Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection.

• Published in: PLOS Neglected Tropical Diseases
• Main Authors: Zhaoyu Guo, Miaomiao Zhao, Zhenhua Liu, Jinxin Zheng, Yanfeng Gong, Lulu Huang, Jingbo Xue, Xiaonong Zhou, Shizhu Li
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2024
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270; Arctic
• Online Access: https://doi.org/10.1371/journal.pntd.0012235; https://doaj.org/article/7393792339b14e6dbbac2de1eeabc32b

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.