Development and validation of a predictive model for toxicity of neoadjuvant chemoradiotherapy in rectal cancer in the CAO/ARO/AIO-04 phase III trial
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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 areaBackground: 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.…
Autor(en): | Markus Diefenhardt, Daniel Martin, Ethan B. Ludmir, Maximilian Fleischmann, Ralf-Dieter Hofheinz, Michael Ghadimi, Rebekka Kosmala, Bülent Polat, Tim Friede, Bruce D. Minsky, Claus Rödel, Emmanouil Fokas |
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URN: | urn:nbn:de:bvb:20-opus-288081 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Medizinische Fakultät / Klinik und Poliklinik für Strahlentherapie |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Cancers |
ISSN: | 2072-6694 |
Erscheinungsjahr: | 2022 |
Band / Jahrgang: | 14 |
Heft / Ausgabe: | 18 |
Aufsatznummer: | 4425 |
Originalveröffentlichung / Quelle: | Cancers (2022) 14:18, 4425. https://doi.org/10.3390/cancers14184425 |
DOI: | https://doi.org/10.3390/cancers14184425 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Freie Schlagwort(e): | chemoradiotherapy; neoadjuvant; rectal cancer; risk score; toxicity |
Datum der Freischaltung: | 18.08.2023 |
Datum der Erstveröffentlichung: | 12.09.2022 |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |