Successful Treatment of Intracranial Hemorrhage with Recombinant Activated Factor VII in a Patient with Newly Diagnosed Acute Myeloid Leukemia: A Case Report and Review of the Literature

Intracranial hemorrhage (ICH) is a common complication in acute myeloid leukemia (AML) patients with an incidence rate of 6.3% [1]. Bleeding disorders related to disseminated intravascular coagulation (DIC) are common complications in AML cases [2]. Recombinant activated Factor VII (rFVIIa [NovoSeven®]) is approved for the treatment of bleeding complications with FVIII or FIX inhibitors in patients with congenital FVII deficiency. Use of rFVIIa for the treatment of acute hemorrhage in patients without hemophilia has been successful [3,4]. Herein, we describe the successful use of rFVIIa in a patient with acute ICH in the setting of newly diagnosed AML

Validation of a Multiomic Model of Plasma Extracellular Vesicle Pd-L1 and Radiomics for Prediction of Response to Immunotherapy in NSCLC

BACKGROUND: Immune-checkpoint inhibitors (ICIs) have showed unprecedent efficacy in the treatment of patients with advanced non-small cell lung cancer (NSCLC). However, not all patients manifest clinical benefit due to the lack of reliable predictive biomarkers. We showed preliminary data on the predictive role of the combination of radiomics and plasma extracellular vesicle (EV) PD-L1 to predict durable response to ICIs. MAIN BODY: Here, we validated this model in a prospective cohort of patients receiving ICIs plus chemotherapy and compared it with patients undergoing chemotherapy alone. This multiparametric model showed high sensitivity and specificity at identifying non-responders to ICIs and outperformed tissue PD-L1, being directly correlated with tumor change. SHORT CONCLUSION: These findings indicate that the combination of radiomics and EV PD-L1 dynamics is a minimally invasive and promising biomarker for the stratification of patients to receive ICIs

Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P \u3c 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer

Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity.

Individual analysis of functional Magnetic Resonance Imaging (fMRI) scans requires user-adjustment of the statistical threshold in order to maximize true functional activity and eliminate false positives. In this study, we propose a novel technique that uses radiomic texture analysis (TA) features associated with heterogeneity to predict areas of true functional activity. Scans of 15 right-handed healthy volunteers were analyzed using SPM8. The resulting functional maps were thresholded to optimize visualization of language areas, resulting in 116 regions of interests (ROIs). A board-certified neuroradiologist classified different ROIs into Expected (E) and Non-Expected (NE) based on their anatomical locations. TA was performed using the mean Echo-Planner Imaging (EPI) volume, and 20 rotation-invariant texture features were obtained for each ROI. Using forward stepwise logistic regression, we built a predictive model that discriminated between E and NE areas of functional activity, with a cross-validation AUC and success rate of 79.84% and 80.19% respectively (specificity/sensitivity of 78.34%/82.61%). This study found that radiomic TA of fMRI scans may allow for determination of areas of true functional activity, and thus eliminate clinician bias

TP53-PTEN-NF1 Depletion in Human Brain Organoids Produces a Glioma Phenotype

Glioblastoma (GBM) is fatal and the study of therapeutic resistance, disease progression, and drug discovery in GBM or glioma stem cells is often hindered by limited resources. This limitation slows down progress in both drug discovery and patient survival. Here we present a genetically engineered human cerebral organoid model with a cancer-like phenotype that could provide a basis for GBM-like models. Specifically, we engineered a doxycycline-inducible vector encoding shRNAs enabling depletion of the TP53, PTEN, and NF1 tumor suppressors in human cerebral organoids. Designated as inducible short hairpin-TP53-PTEN-NF1 (ish-TPN), doxycycline treatment resulted in human cancer-like cerebral organoids that effaced the entire organoid cytoarchitecture, while uninduced ish-TPN cerebral organoids recapitulated the normal cytoarchitecture of the brain. Transcriptomic analysis revealed a proneural GBM subtype. This proof-of-concept study offers a valuable resource for directly investigating the emergence and progression of gliomas within the context of specific genetic alterations in normal cerebral organoids

A validated integrated clinical and molecular glioblastoma long-term survival-predictive nomogram.

Background: Glioblastoma (GBM) is the most common primary malignant brain tumor in adulthood. Despite multimodality treatments, including maximal safe resection followed by irradiation and chemotherapy, the median overall survival times range from 14 to 16 months. However, a small subset of GBM patients live beyond 5 years and are thus considered long-term survivors. Methods: A retrospective analysis of the clinical, radiographic, and molecular features of patients with newly diagnosed primary GBM who underwent treatment at The University of Texas MD Anderson Cancer Center was conducted. Eighty patients had sufficient quantity and quality of tissue available for next-generation sequencing and immunohistochemical analysis. Factors associated with survival time were identified using proportional odds ordinal regression. We constructed a survival-predictive nomogram using a forward stepwise model that we subsequently validated using The Cancer Genome Atlas. Results: Univariate analysis revealed 3 pivotal genetic alterations associated with GBM survival: both high tumor mutational burden ( Conclusions: Our newly devised long-term surviva

Changes in Outcomes and Factors Associated With Survival in Melanoma Patients With Brain Metastases

BACKGROUND: Treatment options for patients with melanoma brain metastasis (MBM) have changed significantly in the last decade. Few studies have evaluated changes in outcomes and factors associated with survival in MBM patients over time. The aim of this study is to evaluate changes in clinical features and overall survival (OS) for MBM patients. METHODS: Patients diagnosed with MBMs from 1/1/2009 to 12/31/2013 (Prior Era; PE) and 1/1/2014 to 12/31/2018 (Current Era; CE) at The University of Texas MD Anderson Cancer Center were included in this retrospective analysis. The primary outcome measure was OS. Log-rank test assessed differences between groups; multivariable analyses were performed with Cox proportional hazards models and recursive partitioning analysis (RPA). RESULTS: A total of 791 MBM patients (PE, n = 332; CE, n = 459) were included in analysis. Median OS from MBM diagnosis was 10.3 months (95% CI, 8.9-12.4) and improved in the CE vs PE (14.4 vs 10.3 months, P \u3c .001). Elevated serum lactate dehydrogenase (LDH) was the only factor associated with worse OS in both PE and CE patients. Factors associated with survival in CE MBM patients included patient age, primary tumor Breslow thickness, prior immunotherapy, leptomeningeal disease, symptomatic MBMs, and whole brain radiation therapy. Several factors associated with OS in the PE were not significant in the CE. RPA demonstrated that elevated serum LDH and prior immunotherapy treatment are the most important determinants of survival in CE MBM patients. CONCLUSIONS: OS and factors associated with OS have changed for MBM patients. This information can inform contemporary patient management and clinical investigations