Crimì Filippo, D'Alessandro Carlo, Zanon Chiara, Celotto Francesco, Salvatore Christian, Interlenghi Matteo, Castiglioni Isabella, Quaia Emilio, Pucciarelli Salvatore, Spolverato Gaya
Institute of Radiology, Department of Medicine-DIMED, University of Padova, 35128 Padova, Italy.
Third Surgical Clinic, Department of Surgical, Oncological and Gastroenterological Sciences (DiSCOG), University of Padova, 35128 Padova, Italy.
Life (Basel). 2024 Nov 22;14(12):1530. doi: 10.3390/life14121530.
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.
We divided MRI-data from 102 patients into a training cohort ( = 72) and a validation cohort ( = 30). In the training cohort, 52 patients were classified as non-responders and 20 as pCR based on histological results from total mesorectal excision.
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%.
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.
随着保留直肠方案在直肠癌治疗中变得越来越普遍，本研究旨在使用治疗前磁共振成像（MRI）和基于影像组学的机器学习方法预测直肠癌患者对术前放化疗（pCRT）的病理完全缓解（pCR）。
我们将102例患者的MRI数据分为训练队列（n = 72）和验证队列（n = 30）。在训练队列中，根据全直肠系膜切除的组织学结果，52例患者被分类为无反应者，20例为pCR。
我们使用影像组学特征训练了各种机器学习模型，以捕捉反应者和无反应者之间的疾病异质性。表现最佳的模型的曲线下面积（ROC-AUC）为73%，准确率为70%，敏感性为78%，阳性预测值（PPV）为80%。在验证队列中，该模型的敏感性为81%，特异性为75%，准确率为80%。
这些结果突出了影像组学和机器学习在预测治疗反应方面的潜力，并支持将先进的成像和计算方法整合到个性化直肠癌管理中。
