Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China.
The Second Clinical Medical College, Guangxi Medical University, No. 22 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China.
BMC Med Imaging. 2022 May 10;22(1):84. doi: 10.1186/s12880-022-00813-6.
To investigate whether radiomics based on ultrasound images can predict lymphovascular invasion (LVI) of rectal cancer (RC) before surgery.
A total of 203 patients with RC were enrolled retrospectively, and they were divided into a training set (143 patients) and a validation set (60 patients). We extracted the radiomic features from the largest gray ultrasound image of the RC lesion. The intraclass correlation coefficient (ICC) was applied to test the repeatability of the radiomic features. The least absolute shrinkage and selection operator (LASSO) was used to reduce the data dimension and select significant features. Logistic regression (LR) analysis was applied to establish the radiomics model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the comprehensive performance of the model.
Among the 203 patients, 33 (16.7%) were LVI positive and 170 (83.7%) were LVI negative. A total of 5350 (90.1%) radiomic features with ICC values of ≥ 0.75 were reported, which were subsequently subjected to hypothesis testing and LASSO regression dimension reduction analysis. Finally, 15 selected features were used to construct the radiomics model. The area under the curve (AUC) of the training set was 0.849, and the AUC of the validation set was 0.781. The calibration curve indicated that the radiomics model had good calibration, and DCA demonstrated that the model had clinical benefits.
The proposed endorectal ultrasound-based radiomics model has the potential to predict LVI preoperatively in RC.
探讨基于超声图像的放射组学是否能在术前预测直肠癌（RC）的血管淋巴管侵犯（LVI）。
回顾性纳入 203 例 RC 患者，分为训练集（143 例）和验证集（60 例）。从 RC 病变的最大灰阶超声图像中提取放射组学特征。采用组内相关系数（ICC）检验放射组学特征的可重复性。应用最小绝对值收缩和选择算子（LASSO）降维并筛选显著特征。采用逻辑回归（LR）分析建立放射组学模型。采用受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估模型的综合性能。
203 例患者中，33 例（16.7%）为 LVI 阳性，170 例（83.7%）为 LVI 阴性。共报告了 5350 个 ICC 值≥0.75 的放射组学特征，随后进行假设检验和 LASSO 回归降维分析。最终，选取 15 个特征构建放射组学模型。训练集的曲线下面积（AUC）为 0.849，验证集的 AUC 为 0.781。校准曲线表明放射组学模型具有良好的校准度，DCA 表明该模型具有临床获益。
该直肠内超声放射组学模型有望在术前预测 RC 的 LVI。
