放射治疗后直肠毒性是前列腺癌患者常见的副作用之一。辐射组学是一种非侵入性、低成本的方法，用于建立预测放射毒性的模型，不具备先前方法的局限性。这些模型是使用个体患者信息开发的，并具有可靠和可接受的性能。本研究通过评估计算机断层扫描（CT）和磁共振（MR）图像的辐射组学特征，利用机器学习（ML）方法预测辐射诱导的直肠毒性进行了研究。 本前瞻性试验纳入了经病理证实为前列腺癌的70名男性患者，适应于三维放射疗法（3DCRT）。使用直肠壁CT和MR图像提取了一阶、形态学和纹理特征。使用最小绝对收缩和选择算子（LASSO）进行特征选择。采用随机森林（RF）、决策树（DT）、逻辑回归（LR）和K最近邻（KNN）等分类器，基于辐射组学、剂量学和临床数据的单独或组合创建模型。使用受试者工作特征曲线（ROC）下的面积（AUC）、准确度、敏感度和特异度评估每个模型的性能。 将MR图像的辐射组学特征与临床和剂量学数据相结合的模型取得了最佳结果，AUC均值为0.79，准确度为77.75%，特异度为82.15%，敏感度为67%。这项研究表明，作为预测辐射诱导的直肠毒性的辐射组学标志，MR图像优于CT图像。 ©2023 Australasian College of Physical Scientists and Engineers in Medicine.

Rectal toxicity is one of the common side effects after radiotherapy in prostate cancer patients. Radiomics is a non-invasive and low-cost method for developing models of predicting radiation toxicity that does not have the limitations of previous methods. These models have been developed using individual patients' information and have reliable and acceptable performance. This study was conducted by evaluating the radiomic features of computed tomography (CT) and magnetic resonance (MR) images and using machine learning (ML) methods to predict radiation-induced rectal toxicity.Seventy men with pathologically confirmed prostate cancer, eligible for three-dimensional radiation therapy (3DCRT) participated in this prospective trial. Rectal wall CT and MR images were used to extract first-order, shape-based, and textural features. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Classifiers such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and K-Nearest Neighbors (KNN) were used to create models based on radiomic, dosimetric, and clinical data alone or in combination. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity were used to assess each model's performance.The best outcomes were achieved by the radiomic features of MR images in conjunction with clinical and dosimetric data, with a mean of AUC: 0.79, accuracy: 77.75%, specificity: 82.15%, and sensitivity: 67%.This research showed that as radiomic signatures for predicting radiation-induced rectal toxicity, MR images outperform CT images.© 2023. Australasian College of Physical Scientists and Engineers in Medicine.