P12.01.B GLIOBLASTOMA RECURRENCE GROWTH PATTERNS - PROGNOSTIC IMPACT AND POTENTIAL PREDICTORS

Abstract BACKGROUND Glioblastoma (GBM) recurrences usually occur locally, but can also develop at a distance from the initial lesion or several sites simultaneously. Although a fundamental influence of growth patterns on prognosis in GBM recurrence is known, there is little preliminary data on prognostic effects and associations of defined stratified growth patterns. Furthermore, possible predictive factors influencing the development of specific growth patterns cannot yet be evaluated with certainty. With this project, we aimed to answer the following questions: (1) Can a more precise analysis of GBM growth patterns in recurrence help with stratification into subgroups? (2) Do GBM growth patterns in relapse correlate with patient prognosis? (3) Can we identify predictors of defined growth patterns? METHODS As part of a retrospective study of two neuro-oncological centers, 251 patients were included, and their demographic data, therapy, tumor, and patient characteristics were retrospectively collected. In addition, the MRI images after initial biopsy/resection and at the time of the first recurrence were evaluated and assigned to defined growth patterns, which were then analyzed for statistical associations with a focus on prediction and prognosis with the previously collected characteristics. RESULTS & CONCLUSIONS We observed apparent differences in progression-free and overall survival between multicentric and multifocal recurrences. This further supports the hypothesis that these are different growth patterns from other origins. Several of the investigated growth patterns in recurrence showed significant associations with longer or shorter progression-free and overall survival compared to the overall collective (e.g. unifocal local and multicentric recurrences, p<0.05). These observations were also confirmed in multivariable survival analyses in which known prognosis-associated factors were included. Patients treated with TumorTreatingFields after subtotal tumor resection presented a significantly prolonged progression-free survival (mPFS 7.2 months vs. 4.8 months; p=0.03), which was due to a longer time to development of local recurrence. This can be interpreted as an indication of improved local tumor control (mPFS 11.1 months vs. 5.2. months; p=0.01). In the collective after subtotal resection, treatment with TumorTreatingFields was accordingly also predictive for the development of non-local tumor recurrence (OR 0.16; p=0.02).

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Journal
Neuro-Oncology
Published
2025-10-01
DOI
https://doi.org/10.1093/neuonc/noaf193.380
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

P12.01.B GLIOBLASTOMA RECURRENCE GROWTH PATTERNS - PROGNOSTIC IMPACT AND POTENTIAL PREDICTORS

Martin Glas, Christoph Kleinschnitz, Niklas Schäfer, Carsten Hagemann et al.
Neuro-Oncology
Radiomics and Machine Learning in Medical Imaging
article

P12.01.B GLIOBLASTOMA RECURRENCE GROWTH PATTERNS - PROGNOSTIC IMPACT AND POTENTIAL PREDICTORS

Martin Glas, Christoph Kleinschnitz, Niklas Schäfer, Carsten Hagemann, Jessica A. Grieger, Sied Kebir, Lazaros Lazaridis, Cornelius Deuschl, Björn Scheffler, Ulrich Sure, Ulrich Herrlinger, Jonas Feldheim, Martin Stuschke, Elke Hattingen, Teresa Schmidt, Christoph Oster, F. Vigués Julià
article en

Abstract

Abstract BACKGROUND Glioblastoma (GBM) recurrences usually occur locally, but can also develop at a distance from the initial lesion or several sites simultaneously. Although a fundamental influence of growth patterns on prognosis in GBM recurrence is known, there is little preliminary data on prognostic effects and associations of defined stratified growth patterns. Furthermore, possible predictive factors influencing the development of specific growth patterns cannot yet be evaluated with certainty. With this project, we aimed to answer the following questions: (1) Can a more precise analysis of GBM growth patterns in recurrence help with stratification into subgroups? (2) Do GBM growth patterns in relapse correlate with patient prognosis? (3) Can we identify predictors of defined growth patterns? METHODS As part of a retrospective study of two neuro-oncological centers, 251 patients were included, and their demographic data, therapy, tumor, and patient characteristics were retrospectively collected. In addition, the MRI images after initial biopsy/resection and at the time of the first recurrence were evaluated and assigned to defined growth patterns, which were then analyzed for statistical associations with a focus on prediction and prognosis with the previously collected characteristics. RESULTS & CONCLUSIONS We observed apparent differences in progression-free and overall survival between multicentric and multifocal recurrences. This further supports the hypothesis that these are different growth patterns from other origins. Several of the investigated growth patterns in recurrence showed significant associations with longer or shorter progression-free and overall survival compared to the overall collective (e.g. unifocal local and multicentric recurrences, p<0.05). These observations were also confirmed in multivariable survival analyses in which known prognosis-associated factors were included. Patients treated with TumorTreatingFields after subtotal tumor resection presented a significantly prolonged progression-free survival (mPFS 7.2 months vs. 4.8 months; p=0.03), which was due to a longer time to development of local recurrence. This can be interpreted as an indication of improved local tumor control (mPFS 11.1 months vs. 5.2. months; p=0.01). In the collective after subtotal resection, treatment with TumorTreatingFields was accordingly also predictive for the development of non-local tumor recurrence (OR 0.16; p=0.02).

Neuro-OncologyVol. 27(Supplement_3)
Goethe University Frankfurt (DE), German Cancer Research Center (DE), University Hospital Bonn (DE), Universitätsklinikum Knappschaftskrankenhaus Bochum (DE), Paracelsus Medizinische Privatuniversität (DE), Essen University Hospital (DE), Universitätsklinikum Würzburg (DE), University Hospital Frankfurt (DE)
Openalex Percentile: Top 59%
Radiomics and Machine Learning in Medical Imaging
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