Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.).
School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China (H.X.).
Acad Radiol. 2024 Jul;31(7):2753-2772. doi: 10.1016/j.acra.2023.07.016. Epub 2023 Aug 27.
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.
为了在局部进展期直肠癌患者中术前预测侧盆淋巴结（LPLN）转移（LPLNM），我们开发了一种基于磁共振成像（MRI）的放射组学模型。
我们回顾性纳入了 263 例接受直肠全系膜切除术和 LPLN 解剖的局部进展期直肠癌患者。使用基线 MRI 图像上原发肿瘤和 LPLN 的放射组学特征构建放射组学模型，并将其放射组学评分进行组合，开发出放射组学评分系统。采用 logistic 回归建立临床预测模型。通过多变量 logistic 回归分析，将放射组学评分与显著的临床危险因素（基线癌胚抗原（CEA）、临床环周切缘状态和 LPLN 短轴直径）相结合，建立混合预测模型。该混合模型以混合临床-放射组学列线图呈现，并对其校准、判别和临床实用性进行评估。
共有 148 例患者纳入分析，并随机分为训练队列（n=104）和独立内部测试队列（n=44）。混合临床-放射组学模型具有最高的判别能力，在测试队列中的 AUC 为 0.843[95%CI，0.706-0.968]，高于临床模型[AUC（95%CI）=0.772（0.589-0.856）]和放射组学模型[AUC（95%CI）=0.731（0.613-0.849）]。混合预测模型也具有良好的校准度，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 的放射组学模型，该模型结合了放射组学评分和显著的临床危险因素。该模型有望为局部进展期直肠癌患者的 LPLNM 个体化术前预测提供帮助。
