Back to search

Article

Predicting EGFR-TKI resistance in EGFR-mutant lung adenocarcinoma from H&E histopathology with weakly supervised multiple-instance learning

2026-08-18

Abstract excerpt

<title>Abstract</title> <p>Background This study sought to construct a deep-learning pipeline using an attention mechanism and multiple-instance learning (MIL) to predict early treatment response to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) directly from routinely available hematoxylin-eosin (H&E) whole-slide images (WSIs) of pretreatment tissue in patients with EGFR-mutant lung ade...

Topics

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

Identifiers and source

Literature Corpus work
d381e801-c2a4-50de-a0f9-6415eddd0485
DOI
10.21203/rs.3.rs-10300980/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Predicting EGFR-TKI resistance in EGFR-mutant lung adenocarcinoma from H&amp;E histopathology with weakly supervised multiple-instance learningDOI 10.21203/rs.3.rs-10300980/v1
Deep learning framework for predicting EGFR mutation status from H&E whole slide images in lung adenocarcinoma.Semantic proximity
Prediction of Epidermal Growth Factor Receptor Mutation Subtypes in Non-Small Cell Lung Cancer From Hematoxylin and Eosin-Stained Slides Using Deep Learning.Semantic proximity
GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status.Semantic proximity
Dual-stage AI system for Pathologist-Free Tumor Detection and subtyping in Oral Squamous Cell CarcinomaSemantic proximity
Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images.Semantic proximity
Prediction of <i>TP53</i> biomarkers and survival outcomes from whole slide images using a vision transformer-based multi-instance learning frameworkSemantic proximity
Contrastive learning-based histopathological features infer molecular subtypes and clinical outcomes of breast cancer from unannotated whole slide images.Semantic proximity
Predicting EGFR mutational status from pathology images using a real-world dataset.Semantic proximity
Multi-task deep learning integrating pretreatment MRI and whole slide images predicts induction chemotherapy response and survival in locally advanced nasopharyngeal carcinomaSemantic proximity
Benchmarking artificial intelligence methods for end-to-end computational pathologySemantic proximity
Multiple instance learning on tile level-pathology images provides accurate and interpretable classification for breast cancer molecular subtypesSemantic proximity
Classification and Interpretation of Histopathology Images: Leveraging Ensemble of EfficientNetV1 and EfficientNetV2 ModelsSemantic proximity
Select a neighboring publication to make it the new centre.