[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128774-en":3,"doc-seo-128774-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128774,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Development and validation of a MRI-radiomics-based machine learning approach in High Grade Glioma to detect early recurrence - Original Research","Development and validation of a MRI-radiomics-based machine learning approach for predicting early recurrence in patients with high-grade glioma addresses the need for individualized risk assessment. The work retrospectively analyzes 248 patients with available 6-month tumor-recurrence outcomes, using manual segmentation and radiomic feature extraction from T1- and T2-weighted MR sequences. Multiple machine-learning models are trained and externally validated, with performance assessed using ROC-based metrics including accuracy, sensitivity, specificity, PPV, and NPV.","TYPE Original Research PUBLISHED 14 November 2024 DOI 10.3389/fonc.2024.1449235  \nOPEN ACCESS  \nEDITED BY  \nDomenico Aquino,  \nIRCCS Carlo Besta Neurological Institute Foundation, Italy  \nREVIEWED BY  \nFulvia Palesi, University of Pavia, Italy Riccardo Pascuzzo,  \nIRCCS Carlo Besta Neurological Institute Foundation, Italy  \n*CORRESPONDENCE  \nRosellina Russo  \n [rosellina.russo@policlinicogemelli.it](rosellina.russo@policlinicogemelli.it)  \n†These authors have contributed equally to this work  \n‡These authors share senior authorship  \nRECEIVED 14 June 2024  \nACCEPTED 16 October 2024  \nPUBLISHED 14 November 2024  \nCITATION  \nPignotti F, Ius T, Russo R, Bagatto D, Beghella Bartoli F, Boccia E, Boldrini L, Chiesa S, Ciardi C, Cusumano D, Giordano C, La Rocca G, Mazzarella C, Mazzucchi E, Olivi A, Skrap M, Tran HE, Varcasia G, Gaudino S and Sabatino G (2024)  \nDevelopment and validation of a MRIradiomics-based machine learning approach in High Grade Glioma to detect early recurrence.  \nFront. Oncol. 14:1449235 .  \ndoi: 10.3389/fonc.2024.1449235  \nCOPYRIGHT  \n© 2024 Pignotti, Ius, Russo, Bagatto, Beghella Bartoli, Boccia, Boldrini, Chiesa, Ciardi, Cusumano, Giordano, La Rocca, Mazzarella, Mazzucchi, Olivi, Skrap, Tran, Varcasia, Gaudino and Sabatino. 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.  \nDevelopment and validation of a MRI-radiomics-based machine learning approach in High Grade Glioma to detect early recurrence  \nFabrizio Pignotti 1,2†, Tamara Ius 3†, Rosellina Russo 4*, Daniele Bagatto 5, Francesco Beghella Bartoli 6, Edda Boccia 6, Luca Boldrini 6, Silvia Chiesa 6, Chiara Ciardi 5,  \nDavide Cusumano 7, Carolina Giordano 4, Giuseppe La Rocca 8, Ciro Mazzarella 6, Edoardo Mazzucchi 1,2, Alessandro Olivi 2,8, Miran Skrap 3, Houng Elena Tran 6, Giuseppe Varcasia 4, Simona Gaudino 2,4‡ and Giovanni Sabatino 1,2,8‡  \n1 Department of Neurosurgery, Mater Olbia Hospital, Olbia, Italy, 2 Institute of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Catholic University, Rome, Italy, 3 Neurosurgery Unit, Head-Neck and NeuroScience Department, University Hospital of Udine, Udine, Italy, 4Advanced Radiodiagnostics Centre, Unità Operativa Semplice Dipartimentale (UOSD) Neuroradiology, Fondazione Policlinico Universitario Agostino Gemelli IRCSS, Rome, Italy, 5 Department of Neuroradiology, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC) “Santa Maria Della Misericordia”, Udine, Italy, 6 Department of Radiology, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCSS, Rome, Italy, 7 Medical Physics Unit, Mater Olbia Hospital, Olbia, Italy, 8 Institute of Neurosurgery, Fondazione Policlinico Universitario Agostino Gemelli IRCSS, Rome, Italy  \nPurpose: Patients diagnosed with High Grade Gliomas (HGG) generally tend to have a relatively negative prognosis with a high risk of early tumor recurrence (TR) after post-operative radio-chemotherapy. The assessment of the pre-operative risk of early versus delayed TR can be crucial to develop a personalized surgical approach. The purpose of this article is to predict TR using MRI radiomic analysis.  \nMethods: Data were retrospectively collected from a database. A total of 248 patients were included based on the availability of 6-month TR results: 188 were used to train the model, the others to externally validate it. After manual segmentation of the tumor, Radiomic features were extracted and different machine learning models were implemented considering a combination of T1 and T2 weighted MR sequences. Receiver Operating Characteristic ","cbCaikqGIA9KfiUP","https://ap.wps.com/l/cbCaikqGIA9KfiUP","pdf",1111268,3,1,11,"English","en",105,"# Purpose\n# Methods\n## Patient cohort and study design\n## Imaging and feature extraction\n## Model training and validation\n# Results\n# Conclusion","[{\"question\":\"What clinical problem does the study address in high-grade glioma patients?\",\"answer\":\"The study targets prediction of early tumor recurrence after post-operative radio-chemotherapy, aiming to support personalized surgical planning and pre-operative counseling.\"},{\"question\":\"How were the prediction models developed and validated?\",\"answer\":\"Retrospective data from 248 patients were used, with 188 patients for training and the remaining patients for external validation. After manual tumor segmentation, radiomic features were extracted from T1- and T2-weighted MRI sequences.\"},{\"question\":\"Which model performed best and how was its performance measured?\",\"answer\":\"The XGBoost model showed the best performance with an AUC of 0.72. Metrics at the optimal threshold included accuracy, sensitivity, specificity, PPV, and NPV derived from ROC analysis.\"}]","Development and validation of a MRI-radiomics-based machine learning approach in High Grade Glioma to detect early recurrence - Original Research | PDF",1786003311,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"development-and-validation-of-a-mri-radiomics-based-machine-learning-approach-in-high-grade-glioma-to-detect-early-recurrence-original-research","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/development-and-validation-of-a-mri-radiomics-based-machine-learning-approach-in-high-grade-glioma-to-detect-early-recurrence-original-research/128774/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-06",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 problem does the study address in high-grade glioma patients?","Question",{"text":76,"@type":77},"The study targets prediction of early tumor recurrence after post-operative radio-chemotherapy, aiming to support personalized surgical planning and pre-operative counseling.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the prediction models developed and validated?",{"text":81,"@type":77},"Retrospective data from 248 patients were used, with 188 patients for training and the remaining patients for external validation. After manual tumor segmentation, radiomic features were extracted from T1- and T2-weighted MRI sequences.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model performed best and how was its performance measured?",{"text":85,"@type":77},"The XGBoost model showed the best performance with an AUC of 0.72. Metrics at the optimal threshold included accuracy, sensitivity, specificity, PPV, and NPV derived from ROC analysis.","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]