ArticleFrontiers in neurology2024
Utilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases.
Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics-based artificial intelligence models in brain tumors: A systematic review and meta-analysis of diagnostic performance.Neuroradiology · 2026Pooled it
- Interpretable habitat radiomics based nomogram for predicting T790M mutation in non-small cell lung cancer with brain metastases.Quantitative imaging in medicine and surgery · 2026Article
- Modality-level attribution and redundancy-aware radiomics for MRI-based differentiation of melanoma and NSCLC brain metastases.Frontiers in oncology · 2026Article
- Development and validation of a perinatal risk prediction model for recurrent respiratory tract infections in moderate-to-late preterm infants: a retrospective cohort study.BMC pediatrics · 2025Article
- Bayesian-Optimized Convolutional Neural Networks for Classifying Primary Tumor Origin of Brain Metastases from MRI.Brain sciences · 2025Article
- GMANet: a CPU-only ultra-lightweight 3D CNN for real-time glioma triage in resource-limited environments.Frontiers in neurologyArticle
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Authors and funding
5 authors.
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Abstract
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] 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.
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