Artificial intelligence for personalized management of vestibular schwannoma: A clinical implementation study within a multidisciplinary decision making environment

Background: The management of patients with Vestibular Schwannoma (VS) relies heavily on precise measurements of tumour size and determining growth trends. Methods: In this study, we introduce a novel computer-assisted approach designed to aid clinical decision-making during Multidisciplinary Meetings (MDM) for patients with VS through the provision of automatically generated tumour volume and standard linear measurements. We conducted two simulated MDMs with the same 50 patients evaluated in both cases to compare our proposed approach against the standard process, focusing on its impact on preparation time and decision-making. Findings: Automated reports provided acceptable information in 72% of cases, as assessed by an expert neuroradiologist, while the remaining 28% required some revision with manual feature extraction. The segmentation models used in this report generation task achieved Dice scores of 0.9392 (± 0.0351) for contrast-enhanced T1 and 0.9331 (± 0.0354) for T2 MRI in delineating whole tumor regions. The automated computer-assisted reports that included additional tumour information initially extended the neuroradiologist's preparation time for the MDM (2m 54s (± 1m and 22s) per case) compared to the standard preparation time (2m 36s (± 1m and 5s) per case). However, the computer-assisted simulated MDM (CAS-MDM) approach significantly improved MDM efficiency, with shorter discussion times per patient (1m 15s (± 0m and 28s) per case) compared to standard simulated MDM (SS-MDM) (1m 21s (± 0m and 44s) per case). Interpretation: This pilot clinical implementation study highlights the potential benefits of integrating automated measurements into clinical decision-making for VS management. An initial learning curve in interpreting new data measurements is quickly mastered and the enhanced communication of growth patterns and more comprehensive assessments ultimately provides clinicians with the tools to offer patients more personalized care. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement N. Wijethilake was supported by the UK Medical Research Council (MR/N013700/1) and the King's College London MRC Doctoral Training Partnership in Biomedical Sciences. This work was supported by core funding from the Wellcome Trust (203148/Z/16/Z) and EPSRC (NS/A000049/1) and an MRC project grant (MC/PC/180520). TV is also supported by a Medtronic/Royal Academy of Engineering Research Chair (RCSRF1819/7/34). ### 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: This study was approved by the NHS Health Research Authority and Research Ethics Committee (18/LO/0532). Because patients were selected retrospectively and the MR images were completely anonymised before analysis, no informed consent was required for the study. 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, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data is partially available on TCIA. The rest will be released on TCIA in the future.