Topological machine learning for Raman spectroscopy: perspectives for pancreatic diseases

The analysis of tissue samples from 17 subjects clinically diagnosed with chronic pancreatitis, ductal adenocarcinoma, or classified as controls has been collected and ana- lyzed by Raman spectroscopy (RS). Such data are classified using a recent methodology which combines machine learning with advanced Topological Data Analysis (TDA) tech- niques, known as Topological Machine Learning (TML). A classification accuracy of 82% was achieved following a cross-validation scheme with patient stratification, suggesting that the combination of RS and topological data analysis holds significant potential for distinguishing between the three diagnostic categories. When restricted to binary classifica- tion (cancer vs. no cancer), performance increases to 88%. This approach offers a promising and fast method to support clinical diagnoses, potentially improving diagnostic accuracy and patient outcomes.