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Radiomics-Based Predictive Model of Radiation-Induced Liver Disease in Hepatocellular Carcinoma Patients Receiving Stereo-Tactic Body Radiotherapy

(1) Background: The application of stereotactic body radiation therapy (SBRT) in hepatocellular carcinoma (HCC) limited the risk of the radiation-induced liver disease (RILD) and we aimed to predict the occurrence of RILD more accurately. (2) Methods: 86 HCC patients were enrolled. We identified key...

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Autores principales: Shen, Po-Chien, Huang, Wen-Yen, Dai, Yang-Hong, Lo, Cheng-Hsiang, Yang, Jen-Fu, Su, Yu-Fu, Wang, Ying-Fu, Lu, Chia-Feng, Lin, Chun-Shu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8945465/
https://www.ncbi.nlm.nih.gov/pubmed/35327398
http://dx.doi.org/10.3390/biomedicines10030597
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author Shen, Po-Chien
Huang, Wen-Yen
Dai, Yang-Hong
Lo, Cheng-Hsiang
Yang, Jen-Fu
Su, Yu-Fu
Wang, Ying-Fu
Lu, Chia-Feng
Lin, Chun-Shu
author_facet Shen, Po-Chien
Huang, Wen-Yen
Dai, Yang-Hong
Lo, Cheng-Hsiang
Yang, Jen-Fu
Su, Yu-Fu
Wang, Ying-Fu
Lu, Chia-Feng
Lin, Chun-Shu
author_sort Shen, Po-Chien
collection PubMed
description (1) Background: The application of stereotactic body radiation therapy (SBRT) in hepatocellular carcinoma (HCC) limited the risk of the radiation-induced liver disease (RILD) and we aimed to predict the occurrence of RILD more accurately. (2) Methods: 86 HCC patients were enrolled. We identified key predictive factors from clinical, radiomic, and dose-volumetric parameters using a multivariate analysis, sequential forward selection (SFS), and a K-nearest neighbor (KNN) algorithm. We developed a predictive model for RILD based on these factors, using the random forest or logistic regression algorithms. (3) Results: Five key predictive factors in the training set were identified, including the albumin–bilirubin grade, difference average, strength, V5, and V30. After model training, the F1 score, sensitivity, specificity, and accuracy of the final random forest model were 0.857, 100, 93.3, and 94.4% in the test set, respectively. Meanwhile, the logistic regression model yielded an F1 score, sensitivity, specificity, and accuracy of 0.8, 66.7, 100, and 94.4% in the test set, respectively. (4) Conclusions: Based on clinical, radiomic, and dose-volumetric factors, our models achieved satisfactory performance on the prediction of the occurrence of SBRT-related RILD in HCC patients. Before undergoing SBRT, the proposed models may detect patients at high risk of RILD, allowing to assist in treatment strategies accordingly.
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spelling pubmed-89454652022-03-25 Radiomics-Based Predictive Model of Radiation-Induced Liver Disease in Hepatocellular Carcinoma Patients Receiving Stereo-Tactic Body Radiotherapy Shen, Po-Chien Huang, Wen-Yen Dai, Yang-Hong Lo, Cheng-Hsiang Yang, Jen-Fu Su, Yu-Fu Wang, Ying-Fu Lu, Chia-Feng Lin, Chun-Shu Biomedicines Article (1) Background: The application of stereotactic body radiation therapy (SBRT) in hepatocellular carcinoma (HCC) limited the risk of the radiation-induced liver disease (RILD) and we aimed to predict the occurrence of RILD more accurately. (2) Methods: 86 HCC patients were enrolled. We identified key predictive factors from clinical, radiomic, and dose-volumetric parameters using a multivariate analysis, sequential forward selection (SFS), and a K-nearest neighbor (KNN) algorithm. We developed a predictive model for RILD based on these factors, using the random forest or logistic regression algorithms. (3) Results: Five key predictive factors in the training set were identified, including the albumin–bilirubin grade, difference average, strength, V5, and V30. After model training, the F1 score, sensitivity, specificity, and accuracy of the final random forest model were 0.857, 100, 93.3, and 94.4% in the test set, respectively. Meanwhile, the logistic regression model yielded an F1 score, sensitivity, specificity, and accuracy of 0.8, 66.7, 100, and 94.4% in the test set, respectively. (4) Conclusions: Based on clinical, radiomic, and dose-volumetric factors, our models achieved satisfactory performance on the prediction of the occurrence of SBRT-related RILD in HCC patients. Before undergoing SBRT, the proposed models may detect patients at high risk of RILD, allowing to assist in treatment strategies accordingly. MDPI 2022-03-03 /pmc/articles/PMC8945465/ /pubmed/35327398 http://dx.doi.org/10.3390/biomedicines10030597 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shen, Po-Chien
Huang, Wen-Yen
Dai, Yang-Hong
Lo, Cheng-Hsiang
Yang, Jen-Fu
Su, Yu-Fu
Wang, Ying-Fu
Lu, Chia-Feng
Lin, Chun-Shu
Radiomics-Based Predictive Model of Radiation-Induced Liver Disease in Hepatocellular Carcinoma Patients Receiving Stereo-Tactic Body Radiotherapy
title Radiomics-Based Predictive Model of Radiation-Induced Liver Disease in Hepatocellular Carcinoma Patients Receiving Stereo-Tactic Body Radiotherapy
title_full Radiomics-Based Predictive Model of Radiation-Induced Liver Disease in Hepatocellular Carcinoma Patients Receiving Stereo-Tactic Body Radiotherapy
title_fullStr Radiomics-Based Predictive Model of Radiation-Induced Liver Disease in Hepatocellular Carcinoma Patients Receiving Stereo-Tactic Body Radiotherapy
title_full_unstemmed Radiomics-Based Predictive Model of Radiation-Induced Liver Disease in Hepatocellular Carcinoma Patients Receiving Stereo-Tactic Body Radiotherapy
title_short Radiomics-Based Predictive Model of Radiation-Induced Liver Disease in Hepatocellular Carcinoma Patients Receiving Stereo-Tactic Body Radiotherapy
title_sort radiomics-based predictive model of radiation-induced liver disease in hepatocellular carcinoma patients receiving stereo-tactic body radiotherapy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8945465/
https://www.ncbi.nlm.nih.gov/pubmed/35327398
http://dx.doi.org/10.3390/biomedicines10030597
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