Assessment of automatic decision-support systems for detecting active T2 lesions in multiple sclerosis patients

Active (new/enlarging) T2 lesion counts are routinely used in the clinical management of multiple sclerosis. Thus, automated tools able to accurately identify active T2 lesions would be of high interest to neuroradiologists for assisting in their clinical activity. To compare the accuracy in detecting active T2 lesions and of radiologically active patients based on different visual and automated methods.Postprint (author's final draft

A Multicenter Longitudinal MRI Study Assessing LeMan-PV Software Accuracy in the Detection of White Matter Lesions in Multiple Sclerosis Patients.

BACKGROUND Detecting new and enlarged lesions in multiple sclerosis (MS) patients is needed to determine their disease activity. LeMan-PV is a software embedded in the scanner reconstruction system of one vendor, which automatically assesses new and enlarged white matter lesions (NELs) in the follow-up of MS patients; however, multicenter validation studies are lacking. PURPOSE To assess the accuracy of LeMan-PV for the longitudinal detection NEL white-matter MS lesions in a multicenter clinical setting. STUDY TYPE Retrospective, longitudinal. SUBJECTS A total of 206 patients with a definitive MS diagnosis and at least two follow-up MRI studies from five centers participating in the Swiss Multiple Sclerosis Cohort study. Mean age at first follow-up = 45.2 years (range: 36.9-52.8 years); 70 males. FIELD STRENGTH/SEQUENCE Fluid attenuated inversion recovery (FLAIR) and T1-weighted magnetization prepared rapid gradient echo (T1-MPRAGE) sequences at 1.5 T and 3 T. ASSESSMENT The study included 313 MRI pairs of datasets. Data were analyzed with LeMan-PV and compared with a manual "reference standard" provided by a neuroradiologist. A second rater (neurologist) performed the same analysis in a subset of MRI pairs to evaluate the rating-accuracy. The Sensitivity (Se), Specificity (Sp), Accuracy (Acc), F1-score, lesion-wise False-Positive-Rate (aFPR), and other measures were used to assess LeMan-PV performance for the detection of NEL at 1.5 T and 3 T. The performance was also evaluated in the subgroup of 123 MRI pairs at 3 T. STATISTICAL TESTS Intraclass correlation coefficient (ICC) and Cohen's kappa (CK) were used to evaluate the agreement between readers. RESULTS The interreader agreement was high for detecting new lesions (ICC = 0.97, Pvalue < 10-20 , CK = 0.82, P value = 0) and good (ICC = 0.75, P value < 10-12 , CK = 0.68, P value = 0) for detecting enlarged lesions. Across all centers, scanner field strengths (1.5 T, 3 T), and for NEL, LeMan-PV achieved: Acc = 61%, Se = 65%, Sp = 60%, F1-score = 0.44, aFPR = 1.31. When both follow-ups were acquired at 3 T, LeMan-PV accuracy was higher (Acc = 66%, Se = 66%, Sp = 66%, F1-score = 0.28, aFPR = 3.03). DATA CONCLUSION In this multicenter study using clinical data settings acquired at 1.5 T and 3 T, and variations in MRI protocols, LeMan-PV showed similar sensitivity in detecting NEL with respect to other recent 3 T multicentric studies based on neural networks. While LeMan-PV performance is not optimal, its main advantage is that it provides automated clinical decision support integrated into the radiological-routine flow. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2

New MS lesion segmentation with deep residual attention gate U-Net utilizing 2D slices of 3D MR images

Multiple sclerosis (MS) is an autoimmune disease that causes lesions in the central nervous system of humans due to demyelinating axons. Magnetic resonance imaging (MRI) is widely used for monitoring and measuring MS lesions. Automated methods for MS lesion segmentation have usually been performed on individual MRI scans. Recently, tracking lesion activity for quantifying and monitoring MS disease progression, especially detecting new lesions, has become an important biomarker. In this study, a unique pipeline with a deep neural network that combines U-Net, attention gate, and residual learning is proposed to perform better new MS lesion segmentation using baseline and follow-up 3D FLAIR MR images. The proposed network has a similar architecture to U-Net and is formed from residual units which facilitate the training of deep networks. Networks with fewer parameters are designed with better performance through the skip connections of U-Net and residual units, which facilitate information propagation without degradation. Attention gates also learn to focus on salient features of the target structures of various sizes and shapes. The MSSEG-2 dataset was used for training and testing the proposed pipeline, and the results were compared with those of other proposed pipelines of the challenge and experts who participated in the same challenge. According to the results over the testing set, the lesion-wise F1 and dice scores were obtained as a mean of 48 and 44.30%. For the no-lesion cases, the number of tested and volume of tested lesions were obtained as a mean of 0.148 and 1.488, respectively. The proposed pipeline outperformed 22 proposed pipelines and ranked 8th in the challenge

Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided

Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI

Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients. Our model (a) predicts future new and enlarging T2 weighted (NE-T2) lesion counts on follow-up MRI on multiple treatments and (b) estimates the conditional average treatment effect (CATE), as defined by the predicted future suppression of NE-T2 lesions, between different treatment options relative to placebo. Our model is validated on a proprietary federated dataset of 1817 multi-sequence MRIs acquired from MS patients during four multi-centre randomized clinical trials. Our framework achieves high average precision in the binarized regression of future NE-T2 lesions on five different treatments, identifies heterogeneous treatment effects, and provides a personalized treatment recommendation that accounts for treatment-associated risk (e.g. side effects, patient preference, administration difficulties).Comment: Accepted to MIDL 202

MSSEG-2 challenge proceedings: Multiple sclerosis new lesions segmentation challenge using a data management and processing infrastructure

International audienceThis proceedings book gathers methodological papers describing the segmenta-tion methods evaluated at the second MICCAI Challenge on Multiple Sclerosisnew lesions segmentation challenge using a data management and processinginfrastructure. This challenge took place as part of an effort of the OFSEP1(French registry on multiple sclerosis aiming at gathering, for research purposes,imaging data, clinical data and biological samples from the French populationof multiple sclerosis subjects) and FLI2(France Life Imaging, devoted to setupa national distributed e-infrastructure to manage and process medical imagingdata). These joint efforts are directed towards automatic segmentation of MRIscans of MS patients to help clinicians in their daily practice. This challengetook place at the MICCAI 2021 conference, on September 23rd 2021.More precisely, the problem addressed in this challenge is as follows. Con-ventional MRI is widely used for disease diagnosis, patient follow-up, monitoringof therapies, and more generally for the understanding of the natural history ofMS. A growing literature is interested in the delineation of new MS lesions onT2/FLAIR by comparing one time point to another. This marker is even morecrucial than the total number and volume of lesions as the accumulation of newlesions allows clinicians to know if a given anti-inflammatory DMD (disease mod-ifying drug) works for the patient. The only indicator of drug efficacy is indeedthe absence of new T2 lesions within the central nervous system. Performingthis new lesions count by hand is however a very complex and time consumingtask. Automating the detection of these new lesions would therefore be a majoradvance for evaluating the patient disease activity.Based on the success of the first MSSEG challenge, we have organized aMICCAI sponsored online challenge, this time on new MS lesions detection3.This challenge has allowed to 1) estimate the progress performed during the2016 - 2021 period, 2) extend the number of patients, and 3) focus on the newlesions crucial clinical marker. We have performed the evaluation task on a largedatabase (100 patients, each with two time points) compiled from the OFSEPcohort with 3D FLAIR images from different centers and scanners. As in ourprevious challenge, we have conducted the evaluation on a dedicated platform(FLI-IAM) to automate the evaluation and remove the potential biases due tochallengers seeing the images on which the evaluation is made

AI Adoption in Real-World Clinical Neuroimaging Applications: Practical Challenges and Solutions

Deep learning has demonstrated a capacity to revolutionise human life in the last several years. Medical imaging, which has a vast data footprint, has emerged as a pioneering area that has seen rapid adoption of deep learning approaches to the domain. The development of new imaging-based algorithms is directed toward improved efficiency and accuracy of disease diagnosis and prognosis; and enhanced disease progression monitoring. These models also have the potential to provide insights into disease pathomechanisms. However, the translation rate of newly described deep learning models into real-world clinical practice is extraordinarily low, despite an exponential increase in the number of research publications over the past few years. The cost of of data collection and annotation required to achieve model performance sufficient for clinical use; and to provide persuasive evidence of utility in real-world settings are significant roadblocks. This thesis investigates solutions to the challenges of adapting deep neural networks to real-world settings. To improve the performance of algorithms, while reducing the costs of meticulous labelling, a novel model Masked Multi-Task Network is proposed for classification using only case-level labels; and a new training approach is proposed to tackle the issue of noisy labels in a federated learning setting. Furthermore, an in-depth analysis of the requirements for sample size used for training is conducted, to guide the development of deep learning models for large-scale adoption. The research presented in this thesis encompasses the clinical validation and technical steps required for the commercialisation of two exemplary neuroimaging deep learning algorithms based on above works. This work also offers valuable insight into the compilation of requisite documentation for medical device registration, providing a valuable resource for researchers who wish to translate their models from the bench to the bedside