Towards Improving Surgical Margins in Tumour Resection Using Mass Spectrometry Imaging

Successful cancer resection is limited by the inability to differentiate between cancer and normal tissue intraoperatively. Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is an emerging and powerful analytical technique that offers a rapid and low cost approach for assessing surgical margins by generating detailed metabolic profiles. However, exploiting this data for tissue characterization based on molecular signals requires machine learning methods to handle its complexity. In this work, we utilize machine learning models for the characterization of tissue using DESI-MSI data obtained from prostate tissue samples. We use ViPRE, a novel open-source software, to annotate a large DESI-MSI dataset. We explore various machine learning models and train test schemes for cancer classification. Cross-validation of our models result in high balanced accuracy, sensitivity and specificity for cancer classification. Furthermore, we simulate the prospective application of perioperative tissue characterization, generating a qualitative visual prediction for whole slides that match pathology annotations. Finally, the application of linear transformation and classification algorithms on DESI-MSI data effectively distinguished between the molecular profiles associated with different cancer grades. Our findings highlight the promise of combining machine learning with large DESI-MSI datasets for tissue characterization, thereby improving surgical margin precision.