Fan Shuxuan, Wang Jing, Hou Yan, Cui Xiaonan, Feng Ziwei, Qi Lisha, Liu Jiaxin, Bian Keyi, Liang Jing, Ye Zhaoxiang, Zheng Sunyi, Ma Wenjuan
Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
School of Public Health, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
Eur J Radiol. 2025 Feb;183:111888. doi: 10.1016/j.ejrad.2024.111888. Epub 2024 Dec 12.
To develop an MRI-based multiregional radiomics model for the noninvasive desmoplastic reaction (DR) classification and prognosis stratification in stage II rectal cancer (RC) patients.
This study retrospectively involved 336 patients with RC from two centers, with 239 from Center 1 divided into training (n = 191) and internal validation (n = 48) datasets at an 8:2 ratio, and 97 from Center 2 serving as external validation dataset. Radiomics features were extracted, and a multiregional radiomics DR (M-RDR) signature was established using multi-level feature selection procedure. The cut-off value for M-RDR was determined using Youden's index. We further evaluated the predictive values of M-RDR on prognosis and adjuvant chemotherapy stratification. The primary outcome was 3-year disease-free survival (DFS), and cox model performance was assessed using AUCs and 95 % confidence intervals.
M-RDR demonstrated a high accuracy in DR classification with AUCs of 0.778 and 0.798 in the training and internal validation datasets. Multivariable analysis confirmed M-RDR as an independent prognostic factor after adjusting for clinicopathological factors.The combined model incorporating M-RDR and clinicopathological factors showed good performance in predicting 3-year DFS, with AUCs of 0.923, 0.908, and 0.891 in the training, internal validation and external validation datasets, respectively. Additionally, patients in the M-RDR-high group who received adjuvant chemotherapy had significantly better DFS compared with those who did not (P < 0.05).
The MRI-based multiregional radiomics model could effectively improve non-invasive DR classification, and was able to enhance postoperative risk stratification and treatment decision-making in stage II RC patients.
建立基于磁共振成像（MRI）的多区域放射组学模型，用于对II期直肠癌（RC）患者进行非侵入性促纤维增生反应（DR）分类及预后分层。
本研究回顾性纳入了来自两个中心的336例RC患者，其中中心1的239例患者按8:2的比例分为训练集（n = 191）和内部验证集（n = 48），中心2的97例患者作为外部验证集。提取放射组学特征，并使用多级特征选择程序建立多区域放射组学DR（M-RDR）特征。使用约登指数确定M-RDR的临界值。我们进一步评估了M-RDR对预后和辅助化疗分层的预测价值。主要结局为3年无病生存期（DFS），使用曲线下面积（AUC）和95%置信区间评估Cox模型性能。
M-RDR在DR分类中表现出较高的准确性，训练集和内部验证集的AUC分别为0.778和0.798。多变量分析证实，在调整临床病理因素后，M-RDR是一个独立的预后因素。结合M-RDR和临床病理因素的联合模型在预测3年DFS方面表现良好，训练集、内部验证集和外部验证集的AUC分别为0.923、0.908和0.891。此外，接受辅助化疗的M-RDR高分组患者的DFS明显优于未接受辅助化疗的患者（P < 0.05）。
基于MRI的多区域放射组学模型可有效改善非侵入性DR分类，并能够增强II期RC患者术后的风险分层和治疗决策。
