Preoperative Two-Dimensional Size of Glioblastoma is Associated with Patient Survival

Do presenting symptoms predict treatment decisions and survival in glioblastoma? Real-world data from 1458 patients in the Swedish brain tumor registry

Background

Glioblastoma is the most common malignant brain tumor in adults. Non-invasive clinical parameters could play a crucial role in treatment planning and serve as predictors of patient survival. Our register-based real-life study aimed to investigate the prognostic value of presenting symptoms.

Methods

Data on presenting symptoms and survival, as well as known prognostic factors, were retrieved for all glioblastoma patients in Sweden registered in the Swedish Brain Tumor Registry between 2018 and 2021. The prognostic impact of different presenting symptoms was calculated using the Cox proportional hazard model.

Results

Data from 1458 adults with pathologically verified IDH wild-type glioblastoma were analyzed. Median survival time was 345 days. The 2-year survival rate was 21.5%. Registered presenting symptoms were focal neurological deficits, cognitive dysfunction, headache, epilepsy, signs of raised intracranial pressure, and cranial nerve symptoms, with some patients having multiple symptoms. Patients with initial cognitive dysfunction had significantly shorter survival than patients without; 265 days (245-285) vs. 409 days (365-453; P < .001). The reduced survival remained after Cox regression adjusting for known prognostic factors. Patients presenting with seizures and patients with headaches had significantly longer overall survival compared to patients without these symptoms, but the difference was not retained in multivariate analysis. Patients with cognitive deficits were less likely to have radical surgery and to receive extensive anti-neoplastic nonsurgical treatment.

Conclusions

This extensive real-life study reveals that initial cognitive impairment acts as an independent negative predictive factor for treatment decisions and adversely affects survival outcomes in glioblastoma patients.

Shape matters: unsupervised exploration of IDH-wildtype glioma imaging survival predictors

OBJECTIVES

This study examines clustering based on shape radiomic features and tumor volume to identify IDH-wildtype glioma phenotypes and assess their impact on overall survival (OS).

MATERIALS AND METHODS

This retrospective study included 436 consecutive patients diagnosed with IDH-wt glioma who underwent preoperative MR imaging. Alongside the total tumor volume, nine distinct shape radiomic features were extracted using the PyRadiomics framework. Different imaging phenotypes were identified using partition around medoids (PAM) clustering on the training dataset (348/436). The prognostic efficacy of these phenotypes in predicting OS was evaluated on the test dataset (88/436). External validation was performed using the public UCSF glioma dataset (n = 397). A decision-tree algorithm was employed to determine the relevance of features associated with cluster affiliation.

RESULTS

PAM clustering identified two clusters in the training dataset: Cluster 1 (n = 233) had a higher proportion of patients with higher sphericity and elongation, while Cluster 2 (n = 115) had a higher proportion of patients with higher maximum 3D diameter, surface area, axis lengths, and tumor volume (p < 0.001 for each). OS differed significantly between clusters: Cluster 1 showed a median OS of 23.8 compared to 11.4 months of Cluster 2 in the holdout test dataset (p = 0.002). Multivariate Cox regression showed improved performance with cluster affiliation over clinical data alone (C index 0.67 vs 0.59, p = 0.003). Cluster-based models outperformed the models with tumor volume alone (evidence ratio: 5.16-5.37).

CONCLUSION

Data-driven clustering reveals imaging phenotypes, highlighting the improved prognostic power of combining shape-radiomics with tumor volume, thereby outperforming predictions based on tumor volume alone in high-grade glioma survival outcomes.

CLINICAL RELEVANCE STATEMENT

Shape-radiomics and volume-based cluster analyses of preoperative MRI scans can reveal imaging phenotypes that improve the prediction of OS in patients with IDH-wild type gliomas, outperforming currently known models based on tumor size alone or clinical parameters.

KEY POINTS

Shape radiomics and tumor volume clustering in IDH-wildtype gliomas are investigated for enhanced prognostic accuracy. Two distinct phenotypic clusters were identified with different median OSs. Integrating shape radiomics and volume-based clustering enhances OS prediction in IDH-wildtype glioma patients.

Physical Health-Related Quality of Life and Postsurgical Outcomes in Brain Tumor Resection Patients

Background  Patient-reported outcome measures (PROMs) have gained traction in assessing patients' health around surgery. Among these, the 29-item Patient-Reported Outcomes Measurement Information System (PROMIS-29) is a widely accepted tool for evaluating overall health, yet its applicability in cranial neurosurgery remains uncertain. Objective  This study aimed to evaluate the predictive value of preoperative PROMIS-29 scores for postoperative outcomes in patients undergoing brain tumor resection. Materials and Methods  We identified adult patients undergoing brain tumor resection at a single neurosurgical center between January 2018 and December 2021. We analyzed physical health (PH) summary scores to determine optimal thresholds for predicting length of stay (LOS), discharge disposition (DD), and 30-day readmission. Bivariate analyses were conducted to examine the distribution of PH scores based on patient characteristics. Multivariate logistic regression models were employed to assess the association between preoperative PH scores and short-term postoperative outcomes. Results  Among 157 patients (mean age 55.4 years, 58.0% female), 14.6% exhibited low PH summary scores. Additionally, 5.7% experienced prolonged LOS, 37.6% had nonroutine DDs, and 19.1% were readmitted within 30 days. Bivariate analyses indicated that patients with low PH summary scores, indicating poorer baseline PH, were more likely to have malignant tumors, nonelective admissions, and adverse outcomes. In multivariate analysis, low PH summary scores independently predicted increased odds of prolonged LOS (odds ratio [OR] = 6.09, p  = 0.003), nonroutine DD (OR = 4.25, p  = 0.020), and 30-day readmission (OR = 3.93, p  = 0.020). Conclusion  The PROMIS-29 PH summary score serves as a valuable predictor of short-term postoperative outcomes in brain tumor patients. Integrating this score into clinical practice can enhance the ability to anticipate meaningful postoperative results.

Preoperative patient-reported physical health-related quality of life predicts short-term postoperative outcomes in brain tumor patients

BACKGROUND

Patient-reported outcome measures (PROMs) are increasingly used to assess patients' perioperative health. The PROM Information System 29 (PROMIS-29) is a well-validated global health assessment instrument for patient physical health, though its utility in cranial neurosurgery is unclear.

OBJECTIVE

To investigate the utility of preoperative PROMIS-29 physical health (PH) summary scores in predicting postoperative outcomes in brain tumor patients.

METHODS

Adult brain tumor patients undergoing resection at a single institution (January 2018-December 2021) were identified and prospectively received PROMIS-29 surveys during pre-operative visits. PH summary scores were constructed and optimum prediction thresholds for length of stay (LOS), discharge disposition (DD), and 30-day readmission were approximated by finding the Youden index of the associated receiver operating characteristic curves. Bivariate analyses were used to study the distribution of low (z-score≤-1) versus high (z-score>-1) PH scores according to baseline characteristics. Logistic regression models quantified the association between preoperative PH summary scores and post-operative outcomes.

RESULTS

A total of 157 brain tumor patients were identified (mean age 55.4±15.4 years; 58.0% female; mean PH score 45.5+10.5). Outcomes included prolonged LOS (24.8%), non-routine discharge disposition (37.6%), and 30-day readmission (19.1%). On bivariate analysis, patients with low PH scores were significantly more likely to be diagnosed with a high-grade tumor (69.6% vs 38.85%, p=0.010) and less likely to have elective surgery (34.8% vs 70.9%, p=0.002). Low PH score was associated with prolonged LOS (26.1% vs 22%, p<0.001), nonroutine discharge (73.9% vs 31.3%, p<0.001) and 30-day readmission (43.5% vs 14.9%, p=0.003). In multivariate analysis, low PH scores predicted greater LOS (odds ratio [OR]=6.09, p=0.003), nonroutine discharge (OR=4.25, p=0.020), and 30-day readmission (OR=3.93, p=0.020).

CONCLUSION

The PROMIS-29 PH summary score predicts short-term postoperative outcomes in brain tumor patients and may be incorporated into prospective clinical workflows.

Rapid Passive Gamma Mapping as an Adjunct to Electrical Stimulation Mapping for Functional Localization in Resection of Primary Brain Neoplasms

OBJECTIVE

We examined the utility of passive high gamma mapping (HGM) as an adjunct to conventional awake brain mapping during glioma resection. We compared functional and survival outcomes before and after implementing intraoperative HGM.

METHODS

This was a retrospective cohort study of 75 patients who underwent a first-time, awake craniotomy for glioma resection. Patients were stratified by whether their operation occurred before or after the implementation of a U.S. Food and Drug Administration-approved high-gamma mapping tool in July 2017.

RESULTS

The preimplementation and postimplementation cohorts included 28 and 47 patients, respectively. Median intraoperative time (261 vs. 261 minutes, P = 0.250) and extent of resection (97.14% vs. 98.19%, P = 0.481) were comparable between cohorts. Median Karnofsky performance status at initial follow-up was similar between cohorts (P = 0.650). Multivariable Cox regression models demonstrated an adjusted hazard ratio for overall survival of 0.10 (95% confidence interval: 0.02-0.43, P = 0.002) for the postimplementation cohort relative to the preimplementation cohort. Progression-free survival adjusted for insular involvement showed an adjusted hazard ratio of 1.00 (95% confidence interval: 0.49-2.06, P = 0.999) following HGM implementation. Falling short of statistical significance, prevalence of intraoperative seizures and/or afterdischarges decreased after HGM implementation as well (12.7% vs. 25%, P = 0.150).

CONCLUSIONS

Our results tentatively indicate that passive HGM is a safe and potentially useful adjunct to electrical stimulation mapping for awake cortical mapping, conferring at least comparable functional and survival outcomes with a nonsignificant lower rate of intraoperative epileptiform events. Considering the limitations of our study design and patient cohort, further investigation is needed to better identify optimal use cases for HGM.

Interpreting deep learning models for glioma survival classification using visualization and textual explanations

BACKGROUND

Saliency-based algorithms are able to explain the relationship between input image pixels and deep-learning model predictions. However, it may be difficult to assess the clinical value of the most important image features and the model predictions derived from the raw saliency map. This study proposes to enhance the interpretability of saliency-based deep learning model for survival classification of patients with gliomas, by extracting domain knowledge-based information from the raw saliency maps.

MATERIALS AND METHODS

Our study includes presurgical T1-weighted (pre- and post-contrast), T2-weighted and T2-FLAIR MRIs of 147 glioma patients from the BraTs 2020 challenge dataset aligned to the SRI 24 anatomical atlas. Each image exam includes a segmentation mask and the information of overall survival (OS) from time of diagnosis (in days). This dataset was divided into training ([Formula: see text]) and validation ([Formula: see text]) datasets. The extent of surgical resection for all patients was gross total resection. We categorized the data into 42 short (mean [Formula: see text] days), 30 medium ([Formula: see text] days), and 46 long ([Formula: see text] days) survivors. A 3D convolutional neural network (CNN) trained on brain tumour MRI volumes classified all patients based on expected prognosis of either short-term, medium-term, or long-term survival. We extend the popular 2D Gradient-weighted Class Activation Mapping (Grad-CAM), for the generation of saliency map, to 3D and combined it with the anatomical atlas, to extract brain regions, brain volume and probability map that reveal domain knowledge-based information.

RESULTS

For each OS class, a larger tumor volume was associated with a shorter OS. There were 10, 7 and 27 tumor locations in brain regions that uniquely associate with the short-term, medium-term, and long-term survival, respectively. Tumors located in the transverse temporal gyrus, fusiform, and palladium are associated with short, medium and long-term survival, respectively. The visual and textual information displayed during OS prediction highlights tumor location and the contribution of different brain regions to the prediction of OS. This algorithm design feature assists the physician in analyzing and understanding different model prediction stages.

CONCLUSIONS

Domain knowledge-based information extracted from the saliency map can enhance the interpretability of deep learning models. Our findings show that tumors overlapping eloquent brain regions are associated with short patient survival.

Prognostic Factors of Survival in Glioblastoma Multiforme Patients-A Retrospective Study

BACKGROUND

Glioblastoma multiforme (GBM) is the most aggressive brain tumor that occurs in adults. In spite of prompt diagnosis and rapidly administered treatment, the survival expectancy is tremendously poor. Extensive research has been performed in order to establish factors to predict the outcome of GBM patients; however, worldwide accepted prognostic markers are still lacking.

METHODS

We retrospectively assessed all adult patients who were diagnosed with primary GBM and underwent surgical treatment during a three-year period (January 2017-December 2019) in the Neurosurgery Department of the Emergency Clinical County Hospital of Târgu Mureș, Romania. Our aim was to find any statistically relevant connections between clinical, imagistic, and histopathological characteristics and patients' survival.

RESULTS

A total of 75 patients were eventually included in our statistical analysis: 40 males and 35 females, with a median age of 61 years. The mean tumor dimension was 45.28 ± 15.52 mm, while the mean survival rate was 4 ± 6.75 months. A univariate analysis demonstrated a statistically significant impact of tumor size, pre-, and postoperative KPSI on survival rate. In addition, a Cox multivariate assessment strengthened previous findings regarding postoperative KPSI (regression coefficient -0.03, HR 0.97, 95% CI (HR) 0.96-0.99, p = 0.002) as a favorable prognostic factor and GBM size (regression coefficient 0.03, HR 1.03, 95% CI (HR) 1.01-1.05, p = 0.005) as a poor prognostic marker for patients' survival.

CONCLUSIONS

The results of our retrospective study are consistent with prior scientific results that provide evidence supporting the importance of clinical (quantified by KPSI) and imagistic (particularly tumor dimensions) features as reliable prognostic factors in GBM patients' survival.

Aggressiveness of Grade 4 Gliomas of Adults

Grade 4 adult gliomas are IDH-mutant astrocytomas and IDH-wildtype glioblastomas. They have a very high mortality rate, with survival at 5 years not exceeding 5%. We aimed to conduct a clinical imaging and morphogenetic characterization of them, as well as to identify the main negative prognostic factors that give them such aggressiveness. We conducted a ten-year retrospective study. We followed the clinical, imaging, and morphogenetic aspects of the cases. We analyzed immunohistochemical markers (IDH1, Ki-67, and nestin) and FISH tests based on the CDKN2A gene. The obtained results were analyzed using SPSS Statistics with the appropriate parameters. The clinical aspects representing negative prognostic factors were represented by patients’ comorbidities: hypertension (HR = 1.776) and diabetes mellitus/hyperglycemia (HR = 2.159). The lesions were mostly supratentorial, and the temporal lobe was the most affected. The mean volume was 88.05 cm3 and produced a midline shift with an average of 8.52 mm. Subtotal surgical resection was a negative prognostic factor (HR = 1.877). The proliferative index did not influence survival rate, whereas CDKN2A gene mutations were shown to have a major impact on survival. We identified the main negative prognostic factors that support the aggressiveness of grade 4 gliomas: patient comorbidities, type of surgical resection, degree of cell differentiation, and CDKN2A gene mutations.

Novel Predictive Models for High-Value Care Outcomes Following Glioblastoma Resection

BACKGROUND

Treating patients with glioblastoma (GBM) requires extensive medical infrastructure. Individualized risk assessment for extended length of stay (LOS), nonroutine discharge disposition, and increased total hospital charges is critical to optimize delivery of care. Our study sought to develop predictive models identifying independent risk factors for these outcomes.

METHODS

We retrospectively reviewed patients undergoing GBM resection at our institution between January 2017 and September 2020. Extended LOS and elevated hospital charges were defined as values in the upper quartile of the cohort. Nonroutine discharge was defined as any disposition other than to home. Multivariate models for each outcome included covariates demonstrating P ≤ 0.10 on bivariate analysis.

RESULTS

We identified 265 patients undergoing GBM resection, with an average age of 58.2 years. 24.5% of patients experienced extended LOS, 22.6% underwent nonroutine discharge, and 24.9% incurred elevated total hospital charges. Decreasing Karnofsky Performance Status (KPS) (P = 0.004), increasing modified 5-factor frailty (mFI-5) index (P = 0.012), lower surgeon experience (P = 0.005), emergent surgery (P < 0.0001), and larger tumor volume (P < 0.0001) predicted extended LOS. Independent predictors of nonroutine discharge included older age (P = 0.02), decreasing KPS (P < 0.0001), and emergent surgery (P = 0.048). Nonprivate insurance (P = 0.011), decreasing KPS (P = 0.029), emergent surgery (P < 0.0001), and larger tumor volume (P = 0.004) predicted elevated hospital charges. These models were incorporated into an open-access online calculator (https://neurooncsurgery3.shinyapps.io/gbm_calculator/).

CONCLUSIONS

Several factors were independent predictors for at least 1 high-value care outcome, with lower KPS and emergent admission associated with each outcome. These models and our calculator may help clinicians provide individualized postoperative risk assessment to glioblastoma patients.

Impact of Levetiracetam Treatment on 5-Aminolevulinic Acid Fluorescence Expression in IDH1 Wild-Type Glioblastoma

Simple Summary

The amino acid 5-aminolevulinic acid (5-ALA) is the benchmark regarding intraoperative imaging tools for glioblastoma (GB) surgery, and is known to facilitate the extent of resection, which results in an enhanced 6 month progression-free survival rate. Recent in vitro studies suggest that antiepileptic drugs (AEDs) result in a reduction in the fluorescence quality in gliomas. To date, there is no large clinical series investigating this issue in a homogeneous cohort. Approximately 25% of all GB patients have a symptomatic epilepsy as the initial symptom at presentation. Hence, this potential dilemma is of paramount importance. We found that the preoperative intake of levetiracetam is a significant risk factor for reduced intraoperative fluorescence in IDH1 wild-type GBs. We believe that this issue must be considered in future external validations, and physicians must carefully evaluate the indication of levetiracetam and avoid a prophylactic levetiracetam treatment in terms of the suspected diagnosis of glioblastoma.

Abstract

The amino acid 5-aminolevulinic acid (5-ALA) is the most established neurosurgical fluorescent dye and facilitates the achievement of gross total resection. In vitro studies raised concerns that antiepileptic drugs (AED) reduce the quality of fluorescence. Between 2013 and 2018, 175 IDH1 wild-type glioblastoma (GB) patients underwent 5-ALA guided surgery. Patients’ data were retrospectively reviewed regarding demographics, comorbidities, medications, tumor morphology, neuropathological characteristics, and their association with intraoperative 5-ALA fluorescence. The fluorescence of 5-ALA was graded in a three point scaling system (grade 0 = no; grade 1 = weak; grade 2 = strong). Univariable analysis shows that the intake of dexamethasone or levetiracetam, and larger preoperative tumor area significantly reduce the intraoperative fluorescence activity (fluorescence grade: 0 + 1). Multivariable binary logistic regression analysis demonstrates the preoperative intake of levetiracetam (adjusted odds ratio: 12.05, 95% confidence interval: 3.91–37.16, p = 0.001) as the only independent and significant risk factor for reduced fluorescence quality. Preoperative levetiracetam intake significantly reduced intraoperative fluorescence. The indication for levetiracetam in suspected GB should be carefully reviewed and prophylactic treatment avoided for this tumor entity. Future comparative trials of neurosurgical fluorescent dyes need a special focus on the influence of levetiracetam on fluorescence intensity. Further trials must validate our findings.

Practice of the new supervised machine learning predictive analytics for glioma patient survival after tumor resection: Experiences in a high-volume Chinese center

OBJECTIVE

This study aims to assess the effectiveness of the Gradient Boosting (GB) algorithm on glioma prognosis prediction and to explore new predictive models for glioma patient survival after tumor resection.

METHODS

A cohort of 776 glioma cases (WHO grades II-IV) between 2010 and 2017 was obtained. Clinical characteristics and biomarker information were reviewed. Subsequently, we constructed the conventional Cox survival model and three different supervised machine learning models, including support vector machine (SVM), random survival forest (RSF), Tree GB, and Component GB. Then, the model performance was compared with each other. At last, we also assessed the feature importance of models.

RESULTS

The concordance indexes of the conventional survival model, SVM, RSF, Tree GB, and Component GB were 0.755, 0.787, 0.830, 0.837, and 0.840, respectively. All areas under the cumulative receiver operating characteristic curve of both GB models were above 0.800 at different survival times. Their calibration curves showed good calibration of survival prediction. Meanwhile, the analysis of feature importance revealed Karnofsky performance status, age, tumor subtype, extent of resection, and so on as crucial predictive factors.

CONCLUSION

Gradient Boosting models performed better in predicting glioma patient survival after tumor resection than other models.

Mean Platelet Volume/Platelet Count Ratio and Risk of Progression in Glioblastoma

Objective

The mean platelet volume/platelet count (MPV/PC) ratio is an emerging biomarker in selected types of cancer. The objective of this study is to analyze the association of MPV/PC ratio with progression and survival in glioblastoma (GB) patients, with consideration of patient demographics, tumor morphology, extent of resection, molecular pathology, and oncological therapy.

Methods

One hundred ninety-one patients with newly diagnosed GB were analyzed retrospectively. MPV/PC ratio groups (≤ or >0.0575) were dichotomized into low-MPV/PC ratio (≤0.0575) and high-MPV/PC ratio (>0.0575) groups according to the most significant split in the log-rank test.

Results

A two-sided Fisher's exact test showed no significant differences in the confounders between the low- and high-MPV/PC ratio groups. The median progression-free survival (PFS) was 9.0 months (95% CI=8.0-10.0) in the low-MPV/PC ratio group (n=164) and 6.0 months (95% CI=3.0-8.9) in the high-MPV/PC group (n=28) (p=0.013). Multivariate Cox regression analysis including the O-6-methylguanine-DNA methyltransferase (MGMT) status, age (≤/>65 years), baseline Karnofsky Performance Status (KPS), and MPV/PC ratio showed high-MPV/PC ratio as a predictor of progression (p =0.04, HR=1.61, 95% CI=1.01-2.57). In the subgroup of IDH1 wild-type GBs, high MPV/PC ratio was still a significant predictor for shortened PFS (p=0.042, HR=1.60, 95% CI=1.02-2.52). MPV/PC ratio showed no significant effect in the overall survival (OS) analysis. Median OS was 15.0 months in the high-MPV/PC ratio group and 21.0 months in the low-MPV/PC ratio group (p=0.22).

Conclusion

MPV/PC ratio may independently predict the progression-free survival rates of patients with glioblastoma multiforme.

Baseline Serum C-Reactive Protein and Plasma Fibrinogen-Based Score in the Prediction of Survival in Glioblastoma

Objective: The present study investigates a score based on baseline C-reactive protein (CRP) and fibrinogen values (FC score) in 173 consecutive glioblastoma (GBM) patients.

Methods: The optimal cut-off value for fibrinogen and CRP was defined as 3.5 g/dl and 3.0 mg/L, respectively, according to previous reports. Patients with elevated CRP and fibrinogen were classified with a score of 2, those with an elevation of only one of these parameters were allocated a score of 1, and those without any abnormalities were assigned a score of 0.

Results: No significant differences in age, gender, tumor area, molecular pathology, physical status, or extent of resection were identified among the three groups defined by this score. Univariate survival analysis demonstrated that a high baseline FC score (≥1) is significantly associated with a shortened overall survival (OS) (HR: 1.52, 95% CI: 1.05–2.20, p = 0.027). A multivariate Cox regression analysis considering age (>65/≤65), extent of resection (GTR/STR), MGMT promoter status (hypermethylated/non-hypermethylated), and FC score (0/≥1) confirmed that an elevated FC score (≥1) is an independent predictor of shortened OS (HR: 1.71, 95% CI: 1.16–2.51, p = 0.006).

Conclusions: The baseline fibrinogen and CRP score thus serves as an independent predictor of OS in GBM. Further investigations of the role of inflammation in the prediction of a prognosis are needed.

Understanding the glioblastoma tumor biology to optimize photodynamic therapy: From molecular to cellular events

Photodynamic therapy (PDT) has recently gained attention as an alternative treatment of malignant gliomas. Glioblastoma (GBM) is the most prevalent within tumors of the central nervous system (CNS). Conventional treatments for this CNS tumor include surgery, radiation, and chemotherapy. Surgery is still being considered as the treatment of choice. Even so, the poor prognosis and/or recurrence of the disease after applying any of these treatments highlight the urgency of exploring new therapies and/or improving existing ones to achieve the definitive eradication of tumor masses and remaining cells. PDT is a therapeutic modality that involves the destruction of tumor cells by reactive oxygen species induced by light, which were previously treated with a photosensitizing agent. However, in recent years, its experimental application has expanded to other effects that could improve overall performance against GBM. In the current review, we revisit the main advances of PDT for GBM management and also, the recent mechanistic insights about cellular and molecular aspects related to tumoral resistance to PDT of GBM.

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

Objective: We evaluate the performance of three MRI methods to determine non-invasively tumor size, as overall survival (OS) and Progression Free Survival (PFS) predictors, in a cohort of wild type, IDH negative, glioblastoma patients. Investigated protocols included bidimensional (2D) diameter measurements, and three-dimensional (3D) estimations by the ellipsoid or semi-automatic segmentation methods.

Methods: We investigated OS in a cohort of 44 patients diagnosed with wild type IDH glioblastoma (58.2 ± 11.4 years, 1.9/1 male/female) treated with neurosurgical resection followed by adjuvant chemo and radiotherapy. Pre-operative MRI images were evaluated to determine tumor mass area and volume, gadolinium enhancement volume, necrosis volume, and FLAIR-T₂ hyper-intensity area and volume. We implemented then multivariate Cox statistical analysis to select optimal predictors for OS and PFS.

Results: Median OS was 16 months (1–42 months), ranging from 9 ± 2.4 months in patients over 65 years, to 18 ± 1.6 months in younger ones. Patients with tumors carrying O⁶-methylguanin-DNA-methyltransferase (MGMT) methylation survived 30 ± 5.2 vs. 13 ± 2.5 months in non-methylated. Our study evidenced high and positive correlations among the results of the three methods to determine tumor size. FLAIR-T₂ hyper-intensity areas (2D) and volumes (3D) were also similar as determined by the three methods. Cox proportional hazards analysis with the 2D and 3D methods indicated that OS was associated to age ≥ 65 years (HR 2.70, 2.94, and 3.16), MGMT methylation (HR 2.98, 3.07, and 2.90), and FLAIR-T₂ ≥ 2,000 mm² or ≥60 cm³ (HR 4.16, 3.93, and 3.72), respectively. Other variables including necrosis, tumor mass, necrosis/tumor ratio, and FLAIR/tumor ratio were not significantly correlated with OS.

Conclusion: Our results reveal a high correlation among measurements of tumor size performed with the three methods. Pre-operative FLAIR-T₂ hyperintensity area and volumes provided, independently of the measurement method, the optimal neuroimaging features predicting OS in primary glioblastoma patients, followed by age ≥ 65 years and MGMT methylation.

Insurance type impacts the economic burden and survival of patients with newly diagnosed glioblastoma

OBJECTIVE

Glioblastoma (GBM) carries a high economic burden for patients and caregivers, much of which is associated with initial surgery. The authors investigated the impact of insurance status on the inpatient hospital costs of surgery for patients with GBM.

METHODS

The authors conducted a retrospective review of patients with GBM (2010-2015) undergoing their first resection at the University of California, San Francisco, and corresponding inpatient hospital costs.

RESULTS

Of 227 patients with GBM (median age 62 years, 37.9% females), 31 (13.7%) had Medicaid, 94 (41.4%) had Medicare, and 102 (44.9%) had private insurance. Medicaid patients had 30% higher overall hospital costs for surgery compared to non-Medicaid patients ($50,285 vs $38,779, p = 0.01). Medicaid patients had higher intensive care unit (ICU; p < 0.01), operating room (p < 0.03), imaging (p < 0.001), room and board (p < 0001), and pharmacy (p < 0.02) costs versus non-Medicaid patients. Medicaid patients had significantly longer overall and ICU lengths of stay (6.9 and 2.6 days) versus Medicare (4.0 and 1.5 days) and privately insured patients (3.9 and 1.8 days, p < 0.01). Medicaid patients had similar comorbidity rates to Medicare patients (67.8% vs 68.1%), and both groups had higher comorbidity rates than privately insured patients (37.3%, p < 0.0001). Only 67.7% of Medicaid patients had primary care providers (PCPs) versus 91.5% of Medicare and 86.3% of privately insured patients (p = 0.009) at the time of presentation. Tumor diameter at diagnosis was largest for Medicaid (4.7 cm) versus Medicare (4.1 cm) and privately insured patients (4.2 cm, p = 0.03). Preoperative (70 vs 90, p = 0.02) and postoperative (80 vs 90, p = 0.03) Karnofsky Performance Scale (KPS) scores were lowest for Medicaid versus non-Medicaid patients, while in subgroup analysis, postoperative KPS score was lowest for Medicaid patients (80, vs 90 for Medicare and 90 for private insurance; p = 0.03). Medicaid patients had significantly shorter median overall survival (10.7 months vs 12.8 months for Medicare and 15.8 months for private insurance; p = 0.02). Quality-adjusted life year (QALY) scores were 0.66 and 1.05 for Medicaid and non-Medicaid patients, respectively (p = 0.036). The incremental cost per QALY was $29,963 lower for the non-Medicaid cohort.

CONCLUSIONS

Patients with GBMs and Medicaid have higher surgical costs, longer lengths of stay, poorer survival, and lower QALY scores. This study indicates that these patients lack PCPs, have more comorbidities, and present later in the disease course with larger tumors; these factors may drive the poorer postoperative function and greater consumption of hospital resources that were identified. Given limited resources and rising healthcare costs, factors such as access to PCPs, equitable adjuvant therapy, and early screening/diagnosis of disease need to be improved in order to improve prognosis and reduce hospital costs for patients with GBM.

Impact of initial midline shift in glioblastoma on survival

The impact of midline shift (MLS) on long-term survival and progression in glioblastoma (GBM) is unknown. The objective of this study was to analyze the influence of mass effect on survival and progression with consideration of the patient demographics, tumor morphology, operative techniques, molecular pathology, and postoperative treatment. One hundred ninety-eight patients with GBM were analyzed retrospectively. Both MLS groups (< or ≥ 10 mm) were compared with regard to survival, progression-free survival (PFS), and postoperative course of Karnofsky Performance Status (KPS). A two-sided Fisher exact test showed no statistically significant differences in the confounders between the low- and high-MLS groups. The median survival was 18.0 months (95% confidence interval (CI) = 15.3-20.7) in the low-MLS group (n = 173) and 9.0 months (95% CI = 4.8-13.2) in the high-MLS group (n = 25) (p = 0.045). In the high-MLS group, 59.1% (13/22) with an initially high MLS had a KPS of less than 70% after 3 months, whereas 20.5% of the low-MLS group had a KPS of less than 70% (p < 0.001). Binary logistic regression analysis including the O-6-methylguanine-DNA methyltransferase (MGMT) status, extent of resection, baseline KPS, and MIB-I index showed low MLS as the only predictor for survival at 12 months (p = 0.046, odds ratio (OR) = 2.70, 95% CI = 1.0-7.2). Median PFS was 6.0 months in the high-MLS group and 9.0 months in the low-MLS group (log-rank test; p = 0.08). An initial midline shift of 10 mm or greater seems to be an imaging characteristic that independently predicts the survival in glioblastoma.