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Deep Learning for Colorectal Cancer Detection in Contrast-Enhanced Ct Without Bowel Preparation: A Retrospective, Multicentre Study
20 Pages Posted: 1 Nov 2023
More...Abstract
Background: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists.
Methods: We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists’ detection performance.
Findings: In the four test sets, the DL model had AUCs ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 91.6%, p<0.0001; 94.9% vs 91.4%, p<0.0001), and significantly improved the accuracy of radiologists (97.7% vs 91.6%, p=0.001; 96.4% vs 91.4%, p<0.0001). In the real-world test set, the DL model delivered sensitivity comparable to that of radiologists who had been informed about clinical indications for most cancer cases (94.3% vs 96.2%, p>0.99), and it detected 2 cases that had been missed by radiologists.
Interpretation: The developed DL model can accurately detect colorectal cancer and improve radiologists’ detection performance, showing its potential as an effective computer-aided detection tool.
Funding: Regional Innovation and Development Joint Fund of National Natural Science Foundation of China, National Science Fund for Distinguished Young Scholars of China
Declaration of Interest: We declare no competing interests.
Ethical Approval: This study was approved by the ethic committee of Guangdong Provincial People’s Hospital. Considering the retrospective nature of this study, the informed consent was waived.
Keywords: colorectal cancer, deep learning, Computer-aided detection, Contrast-enhanced CT
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