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Published on: March 10, 2015
Radiation enteritis associated with temporal sequencing of total neoadjuvant therapy in locally advanced rectal
Chen-Ying Ma1, Yi Fu1, Lou Liu1
1Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Background:
This study aimed to develop and validate a multi-temporal magnetic resonance imaging (MRI)-based delta-radiomics model to accurately predict severe acute radiation enteritis risk in patients undergoing total neoadjuvant therapy (TNT) for locally advanced rectal cancer (LARC).
Methods:
A retrospective analysis was conducted on the data from 92 patients with LARC who received TNT. All patients underwent pelvic MRI at baseline (pre-treatment) and after neoadjuvant radiotherapy (post-RT). Radiomic features of the primary tumor region were extracted from T2-weighted images at both timepoints. Four delta feature strategies were defined (absolute difference, percent change, ratio, and feature fusion) by concatenating pre- and post-RT features. Severe acute radiation enteritis (SARE) was defined as a composite CTCAE-based symptom score of ≥ 3 within the first 2 weeks of radiotherapy. Features were selected via statistical evaluation and least absolute shrinkage and selection operator regression. Support vector machine (SVM) classifiers were trained using baseline, post-RT, delta, and combined radiomic and clinical features. Model performance was evaluated in an independent test set based on the area under the curve (AUC) value and other metrics.
Results:
Only the delta-fusion strategy retained stable radiomic features after selection, and outperformed the difference, percent, and ratio definitions in terms of feature stability and model performance. The SVM model, based on combined delta-fusion radiomics and clinical variables, demonstrated the best predictive performance and generalizability. In the independent test cohort, this combined model demonstrated an AUC value of 0.711, sensitivity of 88.9%, and F1-score of 0.696; these values surpassed those of models built with baseline-only or delta difference features.
Conclusions:
Integrating multi-temporal radiomic features via delta-fusion with clinical factors markedly improved early prediction of SARE in LARC. The delta-fusion approach outperformed conventional delta calculations, and demonstrated superior predictive performance. This highlights its potential in guiding individualized TNT sequencing and proactive toxicity management.
Clinical Registration Number:
NA.
