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 su...
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| Hauptverfasser: | , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
12 September 2022
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| In: |
Cancers
Year: 2022, Jahrgang: 14, Heft: 18, Pages: 1-12 |
| ISSN: | 2072-6694 |
| DOI: | 10.3390/cancers14184425 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.3390/cancers14184425 Verlag, kostenfrei, Volltext: https://www.mdpi.com/2072-6694/14/18/4425 
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| Verfasserangaben: | 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 and Emmanouil Fokas |
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| 245 | 1 | 0 | |a Development and validation of a predictive model for toxicity of neoadjuvant chemoradiotherapy in rectal cancer in the CAO/ARO/AIO-04 phase III trial |c 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 and Emmanouil Fokas |
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| 500 | |a Dieser Artikel gehört zum Special Issue: Advances in radiotherapy and prognosis of rectal cancer | ||
| 500 | |a Gesehen am 18.09.2023 | ||
| 520 | |a 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. | ||
| 650 | 4 | |a chemoradiotherapy | |
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| 700 | 1 | |a Ludmir, Ethan B. |e VerfasserIn |4 aut | |
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