Development and validation of a clinical risk model for postoperative outcome in newly diagnosed glioblastoma: A report of the RANO resect group

Karschnia P, Young JS, Youssef GC, Dono A, Häni L, Sciortino T, Bruno F, Juenger ST, Teske N, Dietrich J, Weller M, Vogelbaum MA, van den Bent M, Beck J, Thon N, Gerritsen JK, Hervey-Jumper S, Cahill DP, Chang SM, Rudà R, Bello L, Schnell O, Esquenazi Y, Ruge MI, Grau SJ, Huang RY, Wen PY, Berger MS, Molinaro AM, Tonn JC (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Neuro-Oncology Oxford University Press

Book Volume: 27

Pages Range: 1046-1060

Journal Issue: 4

DOI: 10.1093/neuonc/noae231

Abstract

Background. Following surgery, patients with newly diagnosed glioblastoma frequently enter clinical trials. Nuanced risk assessment is warranted to reduce imbalances between study arms. Here, we aimed (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 response assessment in neuro-oncology (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 isocitrate dehydrogenase-wildtype glioblastoma. Residual tumor, O6-methylguanine DNA methyltransferase (MGMT) promotor methylation status, age, and postoperative Karnofsky Performance Score were prognostic for survival and incorporated into regression tree analysis. By individually weighting the prognostic factors, an additive score (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 inferior survival.The prognostic value of our risk model was retained in treatment-based subgroups and confirmed in an external validation cohort of 258 patients with glioblastoma. Compared to previously postulated models, goodness-of-fit measurements were superior for our model. Conclusions. The novel RANO risk model serves as an easy-to-use, yet highly prognostic tool for postoperative patient stratification prior to further therapy.The model may serve to guide patient management and reduce imbalances between study arms in prospective trials.

Authors with CRIS profile

Oliver Schnell Lehrstuhl für Neurochirurgie

Involved external institutions

Klinikum der Universität München DE Germany (DE) University of San Francisco (USF) US United States (USA) (US) Harvard University US United States (USA) (US) Universitätsspital Zürich (USZ) CH Switzerland (CH) H. Lee Moffitt Cancer Center & Research Institute US United States (USA) (US) Erasmus University Medical Center (MC) NL Netherlands (NL) Universitätsklinikum Freiburg DE Germany (DE) University of Turin / Università degli Studi di Torino (UNITO) IT Italy (IT) Università degli studi di Milano IT Italy (IT) McGovern Medical School US United States (USA) (US) Universität zu Köln DE Germany (DE)

How to cite

APA:

Karschnia, P., Young, J.S., Youssef, G.C., Dono, A., Häni, L., Sciortino, T.,... Tonn, J.C. (2025). Development and validation of a clinical risk model for postoperative outcome in newly diagnosed glioblastoma: A report of the RANO resect group. Neuro-Oncology, 27(4), 1046-1060. https://doi.org/10.1093/neuonc/noae231

MLA:

Karschnia, Philipp, et al. "Development and validation of a clinical risk model for postoperative outcome in newly diagnosed glioblastoma: A report of the RANO resect group." Neuro-Oncology 27.4 (2025): 1046-1060.

BibTeX: Download