Article

Buyer Beware: confounding factors and biases abound when predicting omics-based biomarkers from histological images

2024-09-19

Abstract excerpt

<title>Abstract</title> <p>Recent advances in computational pathology have introduced deep learning methods to predict molecular biomarkers from routine histology whole slide images (WSIs). In this work, we identify and report some key limitations of these approaches, including the oversight of biomarkers interdependencies during model training and inference. Through statistical analysis, we first demonstrate sig...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 4040c97e-9516-5b66-9647-b99d2697b2b7
DOI: 10.21203/rs.3.rs-4993712/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Buyer Beware: confounding factors and biases abound when predicting omics-based biomarkers from histological imagesDOI 10.21203/rs.3.rs-4993712/v1Read

Buyer Beware: confounding factors and biases abound when predicting omics-based biomarkers from histological imagesSemantic proximity Read

Weak supervision of H&E slides reveals systems-level biology and functional states that govern therapeutic resistanceSemantic proximity Read

Deep learning can predict multi-omic biomarkers from routine pathology images: A systematic large-scale studySemantic proximity Read

Artificial Intelligence Reveals Distinct Prognostic Subgroups of Muscle-Invasive Bladder Cancer on Histology ImagesSemantic proximity Read

Evaluating reproducibility of AI algorithms in digital pathology with DAPPERSemantic proximity Read

Multi-stain modelling of histopathology slides for breast cancer prognosis predictionSemantic proximity Read

DIANNE: Segmentation-Free Localization of Histology Differential AttributesSemantic proximity Read

Deep Learning on Histopathological Images to Predict Breast Cancer Recurrence Risk and Chemotherapy BenefitSemantic proximity Read

A Model-agnostic Computational Method for Discovering Gene–Phenotype Relationships and Inferring Gene Networks via<i>in silico</i>Gene PerturbationSemantic proximity Read

Prediction of OncotypeDX recurrence score using H&E stained WSI imagesSemantic proximity Read

Nuclear Morphology Tracks Molecular Class Unevenly Across Four CancersSemantic proximity Read

Artificial Image Objects for Classification of Breast Cancer Biomarkers with Transcriptome Sequencing Data and Convolutional Neural Network AlgorithmsSemantic proximity Read