Hamed Akbari, Spyridon Bakas, Chiharu Sako, Anahita Fathi Kazerooni, Javier Villanueva-Meyer, Jose A Garcia, Elizabeth Mamourian, Fang Liu, Quy Cao, Russell T Shinohara, Ujjwal Baid, Alexander Getka, Sarthak Pati, Ashish Singh, Evan Calabrese, Susan Chang, Jeffrey Rudie, Aristeidis Sotiras, Pamela LaMontagne, Daniel S Marcus, Mikhail Milchenko, Arash Nazeri, Carmen Balana, Jaume Capellades, Josep Puig, Chaitra Badve, Jill S Barnholtz-Sloan, Andrew E Sloan, Vachan Vadmal, Kristin Waite, Murat Ak, Rivka R Colen, Yae Won Park, Sung Soo Ahn, Jong Hee Chang, Yoon Seong Choi, Seung-Koo Lee, Gregory S Alexander, Ayesha S Ali, Adam P Dicker, Adam E Flanders, Spencer Liem, Joseph Lombardo, Wenyin Shi, Gaurav Shukla, Brent Griffith, Laila M Poisson, Lisa R Rogers, Aikaterini Kotrotsou, Thomas C Booth, Rajan Jain, Matthew Lee, Abhishek Mahajan, Arnab Chakravarti, Joshua D Palmer, Dominic DiCostanzo, Hassan Fathallah-Shaykh, Santiago Cepeda, Orazio Santo Santonocito, Anna Luisa Di Stefano, Benedikt Wiestler, Elias R Melhem, Graeme F Woodworth, Pallavi Tiwari, Pablo Valdes, Yuji Matsumoto, Yoshihiro Otani, Ryoji Imoto, Mariam Aboian, Shinichiro Koizumi, Kazuhiko Kurozumi, Toru Kawakatsu, Kimberley Alexander, Laveniya Satgunaseelan, Aaron M Rulseh, Stephen J Bagley, Michel Bilello, Zev A Binder, Steven Brem, Arati S Desai, Robert A Lustig, Eileen Maloney, Timothy Prior, Nduka Amankulor, MacLean P Nasrallah, Donald M O'Rourke, Suyash Mohan, Christos Davatzikos
{"title":"Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study.","authors":"Hamed Akbari, Spyridon Bakas, Chiharu Sako, Anahita Fathi Kazerooni, Javier Villanueva-Meyer, Jose A Garcia, Elizabeth Mamourian, Fang Liu, Quy Cao, Russell T Shinohara, Ujjwal Baid, Alexander Getka, Sarthak Pati, Ashish Singh, Evan Calabrese, Susan Chang, Jeffrey Rudie, Aristeidis Sotiras, Pamela LaMontagne, Daniel S Marcus, Mikhail Milchenko, Arash Nazeri, Carmen Balana, Jaume Capellades, Josep Puig, Chaitra Badve, Jill S Barnholtz-Sloan, Andrew E Sloan, Vachan Vadmal, Kristin Waite, Murat Ak, Rivka R Colen, Yae Won Park, Sung Soo Ahn, Jong Hee Chang, Yoon Seong Choi, Seung-Koo Lee, Gregory S Alexander, Ayesha S Ali, Adam P Dicker, Adam E Flanders, Spencer Liem, Joseph Lombardo, Wenyin Shi, Gaurav Shukla, Brent Griffith, Laila M Poisson, Lisa R Rogers, Aikaterini Kotrotsou, Thomas C Booth, Rajan Jain, Matthew Lee, Abhishek Mahajan, Arnab Chakravarti, Joshua D Palmer, Dominic DiCostanzo, Hassan Fathallah-Shaykh, Santiago Cepeda, Orazio Santo Santonocito, Anna Luisa Di Stefano, Benedikt Wiestler, Elias R Melhem, Graeme F Woodworth, Pallavi Tiwari, Pablo Valdes, Yuji Matsumoto, Yoshihiro Otani, Ryoji Imoto, Mariam Aboian, Shinichiro Koizumi, Kazuhiko Kurozumi, Toru Kawakatsu, Kimberley Alexander, Laveniya Satgunaseelan, Aaron M Rulseh, Stephen J Bagley, Michel Bilello, Zev A Binder, Steven Brem, Arati S Desai, Robert A Lustig, Eileen Maloney, Timothy Prior, Nduka Amankulor, MacLean P Nasrallah, Donald M O'Rourke, Suyash Mohan, Christos Davatzikos","doi":"10.1093/neuonc/noae260","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification.</p><p><strong>Methods: </strong>We developed a highly reproducible, personalized prognostication, and clinical subgrouping system using machine learning (ML) on routine clinical data, magnetic resonance imaging (MRI), and molecular measures from 2838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, and III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]).</p><p><strong>Results: </strong>The ML model stratified patients into distinct prognostic subgroups with HRs between subgroups I-II and I-III of 1.62 (95% CI: 1.43-1.84, P < .001) and 3.48 (95% CI: 2.94-4.11, P < .001), respectively. Analysis of imaging features revealed several tumor properties contributing unique prognostic value, supporting the feasibility of a generalizable prognostic classification system in a diverse cohort.</p><p><strong>Conclusions: </strong>Our ML model demonstrates extensive reproducibility and online accessibility, utilizing routine imaging data rather than complex imaging protocols. This platform offers a unique approach to personalized patient management and clinical trial stratification in GBM.</p>","PeriodicalId":19377,"journal":{"name":"Neuro-oncology","volume":" ","pages":"1102-1115"},"PeriodicalIF":16.4000,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083074/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuro-oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/neuonc/noae260","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification.
Methods: We developed a highly reproducible, personalized prognostication, and clinical subgrouping system using machine learning (ML) on routine clinical data, magnetic resonance imaging (MRI), and molecular measures from 2838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, and III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]).
Results: The ML model stratified patients into distinct prognostic subgroups with HRs between subgroups I-II and I-III of 1.62 (95% CI: 1.43-1.84, P < .001) and 3.48 (95% CI: 2.94-4.11, P < .001), respectively. Analysis of imaging features revealed several tumor properties contributing unique prognostic value, supporting the feasibility of a generalizable prognostic classification system in a diverse cohort.
Conclusions: Our ML model demonstrates extensive reproducibility and online accessibility, utilizing routine imaging data rather than complex imaging protocols. This platform offers a unique approach to personalized patient management and clinical trial stratification in GBM.
期刊介绍:
Neuro-Oncology, the official journal of the Society for Neuro-Oncology, has been published monthly since January 2010. Affiliated with the Japan Society for Neuro-Oncology and the European Association of Neuro-Oncology, it is a global leader in the field.
The journal is committed to swiftly disseminating high-quality information across all areas of neuro-oncology. It features peer-reviewed articles, reviews, symposia on various topics, abstracts from annual meetings, and updates from neuro-oncology societies worldwide.