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Open AccessArticle

A Novel Comprehensive Clinical Stratification Model to Refine Prognosis of Glioblastoma Patients Undergoing Surgical Resection

1
Neurosurgery Unit, Department of Neuroscience, Santa Maria della Misericordia University Hospital, 33100 Udine, Italy
2
Department of Neurosurgery, Mater Olbia Hospital, 07026 Olbia, Italy
3
Institute of Neurosurgery, Catholic University, 00168 Rome, Italy
4
Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Università degli Studi di Napoli Federico II, 80131 Naples, Italy
5
Department of Medicine, Santa Maria della Misericordia University Hospital, 33100 Udine, Italy
6
Institute of radiology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
7
Experimental and Clinical Pharmacology Unit, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy
8
Neuroradiology Unit, Department of Diagnostic Imaging ASUIUD Udine, 33100 Udine, Italy
9
Institute of Pathology, Santa Maria della Misericordia University Hospital, 33100 Udine, Italy
10
Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
11
Department of Oncology, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy
*
Author to whom correspondence should be addressed.
Cancers 2020, 12(2), 386; https://doi.org/10.3390/cancers12020386
Received: 11 December 2019 / Revised: 29 January 2020 / Accepted: 5 February 2020 / Published: 7 February 2020
(This article belongs to the Special Issue Precision Medicine in Solid Tumors)
Despite recent discoveries in genetics and molecular fields, glioblastoma (GBM) prognosis still remains unfavorable with less than 10% of patients alive 5 years after diagnosis. Numerous studies have focused on the research of biological biomarkers to stratify GBM patients. We addressed this issue in our study by using clinical/molecular and image data, which is generally available to Neurosurgical Departments in order to create a prognostic score that can be useful to stratify GBM patients undergoing surgical resection. By using the random forest approach [CART analysis (classification and regression tree)] on Survival time data of 465 cases, we developed a new prediction score resulting in 10 groups based on extent of resection (EOR), age, tumor volumetric features, intraoperative protocols and tumor molecular classes. The resulting tree was trimmed according to similarities in the relative hazard ratios amongst groups, giving rise to a 5-group classification tree. These 5 groups were different in terms of overall survival (OS) (p < 0.000). The score performance in predicting death was defined by a Harrell’s c-index of 0.79 (95% confidence interval [0.76–0.81]). The proposed score could be useful in a clinical setting to refine the prognosis of GBM patients after surgery and prior to postoperative treatment. View Full-Text
Keywords: glioblastoma prognosis; overall survival; extent of resection; random forest; Decision tree; personalized precision oncology glioblastoma prognosis; overall survival; extent of resection; random forest; Decision tree; personalized precision oncology
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Ius, T.; Pignotti, F.; Della Pepa, G.M.; La Rocca, G.; Somma, T.; Isola, M.; Battistella, C.; Gaudino, S.; Polano, M.; Dal Bo, M.; Bagatto, D.; Pegolo, E.; Chiesa, S.; Arcicasa, M.; Olivi, A.; Skrap, M.; Sabatino, G. A Novel Comprehensive Clinical Stratification Model to Refine Prognosis of Glioblastoma Patients Undergoing Surgical Resection. Cancers 2020, 12, 386.

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