Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models
Background: We conduct a study in developing and validating two radiomics-based models to preoperatively distinguish hepatic epithelioid angiomyolipoma (HEAML) from hepatic carcinoma (HCC) as well as focal nodular hyperplasia (FNH).Methods: Totally, preoperative contrast-enhanced computed tomography...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-10-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fonc.2020.564307/full |
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| author | Wenjie Liang Jiayuan Shao Weihai Liu Shijian Ruan Wuwei Tian Xiuming Zhang Dalong Wan Qiang Huang Yong Ding Yong Ding Wenbo Xiao |
| author_facet | Wenjie Liang Jiayuan Shao Weihai Liu Shijian Ruan Wuwei Tian Xiuming Zhang Dalong Wan Qiang Huang Yong Ding Yong Ding Wenbo Xiao |
| author_sort | Wenjie Liang |
| collection | DOAJ |
| description | Background: We conduct a study in developing and validating two radiomics-based models to preoperatively distinguish hepatic epithelioid angiomyolipoma (HEAML) from hepatic carcinoma (HCC) as well as focal nodular hyperplasia (FNH).Methods: Totally, preoperative contrast-enhanced computed tomography (CT) data of 170 patients and preoperative contrast-enhanced magnetic resonance imaging (MRI) data of 137 patients were enrolled in this study. Quantitative texture features and wavelet features were extracted from the regions of interest (ROIs) of each patient imaging data. Then two radiomics signatures were constructed based on CT and MRI radiomics features, respectively, using the random forest (RF) algorithm. By integrating radiomics signatures with clinical characteristics, two radiomics-based fusion models were established through multivariate linear regression and 10-fold cross-validation. Finally, two diagnostic nomograms were built to facilitate the clinical application of the fusion models.Results: The radiomics signatures based on the RF algorithm achieved the optimal predictive performance in both CT and MRI data. The area under the receiver operating characteristic curves (AUCs) reached 0.996, 0.879, 0.999, and 0.925 for the training as well as test cohort from CT and MRI data, respectively. Then, two fusion models simultaneously integrated clinical characteristics achieved average AUCs of 0.966 (CT data) and 0.971 (MRI data) with 10-fold cross-validation. Through decision curve analysis, the fusion models were proved to be excellent models to distinguish HEAML from HCC and FNH in comparison between the clinical models and radiomics signatures.Conclusions: Two radiomics-based models derived from CT and MRI images, respectively, performed well in distinguishing HEAML from HCC and FNH and might be potential diagnostic tools to formulate individualized treatment strategies. |
| first_indexed | 2024-12-14T04:08:26Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-14T04:08:26Z |
| publishDate | 2020-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-c852e40adb9542779f25da200df0063f2022-12-21T23:17:45ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2020-10-011010.3389/fonc.2020.564307564307Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics ModelsWenjie Liang0Jiayuan Shao1Weihai Liu2Shijian Ruan3Wuwei Tian4Xiuming Zhang5Dalong Wan6Qiang Huang7Yong Ding8Yong Ding9Wenbo Xiao10Department of Radiology, College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, ChinaPolytechnic Institute, Zhejiang University, Hangzhou, ChinaDepartment of Radiology, The People's Hospital of Beilun District, Ningbo, ChinaCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, ChinaCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, ChinaDepartment of Pathology, College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, ChinaDepartment of Hepatobiliary and Pancreatic Surgery, College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, ChinaDepartment of Radiology, College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, ChinaPolytechnic Institute, Zhejiang University, Hangzhou, ChinaCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, ChinaDepartment of Radiology, College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, ChinaBackground: We conduct a study in developing and validating two radiomics-based models to preoperatively distinguish hepatic epithelioid angiomyolipoma (HEAML) from hepatic carcinoma (HCC) as well as focal nodular hyperplasia (FNH).Methods: Totally, preoperative contrast-enhanced computed tomography (CT) data of 170 patients and preoperative contrast-enhanced magnetic resonance imaging (MRI) data of 137 patients were enrolled in this study. Quantitative texture features and wavelet features were extracted from the regions of interest (ROIs) of each patient imaging data. Then two radiomics signatures were constructed based on CT and MRI radiomics features, respectively, using the random forest (RF) algorithm. By integrating radiomics signatures with clinical characteristics, two radiomics-based fusion models were established through multivariate linear regression and 10-fold cross-validation. Finally, two diagnostic nomograms were built to facilitate the clinical application of the fusion models.Results: The radiomics signatures based on the RF algorithm achieved the optimal predictive performance in both CT and MRI data. The area under the receiver operating characteristic curves (AUCs) reached 0.996, 0.879, 0.999, and 0.925 for the training as well as test cohort from CT and MRI data, respectively. Then, two fusion models simultaneously integrated clinical characteristics achieved average AUCs of 0.966 (CT data) and 0.971 (MRI data) with 10-fold cross-validation. Through decision curve analysis, the fusion models were proved to be excellent models to distinguish HEAML from HCC and FNH in comparison between the clinical models and radiomics signatures.Conclusions: Two radiomics-based models derived from CT and MRI images, respectively, performed well in distinguishing HEAML from HCC and FNH and might be potential diagnostic tools to formulate individualized treatment strategies.https://www.frontiersin.org/article/10.3389/fonc.2020.564307/fullhepatic epithelioid angiomyolipomahepatocellular carcinomafocal nodular hyperplasiaradiomicsmachine learning |
| spellingShingle | Wenjie Liang Jiayuan Shao Weihai Liu Shijian Ruan Wuwei Tian Xiuming Zhang Dalong Wan Qiang Huang Yong Ding Yong Ding Wenbo Xiao Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models Frontiers in Oncology hepatic epithelioid angiomyolipoma hepatocellular carcinoma focal nodular hyperplasia radiomics machine learning |
| title | Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models |
| title_full | Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models |
| title_fullStr | Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models |
| title_full_unstemmed | Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models |
| title_short | Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models |
| title_sort | differentiating hepatic epithelioid angiomyolipoma from hepatocellular carcinoma and focal nodular hyperplasia via radiomics models |
| topic | hepatic epithelioid angiomyolipoma hepatocellular carcinoma focal nodular hyperplasia radiomics machine learning |
| url | https://www.frontiersin.org/article/10.3389/fonc.2020.564307/full |
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