Development and Validation of a Machine Learning Radiomics Model based on Multiparametric MRI for Predicting Progesterone Receptor Expression in Meningioma: A Multicenter Study

Guihan Lin; Weiyue Chen; Yongjun Chen; Changsheng Shi; Qianqian Cao; Yang Jing; Weiming Hu; Ting Zhao; Pengjun Chen; Zhihan Yan; Minjiang Chen; Chenying Lu; Shuiwei Xia; Jiansong Ji

doi:10.1016/j.acra.2024.11.019

Development and Validation of a Machine Learning Radiomics Model based on Multiparametric MRI for Predicting Progesterone Receptor Expression in Meningioma: A Multicenter Study

Acad Radiol. 2024 Nov 28:S1076-6332(24)00864-X. doi: 10.1016/j.acra.2024.11.019. Online ahead of print.

Authors

Guihan Lin¹, Weiyue Chen¹, Yongjun Chen², Changsheng Shi³, Qianqian Cao⁴, Yang Jing⁵, Weiming Hu¹, Ting Zhao¹, Pengjun Chen¹, Zhihan Yan⁶, Minjiang Chen¹, Chenying Lu¹, Shuiwei Xia¹, Jiansong Ji⁷

Affiliations

¹ Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Department of Radiology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
² Department of Radiology, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
³ Department of Interventional Vascular Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
⁴ Department of Pathology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
⁵ Huiying Medical Technology Co., Ltd., Room A206, B2, Dongsheng Science and Technology Park, Haidian District, Beijing 100192, China.
⁶ Wenzhou Key Laboratory of Structural and Functional Imaging, Wenzhou, China.
⁷ Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Department of Radiology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China. Electronic address: jjstcty@wmu.edu.cn.

PMID: 39613583
DOI: 10.1016/j.acra.2024.11.019

Abstract

Rationale and objectives: This study aimed to develop and validate a machine learning-based prediction model for preoperatively predicting progesterone receptor (PR) expression in meningioma patients using multiparametric magnetic resonance imaging (MRI).

Materials and methods: The study retrospectively enrolled 739 patients with pathologically confirmed meningioma from three medical centers, dividing them into four cohorts: training (n = 294), internal test (n = 126), external test 1 (n = 217), and external test 2 (n = 102). Radiomics characteristics were derived from T2-weighted and contrast-enhanced T1-weighted MRI images, followed by feature selection. A machine learning-based combined model was developed by incorporating radiomics scores (rad-scores) from the optimal radiomics model along with clinical predictors. The Shapley additive explanation (SHAP) method was employed to visually represent the process of making predictions. The prognostic value of the model was evaluated using Kaplan-Meier analysis.

Results: Among the 739 patients, 299 (40.5%) had negative PR expression confirmed by pathology. Twelve radiomics features derived from multiparametric MRI were selected to build the radiomics model. Tumor location and enhancement pattern were identified as key clinical predictors and were combined with rad-scores to create a combined model utilizing the extreme gradient boosting (XGBoost) algorithm. The combined model demonstrated strong accuracy and robustness, with area under the curve values of 0.907, 0.827, 0.846, and 0.807 across training, internal test, external test 1, and external test 2 cohorts, respectively. The recurrence-free survival analysis indicated that the combined model was able to effectively categorize patients based on recurrence outcomes.

Conclusion: The XGBoost combined model, utilizing multiparametric MRI, shows promise for predicting PR expression in meningioma patients.

Keywords: Machine learning; Magnetic resonance imaging; Meningioma; Progesterone receptor; Radiomics.