Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images
Pancreatic cancer (PC) is a malignant tumor that seriously threatens the survival of patients. Artificial classification has practical difficulties, such as unstable classification accuracy, a heavy workload, and the classification results depend on the subjective judgment of the clinician during th...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9152949/ |
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| author | Min Li Xiaohan Nie Yilidan Reheman Pan Huang Shuailei Zhang Yushuai Yuan Chen Chen Ziwei Yan Cheng Chen Xiaoyi Lv Wei Han |
| author_facet | Min Li Xiaohan Nie Yilidan Reheman Pan Huang Shuailei Zhang Yushuai Yuan Chen Chen Ziwei Yan Cheng Chen Xiaoyi Lv Wei Han |
| author_sort | Min Li |
| collection | DOAJ |
| description | Pancreatic cancer (PC) is a malignant tumor that seriously threatens the survival of patients. Artificial classification has practical difficulties, such as unstable classification accuracy, a heavy workload, and the classification results depend on the subjective judgment of the clinician during the diagnosis and staging of PC. In addition, accurate PC staging could better help clinicians deliver the optimal therapeutic schedule for PC patients of different stages. Therefore, this study proposes a comprehensive medical computer-aided method for preoperative diagnosis and staging of PC based on an ensemble learning-support vector machine (EL-SVM) and computed tomography (CT) images. The least absolute shrinkage and selection operator (LASSO) algorithm was chosen for feature selection. In contrast to no feature selection, the model optimization time decreased by 19.94 seconds while maintaining precision. The EL-SVM learner was used to classify 168 CT images of normal pancreas and different stages of PC. The experimental results demonstrated that the normal pancreas (normal)-pancreatic cancer early stage (early stage) classification accuracy was 86.61%, the normal-pancreatic cancer stage III (stage III) classification accuracy was 87.04%, the normal-pancreatic cancer stage IV (stage IV) classification accuracy was 91.63%, the normal-PC classification accuracy was 87.89%, the early stage-stage III classification accuracy was 75.03%, and the early stage-stage IV classification accuracy was 81.22%, and the stage III-stage IV classification accuracy was 82.48%. Our experimental results prove that our proposed method is feasible and promising for clinical applications for the preoperative diagnosis and staging of PC via CT images. |
| format | Article |
| id | doaj-art-2420b02b804f49bfb526595a3f997919 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2420b02b804f49bfb526595a3f9979192025-08-20T03:15:51ZengIEEEIEEE Access2169-35362020-01-01814170514171810.1109/ACCESS.2020.30129679152949Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT ImagesMin Li0https://orcid.org/0000-0001-9406-4707Xiaohan Nie1https://orcid.org/0000-0002-7417-5386Yilidan Reheman2https://orcid.org/0000-0001-8798-7335Pan Huang3https://orcid.org/0000-0001-8158-2628Shuailei Zhang4https://orcid.org/0000-0003-4440-5248Yushuai Yuan5https://orcid.org/0000-0003-1559-5898Chen Chen6https://orcid.org/0000-0003-1406-5721Ziwei Yan7https://orcid.org/0000-0003-2943-2707Cheng Chen8https://orcid.org/0000-0002-6739-1937Xiaoyi Lv9https://orcid.org/0000-0001-6855-7428Wei Han10https://orcid.org/0000-0002-6595-4919College of Software, Xinjiang University, Urumqi, ChinaThe First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaThe First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi, ChinaCollege of Software, Xinjiang University, Urumqi, ChinaThe First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaPancreatic cancer (PC) is a malignant tumor that seriously threatens the survival of patients. Artificial classification has practical difficulties, such as unstable classification accuracy, a heavy workload, and the classification results depend on the subjective judgment of the clinician during the diagnosis and staging of PC. In addition, accurate PC staging could better help clinicians deliver the optimal therapeutic schedule for PC patients of different stages. Therefore, this study proposes a comprehensive medical computer-aided method for preoperative diagnosis and staging of PC based on an ensemble learning-support vector machine (EL-SVM) and computed tomography (CT) images. The least absolute shrinkage and selection operator (LASSO) algorithm was chosen for feature selection. In contrast to no feature selection, the model optimization time decreased by 19.94 seconds while maintaining precision. The EL-SVM learner was used to classify 168 CT images of normal pancreas and different stages of PC. The experimental results demonstrated that the normal pancreas (normal)-pancreatic cancer early stage (early stage) classification accuracy was 86.61%, the normal-pancreatic cancer stage III (stage III) classification accuracy was 87.04%, the normal-pancreatic cancer stage IV (stage IV) classification accuracy was 91.63%, the normal-PC classification accuracy was 87.89%, the early stage-stage III classification accuracy was 75.03%, and the early stage-stage IV classification accuracy was 81.22%, and the stage III-stage IV classification accuracy was 82.48%. Our experimental results prove that our proposed method is feasible and promising for clinical applications for the preoperative diagnosis and staging of PC via CT images.https://ieeexplore.ieee.org/document/9152949/Pancreatic cancerdiagnosis and stagingEL-SVMCTLASSO |
| spellingShingle | Min Li Xiaohan Nie Yilidan Reheman Pan Huang Shuailei Zhang Yushuai Yuan Chen Chen Ziwei Yan Cheng Chen Xiaoyi Lv Wei Han Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images IEEE Access Pancreatic cancer diagnosis and staging EL-SVM CT LASSO |
| title | Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images |
| title_full | Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images |
| title_fullStr | Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images |
| title_full_unstemmed | Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images |
| title_short | Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images |
| title_sort | computer aided diagnosis and staging of pancreatic cancer based on ct images |
| topic | Pancreatic cancer diagnosis and staging EL-SVM CT LASSO |
| url | https://ieeexplore.ieee.org/document/9152949/ |
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