Attention based multi-instance learning for improved glioblastoma detection using mass spectrometry

Abstract

BACKGROUND: Glioblastoma multiforme (GBM) is the most common and most lethal primary brain tumour in adults with a 5-year survival rate of 5%. The current standard of care and survival rate have remained largely unchanged due to the degree of difficulty in surgically removing these tumours, which plays a crucial role in survival, as better surgical resection leads to longer survival times. Thus, novel technologies need to be identified to improve resection accuracy. METHODS: Our study features a curated database of GBM and normal brain tissue specimens, which we used to train and validate a multi-instance learning model for GBM detection via rapid evaporative ionization mass spectrometry. This method enables real-time tissue typing. The specimens were collected by a surgeon, reviewed by a pathologist, and sampled with an electrocautery device. The dataset comprised 276 normal tissue burns and 321 GBM tissue burns. Our multi-instance learning model was adapted to identify the molecular signatures of GBM, and we employed a patientstratified 4-fold cross-validation approach for model training and evaluation. RESULTS: Our models demonstrated robustness and outperformed baseline models with an improved AUC of 0.95 and accuracy of 0.95 in correctly classifying GBM and normal brain. CONCLUSIONS: This study marks the first application of deep learning to REIMS data for brain tumor tissue characterization. This study sets the foundation for investigating more clinically relevant questions where intraoperative tissue detection in neurosurgery is pertinent.