A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer
PurposeTo evaluate the potential of radiomics approach for predicting No. 14v station lymph node metastasis (14vM) in gastric cancer (GC).MethodsThe contrast enhanced CT (CECT) images with corresponding clinical information of 288 GC patients were retrospectively collected. Patients were separated i...
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Frontiers Media S.A.
2024-10-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2024.1464632/full |
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| author | Tingting Ma Tingting Ma Tingting Ma Tingting Ma Tingting Ma Mengran Zhao Mengran Zhao Mengran Zhao Mengran Zhao Mengran Zhao Xiangli Li Xiangchao Song Xiangchao Song Xiangchao Song Xiangchao Song Lingwei Wang Lingwei Wang Lingwei Wang Lingwei Wang Zhaoxiang Ye Zhaoxiang Ye Zhaoxiang Ye Zhaoxiang Ye |
| author_facet | Tingting Ma Tingting Ma Tingting Ma Tingting Ma Tingting Ma Mengran Zhao Mengran Zhao Mengran Zhao Mengran Zhao Mengran Zhao Xiangli Li Xiangchao Song Xiangchao Song Xiangchao Song Xiangchao Song Lingwei Wang Lingwei Wang Lingwei Wang Lingwei Wang Zhaoxiang Ye Zhaoxiang Ye Zhaoxiang Ye Zhaoxiang Ye |
| author_sort | Tingting Ma |
| collection | DOAJ |
| description | PurposeTo evaluate the potential of radiomics approach for predicting No. 14v station lymph node metastasis (14vM) in gastric cancer (GC).MethodsThe contrast enhanced CT (CECT) images with corresponding clinical information of 288 GC patients were retrospectively collected. Patients were separated into training set (n = 202) and testing set (n = 86). A total of 1,316 radiomics feature were extracted from portal venous phase images of CECT. Seven machine learning (ML) algorithms including naïve Bayes (NB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), random forest (RF), eXtreme gradient boosting (XGBoost) and support vector machine (SVM) were trained for development of optimal radiomics signature. A combined model was established by combining radiomics with important clinicopathological factors. The diagnostic ability of the signature and model were evaluated.ResultsLR algorithm was chosen for signature construction. The radiomics signature exhibited good discrimination accuracy of 14vM with AUCs of 0.83 in the training and 0.77 in the testing set. The risk of 14vM showed significant association with higher radiomics score. A combined model exhibited increased predictive ability and good agreement in the training (AUC = 0.87) and testing (AUC = 0.85) sets.ConclusionThe ML-based radiomics model provided a promising image biomarker for preoperative detection of 14vM and may help the surgeon to decide whether to add 14v dissection to lymphadenectomy. |
| format | Article |
| id | doaj-art-394a0ccd92a94d77a7fdb86ca3071045 |
| institution | OA Journals |
| issn | 2296-858X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-394a0ccd92a94d77a7fdb86ca30710452025-08-20T01:47:50ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2024-10-011110.3389/fmed.2024.14646321464632A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancerTingting Ma0Tingting Ma1Tingting Ma2Tingting Ma3Tingting Ma4Mengran Zhao5Mengran Zhao6Mengran Zhao7Mengran Zhao8Mengran Zhao9Xiangli Li10Xiangchao Song11Xiangchao Song12Xiangchao Song13Xiangchao Song14Lingwei Wang15Lingwei Wang16Lingwei Wang17Lingwei Wang18Zhaoxiang Ye19Zhaoxiang Ye20Zhaoxiang Ye21Zhaoxiang Ye22Department of Radiology, Tianjin Cancer Hospital Airport Hospital, Tianjin, ChinaDepartment of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, ChinaNational Clinical Research Center for Cancer, Tianjin, ChinaTianjin’s Clinical Research Center for Cancer, Tianjin, ChinaThe Key Laboratory of Cancer Prevention and Therapy, Tianjin, ChinaDepartment of Radiology, Tianjin Cancer Hospital Airport Hospital, Tianjin, ChinaDepartment of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, ChinaNational Clinical Research Center for Cancer, Tianjin, ChinaTianjin’s Clinical Research Center for Cancer, Tianjin, ChinaThe Key Laboratory of Cancer Prevention and Therapy, Tianjin, ChinaHealth Management Center, Weifang People’s Hospital, Weifang, ChinaDepartment of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, ChinaNational Clinical Research Center for Cancer, Tianjin, ChinaTianjin’s Clinical Research Center for Cancer, Tianjin, ChinaThe Key Laboratory of Cancer Prevention and Therapy, Tianjin, ChinaDepartment of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, ChinaNational Clinical Research Center for Cancer, Tianjin, ChinaTianjin’s Clinical Research Center for Cancer, Tianjin, ChinaThe Key Laboratory of Cancer Prevention and Therapy, Tianjin, ChinaDepartment of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, ChinaNational Clinical Research Center for Cancer, Tianjin, ChinaTianjin’s Clinical Research Center for Cancer, Tianjin, ChinaThe Key Laboratory of Cancer Prevention and Therapy, Tianjin, ChinaPurposeTo evaluate the potential of radiomics approach for predicting No. 14v station lymph node metastasis (14vM) in gastric cancer (GC).MethodsThe contrast enhanced CT (CECT) images with corresponding clinical information of 288 GC patients were retrospectively collected. Patients were separated into training set (n = 202) and testing set (n = 86). A total of 1,316 radiomics feature were extracted from portal venous phase images of CECT. Seven machine learning (ML) algorithms including naïve Bayes (NB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), random forest (RF), eXtreme gradient boosting (XGBoost) and support vector machine (SVM) were trained for development of optimal radiomics signature. A combined model was established by combining radiomics with important clinicopathological factors. The diagnostic ability of the signature and model were evaluated.ResultsLR algorithm was chosen for signature construction. The radiomics signature exhibited good discrimination accuracy of 14vM with AUCs of 0.83 in the training and 0.77 in the testing set. The risk of 14vM showed significant association with higher radiomics score. A combined model exhibited increased predictive ability and good agreement in the training (AUC = 0.87) and testing (AUC = 0.85) sets.ConclusionThe ML-based radiomics model provided a promising image biomarker for preoperative detection of 14vM and may help the surgeon to decide whether to add 14v dissection to lymphadenectomy.https://www.frontiersin.org/articles/10.3389/fmed.2024.1464632/fullradiomicscomputed tomographygastric cancerlymph node metastasis14v station |
| spellingShingle | Tingting Ma Tingting Ma Tingting Ma Tingting Ma Tingting Ma Mengran Zhao Mengran Zhao Mengran Zhao Mengran Zhao Mengran Zhao Xiangli Li Xiangchao Song Xiangchao Song Xiangchao Song Xiangchao Song Lingwei Wang Lingwei Wang Lingwei Wang Lingwei Wang Zhaoxiang Ye Zhaoxiang Ye Zhaoxiang Ye Zhaoxiang Ye A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer Frontiers in Medicine radiomics computed tomography gastric cancer lymph node metastasis 14v station |
| title | A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer |
| title_full | A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer |
| title_fullStr | A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer |
| title_full_unstemmed | A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer |
| title_short | A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer |
| title_sort | machine learning based radiomics approach for predicting no 14v station lymph node metastasis in gastric cancer |
| topic | radiomics computed tomography gastric cancer lymph node metastasis 14v station |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2024.1464632/full |
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