@article{DiefenhardtMartinLudmiretal.2022,
  author    = {Diefenhardt, Markus and Martin, Daniel and Ludmir, Ethan B. and Fleischmann, Maximilian and Hofheinz, Ralf-Dieter and Ghadimi, Michael and Kosmala, Rebekka and Polat, B{\"u}lent and Friede, Tim and Minsky, Bruce D. and R{\"o}del, Claus and Fokas, Emmanouil},
  title     = {Development and validation of a predictive model for toxicity of neoadjuvant chemoradiotherapy in rectal cancer in the CAO/ARO/AIO-04 phase III trial},
  series = {Cancers},
  volume    = {14},
  journal   = {Cancers},
  number    = {18},
  issn      = {2072-6694},
  doi       = {10.3390/cancers14184425},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-288081},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}