[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120139-en":3,"doc-seo-120139-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120139,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning based ultrasomics noninvasive predicting EGFR expression status in hepatocellular carcinoma patients","Ultrasomics features extracted from gray-scale ultrasound images combined with machine learning algorithms are investigated for noninvasive prediction of epidermal growth factor receptor (EGFR) expression status in hepatocellular carcinoma (HCC). The study analyzes 198 patients with immunohistochemistry-confirmed EGFR status and compares clinical models, ultrasomics models, and integrated clinical-ultrasomics models using AUC, sensitivity, specificity, decision curve analysis, and calibration curves. Results show strong performance for the random forest ultrasomics model and further improved accuracy with the combined model, supporting clinically efficient, noninvasive assessment.","OPEN ACCESS  \nEDITED BY  \nLuis Castro-Sánchez,  \nUniversity of Colima, Mexico  \nREVIEWED BY  \nNaveena Yanamala,  \nThe State University of New Jersey, United States  \nYu-quan Wu,  \nGuangxi Medical University, China  \n*CORRESPONDENCE  \nLianzhong Zhang  \n [zlz8777@zzu.edu.cn](zlz8777@zzu.edu.cn)  \nRECEIVED 19 August 2024  \nACCEPTED 01 November 2024  \nPUBLISHED 19 November 2024  \nCITATION  \nMa Y, Duan S, Ren S, Bu D, Li Y, Cai X and Zhang L (2024) Machine learning based ultrasomics noninvasive predicting EGFR expression status in hepatocellular carcinoma patients.  \nFront. Med. 11:1483291 .  \ndoi: 10.3389/fmed.2024.1483291  \nCOPYRIGHT  \n© 2024 Ma, Duan, Ren, Bu, Li, Cai and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nTYPE Original Research PUBLISHED 19 November 2024 DOI 10.3389/fmed.2024.1483291  \nMachine learning based ultrasomics noninvasive predicting EGFR expression status in hepatocellular carcinoma patients  \nYujing Ma 1, Shaobo Duan 2, Shanshan Ren3, Didi Bu4, Yahong Li4, Xiguo Cai 5 and Lianzhong Zhang 1,6*  \n1 Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou, China,  \n2 Department of Health Management, Henan Provincial People’s Hospital, Zhengzhou, China,  \n3 Department of Ultrasound, Henan Provincial People’s Hospital, Zhengzhou, China, 4Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou, China, 5 Henan Rehabilitation Clinical Medical Research Center, Henan Provincial People’s Hospital, Zhengzhou, China, 6 Henan International Joint Laboratory of Ultrasonic Nanotechnology and Artificial Intelligence in Precision Theragnostic Systems, Henan Provincial People’s Hospital, Zhengzhou, China  \nObjective: To investigate the ability of ultrasomics to noninvasively predict epidermal growth factor receptor (EGFR) expression status in patients with hepatocellular carcinoma (HCC) .  \nMethods: 198 HCC patients were comprised in the study (n = 138 in the training dataset and n = 60 in the test dataset) . EGFR expression was detected by immunohistochemistry. Ultrasomics features from gray-scale ultrasound images were extracted. Intra-class correlation coefficient (ICC) screening, variance filtering, mutual information method, and extreme gradient boosting (XGboost) embedding method were applied for selecting the best features. Random forest (RF), XGBoost, support vector machine (SVM), decision tree (DT), and logistic regression (LR) 5 machine learning algorithms were used to construct clinical models, ultrasomics models, and clinical-ultrasomics combined models, respectively. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, decision curve analysis (DCA), and calibration curve were used to assess the predictive performance of the model.  \nResults: In 198 patients, high EGFR expression was observed in 100 patients and low EGFR expression was observed in 98 patients. The RF machine learning ultrasomics model was found to perform well, with the AUC of the training and test dataset being 0.929 (95%CI, 0.874–0.966) and 0.807 (95%CI, 0.684–0. 897) respectively, the sensitivity being 0.843 and 0.767 respectively, the specificity being 0.857 and 0.800 respectively, and the accuracy being 0.850 and 0.783, respectively. The predictive performance of the combined model established by integrating ultrasomics features and clinical baseline characteristics was improved, with the AUC, sensitivity, specificity, and accuracy of the RF machine learning combined model for the training and test dataset reaching 0.937 (95%CI, 0.884–0.97","cbCaigfQkofM4FCH","https://ap.wps.com/l/cbCaigfQkofM4FCH","pdf",3485289,1,12,"English","en",105,"# Objective\n# Methods\n## Data and feature selection\n## Model construction and evaluation\n# Results\n# Conclusion","[{\"question\":\"What is the objective of this study?\",\"answer\":\"To evaluate whether ultrasomics can noninvasively predict EGFR expression status in patients with hepatocellular carcinoma (HCC).\"},{\"question\":\"How was EGFR expression status determined?\",\"answer\":\"EGFR expression status was detected using immunohistochemistry.\"},{\"question\":\"Which model showed the best predictive performance?\",\"answer\":\"The ultrasomics and combined model established by the random forest (RF) classifier demonstrated the best predictive performance.\"}]","Machine learning based ultrasomics noninvasive predicting EGFR expression status in hepatocellular carcinoma patients | 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