TY - JOUR
T1 - A novel comprehensive clinical stratification model to refine prognosis of glioblastoma patients undergoing surgical resection
AU - Ius, Tamara
AU - Pignotti, Fabrizio
AU - Pepa, Giuseppe Maria Della
AU - La Rocca, Giuseppe
AU - Somma, Teresa
AU - Isola, Miriam
AU - Battistella, Claudio
AU - Gaudino, Simona
AU - Polano, Maurizio
AU - Bo, Michele Dal
AU - Bagatto, Daniele
AU - Pegolo, Enrico
AU - Chiesa, Silvia
AU - Arcicasa, Mauro
AU - Olivi, Alessandro
AU - Skrap, Miran
AU - Sabatino, Giovanni
N1 - Funding Information:
Funding: This work has been supported by: Progetto Ministero della Salute, Giovani Ricercatori 2016 GR-2016-02364678. Application of GLIADEL wafers (BCNU, carmustine) followed by temozolomide and radiotherapy in patients with high-grade glioma: a precision medicine based on molecular landscape. CUP: J26C16000000005 Acknowledgments: We acknowledge the support by the medical staff of the Departments of Pathology and Neuroradiology (Santa Maria della Misericordia University Hospital, Udine, Policlinico Gemelli, Rome).
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/2
Y1 - 2020/2
N2 - 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.
AB - 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.
KW - Decision tree
KW - Extent of resection
KW - Glioblastoma prognosis
KW - Overall survival
KW - Personalized precision oncology
KW - Random forest
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U2 - 10.3390/cancers12020386
DO - 10.3390/cancers12020386
M3 - Article
VL - 12
JO - Cancers
JF - Cancers
SN - 2072-6694
IS - 2
M1 - 386
ER -