A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy
Abstract Background Hepatocellular carcinoma (HCC) is associated with a dismal prognosis, and prediction of the prognosis of HCC can assist in therapeutic decision-makings. An increasing number of studies have shown that the texture parameters of images can reflect the heterogeneity of tumors, and m...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-11-01
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| Series: | Cancer Imaging |
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| Online Access: | http://link.springer.com/article/10.1186/s40644-020-00360-9 |
| _version_ | 1818595476394475520 |
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| author | Qinqin Liu Jing Li Fei Liu Weilin Yang Jingjing Ding Weixia Chen Yonggang Wei Bo Li Lu Zheng |
| author_facet | Qinqin Liu Jing Li Fei Liu Weilin Yang Jingjing Ding Weixia Chen Yonggang Wei Bo Li Lu Zheng |
| author_sort | Qinqin Liu |
| collection | DOAJ |
| description | Abstract Background Hepatocellular carcinoma (HCC) is associated with a dismal prognosis, and prediction of the prognosis of HCC can assist in therapeutic decision-makings. An increasing number of studies have shown that the texture parameters of images can reflect the heterogeneity of tumors, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of this study was to investigate the prognostic value of computed tomography (CT) texture parameters in patients with HCC after hepatectomy and to develop a radiomics nomogram by combining clinicopathological factors and the radiomics signature. Methods In all, 544 eligible patients were enrolled in this retrospective study and were randomly divided into the training cohort (n = 381) and the validation cohort (n = 163). The tumor regions of interest (ROIs) were delineated, and the corresponding texture parameters were extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in the training cohort, and a radiomics signature was established. Then, the radiomics signature was further validated as an independent risk factor for overall survival (OS). The radiomics nomogram was established based on the Cox regression model. The concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram. Results The radiomics signature was formulated based on 7 OS-related texture parameters, which were selected in the training cohort. In addition, the radiomics nomogram was developed based on the following five variables: α-fetoprotein (AFP), platelet-to-lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and radiomics score (Rad-score). The nomogram displayed good accuracy in predicting OS (C-index = 0.747) in the training cohort and was confirmed in the validation cohort (C-index = 0.777). The calibration plots also showed excellent agreement between the actual and predicted survival probabilities. The DCA indicated that the radiomics nomogram showed better clinical utility than the clinicopathologic nomogram. Conclusion The radiomics signature is a potential prognostic biomarker of HCC after hepatectomy. The radiomics nomogram that integrated the radiomics signature can provide a more accurate estimation of OS than the clinicopathologic nomogram for HCC patients after hepatectomy. |
| first_indexed | 2024-12-16T11:16:37Z |
| format | Article |
| id | doaj.art-fd776f92d092470d87b7bacb94d2ffb1 |
| institution | Directory Open Access Journal |
| issn | 1470-7330 |
| language | English |
| last_indexed | 2024-12-16T11:16:37Z |
| publishDate | 2020-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Imaging |
| spelling | doaj.art-fd776f92d092470d87b7bacb94d2ffb12022-12-21T22:33:36ZengBMCCancer Imaging1470-73302020-11-0120111410.1186/s40644-020-00360-9A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomyQinqin Liu0Jing Li1Fei Liu2Weilin Yang3Jingjing Ding4Weixia Chen5Yonggang Wei6Bo Li7Lu Zheng8Department of Liver Surgery, Center of Liver Transplantation, West China Hospital, Sichuan UniversityDepartment of Hepatobiliary Surgery, the Second Affiliated Hospital of Army Medical UniversityDepartment of Liver Surgery, Center of Liver Transplantation, West China Hospital, Sichuan UniversityDepartment of Radiology, West China Hospital, Sichuan UniversityDepartment of Gastrointestinal Surgery, West China Hospital, Sichuan UniversityDepartment of Radiology, West China Hospital, Sichuan UniversityDepartment of Liver Surgery, Center of Liver Transplantation, West China Hospital, Sichuan UniversityDepartment of Liver Surgery, Center of Liver Transplantation, West China Hospital, Sichuan UniversityDepartment of Hepatobiliary Surgery, the Second Affiliated Hospital of Army Medical UniversityAbstract Background Hepatocellular carcinoma (HCC) is associated with a dismal prognosis, and prediction of the prognosis of HCC can assist in therapeutic decision-makings. An increasing number of studies have shown that the texture parameters of images can reflect the heterogeneity of tumors, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of this study was to investigate the prognostic value of computed tomography (CT) texture parameters in patients with HCC after hepatectomy and to develop a radiomics nomogram by combining clinicopathological factors and the radiomics signature. Methods In all, 544 eligible patients were enrolled in this retrospective study and were randomly divided into the training cohort (n = 381) and the validation cohort (n = 163). The tumor regions of interest (ROIs) were delineated, and the corresponding texture parameters were extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in the training cohort, and a radiomics signature was established. Then, the radiomics signature was further validated as an independent risk factor for overall survival (OS). The radiomics nomogram was established based on the Cox regression model. The concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram. Results The radiomics signature was formulated based on 7 OS-related texture parameters, which were selected in the training cohort. In addition, the radiomics nomogram was developed based on the following five variables: α-fetoprotein (AFP), platelet-to-lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and radiomics score (Rad-score). The nomogram displayed good accuracy in predicting OS (C-index = 0.747) in the training cohort and was confirmed in the validation cohort (C-index = 0.777). The calibration plots also showed excellent agreement between the actual and predicted survival probabilities. The DCA indicated that the radiomics nomogram showed better clinical utility than the clinicopathologic nomogram. Conclusion The radiomics signature is a potential prognostic biomarker of HCC after hepatectomy. The radiomics nomogram that integrated the radiomics signature can provide a more accurate estimation of OS than the clinicopathologic nomogram for HCC patients after hepatectomy.http://link.springer.com/article/10.1186/s40644-020-00360-9CT texture analysisHepatocellular carcinomaOverall survivalPredictionNomogram |
| spellingShingle | Qinqin Liu Jing Li Fei Liu Weilin Yang Jingjing Ding Weixia Chen Yonggang Wei Bo Li Lu Zheng A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy Cancer Imaging CT texture analysis Hepatocellular carcinoma Overall survival Prediction Nomogram |
| title | A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy |
| title_full | A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy |
| title_fullStr | A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy |
| title_full_unstemmed | A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy |
| title_short | A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy |
| title_sort | radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy |
| topic | CT texture analysis Hepatocellular carcinoma Overall survival Prediction Nomogram |
| url | http://link.springer.com/article/10.1186/s40644-020-00360-9 |
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