Jiang Xiaoting, Zhai Weiling, Song Jiacheng, Shao Wenhui, Zhang Aining, Duan Shaofeng, Qu Feifei, Cheng Wenjun, Luo Chengyan, Wu Feiyun, Liu Xisheng, Chen Ting
Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China.
Central Research Institute, UIH Group, Shanghai, China.
Magn Reson Imaging. 2025 Apr;117:110298. doi: 10.1016/j.mri.2024.110298. Epub 2024 Dec 5.
This study aimed to investigate the correlation between imaging phenotypes of endometrial cancer (EC) and clinical, pathologic, and molecular characteristics, as well as disease-free survival (DFS).
The clinical, pathologic, and molecular characteristics, along with MRI radiomics features, of 356 patients with EC were collected retrospectively. The patients were divided into 2 groups based on radiomics features using unsupervised machine learning. The obtained characteristics and DFS of patients were compared between the various imaging phenotypes.
The lesions with deep myometrial invasion (DMI), lymphovascular space invasion (LVSI), cervical stromal invasion (CSI), lymph node metastasis, aggressive histologic type, advanced postoperative International Federation of Gynecology and Obstetrics (FIGO) stage, overexpression of p53, and absent expression of estrogen receptor or progesterone receptor were associated with poor DFS. Two clusters were identified and defined as imaging phenotype 1 and 2, respectively. Compared with phenotype 2, phenotype 1 exhibited a higher correlation with DMI (33.7 % vs 13.0 %), LVSI (23.8 % vs 9.2 %), CSI (16.3 % vs 3.8 %), aggressive histologic type (36.0 % vs 17.4 %), and advanced FIGO stage (IB or higher, 43.6 % vs 22.3 %) (p < 0.001). The incidence of p53 overexpression was higher in phenotype 1 than in phenotype 2 (20.2 % vs 8.5 %, p = 0.022). Survival analysis exhibited a higher risk of poor DFS in phenotype 1 than in phenotype 2 (log-rank p = 0.002).
EC imaging phenotypes identified through MRI radiomics features were associated with pathologic, molecular characteristics, and DFS, suggesting potential for preoperative risk stratification.
本研究旨在探讨子宫内膜癌(EC)的影像学表型与临床、病理、分子特征以及无病生存期(DFS)之间的相关性。
回顾性收集356例EC患者的临床、病理、分子特征以及MRI影像组学特征。使用无监督机器学习方法根据影像组学特征将患者分为两组。比较不同影像学表型患者的上述特征及DFS。
具有深肌层浸润(DMI)、脉管间隙浸润(LVSI)、宫颈间质浸润(CSI)、淋巴结转移、侵袭性组织学类型、术后国际妇产科联盟(FIGO)分期晚期、p53过表达以及雌激素受体或孕激素受体表达缺失的病变与DFS较差相关。识别出两个聚类,分别定义为影像学表型1和2。与表型2相比,表型1与DMI(33.7%对13.0%)、LVSI(23.8%对9.2%)、CSI(16.3%对3.8%)、侵袭性组织学类型(36.0%对17.4%)以及FIGO分期晚期(IB期或更高,43.6%对22.3%)的相关性更高(p<0.001)。表型1中p53过表达的发生率高于表型2(20.2%对8.5%,p=0.022)。生存分析显示,表型1的DFS较差风险高于表型2(对数秩检验p=0.002)。
通过MRI影像组学特征识别出的EC影像学表型与病理、分子特征及DFS相关,提示其在术前风险分层方面具有潜力。