- Email: [email protected]
A Feasibility Study on the Identification of Postlumpectomy Seromas by a Radiation Therapist Compared with That by Radiation Oncologists in Radiation Therapy Planning for Early Stage Breast Cancer
Journal of Medical Imaging and Radiation Sciences
Journal of Medical Imaging and Radiation Sciences xx (2018) 1-6
Journal de l’imagerie médicale et des sciences de la radiation
www.elsevier.com/locate/jmir
Research Article
A Feasibility Study on the Identification of Postlumpectomy Seromas by a Radiation Therapist Compared with That by Radiation Oncologists in Radiation Therapy Planning for Early Stage Breast Cancer Sharon Oultram, RT (T), MHSc(Ed), MPhil (Research) Candidatea* and Shane Dempsey, GradDipEpi, DipAppSci, MIR, PhDb b
a Senior Clinical Radiation Therapy Educator, Department of Radiation Oncology, Newcastle, NSW, Australia Associate Professor, Deputy Head of School of Health Sciences, Faculty of Health and Medicine, University of Newcastle, Callaghan, NSW, Australia
ABSTRACT Introduction: Computed tomography (CT) simulation is currently used to identify the tumour bed in patients with early stage breast cancer requiring whole-breast and boost radiation therapy. Postlumpectomy breast seromas as visible on CT data have been identified as a proxy for the tumour bed. This study aims to quantify the incidence of postsurgical tumour bed seromas identified at CT simulation and report how well a radiation therapist (RT) is able to identify these seromas compared with those contoured by radiation oncologists (ROs). Methods and Materials: A study was undertaken on electronic patient records and the CT-simulation datasets of early stage breast cancer patients treated in 2013 at the Calvary Mater Newcastle to identify the presence of seromas. Patient and tumour characteristics were reviewed. Data analysed included seroma volumes, as contoured by the ROs, as part of the standard voluming procedure. One RT rated seroma visibility based on the level of difficulty when viewing the seroma. Results: Out of 108 CT datasets, an RT was able to identify the presence of a seroma in 102 cases vs. 104 as contoured by ROs. The number of observed agreements was 106 (98.15% of the observations), and the number of agreements expected by chance was 98.4 (91.15% of the observations). The Kappa statistics equalled 0.791 (SE of kappa ¼ 0.143). The strength of agreement is considered to be ‘‘substantial.’’ Conclusion: RTs play an integral role in contouring during the planning process, and there is scope to expand this role. This research introduced the first step by confirming that a radiation therapist is able to identify seromas on CT-simulation data when compared those contoured by an RO.
* Corresponding author: Sharon Oultram, RT (T), MHSc(Ed), MPhil (Research) Candidate, Senior Clinical Radiation Therapy Educator, Department
RESUM E Introduction : La simulation par tomodensitometrie (TDM) axiale est actuellement utilisee pour identifier le siege tumoral chez les patientes presentant un cancer du sein au stade precoce necessitant une radiotherapie du sein entier ou en dose concentree (‘‘ boost ’’). Les seromes post-lumpectomie visibles sur les donnees de TDM ont ete identifies comme substituts du siege tumoral. Dans cette etude, les auteurs visent a quantifier l’incidence de seromes du siege tumoral post-chirurgie identifies a la simulation par TDM, et a indiquer dans quelle mesure un radiotherapeute est en mesure d’identifier ces seromes en comparaison de ceux contoures par un radio-oncologue. Methodologie et materiel : Une etude a ete faite a partir des dossiers electroniques des patientes et des ensembles de donnees de simulation des patientes presentant un cancer du sein au stade precoce traitees en 2013 a la clinique Calvary Mater Newcastle afin de determiner la presence de seromes. Les caracteristiques des patientes et des tumeurs ont ete examinees. Les donnees analysees comprenaient le volume des seromes, selon le contourage effectue par les radio-oncologue, dans le cadre de la procedure standard de determination du volume. Un radiotherapeute a classifie la visibilite des seromes selon le niveau de difficulte presente au visionnement du serome. Resultats : Sur 108 ensembles de donnees, un radiotherapeute a ete en mesure de determiner la presence de seromes dans 102 cas, contre 104 cas contoures par les radio-oncologue. Le nombre de cas d’accord observe a ete de 106 (98,15% des observations) tandis que le nombre de cas d’accord prevu par la chance etait de 98.4 (91,15% des observations). Le test de concordance de Kappa donne un resultat de 0,791 (SE de kappa ¼ 0,143). La solidite de l’accord est jugee ‘‘substantielle’’. Conclusion : Les radiotherapeutes jouent un r^ole integral dans le contourage durant le processus de planification et il y a place pour
of Radiation Oncology, Calvary Mater Newcastle, Locked Bag 7 Hunter Region Mail Centre, Newcastle, NSW, 2310, Australia. E-mail address: [email protected] (S. Oultram).
1939-8654/$ - see front matter Ó 2018 Published by Elsevier Inc. on behalf of Canadian Association of Medical Radiation Technologists. https://doi.org/10.1016/j.jmir.2018.02.055
une expansion de ce r^ole. Cette recherche etablit une premiere etape, en confirmant que le radiotherapeute peut reconna^ıtre les seromes
sur les donnees de simulation TRM en comparaison de ceux contoures par un radio-oncologue.
Keywords: Radiotherapy; computed tomography; tumour bed; boost
Introduction Numerous studies have confirmed the efficacy of breast conservation surgery with postoperative external beam radiation therapy to the entire breast in early stage localised breast cancer [1–3], with current 5-year survival rates in New South Wales, Australia, at 97% [4]. Radiation therapy for early stage breast cancer includes treatment of the entire breast and, when deemed necessary, the involved local lymph nodes. Numerous studies have indicated that recurrences can occur near the resected primary tumour site [5–7]. To reduce the possibility of recurrence, radiation therapy may include a boost dose of radiation that targets the primary tumour bed site, and this has proven to be effective in reducing the recurrence rates in these early stage breast cancers [6]. Historically, the tumour bed was predominately identified or localised using the surgical scar as a proxy for its position within the breast [8,9]. However, over time, the improvement in surgical techniques resulted in the relationship with the position of a scar, with the location of the tumour bed being increasingly unreliable [8,10–12]. One of the early solutions to improve localisation of the tumour bed was the placement of metal clips within the tumour bed at the time of surgery which could be seen on radiographs [9]. This technique was dependent on the surgeon’s preference of surgical technique and the number of clips inserted [10]. The literature reports that clips can migrate reducing the accuracy of clips as an image-guidance tool [13]. These earlier methods have given way to computed tomography (CT) in the radiation therapy simulation process. The postsurgical tumour bed site can often be identified on a CT scan as a collection of fluid, known as a seroma, and this seroma can be used as an acceptable proxy for the tumour bed [8,14–17]. Literature has indicated that the seroma will reduce over time, and this has been attributed to the reabsorption of the fluid within the seroma and/or the normal breast tissue replacing the surgical cavity [15,18]. A delay from surgery to CT simulation, primarily due to adjuvant chemotherapy, has been reported to have an effect on seroma visibility [15]. At the Radiation Oncology Treatment Centre (ROTC) where this research was conducted, the seroma is contoured by radiation oncologists (ROs) on the CT data. It is contoured to ensure that it is included in the whole of breast treatment volume, but the seroma is not currently used for the localisation and positioning of the boost on treatment. This CT-defined seroma may, however, be able to be used by radiation therapists (RTs) within an adaptive image-guidance framework for the localisation and positioning of the boost volume on treatment. Quantifying 2
the number of cases that may benefit from this identification may provide important information in making the boost treatment more accurate. Although this study focuses on seromas identified on CT-simulation data, a subsequent study is planned to include the use of ultrasound for seroma identification. This feasibility study aims to quantify the incidence of postsurgical tumour bed seromas identified at CT-simulation and to report how well one RT can identify these seromas compared with those identified by ROs during the treatment planning process. Methods and Materials Ethics Before commencing, the research proposal was submitted for review to the Hunter New England Research Ethics Committee. The ethical approval was granted, HNEHREC 14/05/ 21/5.03. This approval was subsequently submitted and registered by the University of Newcastle Human Research Ethics Administration, H-2014-0277. Methods and Data Collection A study was conducted on the CT-simulation datasets of all patients with early stage breast cancer treated with radiation therapy in 2013 at the Calvary Mater Newcastle (CMN), New South Wales, Australia. In this study, one senior RT, the primary investigator who worked at the ROTC, identified and quantified the presence or absence of a seroma on the deidentified CT-simulation datasets of a sample of patients treated for breast cancer in 2013. The RT had over 30 years of clinical experience, which included experience viewing seromas on CT-simulation and ultrasound data for treatment planning purposes. The incidence of seromas, as reported by the RT, was then compared with the same CT-simulation data where a breast seroma was identified, evidenced by a contour, by an RO, and a level of agreement (Kappa statistic) was determined. The CMN is a large progressive ROTC which provides radiation therapy services, along with a private provider, to a population of approximately 800,000 in the Hunter New England Local Area Health District of NSW, Australia. In 2013, the ROTC had two CT simulators, a Toshiba Aquilion and a GE Lightspeed helical CT simulator. All early stage breast cancer patients at the CMN have CT simulation of the treatment area. The CT-simulation breast protocol for both the CT simulators used 120 kV and a slice thickness of 2.0–2.5 mm. A part of the treatment planning workflow involves a planning RT importing patients’ CT-simulation
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data into the VARIAN EclipseÒ Treatment Planning System; this includes contouring the body, lungs, heart, and bones. The ROs are responsible for contouring the posterior baselines of the ipsilateral breast and postlumpectomy breast seromas. Inclusion criteria for this study included the CTsimulation datasets of early stage (T1 and T2 tumours) breast cancer patients who presented for CT simulation to the ROTC in 2013, with an intact breast and who had one or more CT-simulation scan used for treatment planning. Exclusion criteria included patients with mastectomies. To ensure that the study sample was representative of the early stage breast cancer population at the centre, the RT collected a wide variety of clinical and pathological information about each patient, including stage, size, grade, surgical procedure, timepoints (surgery, CT simulation, radiation therapy), and location of the primary tumour. A saturation sampling technique [19] was used to obtain a sample of patient CT-simulation data that represented all early stage T1 and T2 breast cancer patients. Saturation sampling is a qualitative research method used to obtain a sample that is representative of and generalizable to the population of interest. The representative sample is reached when it is determined that further sampling will add no new information related to the research question. The sampling commenced with the review of patients treated in December 2013, and the sampling technique worked backwards through the preceding months until a point was reached where no new or different patient demographic (eg, age) or tumour-related information (eg, grade, stage, positioning in the breast) was obtained. Data collection occurred between May 20, 2014, and July 19, 2014. The sampling technique resulted in a sample size of 100 patients. There were 108 CT-simulation datasets in total as 8 patients had more than one CT simulation or required planning for bilateral disease. A power calculation was also applied to the sample size of 100 patients, each being rated by an RT and an RO, and this resulted in an estimation of the Kappa agreement statistic of at least 0.7% and 95% confidence interval with a margin of error of 15%. Of the 108 CT datasets retrieved, a review demonstrated that 89 had seromas contoured by one of four ROs as part of the initial treatment development; all ROs were the patients’ referring RO. When asked why the seromas on the CT data set had not been contoured, ROs cited the reasons that no seroma was visible, no intentions to deliver boost RT, or the seroma was in the central position of the breast and it was deemed to be covered by the treatment field. To allow those 19 CT datasets without a seroma contour to be included in the study, an independent RO reviewed the CT datasets and indicated whether a seroma was visible or not visible. All 108 CT datasets were then deidentified from the RT, and all seroma contours were hidden before the RT reviewied and analysed the CT data. All CT datasets were given individual identifiers. The CT datasets were reviewed
by the RT for how well the seromas could be seen on CT. The RT reviewed the CT datasets for the presence or absence of a visible seroma using a Four-Point Seroma Visibility Rating Scale developed for this study. The seroma was rated as being 1. 2. 3. 4.
Easily visible, Visible, Difficult to visualise, or Not visible.
Data Analysis All data collected during the study were entered into Microsoft Excel, GraphPad (GraphPad Software Inc) and Stata IC 14 (StataCorp LP) software. The level of agreement between the RT and RO was assessed using a Kappa statistical method, and the effect of the time delay from surgery to CT simulation on seroma visibility was analysed using the Spearman’s rho statistical method. Results Patient Characteristics The patient and tumour characteristics for the sample were recorded for each patient. Table 1 provides an overview of the patient and tumour characteristics of the study sample. Quantifying the Presence of Seromas Table 2 demonstrates that of the 108 CT datasets reviewed by the RT, 102 (94%) seromas were assessed as visible by the RT. The RT used patient documentation, in particular, patient history and surgical and pathological reports, to aid in the identification of seromas. The use of clips was not the standard practice at the time of surgery for these cases; clips were reported in only 3 cases. In 2 cases, seromas were easily visible, and clips only aided the identification of the tumour bed in 1 case. Table 1 Patient and Tumour Characteristics Patient and Tumour Characteristics (n ¼ 100) Age (years) Mean (range) Laterality (%) Right sided Left sided Pathology (%) Infiltrating ductal carcinoma (IDC) Ductal carcinoma in situ (DCIS) Lobular carcinoma Other subtypes Tumour stage (%) T1 T2 Nodal status (%) Negative Positive
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62 y (37–86) 47 53 74 5 12 9 67 33 75 25
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Table 2 RT Rating of the Visibility of Seromas Using the Four-Point Seroma Visibility Rating Scale
Table 4 Duration Between Surgery, CT Simulation, and Commencement of Radiation Therapy
Visibility Rating
Number (%) of Seromas Identified on CT-Simulation Dataset (n ¼108)
Duration
Patients (n¼100)
Weeks
Easily visible Visible Difficult to visualise No seroma visible
17 41 44 6
Surgery to CT Simulation
Surgery to Radiation Therapy Commencement
<4 4–8 >8–12 >12 Median Range
7 43 15 35 11.2 wk 3.1–38.4 wk
1 19 36 44 12.6 wk 5.7–41 wk
(16) (38) (40) (6)
The clinical agreement between the breast seromas identified by the RO, considering the clinical practice standard, and those identified by the RT was analysed. The ROs identified the presence of 104 seromas in 108 CT datasets, whereas the RT identified 102 seromas. The RT could not identify the presence of 2 seromas that the RO identified. The RO and RT agreed on the absence of seromas in 4 CT datasets. The level of agreement between the RO and RT was calculated using the Kappa statistic. Table 3 demonstrates the results of the RO and RT ratings. The number of observed agreements was 106 (98.15% of the observations), and the number of agreements expected by chance was 98.4 (91.15% of the observations); the Kappa statistics equalled 0.791 (SE of kappa ¼ 0.143). The strength of agreement is considered to be ‘‘substantial’’ [20]. It is reported in literature that the presence of seromas reduces over time [15,18]; hence, the dates between surgery, CT simulation, and the commencement of radiation therapy were recorded. Table 4 demonstrates the duration, median times, and the ranges in weeks between these key timepoints. When statistically analysed, the relationship between the time between surgery and CT simulation and the visibility of the seroma, as rated by the RT, was found to be very weak (Spearman’s rho ¼ 0.1491). Discussion There is limited published research investigating the issue of the presence or absence of a seroma after lumpectomy. Compared with three other published studies that focus on postlumpectomy seromas in early stage breast cancer and the presence of seromas and the factors influencing their incidence [15,18,20], the present study compares well with regards to the patient and tumour characteristics (Table 5).
Table 3 Rating of Seromas by ROs and the RT Radiation Therapist Rating
Radiation Oncologist Rating Present Absent Total
4
Present
Absent
102 0 102
2 4 6
Total
104 4 108
To determine how well the seromas are visualised on CT imaging, the Four-Point Seroma Visibility Rating Scale was developed to aid the RT in identifying seromas. Other studies have developed similar rating systems, with two examples being the British Columbia Cancer Agency Seroma Clarity Score [22] and a two-point grading system used by Mukesh et al. [21]. The Seroma Clarity Score 6-point scale, which ranged from the seroma being not detectable (0) to clearest [5], was not adopted for this study as it was developed for use by ROs. Without an advanced level of knowledge, it was believed to be too difficult for an RT to use this without additional training. Mukesh et al [21] used only a 2-point rating system, which categorised the seroma as being visible or subtle/no seroma. This system was found to be too limited to accurately grade the seromas. Oh et al [15] investigated changes to the tumour bed volume and surgical scar location as a result of breastconserving surgery and whole-breast radiation therapy. Oh et al reported the benefits of using CT data for tumour bed localisation over the use of the surgical scar in the clinical markup of the breast boost. An Australian study by Hansen et al [18] also investigated the use of CT in the localisation of the tumour bed. The results compared well with those of Oh et al [15] and found CT-based localisation of the tumour bed superior to the use of the surgical scar in tumour bed localisation. A large study by Mukesh et al [21] reported on the presence or absence of seromas, similar to the present study, using a 2-point rating system, which categorised the seroma as being visible or subtle/no seroma. The results of the CMN study using the 4-point scale were compared with those of Mukesh. To do this, it was necessary to condense the 4-point scale used in the present study to the 2-point scale used by Mukesh. The ratings easily visible and visible were condensed to a visible rating, and difficult to visualise and no seroma present were condensed to subtle or no seroma. According to the 2-point scale, the results in Table 6 demonstrate that Mukesh reported 17% fewer visible seromas. There are a range of factors such as surgical technique; wound healing; and timepoints between surgery, CT simulation, and radiation therapy which may influence the
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Table 5 A Comparison of Patient and Tumour Characteristics Oultram et aldCMN (n ¼ 100) Age (years) Mean (range) Laterality (%) Right sided Left sided Pathology (%) Infiltrating ductal carcinoma (IDC) Ductal carcinoma in situ (DCIS) Lobular carcinoma Other subtypes Tumour stage (%) T1 T2 Nodal status (%) Negative Positive
62 (37–86)
Oh et al [15] (n ¼ 30)
Hansen et al [18] (n ¼ 50)
Mukesh et al [21] (n ¼ 648)
55.4 (34.5–80.5)
54.5 (29–70)
57 (32–81)
47 53
50 50
74 5 12 9
65 26 3 6
67 33
87 13
88 6
9.9
78 22
75 25
83 17
visibility of seromas. At the CMN, there was primarily one surgical technique used, wide local excision, which did not affect the visibility of the seromas in this study. Postoperative complications were recorded; however, there appeared to be no impact on the visibility of seromas. The timepoints between surgery, CT simulation, and radiation therapy were recorded to determine if a delay between these timepoints affected the visibility of seromas. Chemotherapy has been attributed to the delay between surgery and CT simulation, hence impacting on the visibility of the seroma [15,18]. The CMN study found that 36 patients received chemotherapy before radiation therapy, and the resulting delay to CT simulation ranged from 11.2 to 38.4 weeks. Of these 36 patients, 19 had visible seromas, 16 had seromas that were difficult to visualise, and only 1 had no visible seroma. As stated in the results, the relationship between time and visibility was found to be very weak. There were a number of limitations for this study. This was a single-centre study which involved only one RT and four ROs. This study could be improved if more than one RT reviewed the CT-simulation data, removing potential bias. Another limitation is that the patient information was recorded from one electronic medical record source. The electronic medical record had patient information imported from a number of different surgical and medical oncology providers. Each provider provided information in varying degrees of detail and format, and this resulted in incomplete data
which affected some of the data analysis. The novel FourPoint Seroma Visibility Rating Scale requires further investigation; however, it does have potential for use in training RTs in seroma identification. In addition, it would provide more quantitative data if the RT was able to contour the seromas and compare the volumes with those of the RO-contoured seromas. Conclusion This feasibility study confirms that seromas were identified by an experienced RT on CT-simulation data in up to 94% of all postlumpectomy breast cancer patients when compared with those identified by an RO. Although the RT found 54% of seromas to be easily visible to visible, in the cases where seroma was found to be difficult to visualise, further training and/or an adjunct to CT imaging, such as ultrasonography, may prove beneficial. The next phase of this study, which is currently taking place, is to attempt to correlate the detection of seromas during CT simulation with ultrasonography. As a nonionising radiation, ultrasonography has the potential to be used daily as an on-treatment adaptive imaging technology. The results of this subsequent study may provide evidence to support the use of nonionising adaptive technologies to better align the small postsurgical lumpectomy tumour bed with the high-dose boost radiation field.
Table 6 Comparison of Visibility Oultram et aldCMN, Four-Point Seroma Visibility Rating Scale (n ¼ 108)
Oultram et aldCMN, Condensed 2-Point Scale (n ¼ 108)
Mukesh, 2-Point Visibility Scale (n ¼ 648)
Easily visible Visible Difficult to visualise No seroma visible
58 (54%)
Visible
237 (36.6%)
50 (46%)
Subtle or no seroma
411 (63%)
17 41 44 6
(16%) (38%) (40%) (6%)
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Footnotes Contributors: All authors contributed to the conception or design of the work, the acquisition, analysis, or interpretation of the data. All authors were involved in drafting and commenting on the paper and have approved the final version. Funding: This study did not receive any specific grant from funding agencies in the public, commercial, or not for-profit sectors. Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/ coi_disclosure.pdf and declare: no financial relationships with any organizations that might have an interest in thesubmitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. Ethical approval: Before commencing, the research proposal was submitted for review to the Hunter New England Research Ethics Committee. The ethical approval was granted, HNEHREC 14/05/21/5.03. This approval was subsequently submitted and registered by the University of Newcastle Human Research Ethics Administration, H-2014-0277. References [1] Fisher, B., Anderson, S., & Bryant, J., et al. (2002). Twenty-year followup of a randomised trial comparing total mastectomy, lumpectomy and lumpectomy plus irradiation for the treatment of invasive breast cancer. N Engl J Med 347(16), 1233–1241. [2] Veronesi, U., Casinelli, N., & Mariani, L., et al. (2002). Twenty-year follow-up of a randomized study comparing breast-conserving surgery with radical mastectomy for early breast cancer. N Engl J Med 347(16), 1227–1232. [3] Early Breast Cancer Trialists’ Collaborative G (2005). Effects of radiotherapy and of differences in the extent of surgery for early breast cancer on local recurrence and 15-year survival: an overview of the randomised trials. Lancet 366(9503), 2087–2106. [4] NSW CI (2012). Cancer Survival in NSW 2002-2006. Sydney: Cancer Institute NSW. [5] Jalali, R., Singh, S., & Budrukkar, A. (2007). Techniques of tumour bed boost irradiation in breast conserving therapy: current evidence and suggested guidelines. Acta Oncol 46(7), 879–892. [6] Bartelink, H., Horiot, J., & Poortmans, P., et al. (2001). Recurrence rates after treatment of breast cancer with standard radiotherapy with or without additional radiation therapy. N Engl J Med 345(19), 1378–1387. [7] Polgar, C., Fodor, J., & Orosz, Z., et al. (2002). Electron and highdose-rate brachytherapy boost in the conservative treatment of stage III breast cancer first results of the randomized Budapest boost trial. Strahlentherapie und Onkologie 178(11), 615–623.
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[8] Agrawal, A., Ayantunde, A. A., & Cheung, K. L. (2006). Concepts of seroma formation and prevention in breast cancer surgery. ANZ J Surg 76(12), 1088–1095. [9] Harrington, K. J., Harrison, M., & Bayle, P., et al. (1996). Surgical clips in planning the electron boost in breast cancer: a qualitative and quantitative evaluation. Int J Radiat Oncol Biol Phys 34(3), 579–584. [10] Goldberg, H., Prosnitz, R. G., Olson, J. A., & Marks, L. B. (2005). Definition of postlumpectomy tumor bed for radiotherapy boost field planning: CT versus surgical clips. Int J Radiat Oncol Biol Phys 63(1), 209–213. [11] Wang, W., French, J., & Boyages, J. (2012). Put the felt pen away: Time to move on from a clinical mark-up for a breast boost. J Med Imaging Radiat Oncol 56, 375–378. [12] Schaverien, M. V., Stallard, S., Dodwell, D. J., & Doughty, J. C. (2013). Use of boost radiotherapy in oncoplastic breast conserving surgery- A systematic review. Eur J Surg Oncol 39(11), 1179–1185. [13] Yang, Z., Chen, J., & Hu, W., et al. (2010). Planning the breast boost: How accurately do surgical clips represent the CT seroma? Radiother Oncol 97(3), 530–534. [14] Coles, C. E., Moody, A. M., Wilson, C. B., & Burnet, N. G. (2005). Reduction of radiotherapy-induced late complications in early breast cancer: the role of intensity-modulated radiation therapy and partial breast irradiation: Part II d radiotherapy strategies to reduce radiation-induced late effects. Clin Oncol 17(2), 98–110. [15] Oh, K. S., Kong, F. M., Griffith, K. A., Yanke, B., & Pierce, L. J. (2006). Planning the breast tumor bed boost: changes in the excision cavity volume and surgical scar location after breast-conserving surgery and whole-breast irradiation. Int J Radiat Oncol Biol Phys 66(3), 680–686. [16] Messer, P. M., Kirikuta, I. C., Bratengeier, K., & Flentje, M. (1997). CT planning of boost irradiation in radiotherapy of breast cancer after conservative surgery. Radiother Oncol 42(3), 239–243. [17] Gonzales, E., Saltzstein, E., Riedner, C., & Nelson, B. (2003). Seroma formation following breast cancer surgery. Breast J 2003(9), 385–388. [18] Hansen, C. J., de Winton, E., Guglani, S., Vamvakas, E., Willis, D., & Chua, B. H. (2012). Target localisation for tumour bed radiotherapy in early breast cancer. J Med Imaging Radiat Oncol 56(4), 452–457. [19] M. Mason. Sample Size and Saturation in PhD Studies Using Qualitative Interviews. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research. [S1]. v. 11, n.3, Aug. 2010; [ISSN 1438-5627. Available at: http://www.qualitativeresearch.net/index.php/fos/article/ view/1428. [20] Viera, A., & Garrett, J. (2005). Understanding interobserver agreement: the kappa statistic. Fam Med 37(5), 360–363. [21] Mukesh, M., Barnett, J., & Cumming, J., et al. (2012). Association of breast tumour bed seroma with post-operative complications and late normal tissue toxicity: Results from Cambridge breast IMRT trial. Eur J Cancer Surg 38, 918–924. [22] Kader, H. A., Troung, P. T., & Pai, R., et al. (2008). When is CT-based postoperative seroma most useful to plan partial breast radiotherapy? Evaluation of clinical factors affecting seroma volume and clarity. Int J Radiat Oncol Biol Phys 72(4), 1064–1069.
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Journal of Medical Imaging and Radiation Sciences xx (2018) 1-6
Journal de l’imagerie médicale et des sciences de la radiation
www.elsevier.com/locate/jmir
Research Article
A Feasibility Study on the Identification of Postlumpectomy Seromas by a Radiation Therapist Compared with That by Radiation Oncologists in Radiation Therapy Planning for Early Stage Breast Cancer Sharon Oultram, RT (T), MHSc(Ed), MPhil (Research) Candidatea* and Shane Dempsey, GradDipEpi, DipAppSci, MIR, PhDb b
a Senior Clinical Radiation Therapy Educator, Department of Radiation Oncology, Newcastle, NSW, Australia Associate Professor, Deputy Head of School of Health Sciences, Faculty of Health and Medicine, University of Newcastle, Callaghan, NSW, Australia
ABSTRACT Introduction: Computed tomography (CT) simulation is currently used to identify the tumour bed in patients with early stage breast cancer requiring whole-breast and boost radiation therapy. Postlumpectomy breast seromas as visible on CT data have been identified as a proxy for the tumour bed. This study aims to quantify the incidence of postsurgical tumour bed seromas identified at CT simulation and report how well a radiation therapist (RT) is able to identify these seromas compared with those contoured by radiation oncologists (ROs). Methods and Materials: A study was undertaken on electronic patient records and the CT-simulation datasets of early stage breast cancer patients treated in 2013 at the Calvary Mater Newcastle to identify the presence of seromas. Patient and tumour characteristics were reviewed. Data analysed included seroma volumes, as contoured by the ROs, as part of the standard voluming procedure. One RT rated seroma visibility based on the level of difficulty when viewing the seroma. Results: Out of 108 CT datasets, an RT was able to identify the presence of a seroma in 102 cases vs. 104 as contoured by ROs. The number of observed agreements was 106 (98.15% of the observations), and the number of agreements expected by chance was 98.4 (91.15% of the observations). The Kappa statistics equalled 0.791 (SE of kappa ¼ 0.143). The strength of agreement is considered to be ‘‘substantial.’’ Conclusion: RTs play an integral role in contouring during the planning process, and there is scope to expand this role. This research introduced the first step by confirming that a radiation therapist is able to identify seromas on CT-simulation data when compared those contoured by an RO.
* Corresponding author: Sharon Oultram, RT (T), MHSc(Ed), MPhil (Research) Candidate, Senior Clinical Radiation Therapy Educator, Department
RESUM E Introduction : La simulation par tomodensitometrie (TDM) axiale est actuellement utilisee pour identifier le siege tumoral chez les patientes presentant un cancer du sein au stade precoce necessitant une radiotherapie du sein entier ou en dose concentree (‘‘ boost ’’). Les seromes post-lumpectomie visibles sur les donnees de TDM ont ete identifies comme substituts du siege tumoral. Dans cette etude, les auteurs visent a quantifier l’incidence de seromes du siege tumoral post-chirurgie identifies a la simulation par TDM, et a indiquer dans quelle mesure un radiotherapeute est en mesure d’identifier ces seromes en comparaison de ceux contoures par un radio-oncologue. Methodologie et materiel : Une etude a ete faite a partir des dossiers electroniques des patientes et des ensembles de donnees de simulation des patientes presentant un cancer du sein au stade precoce traitees en 2013 a la clinique Calvary Mater Newcastle afin de determiner la presence de seromes. Les caracteristiques des patientes et des tumeurs ont ete examinees. Les donnees analysees comprenaient le volume des seromes, selon le contourage effectue par les radio-oncologue, dans le cadre de la procedure standard de determination du volume. Un radiotherapeute a classifie la visibilite des seromes selon le niveau de difficulte presente au visionnement du serome. Resultats : Sur 108 ensembles de donnees, un radiotherapeute a ete en mesure de determiner la presence de seromes dans 102 cas, contre 104 cas contoures par les radio-oncologue. Le nombre de cas d’accord observe a ete de 106 (98,15% des observations) tandis que le nombre de cas d’accord prevu par la chance etait de 98.4 (91,15% des observations). Le test de concordance de Kappa donne un resultat de 0,791 (SE de kappa ¼ 0,143). La solidite de l’accord est jugee ‘‘substantielle’’. Conclusion : Les radiotherapeutes jouent un r^ole integral dans le contourage durant le processus de planification et il y a place pour
of Radiation Oncology, Calvary Mater Newcastle, Locked Bag 7 Hunter Region Mail Centre, Newcastle, NSW, 2310, Australia. E-mail address: [email protected] (S. Oultram).
1939-8654/$ - see front matter Ó 2018 Published by Elsevier Inc. on behalf of Canadian Association of Medical Radiation Technologists. https://doi.org/10.1016/j.jmir.2018.02.055
une expansion de ce r^ole. Cette recherche etablit une premiere etape, en confirmant que le radiotherapeute peut reconna^ıtre les seromes
sur les donnees de simulation TRM en comparaison de ceux contoures par un radio-oncologue.
Keywords: Radiotherapy; computed tomography; tumour bed; boost
Introduction Numerous studies have confirmed the efficacy of breast conservation surgery with postoperative external beam radiation therapy to the entire breast in early stage localised breast cancer [1–3], with current 5-year survival rates in New South Wales, Australia, at 97% [4]. Radiation therapy for early stage breast cancer includes treatment of the entire breast and, when deemed necessary, the involved local lymph nodes. Numerous studies have indicated that recurrences can occur near the resected primary tumour site [5–7]. To reduce the possibility of recurrence, radiation therapy may include a boost dose of radiation that targets the primary tumour bed site, and this has proven to be effective in reducing the recurrence rates in these early stage breast cancers [6]. Historically, the tumour bed was predominately identified or localised using the surgical scar as a proxy for its position within the breast [8,9]. However, over time, the improvement in surgical techniques resulted in the relationship with the position of a scar, with the location of the tumour bed being increasingly unreliable [8,10–12]. One of the early solutions to improve localisation of the tumour bed was the placement of metal clips within the tumour bed at the time of surgery which could be seen on radiographs [9]. This technique was dependent on the surgeon’s preference of surgical technique and the number of clips inserted [10]. The literature reports that clips can migrate reducing the accuracy of clips as an image-guidance tool [13]. These earlier methods have given way to computed tomography (CT) in the radiation therapy simulation process. The postsurgical tumour bed site can often be identified on a CT scan as a collection of fluid, known as a seroma, and this seroma can be used as an acceptable proxy for the tumour bed [8,14–17]. Literature has indicated that the seroma will reduce over time, and this has been attributed to the reabsorption of the fluid within the seroma and/or the normal breast tissue replacing the surgical cavity [15,18]. A delay from surgery to CT simulation, primarily due to adjuvant chemotherapy, has been reported to have an effect on seroma visibility [15]. At the Radiation Oncology Treatment Centre (ROTC) where this research was conducted, the seroma is contoured by radiation oncologists (ROs) on the CT data. It is contoured to ensure that it is included in the whole of breast treatment volume, but the seroma is not currently used for the localisation and positioning of the boost on treatment. This CT-defined seroma may, however, be able to be used by radiation therapists (RTs) within an adaptive image-guidance framework for the localisation and positioning of the boost volume on treatment. Quantifying 2
the number of cases that may benefit from this identification may provide important information in making the boost treatment more accurate. Although this study focuses on seromas identified on CT-simulation data, a subsequent study is planned to include the use of ultrasound for seroma identification. This feasibility study aims to quantify the incidence of postsurgical tumour bed seromas identified at CT-simulation and to report how well one RT can identify these seromas compared with those identified by ROs during the treatment planning process. Methods and Materials Ethics Before commencing, the research proposal was submitted for review to the Hunter New England Research Ethics Committee. The ethical approval was granted, HNEHREC 14/05/ 21/5.03. This approval was subsequently submitted and registered by the University of Newcastle Human Research Ethics Administration, H-2014-0277. Methods and Data Collection A study was conducted on the CT-simulation datasets of all patients with early stage breast cancer treated with radiation therapy in 2013 at the Calvary Mater Newcastle (CMN), New South Wales, Australia. In this study, one senior RT, the primary investigator who worked at the ROTC, identified and quantified the presence or absence of a seroma on the deidentified CT-simulation datasets of a sample of patients treated for breast cancer in 2013. The RT had over 30 years of clinical experience, which included experience viewing seromas on CT-simulation and ultrasound data for treatment planning purposes. The incidence of seromas, as reported by the RT, was then compared with the same CT-simulation data where a breast seroma was identified, evidenced by a contour, by an RO, and a level of agreement (Kappa statistic) was determined. The CMN is a large progressive ROTC which provides radiation therapy services, along with a private provider, to a population of approximately 800,000 in the Hunter New England Local Area Health District of NSW, Australia. In 2013, the ROTC had two CT simulators, a Toshiba Aquilion and a GE Lightspeed helical CT simulator. All early stage breast cancer patients at the CMN have CT simulation of the treatment area. The CT-simulation breast protocol for both the CT simulators used 120 kV and a slice thickness of 2.0–2.5 mm. A part of the treatment planning workflow involves a planning RT importing patients’ CT-simulation
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data into the VARIAN EclipseÒ Treatment Planning System; this includes contouring the body, lungs, heart, and bones. The ROs are responsible for contouring the posterior baselines of the ipsilateral breast and postlumpectomy breast seromas. Inclusion criteria for this study included the CTsimulation datasets of early stage (T1 and T2 tumours) breast cancer patients who presented for CT simulation to the ROTC in 2013, with an intact breast and who had one or more CT-simulation scan used for treatment planning. Exclusion criteria included patients with mastectomies. To ensure that the study sample was representative of the early stage breast cancer population at the centre, the RT collected a wide variety of clinical and pathological information about each patient, including stage, size, grade, surgical procedure, timepoints (surgery, CT simulation, radiation therapy), and location of the primary tumour. A saturation sampling technique [19] was used to obtain a sample of patient CT-simulation data that represented all early stage T1 and T2 breast cancer patients. Saturation sampling is a qualitative research method used to obtain a sample that is representative of and generalizable to the population of interest. The representative sample is reached when it is determined that further sampling will add no new information related to the research question. The sampling commenced with the review of patients treated in December 2013, and the sampling technique worked backwards through the preceding months until a point was reached where no new or different patient demographic (eg, age) or tumour-related information (eg, grade, stage, positioning in the breast) was obtained. Data collection occurred between May 20, 2014, and July 19, 2014. The sampling technique resulted in a sample size of 100 patients. There were 108 CT-simulation datasets in total as 8 patients had more than one CT simulation or required planning for bilateral disease. A power calculation was also applied to the sample size of 100 patients, each being rated by an RT and an RO, and this resulted in an estimation of the Kappa agreement statistic of at least 0.7% and 95% confidence interval with a margin of error of 15%. Of the 108 CT datasets retrieved, a review demonstrated that 89 had seromas contoured by one of four ROs as part of the initial treatment development; all ROs were the patients’ referring RO. When asked why the seromas on the CT data set had not been contoured, ROs cited the reasons that no seroma was visible, no intentions to deliver boost RT, or the seroma was in the central position of the breast and it was deemed to be covered by the treatment field. To allow those 19 CT datasets without a seroma contour to be included in the study, an independent RO reviewed the CT datasets and indicated whether a seroma was visible or not visible. All 108 CT datasets were then deidentified from the RT, and all seroma contours were hidden before the RT reviewied and analysed the CT data. All CT datasets were given individual identifiers. The CT datasets were reviewed
by the RT for how well the seromas could be seen on CT. The RT reviewed the CT datasets for the presence or absence of a visible seroma using a Four-Point Seroma Visibility Rating Scale developed for this study. The seroma was rated as being 1. 2. 3. 4.
Easily visible, Visible, Difficult to visualise, or Not visible.
Data Analysis All data collected during the study were entered into Microsoft Excel, GraphPad (GraphPad Software Inc) and Stata IC 14 (StataCorp LP) software. The level of agreement between the RT and RO was assessed using a Kappa statistical method, and the effect of the time delay from surgery to CT simulation on seroma visibility was analysed using the Spearman’s rho statistical method. Results Patient Characteristics The patient and tumour characteristics for the sample were recorded for each patient. Table 1 provides an overview of the patient and tumour characteristics of the study sample. Quantifying the Presence of Seromas Table 2 demonstrates that of the 108 CT datasets reviewed by the RT, 102 (94%) seromas were assessed as visible by the RT. The RT used patient documentation, in particular, patient history and surgical and pathological reports, to aid in the identification of seromas. The use of clips was not the standard practice at the time of surgery for these cases; clips were reported in only 3 cases. In 2 cases, seromas were easily visible, and clips only aided the identification of the tumour bed in 1 case. Table 1 Patient and Tumour Characteristics Patient and Tumour Characteristics (n ¼ 100) Age (years) Mean (range) Laterality (%) Right sided Left sided Pathology (%) Infiltrating ductal carcinoma (IDC) Ductal carcinoma in situ (DCIS) Lobular carcinoma Other subtypes Tumour stage (%) T1 T2 Nodal status (%) Negative Positive
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62 y (37–86) 47 53 74 5 12 9 67 33 75 25
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Table 2 RT Rating of the Visibility of Seromas Using the Four-Point Seroma Visibility Rating Scale
Table 4 Duration Between Surgery, CT Simulation, and Commencement of Radiation Therapy
Visibility Rating
Number (%) of Seromas Identified on CT-Simulation Dataset (n ¼108)
Duration
Patients (n¼100)
Weeks
Easily visible Visible Difficult to visualise No seroma visible
17 41 44 6
Surgery to CT Simulation
Surgery to Radiation Therapy Commencement
<4 4–8 >8–12 >12 Median Range
7 43 15 35 11.2 wk 3.1–38.4 wk
1 19 36 44 12.6 wk 5.7–41 wk
(16) (38) (40) (6)
The clinical agreement between the breast seromas identified by the RO, considering the clinical practice standard, and those identified by the RT was analysed. The ROs identified the presence of 104 seromas in 108 CT datasets, whereas the RT identified 102 seromas. The RT could not identify the presence of 2 seromas that the RO identified. The RO and RT agreed on the absence of seromas in 4 CT datasets. The level of agreement between the RO and RT was calculated using the Kappa statistic. Table 3 demonstrates the results of the RO and RT ratings. The number of observed agreements was 106 (98.15% of the observations), and the number of agreements expected by chance was 98.4 (91.15% of the observations); the Kappa statistics equalled 0.791 (SE of kappa ¼ 0.143). The strength of agreement is considered to be ‘‘substantial’’ [20]. It is reported in literature that the presence of seromas reduces over time [15,18]; hence, the dates between surgery, CT simulation, and the commencement of radiation therapy were recorded. Table 4 demonstrates the duration, median times, and the ranges in weeks between these key timepoints. When statistically analysed, the relationship between the time between surgery and CT simulation and the visibility of the seroma, as rated by the RT, was found to be very weak (Spearman’s rho ¼ 0.1491). Discussion There is limited published research investigating the issue of the presence or absence of a seroma after lumpectomy. Compared with three other published studies that focus on postlumpectomy seromas in early stage breast cancer and the presence of seromas and the factors influencing their incidence [15,18,20], the present study compares well with regards to the patient and tumour characteristics (Table 5).
Table 3 Rating of Seromas by ROs and the RT Radiation Therapist Rating
Radiation Oncologist Rating Present Absent Total
4
Present
Absent
102 0 102
2 4 6
Total
104 4 108
To determine how well the seromas are visualised on CT imaging, the Four-Point Seroma Visibility Rating Scale was developed to aid the RT in identifying seromas. Other studies have developed similar rating systems, with two examples being the British Columbia Cancer Agency Seroma Clarity Score [22] and a two-point grading system used by Mukesh et al. [21]. The Seroma Clarity Score 6-point scale, which ranged from the seroma being not detectable (0) to clearest [5], was not adopted for this study as it was developed for use by ROs. Without an advanced level of knowledge, it was believed to be too difficult for an RT to use this without additional training. Mukesh et al [21] used only a 2-point rating system, which categorised the seroma as being visible or subtle/no seroma. This system was found to be too limited to accurately grade the seromas. Oh et al [15] investigated changes to the tumour bed volume and surgical scar location as a result of breastconserving surgery and whole-breast radiation therapy. Oh et al reported the benefits of using CT data for tumour bed localisation over the use of the surgical scar in the clinical markup of the breast boost. An Australian study by Hansen et al [18] also investigated the use of CT in the localisation of the tumour bed. The results compared well with those of Oh et al [15] and found CT-based localisation of the tumour bed superior to the use of the surgical scar in tumour bed localisation. A large study by Mukesh et al [21] reported on the presence or absence of seromas, similar to the present study, using a 2-point rating system, which categorised the seroma as being visible or subtle/no seroma. The results of the CMN study using the 4-point scale were compared with those of Mukesh. To do this, it was necessary to condense the 4-point scale used in the present study to the 2-point scale used by Mukesh. The ratings easily visible and visible were condensed to a visible rating, and difficult to visualise and no seroma present were condensed to subtle or no seroma. According to the 2-point scale, the results in Table 6 demonstrate that Mukesh reported 17% fewer visible seromas. There are a range of factors such as surgical technique; wound healing; and timepoints between surgery, CT simulation, and radiation therapy which may influence the
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Table 5 A Comparison of Patient and Tumour Characteristics Oultram et aldCMN (n ¼ 100) Age (years) Mean (range) Laterality (%) Right sided Left sided Pathology (%) Infiltrating ductal carcinoma (IDC) Ductal carcinoma in situ (DCIS) Lobular carcinoma Other subtypes Tumour stage (%) T1 T2 Nodal status (%) Negative Positive
62 (37–86)
Oh et al [15] (n ¼ 30)
Hansen et al [18] (n ¼ 50)
Mukesh et al [21] (n ¼ 648)
55.4 (34.5–80.5)
54.5 (29–70)
57 (32–81)
47 53
50 50
74 5 12 9
65 26 3 6
67 33
87 13
88 6
9.9
78 22
75 25
83 17
visibility of seromas. At the CMN, there was primarily one surgical technique used, wide local excision, which did not affect the visibility of the seromas in this study. Postoperative complications were recorded; however, there appeared to be no impact on the visibility of seromas. The timepoints between surgery, CT simulation, and radiation therapy were recorded to determine if a delay between these timepoints affected the visibility of seromas. Chemotherapy has been attributed to the delay between surgery and CT simulation, hence impacting on the visibility of the seroma [15,18]. The CMN study found that 36 patients received chemotherapy before radiation therapy, and the resulting delay to CT simulation ranged from 11.2 to 38.4 weeks. Of these 36 patients, 19 had visible seromas, 16 had seromas that were difficult to visualise, and only 1 had no visible seroma. As stated in the results, the relationship between time and visibility was found to be very weak. There were a number of limitations for this study. This was a single-centre study which involved only one RT and four ROs. This study could be improved if more than one RT reviewed the CT-simulation data, removing potential bias. Another limitation is that the patient information was recorded from one electronic medical record source. The electronic medical record had patient information imported from a number of different surgical and medical oncology providers. Each provider provided information in varying degrees of detail and format, and this resulted in incomplete data
which affected some of the data analysis. The novel FourPoint Seroma Visibility Rating Scale requires further investigation; however, it does have potential for use in training RTs in seroma identification. In addition, it would provide more quantitative data if the RT was able to contour the seromas and compare the volumes with those of the RO-contoured seromas. Conclusion This feasibility study confirms that seromas were identified by an experienced RT on CT-simulation data in up to 94% of all postlumpectomy breast cancer patients when compared with those identified by an RO. Although the RT found 54% of seromas to be easily visible to visible, in the cases where seroma was found to be difficult to visualise, further training and/or an adjunct to CT imaging, such as ultrasonography, may prove beneficial. The next phase of this study, which is currently taking place, is to attempt to correlate the detection of seromas during CT simulation with ultrasonography. As a nonionising radiation, ultrasonography has the potential to be used daily as an on-treatment adaptive imaging technology. The results of this subsequent study may provide evidence to support the use of nonionising adaptive technologies to better align the small postsurgical lumpectomy tumour bed with the high-dose boost radiation field.
Table 6 Comparison of Visibility Oultram et aldCMN, Four-Point Seroma Visibility Rating Scale (n ¼ 108)
Oultram et aldCMN, Condensed 2-Point Scale (n ¼ 108)
Mukesh, 2-Point Visibility Scale (n ¼ 648)
Easily visible Visible Difficult to visualise No seroma visible
58 (54%)
Visible
237 (36.6%)
50 (46%)
Subtle or no seroma
411 (63%)
17 41 44 6
(16%) (38%) (40%) (6%)
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Footnotes Contributors: All authors contributed to the conception or design of the work, the acquisition, analysis, or interpretation of the data. All authors were involved in drafting and commenting on the paper and have approved the final version. Funding: This study did not receive any specific grant from funding agencies in the public, commercial, or not for-profit sectors. Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/ coi_disclosure.pdf and declare: no financial relationships with any organizations that might have an interest in thesubmitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. Ethical approval: Before commencing, the research proposal was submitted for review to the Hunter New England Research Ethics Committee. The ethical approval was granted, HNEHREC 14/05/21/5.03. This approval was subsequently submitted and registered by the University of Newcastle Human Research Ethics Administration, H-2014-0277. References [1] Fisher, B., Anderson, S., & Bryant, J., et al. (2002). Twenty-year followup of a randomised trial comparing total mastectomy, lumpectomy and lumpectomy plus irradiation for the treatment of invasive breast cancer. N Engl J Med 347(16), 1233–1241. [2] Veronesi, U., Casinelli, N., & Mariani, L., et al. (2002). Twenty-year follow-up of a randomized study comparing breast-conserving surgery with radical mastectomy for early breast cancer. N Engl J Med 347(16), 1227–1232. [3] Early Breast Cancer Trialists’ Collaborative G (2005). Effects of radiotherapy and of differences in the extent of surgery for early breast cancer on local recurrence and 15-year survival: an overview of the randomised trials. Lancet 366(9503), 2087–2106. [4] NSW CI (2012). Cancer Survival in NSW 2002-2006. Sydney: Cancer Institute NSW. [5] Jalali, R., Singh, S., & Budrukkar, A. (2007). Techniques of tumour bed boost irradiation in breast conserving therapy: current evidence and suggested guidelines. Acta Oncol 46(7), 879–892. [6] Bartelink, H., Horiot, J., & Poortmans, P., et al. (2001). Recurrence rates after treatment of breast cancer with standard radiotherapy with or without additional radiation therapy. N Engl J Med 345(19), 1378–1387. [7] Polgar, C., Fodor, J., & Orosz, Z., et al. (2002). Electron and highdose-rate brachytherapy boost in the conservative treatment of stage III breast cancer first results of the randomized Budapest boost trial. Strahlentherapie und Onkologie 178(11), 615–623.
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[8] Agrawal, A., Ayantunde, A. A., & Cheung, K. L. (2006). Concepts of seroma formation and prevention in breast cancer surgery. ANZ J Surg 76(12), 1088–1095. [9] Harrington, K. J., Harrison, M., & Bayle, P., et al. (1996). Surgical clips in planning the electron boost in breast cancer: a qualitative and quantitative evaluation. Int J Radiat Oncol Biol Phys 34(3), 579–584. [10] Goldberg, H., Prosnitz, R. G., Olson, J. A., & Marks, L. B. (2005). Definition of postlumpectomy tumor bed for radiotherapy boost field planning: CT versus surgical clips. Int J Radiat Oncol Biol Phys 63(1), 209–213. [11] Wang, W., French, J., & Boyages, J. (2012). Put the felt pen away: Time to move on from a clinical mark-up for a breast boost. J Med Imaging Radiat Oncol 56, 375–378. [12] Schaverien, M. V., Stallard, S., Dodwell, D. J., & Doughty, J. C. (2013). Use of boost radiotherapy in oncoplastic breast conserving surgery- A systematic review. Eur J Surg Oncol 39(11), 1179–1185. [13] Yang, Z., Chen, J., & Hu, W., et al. (2010). Planning the breast boost: How accurately do surgical clips represent the CT seroma? Radiother Oncol 97(3), 530–534. [14] Coles, C. E., Moody, A. M., Wilson, C. B., & Burnet, N. G. (2005). Reduction of radiotherapy-induced late complications in early breast cancer: the role of intensity-modulated radiation therapy and partial breast irradiation: Part II d radiotherapy strategies to reduce radiation-induced late effects. Clin Oncol 17(2), 98–110. [15] Oh, K. S., Kong, F. M., Griffith, K. A., Yanke, B., & Pierce, L. J. (2006). Planning the breast tumor bed boost: changes in the excision cavity volume and surgical scar location after breast-conserving surgery and whole-breast irradiation. Int J Radiat Oncol Biol Phys 66(3), 680–686. [16] Messer, P. M., Kirikuta, I. C., Bratengeier, K., & Flentje, M. (1997). CT planning of boost irradiation in radiotherapy of breast cancer after conservative surgery. Radiother Oncol 42(3), 239–243. [17] Gonzales, E., Saltzstein, E., Riedner, C., & Nelson, B. (2003). Seroma formation following breast cancer surgery. Breast J 2003(9), 385–388. [18] Hansen, C. J., de Winton, E., Guglani, S., Vamvakas, E., Willis, D., & Chua, B. H. (2012). Target localisation for tumour bed radiotherapy in early breast cancer. J Med Imaging Radiat Oncol 56(4), 452–457. [19] M. Mason. Sample Size and Saturation in PhD Studies Using Qualitative Interviews. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research. [S1]. v. 11, n.3, Aug. 2010; [ISSN 1438-5627. Available at: http://www.qualitativeresearch.net/index.php/fos/article/ view/1428. [20] Viera, A., & Garrett, J. (2005). Understanding interobserver agreement: the kappa statistic. Fam Med 37(5), 360–363. [21] Mukesh, M., Barnett, J., & Cumming, J., et al. (2012). Association of breast tumour bed seroma with post-operative complications and late normal tissue toxicity: Results from Cambridge breast IMRT trial. Eur J Cancer Surg 38, 918–924. [22] Kader, H. A., Troung, P. T., & Pai, R., et al. (2008). When is CT-based postoperative seroma most useful to plan partial breast radiotherapy? Evaluation of clinical factors affecting seroma volume and clarity. Int J Radiat Oncol Biol Phys 72(4), 1064–1069.
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