为了预测局部晚期直肠癌患者术前的盆腔淋巴结转移，我们开发了一种基于核磁共振成像（MRI）的影像组学模型。材料和方法：我们回顾性纳入了263例接受全系膜切除和盆腔淋巴结清扫手术的直肠癌患者。利用基线MRI图像中原发病灶和盆腔淋巴结的影像组学特征构建了一个影像组学模型，并将它们的影像组学评分相结合，开发了一个影像组学评分系统。采用逻辑回归分析建立了一个临床预测模型。通过多元逻辑回归分析将影像组学评分与显著的临床危险因素（基线癌胚抗原，临床环形切除缘状态和盆腔淋巴结短轴直径）集成，创建了一个混合预测模型。这个混合模型通过混合临床-影像组学标度图展示，并评估了其标定性、判别性和临床实用性。总共纳入了148例患者进行分析，并随机分为训练组（n = 104）和独立内部测试组（n = 44）。在测试组中，混合临床-影像组学模型显示出最高的判别能力，接受者操作特征曲线下面积（AUC）为0.843（95%置信区间[CI]，0.706-0.968），相比之下，临床模型的AUC [95% CI] = 0.772（0.589-0.856），影像组学模型的AUC [95% CI] = 0.731（0.613-0.849））。混合预测模型也显示了良好的标定性，决策曲线分析证实了其临床实用性。本研究开发了一种基于MRI的混合影像组学模型，结合了影像组学评分和显著的临床危险因素。这个模型有望用于局部晚期直肠癌患者术前个体化的预测盆腔淋巴结转移。本研究的数据可向通讯作者申请获取。版权所有 © 2023 The Association of University Radiologists。由Elsevier Inc.出版。保留所有权利。

To develop a magnetic resonance imaging (MRI)-based radiomics model for preoperative prediction of lateral pelvic lymph node (LPLN) metastasis (LPLNM) in patients with locally advanced rectal cancer MATERIALS AND METHODS: We retrospectively enrolled 263 patients with rectal cancer who underwent total mesorectal excision and LPLN dissection. Radiomics features from the primary lesion and LPLNs on baseline MRI images were utilized to construct a radiomics model, and their radiomics scores were combined to develop a radiomics scoring system. A clinical prediction model was developed using logistic regression. A hybrid predicting model was created through multivariable logistic regression analysis, integrating the radiomics score with significant clinical risk factors (baseline Carcinoembryonic Antigen (CEA), clinical circumferential resection margin status, and the short axis diameter of LPLN). This hybrid model was presented with a hybrid clinical-radiomics nomogram, and its calibration, discrimination, and clinical usefulness were assessed.A total of 148 patients were included in the analysis and randomly divided into a training cohort (n = 104) and an independent internal testing cohort (n = 44). The hybrid clinical-radiomics model exhibited the highest discrimination, with an area under the receiver operating characteristic (AUC) of 0.843 [95% confidence interval (CI), 0.706-0.968] in the testing cohort compared to the clinical model [AUC (95% CI) = 0.772 (0.589-0.856)] and radiomics model [AUC (95% CI) = 0.731 (0.613-0.849)]. The hybrid prediction model also demonstrated good calibration, and decision curve analysis confirmed its clinical usefulness.This study developed a hybrid MRI-based radiomics model that incorporates a combination of radiomics score and significant clinical risk factors. The proposed model holds promise for individualized preoperative prediction of LPLNM in patients with locally advanced rectal cancer.The data presented in this study are available on request from the corresponding author.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.