Vy Vu Pham Thao, Chien Jerry Chin-Wei, Irama Wiwan, Wu Hao-Yang, Wu Tzu-I, Chen Wei-Yu, Liang Chia-Hao, Hung Truong Nguyen Khanh, Lao Wilson T, Chan Wing P
International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University Taipei 110, Taiwan.
Department of Radiology, Thai Nguyen National Hospital Thai Nguyen 24000, Vietnam.
Am J Cancer Res. 2024 Nov 15;14(11):5400-5410. doi: 10.62347/MALY3908. eCollection 2024.
This study evaluated the efficacy of machine learning and radiomics of preoperative multiparameter MRIs in predicting low- vs high-risk histopathologic features and early vs advanced FIGO stage (IA vs IB or higher) in endometrial cancer. This retrospective study of patients with endometrial cancer histologically confirmed from 2008 through 2023 excluded those with: (a) previous treatment for endometrial carcinoma, (b) incomplete MRI examinations or low-quality MR images, (c) incomplete pathology reports, (d) non-visualized tumors on MRI, or (e) distant metastases. In total, 110 radiomic features were extracted using commercial PACS built-in software following segmentation after sagittal T2-weighted imaging (T2WI), contrast enhanced T1-weighted imaging (CE-T1WI), and diffusion weighted imaging (DWI). The radiomic features from each imaging sequence were utilized for initial modeling. A combined model, which included features retained from all 3 sequences, was then established. The area under the receiver operating characteristic curve (AUC) determined the efficacy of each model. For 5 specific histopathologic features, the combined model achieved AUCs of 0.87 (95% CI, 0.85-0.90), 0.90 (95% CI, 0.88-0.92), 0.88 (95% CI, 0.87-0.90), 0.88 (95% CI, 0.86-0.92), and 0.87 (95% CI, 0.86-0.90). This model incorporated 38 radiomic features: 12 from T2WI, 17 from CE-T1WI, and 9 from DWI. In conclusion, an MRI radiomics-based model can differentiate between early- and advanced-stage endometrial cancer and between low- and high-risk histologic markers, giving it the potential to predict high risk and stratify preoperative risk in those with endometrial cancer. The findings may aid personalized preoperative assessments to guide clinical decision-making in endometrial cancer.
本研究评估了术前多参数磁共振成像（MRI）的机器学习和影像组学在预测子宫内膜癌低风险与高风险组织病理学特征以及早期与晚期国际妇产科联盟（FIGO）分期（IA期与IB期或更高分期）方面的效能。这项对2008年至2023年组织学确诊的子宫内膜癌患者的回顾性研究排除了以下患者：（a）既往接受过子宫内膜癌治疗；（b）MRI检查不完整或MR图像质量低；（c）病理报告不完整；（d）MRI上未显示肿瘤；或（e）远处转移。在矢状面T2加权成像（T2WI）、对比增强T1加权成像（CE-T1WI）和扩散加权成像（DWI）后进行分割，使用商用PACS内置软件总共提取了110个影像组学特征。每个成像序列的影像组学特征用于初始建模。然后建立了一个包含从所有3个序列中保留的特征的联合模型。受试者操作特征曲线（AUC）下的面积确定了每个模型的效能。对于5种特定的组织病理学特征，联合模型的AUC分别为0.87（95%CI，0.85 - 0.90）、0.90（95%CI，0.88 - 0.92）、0.88（95%CI，0.87 - 0.90）、0.88（95%CI，0.86 - 0.92）和0.87（95%CI，0.86 - 0.90）。该模型纳入了38个影像组学特征：12个来自T2WI，17个来自CE-T1WI，9个来自DWI。总之，基于MRI影像组学的模型可以区分早期和晚期子宫内膜癌以及低风险和高风险组织学标志物，使其有可能预测子宫内膜癌患者的高风险并对术前风险进行分层。这些发现可能有助于个性化术前评估，以指导子宫内膜癌的临床决策。
