Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection.
Background Schistosomiasis japonica represents a significant public health concern in South Asia. There is an urgent need to optimize existing schistosomiasis diagnostic techniques. This study aims to develop models for the different stages of liver fibrosis caused by Schistosoma infection utilizing...
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ftdoajarticles:oai:doaj.org/article:7393792339b14e6dbbac2de1eeabc32b 2024-09-09T19:28:06+00:00 Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection. Zhaoyu Guo Miaomiao Zhao Zhenhua Liu Jinxin Zheng Yanfeng Gong Lulu Huang Jingbo Xue Xiaonong Zhou Shizhu Li 2024-06-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0012235 https://doaj.org/article/7393792339b14e6dbbac2de1eeabc32b EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0012235 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0012235 https://doaj.org/article/7393792339b14e6dbbac2de1eeabc32b PLoS Neglected Tropical Diseases, Vol 18, Iss 6, p e0012235 (2024) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2024 ftdoajarticles https://doi.org/10.1371/journal.pntd.0012235 2024-08-05T17:49:03Z Background Schistosomiasis japonica represents a significant public health concern in South Asia. There is an urgent need to optimize existing schistosomiasis diagnostic techniques. This study aims to develop models for the different stages of liver fibrosis caused by Schistosoma infection utilizing ultrasound radiomics and machine learning techniques. Methods From 2018 to 2022, we retrospectively collected data on 1,531 patients and 5,671 B-mode ultrasound images from the Second People's Hospital of Duchang City, Jiangxi Province, China. The datasets were screened based on inclusion and exclusion criteria suitable for radiomics models. Liver fibrosis due to Schistosoma infection (LFSI) was categorized into four stages: grade 0, grade 1, grade 2, and grade 3. The data were divided into six binary classification problems, such as group 1 (grade 0 vs. grade 1) and group 2 (grade 0 vs. grade 2). Key radiomic features were extracted using Pyradiomics, the Mann-Whitney U test, and the Least Absolute Shrinkage and Selection Operator (LASSO). Machine learning models were constructed using Support Vector Machine (SVM), and the contribution of different features in the model was described by applying Shapley Additive Explanations (SHAP). Results This study ultimately included 1,388 patients and their corresponding images. A total of 851 radiomics features were extracted for each binary classification problems. Following feature selection, 18 to 76 features were retained from each groups. The area under the receiver operating characteristic curve (AUC) for the validation cohorts was 0.834 (95% CI: 0.779-0.885) for the LFSI grade 0 vs. LFSI grade 1, 0.771 (95% CI: 0.713-0.835) for LFSI grade 1 vs. LFSI grade 2, and 0.830 (95% CI: 0.762-0.885) for LFSI grade 2 vs. LFSI grade 3. Conclusion Machine learning models based on ultrasound radiomics are feasible for classifying different stages of liver fibrosis caused by Schistosoma infection. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 18 6 e0012235 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Zhaoyu Guo Miaomiao Zhao Zhenhua Liu Jinxin Zheng Yanfeng Gong Lulu Huang Jingbo Xue Xiaonong Zhou Shizhu Li Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Background Schistosomiasis japonica represents a significant public health concern in South Asia. There is an urgent need to optimize existing schistosomiasis diagnostic techniques. This study aims to develop models for the different stages of liver fibrosis caused by Schistosoma infection utilizing ultrasound radiomics and machine learning techniques. Methods From 2018 to 2022, we retrospectively collected data on 1,531 patients and 5,671 B-mode ultrasound images from the Second People's Hospital of Duchang City, Jiangxi Province, China. The datasets were screened based on inclusion and exclusion criteria suitable for radiomics models. Liver fibrosis due to Schistosoma infection (LFSI) was categorized into four stages: grade 0, grade 1, grade 2, and grade 3. The data were divided into six binary classification problems, such as group 1 (grade 0 vs. grade 1) and group 2 (grade 0 vs. grade 2). Key radiomic features were extracted using Pyradiomics, the Mann-Whitney U test, and the Least Absolute Shrinkage and Selection Operator (LASSO). Machine learning models were constructed using Support Vector Machine (SVM), and the contribution of different features in the model was described by applying Shapley Additive Explanations (SHAP). Results This study ultimately included 1,388 patients and their corresponding images. A total of 851 radiomics features were extracted for each binary classification problems. Following feature selection, 18 to 76 features were retained from each groups. The area under the receiver operating characteristic curve (AUC) for the validation cohorts was 0.834 (95% CI: 0.779-0.885) for the LFSI grade 0 vs. LFSI grade 1, 0.771 (95% CI: 0.713-0.835) for LFSI grade 1 vs. LFSI grade 2, and 0.830 (95% CI: 0.762-0.885) for LFSI grade 2 vs. LFSI grade 3. Conclusion Machine learning models based on ultrasound radiomics are feasible for classifying different stages of liver fibrosis caused by Schistosoma infection. |
format |
Article in Journal/Newspaper |
author |
Zhaoyu Guo Miaomiao Zhao Zhenhua Liu Jinxin Zheng Yanfeng Gong Lulu Huang Jingbo Xue Xiaonong Zhou Shizhu Li |
author_facet |
Zhaoyu Guo Miaomiao Zhao Zhenhua Liu Jinxin Zheng Yanfeng Gong Lulu Huang Jingbo Xue Xiaonong Zhou Shizhu Li |
author_sort |
Zhaoyu Guo |
title |
Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection. |
title_short |
Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection. |
title_full |
Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection. |
title_fullStr |
Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection. |
title_full_unstemmed |
Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection. |
title_sort |
feasibility of ultrasound radiomics based models for classification of liver fibrosis due to schistosoma japonicum infection. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2024 |
url |
https://doi.org/10.1371/journal.pntd.0012235 https://doaj.org/article/7393792339b14e6dbbac2de1eeabc32b |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 18, Iss 6, p e0012235 (2024) |
op_relation |
https://doi.org/10.1371/journal.pntd.0012235 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0012235 https://doaj.org/article/7393792339b14e6dbbac2de1eeabc32b |
op_doi |
https://doi.org/10.1371/journal.pntd.0012235 |
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PLOS Neglected Tropical Diseases |
container_volume |
18 |
container_issue |
6 |
container_start_page |
e0012235 |
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1809897377815855104 |