Prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast-enhanced CT
AimTo develop a habitat imaging method for preoperative prediction of early postoperative recurrence of hepatocellular carcinoma.MethodsA retrospective cohort study was conducted to collect data on 344 patients who underwent liver resection for HCC. The internal subregion of the tumor was objectivel...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1522501/full |
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| author | Yubo Zhang Yubo Zhang Hongyan Ma Peng Lei Zhiyuan Li Zhao Yan Xinqing Wang |
| author_facet | Yubo Zhang Yubo Zhang Hongyan Ma Peng Lei Zhiyuan Li Zhao Yan Xinqing Wang |
| author_sort | Yubo Zhang |
| collection | DOAJ |
| description | AimTo develop a habitat imaging method for preoperative prediction of early postoperative recurrence of hepatocellular carcinoma.MethodsA retrospective cohort study was conducted to collect data on 344 patients who underwent liver resection for HCC. The internal subregion of the tumor was objectively delineated and the clinical features were also analyzed to construct clinical models. Radiomics feature extraction was performed on tumor subregions of arterial and portal venous phase images. Machine learning classification models were constructed as a fusion model combining the three different models, and the models were assessed.ResultsA comprehensive retrospective analysis was conducted on a cohort of 344 patients who underwent hepatic cancer resection at one of the two centers. it was found that the combined SVM model yielded superior results after comparing various metrics, such as the AUC, accuracy, sensitivity, specificity, and DCA.ConclusionsHabitat analysis of sequential CT images can delineate distinct subregions within a tumor, offering valuable insights for early prediction of postoperative HCC recurrence. |
| format | Article |
| id | doaj-art-2168c892aa83491ea350a7c9cbf8f9d2 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-2168c892aa83491ea350a7c9cbf8f9d22025-01-03T06:47:26ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011410.3389/fonc.2024.15225011522501Prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast-enhanced CTYubo Zhang0Yubo Zhang1Hongyan Ma2Peng Lei3Zhiyuan Li4Zhao Yan5Xinqing Wang6Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan, ChinaSchool of Clinical Medicine, General Hospital of Ningxia Medical University, Yinchuan, ChinaSchool of Clinical Medicine, General Hospital of Ningxia Medical University, Yinchuan, ChinaDepartment of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan, ChinaSchool of Clinical Medicine, General Hospital of Ningxia Medical University, Yinchuan, ChinaSchool of Clinical Medicine, General Hospital of Ningxia Medical University, Yinchuan, ChinaDepartment of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan, ChinaAimTo develop a habitat imaging method for preoperative prediction of early postoperative recurrence of hepatocellular carcinoma.MethodsA retrospective cohort study was conducted to collect data on 344 patients who underwent liver resection for HCC. The internal subregion of the tumor was objectively delineated and the clinical features were also analyzed to construct clinical models. Radiomics feature extraction was performed on tumor subregions of arterial and portal venous phase images. Machine learning classification models were constructed as a fusion model combining the three different models, and the models were assessed.ResultsA comprehensive retrospective analysis was conducted on a cohort of 344 patients who underwent hepatic cancer resection at one of the two centers. it was found that the combined SVM model yielded superior results after comparing various metrics, such as the AUC, accuracy, sensitivity, specificity, and DCA.ConclusionsHabitat analysis of sequential CT images can delineate distinct subregions within a tumor, offering valuable insights for early prediction of postoperative HCC recurrence.https://www.frontiersin.org/articles/10.3389/fonc.2024.1522501/fullcomputed tomography (CT)early recurrencehabitat analysishepatocellular carcinomamachine learning |
| spellingShingle | Yubo Zhang Yubo Zhang Hongyan Ma Peng Lei Zhiyuan Li Zhao Yan Xinqing Wang Prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast-enhanced CT Frontiers in Oncology computed tomography (CT) early recurrence habitat analysis hepatocellular carcinoma machine learning |
| title | Prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast-enhanced CT |
| title_full | Prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast-enhanced CT |
| title_fullStr | Prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast-enhanced CT |
| title_full_unstemmed | Prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast-enhanced CT |
| title_short | Prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast-enhanced CT |
| title_sort | prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast enhanced ct |
| topic | computed tomography (CT) early recurrence habitat analysis hepatocellular carcinoma machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1522501/full |
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