[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117057-en":3,"doc-seo-117057-105":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},117057,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Methodological evaluation of original articles on radiomics and machine learning for outcome predictions based on positron emission tomography (PET)","Despite a large number of radiomics and machine learning studies for positron emission tomography (PET), clinical use remains constrained by methodological shortcomings and insufficient reporting quality. A systematic PubMed search evaluated original articles using 17 predefined criteria proposed by the study authors. Criteria with multiple rating levels were binarized, the adequacy–publication-date relationship was analyzed, and key gaps were quantified. Results showed incomplete validation, limited code sharing, and restricted open datasets, indicating substantial room for adherence to established TRIPOD-aligned guidance.","Article published online: 2023-11-23  \nOriginal Article  \nMethodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET)  \nMethodische Bewertung von Originalartikeln zu Radiomics und Machine Learning für Outcome-Vorhersagen basierend auf der Positronen-Emissions-Tomografie (PET)  \nAuthors  \nJulian Manuel Michael Rogasch1, 2, Kuangyu Shi3, David Kersting4, Robert Seifert4  \nsource: [https://doi.org/10.48350/189344 | downloaded:](https://doi.org/10.48350/189344 | downloaded:) 10.12.2023  \nAffiliations  \n1 Department of Nuclear Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany  \n2 Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin  \n3 Department of Nuclear Medicine, Inselspital University Hospital Bern, Bern, Switzerland  \n4 Department of Nuclear Medicine, University Hospital Essen, Essen, Germany  \nKey words  \nradiomics, positron emission tomography, artificial intelligence, machine learning, TRIPOD, outcome prediction  \nreceived 15.9.2023 accepted 25.10.2023  \nBibliography  \nNuklearmedizin 2023; 62: 361–369  \nDOI 10.1055/a-2198-0545 ISSN 0029-5566  \n© 2023. The Author(s) .  \nThe Author(s) . This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited.([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nGeorg Thieme Verlag KG, Rüdigerstraße 14, 70469 Stuttgart, Germany  \nCorrespondence  \nM.D. Julian Manuel Michael Rogasch  \nDepartment of Nuclear Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany [julian. rogasch@charite.de](julian. rogasch@charite.de)  \nAdditional material is available at [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.1055/a-2198-0545.](10.1055/a-2198-0545.)  \nABSTRACT  \nAim Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described in publications on radiomics and machine learning for PET-based outcome prediction.  \nMethods A systematic search for original articles was run on PubMed. All articles were rated according to 17 criteria proposed by the authors. Criteria with >2 rating categories were binarized into “adequate” or “inadequate”. The association between the number of “adequate” criteria per article and the date of publication was examined.  \nResults One hundred articles were identified (published between 07/2017 and 09/2023) . The median proportion of articles per criterion that were rated “adequate” was 65%(range: 23–98 %) . Nineteen articles (19 %) mentioned neither a test cohort nor cross-validation to separate training from testing. The median number of criteria with an “adequate” rating per article was 12.5 out of 17 (range, 4–17), and this did not increase with later dates of publication (Spearman ’ s r ho, 0.094; p = 0.35) . In 22 articles (22 %), less than half of the items were rated “adequate”. Only 8 % of articles published the source code, and 10% made the dataset openly available. Conclusion Among the articles investigated, methodological weaknesses have been identified, and the degree of compliance with recommendations on methodological quality and reporting shows potential for improvement. Better adherence to established guidelines could increase the clinical significance of radiomics and machine learning for PET-based outcome prediction and finally lead to the widespread use in routine clinical practice.  \nRogasch JMM et al. Methodological evaluatio","cbCaikxqS051EB6V","https://ap.wps.com/l/cbCaikxqS051EB6V","pdf",3305795,1,9,"English","en",105,"# Abstract\n## Aim\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Clinical value of PET-derived information\n## Rationale for radiomics and machine learning\n## Need for methodological standards","[{\"question\":\"Why is clinical applicability limited for PET-based radiomics and machine learning outcome prediction?\",\"answer\":\"Clinical uptake remains limited due to methodological quality problems and shortcomings in how studies report essential details for assessment and reproducibility.\"},{\"question\":\"How were the included original articles evaluated in the study?\",\"answer\":\"A systematic PubMed search identified original articles, which were rated against 17 criteria. Multi-category criteria were binarized into “adequate” versus “inadequate,” and adequacy patterns were analyzed statistically.\"},{\"question\":\"What weaknesses were identified regarding validation and sharing practices?\",\"answer\":\"Many articles did not clearly separate training and testing using a test cohort or cross-validation, and only a small fraction provided source code or openly shared datasets.\"}]","Methodological evaluation of original articles on radiomics and machine learning for outcome predictions based on positron emission tomography (PET) | PDF",1785673486,23,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"methodological-evaluation-of-original-articles-on-radiomics-and-machine-learning-for-outcome-predictions-based-on-positron-emission-tomography-pet","",{"@graph":36,"@context":85},[37,54,68],{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/methodological-evaluation-of-original-articles-on-radiomics-and-machine-learning-for-outcome-predictions-based-on-positron-emission-tomography-pet/117057/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is clinical applicability limited for PET-based radiomics and machine learning outcome prediction?","Question",{"text":75,"@type":76},"Clinical uptake remains limited due to methodological quality problems and shortcomings in how studies report essential details for assessment and reproducibility.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the included original articles evaluated in the study?",{"text":80,"@type":76},"A systematic PubMed search identified original articles, which were rated against 17 criteria. Multi-category criteria were binarized into “adequate” versus “inadequate,” and adequacy patterns were analyzed statistically.",{"name":82,"@type":73,"acceptedAnswer":83},"What weaknesses were identified regarding validation and sharing practices?",{"text":84,"@type":76},"Many articles did not clearly separate training and testing using a test cohort or cross-validation, and only a small fraction provided source code or openly shared datasets.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]