Clinician-interactive AI for RECIST measurements in CT imaging

Abstract

The RECIST criteria are used in computed tomography (CT) imaging to assess changes in tumour burden induced by cancer therapeutics throughout treatment. One of its requirements is frequent measurement of lesion diameters , which is often time consuming for clinicians. We aimed to study clinician-interactive AI, defined as deep learning models that use image annotations as input to assist in radiological measurements. Two annotation types are compared in their enhancement of predictive capabilities: mouse clicks in the tumour region, and bounding boxes surrounding lesions. The model architectures compared in this study are the U-Net, V-Net, AH-Net, and SegRes-Net. Models were trained and tested using a non-small cell lung cancer dataset from the cancer imaging archive (TCIA) consisting of CT scans and corresponding gold-standard lesion segmentations inferred from PET/CT scans. Mouse clicks and bounding boxes, representing clinician input, were artificially generated. The absolute percent error between predicted and ground truth diameters was computed for each model architecture. Bounding box annotations yielded mean absolute percent errors of 4.9 ± 2.1 %, 7.8 ± 3.4 %, 5.6 ± 2.4 % and 5.6 ± 2.3 %, respectively, whereas models using clicks annotations yielded 17.0 ± 7.9 %, 19.8 ± 9.3 %, 21.4 ± 10.9 % and 18.1 ± 7.9%. The corresponding mean dice scores across all model architectures were 0.883 ± 0.004 and 0.760 ± 0.012 for bounding box and click annotations respectively. Models were then implemented in an AI pipeline for clinical use at the BC cancer agency using the Ascinta software package; click annotations yielded qualitatively better results than bounding box annotations.

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