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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基于机器学习的胶质母细胞瘤预后亚组：一项多中心研究。

背景：胶质母细胞瘤是最具侵袭性的成人原发性脑癌，具有显著的异质性，为患者管理、治疗计划和临床试验分层提出了挑战。方法：我们利用机器学习（ML）开发了一个高度可重复的个性化预后和临床亚组系统，该系统基于常规临床数据、MRI和分子测量，来自3大洲22个机构的2,838名不同人口统计学的患者。采用Kaplan-Meier分析（Cox比例模型和风险比[HR]）将患者分为预后良好、中等和不良亚组（I、II、III）。结果：ML模型将患者分为不同的预后亚组，亚组I-II和I-III之间的hr为1.62 （95%CI: 1.43-1.84）。结论：我们的ML模型具有广泛的可重复性和在线可访问性，利用常规成像数据而不是复杂的成像方案。该平台为胶质母细胞瘤的个性化患者管理和临床试验分层提供了独特的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.