[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122714-en":3,"doc-seo-122714-105":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},122714,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma","A retrospective radiogenomic study develops a clinical prediction model to estimate KRAS mutation status in therapy-naive lung adenocarcinoma by integrating 18F-FDG PET radiomics with machine learning. Patients were selected from an institutional database and screened using the NSCLC radiogenomic dataset from TCIA, then randomly split into training, validation, and testing cohorts. Lung tumors were segmented with 3D Slicer, radiomic features were extracted from 18F-FDG PET images, features were reduced using Mann–Whitney U, Spearman correlation, and RFE, and logistic regression models were compared via ROC AUC, calibration, and decision-curve analysis. The final model showed good discriminative performance across cohorts and enabled KRAS status prediction as a potentially useful non-invasive screening approach.","source: [https://doi.org/10.48350/185131 | downloaded:](https://doi.org/10.48350/185131 | downloaded:) 7.8.2023  \n cancers   \nArticle  \nAbility of 18 F-FDG Positron Emission Tomography Radiomicsand Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma  \nRuiyun Zhang 1,2, Kuangyu Shi 3, Wolfgang Hohenforst-Schmidt 4, Claus Steppert 5, Zsolt Sziklavari 6, Christian Schmidkonz 7, Armin Atzinger 7, Arndt Hartmann 2, Michael Vieth 1,* and Stefan Förster 8,9,10, *  \nCitation: Zhang, R.; Shi, K.;  \nHohenforst-Schmidt, W.; Steppert, C.; Sziklavari, Z.; Schmidkonz, C.;  \nAtzinger, A.; Hartmann, A.; Vieth, M.; Förster, S. Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma. Cancers 2023, 15, 3684. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)cancers15143684  \nAcademic Editor: Hanibal Bohnenberger  \nReceived: 18 June 2023  \nRevised: 11 July 2023  \nAccepted: 13 July 2023  \nPublished: 19 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Institute of Pathology, Medizincampus Oberfranken, Klinikum Bayreuth, Friedrich-Alexander-Universität Erlangen-Nürnberg, 95445 Bayreuth, Germany  \n2 Institute of Pathology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany  \n3 Department of Nuclear Medicine, Inselspital Bern, 3010 Bern, Switzerland  \n4 Department of Pneumology, Sana Klinikum Hof, 95032 Hof, Germany  \n5 Department of Pneumology, REGIOMED Klinikum Coburg, 96450 Coburg, Germany  \n6 Department of Thoracic Surgery, Klinikum Coburg, 96450 Coburg, Germany  \n7 Department of Nuclear Medicine, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany  \n8 Department of Nuclear Medicine, Klinikum Bayreuth, 95445 Bayreuth, Germany  \n9 Medizincampus Oberfranken, Universitätsklinikum Erlangen, 95445 Bayreuth, Germany  \n10 Department of Nuclear Medicine, Klinikum rechts der Isar der Technischen Universitaet Muenchen,  \n81675 München, Germany  \n* Correspondence: michael.vieth@uni-bayreuth.de (M.V.); [stefan.foerster@klinikum-bayreuth.de](stefan.foerster@klinikum-bayreuth.de) (S.F.)  \nSimple Summary: Approximately 26.1% of patients diagnosed with lung adenocarcinoma harbour a KRAS mutation, which is associated with a poorer prognosis. Recent advances in targeted therapy, specifically with sotorasib and MRTX849, have shown promise in targeting KRAS mutations. This retrospective study aimed to develop a clinical prediction model that combines clinical–pathological variables and radiomics derived from PET scans to assess the KRAS mutation status in patients with lung adenocarcinoma. This study utilised two different databases and randomly divided into a training, a validation, and a testing dataset to build and evaluate the predictive performance of our model. Our retrospectively developed model demonstrates good predictive accuracy for determining the KRAS mutation status in lung adenocarcinoma patients.  \nAbstract: Objective: Considering the essential role of KRAS mutation in NSCLC and the limited experience of PET radiomic features in KRAS mutation, a prediction model was built in our current analysis. Our model aims to evaluate the status of KRAS mutants in lung adenocarcinoma by combining PET radiomics and machine learning. Method: Patients were retrospectively selected from our database and screened from the NSCLC radiogenomic dataset from TCIA. The dataset was randomly divided into three subgroups. Two open-source software programs, 3D Slicer and Python, were used to s","cbCaii5bxBXJyBbu","https://ap.wps.com/l/cbCaii5bxBXJyBbu","pdf",5144480,1,14,"English","en",105,"# Study objective and rationale\n## Methods and data sources\n## Radiomics feature extraction and selection\n## Prediction model building and evaluation\n## Results and conclusions","[{\"question\":\"What is the objective of the study?\",\"answer\":\"To build a prediction model that estimates KRAS mutation status in therapy-naive lung adenocarcinoma by combining 18F-FDG PET radiomics with machine learning.\"},{\"question\":\"How were PET radiomic features generated and selected?\",\"answer\":\"Lung tumors were segmented using 3D Slicer and radiomic features were extracted from 18F-FDG PET images. Feature selection used Mann–Whitney U test, Spearman correlation, and RFE before model training.\"},{\"question\":\"How was the model evaluated for predictive performance?\",\"answer\":\"Predictive ability was compared using ROC AUC, while calibration plots assessed agreement between observed and predicted values. Decision curve analysis was used to examine clinical impact of the best model.\"}]","Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma | PDF",1785812495,35,{"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},"ability-of-18f-fdg-positron-emission-tomography-radiomics-and-machine-learning-in-predicting-kras-mutation-status-in-therapy-naive-lung-adenocarcinoma","",{"@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/ability-of-18f-fdg-positron-emission-tomography-radiomics-and-machine-learning-in-predicting-kras-mutation-status-in-therapy-naive-lung-adenocarcinoma/122714/",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","2026-08-04",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},"What is the objective of the study?","Question",{"text":75,"@type":76},"To build a prediction model that estimates KRAS mutation status in therapy-naive lung adenocarcinoma by combining 18F-FDG PET radiomics with machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were PET radiomic features generated and selected?",{"text":80,"@type":76},"Lung tumors were segmented using 3D Slicer and radiomic features were extracted from 18F-FDG PET images. Feature selection used Mann–Whitney U test, Spearman correlation, and RFE before model training.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the model evaluated for predictive performance?",{"text":84,"@type":76},"Predictive ability was compared using ROC AUC, while calibration plots assessed agreement between observed and predicted values. Decision curve analysis was used to examine clinical impact of the best model.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"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":106,"slug":138},19,"General","general"]