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.
We describe an interesting case of a patient who presented with a large adnexal mass, first favored to be mucinous carcinoma of the gynecologic origin. The primary tumour site was ascertained after the patient's small bowel was resected by identifying an adenomatous component evolving into an invasive adenocarcinoma identical in morphology and immunophenotype to the ovarian tumour. Notably, both tumours were found to harbor a BRAF K601E mutation, which is extremely rare for a primary of the ovary. BRAF mutations are present in a subset of large bowel and small bowel adenocarcinoma, but our case shows the first instance of a BRAF K601E mutation being present in a small bowel adenocarcinoma, to the best of our knowledge. This case serves as a great illustration of the pivotal role of molecular diagnostics in modern pathology in arriving at the correct diagnosis. Additionally, it is an excellent example of how clinical-radiologic-pathologic-molecular correlation plays into the landscape of molecular pathology to deliver optimal care for the patient.
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