Deep feature batch correction using ComBat for machine learning applications in computational pathology.

Background: Developing artificial intelligence (AI) models for digital pathology requires large datasets from multiple sources. However, without careful implementation, AI models risk learning confounding site-specific features in datasets instead of clinically relevant information, leading to overestimated performance, poor generalizability to real-world data, and potential misdiagnosis.

Methods: Whole-slide images (WSIs) from The Cancer Genome Atlas (TCGA) colon (COAD), and stomach adenocarcinoma datasets were selected for inclusion in this study.

Surrogate Biomarker Prediction from Whole-Slide Images for Evaluating Overall Survival in Lung Adenocarcinoma.

Background: Recent advances in computational pathology have shown potential in predicting biomarkers from haematoxylin and eosin (H&E) whole-slide images (WSI). However, predicting the outcome directly from WSIs remains a substantial challenge. In this study, we aimed to investigate how gene expression, predicted from WSIs, could be used to evaluate overall survival (OS) in patients with lung adenocarcinoma (LUAD).

Altered expression of ACOX2 in non-small cell lung cancer.

Peroxisomes are organelles that play essential roles in many metabolic processes, but also play roles in innate immunity, signal transduction, aging and cancer. One of the main functions of peroxisomes is the processing of very-long chain fatty acids into metabolites that can be directed to the mitochondria. One key family of enzymes in this process are the peroxisomal acyl-CoA oxidases (ACOX1, ACOX2 and ACOX3), the expression of which has been shown to be dysregulated in some cancers.

Pathogenic Variants as Biomarkers for Risk in Prostate Cancer.

Studies have demonstrated that men with Prostate Cancer (PCa) harboring genetic aberrations, are more likely to have worse disease and a poorer prognosis. A mutation in is known to confer the highest risk of PCa for men (8.6 fold in men ≤65 years) making genes a conceivable genomic biomarker for risk in PCa.

Correlation of integrated ERG/PTEN assessment with biochemical recurrence in prostate cancer.

Introduction: Prostate cancer is a heterogeneous disease, with a complex molecular landscape that evolves throughout disease progression. Common alterations in genes such as ERG and PTEN have been attributed to worse prognosis. This study aimed to further examine the clinical relevance of PTEN and ERG expression in a cohort of patients with prostate cancer post radical prostatectomy.

Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics.

Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the individual through developments in immunotherapies and targeted therapies. However, long turnaround times and high costs of molecular testing hinder the widespread implementation of targeted cancer treatments. Meanwhile, gold-standard histopathological assessment carried out by a trained pathologist is widely regarded as routine and mandatory in most cancers.