SURG-21. DEVELOPMENT AND VALIDATION OF A CLINICAL RISK SCORE FOR POSTOPERATIVE OUTCOME IN NEWLY DIAGNOSED GLIOBLASTOMA: A REPORT OF THE RANO<i>RESECT</i> GROUP

Abstract BACKGROUND Following resection or biopsy, patients with newly diagnosed glioblastoma frequently enter clinical trials. Nuanced risk assessment is warranted to reduce imbalances between study arms. Here, we aim to make use of our RANO classification for extent of resection (I) to analyze the interactive effects of residual tumor with clinical and molecular factors on outcome and (II) to define a postoperative risk assessment tool. METHODS The RANO resect group retrospectively compiled an international, seven-center training cohort of patients with newly diagnosed glioblastoma. The combined associations of residual tumor with molecular or clinical factors and survival were analyzed, and recursive partitioning analysis was performed for risk modeling. The resulting model was prognostically verified in a separate external validation cohort. RESULTS Our training cohort compromised 1003 patients with newly diagnosed IDH-wildtype glioblastoma, including 744 patients who underwent adjuvant radiochemotherapy (TMZ/RT→TMZ). Residual tumor, MGMT promotor methylation status, age, and KPS were prognostic of overall survival and forwarded into regression tree analysis. By combining eleven terminal nodes and individually weighting the prognostic factors, an additive scale (range, 0-9 points) integrating these four variables distinguished patients with low (0-2 points), intermediate (3-5 points), and high risk (6-9 points) for poor postoperative outcome (median overall survival: 24 vs. 16 vs. 6 months; p&amp;lt;0.01). The presence of the three risk groups was confirmed in the subgroups of patients treated with or without RT/TMZ→TMZ. The prognostic value of the risk score was verified in a external single-center validation cohort of newly diagnosed glioblastoma (n = 258, p&amp;lt;0.01). Compared to previously postulated models, goodness-of-fit measurements were superior for our score. CONCLUSIONS The novel RANO risk score serves as an easy-to-use, yet highly prognostic tool for postoperative patient stratification prior to further therapy. The score may serve to guide patient management and reduce imbalances between study arms in prospective trials.

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Journal
Neuro-Oncology
Published
2024-11-01
DOI
https://doi.org/10.1093/neuonc/noae165.1101
Primary Topic
Meta-analysis and systematic reviews
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article
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article

SURG-21. DEVELOPMENT AND VALIDATION OF A CLINICAL RISK SCORE FOR POSTOPERATIVE OUTCOME IN NEWLY DIAGNOSED GLIOBLASTOMA: A REPORT OF THE RANORESECT GROUP

J. C. Tonn, Jacob S. Young, Mitchel Stuart Berger, Levin Häni et al.
Neuro-Oncology
Meta-analysis and systematic reviews
article

SURG-21. DEVELOPMENT AND VALIDATION OF A CLINICAL RISK SCORE FOR POSTOPERATIVE OUTCOME IN NEWLY DIAGNOSED GLIOBLASTOMA: A REPORT OF THE RANORESECT GROUP

J. C. Tonn, Jacob S. Young, Mitchel Stuart Berger, Levin Häni, Martin J. van den Bent, Stefan Josef Grau, Daniel P. Cahill, Michael Weller, Patrick Yung Wen, Jasper K W Gerritsen, Gilbert Youssef, Annette M. Molinaro, Philipp Karschnia, Shawn L. Hervey‐Jumper, Oliver Schnell, Francesco Bruno, Jörg Dietrich, Nico Teske, Raymond Y. Huang, Niklas Thon, Antonio Dono, Jürgen Beck, Maximilian I. Ruge, Tommaso Sciortino, Lorenzo Bello, Yoshua Esquenazi, Roberta Rudà, Stephanie T Juenger, Michael A. Vogelbaum, Susan M. Chang
article en

Abstract

Abstract BACKGROUND Following resection or biopsy, patients with newly diagnosed glioblastoma frequently enter clinical trials. Nuanced risk assessment is warranted to reduce imbalances between study arms. Here, we aim to make use of our RANO classification for extent of resection (I) to analyze the interactive effects of residual tumor with clinical and molecular factors on outcome and (II) to define a postoperative risk assessment tool. METHODS The RANO resect group retrospectively compiled an international, seven-center training cohort of patients with newly diagnosed glioblastoma. The combined associations of residual tumor with molecular or clinical factors and survival were analyzed, and recursive partitioning analysis was performed for risk modeling. The resulting model was prognostically verified in a separate external validation cohort. RESULTS Our training cohort compromised 1003 patients with newly diagnosed IDH-wildtype glioblastoma, including 744 patients who underwent adjuvant radiochemotherapy (TMZ/RT→TMZ). Residual tumor, MGMT promotor methylation status, age, and KPS were prognostic of overall survival and forwarded into regression tree analysis. By combining eleven terminal nodes and individually weighting the prognostic factors, an additive scale (range, 0-9 points) integrating these four variables distinguished patients with low (0-2 points), intermediate (3-5 points), and high risk (6-9 points) for poor postoperative outcome (median overall survival: 24 vs. 16 vs. 6 months; p&amp;lt;0.01). The presence of the three risk groups was confirmed in the subgroups of patients treated with or without RT/TMZ→TMZ. The prognostic value of the risk score was verified in a external single-center validation cohort of newly diagnosed glioblastoma (n = 258, p&amp;lt;0.01). Compared to previously postulated models, goodness-of-fit measurements were superior for our score. CONCLUSIONS The novel RANO risk score serves as an easy-to-use, yet highly prognostic tool for postoperative patient stratification prior to further therapy. The score may serve to guide patient management and reduce imbalances between study arms in prospective trials.

Neuro-OncologyVol. 26(Supplement_8)
Brigham and Women's Hospital (US), Harvard University (US), University of Freiburg (DE), University of Cologne (DE), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), University of Milan (IT), University of Zurich (CH), University Hospital of Bern (CH), University Medical Center Freiburg (DE), Moffitt Cancer Center (US), Massachusetts General Hospital (US), University Hospital of Zurich (CH), Dana-Farber Cancer Institute (US), Erasmus MC Cancer Institute (NL), Klinikum Fulda (DE), Deutsches Konsortium für Translationale Krebsforschung (DE), University of Turin (IT), University of San Francisco (US), Universidade São Francisco (BR), Ludwig-Maximilians-Universität München (DE), The University of Texas Health Science Center at Houston (US)
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Meta-analysis and systematic reviews
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