Philipp Karschnia, Jacob S Young, Gilbert C Youssef, Antonio Dono, Levin Häni, Tommaso Sciortino, Francesco Bruno, Stephanie T Juenger, Nico Teske, Jorg Dietrich, Michael Weller, Michael A Vogelbaum, Martin van den Bent, Juergen Beck, Niklas Thon, Jasper K W Gerritsen, Shawn Hervey-Jumper, Daniel P Cahill, Susan M Chang, Roberta Rudà, Lorenzo Bello, Oliver Schnell, Yoshua Esquenazi, Maximilian I Ruge, Stefan J Grau, Raymond Y Huang, Patrick Y Wen, Mitchel S Berger, Annette M Molinaro, Joerg-Christian Tonn
{"title":"新诊断胶质母细胞瘤术后结果临床风险模型的开发与验证:RANO resect 小组的报告。","authors":"Philipp Karschnia, Jacob S Young, Gilbert C Youssef, Antonio Dono, Levin Häni, Tommaso Sciortino, Francesco Bruno, Stephanie T Juenger, Nico Teske, Jorg Dietrich, Michael Weller, Michael A Vogelbaum, Martin van den Bent, Juergen Beck, Niklas Thon, Jasper K W Gerritsen, Shawn Hervey-Jumper, Daniel P Cahill, Susan M Chang, Roberta Rudà, Lorenzo Bello, Oliver Schnell, Yoshua Esquenazi, Maximilian I Ruge, Stefan J Grau, Raymond Y Huang, Patrick Y Wen, Mitchel S Berger, Annette M Molinaro, Joerg-Christian Tonn","doi":"10.1093/neuonc/noae231","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 KPS 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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":19377,"journal":{"name":"Neuro-oncology","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and validation of a clinical risk model for postoperative outcome in newly diagnosed glioblastoma: a report of the RANO resect group.\",\"authors\":\"Philipp Karschnia, Jacob S Young, Gilbert C Youssef, Antonio Dono, Levin Häni, Tommaso Sciortino, Francesco Bruno, Stephanie T Juenger, Nico Teske, Jorg Dietrich, Michael Weller, Michael A Vogelbaum, Martin van den Bent, Juergen Beck, Niklas Thon, Jasper K W Gerritsen, Shawn Hervey-Jumper, Daniel P Cahill, Susan M Chang, Roberta Rudà, Lorenzo Bello, Oliver Schnell, Yoshua Esquenazi, Maximilian I Ruge, Stefan J Grau, Raymond Y Huang, Patrick Y Wen, Mitchel S Berger, Annette M Molinaro, Joerg-Christian Tonn\",\"doi\":\"10.1093/neuonc/noae231\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Following surgery, patients with newly diagnosed glioblastoma frequently enter clinical trials. 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Residual tumor, O6-methylguanine DNA methyltransferase (MGMT) promotor methylation status, age, and postoperative KPS 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.</p><p><strong>Conclusions: </strong>The novel RANO risk model serves as an easy-to-use, yet highly prognostic tool for postoperative patient stratification prior to further therapy. 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Development and validation of a clinical risk model for postoperative outcome in newly diagnosed glioblastoma: a report of the RANO resect group.
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 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 KPS 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.
期刊介绍:
Neuro-Oncology, the official journal of the Society for Neuro-Oncology, has been published monthly since January 2010. Affiliated with the Japan Society for Neuro-Oncology and the European Association of Neuro-Oncology, it is a global leader in the field.
The journal is committed to swiftly disseminating high-quality information across all areas of neuro-oncology. It features peer-reviewed articles, reviews, symposia on various topics, abstracts from annual meetings, and updates from neuro-oncology societies worldwide.