Life (Basel, Switzerland) · 2024
A Machine Learning Model Based on MRI Radiomics to Predict Response to Chemoradiation Among Patients with Rectal Cancer.
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Crimì Filippo, D'Alessandro Carlo, Zanon Chiara, Celotto Francesco, Salvatore Christian, Interlenghi Matteo, Castiglioni Isabella, Quaia Emilio, Pucciarelli Salvatore, Spolverato Gaya
Аннотация
BACKGROUND: With rectum-sparing protocols becoming more common for rectal cancer treatment, this study aimed to predict the pathological complete response (pCR) to preoperative chemoradiotherapy (pCRT) in rectal cancer patients using pre-treatment MRI and a radiomics-based machine learning approach.
METHODS: We divided MRI-data from 102 patients into a training cohort (n = 72) and a validation cohort (n = 30). In the training cohort, 52 patients were classified as non-responders and 20 as pCR based on histological results from total mesorectal excision.
RESULTS: We trained various machine learning models using radiomic features to capture disease heterogeneity between responders and non-responders. The best-performing model achieved a receiver operating characteristic area under the curve (ROC-AUC) of 73% and an accuracy of 70%, with a sensitivity of 78% and a positive predictive value (PPV) of 80%. In the validation cohort, the model showed a sensitivity of 81%, specificity of 75%, and accuracy of 80%.
CONCLUSIONS: These results highlight the potential of radiomics and machine learning in predicting treatment response and support the integration of advanced imaging and computational methods for personalized rectal cancer management.
METHODS: We divided MRI-data from 102 patients into a training cohort (n = 72) and a validation cohort (n = 30). In the training cohort, 52 patients were classified as non-responders and 20 as pCR based on histological results from total mesorectal excision.
RESULTS: We trained various machine learning models using radiomic features to capture disease heterogeneity between responders and non-responders. The best-performing model achieved a receiver operating characteristic area under the curve (ROC-AUC) of 73% and an accuracy of 70%, with a sensitivity of 78% and a positive predictive value (PPV) of 80%. In the validation cohort, the model showed a sensitivity of 81%, specificity of 75%, and accuracy of 80%.
CONCLUSIONS: These results highlight the potential of radiomics and machine learning in predicting treatment response and support the integration of advanced imaging and computational methods for personalized rectal cancer management.