Artificial intelligence for personalized management of vestibular schwannoma: A clinical implementation study within a multidisciplinary decision making environment
Authors: Wijethilake Navodini , Connor Steve , Oviedova Anna , Burger Rebecca , Sagun Jeromel De Leon De , Hitchings Amanda , Abougamil Ahmed , Giannis Theofanis , Syrris Christoforos , Chia Kazumi , Al-Salihi Omar , Obholzer Rupert , Jiang Dan , Maratos Eleni , Barazi Sinan , Thomas Nick , Vercauteren Tom , Shapey Jonathan 
Published: November 2023

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 neuro-radiologist’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.

Funding

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).

Graphical Abstract

Highlights

The first study to evaluate the impact of AI assisted reporting in a clinical setting.

AI generated segmentations can be used to provide a clinical guideline driven report facilitating personalized patient management

Volumetric tumour measurements provide a more comprehensive assessment of tumour growth.