Incorporating Supramaximal Resection into Survival Stratification of IDH-Wildtype Glioblastoma: A Refined Multi-institutional Recursive Partitioning Analysis.

in Clinical cancer research : an official journal of the American Association for Cancer Research by Yae Won Park, Kyu Sung Choi, Martha Foltyn-Dumitru, Gianluca Brugnara, Rouzbeh Banan, Sooyon Kim, Kyunghwa Han, Ji Eun Park, Tobias Kessler, Martin Bendszus, Sandro Krieg, Wolfgang Wick, Felix Sahm, Seung Hong Choi, Ho Sung Kim, Jong Hee Chang, Se Hoon Kim, Doonyaporn Wongsawaeng, Jeffrey Michael Pollock, Seung-Koo Lee, Ramon Francisco Barajas, Philipp Vollmuth, Sung Soo Ahn

TLDR

  • The study is about developing a new way to classify patients with a type of brain tumor called glioblastoma. The researchers wanted to create a more accurate way to predict how long patients would live based on their age, how well their tumor responded to treatment, and how much of the tumor was removed during surgery. The study included a group of patients with IDH-wildtype glioblastomas from a single institution and three other institutions. The researchers compared the new model to an older model and found that the new model was better at predicting how long patients would live based on their age, how well their tumor responded to treatment, and how much of the tumor was removed during surgery. The study's key findings highlight the importance of considering both clinical and molecular factors when predicting how long patients with glioblastoma will live.

Abstract

To propose a novel recursive partitioning analysis (RPA) classification model in patients with IDH-wildtype glioblastomas that incorporates the recently expanded conception of the extent of resection (EOR) in terms of both supramaximal and total resections. This multicenter cohort study included a developmental cohort of 622 patients with IDH-wildtype glioblastomas from a single institution (Severance Hospital) and validation cohorts of 536 patients from three institutions (Seoul National University Hospital, Asan Medical Center, and Heidelberg University Hospital). All patients completed standard treatment including concurrent chemoradiotherapy and underwent testing to determine their IDH mutation and MGMTp methylation status. EORs were categorized into either supramaximal, total, or non-total resections. A novel RPA model was then developed and compared to a previous RTOG RPA model. In the developmental cohort, the RPA model included age, MGMTp methylation status, KPS, and EOR. Younger patients with MGMTp methylation and supramaximal resections showed a more favorable prognosis (class I: median overall survival [OS] 57.3 months), while low-performing patients with non-total resections and without MGMTp methylation showed the worst prognosis (class IV: median OS 14.3 months). The prognostic significance of the RPA was subsequently confirmed in the validation cohorts, which revealed a greater separation between prognostic classes for all cohorts compared to the previous RTOG RPA model. The proposed RPA model highlights the impact of supramaximal versus total resections and incorporates clinical and molecular factors into survival stratification. The RPA model may improve the accuracy of assessing prognostic groups.

Overview

  • The study aims to develop a novel recursive partitioning analysis (RPA) classification model in patients with IDH-wildtype glioblastomas that incorporates the recently expanded conception of the extent of resection (EOR) in terms of both supramaximal and total resections. The study includes a developmental cohort of 622 patients with IDH-wildtype glioblastomas from a single institution (Severance Hospital) and validation cohorts of 536 patients from three institutions (Seoul National University Hospital, Asan Medical Center, and Heidelberg University Hospital). The study compares the novel RPA model to a previous RTOG RPA model and evaluates its prognostic significance in the developmental and validation cohorts. The primary objective of the study is to develop a more accurate prognostic model for patients with IDH-wildtype glioblastomas that incorporates both clinical and molecular factors.

Comparative Analysis & Findings

  • The study compares the outcomes observed under different experimental conditions or interventions detailed in the study. The novel RPA model developed in the study includes age, MGMTp methylation status, KPS, and EOR. The study identifies significant differences in the results between the developmental and validation cohorts, with the validation cohorts revealing a greater separation between prognostic classes for all cohorts compared to the previous RTOG RPA model. The study also identifies significant differences in the results between patients with supramaximal versus total resections, with younger patients with MGMTp methylation and supramaximal resections showing a more favorable prognosis (class I: median overall survival [OS] 57.3 months), while low-performing patients with non-total resections and without MGMTp methylation showed the worst prognosis (class IV: median OS 14.3 months). The study's key findings highlight the impact of supramaximal versus total resections and incorporate clinical and molecular factors into survival stratification, which may improve the accuracy of assessing prognostic groups.

Implications and Future Directions

  • The study's findings have significant implications for the field of research or clinical practice. The proposed RPA model highlights the importance of incorporating both clinical and molecular factors into survival stratification for patients with IDH-wildtype glioblastomas. The study also identifies the impact of supramaximal versus total resections on prognosis, which may inform treatment decisions for patients with IDH-wildtype glioblastomas. Future research directions could build on the results of this study by incorporating additional clinical and molecular factors into the RPA model, exploring the impact of other types of resections on prognosis, and evaluating the model's performance in other patient populations.
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