[HTML][HTML] Digital pathology and artificial intelligence in translational medicine and clinical practice
V Baxi, R Edwards, M Montalto, S Saha - Modern Pathology, 2022 - Elsevier
Traditional pathology approaches have played an integral role in the delivery of diagnosis,
semi-quantitative or qualitative assessment of protein expression, and classification of …
semi-quantitative or qualitative assessment of protein expression, and classification of …
Artificial intelligence to identify genetic alterations in conventional histopathology
Precision oncology relies on the identification of targetable molecular alterations in tumor
tissues. In many tumor types, a limited set of molecular tests is currently part of standard …
tissues. In many tumor types, a limited set of molecular tests is currently part of standard …
The impact of site-specific digital histology signatures on deep learning model accuracy and bias
Abstract The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital
histology. Deep learning (DL) models have been trained on TCGA to predict numerous …
histology. Deep learning (DL) models have been trained on TCGA to predict numerous …
Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer
Abstract Programmed death ligand-1 (PD-L1) has been recently adopted for breast cancer
as a predictive biomarker for immunotherapies. The cost, time, and variability of PD-L1 …
as a predictive biomarker for immunotherapies. The cost, time, and variability of PD-L1 …
[HTML][HTML] The ethical, legal and social implications of using artificial intelligence systems in breast cancer care
Breast cancer care is a leading area for development of artificial intelligence (AI), with
applications including screening and diagnosis, risk calculation, prognostication and clinical …
applications including screening and diagnosis, risk calculation, prognostication and clinical …
Determining breast cancer biomarker status and associated morphological features using deep learning
Background Breast cancer management depends on biomarkers including estrogen
receptor, progesterone receptor, and human epidermal growth factor receptor 2 …
receptor, progesterone receptor, and human epidermal growth factor receptor 2 …
[HTML][HTML] Artificial intelligence in digital breast pathology: techniques and applications
Breast cancer is the most common cancer and second leading cause of cancer-related
death worldwide. The mainstay of breast cancer workup is histopathological diagnosis …
death worldwide. The mainstay of breast cancer workup is histopathological diagnosis …
Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains
For newly diagnosed breast cancer, estrogen receptor status (ERS) is a key molecular
marker used for prognosis and treatment decisions. During clinical management, ERS is …
marker used for prognosis and treatment decisions. During clinical management, ERS is …
A guide to artificial intelligence for cancer researchers
Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …
a readily accessible tool for cancer researchers. AI-based tools can boost research …
Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer
A Levy-Jurgenson, X Tekpli, VN Kristensen… - Scientific reports, 2020 - nature.com
Digital analysis of pathology whole-slide images is fast becoming a game changer in cancer
diagnosis and treatment. Specifically, deep learning methods have shown great potential to …
diagnosis and treatment. Specifically, deep learning methods have shown great potential to …