Fu Chunlong, Shao Tingting, Hou Min, Qu Jiali, Li Ping, Yang Zebin, Shan Kangfei, Wu Meikang, Li Weida, Wang Xuan, Zhang Jingfeng, Luo Fanghong, Zhou Long, Sun Jihong, Zhao Fenhua
Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China.
Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Front Oncol. 2023 Feb 20;13:1078863. doi: 10.3389/fonc.2023.1078863. eCollection 2023.
This study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC).
In 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation.
A total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04).
A comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.
本研究旨在建立一种有效的模型，用于术前预测直肠癌（RC）患者的肿瘤结节（TDs）。
在500例患者中，使用高分辨率T2加权（HRT2）成像和扩散加权成像（DWI）等模态从磁共振成像（MRI）中提取影像组学特征。开发了基于机器学习（ML）和深度学习（DL）的影像组学模型，并将其与临床特征相结合用于TD预测。通过五折交叉验证，使用曲线下面积（AUC）评估模型的性能。
为每位患者提取了总共564个量化肿瘤强度、形状、方向和纹理的影像组学特征。HRT2-ML、DWI-ML、合并-ML、HRT2-DL、DWI-DL和合并-DL模型的AUC分别为0.62±0.02、0.64±0.08、0.69±0.04、0.57±0.06、0.68±0.03和0.59±0.04。临床-ML、临床-HRT2-ML、临床-DWI-ML、临床合并-ML、临床-DL、临床-HRT2-DL、临床-DWI-DL和临床合并-DL模型的AUC分别为0.81±0.06、0.79±0.02、0.81±0.02、0.83±0.01、0.81±0.04、0.83±0.04、0.90±0.04和0.83±0.05。临床-DWI-DL模型具有最佳预测性能（准确率0.84±0.05，灵敏度0.94±0.13，特异性0.79±0.04）。
结合MRI影像组学特征和临床特征的综合模型在RC患者的TD预测中表现出良好性能。这种方法有可能帮助临床医生进行RC患者的术前分期评估和个性化治疗。
