Article

Integration of CA attention and KAN algorithm to predict EGFR mutation status in lung cancer

2025-02-25

Abstract excerpt

Epidermal Growth Factor Receptor (EGFR) mutations are critical biomarkers for targeted therapies in non-small cell lung cancer (NSCLC). However, conventional diagnostic methods rely on invasive tissue biopsies, which are costly, time-consuming, and pose significant limitations. As an alternative, non-invasive approaches using lung CT imaging to predict EGFR mutation status have gained attention for their rapid and...

Topics

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

Identifiers and source

Literature Corpus work: 88de8cd7-f778-5659-bd1a-5a786b0c06e0
DOI: 10.1101/2025.02.20.25322637

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Integration of CA attention and KAN algorithm to predict EGFR mutation status in lung cancerDOI 10.1101/2025.02.20.25322637Read

[Multi-classification prediction model of lung cancer tumor mutation burden based on residual network].Semantic proximity Read

Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learningSemantic proximity Read

Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung AdenocarcinomaSemantic proximity Read

Deep learning framework for predicting EGFR mutation status from H&E whole slide images in lung adenocarcinoma.Semantic proximity Read

Using Vision Transformer for high robustness and generalization in predicting EGFR mutation status in lung adenocarcinoma.Semantic proximity Read

Hybrid deep multi-task learning radiomics approach for predicting EGFR mutation status of non-small cell lung cancer in CT images.Semantic proximity Read

AI-Driven Computational Models for Lung Cancer Diagnosis: A Systematic Review and Meta-AnalysisSemantic proximity Read

A progressive phased attention model fusing histopathology and genomic features for lung cancer stagingSemantic proximity Read

Brain metastasis magnetic resonance imaging-based deep learning for predicting epidermal growth factor receptor (EGFR) mutation and subtypes in metastatic non-small cell lung cancerSemantic proximity Read

Integrating image and gene-data with a semi-supervised attention model for prediction of KRAS gene mutation status in non-small cell lung cancer.Semantic proximity Read

CNNcon: A Quantitative Imaging Tool for Lung CT Image Feature AnalysisSemantic proximity Read

An Optimized EfficientNet-B3 Based Lung Cancer Detection Framework with Stratified Splitting and Deployment-Ready Torch Script IntegrationSemantic proximity Read