Defining a radiomics feature selection method for predicting response to transarterial chemoembolization in hepatocellular carcinoma patients
Aim: To assess the utility of different radiomics feature selection methods in predicting transarterial chemoembolization (TACE) response in hepatocellular carcinoma (HCC) patients. Materials and methods: This study employed a dataset of 136 paired MR T1-weighted contrast-enhanced abdominal images w...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-03-01
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| Series: | Meta-Radiology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950162824000201 |
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| _version_ | 1846170003735314432 |
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| author | Helen Zhang Li Yang Amanda Laguna Jing Wu Beiji Zou Alireza Mohseni Rajat S. Chandra Tej I. Mehta Hossam A. Zaki Paul Zhang Zhicheng Jiao Ihab R. Kamel Harrison X. Bai |
| author_facet | Helen Zhang Li Yang Amanda Laguna Jing Wu Beiji Zou Alireza Mohseni Rajat S. Chandra Tej I. Mehta Hossam A. Zaki Paul Zhang Zhicheng Jiao Ihab R. Kamel Harrison X. Bai |
| author_sort | Helen Zhang |
| collection | DOAJ |
| description | Aim: To assess the utility of different radiomics feature selection methods in predicting transarterial chemoembolization (TACE) response in hepatocellular carcinoma (HCC) patients. Materials and methods: This study employed a dataset of 136 paired MR T1-weighted contrast-enhanced abdominal images with liver tumor masks before and after TACE. TACE response for each image pair was classified by European Association for the Study of the Liver (EASL) and modified Response Evaluation Criteria in Solid Tumors (mRECIST) guidelines. 100D feature vectors were generated for the paired tumor areas. Eighteen existing feature selection methods were employed to select the top-k features to train and test a non-linear support vector machine (SVM) with a Gaussian kernel. Five-cross validation was performed to identify the highest performing feature selection methods. Results: For all benchmarks, a L0-based method selecting the top-5 or top-10 features achieved the highest performance. For images classified with EASL criteria that were analyzed with the L0-based method, the accuracy (ACC), area under curve (AUC), and balanced F score (F1-score) were 0.75 ± 0.06, 0.75 ± 0.09, and 0.80 ± 0.05, respectively. For images classified with mRECIST criteria that were analyzed with the L0-based method, the ACC, AUC, and F1-score were 0.75 ± 0.07, 0.71 ± 0.16, and 0.82 ± 0.04, respectively. Conclusion: A L0-based method that selected the top-5/10 most important features predicted TACE response in HCC patients with the highest accuracy under both EASL and mRECIST criteria. This proof-of-concept investigation represents a step forward in the development of a reliable clinical decision-making tool for management of intermediate HCC patients undergoing TACE. |
| format | Article |
| id | doaj-art-41f4dbda92a24c60b241fc4c5b00b4e1 |
| institution | Kabale University |
| issn | 2950-1628 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Meta-Radiology |
| spelling | doaj-art-41f4dbda92a24c60b241fc4c5b00b4e12024-11-12T05:22:42ZengKeAi Communications Co., Ltd.Meta-Radiology2950-16282024-03-0121100067Defining a radiomics feature selection method for predicting response to transarterial chemoembolization in hepatocellular carcinoma patientsHelen Zhang0Li Yang1Amanda Laguna2Jing Wu3Beiji Zou4Alireza Mohseni5Rajat S. Chandra6Tej I. Mehta7Hossam A. Zaki8Paul Zhang9Zhicheng Jiao10Ihab R. Kamel11Harrison X. Bai12The Warren Alpert Medical School of Brown University, Providence, RI, USAThe Warren Alpert Medical School of Brown University, Providence, RI, USA; School of Informatics, Hunan University of Chinese Medicine, Changsha, PR ChinaThe Warren Alpert Medical School of Brown University, Providence, RI, USA; Johns Hopkins University, Baltimore, MD, USADepartment of Radiology, Second Xiangya Hospital, Changsha, PR ChinaSchool of Informatics, Hunan University of Chinese Medicine, Changsha, PR China; School of Computer Science and Engineering, Central South University, Changsha, PR ChinaJohns Hopkins University, Baltimore, MD, USAPerelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USAJohns Hopkins University, Baltimore, MD, USAThe Warren Alpert Medical School of Brown University, Providence, RI, USADepartment of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USARhode Island Hospital, Providence, RI, USADepartment of Radiology, University of Colorado School of Medicine, Aurora, CO, USAJohns Hopkins University, Baltimore, MD, USA; Corresponding author.Aim: To assess the utility of different radiomics feature selection methods in predicting transarterial chemoembolization (TACE) response in hepatocellular carcinoma (HCC) patients. Materials and methods: This study employed a dataset of 136 paired MR T1-weighted contrast-enhanced abdominal images with liver tumor masks before and after TACE. TACE response for each image pair was classified by European Association for the Study of the Liver (EASL) and modified Response Evaluation Criteria in Solid Tumors (mRECIST) guidelines. 100D feature vectors were generated for the paired tumor areas. Eighteen existing feature selection methods were employed to select the top-k features to train and test a non-linear support vector machine (SVM) with a Gaussian kernel. Five-cross validation was performed to identify the highest performing feature selection methods. Results: For all benchmarks, a L0-based method selecting the top-5 or top-10 features achieved the highest performance. For images classified with EASL criteria that were analyzed with the L0-based method, the accuracy (ACC), area under curve (AUC), and balanced F score (F1-score) were 0.75 ± 0.06, 0.75 ± 0.09, and 0.80 ± 0.05, respectively. For images classified with mRECIST criteria that were analyzed with the L0-based method, the ACC, AUC, and F1-score were 0.75 ± 0.07, 0.71 ± 0.16, and 0.82 ± 0.04, respectively. Conclusion: A L0-based method that selected the top-5/10 most important features predicted TACE response in HCC patients with the highest accuracy under both EASL and mRECIST criteria. This proof-of-concept investigation represents a step forward in the development of a reliable clinical decision-making tool for management of intermediate HCC patients undergoing TACE.http://www.sciencedirect.com/science/article/pii/S2950162824000201RadiomicsFeature selectionMachine learningHepatocellular carcinomaTransarterial chemoembolization |
| spellingShingle | Helen Zhang Li Yang Amanda Laguna Jing Wu Beiji Zou Alireza Mohseni Rajat S. Chandra Tej I. Mehta Hossam A. Zaki Paul Zhang Zhicheng Jiao Ihab R. Kamel Harrison X. Bai Defining a radiomics feature selection method for predicting response to transarterial chemoembolization in hepatocellular carcinoma patients Meta-Radiology Radiomics Feature selection Machine learning Hepatocellular carcinoma Transarterial chemoembolization |
| title | Defining a radiomics feature selection method for predicting response to transarterial chemoembolization in hepatocellular carcinoma patients |
| title_full | Defining a radiomics feature selection method for predicting response to transarterial chemoembolization in hepatocellular carcinoma patients |
| title_fullStr | Defining a radiomics feature selection method for predicting response to transarterial chemoembolization in hepatocellular carcinoma patients |
| title_full_unstemmed | Defining a radiomics feature selection method for predicting response to transarterial chemoembolization in hepatocellular carcinoma patients |
| title_short | Defining a radiomics feature selection method for predicting response to transarterial chemoembolization in hepatocellular carcinoma patients |
| title_sort | defining a radiomics feature selection method for predicting response to transarterial chemoembolization in hepatocellular carcinoma patients |
| topic | Radiomics Feature selection Machine learning Hepatocellular carcinoma Transarterial chemoembolization |
| url | http://www.sciencedirect.com/science/article/pii/S2950162824000201 |
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