MRI‐Based 2.5D Multi‐Instance Deep Learning for Differentiating Pathological Subtypes of Brain Metastases From Lung Cancer

BACKGROUND: Distinct treatment strategies for small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) brain metastases require accurate noninvasive subtyping. PURPOSE: To develop and externally validate a biparametric MRI-based 2.5D multi-instance learning (MIL) deep learning (DL) model for discriminating SCLC from NSCLC brain metastases, with performance compared against clinical, radiomics, and 3D-DL models. STUDY TYPE: Retrospective. POPULATION: This study included 366 patients from four institutions. Center 1 provided the training set (n = 199; mean age, 63.88 ± 10.79 years; 72.36% male) and an internal tuning set (n = 85; mean age, 64.73 ± 9.53 years; 67.06% male). Centers 2-4 were pooled into a single external test set (n = 82; mean age, 62.26 ± 10.60 years; 63.41% male). FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T; contrast-enhanced T1-weighted imaging and fluid-attenuated inversion-recovery imaging. ASSESSMENT: The 2.5D-MIL model was benchmarked against clinical, radiomics, and 3D-DL models and evaluated on internal tuning and pooled external test sets. STATISTICAL TESTS: Shapiro-Wilk, Student's t-test, Mann-Whitney U, chi-square, univariable and multivariable logistic regression, and DeLong tests were used. Performance measures included the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals and classification and calibration metrics. Statistical significance was defined as two-sided p < 0.05. RESULTS: In the internal tuning set, AUCs were 0.842 (2.5D-MIL), 0.740 (clinical), 0.710 (radiomics), and 0.747 (3D-DL). In the external test set, AUCs were 0.826 (2.5D-MIL), 0.826 (clinical), 0.696 (radiomics), and 0.664 (3D-DL). Differences between 2.5D-MIL and the clinical (p = 1.000), radiomics (p = 0.139), and 3D-DL (p = 0.119) models were not significant. DATA CONCLUSION: The MRI-based 2.5D-MIL model showed potential for noninvasive differentiation of SCLC from NSCLC brain metastases. Larger prospective studies are needed before clinical use. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

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Journal
Journal of Magnetic Resonance Imaging
Published
2026-10-13
DOI
https://doi.org/10.1002/jmri.70588
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

MRI‐Based 2.5D Multi‐Instance Deep Learning for Differentiating Pathological Subtypes of Brain Metastases From Lung Cancer

Long Jiang Zhang, Bo Zhang, Tian Ye, Jinling Zhu et al.
Journal of Magnetic Resonance Imaging
Radiomics and Machine Learning in Medical Imaging
article

MRI‐Based 2.5D Multi‐Instance Deep Learning for Differentiating Pathological Subtypes of Brain Metastases From Lung Cancer

Long Jiang Zhang, Bo Zhang, Tian Ye, Jinling Zhu, Ruizhe Xu, Cairui Liu, Xin Xie, Li Zou
article en

Abstract

BACKGROUND: Distinct treatment strategies for small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) brain metastases require accurate noninvasive subtyping. PURPOSE: To develop and externally validate a biparametric MRI-based 2.5D multi-instance learning (MIL) deep learning (DL) model for discriminating SCLC from NSCLC brain metastases, with performance compared against clinical, radiomics, and 3D-DL models. STUDY TYPE: Retrospective. POPULATION: This study included 366 patients from four institutions. Center 1 provided the training set (n = 199; mean age, 63.88 ± 10.79 years; 72.36% male) and an internal tuning set (n = 85; mean age, 64.73 ± 9.53 years; 67.06% male). Centers 2-4 were pooled into a single external test set (n = 82; mean age, 62.26 ± 10.60 years; 63.41% male). FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T; contrast-enhanced T1-weighted imaging and fluid-attenuated inversion-recovery imaging. ASSESSMENT: The 2.5D-MIL model was benchmarked against clinical, radiomics, and 3D-DL models and evaluated on internal tuning and pooled external test sets. STATISTICAL TESTS: Shapiro-Wilk, Student's t-test, Mann-Whitney U, chi-square, univariable and multivariable logistic regression, and DeLong tests were used. Performance measures included the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals and classification and calibration metrics. Statistical significance was defined as two-sided p < 0.05. RESULTS: In the internal tuning set, AUCs were 0.842 (2.5D-MIL), 0.740 (clinical), 0.710 (radiomics), and 0.747 (3D-DL). In the external test set, AUCs were 0.826 (2.5D-MIL), 0.826 (clinical), 0.696 (radiomics), and 0.664 (3D-DL). Differences between 2.5D-MIL and the clinical (p = 1.000), radiomics (p = 0.139), and 3D-DL (p = 0.119) models were not significant. DATA CONCLUSION: The MRI-based 2.5D-MIL model showed potential for noninvasive differentiation of SCLC from NSCLC brain metastases. Larger prospective studies are needed before clinical use. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Journal of Magnetic Resonance Imaging
Nanjing General Hospital of Nanjing Military Command (CN), Soochow University (CN), Second Affiliated Hospital of Soochow University (CN), Nanjing University (CN)
Openalex Percentile: Top 13%
Radiomics and Machine Learning in Medical Imaging
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