Prediction of Response to Neoadjuvant Radiotherapy in Patients With Locally Advanced Rectal Cancer by Means of Sequential 18FDG-PET

Int. J. Radiation Oncology Biol. Phys., Vol. 80, No. 1, pp. 91–96, 2011 Copyright Ó 2011 Elsevier Inc. Printed in the USA. All rights reserved 0360-3016/$–see front matter

doi:10.1016/j.ijrobp.2010.01.021

CLINICAL INVESTIGATION

Rectum

PREDICTION OF RESPONSE TO NEOADJUVANT RADIOTHERAPY IN PATIENTS WITH LOCALLY ADVANCED RECTAL CANCER BY MEANS OF SEQUENTIAL 18FDG-PET HENDRIK EVERAERT, M.D., PH.D.,* ANNE HOORENS, M.D., PH.D.,y CHRISTIAN VANHOVE, PH.D.,* ALEXANDRA SERMEUS, M.D.,z GAETANE CEULEMANS, M.D.,* BENEDIKT ENGELS, M.D.,x MARIEKE VERMEERSCH, PH.D.,x DIRK VERELLEN, PH.D.,x DANIEL URBAIN, M.D., PH.D.,z GUY STORME, M.D., PH.D.,x AND MARK DE RIDDER, M.D., PH.D.x Departments of Nuclear Medicine,* Pathology,y Gastroenterology,z and Radiation Oncology,x UZ Brussel, Vrije Universiteit Brussel, Brussels, Belgium Purpose: Morphologic imaging techniques perform poorly in assessing the response to preoperative radiotherapy (RT), mainly because of desmoplastic reactions. The aim of this study was to investigate the potential of sequential 18-fluoro-2-deoxy-D-glucose (18FDG-PET) in assessing the response of rectal cancer to neoadjuvant RT and to determine which parameters can be used as surrogate markers for histopathologic response. Methods and Materials: 18FDG-PET scans were acquired before and during the 5th week after the end of RT. Tracer uptake was assessed semiquantitatively using standardized uptake values (SUV). The percentage differences (%D) between pre- and post-RT scans in SUVmax, SUVmean, metabolic volume (MV), and total glycolytic volume (tGV) were calculated. Results: Forty-five consecutive patients with histologically confirmed rectal adenocarcinoma were enrolled. After neoadjuvant RT, 20 of the 45 patients were classified as histopathologic responders and 25 as non-responders. Intense 18F-FDG uptake was seen in all tumors before neoadjuvant RT (average SUVmax 12.9 ± 6.0). When patients were classified as histologic responders and nonresponders, significant differences in %DSUVmax (55.8% vs. 37.4%, p = 0.023) and %DSUVmean (40.1% vs. 21.0%, p = 0.001) were observed between the two groups. For %DMV and %DtGV, decreases were more prominent in responders but were not significantly different from those in nonresponders. As demonstrated by receiver operating characteristic analysis, %DSUVmean was a more powerful discriminator than was %DSUVmax. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for optimal threshold of %DSUVmean (24.5%) were 80%, 72%, 76%, 70%, and 82% respectively. Conclusion: Sequential 18FDG-PET allows assessment of the response to preoperative RT. Both %DSUVmean and %DSUVmax correlate with histopathologic response and can be used to evaluate and compare the effectiveness of different neoadjuvant treatment strategies. The maximum accuracy figures and the positive predictive value figures for both D%SUVmean and D%SUVmax are, however, too low to justify modification of the standard treatment protocol of an individual patient. Ó 2011 Elsevier Inc. Rectal cancer, Preoperative radiotherapy, Response evaluation, 18FDG-PET, Histologic regression.

individual patient. It remains, indeed, a matter of debate whether concomitant chemotherapy should be administered to all patients with T3–4 disease and whether sphincter preservation may be made on the basis of response to the preoperative treatment (4, 5). Additionally, a standardized imaging protocol for assessing tumor response would be of great value for radiobiologic studies, aiming at identifying prognostic markers or evaluating new radiosensitizers.

INTRODUCTION Preoperative (chemo)radiotherapy followed by total mesorectal excision has become the standard of care in locally advanced rectal cancer (1–3). Unfortunately, not all patients benefit equally from neoadjuvant treatment, and an individual assessment of response to neoadjuvant therapy using imaging techniques could be of great value for tailoring the neoadjuvant regimen and the surgical approach to the Reprint requests to: Mark De Ridder, M.D., Ph.D., Oncology Center, UZ Brussel, Laarbeeklaan 101, B-1090 Brussels, Belgium. Tel: (32) 2-4776147; Fax: (32) 2-4776212; E-mail: mark.deridder@ uzbrussel.be Supported by grants from the Foundation against Cancer, Foundation of Public Interest (219.2008), the ‘‘Wetenschappelijk Fonds W. Gepts UZ Brussel,’’ the Belgian government within the

framework of the translational research in the oncology care programs (NKP_29_045) and the ‘‘Fonds voor Wetenschappelijk Onderzoek–Vlaanderen’’ (G.0134.10). Conflict of interest: none. Received Dec 8, 2009, and in revised form Jan 19, 2010. Accepted for publication Jan 19, 2010. 91

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Conventional imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and endorectal ultrasound, although successfully used in the staging of rectal cancer, are known to perform poorly after neoadjuvant therapy. Because of their inability to distinguish desmoplastic reactions and fibrosis from viable tumor cells, these imaging techniques are of limited value in restaging after neoadjuvant treatment (6–8). The potential role of dynamic contrast-enhanced MRI (DCE-MRI) as a noninvasive tool to predict response to treatment in colorectal cancer was reported about 10 years ago. These studies, based on a limited number of patients, concluded that alterations in microcirculation measured by DCE-MRI could be used to monitor tumor response to (chemo)radiotherapy (9, 10). Reports including larger number of patients confirming these initial results are awaited. In contrast to conventional imaging modalities, metabolic imaging with 18FDG-PET allows the discrimination of fibrosis from viable tumor tissue. In addition, there seems to be a rather strong relationship between 18FDG uptake and cancer cell numbers in several studies (11–13). More than a decade ago, 18FDG-PET with quantitative measurement of tracer accumulation was introduced with success to evaluate the effectiveness of breast cancer treatment (14). Since then, therapy-induced modifications in glucose metabolism have been reported and used in a variety of other cancer types, including rectal cancer, to measure the effect of cytotoxic therapies (15–17). The aim of the current study was to investigate whether semiquantitative measurement of 18FDG accumulation at the primary tumor site using (sequential) PET could be applied to assess the effects of neoadjuvant radiotherapy (RT), using histopathology as a gold standard. In addition, we investigated the potential of several parameters, reflecting tumor aggressiveness and/or tumor burden, derived from 18FDG-PET studies in their ability to discriminate responders from nonresponders. PATIENTS AND METHODS Preoperative RT Forty-five consecutive patients (34 male and 11 female, aged 65.4  12.5 years) with histologically confirmed locally advanced (cT3/ T4) rectal adenocarcinoma were enrolled in a Phase II study, evaluating helical tomotherapy and daily megavolt CT positioning (Tomotherapy Hi Art II system.). Staging and treatment have been previously described (18–20). Essentially, a dose of 46 Gy, in daily fractions of 2 Gy, was delivered to the presacral space and the perineum if abdominoperineal resection was deemed necessary. No concomitant chemotherapy was administered, but the dose of radiation was increased by a simultaneous integrated boost to 55.2 Gy when the circumferential resection margin was less than 2 mm.

18FDG-PET or PET/CT acquisition, reconstruction, and quantification The PET or PET/CT scans were acquired before and during the 5th week after the end of RT using a dedicated PET camera (Ecat Accel, Siemens, Hoffman Estates, IL, USA) in 40 individuals or

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a PET/CT camera for the remaining five (Gemini TF, Philips Medical Systems, OH, USA). The patients fasted for at least 6 hours. Prescanning glucose levels were systematically checked and ranged from 72 to 185 mg/dL (107  17 mg/dL). The activity of 18FDG administered averaged 447  68 MBq for the Ecat Accel and 317  48 MBq when the Gemini TF camera was used. Whole body images corrected for attenuation were acquired, starting 60 minutes after tracer administration. The acquisition and reconstruction parameters were as follows: (1) For Ecat Accel: 3 minutes emission, 2 minutes transmission per bed position, iterative reconstruction of emission data using OSEM (2 iterations, 16 subsets), scatter correction and postreconstruction filtering (6 mm Gauss), forward projection of attenuation correction factors (obtained with 68Ge sources) for each line of response. The resulting images had a transaxial resolution of 6.0 mm. (2) For Gemini TF: 1 minute emission per bed position, iterative reconstruction of emission data using BLOB-OS (3 iterations, 33 subsets), scatter correction, forward projection of CT-derived attenuation correction factors for each line of response. The resulting transaxial resolution of the images was 4.7 mm. The uptake of 18FDG within the tumor was measured using a three-dimensional volume of interest placed over the lesion, carefully avoiding the urinary bladder. Activity measurements were corrected for dose administered, body weight, and decay and were expressed in standardized uptake values (SUV). No correction for glycemia, lean body mass, or body surface was made. The following response parameters were acquired before the start of RT and in the 5th week after completion of RT: SUVmax, SUVmean (average SUV of tumoral pixels with SUV $2.5), metabolic volume (MV, sum of tumor pixels with SUV $2.5) and total glycolytic volume (tGV, MV  SUVmean). In addition, the response indices (percentage difference (%D) between pre- and post-RT scans) for the different parameters were calculated.

Surgery and histologic regression All patients underwent surgery in the 6th week after completion of RT: standard total mesorectal excision was carried out for tumors of the middle and lower third of the rectum and partial mesorectal excision for tumors of the upper third of the rectum (21). Tumor regression (fraction of tumor replaced by fibrous tissue) was graded by histologic evaluation of the surgical specimens according to the criteria described by Dworak et al. (22). Grading of regression was established as follows: Grade 0: no regression Grade 1: dominant tumor mass with obvious fibrosis and/or vasculopathy Grade 2: dominantly fibrotic changes with few tumor cells or groups (easy to find) Grade 3: very few (difficult to find microscopically) tumor cells in fibrotic tissue with or without mucous substance Grade 4: no tumor cells, only fibrotic mass (total regression or response)

Statistical analysis Statistical analysis was performed with GraphPad Prism (GraphPad Software 5.0b, Inc.). The results were expressed in mean and standard deviation. For the comparison of the different response parameters and indices obtained after RT vs. baseline, the Wilcoxon signed rank test (paired, two-tailed) was applied. To evaluate correlations between the different response indices and the stratification of patients according to tumor regression grade, the KruskalWallis test and the Dunn multiple comparison test was used.

18FDG-PET response evaluation for rectal carcinoma d H. EVERAERT et al.

Table 1. Positron emission tomography (RT) parameters obtained at baseline and after neoadjuvant radiotherapy Parameter

Baseline

Post-RT

p (Wilcoxon test)

SUVmax SUVmean MV (mL) tGV (mL)

12.9  6.0 5.2  1.5 60.2  68.2 333.5  423

6.2  2.7 3.5  0.9 15.5  20.5 58.9  75.9

<0.0001 <0.0001 <0.0001 <0.0001

Response parameters and indices for which a significant difference between histologic responders and nonresponders was observed, were further evaluated by receiver operating characteristic analysis. All test were performed at a 95% confidence level.

RESULTS Histopathologic analysis According to the criteria of Dworak et al. (22), tumor regression in the surgical specimens was graded as G0 in 2 (4%), G1 in 23 (51%), G2 in 11 (24%), G3 in 3 (7%), and G4 in 6 (13%) patients. Response parameters and histologic regression The values obtained at baseline and after RT for the different PET response parameters are summarized in Table 1. For all parameters, baseline values were significantly higher in comparison with values obtained after neoadjuvant RT. Figure 1. demonstrates the relationship between the different response parameters (%DSUVmax, %DSUVmean, %DMV, and %DtGV) and histopathologic tumor regression grade. Although larger decreases were associated with higher regression scores for all parameters considered, the differences between patient groups (G0, G1, G2, G3, and G4) were not significant according to the Dunn multiple comparison test. Histopathologic responders vs. nonresponders When patients were regrouped, there were 25 responders (R = G2–G4) and 20 nonresponders (NR = G0–G1). Significant differences were seen for %DSUVmax (55.8  25.7 in R vs. 37.4  27.6 in NR; p = 0.023) and %DSUVmean (40.1  21.8 in R vs. 21.0  17.9 in NR; p = 0.001) (Table 2). For %D MV and %D tGV, a similar observation could be made: larger values in R, but not statistically different from the values in NR. %DSUVmean vs. %DSUVmax As shown in the receiver operating characteristic analysis, %DSUVmean was superior in comparison with to %DSUVmax (area under the curve: 0.786 vs. 0.700) in the capability to discriminate responders from nonresponders (Fig. 2.). To identify the optimal threshold to separate responders from nonresponders, the cutoff point rendering the maximal accuracy was taken. For %DSUVmean the maximum accuracy was 76%, corresponding to an optimal threshold of 24.5%, whereas for %DSUVmax the maximum accuracy was 73%, corresponding to an optimal threshold of 39%. Table 3 summarizes the sensitivity, specificity, positive predictive value (PPV), and negative predictive value for the optimal thresh-

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120 100 80 %D SUVmean %D SUVmax %D MV %D tGV

60 40 20 0

G0

G1

G2

G3

G4

Fig. 1. Bar plot showing the mean and standard deviation of the response parameters in the different groups of patients classified according to the histopathologic response after neoadjuvant radiotherapy. For all parameters, a clear correlation with histopathologic regression score can be observed. The Dunn multiple comparison test demonstrated that the difference between groups was not statistically significant.

old levels. From Fig. 3, showing the relationship between threshold levels and PPV for both %DSUVmean and %DSUVmax, it can be concluded that a PPV >90% cannot be obtained whatever cutoff value is used. DISCUSSION Patients with locally advanced rectal cancer have a higher risk in comparison with less advanced stages for local recurrence, even after radical surgery. Combining radical surgery with neoadjuvant RT or radiochemotherapy is considered the best strategy to prevent local recurrence in this high-risk population (1, 3, 23). However, the response to neoadjuvant therapy is variable. Complete tumor remission (ypT0 in surgical specimens) in the rectal wall is observed in up to 15–30% of patients who undergo neoadjuvant chemoradiation and is a significant favorable prognostic factor (24, 25). Therefore, there is a definite need for surrogate markers that allow evaluation of the effect of neoadjuvant therapy. This might help optimize the therapeutic approach in an individual patient, primarily by reducing the extent and invasiveness of surgery. Recent reports suggest that patients with a good response to neoadjuvant chemoradiation may benefit from sphincterand organ-preserving strategies, such as local excision and contact X-ray therapy, whereas intraoperative RT might considered in poor responders (4, 26–28). Modification of surgery requires a reliable test characterized by very high PPV to avoid undertreatment of individuals who still have viable tumor tissue, because this will almost certainly result in early local recurrence. Ideally, the information should be obtained easily and noninvasively, using imaging techniques that are widely accessible. Computed tomography, MRI, and echoendoscopy, which are accurate imaging modalities in staging rectal cancer, are, however, not well suited for restaging after neoadjuvant therapy because of their inability to differentiate viable tumor cells from posttherapeutic desmoplastic reactions and fibrosis (8). This explains the poor performance of conventional imaging techniques after (neoadjuvant) (chemo)radiation to

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Table 2. Response parameters in responders (G2–G4) and nonresponders (G0–G1) Response parameter

Responders

Nonresponders

p (Wilcoxon test)

D % SUVmean D % SUVmax D % MV D % tGV

40.1  21.8 55.8  25.7 72.9  29.6 80.2  22.5

21.0  17.9 37.4  27.6 62.7  27.5 68.1  24.4

0.001 0.023 n.s. n.s.

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Table 3. Maximum accuracy (Max Acc) for D % SUVmean and D % SUVmax with corresponding optimal thresholds (Opt threshold) and resulting sensitivity (Sens), specificity (Spec), positive predictive value (PPV), and negative predictive value (NPV) Parameter D % SUVmean D % SUVmax

Max Acc Opt Threshold Sens Spec PPV NPV 76 73

24.5 39

80 90

72 60

70 64

82 88

Abbreviation: n.s. = not significant.

differentiate responders from nonresponders (6, 29). The use of DCE-MRI to assess tumor response to (chemo)radiation has recently regained interest and is currently being investigated in various tumor types and tumor models. Although DCE-MRI has been described in the past as ‘‘promising’’ for the evaluation of neoadjuvant treatment of colorectal cancer, more recent data, including larger patient cohorts, that could confirm these findings are lacking (9, 10). Metabolic imaging to assess the effect of neoadjuvant therapy has been successfully used in a variety of different tumor types, including rectal cancer (30, 31). In these studies, several different parameters have been used to characterize the tumor during therapy. Although today SUVmax is most commonly used for this purpose, the optimal method for quantitative analysis of 18FDG-PET is still debated (32). In this study, sequential 18FDG-PET was used to evaluate the effect of neoadjuvant RT, taking into account response parameters that reflect tumor aggressiveness (SUVmax, SUVmean) and also tumor burden (MV and tGV). Inasmuch as no manual delineation of the tumor is required to obtain these parameters, the reproducibility of the measurements will be 100% if the volume of interest does not include hot spots such as the bladder. In addition, an identical imaging protocol was used for both baseline and follow-up (same scanner, comparable injected dose of 18FDG, same uptake time) to minimize the interstudy variability. A definite correlation between %D and histopathologic regression score was observed for all response parame-

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ters. From this correlation it can be concluded that the influx of activated macrophages, neutrophils, fibroblasts, and granulation tissue induced by RT, which may account for a considerable accumulation of FDG in noncancerous parts of tumors, was not obscuring true tumor cell loss (15, 33). Also, stunning of tumor cells—a phenomenon that corresponds to a therapy-induced reversible decrease in glucose metabolism—does not seem to be of major importance (30). The differences between the five groups categorized according to the histologic regression score, however, were not statistically significant, most probably because of the limited number of patients in each group. When the patients were regrouped as either histopathologic responders or nonresponders, significant differences were observed for %DSUVmean and %DSUVmax. This confirms observations made by several other investigators who also demonstrated significant correlations between %DSUVmax, %DSUVmean, and semiquantitative histologic response after neoadjuvant therapy (30, 31, 34–40). For parameters reflecting tumor burden (MV, tGV), the observed differences were not significant. A receiver operating characteristic analysis demonstrated that in our study %DSUVmean was a more powerful than %DSUVmax in discriminating responders from nonresponders who had been treated. This might be explained by the fact that the use of a single pixel to describe the behavior of a mass may not always be representative and is particularly prone to statistical noise. In addition, it cannot be verified whether the pixel with the maximum SUV value in the baseline study is the same as in the study after treatment. For both response parameters, the %D threshold that rendered the highest accuracy to predict histopathologic response was determined.

80 60 40 20 0

%D SUVmean (AUC: 0.786) %D SUVmax (AUC: 0.700)

0

20

40

60

80

100

Fig. 2. Receiver operating characteristic curve analysis for D % SUVmean and D % SUVmax in discriminating responders from nonresponders. X-axis: 100 specificity; Y-axis: sensitivity. The larger area under the curve (AUC) for D%SUVmean in comparison to D%SUVmax indicates that this parameter has a higher predictive value.

Fig. 3. Scatterplots showing the positive predictive value as a function of different threshold-levels for D%SUVmean and D%SUVmax.

18FDG-PET response evaluation for rectal carcinoma d H. EVERAERT et al.

This optimal threshold of %D of 24.5 for SUVmean, which is considerably lower than the threshold of 57.7% used by Rosenberg et al. (40) 5 weeks after chemotherapy and 52% reported by Cascini et al. (39) 12 days after chemoradiotherapy, respectively. For %DSUVmax the optimal threshold in our study was 39%, which closely corresponds with threshold levels of 36% applied by Amthauer et al. (30), 36% by Denecke et al. (41), and 40% by Siegel et al. (37). It remains to be investigated whether these results obtained in patients treated with neoadjuvant RT can be extrapolated to regimens in which chemoradiation is used in a preoperative setting. We believe that both %DSUVmean and %DSUVmax can be used to evaluate and compare different neoadjuvant treatment strategies, but the associated PPV is too low to allow identification of patients that might benefit from reduced surgery. From the investigated relationship between %D threshold levels and the corresponding PPV, it became clear that for neither SUVmean nor DSUVmax a threshold level rendering a PPV >90% could be determined. This indicates that PET cameras of the newer generation, with improved sensitivity and spatial resolution, still fail to detect minimal tumor

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burden and to differentiate it from absence of residual tumor. The observation made in the early days of PET, that rectal cancer patients with a complete metabolic response after RT had a high incidence of early local recurrences if surgery was not performed, is probably still valid today (42).

CONCLUSION Response parameters corresponding to tumor aggressiveness, derived from sequential 18FDG-PET studies, can be considered as valuable surrogate markers of histopathologic response in rectal cancer patients after neoadjuvant RT. Both %DSUVmean and %DSUVmax can be used to evaluate and compare the effectiveness of different neoadjuvant treatment strategies. Measuring %DSUVmean was more powerful than %DSUVmax in its ability to differentiate responders from nonresponders. The maximum accuracy figures and the PPV figures for both D%SUVmean and D%SUVmax are, however, too low to justify modification of the standard treatment protocol of an individual patient.

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