Serrated polyps have emerged as a new important target lesion in colorectal cancer screening. Although artificial intelligence (AI) can be used to detect serrated polyps at a high accuracy in CT colonography, it is also important to understand the uncertainties regarding the decisions made by the AI. In this pilot study, we explored the quantification of the uncertainty in 3D deep learning for the detection of serrated polyps in CT colonography. The uncertainty was estimated by use of a Monte-Carlo dropout method, and quantified by characterizing the variance of the predictions made on the Monte-Carlo samples. For a preliminary evaluation, we performed a 10-fold per-patient cross-validation to compare the accuracies and uncertainties of the detections made by the two 3D DenseNet models that we previously identified as having a high performance in the detection of serrated polyps. The materials included 94 clinical CT colonography cases with biopsy-confirmed serrated polyps. Our preliminary results indicate that both 3D DenseNets were able to detect serrated polyps at a high accuracy and high certainty. However, the 3D DenseNet with a larger number of input convolutions yielded more consistent certainties in the detection of different clinical pathologies of polyps than did the DenseNet with a smaller number of input convolutions. Our results indicate that the uncertainty quantification can provide constructive quantitative insights regarding the quality of the detections made by AI, and that serrated polyps can be detected automatically in CT colonography not only at a high detection accuracy but also at a high certainty.
Serrated polyps were historically believed to be benign lesions that have no cancer potential. However, recent studies have revealed a molecular pathway where serrated polyps can develop into colorectal cancers. Because serrated polyps tend to be flat and pale lesions, they are challenging to detect in colonoscopy, whereas CT colonography can detect serrated polyps based on a phenomenon called contrast coating. However, the differentiation of contrast coating from tagged feces requires great skill from the reader. The purpose of this pilot study was to explore the performance of 3D deep learning in the detection of serrated polyps. The materials included 94 CT colonography cases with biopsy-confirmed serrated polyps. We explored how to adapt the architecture of our baseline 3D DenseNet into the limited dataset by modification of the architectural parameters. The detection performance of the different 3D DenseNets and a reference 3D ResNet and a 3D AlexNet were compared by use of 10-fold cross-validation in terms of their sensitivity and false-positive rate within a clinically meaningful performance range by use of the free-response operating characteristic analysis. Our preliminary results indicate that the optimized 3D DenseNet can yield a high detection performance for serrated polyps that is comparable to those of state-of-the-art conventional CADe systems for traditional polyps in CT colonography.
Serrated polyps were previously believed to be benign lesions with no cancer potential. However, recent studies have revealed a novel molecular pathway where also serrated polyps can develop into colorectal cancer. CT colonography (CTC) can detect serrated polyps using the radiomic biomarker of contrast coating, but this requires expertise from the reader and current computer-aided detection (CADe) systems have not been designed to detect the contrast coating. The purpose of this study was to develop a novel CADe method that makes use of deep learning to detect serrated polyps based on their contrast-coating biomarker in CTC. In the method, volumetric shape-based features are used to detect polyp sites over soft-tissue and fecal-tagging surfaces of the colon. The detected sites are imaged using multi-angular 2D image patches. A deep convolutional neural network (DCNN) is used to review the image patches for the presence of polyps. The DCNN-based polyp-likelihood estimates are merged into an aggregate likelihood index where highest values indicate the presence of a polyp. For pilot evaluation, the proposed DCNN-CADe method was evaluated with a 10-fold cross-validation scheme using 101 colonoscopy-confirmed cases with 144 biopsy-confirmed serrated polyps from a CTC screening program, where the patients had been prepared for CTC with saline laxative and fecal tagging by barium and iodine-based diatrizoate. The average per-polyp sensitivity for serrated polyps ≥6 mm in size was 93±7% at 0:8±1:8 false positives per patient on average. The detection accuracy was substantially higher that of a conventional CADe system. Our results indicate that serrated polyps can be detected automatically at high accuracy in CTC.
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