Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence-Assisted Cancer Diagnosis.

The potential of artificial intelligence (AI) in digital pathology is limited by technical inconsistencies in the production of whole slide images (WSIs). This causes degraded AI performance and poses a challenge for widespread clinical application, as fine-tuning algorithms for each site is impractical. Changes in the imaging workflow can also compromise diagnostic accuracy and patient safety.

Prediction of gene expression-based breast cancer proliferation scores from histopathology whole slide images using deep learning.

Background: In breast cancer, several gene expression assays have been developed to provide a more personalised treatment. This study focuses on the prediction of two molecular proliferation signatures: an 11-gene proliferation score and the MKI67 proliferation marker gene. The aim was to assess whether these could be predicted from digital whole slide images (WSIs) using deep learning models.

Clinical evaluation of deep learning-based risk profiling in breast cancer histopathology and comparison to an established multigene assay.

Purpose: To evaluate the Stratipath Breast tool for image-based risk profiling and compare it with an established prognostic multigene assay for risk profiling in a real-world case series of estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative early breast cancer patients categorized as intermediate risk based on classic clinicopathological variables and eligible for chemotherapy.

Methods: In a case series comprising 234 invasive ER-positive/HER2-negative tumors, clinicopathological data including Prosigna results and corresponding HE-stained tissue slides were retrieved. The digitized HE slides were analysed by Stratipath Breast.

Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer.

Background: Histological grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and inter-laboratory variability, causing uncertainty in grade assignments. To address this challenge, we developed and validated a three-level NHG-like deep learning-based histological grade model (predGrade).

Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information.

Background: Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs).

Using deep learning to detect patients at risk for prostate cancer despite benign biopsies.

Routine transrectal ultrasound-guided systematic prostate biopsy only samples a small volume of the prostate and tumors between biopsy cores can be missed, leading to low sensitivity to detect clinically relevant prostate cancers (PCa). Deep learning may enable detection of PCa despite benign biopsies. We included 14,354 hematoxylin-eosin stained benign prostate biopsies from 1,508 men in two groups: men without established PCa diagnosis and men with at least one core biopsy diagnosed with PCa.

Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression-based convolutional neural networks.

Motivation: Molecular phenotyping by gene expression profiling is central in contemporary cancer research and in molecular diagnostics but remains resource intense to implement. Changes in gene expression occurring in tumours cause morphological changes in tissue, which can be observed on the microscopic level. The relationship between morphological patterns and some of the molecular phenotypes can be exploited to predict molecular phenotypes from routine haematoxylin and eosin-stained whole slide images (WSIs) using convolutional neural networks (CNNs).

Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression-Morphology Analysis in Breast Cancer.

Molecular profiling is central in cancer precision medicine but remains costly and is based on tumor average profiles. Morphologic patterns observable in histopathology sections from tumors are determined by the underlying molecular phenotype and therefore have the potential to be exploited for prediction of molecular phenotypes. We report here the first transcriptome-wide expression-morphology (EMO) analysis in breast cancer, where individual deep convolutional neural networks were optimized and validated for prediction of mRNA expression in 17,695 genes from hematoxylin and eosin-stained whole slide images.

Double-stranded RNA-induced dopaminergic neuronal loss in the substantia nigra in the presence of Mac1 receptor.

Background: The underlying mechanism of viral infection as a risk factor for Parkinson's disease (PD), the second most common neurodegenerative disease, remains unclear.

Objective: We used Mac-1 and gp91 transgene animal models to investigate the mechanisms by which poly I:C, a mimic of virus double-stranded RNA, induces PD neurodegeneration.

Method: Poly I:C was stereotaxically injected into the substantia nigra (SN) of wild-type (WT), Mac-1-knockout (Mac-1) and gp91 -knockout (gp91 ) mice (10 μg/μl), and nigral dopaminergic neurodegeneration, α-synuclein accumulation and neuroinflammation were evaluated.

Benzo(a)pyrene exposure induced neuronal loss, plaque deposition, and cognitive decline in APP/PS1 mice.

Background: Exposure to benzo(a)pyrene (BaP) was associated with cognitive impairments and some Alzheimer's disease (AD)-like pathological changes. However, it is largely unknown whether BaP exposure participates in the disease progression of AD.

Objectives: To investigate the effect of BaP exposure on AD progression and its underlying mechanisms.

Ultrafine carbon particles promote rotenone-induced dopamine neuronal loss through activating microglial NADPH oxidase.

Background: Atmospheric ultrafine particles (UFPs) and pesticide rotenone were considered as potential environmental risk factors for Parkinson's disease (PD). However, whether and how UFPs alone and in combination with rotenone affect the pathogenesis of PD remains largely unknown.

Methods: Ultrafine carbon black (ufCB, a surrogate of UFPs) and rotenone were used individually or in combination to determine their roles in chronic dopaminergic (DA) loss in neuron-glia, and neuron-enriched, mix-glia cultures.

Source attributions of heavy metals in rice plant along highway in Eastern China.

Air and soil pollution from traffic has been considered as a critical issue to crop production and food safety, however, few efforts have been paid on distinguish the source origin of traffic-related contaminants in rice plant along highway. Therefore, we investigated metals (Pb, Cd, Cr, Zn and Cu) concentrations and stable Pb isotope ratios in rice plants exposed and unexposed to highway traffic pollution in Eastern China in 2008. Significant differences in metals concentrations between the exposed and unexposed plants existed in leaf for Pb, Cd and Zn, in stem only for Zn, and in grain for Pb and Cd.