A combined model integrating radiomics and deep learning based on multiparametric magnetic resonance imaging for classification of brain metastases.

Abstract

Background: Radiomics and deep learning (DL) can individually and efficiently identify the pathological type of brain metastases (BMs).

Purpose: To investigate the feasibility of utilizing multi-parametric MRI-based deep transfer learning radiomics (DTLR) for the classification of lung adenocarcinoma (LUAD) and non-LUAD BMs.

Material and methods: A retrospective analysis was performed on 342 patients with 1389 BMs. These instances were randomly assigned to a training set of 273 (1179 BMs) and a testing set of 69 (210 BMs) in an 8:2 ratio. Eight machine learning algorithms were employed to construct the radiomics models. A DL model was developed using four pre-trained convolutional neural networks (CNNs). The DTLR model was formulated by integrating the optimal performing radiomics model and the DL model using a classification probability averaging approach. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were utilized to assess the performance and clinical utility of the models.

Results: The AUC for the optimal radiomics and DL model in the testing set were 0.824 (95% confidence interval [CI]= 0.726-0.923) and 0.775 (95% CI=0.666-0.884), respectively. The DTLR model demonstrated superior discriminatory power, achieving an AUC of 0.880 (95% CI=0.803-0.957). In addition, the DTLR model exhibited good consistency between actual and predicted probabilities based on the calibration curve and DCA analysis, indicating its significant clinical value.

Conclusion: Our study's DTLR model demonstrated high diagnostic accuracy in distinguishing LUAD from non-LUAD BMs. This method shows potential for the non-invasive identification of the histological subtype of BMs.