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We provide in this paper a comprehensive comparison of various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. We evaluate the generalizability of out-of-domain ImageNet representations for a target domain of histopathological images, and study the impact of in-domain adaptation using self-supervised and multi-task learning approaches for pretraining the models using the medium-to-large scale datasets of histopathological images. A semi-supervised learning approach is furthermore proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images (WSI). The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning with balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the pathologist's efforts for annotation. Finally, we provide a visualization tool working at WSI level which generates heatmaps that highlight tumor areas; thus, providing insights to pathologists concerning the most informative parts of the WSI.
Purpose: Molecular diagnostics including next generation gene sequencing are increasingly used to determine options for individualized therapies in brain tumor patients. We aimed to evaluate the decision-making process of molecular targeted therapies and analyze data on tolerability as well as signals for efficacy.
Methods: Via retrospective analysis, we identified primary brain tumor patients who were treated off-label with a targeted therapy at the University Hospital Frankfurt, Goethe University. We analyzed which types of molecular alterations were utilized to guide molecular off-label therapies and the diagnostic procedures for their assessment during the period from 2008 to 2021. Data on tolerability and outcomes were collected.
Results: 413 off-label therapies were identified with an increasing annual number for the interval after 2016. 37 interventions (9%) were targeted therapies based on molecular markers. Glioma and meningioma were the most frequent entities treated with molecular matched targeted therapies. Rare entities comprised e.g. medulloblastoma and papillary craniopharyngeoma. Molecular targeted approaches included checkpoint inhibitors, inhibitors of mTOR, FGFR, ALK, MET, ROS1, PIK3CA, CDK4/6, BRAF/MEK and PARP. Responses in the first follow-up MRI were partial response (13.5%), stable disease (29.7%) and progressive disease (46.0%). There were no new safety signals. Adverse events with fatal outcome (CTCAE grade 5) were not observed. Only, two patients discontinued treatment due to side effects. Median progression-free and overall survival were 9.1/18 months in patients with at least stable disease, and 1.8/3.6 months in those with progressive disease at the first follow-up MRI.
Conclusion: A broad range of actionable alterations was targeted with available molecular therapeutics.
However, efficacy was largely observed in entities with paradigmatic oncogenic drivers, in particular with BRAF mutations. Further research on biomarker-informed molecular matched therapies is urgently necessary.
Purpose: Perfusion-weighted MRI (PWI) and O-(2-[18F]fluoroethyl-)-l-tyrosine ([18F]FET) PET are both applied to discriminate tumor progression (TP) from treatment-related changes (TRC) in patients with suspected recurrent glioma. While the combination of both methods has been reported to improve the diagnostic accuracy, the performance of a sequential implementation has not been further investigated. Therefore, we retrospectively analyzed the diagnostic value of consecutive PWI and [18F]FET PET.
Methods: We evaluated 104 patients with WHO grade II–IV glioma and suspected TP on conventional MRI using PWI and dynamic [18F]FET PET. Leakage corrected maximum relative cerebral blood volumes (rCBVmax) were obtained from dynamic susceptibility contrast PWI. Furthermore, we calculated static (i.e., maximum tumor to brain ratios; TBRmax) and dynamic [18F]FET PET parameters (i.e., Slope). Definitive diagnoses were based on histopathology (n = 42) or clinico-radiological follow-up (n = 62). The diagnostic performance of PWI and [18F]FET PET parameters to differentiate TP from TRC was evaluated by analyzing receiver operating characteristic and area under the curve (AUC).
Results: Across all patients, the differentiation of TP from TRC using rCBVmax or [18F]FET PET parameters was moderate (AUC = 0.69–0.75; p < 0.01). A rCBVmax cutoff > 2.85 had a positive predictive value for TP of 100%, enabling a correct TP diagnosis in 44 patients. In the remaining 60 patients, combined static and dynamic [18F]FET PET parameters (TBRmax, Slope) correctly discriminated TP and TRC in a significant 78% of patients, increasing the overall accuracy to 87%. A subgroup analysis of isocitrate dehydrogenase (IDH) mutant tumors indicated a superior performance of PWI to [18F]FET PET (AUC = 0.8/< 0.62, p < 0.01/≥ 0.3).
Conclusion: While marked hyperperfusion on PWI indicated TP, [18F]FET PET proved beneficial to discriminate TP from TRC when PWI remained inconclusive. Thus, our results highlight the clinical value of sequential use of PWI and [18F]FET PET, allowing an economical use of diagnostic methods. The impact of an IDH mutation needs further investigation.
Background: With refinements in diagnosis and therapy of gliomas, the importance of survival time as the sole outcome parameter has decreased, and patient-centered outcome parameters have gained interest. Pursuing a profession is an indispensable component of human happiness. The aim of this study was to analyze the professional outcomes besides their neuro-oncological and functional evaluation after surgery for gliomas in eloquent areas.
Methods: We assessed neuro-oncological and functional outcomes of patients with gliomas WHO grades II and III undergoing surgery between 2012 and 2018. All patients underwent routine follow-up and adjuvant treatment. Treatment and survival parameters were collected prospectively. Repercussions of the disease on the patients’ professional status, socio-economic situation, and neurocognitive function were evaluated retrospectively with questionnaires.
Results: We analyzed data of 58 patients with gliomas (WHO II: 9; III: 49). Median patient age was 35.8 years (range 21–63 years). Awake surgery techniques were applied in 32 patients (55.2%). Gross total and subtotal tumor resections were achieved in 33 (56.9%) and 17 (29.3%) patients, respectively, whereas in 8 patients (13.8%) resection had to remain partial. Most patients (n = 46; 79.3%) received adjuvant treatment. Median follow up was 43.8 months (range 11–82 months). After treatment 41 patients (70.7%) were able to resume a working life. Median time until returning to work was 8.0 months (range 0.2–22.0 months). To be younger than 40 at the time of the surgery was associated with a higher probability to return to work (p < .001). Multivariable regression analysis showed that patient age < 40 years as well as occupational group and self-reported fatigue were factors independently associated with the ability to return to work.
Conclusion: The ability to resume professional activities following brain tumor surgery is an important patient-oriented outcome parameter. We found that the majority of patients with gliomas were able to return to work following surgical and adjuvant treatment. Preservation of neurological function is of utmost relevance for individual patients´ quality of life.
Altered metabolism in tumor cells is increasingly recognized as a core component of the neoplastic phenotype. Because p53 has emerged as a master metabolic regulator, we hypothesized that the presence of wild-type p53 in glioblastoma cells could confer a selective advantage to these cells under the adverse conditions of the glioma microenvironment. Here, we report on the effects of the p53-dependent effector Tp53-induced glycolysis and apoptosis regulator (TIGAR) on hypoxia-induced cell death. We demonstrate that TIGAR is overexpressed in glioblastomas and that ectopic expression of TIGAR reduces cell death induced by glucose and oxygen restriction. Metabolic analyses revealed that TIGAR inhibits glycolysis and promotes respiration. Further, generation of reactive oxygen species (ROS) levels was reduced whereas levels of reduced glutathione were elevated in TIGAR-expressing cells. Finally, inhibiting the transketolase isoenzyme transketolase-like 1 (TKTL1) by siRNA reversed theses effects of TIGAR. These findings suggest that glioma cells benefit from TIGAR expression by (i) improving energy yield from glucose via increased respiration and (ii) enhancing defense mechanisms against ROS. Targeting metabolic regulators such as TIGAR may therefore be a valuable strategy to enhance glioma cell sensitivity toward spontaneously occurring or therapy-induced starvation conditions or ROS-inducing therapeutic approaches.