Radiomics analysis of multiparametric MRI for preoperative prediction of microsatellite instability status in endometrial cancer: a dual-center study

Yaju Jia; Lina Hou; Jintao Zhao; Jialiang Ren; Dandan Li; Haiming Li; Yanfen Cui

doi:10.3389/fonc.2024.1333020

Radiomics analysis of multiparametric MRI for preoperative prediction of microsatellite instability status in endometrial cancer: a dual-center study

Front Oncol. 2024 Jan 29:14:1333020. doi: 10.3389/fonc.2024.1333020. eCollection 2024.

Authors

Yaju Jia^#^{1

2}, Lina Hou^#¹, Jintao Zhao¹, Jialiang Ren³, Dandan Li¹, Haiming Li^{4

5}, Yanfen Cui¹

Affiliations

¹ Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China.
² Department of Radiology, Shanxi Traditional Chinese Medical Hospital, Taiyuan, China.
³ Department of Pharmaceuticals Diagnostics, GE HealthCare, Beijing, China.
⁴ Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
⁵ Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.

^# Contributed equally.

Abstract

Objective: To develop and validate a multiparametric MRI-based radiomics model for prediction of microsatellite instability (MSI) status in patients with endometrial cancer (EC).

Methods: A total of 225 patients from Center I including 158 in the training cohort and 67 in the internal testing cohort, and 132 patients from Center II were included as an external validation cohort. All the patients were pathologically confirmed EC who underwent pelvic MRI before treatment. The MSI status was confirmed by immunohistochemistry (IHC) staining. A total of 4245 features were extracted from T2-weighted imaging (T2WI), contrast enhanced T1-weighted imaging (CE-T1WI) and apparent diffusion coefficient (ADC) maps for each patient. Four feature selection steps were used, and then five machine learning models, including Logistic Regression (LR), k-Nearest Neighbors (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF), were built for MSI status prediction in the training cohort. Receiver operating characteristics (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of these models.

Results: The SVM model showed the best performance with an AUC of 0.905 (95%CI, 0.848-0.961) in the training cohort, and was subsequently validated in the internal testing cohort and external validation cohort, with the corresponding AUCs of 0.875 (95%CI, 0.762-0.988) and 0.862 (95%CI, 0.781-0.942), respectively. The DCA curve demonstrated favorable clinical utility.

Conclusion: We developed and validated a multiparametric MRI-based radiomics model with gratifying performance in predicting MSI status, and could potentially be used to facilitate the decision-making on clinical treatment options in patients with EC.

Keywords: adjuvant therapy (post-operative); endometrial neoplasms; magnetic resonance imaging; microsatellite instability; radiomics.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the National Natural Science Foundation of China (No. 82001789, 82271940, 81901704), the China Postdoctoral Science Foundation (No. 2021M700897), the Applied Basic Research Projects of Shanxi Province, China, Outstanding Youth Foundation (202103021222014), the Project of Shanxi Provincial Health Commission (No.2021XM51, and 2019058), Natural Science Foundation of Shanghai (22ZR1412500).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.