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Cluster-based prognostication in glioblastoma: Unveiling heterogeneity based on diffusion and perfusion similarities.
Foltyn-Dumitru, Martha; Kessler, Tobias; Sahm, Felix; Wick, Wolfgang; Heiland, Sabine; Bendszus, Martin; Vollmuth, Philipp; Schell, Marianne.
Afiliação
  • Foltyn-Dumitru M; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
  • Kessler T; Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
  • Sahm F; Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany.
  • Wick W; Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Heiland S; Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany.
  • Bendszus M; Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany.
  • Vollmuth P; Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Schell M; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
Neuro Oncol ; 26(6): 1099-1108, 2024 Jun 03.
Article em En | MEDLINE | ID: mdl-38153923
ABSTRACT

BACKGROUND:

While the association between diffusion and perfusion magnetic resonance imaging (MRI) and survival in glioblastoma is established, prognostic models for patients are lacking. This study employed clustering of functional imaging to identify distinct functional phenotypes in untreated glioblastomas, assessing their prognostic significance for overall survival.

METHODS:

A total of 289 patients with glioblastoma who underwent preoperative multimodal MR imaging were included. Mean values of apparent diffusion coefficient normalized relative cerebral blood volume and relative cerebral blood flow were calculated for different tumor compartments and the entire tumor. Distinct imaging patterns were identified using partition around medoids (PAM) clustering on the training dataset, and their ability to predict overall survival was assessed. Additionally, tree-based machine-learning models were trained to ascertain the significance of features pertaining to cluster membership.

RESULTS:

Using the training dataset (231/289) we identified 2 stable imaging phenotypes through PAM clustering with significantly different overall survival (OS). Validation in an independent test set revealed a high-risk group with a median OS of 10.2 months and a low-risk group with a median OS of 26.6 months (P = 0.012). Patients in the low-risk cluster had high diffusion and low perfusion values throughout, while the high-risk cluster displayed the reverse pattern. Including cluster membership in all multivariate Cox regression analyses improved performance (P ≤ 0.004 each).

CONCLUSIONS:

Our research demonstrates that data-driven clustering can identify clinically relevant, distinct imaging phenotypes, highlighting the potential role of diffusion, and perfusion MRI in predicting survival rates of glioblastoma patients.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Encefálicas / Glioblastoma / Imagem de Difusão por Ressonância Magnética Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Encefálicas / Glioblastoma / Imagem de Difusão por Ressonância Magnética Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2024 Tipo de documento: Article