Evaluation of statistical detection of change algorithm for triaging multiple sclerosis patients with new lesion activity on longitudinal brain MRI

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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported in part by grants from the National Institutes of Health (R01 NS105144, R01 NS090464, R01 NS104283) and the National Multiple Sclerosis Society (RR-1602-07671). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of Weill Cornell Medicine gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors. * SDC : statistical detection of change SPACE : sampling perfection with application optimized contrasts using different flip angle evolution FSL : FMRIB Software Library