Decision support system in radiation therapy treatment planning

Abstract

The radiation therapy treatment planning involves complex decision-making processes that rely heavily on the expertise of clinicians. This research introduces an innovative decision support system (DSS) designed to enhance personalized treatment recommendations in radiation therapy by leveraging advanced informatics methodologies. This research introduces an innovative decision support system for personalized radiation therapy treatment recommendations using advanced informatics methodologies. To compare patient profiles based on anatomical characteristics, it employs the integration of Gower's similarity measure and Earth Mover's Distance (EMD) analysis, enabling a nuanced assessment of dissimilarities. The DSS computes EMD values for spatial target signatures (STS) and overlap volume histograms (OVH) across multiple regions of interest (ROIs). The effectiveness of the EMD metric is in quantifying dissimilarities between radiation therapy plans is validated through a testing methodology. This involves randomly shifting the contours of organs at risk (OARs) and target volumes while maintaining their original shape and size, allowing us to evaluate the EMD metric's sensitivity to anatomical location variability, and its accuracy in identifying similar treatment plans. The implementation of this DSS involves the acquisition of diverse patient datasets from multiple institutions, ensuring the generalizability of the research outcomes. The data is efficiently organized and managed using MongoDB, a NoSQL database solution that allows for the storage of complex and varied data types.The successful integration of these advanced informatics methodologies paves the way for more personalized, precise, and effective radiation therapy treatment planning.