Cancer tissue classification using supervised machine learning applied to MALDI mass spectrometry imaging

With the current clinical available technologies, not all cancers are staged accurately. Because of this a large percentage of patients are misclassified before treatment, leading to under or over treatment. Therefore, a classification system based on the molecular feature is required to deter-mine the tumour behavior and metastatic potential. Here, we have shown the diagnostic potential of MALDI MSI using supervised machine learning approach in distinguishing cancerous colorectal tissue from normal with an overall accuracy of 98%. Also, shown is the capability of the technique in predicting the presence of metastasis in endometrial cancer with an overall accuracy of 80%. The development of such a model can help in determining the optimum treatment for cancerous pa-tients, reduce morbidity and better patient outcome.