Localization of Colorectal Cancer Lesions in Contrast-Computed Tomography Images via a Deep Learning Approach
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 tortu...
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MDPI AG
2023-08-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/8/972 |
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| author | Prasan Kumar Sahoo Pushpanjali Gupta Ying-Chieh Lai Sum-Fu Chiang Jeng-Fu You Djeane Debora Onthoni Yih-Jong Chern |
| author_facet | Prasan Kumar Sahoo Pushpanjali Gupta Ying-Chieh Lai Sum-Fu Chiang Jeng-Fu You Djeane Debora Onthoni Yih-Jong Chern |
| author_sort | Prasan Kumar Sahoo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T00:06:39Z |
| format | Article |
| id | doaj.art-977ce2e493b34e8cad5d66f2f992eb15 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-11T00:06:39Z |
| publishDate | 2023-08-01 |
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| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-977ce2e493b34e8cad5d66f2f992eb152023-11-19T00:18:34ZengMDPI AGBioengineering2306-53542023-08-0110897210.3390/bioengineering10080972Localization of Colorectal Cancer Lesions in Contrast-Computed Tomography Images via a Deep Learning ApproachPrasan Kumar Sahoo0Pushpanjali Gupta1Ying-Chieh Lai2Sum-Fu Chiang3Jeng-Fu You4Djeane Debora Onthoni5Yih-Jong Chern6Department of Computer Science and Information Engineering, Chang Gung University, Guishan, Taoyuan 33302, TaiwanDepartment of Computer Science and Information Engineering, Chang Gung University, Guishan, Taoyuan 33302, TaiwanDepartment of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, New Taipei City 33305, TaiwanDivision of Colon and Rectal Surgery, Chang Gung Memorial Hospital, Linkou, New Taipei City 33305, TaiwanDivision of Colon and Rectal Surgery, Chang Gung Memorial Hospital, Linkou, New Taipei City 33305, TaiwanDepartment of Computer Science and Information Engineering, Chang Gung University, Guishan, Taoyuan 33302, TaiwanDivision of Colon and Rectal Surgery, Chang Gung Memorial Hospital, Linkou, New Taipei City 33305, TaiwanAbdominal 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.https://www.mdpi.com/2306-5354/10/8/972colorectal cancerdeep learninglocalizationcomputed tomography |
| spellingShingle | Prasan Kumar Sahoo Pushpanjali Gupta Ying-Chieh Lai Sum-Fu Chiang Jeng-Fu You Djeane Debora Onthoni Yih-Jong Chern Localization of Colorectal Cancer Lesions in Contrast-Computed Tomography Images via a Deep Learning Approach Bioengineering colorectal cancer deep learning localization computed tomography |
| title | Localization of Colorectal Cancer Lesions in Contrast-Computed Tomography Images via a Deep Learning Approach |
| title_full | Localization of Colorectal Cancer Lesions in Contrast-Computed Tomography Images via a Deep Learning Approach |
| title_fullStr | Localization of Colorectal Cancer Lesions in Contrast-Computed Tomography Images via a Deep Learning Approach |
| title_full_unstemmed | Localization of Colorectal Cancer Lesions in Contrast-Computed Tomography Images via a Deep Learning Approach |
| title_short | Localization of Colorectal Cancer Lesions in Contrast-Computed Tomography Images via a Deep Learning Approach |
| title_sort | localization of colorectal cancer lesions in contrast computed tomography images via a deep learning approach |
| topic | colorectal cancer deep learning localization computed tomography |
| url | https://www.mdpi.com/2306-5354/10/8/972 |
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