Improving assessment of lesions in longitudinal CT scans: a bi-institutional reader study on an AI-assisted registration and volumetric segmentation workflow

Alessa Hering, Max Westphal, Annika Gerken, Haidara Almansour, Michael Maurer, Benjamin P. Geisler, Temke Kohlbrandt, Thomas Eigentler, Teresa Amaral, Nikolas Lessmann, Sergios Gatidis, Horst K. Hahn, Konstantin Nikolaou, Ahmed E. Othman, Jan Hendrik Moltz, Felix Peisen

Abstract: AI-assisted techniques for lesion registration and segmentation have the potential to make CT-based tumor follow-up assessment faster and less reader-dependent. However, empirical evidence on the advantages of AI-assisted volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans is lacking. The aim of this study was to assess the efficiency, quality, and inter-reader variability of an AI-assisted workflow for volumetric segmentation of lymph node and soft tissue metastases in follow-up CT scans. Three hypotheses were tested: (H1) Assessment time for follow-up lesion segmentation is reduced using an AI-assisted workflow. (H2) The quality of the AI-assisted segmentation is non-inferior to the quality of fully manual segmentation. (H3) The inter-reader variability of the resulting segmentations is reduced with AI assistance.