishi, PET imaging of musculoskeletal tumours with fluorine-18 α-methyltyrosine: comparison with fluorine-18 fluorodeoxyglucose PET, European Journal of Nuclear Medicine 27(10) (2000) 1509-1517. [50] M.K. Schuler, S. Richter, B. Beuthien-Baumann, I. Platxek, J. Kotzerke, J. van den Hoff, G. Ehninger, P. Reichardt, PET/MRI Imaging in High-Risk Sarcoma: First Findings and Solving Clinical Problems, Case Reports in Oncological Medicine 2013 (2013). [51] R.G. Barr, Elastography in clinical practice, Radiologic clinics of North America 52(6) (2014) 1145-62. [52] P. Ricci, E. Maggini, E. Mancuso, P. Lodise, V. Cantisani, C. Catalano, Clinical application of breast elastography: state of the art, European journal of radiology 83(3) (2014) 429-37. [53] C. De Bazelaire, N.M. Rofsky, G. Duhamel, M.D. Michaelson, D. George, D.C. Alsop, Arterial spin labeling blood flow magnetic resonance imaging for the
characterization of metastatic renal cell carcinoma(1), Academic radiology 12(3) (2005) 347-57. [54] R.S. Lanzman, P.M. Robson, M.R. Sun, A.D. Patel, K. Mentore, A.A. Wagner, E.M. Genega, N.M. Rofsky, D.C. Alsop, I. Pedrosa, Arterial spin-labeling MR imaging of renal masses: correlation with histopathologic findings, Radiology 265(3) (2012) 799-808. [55] R. Schor-Bardach, D.C. Alsop, I. Pedrosa, S.A. Solazzo, X. Wang, R.P. Marquis, M.B. Atkins, M. Regan, S. Signoretti, R.E. Lenkinski, S.N. Goldberg, Does arterial spinlabeling MR imaging-measured tumor perfusion correlate with renal cell cancer response to antiangiogenic therapy in a mouse model?, Radiology 251(3) (2009) 731-42. [56] G. Jia, R. Abaza, J.D. Williams, D.L. Zynger, J. Zhou, Z.K. Shah, M. Patel, S. Sammet, L. Wei, R.R. Bahnson, M.V. Knopp, Amide proton transfer MR imaging of prostate cancer: a preliminary study, Journal of magnetic resonance imaging : JMRI 33(3) (2011) 647-54. [57] C.K. Jones, M.J. Schlosser, P.C. van Zijl, M.G. Pomper, X. Golay, J. Zhou, Amide proton transfer imaging of human brain tumors at 3T, Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 56(3) (2006) 585-92. [58] J. Zhou, J.O. Blakeley, J. Hua, M. Kim, J. Laterra, M.G. Pomper, P.C. van Zijl, Practical data acquisition method for human brain tumor amide proton transfer (APT) imaging, Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 60(4) (2008) 842-9. [59] J. Zhou, E. Tryggestad, Z. Wen, B. Lal, T. Zhou, R. Grossman, S. Wang, K. Yan, D.X. Fu, E. Ford, B. Tyler, J. Blakeley, J. Laterra, P.C. van Zijl, Differentiation between glioma and radiation necrosis using molecular magnetic resonance imaging of endogenous proteins and peptides, Nature medicine 17(1) (2011) 130-4.
Figure 1a – Sagittal STIR (A), Axial FST2 (B), and FST1 pre- (C) and post- (D) contrast MR images were obtained in this 42 year old female which were suggested to be typical of a soft tissue sarcoma and biopsy was obtained demonstrating myositis ossificans (arrows).
Figure 1b – Radiographs obtained after biopsy demonstrate mass like soft tissue ossification (arrows) which, if obtained prior to MRI, could have averted an unnecessary biopsy.
Figure 2 – Contrast enhanced CT images demonstrated peripheral enhancement and considerable central necrosis in this 52 year old man with a high grade myxofibrosarcoma of the medial left thigh.
Figure 3 –MRI showing typical appearances of a lipoma (A), vascular malformation (B), PNST (C), giant cell tumor of tendon sheath (D) (arrows).
Figure 4 – Sagittal STIR (A) and axial FST2 (B) images on this 82 year old woman demonstrate areas of infiltrative high signal lesion on fluid sensitive sequences (arrows) at peripheral regions of the mass. Biopsy demonstrated a grade 3 pleomorphic undifferentiated sarcoma of the distal upper left arm.
A
B
C Figure 5 – Diffusion weighted images with color coded ADC maps in a patient with a benign peripheral nerve sheath tumor (minimum ADC = 1.72x10-3 mm2/s and mean ADC = 2.48x10-3) (A), a malignant peripheral nerve sheath tumor (minimum ADC = 1.17 x10-3 and mean ADC = 1.31 x10-3) (B), and the above patient in Fig. 4 with grade 3 pleomorphic undifferentiated sarcoma (minimum ADC = 0.51 x10-3 and mean ADC = 0.81 x10-3) (C).
Figure 6 – Multiple contrast enhanced T1 perfusion images from a 52 year old man with a large high grade myxofibrosarcoma. (A) Coronal MIP arterial phase. (B) Coronal MIP delayed phase. Notice hypervascularity with early fillin, tumor blush and AV shunting. (C) Maximum Relative Enhancement (%). (D) Wash In Rate (s-1). (E) Area Under the Curve. (F) Wash Out Rate (s-1) demonstrating hyperperfusion characteristics.
Figure 7 – F18-FDG PET-CT fused image from a patient with grade 3 synovial sarcoma (SUV max 3.7 and mean 2.1) demonstrating central hypometabolism suggesting necrosis.
Table 1. National Cancer Institute sarcoma grading system
Grade 1
Well differentiated liposarcoma Myxoid liposarcoma Subcutaneous myxoid MFH Well differentiated malignant hemangiopericytoma1 Well differentiated fibrosarcoma Well differentiated leiomyosarcoma2 Malignant Schwannoma (MPNST)3 Myxoid chondrosarcoma4
Grade 2
Other histologic subtypes with <15% necrosis.
Grade 3
Extraskeletal Ewing’s sarcoma Primitive neuroectodermal tumor (PNET) Extraskeletal osteosarcoma Mesenchymal chondrosarcoma Malignant Triton Tumor Other histologic types with >15% necrosis
With <1 mitotic figure (MF)/high power field (HPF), no necrosis and no hemorrhagic areas orderly fascicular pattern plus no pleomorphism, no necrosis, and <6 MF/10 HPF 3 If resembles neurofibroma plus mitotic figures with increased cellularity but <6 MF/10 HPF 4 Uniformly myxoid and hypocellular with no mitotic activity 1
2 With
Table 2 FNCLCC grading system Tumor Differentiation Score 1 Sarcomas closely resembling normal adult mesenchymal tissue (eg, well differentiated liposarcoma). Score 2 Sarcomas for which histological typing is certain (eg, myxoid liposarcoma). Score 3 Embryonal and undifferentiated sarcomas, sarcomas of doubtful type, synovial sarcomas, osteosarcomas, PNET
Mitotic Count Score 1 0-9 mitoses per 10 HPF* Score 2 10-19 mitoses per 10 HPF Score 3 >20 mitoses per 10 HPF
Tumor Necrosis Score 0 No necrosis Score 1 <50% tumor necrosis Score 2 >50% tumor necrosis
Histological Grade Grade 1 Total score of 2-3 Grade 2 Total score of 4-5 Grade 3 Total score of 6-8 Modified from Coindre [60]. *HPF measures 0.1734 mm2
S0720-048X(16)30143-7 http://dx.doi.org/doi:10.1016/j.ejrad.2016.05.003 EURR 7469
To appear in:
European Journal of Radiology
Received date: Revised date: Accepted date:
22-3-2016 2-5-2016 9-5-2016
Please cite this article as: Fisher Stephen M, Joodi Robert, Madhuranthakam Ananth ¨ Orhan K, Sharma Rohit, Chhabra Avneesh.Current Utilities of Imaging J, Oz in Grading Musculoskeletal Soft Tissue Sarcomas.European Journal of Radiology http://dx.doi.org/10.1016/j.ejrad.2016.05.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Title: Current Utilities of Imaging in Grading Musculoskeletal Soft Tissue Sarcomas Authors: Stephen M. Fisher, M.D.a Robert Joodi, M.D.a Ananth J. Madhuranthakam, PhDa Orhan K. Öz, M.D., PhDa Rohit Sharma, M.D.b Avneesh Chhabra, M.D.a,c Author Affiliations: 1. Radiology, University of Texas Southwestern Medical Center, Dallas, TX, United States 2. Surgical Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States 3. Orthopaedic Surgery, University of Texas Southwestern Medical Center, Dallas, TX, United States
Corresponding Author: Dr. Stephen Fisher Department of Radiology University of Texas Southwestern Medical Center 5323 Harry Hines Blvd Dallas, TX 75390 United States 1 (214) 648-0522 [email protected]
Highlights: 1. Non-invasive grading of soft tissue sarcomas is a desirable goal. 2. PET measurements currently correlate most closely with soft tissue sarcoma grade. 3. Emerging MRI techniques hold promise for development of better prognosis. 4. Molecular imaging probes beyond FDG provide functional tumor biology information.
Abstract: The care of patients with musculoskeletal malignancies has increasingly become a multidisciplinary function. Radiologists play an important role in many areas of these patients’ care including initial diagnosis, staging, in many cases guiding therapy, and monitoring treatment response. However, the gold standard for the final diagnosis of these diseases remains the histopathologic proof. Intense efforts have been made to develop non-invasive methods of determining the tumor grade, or a surrogate, in order to predict biologic behavior, aid early treatment decisions, and provide prognostic information. Multiple imaging modalities have been employed in this domain– including computed tomography (CT); anatomic magnetic resonance (MR) imaging techniques; functional MR imaging sequences such as dynamic contrast enhancement (DCE), diffusion weighted imaging (DWI), MR spectroscopy (MRS); and positron emission tomography (PET). This article reviews current available literature in this realm and highlights future directions towards the potential of non-invasive imaging in grading of soft tissue sarcomas. Keywords: MRI; CT; PET; Sarcoma; Grading
Introduction Soft tissue sarcomas are a rare, heterogeneous group of over fifty distinct malignancies that represent less than 1% of all malignant tumors with an estimated 11,280 new diagnoses annually in the United States [1, 2]. The 5-year mortality for high-grade lesions (grade II or III) has been reported to be 30-60% and 10% for low-grade lesions [3, 4]. Histologic grade is widely regarded as the most important independent predictor of metastasis-free and overall survival [5]. The gold standard for the final diagnosis is histopathology, with samples usually acquired by fine needle aspiration (FNA), core biopsy and/or excisional biopsy. A pathologist then determines the apparent cellular origin, amount of tissue differentiation, as well as degree of necrosis and mitosis. Accurate grading is important for patients with soft tissue sarcoma as those with high-grade tumors may benefit from neoadjuvant chemotherapy and/or radiotherapy, whereas those with lower grade can be spared of potential harmful toxicities related to such treatments. Two main pathologic grading methods are popular and commonly used in the treatment of sarcomas: the National Cancer Institute (NCI) system for sarcoma grading, (Table 1), and the Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC) or French Federation of Cancer Centers grading system, (Table 2). Both take into account the cellular differentiation and degree of necrosis, but only the FNCLCC system incorporates the mitotic activity directly. These systems have been shown to be nearly equivalent in predicting metastasis-free and overall survival [6, 7]. Other schemes also exist with unique factors for consideration such as the Scandinavian Sarcoma Group prognostic system [8], which incorporates peripheral growth pattern, and the Memorial Sloan Kettering Cancer Center (MSKCC) sarcoma-specific mortality nomogram [4], a tool that incorporates FNCLCC grade with tumor size, depth, site, and patient age to predict a 10-year probability of death from sarcoma. Molecular and genetic approaches to understanding sarcoma tumor biology beyond histologic subtyping and other morphologic features have been developed in the last decade, which take advantage of gene expression
profiles[9, 10] to differentiate specific tumors as high or low risk, although none have been yet validated in clinical trials. Thus, as it stands currently, the differentiation between high-grade and low-grade lesions drives therapeutic decision-making. The question remains what and how much role imagers can take in aiding the clinicians and pathologists towards an accurate, non-invasive evaluation of tumor grade. Multiple imaging modalities have been employed for non-invasive imaging biomarkers and tumor grading– including computed tomography (CT); anatomic magnetic resonance (MR) imaging techniques; functional MR imaging sequences such as dynamic contrast enhancement (DCE), diffusion weighted imaging (DWI), MR spectroscopy (MRS); and positron emission tomography (PET). This article reviews current available literature in this realm and illustrates future directions towards the potential of non-invasive imaging in grading of soft tissue sarcomas.
Imaging Sarcomas General markers of high-grade sarcomas Most features of sarcomas associated with aggressiveness are histologic and therefore determined by pathology, with the exception of size and depth of invasion. However, many microscopic features have correlative findings on imaging studies and require evaluation. These include extent of necrosis, peripheral growth pattern, neurovascular invasion, cellularity, expression of GLUT-1, cell turnover rates, and neovascularity[5, 8, 11]. These are discussed in a modality-specific fashion below. Radiography Radiographs of the involved extremity are crucial for initial screening due to the ability to detect and define cortical bone involvement and abnormal soft tissue calcifications that otherwise could be missed on MRI [12]. Certain findings may help characterize a lesion as non-malignant, such as with mature peripheral calcification
and/or heterotopic ossification; fat cleft around a lesion, suggesting slow growth, local steal phenomenon in a slow flow vascular malformation or benign peripheral nerve sheath tumor, ossified lipoma; or phleboliths in a hemangioma. Development of
calcifications
in
certain
known
tumors,
such
as
neurofibroma,
pheochromocytoma, leiomyoma and neuroendocrine tumors may serve as indirect marker of developing or progressing necrosis and malignancy. Certain soft tissue sarcomas have a propensity to calcify more than others—such as synovial sarcoma, leiomyosarcoma
and
undifferentiated
pleomorphic
sarcoma.
Beyond this,
radiographs provide little additional information regarding the tumor grade over cross-sectional modalities. In many cases, patients present with trivial trauma or no trauma history; however, there is a sarcoma mimicking a tender mass. If radiographs are not obtained immediately, biopsy may be performed and the lesion may be overdiagnosed as aggressive (Figure 1). Ultrasonography and Computed Tomography The utility of ultrasound (US) is usually limited to differentiating cystic from solid lesions. A subcutaneous echogenic lesion with thin capsule is usually diagnostic of benign lipoma. Other lesions that can be diagnosed as benign using Doppler imaging include hematoma, abscess, glomus tumor and vascular malformations, due to their classic locations, history, clinical findings and appearances [13, 14]. Connection to nerves can aid in the diagnosis of peripheral nerve sheath tumors (PNST) or intraand perineural ganglion cysts but detection of malignancy in PNST is challenging unless central necrosis or local invasion is identified in the setting of neurofibroma. Tumor grading of solid lesions is otherwise limited on US. On the other hand, detection of echogenic foci with shadowing in a lesion which is tender could suggest heterotopic ossification rather than sarcoma. Computed tomography is limited for grading sarcomas; and the lesions are usually incidentally noted on studies performed for other reasons. Estimates can be made of the degree of necrosis, tumor extent including bony involvement, and mass effect on surrounding structures [15] (Figure 2). It is also the initial modality of choice for the
evaluation of extent of abdominal and thoracic soft tissue masses due to widespread availability, ability to characterize macroscopic fat, the type and degree of calcification, evaluation of cortical lesions with soft tissue edema such as osteoid osteoma, stress fractures, cortical metastasis and infections, and the speed with which urgent complications can be evaluated [15]. Evaluation of bone and calcium distribution is enhanced with CT over radiography, allowing more detailed description of calcification patterns – central, peripheral, random. Dynamic contrast enhanced images on wide detector CT or 4D CT have been used to detect not only soft tissue lesions but also, similar to MRI, differentiate osteoid osteomas from other lytic osseous lesions as well as to identify bone marrow edema patterns in patients with lytic bone lesions. In addition, one can differentiate high flow and high blood volume lesions from slow flow lesions [16, 17]. Magnetic Resonance Imaging MR imaging provides the best soft tissue contrast among all imaging modalities with excellent spatial resolution on 1.5 Tesla and higher field strength scanners employing multi-array coils and parallel imaging. Anatomic MR imaging Most institutes perform tumor imaging using 1-2 plane T1-weighted (T1W) images and 3 plane fluid sensitive, fat suppressed imaging. Administration of gadolinium agents varies but typical protocols include pre- and post-contrast fat saturated, T1W images. Three-dimensional imaging is also possible on most current scanners providing both T1W and T2W contrasts. Despite these advantages in the anatomic imaging, the confidence with which a soft tissue tumor can be accurately graded compared to histology is still relatively low [18]. While MR imaging excels at determining cystic versus solid components in the lesion, and for characterizing specific tumor subtypes (such as lipoma, vascular malformation, peripheral nerve sheath tumors and tendosynovial giant cell tumor of tendon sheath) (Figure 3), poor accuracies of 22-58% have been reported for the prediction of histological subtype
in malignant lesions [19, 20]. Recently, illustrating the utility of expert opinion, higher sensitivities and specificities have been reported for the categorical determination of benign or malignant, up to 100% and 89 % respectively [21]. One area in which MR imaging demonstrates added value in evaluation of tumor grade is in determination of the peripheral growth pattern, suggested as the most important prognostic factor in recent work [22]. Fernebro et al. [11] demonstrated that peripheral tumor infiltration characterization as focal or diffuse on MR imaging, correlated well with histology after resection, and that patients with diffuse infiltration fared worse than those with focal infiltration. Similar findings were recently reported by Zhao et. al. [23], with the additional discovery that the presence of peri-tumoral contrast enhancement was predictive of high grade sarcomas. In the series of 156 patients, the sensitivity of this finding was 91% and specificity was 57% (Figure 4). Functional MR imaging The positive correlation between increased cellularity and tumors of higher grade [6] provides an opportunity to exploit this relationship by employing diffusion weighted imaging (DWI). The tumors with high cellularity will generally restrict diffusion to a greater degree than those with lower cellularity [24]. Additionally, if the lesions are of peripheral neural origin, such as in patients with neurocutaneous syndromes, decreasing ADC values can suggest malignant transformation [25-27] (Figure 5). Reports have also been published regarding the use of DWI in the posttreatment setting to monitor for residual or recurrent disease, due to its ability to discriminate necrosis from viable tumor. Dudeck et al. [28] have reported close correlation between the increase in ADC after chemotherapy with the decrease in tumor size. Pitfalls in the use of quantitative DWI for characterization of soft tissue tumors exist, and are related to the effects of perfusion at lower b values (up to 300 s/mm2). This can be partly rectified by using higher b-values; e.g. in the range 500-800 s/mm2 [24]. However, accurate determination of quantitative ADC values needs increased
number of b-values (preferably more than 4) and the use of advanced fitting algorithms such as maximum likelihood estimators (MLE) [29]. Other pitfalls include tumors with excessive fatty components or fibrosis leading to low ADC values, displacement of lesion position due to ghosting artifacts, degradation of images due to poor fat suppression, motion, susceptibility artifacts, and superficial location of the lesion. Presently, it is unclear which measurements of diffusion are most representative of local tumor behavior, i.e. mean ADC, minimum ADC, kurtosis, etc. Tumors are heterogeneous and these images should be evaluated in conjunction with anatomic images. Magnetic resonance spectroscopy (MRS) has the potential to offer prognostic information for patients with STS. This functional imaging method provides information about the microscopic metabolic environment within a region of interest. There is particular interest in choline metabolism as elevated levels of choline have been found in malignant soft tissue lesions due to excessive cellular membrane turnover. Fayad et al. [30] reported significant differences in the signal to noise ratio of the choline peak of malignant lesions compared to benign lesions. Although not directly related to soft tissue sarcomas, the same group found that detection of trimethylamine peaks within peripheral nerve sheath tumors was 100% sensitive but only 50% specific for malignancy in a small study of 18 cases [30, 31]. Moreover, the same paper demonstrated that quantification of the choline fraction increases the specificity of MRS for discrimination of benign and malignant peripheral nerve sheath tumors, particularly of interest in neurofibromatosis due to increased risk of malignant transformation. This type of study has yet to be published with data from sarcomas. Dynamic contrast enhanced sequences are useful for the evaluation of tumor perfusion and are well-established in the musculoskeletal literature. The operating principle is that qualitative and quantitative methods will demonstrate rapid arterial enhancement due to increased perfusion at the microscopic level. Early arterial enhancement on temporally resolved MR angiography technique or multi-
phase dynamic contrast enhanced imaging is useful to detect highly vascular lesions (Figure 6)[32, 33]. However, little is known of its ability to discriminate high and low grade sarcomas. Arterial spin labeling (ASL) is another technique for measuring tissue perfusion characteristics, which have been used in brain imaging. However, performing this technique in the extremities is time and resource intensive and not yet ready for routine clinical practice. Positron Emission Tomography By exploiting the increased metabolic rate of tumors, fluorodeoxyglucose (18F-FDG) is used to detect and evaluate the glucose consumption of a variety of lesions. Early studies of the correlation between FDG avidity and grading of soft tissue sarcomas were published nearly thirty years ago. The first study in the literature was published in 1988 by Kern et al. [34] who showed in five patients that the apparent glucose utilization rate correlated with tumor grade. The sample included three stage 1 lesions, one stage 2 lesion and one stage 3 lesions, of different histologic origins. During the following years, additional studies with small patient numbers employed 18F-FTG PET to show that high-grade soft tissue neoplasms display higher glucose utilization."[35-39] (Figure 7). In the study by Nieweg et al. [36] regional glucose metabolic rate (RMRgl) was shown to correlate with tumor grade, but no significant correlation between standardized uptake values and tumor grade was found. Eary et al. [37] published one of the larger studies, including 70 patients, which showed that there were significant differences between high, intermediate and low grade lesions when using FDG metabolic rate (MRFDG) and dose uptake ratio (DUR) compared to NCI tumor grade. These findings were significant with both parameters of glucose utilization, with MRFDG showing a higher sensitivity. In 2003, Ioannidis et al. [40] published results of a meta-analysis, which included all prior published reports or studies of patients where 18F-FDG and biopsy results were available. In 8 of the included studies, SUV and tumor grade data were
available. These showed that for intermediate-high grade lesions, sensitivity of SUV cutoff of >2.0 and >3.0 were 89.4% and 68.2%, respectively. There were no significant differences between low-grade and benign lesions. Similar correlations between SUV and tumor grade were reported in the systematic review by Bastiaannet et al. [41].to. Evaluation of pulmonary metastatic disease is a limitation of FDG-PET in the setting of soft tissue and bone osteosarcomas. Franzius et al. published initial results indicating that CT was superior to FDG-PET in detecting pulmonary metastases [42]. Their findings were subsequently confirmed in 2 other studies by Igarau et al. [43, 44]. Small lesions (<1 cm) may not retain sufficient FDG for enough contrast to noise ratio but these can be seen on the CT portion of the examination. It is not uncommon for the pulmonary metastases to be lower in FDG avidity than the primary tumor itself, which might be related to the factors, such as heterogeneity of the primary lesion or impact of the lung microenvironment on the tumor metastases. Additional prognostic information can be obtained with FDG-PET/CT by evaluating the extent of tumor necrosis. PET/CT demonstrates necrosis accurately, and it has been shown that the degree or quantification of necrosis correlates with survival outcomes. Rakheja et al[45] found significant differences in 48-month overall and progression free survival among patient groups when they were divided based on the degree of necrosis (0%, 0.1-66%, and >66%), even when adjusted for SUVmax. These findings are echoed in the work performed by other groups as well where it is shown that FDG-PET/CT can demonstrate necrosis reliably and that the presence of these findings correlates with the tumor grade [37, 46]. With the proliferation of new PET radiopharmaceuticals, physiologic evaluation of tumor biology will be enhanced. Thymidine analogs such as 18F-Fluorothymidine (FLT) correlate well with tumor cell proliferation and tumor grade, and in a small study, 18F-FLT has been shown to be superior to 18F-FDG for the discrimination of higher from lower grade soft tissue and bone malignancies [47]. In addition to radiolabeled nucleotide analogs, amino acid derivatives have shown positive results in many small studies. 18F-fluoromethyltyrosine (FMT) is one such analog, which is
brought into tumor cells exclusively by the amino acid transport system [48]. In a study of 75 patients, FMT-PET was found to be 81.3% accurate when used to discriminate between malignant and benign lesions, compared to FDG-PET which was only 68% accurate [49]. Little has been published on the added value of hybrid PET/MRI imaging in the setting of pre-treatment soft tissue sarcomas; however, one study examined the value in four different cases. The latter showed important clinical information regarding local treatment response, chemotherapy timing, as well as some indication towards the tumor grade in the setting of recurrence [50]. Future directions To summarize, one can see that functional techniques, such as MR imaging (DWI, MRS, DCE) and PET imaging (especially FLT and FMT) hold the most promise in generating biomarkers for non-invasive differentiation of lower grade from higher grade lesions. Other techniques, such as elastography[51, 52], arterial spin labeling[53-55] and endogenous CEST imaging [56-59]have been used in other tumors in the kidneys, breast, prostate and brain, but not in soft tissue sarcomas. These techniques also require sequence optimization and complicated postprocessing algorithms. With the availability of PET-MR scanners, it would be possible to compare the utility of different imaging pulse sequences and functional techniques in the same setting. Once more data from newer techniques are available, one could envision a decision model that would take into account the clinical features, qualitative and quantitate information from various imaging techniques and predict the probability of the low grade from intermediate and highgrade lesions. Conclusions With current data, functional MR imaging techniques and new PET tracers hold the most promise in evaluating tumor grade. Established methods for evaluating tumor grade are primarily within the FDG-PET/CT domain; however, recent publications have shown features on standard MR sequences that correlate with high grade soft tissue sarcomas. Imaging can play an important role in the pre-operative work up of patients with soft tissue sarcomas and affect the decision for neoadjuvant chemotherapy or radiation treatment; however, histopathology is still thought to be the gold standard for final diagnosis at present time, despite its limitations in sampling error allowing accurate assessment of tumor necrosis. Conflicts of Interest: None
References [1] K.L. Sherman, J.D. Wayne, J. Chung, M. Agulnik, S. Attar, J.P. Hayes, W.B. Laskin, T.D. Peabody, D.J. Bentrem, R.E. Pollock, K.Y. Bilimoria, Assessment of multimodality therapy use for extremity sarcoma in the United States, Journal of surgical oncology 109(5) (2014) 395-404. [2] R. Siegel, Cancer statistics, CA-A CANCER JOURNAL FOR CLINICIANS 62(1) (2012) 10-29. [3] J.N. Cormier, R.E. Pollock, Soft tissue sarcomas, CA Cancer J Clin 54(2) (2004) 94109. [4] L. Mariani, R. Miceli, M.W. Kattan, M.F. Brennan, M. Colecchia, M. Fiore, P.G. Casali, A. Gronchi, Validation and adaptation of a nomogram for predicting the survival of patients with extremity soft tissue sarcoma using a three-grade system, Cancer 103(2) (2005) 402-8. [5] E.A. Levine, Prognostic factors in soft tissue sarcoma, Semin Surg Oncol 17(1) (1999) 23-32. [6] A.M. Oliveira, A.G. Nascimento, Grading in soft tissue tumors: principles and problems, Skeletal radiology 30(10) (2001) 543-59. [7] L. Guillou, J.M. Coindre, F. Bonichon, B.B. Nguyen, P. Terrier, F. Collin, M.O. Vilain, A.M. Mandard, V. Le Doussal, A. Leroux, J. Jacquemier, H. Duplay, X. Sastre-Garau, J. Costa, Comparative study of the National Cancer Institute and French Federation of Cancer Centers Sarcoma Group grading systems in a population of 410 adult patients with soft tissue sarcoma, Journal of clinical oncology : official journal of the American Society of Clinical Oncology 15(1) (1997) 350-62. [8] A. Carneiro, P.O. Bendahl, J. Engellau, H.A. Domanski, C.D. Fletcher, P. Rissler, A. Rydholm, M. Nilbert, A prognostic model for soft tissue sarcoma of the extremities and trunk wall based on size, vascular invasion, necrosis, and growth pattern, Cancer 117(6) (2011) 1279-87. [9] A. Neuville, F. Chibon, J.M. Coindre, Grading of soft tissue sarcomas: from histological to molecular assessment, Pathology 46(2) (2014) 113-20. [10] T.O. Nielsen, R.B. West, Translating gene expression into clinical care: sarcomas as a paradigm, Journal of clinical oncology : official journal of the American Society of Clinical Oncology 28(10) (2010) 1796-805. [11] J. Fernebro, M. Wiklund, K. Jonsson, P.O. Bendahl, A. Rydholm, M. Nilbert, J. Engellau, Focus on the tumour periphery in MRI evaluation of soft tissue sarcoma: infiltrative growth signifies poor prognosis, Sarcoma 2006 (2006) 21251. [12] B.J. Manaster, Soft-tissue masses: optimal imaging protocol and reporting, AJR. American journal of roentgenology 201(3) (2013) 505-14. [13] H.J. Baek, S.J. Lee, K.H. Cho, H.J. Choo, S.M. Lee, Y.H. Lee, K.J. Suh, T.Y. Moon, J.G. Cha, J.H. Yi, M.H. Kim, S.J. Jung, J.H. Choi, Subungual tumors: clinicopathologic correlation with US and MR imaging findings, Radiographics 30(6) (2010) 1621-36. [14] A.T. Ahuja, P. Richards, K.T. Wong, E.H.Y. Yuen, A.D. King, Accuracy of Highresolution Sonography Compared with Magnetic Resonance Imaging in the Diagnosis of Head and Neck Venous Vascular Malformations, Clinical Radiology 58(11) (2003) 869-875.
[15] B. Amini, A.C. Jessop, D.M. Ganeshan, W.W. Tseng, J.E. Madewell, Contemporary imaging of soft tissue sarcomas, Journal of surgical oncology (2014). [16] P.A. Gondim Teixeira, G. Hossu, S. Lecocq, M. Razeto, M. Louis, A. Blum, Bone marrow edema pattern identification in patients with lytic bone lesions using digital subtraction angiography-like bone subtraction on large-area detector computed tomography, Invest Radiol 49(3) (2014) 156-64. [17] P.A. Gondim Teixeira, S. Lecocq, M. Louis, S. Aptel, A. Raymond, F. Sirveaux, A. Blum, Wide area detector CT perfusion: can it differentiate osteoid osteomas from other lytic bone lesions?, Diagn Interv Imaging 95(6) (2014) 587-94. [18] L.M. Fayad, M.A. Jacobs, X. Wang, J.A. Carrino, D.A. Bluemke, Musculoskeletal tumors: how to use anatomic, functional, and metabolic MR techniques, Radiology 265(2) (2012) 340-56. [19] J.L. Gielen, A.M. De Schepper, F. Vanhoenacker, P.M. Parizel, X.L. Wang, R. Sciot, J. Weyler, Accuracy of MRI in characterization of soft tissue tumors and tumor-like lesions. A prospective study in 548 patients, European radiology 14(12) (2004) 2320-30. [20] T.H. Berquist, R.L. Ehman, B.F. King, C.G. Hodgman, D.M. Ilstrup, Value of MR imaging in differentiating benign from malignant soft-tissue masses: study of 95 lesions, AJR. American journal of roentgenology 155(6) (1990) 1251-5. [21] F.M. Vanhoenacker, K. Van Looveren, K. Trap, J. Desimpelaere, K. Wouters, P. Van Dyck, P.M. Parizel, A.M. De Schepper, Grading and characterization of soft tissue tumors on magnetic resonance imaging: the value of an expert second opinion report, Insights into imaging 3(2) (2012) 131-8. [22] J. Engellau, P.O. Bendahl, A. Persson, H.A. Domanski, M. Akerman, P. Gustafson, T.A. Alvegard, M. Nilbert, A. Rydholm, Improved prognostication in soft tissue sarcoma: independent information from vascular invasion, necrosis, growth pattern, and immunostaining using whole-tumor sections and tissue microarrays, Human pathology 36(9) (2005) 994-1002. [23] F. Zhao, S. Ahlawat, S.J. Farahani, K.L. Weber, E.A. Montgomery, J.A. Carrino, L.M. Fayad, Can MR imaging be used to predict tumor grade in soft-tissue sarcoma?, Radiology 272(1) (2014) 192-201. [24] C.S. van Rijswijk, P. Kunz, P.C. Hogendoorn, A.H. Taminiau, J. Doornbos, J.L. Bloem, Diffusion-weighted MRI in the characterization of soft-tissue tumors, Journal of magnetic resonance imaging : JMRI 15(3) (2002) 302-7. [25] A. Chhabra, T. Soldatos, Soft-tissue lesions: when can we exclude sarcoma?, AJR. American journal of roentgenology 199(6) (2012) 1345-57. [26] A. Chhabra, R.S. Thakkar, G. Andreisek, M. Chalian, A.J. Belzberg, J. Blakeley, A. Hoke, G.K. Thawait, J. Eng, J.A. Carrino, Anatomic MR imaging and functional diffusion tensor imaging of peripheral nerve tumors and tumorlike conditions, AJNR. American journal of neuroradiology 34(4) (2013) 802-7. [27] S. Demehri, A. Belzberg, J. Blakeley, L.M. Fayad, Conventional and functional MR imaging of peripheral nerve sheath tumors: initial experience, AJNR. American journal of neuroradiology 35(8) (2014) 1615-20. [28] O. Dudeck, M. Zeile, D. Pink, M. Pech, P.U. Tunn, P. Reichardt, W.D. Ludwig, B. Hamm, Diffusion-weighted magnetic resonance imaging allows monitoring of
anticancer treatment effects in patients with soft-tissue sarcomas, Journal of magnetic resonance imaging : JMRI 27(5) (2008) 1109-13. [29] Y. Peng, Y. Jiang, C. Yang, J.B. Brown, T. Antic, I. Sethi, C. Schmid-Tannwald, M.L. Giger, S.E. Eggener, A. Oto, Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score--a computer-aided diagnosis development study, Radiology 267(3) (2013) 787-796. [30] L.M. Fayad, P.B. Barker, M.A. Jacobs, J. Eng, K.L. Weber, P. Kulesza, D.A. Bluemke, Characterization of musculoskeletal lesions on 3-T proton MR spectroscopy, AJR. American journal of roentgenology 188(6) (2007) 1513-20. [31] L.M. Fayad, X. Wang, J.O. Blakeley, D.J. Durand, M.A. Jacobs, S. Demehri, T.K. Subhawong, T. Soldatos, P.B. Barker, Characterization of peripheral nerve sheath tumors with 3T proton MR spectroscopy, AJNR. American journal of neuroradiology 35(5) (2014) 1035-41. [32] L.M. Fayad, C. Mugera, T. Soldatos, A. Flammang, F. del Grande, Technical innovation in dynamic contrast-enhanced magnetic resonance imaging of musculoskeletal tumors: an MR angiographic sequence using a sparse k-space sampling strategy, Skeletal radiology 42(7) (2013) 993-1000. [33] C.S. van Rijswijk, M.J. Geirnaerdt, P.C. Hogendoorn, A.H. Taminiau, F. van Coevorden, A.H. Zwinderman, T.L. Pope, J.L. Bloem, Soft-tissue tumors: value of static and dynamic gadopentetate dimeglumine-enhanced MR imaging in prediction of malignancy, Radiology 233(2) (2004) 493-502. [34] K.A. Kern, A. Brunetti, J.A. Norton, A.E. Chang, M. Malawer, E. Lack, R.D. Finn, S.A. Rosenberg, S.M. Larson, Metabolic imaging of human extremity musculoskeletal tumors by PET, Journal of nuclear medicine : official publication, Society of Nuclear Medicine 29(2) (1988) 181-6. [35] L.P. Adler, H.F. Blair, J.T. Makley, R.P. Williams, M.J. Joyce, G. Leisure, N. Alkaisi, F. Miraldi, Noninvasive Grading of Musculoskeletal Tumors Using Pet, Journal of Nuclear Medicine 32(8) (1991) 1508-1512. [36] O.E. Nieweg, J. Pruim, R.J. van Ginkel, H.J. Hoekstra, A.M. Paans, W.M. Molenaar, H.S. Koops, W. Vaalburg, Fluorine-18-fluorodeoxyglucose PET imaging of soft-tissue sarcoma, Journal of nuclear medicine : official publication, Society of Nuclear Medicine 37(2) (1996) 257-61. [37] J.F. Eary, E.U. Conrad, J.D. Bruckner, A. Folpe, K.J. Hunt, D.A. Mankoff, A.T. Howlett, Quantitative [F-18]fluorodeoxyglucose positron emission tomography in pretreatment and grading of sarcoma, Clin Cancer Res 4(5) (1998) 1215-20. [38] M. Schulte, D. Brecht-Krauss, B. Heymer, A. Guhlmann, E. Hartwig, M.R. Sarkar, C.G. Diederichs, M. Schultheiss, J. Kotzerke, S.N. Reske, Fluorodeoxyglucose positron emission tomography of soft tissue tumours: is a non-invasive determination of biological activity possible?, European Journal of Nuclear Medicine 26(6) (1999) 599-605. [39] M. Schulte, D. Brecht-Krauss, B. Heymer, A. Guhlmann, E. Hartwig, M.R. Sarkar, C.G. Diederichs, A. Von Baer, J. Kotzerke, S.N. Reske, Grading of tumors and tumorlike lesions of bone: evaluation by FDG PET, Journal of nuclear medicine : official publication, Society of Nuclear Medicine 41(10) (2000) 1695-701.
[40] J.P. Ioannidis, J. Lau, 18F-FDG PET for the diagnosis and grading of soft-tissue sarcoma: a meta-analysis, Journal of nuclear medicine : official publication, Society of Nuclear Medicine 44(5) (2003) 717-24. [41] E. Bastiaannet, H. Groen, P.L. Jager, D.C. Cobben, W.T. van der Graaf, W. Vaalburg, H.J. Hoekstra, The value of FDG-PET in the detection, grading and response to therapy of soft tissue and bone sarcomas; a systematic review and meta-analysis, Cancer treatment reviews 30(1) (2004) 83-101. [42] C. Franzius, H.E. Daldrup-Link, J. Sciuk, E.J. Rummeny, S. Bielack, H. Jurgens, O. Schober, FDG-PET for detection of pulmonary metastases from malignant primary bone tumors: comparison with spiral CT, Ann Oncol 12(4) (2001) 479-86. [43] A. Iagaru, S. Chawla, L. Menendez, P.S. Conti, 18F-FDG PET and PET/CT for detection of pulmonary metastases from musculoskeletal sarcomas, Nuclear medicine communications 27(10) (2006) 795-802. [44] A. Iagaru, A. Quon, I.R. McDougall, S.S. Gambhir, F-18 FDG PET/CT evaluation of osseous and soft tissue sarcomas, Clin Nucl Med 31(12) (2006) 754-60. [45] R. Rakheja, W. Makis, R. Tulbah, S. Skamene, C. Holcroft, A. Nahal, R. Turcotte, M. Hickeson, Necrosis on FDG PET/CT correlates with prognosis and mortality in sarcomas, AJR. American journal of roentgenology 201(1) (2013) 170-7. [46] M.H. Schwarzbach, A. Dimitrakopoulou-Strauss, F. Willeke, U. Hinz, L.G. Strauss, Y.M. Zhang, G. Mechtersheimer, N. Attigah, T. Lehnert, C. Herfarth, Clinical value of [18-F]] fluorodeoxyglucose positron emission tomography imaging in soft tissue sarcomas, Ann Surg 231(3) (2000) 380-6. [47] A.K. Buck, K. Herrmann, C.M. Buschenfelde, M.E. Juweid, M. Bischoff, G. Glatting, G. Weirich, P. Moller, H.J. Wester, K. Scheidhauer, T. Dechow, C. Peschel, M. Schwaiger, S.N. Reske, Imaging bone and soft tissue tumors with the proliferation marker [18F]fluorodeoxythymidine, Clin Cancer Res 14(10) (2008) 2970-7. [48] H.A. Wieder, K.L. Pomykala, M.R. Benz, A.K. Buck, K. Herrmann, PET Tracers in Musculoskeletal Disease beyond FDG, Semin. Musculoskelet. Radiol. 18(2) (2014) 123-132. [49] H. Watanabe, T. Inoue, T. Shinozaki, T. Yanagawa, A.R. Ahmed, K. Tomiyoshi, N. Oriuchi, M. Tokunaga, J. Aoki, K. Endo, K. Takagishi, PET imaging of musculoskeletal tumours with fluorine-18 α-methyltyrosine: comparison with fluorine-18 fluorodeoxyglucose PET, European Journal of Nuclear Medicine 27(10) (2000) 1509-1517. [50] M.K. Schuler, S. Richter, B. Beuthien-Baumann, I. Platxek, J. Kotzerke, J. van den Hoff, G. Ehninger, P. Reichardt, PET/MRI Imaging in High-Risk Sarcoma: First Findings and Solving Clinical Problems, Case Reports in Oncological Medicine 2013 (2013). [51] R.G. Barr, Elastography in clinical practice, Radiologic clinics of North America 52(6) (2014) 1145-62. [52] P. Ricci, E. Maggini, E. Mancuso, P. Lodise, V. Cantisani, C. Catalano, Clinical application of breast elastography: state of the art, European journal of radiology 83(3) (2014) 429-37. [53] C. De Bazelaire, N.M. Rofsky, G. Duhamel, M.D. Michaelson, D. George, D.C. Alsop, Arterial spin labeling blood flow magnetic resonance imaging for the
characterization of metastatic renal cell carcinoma(1), Academic radiology 12(3) (2005) 347-57. [54] R.S. Lanzman, P.M. Robson, M.R. Sun, A.D. Patel, K. Mentore, A.A. Wagner, E.M. Genega, N.M. Rofsky, D.C. Alsop, I. Pedrosa, Arterial spin-labeling MR imaging of renal masses: correlation with histopathologic findings, Radiology 265(3) (2012) 799-808. [55] R. Schor-Bardach, D.C. Alsop, I. Pedrosa, S.A. Solazzo, X. Wang, R.P. Marquis, M.B. Atkins, M. Regan, S. Signoretti, R.E. Lenkinski, S.N. Goldberg, Does arterial spinlabeling MR imaging-measured tumor perfusion correlate with renal cell cancer response to antiangiogenic therapy in a mouse model?, Radiology 251(3) (2009) 731-42. [56] G. Jia, R. Abaza, J.D. Williams, D.L. Zynger, J. Zhou, Z.K. Shah, M. Patel, S. Sammet, L. Wei, R.R. Bahnson, M.V. Knopp, Amide proton transfer MR imaging of prostate cancer: a preliminary study, Journal of magnetic resonance imaging : JMRI 33(3) (2011) 647-54. [57] C.K. Jones, M.J. Schlosser, P.C. van Zijl, M.G. Pomper, X. Golay, J. Zhou, Amide proton transfer imaging of human brain tumors at 3T, Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 56(3) (2006) 585-92. [58] J. Zhou, J.O. Blakeley, J. Hua, M. Kim, J. Laterra, M.G. Pomper, P.C. van Zijl, Practical data acquisition method for human brain tumor amide proton transfer (APT) imaging, Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 60(4) (2008) 842-9. [59] J. Zhou, E. Tryggestad, Z. Wen, B. Lal, T. Zhou, R. Grossman, S. Wang, K. Yan, D.X. Fu, E. Ford, B. Tyler, J. Blakeley, J. Laterra, P.C. van Zijl, Differentiation between glioma and radiation necrosis using molecular magnetic resonance imaging of endogenous proteins and peptides, Nature medicine 17(1) (2011) 130-4.
Figure 1a – Sagittal STIR (A), Axial FST2 (B), and FST1 pre- (C) and post- (D) contrast MR images were obtained in this 42 year old female which were suggested to be typical of a soft tissue sarcoma and biopsy was obtained demonstrating myositis ossificans (arrows).
Figure 1b – Radiographs obtained after biopsy demonstrate mass like soft tissue ossification (arrows) which, if obtained prior to MRI, could have averted an unnecessary biopsy.
Figure 2 – Contrast enhanced CT images demonstrated peripheral enhancement and considerable central necrosis in this 52 year old man with a high grade myxofibrosarcoma of the medial left thigh.
Figure 3 –MRI showing typical appearances of a lipoma (A), vascular malformation (B), PNST (C), giant cell tumor of tendon sheath (D) (arrows).
Figure 4 – Sagittal STIR (A) and axial FST2 (B) images on this 82 year old woman demonstrate areas of infiltrative high signal lesion on fluid sensitive sequences (arrows) at peripheral regions of the mass. Biopsy demonstrated a grade 3 pleomorphic undifferentiated sarcoma of the distal upper left arm.
A
B
C Figure 5 – Diffusion weighted images with color coded ADC maps in a patient with a benign peripheral nerve sheath tumor (minimum ADC = 1.72x10-3 mm2/s and mean ADC = 2.48x10-3) (A), a malignant peripheral nerve sheath tumor (minimum ADC = 1.17 x10-3 and mean ADC = 1.31 x10-3) (B), and the above patient in Fig. 4 with grade 3 pleomorphic undifferentiated sarcoma (minimum ADC = 0.51 x10-3 and mean ADC = 0.81 x10-3) (C).
Figure 6 – Multiple contrast enhanced T1 perfusion images from a 52 year old man with a large high grade myxofibrosarcoma. (A) Coronal MIP arterial phase. (B) Coronal MIP delayed phase. Notice hypervascularity with early fillin, tumor blush and AV shunting. (C) Maximum Relative Enhancement (%). (D) Wash In Rate (s-1). (E) Area Under the Curve. (F) Wash Out Rate (s-1) demonstrating hyperperfusion characteristics.
Figure 7 – F18-FDG PET-CT fused image from a patient with grade 3 synovial sarcoma (SUV max 3.7 and mean 2.1) demonstrating central hypometabolism suggesting necrosis.
Table 1. National Cancer Institute sarcoma grading system
Grade 1
Well differentiated liposarcoma Myxoid liposarcoma Subcutaneous myxoid MFH Well differentiated malignant hemangiopericytoma1 Well differentiated fibrosarcoma Well differentiated leiomyosarcoma2 Malignant Schwannoma (MPNST)3 Myxoid chondrosarcoma4
Grade 2
Other histologic subtypes with <15% necrosis.
Grade 3
Extraskeletal Ewing’s sarcoma Primitive neuroectodermal tumor (PNET) Extraskeletal osteosarcoma Mesenchymal chondrosarcoma Malignant Triton Tumor Other histologic types with >15% necrosis
With <1 mitotic figure (MF)/high power field (HPF), no necrosis and no hemorrhagic areas orderly fascicular pattern plus no pleomorphism, no necrosis, and <6 MF/10 HPF 3 If resembles neurofibroma plus mitotic figures with increased cellularity but <6 MF/10 HPF 4 Uniformly myxoid and hypocellular with no mitotic activity 1
2 With
Table 2 FNCLCC grading system Tumor Differentiation Score 1 Sarcomas closely resembling normal adult mesenchymal tissue (eg, well differentiated liposarcoma). Score 2 Sarcomas for which histological typing is certain (eg, myxoid liposarcoma). Score 3 Embryonal and undifferentiated sarcomas, sarcomas of doubtful type, synovial sarcomas, osteosarcomas, PNET
Mitotic Count Score 1 0-9 mitoses per 10 HPF* Score 2 10-19 mitoses per 10 HPF Score 3 >20 mitoses per 10 HPF
Tumor Necrosis Score 0 No necrosis Score 1 <50% tumor necrosis Score 2 >50% tumor necrosis
Histological Grade Grade 1 Total score of 2-3 Grade 2 Total score of 4-5 Grade 3 Total score of 6-8 Modified from Coindre [60]. *HPF measures 0.1734 mm2