Journal Article

Ultra-fast deep-learned CNS tumour classification during surgery

Tops, B. B. J., van der Lugt, J., Pagès-Gallego, M., Verburg, N., Wesseling, P., Vermeulen, C., Kooi, E. J., de Witt Hamer, P., Kester, L., Dankmeijer, L., van Baarsen, K., de Ridder, J., Kranendonk, M. E. G., Hoving, E. W.

2023

Overview

Central nervous system tumours represent one of the most lethal cancer types, particularly among children 1 . Primary treatment includes neurosurgical resection of the tumour, in which a delicate balance must be struck between maximizing the extent of resection and minimizing risk of neurological damage and comorbidity 2 , 3 . However, surgeons have limited knowledge of the precise tumour type prior to surgery. Current standard practice relies on preoperative imaging and intraoperative histological analysis, but these are not always conclusive and occasionally wrong. Using rapid nanopore sequencing, a sparse methylation profile can be obtained during surgery 4 . Here we developed Sturgeon, a patient-agnostic transfer-learned neural network, to enable molecular subclassification of central nervous system tumours based on such sparse profiles. Sturgeon delivered an accurate diagnosis within 40 minutes after starting sequencing in 45 out of 50 retrospectively sequenced samples (abstaining from diagnosis of the other 5 samples). Furthermore, we demonstrated its applicability in real time during 25 surgeries, achieving a diagnostic turnaround time of less than 90 min. Of these, 18 (72%) diagnoses were correct and 7 did not reach the required confidence threshold. We conclude that machine-learned diagnosis based on low-cost intraoperative sequencing can assist neurosurgical decision-making, potentially preventing neurological comorbidity and avoiding additional surgeries. Sturgeon is a pretrained neural network that uses incremental results from nanopore sequencing to rapidly classify central nervous system tumours and can be used to aid critical decision-making during surgery.

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 45/22 / 45/23 / 631/1647/48 / 631/61/514/2254 / 631/67/1922 / 692/53/2421 / Central nervous system / Central Nervous System Neoplasms - classification / Central Nervous System Neoplasms - diagnosis / Central Nervous System Neoplasms - genetics / Central Nervous System Neoplasms - surgery / Child / Classification / Clinical Decision-Making - methods / Comorbidity / Decision making / Deep Learning - standards / Diagnosis / DNA methylation / Genomes / Humanities and Social Sciences / Humans / Intraoperative Care - methods / Machine learning / Methylation / multidisciplinary / Nervous system / Neural networks / Neurosurgery / Retrospective Studies / Risk reduction / Science / Science (multidisciplinary) / Sequence Analysis, DNA - methods / Simulation / Surgery / Time Factors / Tumors