本研究旨在构建和验证一种基于磁共振成像（MRI）的放射组学模型，利用机器学习预测直肠癌患者的肿瘤蛋白p53基因状态。该研究纳入了300例行根治手术的直肠癌患者的临床和影像数据，并纳入了166例病理报告显示存在p53基因突变的患者。根据检查时间，将这些患者分配到训练组（n=210）或验证组（n=90）（比例为7:3）。利用训练数据集，通过降维分析了每位患者T2加权影像（T2WI）中原发肿瘤病灶的放射组学特征。多元逻辑回归用于筛选预测特征，并将其与放射组学模型相结合，构建一个预测p53基因状态的诺莫图。使用受试者工作特征曲线（ROC曲线）评估了诺莫图在训练和验证数据集中的准确性和可靠性。利用训练和验证集合进行放射组学模型的诊断效能分别为0.828和0.795，敏感性分别为0.825和0.891，特异性分别为0.722和0.659。利用训练和验证数据集进行诺莫图的诊断效能分别为0.86和0.847，敏感性分别为0.758和0.869，特异性分别为0.833和0.75。基于机器学习的放射组学诺莫图能够预测p53基因状态，并促进术前分子病理诊断。© 2023国际癌症影像学会（ICIS）。

The current study aimed to construct and validate a magnetic resonance imaging (MRI)-based radiomics nomogram to predict tumor protein p53 gene status in rectal cancer patients using machine learning.Clinical and imaging data from 300 rectal cancer patients who underwent radical resections were included in this study, and a total of 166 patients with p53 mutations according to pathology reports were included in these patients. These patients were allocated to the training (n = 210) or validation (n = 90) cohorts (7:3 ratio) according to the examination time. Using the training data set, the radiomic features of primary tumor lesions from T2-weighted images (T2WI) of each patient were analyzed by dimensionality reduction. Multivariate logistic regression was used to screen predictive features, which were combined with a radiomics model to construct a nomogram to predict p53 gene status. The accuracy and reliability of the nomograms were assessed in both training and validation data sets using receiver operating characteristic (ROC) curves.Using the radiomics model with the training and validation cohorts, the diagnostic efficacies were 0.828 and 0.795, the sensitivities were 0.825 and 0.891, and the specificities were 0.722 and 0.659, respectively. Using the nomogram with the training and validation data sets, the diagnostic efficacies were 0.86 and 0.847, the sensitivities were 0.758 and 0.869, and the specificities were 0.833 and 0.75, respectively.The radiomics nomogram based on machine learning was able to predict p53 gene status and facilitate preoperative molecular-based pathological diagnoses.© 2023. International Cancer Imaging Society (ICIS).