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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.

Background: Early efficacy outcome measures in rectal cancer after total neoadjuvant treatment are increasingly investigated. We examined the prognostic role of pathological complete response (pCR), tumor regression grading (TRG) and neoadjuvant rectal (NAR) score for disease-free survival (DFS) in patients with rectal carcinoma treated within the CAO/ARO/AIO-12 randomized phase 2 trial. Methods: Distribution of pCR, TRG and NAR score was analyzed using the Pearson’s chi-squared test. Univariable analyses were performed using the log-rank test, stratified by treatment arm. Discrimination ability of non-pCR for DFS was assessed by analyzing the ROC curve as a function of time. Results: Of the 311 patients enrolled, 306 patients were evaluable (Arm A:156, Arm B:150). After a median follow-up of 43 months, the 3-year DFS was 73% in both groups (HR, 0.95, 95% CI, 0.63–1.45, p = 0.82). pCR tended to be higher in Arm B (17% vs. 25%, p = 0.086). In both treatment arms, pCR, TRG and NAR were significant prognostic factors for DFS, whereas survival in subgroups defined by pCR, TRG or NAR did not significantly differ between the treatment arms. The discrimination ability of non-pCR for DFS remained constant over time (C-Index 0.58) but was slightly better in Arm B (0.61 vs. 0.56). Conclusion: Although pCR, TRG and NAR were strong prognostic factors for DFS in the CAO/ARO/AIO-12 trial, their value in selecting one TNT approach over another could not be confirmed. Hence, the conclusion of a long-term survival benefit of one treatment arm based on early surrogate endpoints should be stated with caution.