Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects.

The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa).

Deep Survival Analysis With Clinical Variables for COVID-19.

Objective: Millions of people have been affected by coronavirus disease 2019 (COVID-19), which has caused millions of deaths around the world. Artificial intelligence (AI) plays an increasing role in all areas of patient care, including prognostics. This paper proposes a novel predictive model based on one dimensional convolutional neural networks (1D CNN) to use clinical variables in predicting the survival outcome of COVID-19 patients.

Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-Ray Images.

This article proposes to encode the distribution of features learned from a convolutional neural network (CNN) using a Gaussian mixture model (GMM). These parametric features, called GMM-CNN, are derived from chest computed tomography (CT) and X-ray scans of patients with coronavirus disease 2019 (COVID-19). We use the proposed GMM-CNN features as input to a robust classifier based on random forests (RFs) to differentiate between COVID-19 and other pneumonia cases.

Future artificial intelligence tools and perspectives in medicine.

Purpose Of Review: Artificial intelligence has become popular in medical applications, specifically as a clinical support tool for computer-aided diagnosis. These tools are typically employed on medical data (i.e.

Deep CNN models for predicting COVID-19 in CT and x-ray images.

Coronavirus disease 2019 (COVID-19) is a new infection that has spread worldwide and with no automatic model to reliably detect its presence from images. We aim to investigate the potential of deep transfer learning to predict COVID-19 infection using chest computed tomography (CT) and x-ray images. Regions of interest (ROI) corresponding to ground-glass opacities (GGO), consolidations, and pleural effusions were labeled in 100 axial lung CT images from 60 COVID-19-infected subjects.

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review.

The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor's grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa.

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder.

Background: Emerging evidence suggests the presence of neuroanatomical abnormalities in subjects with autism spectrum disorder (ASD). Identifying anatomical correlates could thus prove useful for the automated diagnosis of ASD. Radiomic analyses based on MRI texture features have shown a great potential for characterizing differences occurring from tissue heterogeneity, and for identifying abnormalities related to these differences.

A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome.

Objective: Predicting the survival outcome of patients with glioblastoma multiforme (GBM) is of key importance to clinicians for selecting the optimal course of treatment. The goal of this study was to evaluate the usefulness of geometric shape features, extracted from MR images, as a potential non-invasive way to characterize GBM tumours and predict the overall survival times of patients with GBM.

Methods: The data of 40 patients with GBM were obtained from the Cancer Genome Atlas and Cancer Imaging Archive.

Resistance to ursolic acid-induced apoptosis through involvement of melanogenesis and COX-2/PGE2 pathways in human M4Beu melanoma cancer cells.

Melanoma is one of the most aggressive forms of cancer with a continuously growing incidence worldwide and is usually resistant to chemotherapy agents, which is due in part to a strong resistance to apoptosis. Previously, we had showed that B16-F0 murine melanoma cells undergoing apoptosis are able to delay their own death induced by ursolic acid (UA), a natural pentacyclic triterpenoid compound. We had demonstrated that tyrosinase and TRP-1 up-regulation in apoptotic cells and the subsequent production of melanin were implicated in an apoptosis resistance mechanism.

Resistance to 3-HTMC-Induced Apoptosis Through Activation of PI3K/Akt, MEK/ERK, and p38/COX-2/PGE Pathways in Human HT-29 and HCT116 Colorectal Cancer Cells.

Increasing incidence and mortality of colorectal cancer brings the necessity to uncover new possibilities in its prevention and treatment. Chalcones have been identified as interesting compounds having chemopreventive and antitumor properties. In this study, we investigated the effects of the synthetic chalcone derivative 3-hydroxy-3',4,4',5'-tetra-methoxy-chalcone (3-HTMC) on proliferation, cell cycle distribution, apoptosis, and its mechanism of action in human colorectal HT-29 (COX-2 sufficient) and HCT116 (COX-2 deficient) cancer cells.

Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery.

Purpose: This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma.

Materials And Methods: In the proposed approach, the region of interest containing PT is first extracted from multispectral images using active contour segmentation.