Localization of Colorectal Cancer Lesions in Contrast-Computed Tomography Images via a Deep Learning Approach

Author:

Sahoo Prasan Kumar12ORCID,Gupta Pushpanjali1ORCID,Lai Ying-Chieh34,Chiang Sum-Fu56,You Jeng-Fu56,Onthoni Djeane Debora1ORCID,Chern Yih-Jong57ORCID

Affiliation:

1. Department of Computer Science and Information Engineering, Chang Gung University, Guishan, Taoyuan 33302, Taiwan

2. Department of Neurology, Chang Gung Memorial Hospital, Linkou, New Taipei City 33305, Taiwan

3. Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, New Taipei City 33305, Taiwan

4. Department of Metabolomics Core Lab, Chang Gung Memorial Hospital, Linkou, New Taipei City 33305, Taiwan

5. Division of Colon and Rectal Surgery, Chang Gung Memorial Hospital, Linkou, New Taipei City 33305, Taiwan

6. College of Medicine, Chang Gung University, Guishan, Taoyuan 33302, Taiwan

7. Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Guishan, Taoyuan 33302, Taiwan

Abstract

Abdominal computed tomography (CT) is a frequently used imaging modality for evaluating gastrointestinal diseases. The detection of colorectal cancer is often realized using CT before a more invasive colonoscopy. When a CT exam is performed for indications other than colorectal evaluation, the tortuous structure of the long, tubular colon makes it difficult to analyze the colon carefully and thoroughly. In addition, the sensitivity of CT in detecting colorectal cancer is greatly dependent on the size of the tumor. Missed incidental colon cancers using CT are an emerging problem for clinicians and radiologists; consequently, the automatic localization of lesions in the CT images of unprepared bowels is needed. Therefore, this study used artificial intelligence (AI) to localize colorectal cancer in CT images. We enrolled 190 colorectal cancer patients to obtain 1558 tumor slices annotated by radiologists and colorectal surgeons. The tumor sites were double-confirmed via colonoscopy or other related examinations, including physical examination or image study, and the final tumor sites were obtained from the operation records if available. The localization and training models used were RetinaNet, YOLOv3, and YOLOv8. We achieved an F1 score of 0.97 (±0.002), a mAP of 0.984 when performing slice-wise testing, 0.83 (±0.29) sensitivity, 0.97 (±0.01) specificity, and 0.96 (±0.01) accuracy when performing patient-wise testing using our derived model YOLOv8 with hyperparameter tuning.

Funder

NATIONAL SCIENCE and TECHNOLOGY COUNCIL (NSTC), TAIWAN

Publisher

MDPI AG

Subject

Bioengineering

Reference33 articles.

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5. Gupta, P., Chiang, S.-F., Sahoo, P.K., Mohapatra, S.K., You, J.-F., Onthoni, D.D., Hung, H.-Y., Chiang, J.-M., Huang, Y., and Tsai, W.-S. (2019). Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach. Cancers, 11.

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