Development and Cross-Institutional Validation of a Comprehensive Machine Learning Model Predicting Response to Neoadjuvant Therapy for Rectal Cancer

View Full Paper

Ask AI

Bookmark

Authors

Discussion

A multi-omics model predicts pathological complete response in rectal cancer, indicating clinical characteristics are vital.

The multi-omics model achieved a mean AUC of 0.90 in training sets for predicting pCR in rectal cancer.
Clinical characteristics significantly improve prediction accuracy, as shown by an AUC of 0.85 in validation sets.
Integrating radiomics and dosiomics models contributes unique insights to the final predictive model for LARC patients.
Validation involved a comprehensive analysis across multiple centers, highlighting the robustness of the findings.

Li et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark

View Full Paper

Ask AI

Bookmark

Authors

A multi-omics model predicts pathological complete response in rectal cancer, indicating clinical characteristics are vital.

The multi-omics model achieved a mean AUC of 0.90 in training sets for predicting pCR in rectal cancer.
Clinical characteristics significantly improve prediction accuracy, as shown by an AUC of 0.85 in validation sets.
Integrating radiomics and dosiomics models contributes unique insights to the final predictive model for LARC patients.
Validation involved a comprehensive analysis across multiple centers, highlighting the robustness of the findings.

Li et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark