[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122015-en":3,"doc-seo-122015-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},122015,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",7,"Healthcare","A prognostic model for thermal ablation of benign thyroid nodules based on interpretable machine learning","Benign thyroid nodules are increasingly detected, and ultrasound-guided thermal ablation is used to reduce nodule volume and relieve symptoms. However, lesion absorption varies substantially between individuals, and an effective preoperative prediction model for ablation outcomes is lacking. This study prospectively develops six machine learning models to predict the volume reduction ratio (VRR) and uses SHAP to interpret feature contributions to nodule shrinkage after treatment.","TYPE Original Research PUBLISHED 19 August 2024  \nDOI 10.3389/fendo.2024.1433192  \nOPEN ACCESS  \nEDITED BY  \nCristina Alina Silaghi,  \nUniversity of Medicine and Pharmacy Iuliu Hatieganu, Romania  \nREVIEWED BY  \nKyriakos Vamvakidis,  \nHenry Dunant Hospital, Greece Roberto Novizio,  \nAgostino Gemelli University Polyclinic (IRCCS), Italy  \n*CORRESPONDENCE Shuiping Li  \n[49899530@qq.com](49899530@qq.com)  \n†These authors have contributed equally to this work  \nRECEIVED 15 May 2024  \nACCEPTED 01 August 2024  \nPUBLISHED 19 August 2024  \nCITATION  \nLi Z, Nie W, Liu Q, Lin M, Li X, Zhang J, Liu T, Deng Y and Li S (2024) A prognostic model for thermal ablation of benign thyroid nodules based on interpretable machine learning.  \nFront. Endocrinol. 15:1433192 .  \ndoi: 10.3389/fendo.2024.1433192  \nCOPYRIGHT  \n© 2024 Li, Nie, Liu, Lin, Li, Zhang, Liu, Deng and Li. 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.  \nA prognostic model for thermal ablation of benign thyroid nodules based on interpretable machine learning  \nZuolin Li 1†, Wei Nie 2†, Qingfa Liu 3, Min Lin 1, Xiaolian Li 1,  \nJiantang Zhang 1, Tengfu Liu 1, Yongluo Deng 1 and Shuiping Li 1*  \n1 Department of Ultrasound, Longyan First Affiliated Hospital of Fujian Medical University,  \nLongyan, China, 2 Department of Information, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China, 3School of Information Engineering, Minxi Vocational & Technical College, Longyan, China  \nIntroduction: The detection rate of benign thyroid nodules is increasing every year, with some affected patients experiencing symptoms. Ultrasound-guided thermal ablation can reduce the volume of nodules to alleviate symptoms. As the degree and speed of lesion absorption vary greatly between individuals, an effective model to predict curative effect after ablation is lacking. This study aims to predict the efficacy of ultrasound-guided thermal ablation for benign thyroid nodules using machine learning and explain the characteristics affecting the nodule volume reduction ratio (VRR) .  \nDesign: Prospective study  \nPatients: The clinical and ultrasonic characteristics of patients who underwent ultrasound-guided thermal ablation of benign thyroid nodules at our hospital between January 2020 and January 2023 were recorded.  \nMeasurements: Six machine learning models (logistic regression, support vector machine, decision tree, random forest, eXtreme Gradient Boosting [XGBoost], and Light Gradient Boosting Machine [LGBM]) were constructed to predict efficacy; the effectiveness of each model was evaluated, and the optimal model selected. SHapley Additive exPlanations (SHAP) was used to visualize the decision process of the optimal model and analyze the characteristics affecting the VRR.  \nResults: In total, 518 benign thyroid nodules were included: 356 in the satisfactory group (VRR ≥70% 1 year after operation) and 162 in the unsatisfactory group. The optimal XGBoost model predicted satisfactory efficacy with 78.9% accuracy, 88.8% precision, 79.8% recall rate, an F1 value of 0. 84 F1, and an area under the curve of 0 .86. The top five characteristics that  \nFrontiers in Endocrinology 01 [frontiersin.org](frontiersin.org)  \naffected VRRs were the proportion of solid components \u003C 20%, initial nodule volume, blood flow score, peripheral blood flow pattern, and proportion of solid components 50–80% .  \nConclusions: The models, based on interpretable machine learning, predicted the VRR after thermal ablation for benign thyroid nodules, which provided a reference for preoperative treatment decisions.  \nKEYWORDS  ","cbCaiofAWqtyJMr9","https://ap.wps.com/l/cbCaiofAWqtyJMr9","pdf",1308641,1,9,"English","en",105,"# Introduction\n## Study goal and rationale\n# Methods\n## Study design and cohort\n## Prediction models and evaluation\n## Interpretability with SHAP\n# Results\n## Cohort and model performance\n## Key factors affecting VRR\n# Conclusions","[{\"question\":\"What clinical outcome does the model predict after thermal ablation?\",\"answer\":\"The model predicts the volume reduction ratio (VRR) to estimate curative effect after ultrasound-guided thermal ablation of benign thyroid nodules.\"},{\"question\":\"How is model performance evaluated and which model performs best?\",\"answer\":\"Six machine learning models are built and compared, and the optimal XGBoost model is selected based on predictive metrics such as accuracy, precision, recall, F1 score, and AUC.\"},{\"question\":\"How does the study explain why the model makes a prediction?\",\"answer\":\"SHapley Additive exPlanations (SHAP) is used to visualize the decision process of the optimal model and identify the characteristics most associated with VRR.\"}]","A prognostic model for thermal ablation of benign thyroid nodules based on interpretable machine learning | PDF",1785808299,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"a-prognostic-model-for-thermal-ablation-of-benign-thyroid-nodules-based-on-interpretable-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-prognostic-model-for-thermal-ablation-of-benign-thyroid-nodules-based-on-interpretable-machine-learning/122015/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What clinical outcome does the model predict after thermal ablation?","Question",{"text":76,"@type":77},"The model predicts the volume reduction ratio (VRR) to estimate curative effect after ultrasound-guided thermal ablation of benign thyroid nodules.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is model performance evaluated and which model performs best?",{"text":81,"@type":77},"Six machine learning models are built and compared, and the optimal XGBoost model is selected based on predictive metrics such as accuracy, precision, recall, F1 score, and AUC.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the study explain why the model makes a prediction?",{"text":85,"@type":77},"SHapley Additive exPlanations (SHAP) is used to visualize the decision process of the optimal model and identify the characteristics most associated with VRR.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]