Temozolomide for patients with wild-type isocitrate dehydrogenase (IDH) 1 glioblastoma using propensity score matching

Deep learning for image classification between primary central nervous system lymphoma and glioblastoma in corpus callosal tumors

Objectives

It can be challenging in some situations to distinguish primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM) based on magnetic resonance imaging (MRI) scans, especially those involving the corpus callosum. The objective of this study was to assess the diagnostic performance of deep learning (DL) models between PCNSLs and GBMs in corpus callosal tumors.

Materials and Methods

The axial T1-weighted gadolinium-enhanced MRI scans of 274 individuals with pathologically confirmed PCNSL (n = 94) and GBM (n = 180) were examined. After image pooling, pre-operative MRI scans were randomly split with an 80/20 procedure into a training dataset (n = 709) and a testing dataset (n = 177) for DL model development. Therefore, the DL model was deployed as a web application and validated with the unseen images (n = 114) and area under the receiver operating characteristic curve (AUC); other outcomes were calculated to assess the discrimination performance.

Results

The first baseline DL model had an AUC of 0.77 for PCNSL when evaluated with unseen images. The 2nd model with ridge regression regularization and the 3rd model with drop-out regularization increased an AUC of 0.83 and 0.84. In addition, the last model with data augmentation yielded an AUC of 0.57.

Conclusion

DL with regularization may provide useful diagnostic information to help doctors distinguish PCNSL from GBM.

Risk Factors Associated with Malignant Transformation of Astrocytoma: Competing Risk Regression Analysis

Background  Malignant transformation (MT) of low-grade astrocytoma (LGA) triggers a poor prognosis in benign tumors. Currently, factors associated with MT of LGA have been inconclusive. The present study aims to explore the risk factors predicting LGA progressively differentiated to malignant astrocytoma.

Methods  The study design was a retrospective cohort study of medical record reviews of patients with LGA. Using the Fire and Gray method, the competing risk regression analysis was performed to identify factors associated with MT, using both univariate and multivariable analyses. Hence, the survival curves of the cumulative incidence of MT of each covariate were constructed following the final model.

Results  Ninety patients with LGA were included in the analysis, and MT was observed in 14.4% of cases in the present study. For MT, 53.8% of patients with MT transformed to glioblastoma, while 46.2% differentiated to anaplastic astrocytoma. Factors associated with MT included supratentorial tumor (subdistribution hazard ratio [SHR] 4.54, 95% confidence interval [CI] 1.08–19.10), midline shift > 1 cm (SHR 8.25, 95% CI 2.18–31.21), and nontotal resection as follows: subtotal resection (SHR 5.35, 95% CI 1.07–26.82), partial resection (SHR 10.90, 95% CI 3.13–37.90), and biopsy (SHR 11.10, 95% CI 2.88–42.52).

Conclusion  MT in patients with LGA significantly changed the natural history of the disease to an unfavorable prognosis. Analysis of patients' clinical characteristics from the present study identified supratentorial LGA, a midline shift more than 1 cm, and extent of resection as risk factors associated with MT. The more extent of resection would significantly help to decrease tumor burden and MT. In addition, future molecular research efforts are warranted to explain the pathogenesis of MT.

Prognostic Impact of the Combination of MGMT Methylation and TERT Promoter Mutation in Glioblastoma

Background  The concept of combinational analysis between the methylation of O ⁶ -methylguanine-DNA methyltransferase ( MGMT ) and telomerase reverse transcriptase promoter ( pTERT ) mutation in glioblastoma (GBM) has been reported. The main study objective was to determine the prognosis of patients with GBM based on MGMT/pTERT classification, while the secondary objective was to estimate the temozolomide effect on the survival time of GBM with MGMT/pTERT classification. Methods  A total of 50 GBM specimens were collected after tumor resection and were selected for investigating MGMT methylation and pTERT mutation. Clinical imaging and pathological characteristics were retrospectively analyzed. Patients with MGMT/pTERT classification were analyzed using survival analysis to develop the nomogram for forecasting and individual prognosis. Results  All patients underwent resection (total resection: 28%, partial resection: 64%, biopsy: 8%). Thirty-two percent of all cases received adjuvant temozolomide with radiotherapy. Sixty-four percent of the case was found methylated MGMT , and 56% of the present cohort found pTERT mutation. Following combinational analysis of biomarkers, results showed that the GBMs with methylated MGMT and wild-type pTERT had a superior prognosis compared with other subtypes. Using Cox regression analysis with multivariable analysis, the extent of resection, postoperative chemoradiotherapy, MGMT/pTERT classification were associated with a favorable prognosis. Hence, a web-based nomogram was developed for deploying individual prognostication. Conclusions  The interaction of MGMT methylation and pTERT mutation was confirmed for predicting prognosis. The results from the present study could help physicians create treatment strategies for GBM patients in real-world situations.

Molecular Landscape for Malignant Transformation in Diffuse Astrocytoma

Background  Malignant transformation (MT) of low-grade gliomas changes dramatically the natural history to poor prognosis. Currently, factors associated with MT of gliomas have been inconclusive, in particular, diffuse astrocytoma (DA).

Objective  The present study aimed to explore the molecular abnormalities related to MT in the same patients with different MT stages.

Methods  Twelve specimens from five DA patients with MT were genotyped using next-generation sequencing (NGS) to identify somatic variants in different stages of MT. We used cross-tabulated categorical biological variables and compared the mean of continuous variables to assess for association with MT.

Results  Ten samples succussed to perform NGS from one male and four females, with ages ranging from 28 to 58 years. The extent of resection was commonly a partial resection following postoperative temozolomide with radiotherapy in 25% of cases. For molecular findings, poly-T-nucleotide insertion in isocitrate dehydrogenase 1 (IDH1) was significantly related to MT as a dose–response relationship (Mann–Whitney's U test, p  = 0.02). Also, mutations of KMT2C and GGT1 were frequently found in the present cohort, but those did not significantly differ between the two groups using Fisher's exact test.

Conclusion  In summary, we identified a novel relationship between poly-T insertion polymorphisms that established the pathogenesis of MT in DA. A further study should be performed to confirm the molecular alteration with more patients.

Predicting the Health-related Quality of Life in Patients Following Traumatic Brain Injury

Background  Traumatic brain injury (TBI) commonly causes death and disability that can result in productivity loss and economic burden. The health-related quality of life (HRQoL) has been measured in patients suffering from TBI, both in clinical and socioeconomic perspectives. The study aimed to assess the HRQoL in patients following TBI using the European quality of life measure-5 domain-5 level (EQ-5D-5L) questionnaire and develop models for predicting the EQ-5D-5L index score in patients with TBI. Method  A cross-sectional study was performed with 193 TBI patients who had completed the EQ-5D-5L questionnaire. The clinical characteristics, Glasgow coma scale (GCS) score, treatment, and Glasgow outcome scale (GOS) were collected. The total data was divided into training data (80%) and testing data (20%); hence, the factors affecting the EQ-5D-5L index scores were used to develop the predictive model with linear and nonlinear regression. The performances of the predictive models were estimated with the adjusted coefficient of determination (R ² ) and the root mean square error (RMSE). Results  A good recovery was found at 96.4%, while 2.1% displayed an unfavorable outcome. Moreover, the mean EQ-5D-5L index scores were 0.91558 (standard deviation [SD] 1.09639). GCS score, pupillary light reflex, surgery, and GOS score significantly correlated with the HRQoL scores. The multiple linear regression model had a high adjusted R ² of 0.6971 and a low RMSE of 0.06701, while the polynomial regression developed a nonlinear model that had the highest adjusted R ² of 0.6843 and the lowest RMSE of 0.06748. Conclusions  A strong positive correlation between the physician-based outcome as GOS and HRQoL was observed. Furthermore, both the linear and nonlinear regression models were acceptable approaches to predict the HRQoL of patients after TBI. There would be limitations for estimating the HRQoL in unconscious or intubated patients. The HRQoL obtained from the predictive models would be an alternative method to resolve this problem.

The clinical characteristics and prognostic factors of multiple lesions in glioblastomas

OBJECTIVE

Multiple glioblastomas (GBM) are the uncommon presentation of the disease. We aimed to identify the variables associated with the survival of patients with multiple GBMs according to the updated WHO classification.

PATIENTS AND METHODS

We retrospectively reviewed 173 patients with newly diagnosed GBM between January 2003 and December 2018 and analyzed patients with multiple lesions at the time of diagnosis. The clinical, radiographic, and biomarkers were evaluated for descriptive analysis. The median overall survival and the Kaplan-Meier curves of the multiple GBMs were estimated. Furthermore, the Cox proportional hazard regression was the estimated hazard ratio for death according to various factors. Moreover, Schoenfeld's global test was performed for estimating assumptions.

RESULTS

Of these, 30 (17.3%) of all GBMs were multiple GBMs, and multifocal and multicentric GBMs were found in 27 (90%) and 3 (10%), respectively. The median survival of the multiple GBMs was significantly shorter than solitary GBM (6 vs. 12 months, p = 0.003). Using Cox proportional hazards regression, the independent prognostic factors of multiple GBMs were concomitant Temozolomide with radiotherapy, wild-type IDH1, methylated MGMT promoter methylation in univariate analysis. In multivariable analysis, concomitant Temozolomide (TMZ) with radiotherapy (RT) was the strongest predictor associated with prognosis in multiple GBMs (0.40, 95%CI 0.16-0.97).

CONCLUSIONS

Multiple lesions are uncommon findings in glioblastoma with poor prognostic features. Concomitant TMZ with RT was the strongest predictor of prognosis. In the future., IDH1 mutation and MGMT promoter methylation should be further explored as prognostic factors.