MRI‐based radiomics nomogram for the preoperative prediction of deep myometrial invasion of FIGO stage I endometrial carcinoma

列线图 医学 阶段(地层学) 接收机工作特性 磁共振成像 放射科 曼惠特尼U检验 无线电技术 逻辑回归 组内相关 阿卡克信息准则 核医学 肿瘤科 内科学 机器学习 计算机科学 生物 临床心理学 古生物学 心理测量学
作者
Mingli Zhao,Feng Wen,Jiaxin Shi,Jing Song,Jiaqi Zhao,Qingling Song,Qingyuan Lai,Yahong Luo,Tao Yu,Xiran Jiang,Wenyan Jiang,Yue Dong
出处
期刊:Medical Physics [Wiley]
卷期号:49 (10): 6505-6516 被引量:13
标识
DOI:10.1002/mp.15835
摘要

Endometrial carcinoma (EC) is one of the most common gynecological malignancies with an increasing incidence, and an accurate preoperative diagnosis of deep myometrial invasion (DMI) is crucial for personalized treatment.To determine the predictive value of a magnetic resonance imaging (MRI)-based radiomics nomogram for the presence of DMI in the International Federation of Gynecology and Obstetrics (FIGO) stage I EC.We retrospectively collected 163 patients with pathologically confirmed stage I EC from two centers and divided all samples into a training group (Center 1) and a validation group (Center 2). Clinical and routine imaging indicators were analyzed by logistical regression to construct a conventional diagnostic model (M1). Radiomics features extracted from the axial T2-weighted and axial contrast-enhanced T1-weighted (CE-T1W) images were treated with the intraclass correlation coefficient, Mann-Whitney U test, least absolute shrinkage and selection operator, and logistic regression analysis with Akaike information criterion to build a combined radiomics signature (M2). A nomogram (M3) was constructed by M1 and M2. Calibration and decision curves were drawn to evaluate the nomogram in the training and validation cohorts. The diagnostic performance of each indicator and model was evaluated by the area under the receiver operating characteristic curve (AUC).The four most significant radiomics features were finally selected from the CE-T1W MRI. For the diagnosis of DMI, the AUCT /AUCV of M1 was 0.798/0.738, the AUCT /AUCV of M2 was 0.880/0.852, and the AUCT /AUCV of M3 was 0.936/0.871 in the training and validation groups, respectively. The calibration curves showed that M3 was in good agreement with the ideal values. The decision curve analysis suggested potential clinical application values of the nomogram.A nomogram based on MRI radiomics and clinical imaging indicators can improve the diagnosis of DMI in patients with FIGO stage I EC.
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