Differences in regions of interest to identify deeply invasive colorectal cancers: Computer-aided diagnosis vs expert endoscopists

Abstract

Background and study aims Diagnostic performance of a computer-aided diagnosis (CAD) system for deep submucosally invasive (T1b) colorectal cancer was excellent, but the “regions of interest” (ROI) within images are not obvious. Class activation mapping (CAM) enables identification of the ROI that CAD utilizes for diagnosis. The purpose of this study was a quantitative investigation of the difference between CAD and endoscopists.

Patients and methods Endoscopic images collected for validation of a previous study were used, including histologically proven T1b colorectal cancers (n = 82; morphology: flat 36, polypoid 46; median maximum diameter 20 mm, interquartile range 15–25 mm; histological subtype: papillary 5, well 51, moderate 24, poor 2; location: proximal colon 26, distal colon 27, rectum 29). Application of CAM was limited to one white light endoscopic image (per lesion) to demonstrate findings of T1b cancers. The CAM images were generated from the weights of the previously fine-tuned ResNet50. Two expert endoscopists depicted the ROI in identical images. Concordance of the ROI was rated by intersection over union (IoU) analysis.

Results Pixel counts of ROIs were significantly lower using 165K[x103] [108K-227K] than by endoscopists (300K [208K-440K]; P < 0.0001) and median [interquartile] of the IoU was 0.198 [0.024-0.349]. IoU was significantly higher in correctly identified lesions (n = 54, 0.213 [0.116-0.364]) than incorrect ones (n=28, 0.070 [0.000-0.2750, P= 0.033).

Concusions IoU was larger in correctly diagnosed T1b colorectal cancers. Optimal annotation of the ROI may be the key to improving diagnostic sensitivity of CAD for T1b colorectal cancers.

Keywords

Endoscopy Lower GI Tract - Colorectal cancer - Diagnosis and imaging (inc chromoendoscopy, NBI, iSCAN, FICE, CLE...)

Publikationsverlauf

Eingereicht: 09. Januar 2024

Angenommen nach Revision: 23. August 2024

Accepted Manuscript online:
04. September 2024

Artikel online veröffentlicht:
07. November 2024

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