Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction.

Purpose: Personalized interpretation of medical images is critical for optimum patient care, but current tools available to physicians to perform quantitative analysis of patient's medical images in real time are significantly limited. In this work, we describe a novel platform within PACS for volumetric analysis of images and thus development of large expert annotated datasets in parallel with radiologist performing the reading that are critically needed for development of clinically meaningful AI algorithms. Specifically, we implemented a deep learning-based algorithm for automated brain tumor segmentation and radiomics extraction, and embedded it into PACS to accelerate a supervised, end-to- end workflow for image annotation and radiomic feature extraction.

Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries.

Background: While there are innumerable machine learning (ML) research algorithms used for segmentation of gliomas, there is yet to be a US FDA cleared product. The aim of this study is to explore the systemic limitations of research algorithms that have prevented translation from concept to product by a review of the current research literature.

Methods: We performed a systematic literature review on 4 databases.

Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis.

Objectives: To systematically review, assess the reporting quality of, and discuss improvement opportunities for studies describing machine learning (ML) models for glioma grade prediction.

Methods: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA) statement. A systematic search was performed in September 2020, and repeated in January 2021, on four databases: Embase, Medline, CENTRAL, and Web of Science Core Collection.

Machine Learning Applications for Differentiation of Glioma from Brain Metastasis-A Systematic Review.

Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imaging (MRI) due to the similarity of imaging features in specific clinical circumstances. Multiple studies have investigated the use of machine learning (ML) models for non-invasive differentiation of glioma from brain metastasis. Many of the studies report promising classification results, however, to date, none have been implemented into clinical practice.

Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic Review.

Purpose: Machine learning has been applied to the diagnostic imaging of gliomas to augment classification, prognostication, segmentation, and treatment planning. A systematic literature review was performed to identify how machine learning has been applied to identify gliomas in datasets which include non-glioma images thereby simulating normal clinical practice.

Materials And Methods: Four databases were searched by a medical librarian and confirmed by a second librarian for all articles published prior to February 1, 2021: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science-Core Collection.

The Utility of Imaging Parameters in Predicting Long-Term Clinical Improvement After Shunt Surgery in Patients with Idiopathic Normal Pressure Hydrocephalus.

Objective: It is difficult to predict which patients with idiopathic normal pressure hydrocephalus (iNPH) will improve after shunt surgery. This study investigated the association between preoperative imaging parameters in patients with iNPH and long-term outcome after shunt placement.

Methods: Patients with iNPH who showed a response to large-volume cerebrospinal fluid drainage and subsequently underwent ventriculoperitoneal shunt surgery were reviewed.

The Subjective Experience of Patients Undergoing Shunt Surgery for Idiopathic Normal Pressure Hydrocephalus.

Objective: The counseling of patients with idiopathic normal pressure hydrocephalus (iNPH) is difficult; there is variability in the diagnostic criteria, and a definitive diagnosis can be made only postoperatively. A patient's clinical response to shunting is also difficult to predict. This study examines the subjective experience of patients treated for iNPH, to identify the challenges patients face and to improve patient outcomes and satisfaction.

Pretreatment predictors of adjuvant chemoradiation in patients receiving transoral robotic surgery for squamous cell carcinoma of the oropharynx: a case control study.

Background: The purpose of this study was to identify preoperative patient characteristics associated with the incidence of positive surgical margins or lymph node extracapsular extension (ECE), which necessitate adjuvant chemoradiation after transoral robotic surgery (TORS).

Methods: We conducted a single institution retrospective study of 34 consecutive patients with primary oropharyngeal cancer who underwent TORS. All imaging was reviewed by a single neuroradiologist.

Microbiota-Dependent Priming of Antiviral Intestinal Immunity in Drosophila.

Enteric pathogens must overcome intestinal defenses to establish infection. In Drosophila, the ERK signaling pathway inhibits enteric virus infection. The intestinal microflora also impacts immunity but its role in enteric viral infection is unknown.

IGFBP3 promotes esophageal cancer growth by suppressing oxidative stress in hypoxic tumor microenvironment.

Insulin-like growth factor binding protein 3 (IGFBP3), a hypoxia-inducible gene, regulates a variety of cellular processes including cell proliferation, senescence, apoptosis and epithelial-mesenchymal transition (EMT). IGFBP3 has been linked to the pathogenesis of cancers. Most previous studies focus upon proapoptotic tumor suppressor activities of IGFBP3.

ERK signaling couples nutrient status to antiviral defense in the insect gut.

A unique facet of arthropod-borne virus (arbovirus) infection is that the pathogens are orally acquired by an insect vector during the taking of a blood meal, which directly links nutrient acquisition and pathogen challenge. We show that the nutrient responsive ERK pathway is both induced by and restricts disparate arboviruses in Drosophila intestines, providing insight into the molecular determinants of the antiviral "midgut barrier." Wild-type flies are refractory to oral infection by arboviruses, including Sindbis virus and vesicular stomatitis virus, but this innate restriction can be overcome chemically by oral administration of an ERK pathway inhibitor or genetically via the specific loss of ERK in Drosophila intestinal epithelial cells.

Notch receptor inhibition reveals the importance of cyclin D1 and Wnt signaling in invasive esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive forms of squamous cell carcinomas. Common genetic lesions in ESCC include p53 mutations and EGFR overexpression, both of which have been implicated in negative regulation of Notch signaling. In addition, cyclin D1 is overexpressed in ESCC and can be activated via EGFR, Notch and Wnt signaling.

Hypoxia induces IGFBP3 in esophageal squamous cancer cells through HIF-1α-mediated mRNA transcription and continuous protein synthesis.

Insulin-like growth factor binding protein (IGFBP)-3 regulates cell proliferation and apoptosis in esophageal squamous cell carcinoma (ESCC) cells. We have investigated how the hypoxic tumor microenvironment in ESCC fosters the induction of IGFBP3. RNA interference experiments revealed that hypoxia-inducible factor (HIF)-1α, but not HIF-2α, regulates IGFBP3 mRNA induction.