Deep learning for discrimination of active and inactive lesions in multiple sclerosis using non-contrast FLAIR MRI: A multicenter study

AmirAbbas Amini; Azin Shayganfar; Zahra Amini; Leila Ostovar; Somayeh HajiAhmadi; Navid Chitsaz; Masoud Rabbani; Raheleh Kafieh

doi:10.1016/j.msard.2024.105642

Deep learning for discrimination of active and inactive lesions in multiple sclerosis using non-contrast FLAIR MRI: A multicenter study

Mult Scler Relat Disord. 2024 Apr 21:87:105642. doi: 10.1016/j.msard.2024.105642. Online ahead of print.

Authors

AmirAbbas Amini¹, Azin Shayganfar², Zahra Amini¹, Leila Ostovar², Somayeh HajiAhmadi², Navid Chitsaz², Masoud Rabbani², Raheleh Kafieh³

Affiliations

¹ School of Advanced Technologies in Medicine, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
² Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
³ Department of Engineering, Durham University, Durham, UK. Electronic address: raheleh.kafieh@durham.ac.uk.

PMID: 38703520
DOI: 10.1016/j.msard.2024.105642

Abstract

Background: Within the domain of multiple sclerosis (MS), the precise discrimination between active and inactive lesions bears immense significance. Active lesions are enhanced on T1-weighted MRI images after administration of gadolinium-based contrast agents, which brings about associated complexities. This study investigates the potential of deep learning to differentiate between active and inactive lesions in MS using non-contrast FLAIR-type MRI data, presenting a non-invasive alternative to conventional gadolinium-based MRI methods.

Methods: The dataset encompasses 9097 lesion images collected from 130 MS patients across four distinct imaging centers, with post-contrast T1-weighted images as the benchmark reference. We initially identified and labeled the lesions and carefully selected corresponding regions of interest (ROIs). These ROIs were employed as inputs for a convolutional neural network (CNN) to predict lesion status. Also, transfer learning was utilized, incorporating 12 pre-trained CNN models. Subsequently, an ensemble technique was applied to 3 of best models, followed by a systematic comparison of the results.

Results: Through a 5-fold cross-validation, our custom designed network exhibited an average accuracy of 85 %, a sensitivity of 95 %, a specificity of 75 %, and an AUC value of 0.90. Among the pre-trained models, ResNet50 emerged as the most effective, achieving a specificity of 58 %, an accuracy of 75 %, a sensitivity of 91 %, and an AUC value of 0.81. Our comprehensive evaluations encompassed the receiver operating characteristic curve, precision-recall curve, and confusion matrix analyses.

Conclusion: The findings underscore the efficacy of the proposed CNN, trained on FLAIR MRI data ROIs, in accurately discerning active and inactive lesions without reliance on contrast agents. Our multicenter study of 130 patients with diverse imaging devices outperforms the other single-center studies, achieving superior sensitivity and specificity. Unlike studies using multiple modalities, our exclusive use of FLAIR images streamlines the process, and our streamlined approach, excluding conventional pre-processing, demonstrates efficiency. The external validation conducted on diverse datasets, coupled with the analysis of dilated masks, underscores the adaptability and efficacy of our custom CNN model in discerning between active and inactive lesions.

Keywords: Deep learning; Multiple sclerosis (MS); Non-contrast MRI.