A graph-theoretic approach for the analysis of lesion changes and lesions detection review in longitudinal oncological imaging

Beniamin Di Veroli, Richard Lederman, Yigal Shoshan, Jacob Sosna, Leo Joskowicz

Abstract: Highlights•The identification of missed and wrongly identified lesions in radiological scans is related to suspicious patterns of lesion changes and requires the examination of longitudinal patient sequences.•A new generic model-based method for the volumetric analysis of lesions and their changes in longitudinal scans based on lesion matching, classification of changes in individual lesions, and detection of patterns of lesion changes.•A new workflow that guides clinicians in the detection of missed and wrongly identified lesions in manual and computed lesion annotations using the analysis of lesion changes.•A new heuristic method for the automatic revision of ground truth lesion annotations in longitudinal scans based on the workflow.•Experimental results on patient studies with ≥3 examinations of metastatic lesions in lung, liver, and brain studies (67 patients, 190 CT and MRI scans, 2295 lesions) yielded a precision of 0.92–1.0, recall of 0.91–0.99 for lesion marching, and an accuracy of 0.87–0.97 for changes in individual lesions and 0.80–0.94 for patterns of lesions changes.