Graph-based analysis of mass spectrometry data for tissue characterization with application in basal cell carcinoma surgery

Abstract

<strong>PURPOSE:</strong> Basal Cell Carcinoma (BCC) is the most common cancer in the world. Surgery is the standard treatment and margin assessment is used to evaluate the outcome. The presence of cancerous cells at the edge of resected tissue i.e., positive margin, can negatively impact patient outcomes and increase the probability of cancer recurrence. Novel mass spectrometry technologies paired with machine learning can provide surgeons with real-time feedback about margins to eliminate the need for resurgery. To our knowledge, this is the first study to report the performance of cancer detection using Graph Convolutional Networks (GCN) on mass spectrometry data from resected BCC samples. <strong>METHODS:</strong> The dataset used in this study is a subset of an ongoing clinical data acquired by our group and annotated with the help of a trained pathologist. There is a total number of 190 spectra in this dataset, including 127 normal and 63 BCC samples. We propose single-layer and multi-layer conversion methods to represent each mass spectrum as a structured graph. The graph classifier is developed based on the deep GCN structure to distinguish between cancer and normal spectra. The results are compared with the state of the art in mass spectra analysis. <strong>RESULTS:</strong> The classification performance of GCN with multi-layer representation without any data augmentation is comparable to the previous studies that have used augmentation. <strong>CONCLUSION:</strong> The results indicate the capability of the proposed graph-based analysis of mass spectrometry data for tissue characterization or real-time margin assessment during cancer surgery.