Comparison of 2D and 2.5D deep learning features based on multi-parametric magnetic resonance imaging for brain metastases classification

Objective This study aims to compare deep learning (DL) features extracted from 2D and 2.5D data via multiparametric magnetic resonance imaging (MRI) to classify brain metastases (BMs) originating from lung cancer (LC), breast cancer (BC), and gastrointestinal cancer (GIC). Methods This retrospective study analyzed MR images from 328 patients with brain metastases (BMs), which were randomly divided into training (N = 229) and test (N = 99) sets at a 7:3 ratio. From the primary lesion slice, we obtained adjacent slices in both the superior-inferior and anterior-posterior directions, constructing a series of two-dimensional (2D) images. DL features were extracted from these slices via pretrained convolutional neural networks (CNNs), including DenseNet121, ResNet50, and ResNet101. A multi-instance learning (MIL) framework was then applied to integrate features into a comprehensive representation. The 2D model, which uses the tumor’s maximal cross-sectional slice as input, followed an identical processing pipeline to that of the 2.5D model. All feature sets were evaluated via machine learning algorithms. Diagnostic performance was assessed via fivefold cross-validation, with accuracy and area under the curve (AUC) metrics quantified for analysis. Results The best classification results were obtained from multiparametric MR images combined with ResNet50. In the test set, the accuracy and AUC of the optimal 2.5D model were 0.906 and 0.961 (95% confidence interval [CI], 0.926–0.978), respectively. The accuracy and AUC of the optimal 2D model were 0.660 and 0.686 (95% CI, 0.622–0.751), respectively. Conclusions On the basis of multiparametric MRI data, the 2.5D-based DL model is feasible for distinguishing the origins of BMs.