MRI‐based radiomics nomogram for the preoperative prediction of deep myometrial invasion of FIGO stage I endometrial carcinoma. Issue 10 (15th August 2022)

• Record Type: Journal Article
• Authors: Zhao, Mingli; Wen, Feng; Shi, Jiaxin; Song, Jing; Zhao, Jiaqi; Song, Qingling; Lai, Qingyuan; Luo, Yahong; Yu, Tao; Jiang, Xiran; Jiang, Wenyan; Dong, Yue
• Is Part Of: Medical physics. Volume 49:Issue 10(2022)
• Page Start: 6505
• Page End: 6516
• Publication Date: 2022-08-15
• Subjects: deep myometrial invasion -- endometrial carcinoma -- magnetic resonance imaging -- nomogram -- radiomics; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15835
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Background: 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. Objective: 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. Methods: 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). Result: 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. Conclusion: A nomogram based on MRI radiomics and clinical imaging indicators can improve the diagnosis of DMI in patients with FIGO stage I EC.