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

Dilakshan Srikanthan; Martin Kaufmann; Amoon Jamzad; Ayesha Syeda; Alice Santilli; Alireza Sedghi; Gabor Fichtinger; Jamie Purzner; John Rudan; Teresa Purzner; Parvin Mousavi

doi:10.1117/12.2654436

3 April 2023 Attention-based multi-instance learning for improved glioblastoma detection using mass spectrometry

Dilakshan Srikanthan, Martin Kaufmann, Amoon Jamzad, Ayesha Syeda, Alice Santilli, Alireza Sedghi, Gabor Fichtinger, Jamie Purzner, John Rudan, Teresa Purzner, Parvin Mousavi

Author Affiliations +

Proceedings Volume 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling; 1246611 (2023) https://doi.org/10.1117/12.2654436
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Glioblastoma Multiforme (GBM) is the most common and most lethal primary brain tumor in adults with a five-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 tumors, 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. 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 patient-stratified four-fold cross-validation approach for model training and evaluation. 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. 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.

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Dilakshan Srikanthan, Martin Kaufmann, Amoon Jamzad, Ayesha Syeda, Alice Santilli, Alireza Sedghi, Gabor Fichtinger, Jamie Purzner, John Rudan, Teresa Purzner, and Parvin Mousavi "Attention-based multi-instance learning for improved glioblastoma detection using mass spectrometry", Proc. SPIE 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 1246611 (3 April 2023); https://doi.org/10.1117/12.2654436

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