Outcome of Colorectal Cancer Patients Treated with Combination Bevacizumab Therapy: A Pooled Retrospective Analysis of Three European Cohorts from the Angiopredict Initiative

Background/Aims: This study is aimed at analyzing the survival rates and prognostic factors of stage IV colorectal cancer patients from 3 European cohorts undergoing combination chemotherapy with bevacizumab. Methods: Progression free-survival (PFS) and overall survival (OS) were analyzed in 172 patients using the Kaplan–Meier method and uni- and multivariable Cox proportional hazards regression models. Results: The median PFS was 9.7 and the median OS 27.4 months. Patients treated at centers in Germany (n = 97), Ireland (n = 32), and The Netherlands (n = 43) showed a median PFS of 9.9, 9.2, and 9.7 months, OS of 34.0, 20.5, and 25.1 months, respectively. Patients >65 years had a significantly shorter PFS (9.5 vs. 9.8 months) but not OS (27.4 vs. 27.5 months) than younger patients. High tumor grade (G3/4) was associated with a shorter PFS, T4 classification with both shorter PFS and OS. Fluoropyrimidine (FP) chemotherapy backbones (doublets and single) had comparable outcomes, while patients not receiving FP backbones had a shorter PFS. In multivariable analysis, age and non-FP backbone were associated with inferior PFS, T4 classification and therapy line >2nd were significantly associated with poor PFS and OS. Conclusion: The observed survival rates confirm previous studies and demonstrate reproducible benefits of combination bevacizumab regimens. Classification T4, non-FP chemotherapy backbone, and age >65 were associated with inferior outcome

Are We There Yet? The Value of Deep Learning in a Multicenter Setting for Response Prediction of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiotherapy

This retrospective study aims to evaluate the generalizability of a promising state-of-the-art multitask deep learning (DL) model for predicting the response of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (nCRT) using a multicenter dataset. To this end, we retrained and validated a Siamese network with two U-Nets joined at multiple layers using pre- and post-therapeutic T2-weighted (T2w), diffusion-weighted (DW) images and apparent diffusion coefficient (ADC) maps of 83 LARC patients acquired under study conditions at four different medical centers. To assess the predictive performance of the model, the trained network was then applied to an external clinical routine dataset of 46 LARC patients imaged without study conditions. The training and test datasets differed significantly in terms of their composition, e.g., T-/N-staging, the time interval between initial staging/nCRT/re-staging and surgery, as well as with respect to acquisition parameters, such as resolution, echo/repetition time, flip angle and field strength. We found that even after dedicated data pre-processing, the predictive performance dropped significantly in this multicenter setting compared to a previously published single- or two-center setting. Testing the network on the external clinical routine dataset yielded an area under the receiver operating characteristic curve of 0.54 (95% confidence interval [CI]: 0.41, 0.65), when using only pre- and post-therapeutic T2w images as input, and 0.60 (95% CI: 0.48, 0.71), when using the combination of pre- and post-therapeutic T2w, DW images, and ADC maps as input. Our study highlights the importance of data quality and harmonization in clinical trials using machine learning. Only in a joint, cross-center effort, involving a multidisciplinary team can we generate large enough curated and annotated datasets and develop the necessary pre-processing pipelines for data harmonization to successfully apply DL models clinically

Development and Validation of a Predictive Model for Toxicity of Neoadjuvant Chemoradiotherapy in Rectal Cancer in the CAO/ARO/AIO-04 Phase III Trial

Background: There is a lack of predictive models to identify patients at risk of high neoadjuvant chemoradiotherapy (CRT)-related acute toxicity in rectal cancer. Patient and Methods: The CAO/ARO/AIO-04 trial was divided into a development (n = 831) and a validation (n = 405) cohort. Using a best subset selection approach, predictive models for grade 3–4 acute toxicity were calculated including clinicopathologic characteristics, pretreatment blood parameters, and baseline results of quality-of-life questionnaires and evaluated using the area under the ROC curve. The final model was internally and externally validated. Results: In the development cohort, 155 patients developed grade 3–4 toxicities due to CRT. In the final evaluation, 15 parameters were included in the logistic regression models using best-subset selection. BMI, gender, and emotional functioning remained significant for predicting toxicity, with a discrimination ability adjusted for overfitting of AUC 0.687. The odds of experiencing high-grade toxicity were 3.8 times higher in the intermediate and 6.4 times higher in the high-risk group (p < 0.001). Rates of toxicity (p = 0.001) and low treatment adherence (p = 0.007) remained significantly different in the validation cohort, whereas discrimination ability was not significantly worse (DeLong test 0.09). Conclusion: We developed and validated a predictive model for toxicity using gender, BMI, and emotional functioning. Such a model could help identify patients at risk for treatment-related high-grade toxicity to assist in treatment guidance and patient participation in shared decision making

Pre- and Postoperative Capecitabine Without or With Oxaliplatin in Locally Advanced Rectal Cancer: PETACC 6 Trial by EORTC GITCG and ROG, AIO, AGITG, BGDO, and FFCD.

PURPOSE: The PETACC 6 trial investigates whether the addition of oxaliplatin to preoperative capecitabine-based chemoradiation and postoperative capecitabine improves disease-free survival (DFS) in locally advanced rectal cancer. METHODS: Between November 2008 and September 2011, patients with rectal adenocarcinoma within 12 cm from the anal verge, T3/4 and/or node positive, were randomly assigned to 5 weeks preoperative capecitabine-based chemoradiation (45-50.4 Gy) followed by six cycles of adjuvant capecitabine, both without (control arm, 1) or with (experimental arm, 2) oxaliplatin. The primary end point was improvement of 3-year DFS by oxaliplatin from 65% to 72% (hazard ratio [HR], 0.763). RESULTS: A total of 1,094 patients were randomly assigned (intention to treat), and 1,068 eligible patients started their allocated treatment (arm 1, 543; arm 2, 525), with completion of protocol treatment in 68% (arm 1) v 54% (arm 2). A higher rate of grade 3/4 adverse events was reported in the experimental arm (14.4% v 37.3% and 23.4% v 46.6% for neoadjuvant and adjuvant treatment, respectively). At a median follow-up of 68 months (interquartile range, 58-74 months), 157 and 156 DFS events were observed in arms 1 and 2, respectively (adjusted HR, 1.02; 95% CI, 0.82 to 1.28; P = .835). Three-year DFS rate was not different, with 76.5% (95% CI, 72.7% to 79.9%) in arm 1, which is higher than anticipated, and 75.8% (95% CI, 71.9% to 79.3%) in arm 2. The 7-year DFS and overall survival (OS) rates were not different as well, with DFS of 66.1% v 65.5% (HR, 1.02) and OS of 73.5% v 73.7% (HR, 1.19) in arms 1 and 2, respectively. Subgroup analyses revealed heterogeneity in treatment effect according to German versus non-German site location, without detectable confounding factors in multivariable analysis. CONCLUSION: The addition of oxaliplatin to preoperative capecitabine-based chemoradiation and postoperative adjuvant chemotherapy impairs tolerability and feasibility and does not improve efficacy.status: Published onlin