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Artificial intelligence−enhanced white-light colonoscopy with attention guidance predicts colorectal cancer invasion depth

Published:April 10, 2021DOI:https://doi.org/10.1016/j.gie.2021.03.936

      Background and Aims

      Endoscopic submucosal dissection (ESD) and EMR are applied in treating superficial colorectal neoplasms but are contraindicated by deeply invasive colorectal cancer (CRC). The invasion depth of neoplasms can be examined by an automated artificial intelligence (AI) system to determine the applicability of ESD and EMR.

      Methods

      A deep convolutional neural network with a tumor localization branch to guide invasion depth classification was constructed on the GoogLeNet architecture. The model was trained using 7734 nonmagnified white-light colonoscopy (WLC) images supplemented by image augmentation from 657 lesions labeled with histopathologic analysis of invasion depth. An independent testing dataset consisting of 1634 WLC images from 156 lesions was used to validate the model.

      Results

      For predicting noninvasive and superficially invasive neoplasms, the model achieved an overall accuracy of 91.1% (95% confidence interval [CI], 89.6%-92.4%), with 91.2% sensitivity (95% CI, 88.8%-93.3%) and 91.0% specificity (95% CI, 89.0%-92.7%) at an optimal cutoff of .41 and the area under the receiver operating characteristic (AUROC) curve of .970 (95% CI, .962-.978). Inclusion of the advanced CRC data significantly increased the sensitivity in differentiating superficial neoplasms from deeply invasive early CRC to 65.3% (95% CI, 61.9%-68.8%) with an AUROC curve of .729 (95% CI, .699-.759), similar to experienced endoscopists (.691; 95% CI, .624-.758).

      Conclusions

      We have developed an AI-enhanced attention-guided WLC system that differentiates noninvasive or superficially submucosal invasive neoplasms from deeply invasive CRC with high accuracy, sensitivity, and specificity.

      Graphical abstract

      Abbreviations:

      AEWL (artificial intelligence–enhanced attention-guided white light colonoscopy), AI (artificial intelligence), AUROC (area under the receiver operating characteristic curve), CAD (computer-aided diagnosis), CNN (convolutional neural network), CRC (colorectal cancer), ER (endoscopic resection), ESD (endoscopic submucosal dissection), IEE (image-enhanced endoscopy), ME (magnifying endoscopy), NPV (negative predictive value), PPV (positive predictive value), ROC (receiver operating characteristic), WLC (white-light colonoscopy)
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      Linked Article

      • Challenge to the “impossible”
        Gastrointestinal EndoscopyVol. 94Issue 3
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          A Dutch research team engaged in the national colorectal cancer screening program published an “amazing” result in 2020.1 As many as 60% of T1 (submucosally invasive) colorectal cancers detected during the program were misdiagnosed as adenomas by the on-site endoscopists and thus could be susceptible to inappropriate treatment intervention. Is this surprisingly low sensitivity for cancer recognition (40% in this case) reality? We would say yes, although many retrospective studies assessing advanced endoscopic modalities have suggested >90% sensitivities in predicting T1 cancers.
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