Volumetric quantification of glioblastoma: experiences with different measurement techniques and impact on survival

Abstract

The potential impact of different radiological features of glioblastoma multiforme (GBM) on overall survival (OS) like tumor volume, peritumoral edema (PTE), necrosis volume, necrosis-tumor ratio (NTR) and edema-tumor ratio (ETR) is still very controversial. To determine the influence of volumetric data on OS und to compare different measuring techniques described in literature. We prospectively evaluated preoperative MR images from 30 patients harboring a primary supratentorial GBM. All patients received gross-total tumor resection followed by standard radiation and chemotherapy (temozolomide). By 3D semi-automated segmentation, we measured tumor volume, necrosis volume, PTE, postoperative residual tumor volume and calculated ETR, NTR and the extent of resection. After critical review of the existing literature we compared alternative measuring techniques with the gold standard of 3D segmentation. Statistical analysis showed a significant impact of the preoperative tumor and necrosis volumes on OS (p = 0.041, respectively p = 0.039). Furthermore, NTR also showed a significant association with OS (p = 0.005). Comparison of previously described measuring techniques and scorings with our results showed that no other technique is reliable and accurate enough as a predictive tool. The critical review of previously published studies revealed mainly inaccurate measurement techniques and patient selection as potential reasons for inconsistent results. Preoperatively measured necrosis volume and NTR are the most important radiological features of GBM with a strong influence on OS. No other measuring techniques are specific enough and comparable with 3D segmentation.

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

“…Age, performance status, extent of surgical resection and MGMT methylation status are well known prognostic factors for GBM patients [4,[21][22][23][24][25][26][27][28][29][30][31][32][33][34][39][40][41][42][43][44]. Nevertheless, the high degree of clinical/molecular heterogeneity found among GBM patients do not generally allow us to correctly classify GBM patients with the use of a single predictor or a few predictors.…”

“…To classify GBM cases according to this heterogeneity, different prognostic factors have been suggested for GBM, including age, performance status, specific molecular markers [e.g., MGMT methylation (O 6 -methylguanine-DNA methyl-transferase), mutation of IDH1, IDH2(isocitrate dehydrogenase) or TERT (telomerase reverse transcriptase), 1p19q codeletion, overexpression of EGFR (epidermal growth factor receptor)], the size of necrosis and the extent of resection (EOR) [15][16][17][18][19][20][21][22]. The role of EOR in improving survival in patients with GBM has widely been demonstrated, with more extensive resections providing added advantages [8,9,16,18,19,[23][24][25][26][27][28][29][30][31][32][33].…”

Assessment of Pre-operative Measurements of Tumor Size by MRI Methods as Survival Predictors in Wild Type IDH Glioblastoma

“…Present results confirm that, the higher the volume of the FLAIR-T 2 image, the worse the prognosis is. However, there is some controversy in this respect, since some studies have shown correlations between FLAIR-T 2 hyper-intensities with longer OS ( 13 – 18 , 40 , 47 ), while others have not been able to prove it ( 12 , 13 , 48 , 49 ). Many factors, including the non-exclusion of cystic cavities, the influence of corticosteroid treatment, and the inclusion of both, IDH-mutated, and IDH wild-type tumors, may further underlie these discrepancies.…”

“…Notably, OS and PFS in glioblastomas have been previously associated to various compartmental tumor volumes including: the volume of necrosis ( 12 – 14 ), the volume of contrast-enhancing tumor ( 11 ), the volume of FLAIR-T 2 hyper-intensity ( 13 – 18 ), or the tumor/necrosis volume ratio ( 13 , 19 ). Unfortunately, results and conclusions from many of these studies remain contradictory.…”

“…Discrepancies may originate, at least partially, from the diversity of volumetric techniques implemented and, in some cases, from incomplete morphological or molecular characterizations of the tumors investigated. Together, these limitations result in a plethora of uncertainties, preventing more accurate predictions ( 6 , 13 ). On these grounds, assessment of the influence of the different methods of pre-surgical tumor volume determination, their relationship with the underlying clinical and genetic biomarkers, or even with the efficacy of the neurosurgical resections, entail vital relevance to improve the accuracy of current OS and PFS predictions.…”