Department of Ultrasound, Putuo People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234 Gucui Road, Hangzhou, Zhejiang Province, 310012, China.
BMC Med Imaging. 2023 Oct 27;23(1):168. doi: 10.1186/s12880-023-01123-1.
To explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer.
Data from 259 patients with postoperative pathological confirmation of rectal adenocarcinoma who had received enhanced MRI and Ki-67 detection was divided into 4 cohorts: training (139 cases), internal validation (in-valid, 60 cases), and external validation (ex-valid, 60 cases) cohorts. The patients were divided into low and high Ki-67 expression groups. In the training cohort, DWI, T2WI, and contrast enhancement T1WI (CE-T1) sequence radiomics features were extracted from MRI images. Radiomics marker scores and regression coefficient were then calculated for data fitting to construct a radscore model. Subsequently, clinical features with statistical significance were selected to construct a combined model for preoperative individualized prediction of rectal cancer Ki-67 expression. The models were internally and externally validated, and the AUC of each model was calculated. Calibration and decision curves were used to evaluate the clinical practicality of nomograms.
Three models for predicting rectal cancer Ki-67 expression were constructed. The AUC and Delong test results revealed that the combined model had better prediction performance than other models in three chohrts. A decision curve analysis revealed that the nomogram based on the combined model had relatively good clinical performance, which can be an intuitive prediction tool for clinicians.
The multiparametric MRI radiomics model can provide a noninvasive and accurate auxiliary tool for preoperative evaluation of Ki-67 expression in patients with rectal cancer and can support clinical decision-making.
探讨多参数 MRI 标志物在预测直肠癌患者 Ki-67 表达中的价值。
回顾性分析 259 例经术后病理证实为直肠腺癌且接受增强 MRI 和 Ki-67 检测的患者的临床资料，将其分为 4 组：训练组（139 例）、内部验证组（in-valid，60 例）和外部验证组（ex-valid，60 例）。将患者分为低表达和高表达 Ki-67 两组。在训练组中，从 MRI 图像中提取弥散加权成像（DWI）、T2 加权成像（T2WI）和对比增强 T1 加权成像（CE-T1）序列的放射组学特征。计算放射组学标志物评分和回归系数，对数据进行拟合构建 radscore 模型。然后选择有统计学意义的临床特征构建联合模型，用于术前预测直肠癌 Ki-67 表达的个体化。对模型进行内部和外部验证，计算各模型的 AUC。绘制校准曲线和决策曲线，评价列线图的临床实用性。
构建了三种预测直肠癌 Ki-67 表达的模型。AUC 和 Delong 检验结果表明，联合模型在三组验证队列中的预测性能均优于其他模型。决策曲线分析表明，基于联合模型的列线图具有较好的临床性能，可为临床医生提供直观的预测工具。
多参数 MRI 放射组学模型可为术前评估直肠癌患者 Ki-67 表达提供一种非侵入性、准确的辅助工具，有助于临床决策。
