A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival.

By M. Lacroix, D. Abi‐Said, D. Fourney, Z. Gokaslan, Weiming Shi, F. DeMonte, F. Lang, I. McCutcheon, S. Hassenbusch, Eric C. Holland and 4 more. Journal of Neurosurgery, 2001

3,058 citations recorded by the source corpus.

Subfields: Epidemiology, Genetics, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/b9e9e13cda97114004a064258827364d7ee4a1e2

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Arvid Lundervold

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Citations in the corpus

Survival in glioblastoma: a review on the impact of treatment modalities (2016, methodology): “It has been classically reported that surgical resection alone results in a median survival gain of 6 months approximately [19].”
The epidemiology of glioma in adults: a "state of the science" review. (2014, background): “The most conclusive prognostic factors for glioblastoma are extent of tumor resection, age at diagnosis, and Karnofsky performance status.(16,17) Survival also varies significantly by grade across all glioma subtypes.”
Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine (2018, positive): “It has been shown in large studies that the extent of the resection of tumor volume of 98% or more is associated with longer survival time.(5) Furthermore, Hammoud et al(6) showed that MRI features such as sign of little or no necrosis and lower tumor enhancement are associated with longer survival ”
GLIOMA EXTENT OF RESECTION AND ITS IMPACT ON PATIENT OUTCOME (2008, background): “Four of these studies included volumetric analysis of EOR (18, 19, 22, 34) (Table 5).”
High-grade glioma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. (2014, background): “resection is of prognostic value; it may be beneficial to attempt maximal tumour resection provided that neurological function is not compromised by the extent of resection [II, C] [25].”
Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion. (2003, background): “Thus , in terms of prognosis the model considers ' ' ideal ' ' patients unencum - bered by high age and low KPS [ 49 ] that prevent such patients from reaching their predicted survival time .”
Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. (2016, methodology): “Traditionally, clinical variables such as age, Karnofsky performance score, and extent of resection have been used to predict overall survival.(23) The inclusion of imaging features, however, improves upon survival prediction compared with the use of clinical features alone.”
Glioblastoma: Overview of Disease and Treatment (2016, background): “Statistically significant associations between greater extent of resection and longer progression-free survival (PFS) and overall survival (OS) have been seen in several studies (Keles, Anderson, & Berger, 1999; Lacroix et al., 2001; Mukherjee & QuiñonesHinojosa, 2011; Stummer et al., 2006).”
Epidemiologic and Molecular Prognostic Review of Glioblastoma (2014, background): “Several variables affect the prognosis of patients with GBM, including age, preoperative performance status, tumor location, preoperative imaging characteristics of the tumor and extent of resection (7, 44, 47).”
3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients (2016, neutral): “Tumor WHO grading, imaging phenotype, and other clinical data have been studied in their relationship to OS [1,6].”
EXTENT OF RESECTION AND SURVIVAL IN GLIOBLASTOMA MULTIFORME: IDENTIFICATION OF AND ADJUSTMENT FOR BIAS (2008, background): “(8), this threshold seemed to be associated with a resection of 98% of preoperative tumor volume.”
Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme (2009, background): “It is known that different tumor types affect the surrounding healthy region differently, and therefore studies have used the tumor mass effect as a descriptor for classifying gliomas according to their clinical grade (20) or as an independent predictor of survival (39).”