Utilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases

Aug 4, 2026

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A machine learning–based clinical and radiomics framework is developed to predict the primary site of brain metastases using multiparametric MRI. Retrospective data from 202 patients with 439 brain metastases are analyzed, extracting 3,404 quantitative features after semi-automatic segmentation across T1WI, T2WI, FLAIR, and T1-CE. Features are selected via ANOVA, RFE, and Kruskal–Wallis tests, with five-fold cross-validation and independent testing. The radiomics model separates gastrointestinal from lung and breast metastases with high AUC, while a combined radiomics-plus-clinical model further improves discrimination.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Objective

Materials and methods

Patient cohort and data split

Image features and radiomics workflow

Model building and validation

Results

Conclusion

Highlights

Introduction

TYPE Original Research PUBLISHED 06 January 2025 DOI 10.3389/fneur.2024.1474461 OPEN ACCESS EDITED BY Luana Conte, University of Salento, Italy REVIEWED BY Martin Kocher, University of Cologne, Germany Guohua Zhao, First Affiliated Hospital of Zhengzhou University, China *CORRESPONDENCE Qiang Yue [scu_yq@163.com](scu_yq@163.com) RECEIVED 01 August 2024 ACCEPTED 22 November 2024 PUBLISHED 06 January 2025 CITATION Yang WL, Su XR, Li S, Zhao KY and Yue Q (2025) Utilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases. Front. Neurol. 15:1474461 . doi: 10.3389/fneur.2024.1474461 COPYRIGHT © 2025 Yang, Su, Li, Zhao and Yue. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Utilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases W. L. Yang 1,2, X. R. Su3, S. Li 1, K. Y. Zhao4 and Q. Yue 1* 1 Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China, 2 Department of Radiology, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China, 3 Department of Radiology, West China Hospital of Medicine, Huaxi MR Research Center (HMRRC), Chengdu, Sichuan, China, 4West China Hospital of Sichuan University, Chengdu, Sichuan, China Objective: To develop a machine learning-based clinical and/or radiomics model for predicting the primary site of brain metastases using multiparametric magnetic resonance imaging (MRI) . Materials and methods: A total of 202 patients (87 males, 115 females) with 439 brain metastases were retrospectively included, divided into training sets (brain metastases of lung cancer [BMLC] n = 194, brain metastases of breast cancer [BMBC] n = 108, brain metastases of gastrointestinal tumor [BMGiT] n = 48) and test sets (BMLC n = 50, BMBC n = 27, BMGiT n = 12) . A total of 3,404 quantitative image features were obtained through semi-automatic segmentation from MRI images (T1WI, T2WI, FLAIR, and T1-CE) . Intra-class correlation coefficient (ICC) was used to examine segmentation stability between two radiologists. Radiomics features were selected using analysis of variance (ANOVA), recursive feature elimination (RFE), and Kruskal–Wallis test. Three machine learning classifiers were used to build the radiomics model, which was validated using five-fold cross-validation on the training set. A comprehensive model combining radiomics and clinical features was established, and the diagnostic performance was compared by area under the curve (AUC) and evaluated in an independent test set. Results: The radiomics model differentiated BMGiT from BMLC (13 features, AUC = 0.915 ± 0.071) or BMBC (20 features, AUC = 0.954 ± 0.064) with high accuracy, while the classification between BMLC and BMBC was unsatisfactory (11 features, AUC = 0.729 ± 0. 114) . However, the combined model incorporating radiomics and clinical features improved the predictive performance, with AUC values of 0.965 for BMLC vs. BMBC, 0.991 for BMLC vs. BMGiT, and 0.935 for BMBC vs. BMGiT. Conclusion: The machine learning-based radiomics model demonstrates significant potential in distinguishing the primary sites of brain metastases, and may assist screening of primary tumor when brain metastasis is suspected whereas history of primary tumor is absent. KEYWORDS radiomics, machine learning, magnetic resonance imaging, brain metastases, support vector machine (SVM), logistic regression (

FAQs

What is the main objective of the study?

To develop a machine learning-based model using MRI radiomics (and clinical features) to predict the primary site of brain metastases.

How were MRI radiomics features obtained and selected?

Features were extracted from multiparametric MRI (T1WI, T2WI, FLAIR, T1-CE) using semi-automatic segmentation, yielding 3,404 quantitative features. Selection used ANOVA, recursive feature elimination (RFE), and the Kruskal–Wallis test.

Which model showed better performance and what were the key findings?

While the radiomics model distinguished gastrointestinal metastases from lung and breast metastases with high accuracy, it performed poorly for separating lung from breast. Adding clinical features improved discrimination across tumor-type pairs, producing high AUC values in the independent test.

Aug 4, 2026

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