한빛사논문
- 조회402
Yae Won Park1, Kyu Sung Choi2, Martha Foltyn-Dumitru3, Gianluca Brugnara3, Rouzbeh Banan4, Sooyon Kim5, Kyunghwa Han1, Ji Eun Park6, Tobias Kessler7,8, Martin Bendszus3, Sandro Krieg9, Wolfgang Wick7,8, Felix Sahm4,10, Seung Hong Choi2, Ho Sung Kim6, Jong Hee Chang11, Se Hoon Kim12, Doonyaporn Wongsawaeng13,14, Jeffrey Michael Pollock14, Seung-Koo Lee1, Ramon Francisco Barajas Jr14,15,16, Philipp Vollmuth17,18,19, and Sung Soo Ahn1
1Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
2Department of Radiology, Seoul National University Hospital, Seoul, Korea.
3Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
4Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany.
5Department of Statistics and Data Science, Yonsei University, Seoul, Korea.
6Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Seoul, Korea.
7Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany.
8Clinical Cooperation Unit Neurooncology, German Cancer Consortium(DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
9Department of Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany.
10Clinical Cooperation Unit Neuropathology, German Cancer Consortium(DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
11Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea.
12Department of Pathology, Yonsei University College of Medicine, Seoul, Korea.
13Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
14Department of Radiology, Neuroradiology Section, Oregon Health & Science University, Portland, Oregon.
15Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon.
16Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon.
17Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.
18Division for Computational Radiology and Clinical AI (CCIBonn.ai), Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany.
19Medical Faculty Bonn, University of Bonn, Bonn, Germany.
Y.W. Park and K.S. Choi contributed equally to this article.
Corresponding Authors: Ji Eun Park, and Sung Soo Ahn
Abstract
Purpose: 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.
Experimental design: 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.
Results: 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.
Conclusions: 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.
논문정보
- Research article
- 2024년 06월 (BRIC 등록일 2024-06-04)
- 국내(교신)+국외 연구진
- 의약학 > 종양의학
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