3D volume growth rate evaluation in the EORTC-BTG-1320 clinical trial for recurrent WHO grade 2 and 3 meningiomas.

meningioma response criteria volumetric assessment

Journal

Neuro-oncology
ISSN: 1523-5866
Titre abrégé: Neuro Oncol
Pays: England
ID NLM: 100887420

Informations de publication

Date de publication:
07 Mar 2024
Historique:
received: 20 12 2023
medline: 7 3 2024
pubmed: 7 3 2024
entrez: 7 3 2024
Statut: aheadofprint

Résumé

We previously reported that tumor 3D volume growth rate (3DVGR) classification could help in the assessment of drug activity in patients with meningioma using three main classes and a total of five subclasses: class 1: decrease; 2: stabilization or severe slowdown; 3: progression. The EORTC-BTG-1320 clinical trial was a randomized phase II trial evaluating the efficacy of trabectedin for recurrent WHO 2 or 3 meningioma. Our objective was to evaluate the discriminative value of 3DVGR classification in the EORTC-BTG-1320. All patients with at least one available MRI before trial inclusion were included. 3D volume was evaluated on consecutive MRI until progression. 2D imaging response was centrally assessed by MRI modified Macdonald criteria. Clinical benefit was defined as neurological or functional status improvement or steroid decrease or discontinuation. Sixteen patients with a median age of 58.5 years were included. Best 3DVGR classes were: 1, 2A, 3A and 3B in 2 (16.7%), 4 (33.3%), 2 (16.7%) and 4 (33.3%) patients, respectively. All patients with progression-free survival longer than 6 months had best 3DVGR class 1 or 2. 3DVGR classes 1 and 2 (combined) had a median overall survival of 34.7 months versus 7.2 months for class 3 (p=0.061). All class 1 patients (2/2), 75% of class 2 patients (3/4) and only 10% of class 3 patients (1/10) had clinical benefit. Tumor 3DVGR classification may be helpful to identify early signals of treatment activity in meningioma clinical trials.

Sections du résumé

BACKGROUND BACKGROUND
We previously reported that tumor 3D volume growth rate (3DVGR) classification could help in the assessment of drug activity in patients with meningioma using three main classes and a total of five subclasses: class 1: decrease; 2: stabilization or severe slowdown; 3: progression. The EORTC-BTG-1320 clinical trial was a randomized phase II trial evaluating the efficacy of trabectedin for recurrent WHO 2 or 3 meningioma. Our objective was to evaluate the discriminative value of 3DVGR classification in the EORTC-BTG-1320.
METHODS METHODS
All patients with at least one available MRI before trial inclusion were included. 3D volume was evaluated on consecutive MRI until progression. 2D imaging response was centrally assessed by MRI modified Macdonald criteria. Clinical benefit was defined as neurological or functional status improvement or steroid decrease or discontinuation.
RESULTS RESULTS
Sixteen patients with a median age of 58.5 years were included. Best 3DVGR classes were: 1, 2A, 3A and 3B in 2 (16.7%), 4 (33.3%), 2 (16.7%) and 4 (33.3%) patients, respectively. All patients with progression-free survival longer than 6 months had best 3DVGR class 1 or 2. 3DVGR classes 1 and 2 (combined) had a median overall survival of 34.7 months versus 7.2 months for class 3 (p=0.061). All class 1 patients (2/2), 75% of class 2 patients (3/4) and only 10% of class 3 patients (1/10) had clinical benefit.
CONCLUSIONS CONCLUSIONS
Tumor 3DVGR classification may be helpful to identify early signals of treatment activity in meningioma clinical trials.

Identifiants

pubmed: 38452246
pii: 7624106
doi: 10.1093/neuonc/noae037
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Auteurs

E Tabouret (E)

Aix-Marseille Univ, APHM, CNRS, INP, Inst Neurophysiopathol, CHU Timone, Service de Neurooncologie, Marseille, France.

J Furtner (J)

Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Faculty of Medicine and Dentistry, Danube Private University, 3500 Krems, Austria.

T Graillon (T)

Aix-Marseille Univ, APHM, CHU Timone, Service de Neuro-chirurgie, Marseille, France.

A Silvani (A)

Department of Neuro-Oncology, IRCCS Fondazione Istituto Neurologico Carlo Besta, Milan, Italy.

E Le Rhun (E)

Department of Neurosurgery, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091 Zurich, Switzerland.
Department of Neurology, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091 Zurich, Switzerland.

R Soffietti (R)

Division of Neuro-Oncology, University of Torino, Italy.

G Lombardi (G)

Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy.

J M Sepúlveda-Sánchez (JM)

Hospital Universitario e Instituto de Investigación 12 de Octubre, Unidad Multidisciplinar de Neuro-Oncología, Madrid, Spain.

P Brandal (P)

Department of Oncology and Institute for Cancer Genetics and Informatics, Oslo University Hospital, P.O. Box 4953 Nydalen, 0424 Oslo, Norway.

M Bendszus (M)

Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.

V Golfinopoulos (V)

EORTC Headquarters, Brussels, Belgium.

T Gorlia (T)

EORTC Headquarters, Brussels, Belgium.

M Weller (M)

Department of Neurology, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091 Zurich, Switzerland.

F Sahm (F)

Dept. of Neuropathology, University Hospital Heidelberg, Heidelberg University, and German Consortium for Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ).

W Wick (W)

Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg University & German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.

M Preusser (M)

Division of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria.

Classifications MeSH