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

Distinct treatment strategies for small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) brain metastases require accurate noninvasive subtyping.

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.

Retrospective.

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).

1.5 T and 3.0 T; contrast-enhanced T1-weighted imaging and fluid-attenuated inversion-recovery imaging.

The 2.5D-MIL model was benchmarked against clinical, radiomics, and 3D-DL models and evaluated on internal tuning and pooled external test sets.

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.

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.

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.

3.

Stage 2.