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Purpose Personalized treatments based on predictions for patient outcome require early characterization of a rectal cancer patient’s sensitivity to treatment. This study has two aims: (1) identify the main patterns of recurrence and response to the treatments (2) evaluate pathologic complete response (pCR) and two-year disease-free survival (2yDFS) for overall survival (OS) and their potential to be relevant intermediate endpoints to predict. Methods Pooled and treatment subgroup analyses were performed on five large European rectal cancer trials (2795 patients), who all received long-course radiotherapy with or without concomitant and/or adjuvant chemotherapy. The ratio of distant metastasis (DM) and local recurrence (LR) rates was used to identify patient characteristics that increase the risk of recurrences. Findings The DM/LR ratio decreased to a plateau in the first 2 years, revealing it to be a critical follow-up period. According to the patterns of recurrences, three patient groups were identified: 5–15% had pCR and were disease free after 2 years (excellent prognosis), 65–75% had no pCR but were disease free (good prognosis) and 15–30% had neither pCR nor 2yDFS (poor prognosis). Interpretation Compared with pCR, 2yDFS is a stronger predictor of OS. To adapt treatment most efficiently, accurate prediction models should be developed for pCR to select patients for organ preservation and for 2yDFS to select patients for more intensified treatment strategies.

Purpose To develop and externally validate a predictive model for pathologic complete response (pCR) for locally advanced rectal cancer (LARC) based on clinical features and early sequential 18 F-FDG PETCT imaging. Materials and methods Prospective data (i.a. THUNDER trial) were used to train ( N =112, MAASTRO Clinic) and validate ( N =78, Universita Cattolica del S. Cuore) the model for pCR (ypT0N0). All patients received long-course chemoradiotherapy (CRT) and surgery. Clinical parameters were age, gender, clinical tumour (cT) stage and clinical nodal (cN) stage. PET parameters were SUV max , SUV mean , metabolic tumour volume (MTV) and maximal tumour diameter, for which response indices between pre-treatment and intermediate scan were calculated. Using multivariate logistic regression, three probability groups for pCR were defined. Results The pCR rates were 21.4% (training) and 23.1% (validation). The selected predictive features for pCR were cT-stage, cN-stage, response index of SUV mean and maximal tumour diameter during treatment. The models' performances (AUC) were 0.78 (training) and 0.70 (validation). The high probability group for pCR resulted in 100% correct predictions for training and 67% for validation. The model is available on the website www.predictcancer.org. Conclusions The developed predictive model for pCR is accurate and externally validated. This model may assist in treatment decisions during CRT to select complete responders for a wait-and-see policy, good responders for extra RT boost and bad responders for additional chemotherapy.

PURPOSE/OBJECTIVE: Chemoradiation (CRT) has been shown to lead to downsizing of an important portion of rectal cancers. In order to tailor treatment at an earlier stage during treatment, predictive models are being developed. Adding blood biomarkers may be attractive for prediction, as they can be collected very easily and determined with excellent reproducibility in clinical practice. The hypothesis of this study was that blood biomarkers related to tumor load, hypoxia and inflammation can help to predict response to CRT in rectal cancer. MATERIAL/METHODS: 295 patients with locally advanced rectal cancer who were planned to undergo CRT were prospectively entered into a biobank protocol (NCT01067872). Blood samples were drawn before start of CRT. Nine biomarkers were selected, based on a previously defined hypothesis, and measured in a standardized way by a certified lab: CEA, CA19-9, LDH, CRP, IL-6, IL-8, CA IX, osteopontin and 25-OH-vitamin D. Outcome was analyzed in two ways: pCR vs. non-pCR and responders (defined as ypT0-2N0) vs. non-responders (all other ypTN stages). RESULTS: 276 patients could be analyzed. 20.7% developed a pCR and 47.1% were classified as responders. In univariate analysis CEA (p=0.001) and osteopontin (p=0.012) were significant predictors for pCR. Taking response as outcome CEA (p

International audience; Background. The objectives of this study were to investigate the predictive value of sequential (18)F-FDG PET scans for pathological tumor response grade (TRG) after preoperative chemoradiotherapy (PCRT) in locally advanced rectal cancer (LARC) and the impact of partial volume effects correction (PVC). Methods. Twenty-eight LARC patients were included. Responders and non-responders status were determined in histopathology. PET indices [SUV max and mean, volume and total lesion glycolysis (TLG)] at baseline and their evolution after one and two weeks of PCRT were extracted by delineation of the PET images, with or without PVC. Their predictive value was investigated using Mann-Whitney-U tests and ROC analysis. Results. Within baseline parameters, only SUV(mean) was correlated with response. No evolution after one week was predictive of the response, whereas after two weeks all the parameters except volume were, the best prediction being obtained with TLG (AUC 0.79, sensitivity 63%, specificity 92%). PVC had no significant impact on these results. Conclusion. Several PET indices at baseline and their evolution after two weeks of PCRT are good predictors of response in LARC, with or without PVC, whereas results after one week are suboptimal. Best predictor was TLG reduction after two weeks, although baseline SUV(mean) had smaller but similar predictive power.

Background and purpose Previously, we showed a good correlation between pathology and an automatically generated PET-contour in rectal cancer. This study analyzed the effect of the use of PET–CT scan on the interobserver variation in GTV definition in rectal cancer and the influence of PET–CT on treatment volumes. Materials and methods Forty two patients diagnosed with rectal cancer underwent an FDG–PET–CT for radiotherapy planning. An automatic contour was created on PET-scan using the source-to-background ratio. The GTV was delineated by 5 observers in 3 rounds: using CT and MRI, using CT, MRI and PET and using CT, MRI and PET auto-contour. GTV volumes were compared and concordance indices (CI) were calculated. Since the GTV is only a small portion of the treatment volume in rectal cancer, a separate analysis was performed to evaluate the influence of PET on the definition of the CTV used in daily clinical practice and the caudal extension of the treatment volumes. Results GTV volumes based on PET were significantly smaller. CIs increased significantly using PET and the best interobserver agreement was observed using PET auto-contours. Furthermore, we found that in up to 29% of patients the CTV based on PET extended outside the CTV used in clinical practice. The caudal border of the treatment volume can be tailored using PET-scan in low seated tumors. Influence of PET on the position of the caudal border was most pronounced in high seated tumors. Conclusion PET–CT increases the interobserver agreement in the GTV definition in rectal cancer, helps to avoid geographical misses and allows tailoring the caudal border of the treatment volume.

Purpose To develop a positron emission tomography (PET)-based response prediction model to differentiate pathological responders from nonresponders. The predictive strength of the model was validated in a second patient group, treated and imaged identical to the patients on which the predictive model was based. Methods and Materials Fifty-one rectal cancer patients were prospectively included in this study. All patients underwent fluorodeoxyglucose (FDG) PET-computed tomography (CT) imaging both before the start of chemoradiotherapy (CRT) and after 2 weeks of treatment. Preoperative treatment with CRT was followed by a total mesorectal excision. From the resected specimen, the tumor regression grade (TRG) was scored according to the Mandard criteria. From one patient group (n = 30), the metabolic treatment response was correlated with the pathological treatment response, resulting in a receiver operating characteristic (ROC) curve based cutoff value for the reduction of maximum standardized uptake value (SUVmax) within the tumor to differentiate pathological responders (TRG 1–2) from nonresponders (TRG 3–5). The applicability of the selected cutoff value for new patients was validated in a second patient group (n = 21). Results When correlating the metabolic and pathological treatment response for the first patient group using ROC curve analysis (area under the curve = 0.98), a cutoff value of 48% SUVmax reduction was selected to differentiate pathological responders from nonresponders (specificity of 100%, sensitivity of 64%). Applying this cutoff value to the second patient group resulted in a specificity and sensitivity of, respectively, 93% and 83%, with only one of the pathological nonresponders being false positively predicted as pathological responding. Conclusions For rectal cancer, an accurate PET-based prediction of the pathological treatment response is feasible already after 2 weeks of CRT. The presented predictive model could be used to select patients to be considered for less invasive surgical interventions or even a “wait and see” policy. Also, based on the predicted response, early modifications of the treatment protocol are possible, which might result in an improved clinical outcome.

Purpose To determine the optimal time point for repeated 18 F-fluorodeoxyglucose-positron emission tomography (PET)-CT imaging during preoperative radiochemotherapy (RCT) and the best predictive factor for the prediction of pathological treatment response in patients with locally advanced rectal cancer. Methods and Materials A total of 30 patients referred for preoperative RCT treatment were included in this prospective study. All patients underwent sequential PET-CT imaging at four time points: prior to therapy, at day 8 and 15 during RCT, and shortly before surgery. Tumor metabolic treatment responses were correlated with the pathological responses by evaluation of the tumor regression grade (TRG) and the pathological TN (ypT) stage of the resected specimen. Results Based on their TRG evaluations, 13 patients were classified as pathological responders, whereas 17 patients were classified as pathological nonresponders. The response index (RI) for the maximum standardized uptake value (SUV max ) on day 15 of RCT was found to be the best predictive factor for the pathological response (area under the curve [AUC] = 0.87) compared to the RI on day 8 (AUC = 0.78) or the RI of presurgical PET imaging (AUC = 0.66). A cutoff value of 43% for the reduction of SUV max resulted in a sensitivity of 77% and a specificity of 93%. Conclusions The SUV max -based RI calculated after the first 2 weeks of RCT provided the best predictor of pathological treatment response, reaching AUCs of 0.87 and 0.84 for the TRG and the ypT stage, respectively. However, a few patients presented with peritumoral inflammatory reactions, which led to mispredictions. Exclusion of these patients further enhanced the predictive accuracy of PET imaging to AUCs of 0.97 and 0.89 for TRG and ypT, respectively.

Purpose: To quantify the influence of fluctuating blood glucose level (BGLs) and the timing of PET acquisition on PET-based predictions of the pathological treatment response in rectal cancer. Material and methods: Thirty patients, diagnosed with locally advanced-rectal-cancer (LARC), were included in this prospective study. Sequential FDG-PET-CT investigations were performed at four time points during and after pre-operative radiochemotherapy (RCT). All PET-data were normalized for the BGL measured shortly before FDG injection. The metabolic treatment response of the tumor was correlated with the pathological treatment response. Results: During RCT, strong intra-patient BGL-fluctuations were observed, ranging from -38.7 to 95.6%. BGL-normalization of the SUVs revealed differences ranging from -54.7 to 34.7% (p

Background Postoperative radiation therapy for stage I endometrial cancer improves locoregional control but is without survival benefit. To facilitate treatment decision support for individual patients, accurate statistical models to predict locoregional relapse (LRR), distant relapse (DR), overall survival (OS), and disease-free survival (DFS) are required. Methods and Materials Clinical trial data from the randomized Post Operative Radiation Therapy for Endometrial Cancer (PORTEC-1; N=714 patients) and PORTEC-2 (N=427 patients) trials and registered group (grade 3 and deep invasion, n=99) were pooled for analysis (N=1240). For most patients (86%) pathology review data were available; otherwise original pathology data were used. Trial variables which were clinically relevant and eligible according to data constraints were age, stage, given treatment (pelvic external beam radiation therapy (EBRT), vaginal brachytherapy (VBT), or no adjuvant treatment, FIGO histological grade, depth of invasion, and lymph-vascular invasion (LVSI). Multivariate analyses were based on Cox proportional hazards regression model. Predictors were selected based on a backward elimination scheme. Model results were expressed by the c-index (0.5-1.0; random to perfect prediction). Two validation sets (n=244 and 291 patients) were used. Results Accuracy of the developed models was good, with training accuracies between 0.71 and 0.78. The nomograms validated well for DR (0.73), DFS (0.69), and OS (0.70), but validation was only fair for LRR (0.59). Ranking of variables as to their predictive power showed that age, tumor grade, and LVSI were highly predictive for all outcomes, and given treatment for LRR and DFS. The nomograms were able to significantly distinguish low- from high-probability patients for these outcomes. Conclusions The nomograms are internally validated and able to accurately predict long-term outcome for endometrial cancer patients with observation, pelvic EBRT, or VBT after surgery. These models facilitate decision support in daily clinical practice and can be used for patient counseling and shared decision making, selecting patients who benefit most from adjuvant treatment, and generating new hypotheses.

Purpose: To determine the impact of chemoradiation therapy (CRT) on overall survival (OS) after resection of pancreatic adenocarcinoma. Methods and Materials: A multicenter retrospective review of 955 consecutive patients who underwent complete resection with macroscopically negative margins (R0-1) for invasive carcinoma (T1-4; N0-1; M0) of the pancreas was performed. Exclusion criteria included metastatic or unresectable disease at surgery, macroscopic residual disease (R2), treatment with intraoperative radiation therapy (IORT), and a histological diagnosis of no ductal carcinoma, or postoperative death (within 60 days of surgery). In all, 623 patients received postoperative radiation therapy (RT), 575 patients received concurrent chemotherapy (CT), and 462 patients received adjuvant CT. Results: Median follow-up was 21.0 months. Median OS after adjuvant CRT was 39.9 versus 24.8 months after no adjuvant CRT (P20 mm (HR = 1.14; CI = 1.05-1.23; P=.002). Multivariate analysis also showed a better prognosis in patients treated in centers with >10 pancreatic resections per year (HR = 0.87; CI = 0.78-0.97; P=.014). Conclusion: This study represents the largest comparative study on adjuvant therapy in patients after resection of carcinoma of the pancreas. Overall survival was better in patients who received adjuvant CRT.

Extensive, multifactorial data sharing is a crucial prerequisite for current and future (radiotherapy) research. However, the cost, time and effort to achieve this are often a roadblock. We present an open-source based data-sharing infrastructure between two radiotherapy departments, allowing seamless exchange of de-identified, automatically translated clinical and biomedical treatment data.

Purpose: To develop and validate an accurate predictive model and a nomogram for pathologic complete response (pCR) after chemoradiotherapy (CRT) for rectal cancer based on clinical and sequential PET-CT data. Accurate prediction could enable more individualised surgical approaches, including less extensive resection or even a wait-and-see policy. Methods and materials: Population based databases from 953 patients were collected from four different institutes and divided into three groups: clinical factors (training: 677 patients, validation: 85 patients), pre-CRT PET-CT (training: 114 patients, validation: 37 patients) and post-CRT PET-CT (training: 107 patients, validation: 55 patients). A pCR was defined as ypT0N0 reported by pathology after surgery. The data were analysed using a linear multivariate classification model (support vector machine), and the model's performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Results: The occurrence rate of pCR in the datasets was between 15% and 31%. The model based on clinical variables (AUC(train) = 0.61 +/- 0.03, AUC(validation) = 0.69 +/- 0.08) resulted in the following predictors: cT- and cN-stage and tumour length. Addition of pre-CRT PET data did not result in a significantly higher performance (AUC(train) = 0.68 +/- 0.08, AUC(validation) = 0.68 +/- 0.10) and revealed maximal radioactive isotope uptake (SUV(max)) and tumour location as extra predictors. The best model achieved was based on the addition of post-CRT PET-data (AUC(train) = 0.83 +/- 0.05, AUC(validation) = 0.86 +/- 0.05) and included the following predictors: tumour length, post-CRT SUV(max) and relative change of SUV(max). This model performed significantly better than the clinical model (p(train)

Evidence is accumulating that radiotherapy of non-small cell lung cancer patients can be optimized by escalating the tumour dose until the normal tissue tolerances are met. To further improve the therapeutic ratio between tumour control probability and the risk of normal tissue complications, we firstly need to exploit inter patient variation. This variation arises, e.g. from differences in tumour shape and size, lung function and genetic factors. Secondly improvement is achieved by taking into account intra-tumour and intra-organ heterogeneity derived from molecular and functional imaging. Additional radiation dose must be delivered to those parts of the tumour that need it the most, e.g. because of increased radio-resistance or reduced therapeutic drug uptake, and away from regions inside the lung that are most prone to complication. As the delivery of these treatments plans is very sensitive for geometrical uncertainties, probabilistic treatment planning is needed to generate robust treatment plans. The administration of these complicated dose distributions requires a quality assurance procedure that can evaluate the treatment delivery and, if necessary, adapt the treatment plan during radiotherapy.

Background and purpose: The purpose of this study was to prospectively investigate metabolic changes of rectal tumors after 1 week of treatment of either radiochemotherapy (28 x 1.8 Gy + Capecitabine) (RCT) or hypofractionated radiotherapy (5 x 5 Gy) alone (RT). Materials and methods: Fourty-six rectal cancer patients, 25 RCT- and 21 RT-patients, were included in this study. Sequential FDG-PET-CT scans were performed for each of the included patients both prior to treatment and after the first week of treatment. Consecutively, the metabolic treatment response of the tumor was evaluated. Results: For the patients referred for pre-operative RCT, significant reductions of SUV(mean) (p