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William Bolton, Piravin Ramakrishnan, Dharsshini Reveendran, Vassili Crispi, Oluwaseyi Adebola, Rohit Sinha, Richard Digby, Aruna Chakrabarty, Ryan Mathew, DIAGNOSTIC ACCURACY AND PROCESS EVALUATION WITH HEALTH ECONOMICS ANALYSIS OF AN ULTRA-FAST DIGITAL CONFOCAL MICROSCOPY SCANNER FOR REAL-TIME INTRA-OPERATIVE BRAIN TUMOUR DIAGNOSIS, Neuro-Oncology, Volume 25, Issue Supplement_3, October 2023, Page iii9, https://doi.org/10.1093/neuonc/noad147.036
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Abstract
Real-time intra-operative brain tumour tissue analysis in theatre can shorten turnaround times and facilitate repeated sampling; the latter can improve diagnostic accuracy and guide resection to maximise extent. We were the first team in the world to demonstrate the feasibility of an ultra-fast confocal microscopy scanner (Histolog®, SamanTree Medical SA) for real-time intra-operative brain tumour tissue diagnosis. The aim of this study is to demonstrate the diagnostic accuracy of Histolog® within a larger cohort of patients with brain tumours.
An observational study with varying brain tumour types was conducted in a Tessa Jowell Brain Tumour Centre of Excellence. Adult patients undergoing brain biopsies or tumour debulking surgery were included. Multiple, freshly excised tissue samples were stained, and images captured within 60 seconds on Histolog® in the OR in parallel to current diagnostic pathways which included frozen section and smear cytology. Quantitative blinded concordance analysis between Histolog® images and gold standard histopathology was performed by a Consultant Neuropathologist.
A total of 50 cases are included in this analysis. This will include the quantitative analysis of n=50 cases and the concordance rating between Histolog® and gold standard traditional (frozen section and smear cytology) intra- operative histopathological diagnosis. The pilot data (n=12) that led to this larger study indicated a concordance rate of 83%.
Histolog® demonstrates non-inferior diagnostic accuracy when compared to current gold standard but significantly reduces the intra-operative time to achieve diagnosis. Future studies will investigate the effect of real- time margin zone analysis and explore machine learning for automatic computer aided diagnosis.