Radiomics analysis of brain–tumor interface on multiparametric MRI for prediction of brain metastasis primary site
Abstract
Objectives: Brain metastasis (BM) frequently occurs in advanced cancer patients, and accurate identification of the primary origin is essential for personalized treatment. Conventional imaging often lacks sensitivity and specificity for BM source prediction. This study aimed to develop and validate a multiparametric MRI-based radiomics model emphasizing brain–tumor interface (BTI) features to improve noninvasive BM origin identification.
Materials and methods: The primary cohort included 429 patients (740 BMs) from two centers, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), breast cancer (BC), and gastrointestinal cancer (GIC), and an independent validation cohort of 33 patients (84 BMs) from a third center. All underwent pre-treatment 3.0 T MRI with T1-weighted contrast-enhanced (T1CE) and T2-weighted (T2W) sequences. Radiomic features were extracted from tumor core and BTI regions (2, 4, 6, 8 mm). Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, and radiomics signatures (RS) were evaluated using receiver operating characteristic (ROC) analysis.
Results: The constructed RS-BTI4 based on the 4-mm BTI region generated the best overall performance, with area under the ROC curve (AUCs) in the training cohort were 0.855 (LC/NLC), 0.910 (SCLC/NSCLC), and 0.890 (BC/GIC); in validation cohort, 0.726, 0.856, and 0.809; and in external validation cohort, 0.739, 0.750, and 0.792, respectively. RS-BTI4 consistently outperformed models based on other BTI radial widths and the whole tumor region in identifying the primary origin of BM.
Conclusion: BTI-derived radiomic features demonstrate strong potential for noninvasive identification of BM origin, with the 4-mm BTI region showing optimal predictive performance.
Key Points: Question Conventional MRI cannot reliably identify the primary origin of brain metastases, particularly when the primary tumor remains unknown at diagnosis.
Findings The 4-mm brain–tumor interface model showed the most favorable overall performance, measured by area under the curve, across three binary classification tasks and validation cohorts.
Critical relevance statement Integrating brain–tumor interface radiomics with multiparametric MRI may improve noninvasive estimation of BM origin and support more targeted diagnostic evaluation in clinical radiology.
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BibTeX
@article{jiang2026radiomics,
title = {{Radiomics analysis of brain–tumor interface on multiparametric MRI for prediction of brain metastasis primary site}},
author = {Mingchen Jiang and Bingzhu Han and Yufei Huang and Xinmeng Rong and Wenjie Zhao and Xueyan Ge and Ronghui Ju and Wenyan Jiang},
journal = {Insights into Imaging},
year = {2026},
month = nov,
doi = {10.1186/s13244-026-02464-5},
url = {https://link.springer.com/article/10.1186/s13244-026-02464-5},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Radiomics analysis of brain–tumor interface on multiparametric MRI for prediction of brain metastasis primary site
AU - Mingchen Jiang
AU - Bingzhu Han
AU - Yufei Huang
AU - Xinmeng Rong
AU - Wenjie Zhao
AU - Xueyan Ge
AU - Ronghui Ju
AU - Wenyan Jiang
PY - 2026
DA - 2026/11/06
JO - Insights into Imaging
DO - 10.1186/s13244-026-02464-5
UR - https://link.springer.com/article/10.1186/s13244-026-02464-5
AB - Objectives: Brain metastasis (BM) frequently occurs in advanced cancer patients, and accurate identification of the primary origin is essential for personalized treatment. Conventional imaging often lacks sensitivity and specificity for BM source prediction. This study aimed to develop and validate a multiparametric MRI-based radiomics model emphasizing brain–tumor interface (BTI) features to improve noninvasive BM origin identification. Materials and methods: The primary cohort included 429 patients (740 BMs) from two centers, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), breast cancer (BC), and gastrointestinal cancer (GIC), and an independent validation cohort of 33 patients (84 BMs) from a third center. All underwent pre-treatment 3.0 T MRI with T1-weighted contrast-enhanced (T1CE) and T2-weighted (T2W) sequences. Radiomic features were extracted from tumor core and BTI regions (2, 4, 6, 8 mm). Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, and radiomics signatures (RS) were evaluated using receiver operating characteristic (ROC) analysis. Results: The constructed RS-BTI4 based on the 4-mm BTI region generated the best overall performance, with area under the ROC curve (AUCs) in the training cohort were 0.855 (LC/NLC), 0.910 (SCLC/NSCLC), and 0.890 (BC/GIC); in validation cohort, 0.726, 0.856, and 0.809; and in external validation cohort, 0.739, 0.750, and 0.792, respectively. RS-BTI4 consistently outperformed models based on other BTI radial widths and the whole tumor region in identifying the primary origin of BM. Conclusion: BTI-derived radiomic features demonstrate strong potential for noninvasive identification of BM origin, with the 4-mm BTI region showing optimal predictive performance. Key Points: Question Conventional MRI cannot reliably identify the primary origin of brain metastases, particularly when the primary tumor remains unknown at diagnosis. Findings The 4-mm brain–tumor interface model showed the most favorable overall performance, measured by area under the curve, across three binary classification tasks and validation cohorts. Critical relevance statement Integrating brain–tumor interface radiomics with multiparametric MRI may improve noninvasive estimation of BM origin and support more targeted diagnostic evaluation in clinical radiology.
SN - 1869-4101
ER -