Machine Learning-based Prognostic Subgrouping of Glioblastoma: A Multi-center Study.

IF 16.4 1区医学 Q1 CLINICAL NEUROLOGY

Neuro-oncology Pub Date : 2024-12-12 DOI:10.1093/neuonc/noae260

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, Mac Lean P Nasrallah, Donald M O'Rourke, Suyash Mohan, Christos Davatzikos

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引用次数: 0

Abstract

Background: Glioblastoma 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, MRI, and molecular measures from 2,838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, 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<0.001) and 3.48 (95%CI: 2.94-4.11, p<0.001), 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 for personalized patient management and clinical trial stratification in glioblastoma.

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来源期刊

Neuro-oncology 医学-临床神经学

CiteScore

27.20

自引率

6.30%

发文量

1434

审稿时长

3-8 weeks

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.