Objective: Prediction of disease progression is challenging in multiple sclerosis (MS) as the sequence of lesion development and retention of inflammation within a subset of chronic lesions is heterogeneous among patients. We investigated the sequence of lesion-related regional structural disconnectivity across the spectrum of disability and cognitive impairment in MS. Methods: In a full cohort of 482 patients, the Expanded Disability Status Scale was used to classify patients into (i) no or mild vs (ii) moderate or severe disability groups. In 363 out of 482 patients, Quantitative Susceptibility Mapping was used to identify paramagnetic rim lesions (PRL), which are maintained by a rim of iron-laden innate immune cells. In 171 out of 482 patients, Brief International Cognitive Assessment was used to identify subjects with cognitive impairment. Network Modification Tool was used to estimate the regional structural disconnectivity due to MS lesions. Discriminative event-based modeling was applied to investigate the sequence of regional structural disconnectivity due to all representative lesions across the spectrum of disability and cognitive impairment. Results: Structural disconnection in the ventral attention and subcortical networks was an early biomarker of moderate or severe disability. The earliest biomarkers of disability progression were structural disconnections due to PRL in the motor-related regions. Subcortical structural disconnection was an early biomarker of cognitive impairment. Interpretation: MS lesion-related structural disconnections in the subcortex is an early biomarker for both disability and cognitive impairment in MS. PRL-related structural disconnection in the motor cortex may identify the patients at risk for moderate or severe disability in MS.

Background and Purpose: Identification of new MS lesions on longitudinal MRI by human readers is time-consuming and prone to error. Our objective was to evaluate the improvement in a subject-level detection performance by readers when assisted by the automated statistical detection of change (SDC) algorithm. Materials and Methods: A total of 200 MS patients with mean inter-scan interval of 13.2 ± 2.4 months were included. SDC was applied to the baseline and follow-up FLAIR images to detect potential new lesions for confirmation by readers (Reader+SDC method). This method was compared with readers operating in the clinical workflow (Reader method) for a subject-level detection of new lesions. Results: Reader+SDC found 30 subjects (15.0%) with at least one new lesion, while Reader detected 16 subjects (8.0%). As a subject-level triage tool, SDC achieved a perfect sensitivity of 1.00 (95% CI: [0.88, 1.00]) and a moderate specificity of 0.67 (95% CI: [0.59, 0.74]). The agreement on a subject-level was 0.91 (95% CI: [0.87, 0.95]) between Reader+SDC and Reader, and 0.72 (95% CI: [0.66, 0.78]) between Reader+SDC and SDC. Conclusion: SDC improves the detection accuracy of human readers and can serve as a time-saving patient triage tool for detecting new MS lesion activity on longitudinal FLAIR images.

BACKGROUND AND PURPOSE: Identification of new MS lesions on longitudinal MR imaging by human readers is time-consuming and prone to error. Our objective was to evaluate the improvement in the performance of subject-level detection by readers when assisted by the automated statistical detection of change algorithm. MATERIALS AND METHODS:A total of 200 patients with MS with a mean interscan interval of 13.2 (SD, 2.4) months were included. Statistical detection of change was applied to the baseline and follow-up FLAIR images to detect potential new lesions for confirmation by readers (Reader 1 statistical detection of change method). This method was compared with readers operating in the clinical workflow (Reader method) for a subject-level detection of new lesions. RESULTS:Reader 1 statistical detection of change found 30 subjects (15.0%) with at least 1 new lesion, while Reader detected 16 subjects (8.0%). As a subject-level screening tool, statistical detection of change achieved a perfect sensitivity of 1.00 (95% CI, 0.88-1.00) and a moderate specificity of 0.67 (95% CI, 0.59-0.74). The agreement on a subject level was 0.91 (95% CI, 0.87-0.95) between Reader 1 statistical detection of change and Reader, and 0.72 (95% CI, 0.66-0.78) between Reader 1 statistical detection of change and statistical detection of change. CONCLUSIONS:The statistical detection of change algorithm can serve as a time-saving screening tool to assist human readers in verifying 3D FLAIR images of patients with MS with suspected new lesions. Our promising results warrant further evaluation of statistical detection of change in prospective multireader clinical studies.