Back to search

Article

Clinical decision support system to predict the efficacy for EGFR-TKIs based on artificial neural network

2022-05-05

Abstract excerpt

<h4>Background: </h4> The efficacy of epidermal growth factor receptor (EGFR) -tyrosine kinase inhibitor (TKI) was affected by numerous factors. We developed and validated an artificial neural network (ANN) system based on clinical characteristics and next-generation sequencing (NGS) to support clinical decisions. Methods A multicenter retrospective non-interventional study was conducted. 196 untreated patients f...

Topics

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

Identifiers and source

Literature Corpus work
79c4f7c2-8ebf-5859-935e-37594eab9b48
DOI
10.21203/rs.3.rs-1598259/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.
Clinical decision support system to predict the efficacy for EGFR-TKIs based on artificial neural networkDOI 10.21203/rs.3.rs-1598259/v1
A clinical decision support system to predict the efficacy for EGFR-TKIs based on artificial neural network.Semantic proximity
Predictive models for patients with lung carcinomas to identify EGFR mutation status via an artificial neural network based on multiple clinical information.Semantic proximity
Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer with EGFR mutationSemantic proximity
Prediction of 2-Year Mortality in Patients with Advanced Non-Small Cell Lung Cancer Treated with Immune Checkpoint Inhibitors Based on a Machine Learning AlgorithmSemantic proximity
Predicting EGFR Mutation Status in Non-Small Cell Lung Cancer Using Artificial Intelligence: A Systematic Review and Meta-Analysis.Semantic proximity
EfficientNet-Based System for Detecting EGFR-Mutant Status and Predicting Prognosis of Tyrosine Kinase Inhibitors in Patients with NSCLC.Semantic proximity
To establish a prognostic model of epidermal growth factor receptor mutated non-small cell lung cancer patients based on Least Absolute Shrinkage and Selection Operator regression.Semantic proximity
Prediction of oncogene mutation status in non-small cell lung cancer: A systematic review and meta-analysis with a special focus on artificial-intelligence-based methodsSemantic proximity
Efficient 18F-fluorodeoxyglucose positron emission tomography/computed tomography-based machine learning model for predicting epidermal growth factor receptor mutations in non-small cell lung cancer.Semantic proximity
Development and Validation of Machine Learning Models to Predict Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer: A Multi-Center Retrospective Radiomics Study.Semantic proximity
Prediction of oncogene mutation status in non-small cell lung cancer: a systematic review and meta-analysis with a special focus on artificial intelligence-based methods.Semantic proximity
Establishment of prognostic nomograms for predicting the progression free survival of EGFR-sensitizing mutation, advanced lung cancer patients treated with EGFR-TKIs.Semantic proximity
Select a neighboring publication to make it the new centre.