Yoo Jeongin, Han Jun Young, Chang Won, Hur Bo Yun, Kim Jae Hyun, Choi Yunhee, Kim Soo Jin, Kim Se Hyung
Department of Radiology, Seoul National University Hospital, 101 Daehakro, Jongno-gu, Seoul, 03080, Korea.
Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
Sci Rep. 2025 Apr 29;15(1):15071. doi: 10.1038/s41598-025-99029-1.
MRI has relatively low sensitivity and specificity in detecting lymph node metastases. This study aimed to develop and validate an MRI radiomics-based model for predicting lateral pelvic lymph node (LPLN) metastasis in rectal cancer patients who underwent LPLN dissection, and to compare its performance with that of radiologists. This multicenter retrospective study included 336 rectal cancer patients (199 men; mean age, 58.9 years ± 11.1 [standard deviation]) who underwent LPLN dissection. Patients were divided into development (n = 190) and validation (n = 146) cohorts. Radiomics features were extracted from MR images, and the Least Absolute Shrinkage and Selection Operator regression was used to construct radiomics and clinical-radiomics models. Model performance was compared with radiologists using receiver operating characteristic (ROC) analysis. Malignant LPLN was diagnosed in 32.4% of the development cohort (65/190) and 32.9% of the validation cohort (48/146) (P = 0.798). Seven radiomics features and two clinical features were selected. The radiomics and clinical-radiomics models demonstrated area under the curves (AUCs) of 0.819 and 0.830 in the development cohort and 0.821 and 0.829 in the validation cohort, respectively. The optimal cut-off (- 0.47) yielded sensitivities of 72.3% and 45.8% and specificities of 82.4% and 87.8% in the development and validation cohorts, respectively. Decision curve analysis indicated no additional net benefit from the clinical-radiomics model compared to the radiomics-only model. Radiologists' AUCs were significantly lower than that of the radiomics model (0.842) and improved with radiomics probability scores (0.734 vs. 0.801; 0.668 vs. 0.791). The MRI-based radiomics model significantly improves the prediction of LPLN metastasis in rectal cancer, outperforming conventional criteria used by radiologists.Trial registration: Retrospectively registered.
磁共振成像（MRI）在检测淋巴结转移方面的敏感性和特异性相对较低。本研究旨在开发并验证一种基于MRI影像组学的模型，用于预测接受侧盆腔淋巴结（LPLN）清扫的直肠癌患者的LPLN转移情况，并将其性能与放射科医生的表现进行比较。这项多中心回顾性研究纳入了336例接受LPLN清扫的直肠癌患者（199例男性；平均年龄58.9岁±11.1[标准差]）。患者被分为开发队列（n = 190）和验证队列（n = 146）。从MR图像中提取影像组学特征，并使用最小绝对收缩和选择算子回归来构建影像组学和临床-影像组学模型。使用受试者工作特征（ROC）分析将模型性能与放射科医生进行比较。在开发队列中，32.4%（65/190）的患者被诊断为恶性LPLN，在验证队列中这一比例为32.9%（48/146）（P = 0.798）。选择了七个影像组学特征和两个临床特征。影像组学模型和临床-影像组学模型在开发队列中的曲线下面积（AUC）分别为0.819和0.830，在验证队列中分别为0.821和0.829。最佳截断值（-0.47）在开发队列和验证队列中的敏感性分别为72.3%和45.8%，特异性分别为82.4%和87.8%。决策曲线分析表明，与仅影像组学模型相比，临床-影像组学模型没有额外的净收益。放射科医生的AUC显著低于影像组学模型（0.842），并且随着影像组学概率评分而提高（0.734对0.801；0.668对0.791）。基于MRI的影像组学模型显著提高了直肠癌LPLN转移的预测能力，优于放射科医生使用的传统标准。试验注册：回顾性注册。
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