[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120583-en":3,"doc-seo-120583-105":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},120583,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Regression and machine learning approaches identify potential risk factors for glioblastoma multiforme - Research report","Glioblastoma multiforme is a highly lethal cancer with a 5-year survival rate below 10%, making identification of risk factors critical for stratifying high-risk individuals. Current evidence remains limited because the disease’s complexity and heterogeneity can render conventional epidemiologic methods insufficient. Using UK Biobank data (576 cases, 302,602 controls), the study first tests 369 exposures with traditional regression to find significant associations, then filters features by completion rate and correlation. Two machine-learning models, support vector machine and multi-layer perceptron, are trained after addressing within-subpopulation imbalance and analyzed using Shapley Additive explanations. Traditional regression yields 38 associations and machine learning highlights contributions of age, genetics, IGF1 blood levels, and right-hand grip strength, with potential confounding by endogenous testosterone. Integrating ML with regression improves discovery of GBM risk factors.","[https://doi.org/10.1093/braincomms/fcaf187](https://doi.org/10.1093/braincomms/fcaf187) BRAIN COMMUNICATIONS 2025: fcaf187 | 1  \nBRAIN COMMUNICATIONS  \nRegression and machine learning approaches identify potential risk factors for glioblastoma multiforme  \nAlessio Felici, 1,2 Giulia Peduzzi, 1 Roberto Pellungrini,3 Daniele Campa 1,* and Federico Canzian2,*  \n* These authors contributed equally to this work.  \nGlioblastoma multiforme is a lethal disease, with a 5-year survival rate of \u003C 10% . The identification of risk factors for glioblastoma multiforme is essential for the understanding of this disease and could facilitate more effective stratification of high-risk individuals. However, our current knowledge of glioblastoma multiforme risk factors is limited. Given the complexity and heterogeneity of the disease, traditional epidemiological approaches may be insufficient to study risk factors for glioblastoma multiforme. The combination of traditional approaches with machine learning models could prove effective in identifying relevant factors for glioblastoma multiforme risk. In this study, we developed glioblastoma multiformerisk models in the UK Biobank cohort using 576 glioblastoma multiforme cases and 302 602 controls. First, 369 exposures were tested with traditional regression models in a case–control study and significant associations were identified. Subsequently, significant features were filtered based on their completion rate and correlation. The selected exposures were then used to develop two machine learning models: a support vector machine and a Multi-Layer Perceptron. To address the imbalance within the subpopulation, two controls per case with full data were selected, resulting in 442 glioblastoma multiforme cases and 884 controls being analysed with the machine learning models. Relevant factors for glioblastoma multiforme risk were identified by explaining the results of the two models with Shapley Additive explanations. Traditional regression methods identified 38 significant associations between environmental exposures and glioblastoma multiforme risk under the Bonferroni threshold (P \u003C 1.35 × 10−4) . Subsequent filtration results in the selection of 12 exposures, which were then analysed with age, sex and a polygenic score using the two machine learning models. Support vector machine and the multi-layer perceptron demonstrated a good sensitivity (0.91 and 0.82, respectively). In addition to age and genetics, Shapley Additive explanations demonstrated significant contributions of insulin-like growth factor 1 blood levels and the right-hand grip strength on the predictions made by the models, with the latter effect potentially being confounded by endogenous testosterone levels. The integration of machine learning with traditional models has the potential to enhance the identification of risk factors for glioblastoma multiforme.  \n1 Department of Biology, University of Pisa, Pisa 56126, Italy  \n2 Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany  \n3 Classe di Scienze, Scuola Normale Superiore, Pisa 56126, Italy  \nCorrespondence to: Federico Canzian, PhD  \nGenomic Epidemiology Group, German Cancer Research Center (DKFZ) Im Neuenheimer Feld 280, Heidelberg 69120, Germany  \nE-mail: [f.canzian@dkfz.de](f.canzian@dkfz.de)  \nKeywords: Glioblastoma multiforme; machine learning; epidemiology; IGF1; genomics  \nReceived December 28, 2024. Revised April 24, 2025. Accepted May 25, 2025. Advance access publication May 27, 2025 © The Author(s) 2025. Published by Oxford University Press on behalf of the Guarantors of Brain.  \nThis is an Open Access article distributed under the terms of the Creative Commons Attribution License ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.  \n2 | BRAIN COMMUNICATIONS 2025, fc","cbCaigTNIAB75MlM","https://ap.wps.com/l/cbCaigTNIAB75MlM","pdf",1297489,1,12,"English","en",105,"# Introduction\n# Materials and methods\n## Study subjects","[{\"question\":\"Why are traditional epidemiological methods insufficient for identifying glioblastoma multiforme risk factors?\",\"answer\":\"Glioblastoma multiforme is complex and heterogeneous, and this can limit what conventional epidemiologic approaches can capture when studying risk factors.\"},{\"question\":\"What modeling strategy was used to study GBM risk in the UK Biobank cohort?\",\"answer\":\"The study used a two-stage approach: traditional regression to test associations between exposures and GBM risk, followed by two machine-learning models (SVM and multi-layer perceptron) using filtered exposures and explanation via Shapley Additive explanations.\"},{\"question\":\"Which factors contributed to GBM risk predictions according to the model explanations?\",\"answer\":\"Beyond age and genetics, Shapley Additive explanations indicated significant contributions from insulin-like growth factor 1 (IGF1) blood levels and right-hand grip strength, with the latter potentially confounded by endogenous testosterone levels.\"}]","Regression and machine learning approaches identify potential risk factors for glioblastoma multiforme - 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