Surgery vs. biopsy in the treatment of butterfly glioblastoma: a systematic review and meta-analysis

: Butterfly glioblastomas (bGBM) are grade IV gliomas that spread to bilateral hemispheres by infiltrating the corpus callosum. Data on the effect of surgery are limited to small case series. The aim of this meta-analysis was to compare resection vs. biopsy in terms of survival outcomes and postoperative complications. A systematic review of the literature was conducted using PubMed, EMBASE, and Cochrane databases through March 2021 in accordance with the PRISMA checklist. Pooled hazard ratios were calculated and meta-analyzed in a random-effects model including assessment of heterogeneity. Out of 3367 articles, seven studies were included with 293 patients. Surgical resection was significantly associated with longer overall survival (HR 0.39, 95%CI 0.2-0.55) than biopsy. Low heterogeneity was observed (I2: 0%). In further analysis, the effect persisted in extent of resection subgroups of both ≥80% and <80%. No statistically significant difference between surgery and biopsy was detected in terms of postoperative complications, although these were numerically larger for surgery. In patients with bGBM, surgical resection was associated with longer survival prospects compared with biopsy

Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice

Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter-user variability. Fully-automated algorithms for meningioma segmentation have the potential to bring volumetric analysis into clinical and research workflows by increasing accuracy and efficiency, reducing inter-user variability and saving time. Previous research has focused solely on segmentation tasks without assessment of impact and usability of deep learning solutions in clinical practice. Herein, we demonstrate a three-dimensional convolutional neural network (3D-CNN) that performs expert-level, automated meningioma segmentation and volume estimation on MRI scans. A 3D-CNN was initially trained by segmenting entire brain volumes using a dataset of 10,099 healthy brain MRIs. Using transfer learning, the network was then specifically trained on meningioma segmentation using 806 expert-labeled MRIs. The final model achieved a median performance of 88.2% reaching the spectrum of current inter-expert variability (82.6-91.6%). We demonstrate in a simulated clinical scenario that a deep learning approach to meningioma segmentation is feasible, highly accurate and has the potential to improve current clinical practice

Automatic assessment of glioma burden: A deep learning algorithm for fully automated volumetric and bi-dimensional measurement

Background Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO). Methods Two cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment “baseline” MRIs) from 1 institution. Results The automatically generated FLAIR hyperintensity volume, contrast-enhancing tumor volume, and AutoRANO were highly repeatable for the double-baseline visits, with an intraclass correlation coefficient (ICC) of 0.986, 0.991, and 0.977, respectively, on the cohort of postoperative GBM patients. Furthermore, there was high agreement between manually and automatically measured tumor volumes, with ICC values of 0.915, 0.924, and 0.965 for preoperative FLAIR hyperintensity, postoperative FLAIR hyperintensity, and postoperative contrast-enhancing tumor volumes, respectively. Lastly, the ICCs for comparing manually and automatically derived longitudinal changes in tumor burden were 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively. Conclusions Our automated algorithm demonstrates potential utility for evaluating tumor burden in complex posttreatment settings, although further validation in multicenter clinical trials will be needed prior to widespread implementation

Wpływ wydajności elektrowni, jakości węgla brunatnego oraz cząstek nieorganicznych na emisję dwutlenku węgla i konkurencyjność greckiego węgla brunatnego

Greece, mining approximately 70 Mt annually, is the second lignite producer in the EU and fifth largest in the world. Lignite is a key strategic fuel for Greece because it's a very cheap and stable source of energy that is readily available in large quantities. The future exploitation of lignite and hard coal deposits in Greece and Europe depends on the possibility of the coal industry to adapt the environmental requirements of Kyoto Protocol, regarding the control of greenhouse gas emissions. The CO2 charges through the Emission Trading Scheme (ETS) will increase the operating cost of existing coal fired power plants and currently is the main reason for a lack of investment in coal fired units in many EU member states. CO2 reductions through fuel switching (from coal to gas) have become increasingly expensive and risks jeopardising European competitiveness. Furthermore technologies for CO2 capture in fossil-fuel power plants and CO2 sequestration could be exploited only in the longer term (after 2020). To remain lignite as a major component of the Greek energy mix, technological solutions and policies are needed which will enable lignite to contribute to the solutions for climate change. In the short and medium term the increased power plant efficiency and the continuous application of qualitative criteria in lignite deposit exploitations (improved calorific value and reduced CaCO3 content) will be proved as the only capable of delivering competitive lignite/coal - fired generation and contributing to preserve resources and reduce CO2 emissions.Grecja, gdzie wydobywa się około 70 mln ton węgla brunatnego rocznie, jest drugim producentem tego surowca w UE i piątym z największych na świecie. Węgiel brunatny jest strategicznym surowcem energetycznym w Grecji, ponieważ jest to bardzo tanie i stabilne źródło energii, łatwo dostępne w dużych ilościach. Przyszłość eksploatacji złóż węgla brunatnego i kamiennego w Grecji i Europie zależy od możliwości przystosowania się przemysłu węglowego do wymagań środowiskowych Protokołu z Kioto, związanych z emisją gazów cieplarnianych. Limity emisji CO2 ustalone przez uregulowania dotyczące handlu emisjami zwiększą koszty eksploatacji istniejących elektrowni węglowych i są obecnie głównym powodem braku inwestycji w tych elektrowniach w wielu krajach UE. Zmniejszenie emisji CO2 poprzez zmianę paliwa (z węgla na gaz) staje się coraz droższe i zagraża konkurencyjności Europy. Co więcej, technologie przechwytywania CO2 w elektrowniach zasilanych paliwami stałymi oraz separacji CO2 mogą być stosowane jedynie w perspektywie długookresowej (po 2020 roku). Aby móc wykorzystać węgiel brunatny jako główny surowiec energetyczny w Grecji, koniecznie jest znalezienie rozwiązań technologicznych i przyjęcie odpowiedniej polityki, co pozwoli na ograniczenie wpływu sektora węglowego na zmiany klimatu. W średnio- i krótkoterminowej perspektywie możliwe jest jedynie zwiększenie wydajności elektrowni węglowych oraz ciągłe egzekwowanie kryteriów jakościowych przy eksploatacji złóż węgla (zwiększenie kaloryczności i zmniejszenie zawartości CaCO3). Pozwoli to na ochronę złóż oraz zmniejszenie emisji CO2

Improved outcomes associated with maximal extent of resection for butterfly glioblastoma: insights from institutional and national data

Background Butterfly glioblastomas (bGBMs) are grade IV gliomas that infiltrate the corpus callosum and spread to bilateral cerebral hemispheres. Due to the rarity of cases, there is a dearth of information in existing literature. Herein, we evaluate clinical and genetic characteristics, associated predictors, and survival outcomes in an institutional series and compare them to a national cohort. Methods We identified all adult patients with bGBM treated at Brigham & Women's Hospital (2008-2018). The National Cancer Database (NCDB) was also queried for bGBM patients. Survival was analyzed with Kaplan-Meier methods, and Cox models were built to assess for predictive factors. Results Of 993 glioblastoma patients, 62 cases (6.2%) of bGBM were identified. Craniotomy for resection was attempted in 26 patients (41.9%), with a median volumetric extent of resection (vEOR) of 72.3% (95% confidence interval [95%CI] 58.3-82.1). The IDH1 R132H mutation was detected in two patients (3.2%), and MGMT promoter was methylated in 55.5% of the assessed cases. In multivariable regression, factors predictive of longer OS were increased vEOR, MGMT promoter methylation, and receipt of adjuvant therapy. Median OS for the resected cases was 11.5 months (95%CI 7.7-18.8) vs. 6.3 (95%CI 5.1-8.9) for the biopsied. Of 21,353 GBMs, 719 (3.37%) bGBM patients were identified in the NCDB. Resection was more likely to be pursued in recent years, and GTR was independently associated with prolonged OS (p < 0.01). Conclusion Surgical resection followed by adjuvant chemoradiation is associated with significant survival gains and should be pursued in carefully selected bGBM patients

Contemporary assessment of extent of resection in molecularly defined categories of diffuse low-grade glioma: a volumetric analysis

OBJECTIVE While the effect of increased extent of resection (EOR) on survival in diffuse infiltrating low-grade glioma (LGG) patients is well established, there is still uncertainty about the influence of the new WHO molecular subtypes. The authors designed a retrospective analysis to assess the interplay between EOR and molecular classes.METHODS The authors retrospectively reviewed the records of 326 patients treated surgically for hemispheric WHO grade II LGG at Brigham and Women's Hospital and Massachusetts General Hospital (2000-2017). EOR was calculated volumetrically and Cox proportional hazards models were built to assess for predictive factors of overall survival (OS), progression-free survival (PFS), and malignant progression-free survival (MPFS).RESULTS There were 43 deaths (13.2%; median follow-up 5.4 years) among 326 LGG patients. Median preoperative tumor volume was 31.2 cm(3) (IQR 12.9-66.0), and median postoperative residual tumor volume was 5.8 cm(3) (IQR 1.1-20.5). On multivariable Cox regression, increasing postoperative volume was associated with worse OS (HR 1.02 per cm(3); 95% CI 1.00-1.03; p = 0.016), PFS (HR 1.01 per cm(3); 95% CI 1.00-1.02; p = 0.001), and MPFS (HR 1.01 per cm(3); 95% CI 1.00-1.02; p = 0.035). This result was more pronounced in the worse prognosis subtypes of IDH-mutant and IDH-wildtype astrocytoma, for which differences in survival manifested in cases with residual tumor volume of only 1 cm(3). In oligodendroglioma patients, postoperative residuals impacted survival when exceeding 8 cm(3). Other significant predictors of OS were age at diagnosis, IDH-mutant and IDH-wildtype astrocytoma classes, adjuvant radiotherapy, and increasing preoperative volume.CONCLUSIONS The results corroborate the role of EOR in survival and malignant transformation across all molecular subtypes of diffuse LGG. IDH-mutant and IDH-wildtype astrocytomas are affected even by minimal postoperative residuals and patients could potentially benefit from a more aggressive surgical approach