Analytical Validation of the PreciseDx Digital Prognostic Breast Cancer Test in Early-Stage Breast Cancer.

Background: PreciseDx Breast (PDxBr) is a digital test that predicts early-stage breast cancer recurrence within 6-years of diagnosis.

Materials And Methods: Using hematoxylin and eosin-stained whole slide images of invasive breast cancer (IBC) and artificial intelligence-enabled morphology feature array, microanatomic features are generated. Morphometric attributes in combination with patient's age, tumor size, stage, and lymph node status predict disease free survival using a proprietary algorithm.

Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years.

Background: Breast cancer (BC) grading plays a critical role in patient management despite the considerable inter- and intra-observer variability, highlighting the need for decision support tools to improve reproducibility and prognostic accuracy for use in clinical practice. The objective was to evaluate the ability of a digital artificial intelligence (AI) assay (PDxBr) to enrich BC grading and improve risk categorization for predicting recurrence.

Methods: In our population-based longitudinal clinical development and validation study, we enrolled 2075 patients from Mount Sinai Hospital with infiltrating ductal carcinoma of the breast.

Dense Convolutional Neural Network for Detection of Cancer from CT Images.

In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages.

Light Weighted Healthcare CNN Model to Detect Prostate Cancer on Multiparametric MRI.

The SEMRCNN model is proposed for autonomously extracting prostate cancer locations from regions of multiparametric magnetic resonance imaging (MP-MRI). Feature maps are explored in order to provide fine segmentation based on the candidate regions. Two parallel convolutional networks retrieve these maps of apparent diffusion coefficient (ADC) and T2W images, which are then integrated to use the complimentary information in MP-MRI.

Affinity purification of human alpha galactosidase utilizing a novel small molecule biomimetic of alpha-D-galactose.

Alpha galactosidase (a-Gal) is an acidic hydrolase that plays a critical role in hydrolyzing the terminal alpha-galactoyl moiety from glycolipids and glycoproteins. There are over a hundred mutations reported for the GLA gene that encodes a-Gal that result in reduced protein synthesis, protein instability, and reduction in function. The deficiencies of a-Gal can cause Fabry disease, a rare lysosomal storage disorder (LSD) caused by the failure to catabolize alpha-d-galactoyl glycolipid moieties.