ArticleJournal of cancer research and clinical oncology2023
A clinical decision support system to predict the efficacy for EGFR-TKIs based on artificial neural network.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Adverse Event Profile of Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors for Non-small Cell Lung Cancer: An Updated Meta-analysis.Targeted oncology · 2024Pooled it
- A review of deep learning-based multimodal data integration of lung cancer.Clinical and experimental medicine · 2026Review
- Predicting primary resistance to third-generation EGFR-TKIs in lung adenocarcinoma using a multisource cross-modal transformer model.NPJ precision oncology · 2026Article
- Integration of epidemiological surveys, network pharmacology, and molecular docking simulation to explore the potential key targets of propofol-induced euphoria.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- GLMP promotes EGFR-TKI resistance by activating autophagy and RhoA pathway in non-small cell lung cancer.NPJ precision oncology · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Multimodal prediction of tyrosine kinase inhibitors therapy outcomes in advanced EGFR-mutated NSCLC patients.Journal of translational medicine · 2025Article
- STK11 genetic alterations in metastatic EGFR mutant lung cancer.Scientific reports · 2025Article
- Anlotinib reverses osimertinib resistance by inhibiting epithelial-to-mesenchymal transition and angiogenesis in non-small cell lung cancer.Journal of biomedical research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe efficacy of epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) was affected by numerous factors. In the study, we developed and validated an artificial neural network (ANN) system based on clinical characteristics and next-generation sequencing (NGS) to support clinical decisions.
methodsA multicenter retrospective non-interventional study was conducted. 240 patients from three hospitals with advanced non-small cell lung cancer (NSCLC) and EGFR mutation were tested by NGS before the first treatment. All patients received formal EGFR-TKIs treatment. Five different models were individually trained to predict the efficacy of EGFR-TKIs based on one medical center with 188 patients. Two independent cohorts from other medical centers were collected for external validation.
resultsCompared with logistic regression, four machine learning methods showed better predicting abilities for EGFR-TKIs. The inclusion of NGS tests improved the predictive power of models. ANN performed best on the dataset with mutations TP53, RB1, PIK3CA, EGFR mutation sites, and tumor mutation burden (TMB). The prediction accuracy, recall and AUC were 0.82, 0.82, and 0.82, respectively in our final model. In the external validation set, ANN still showed good performance and differentiated patients with poor outcomes. Finally, a clinical decision support software based on ANN was developed and provided a visualization interface for clinicians.
conclusionThis study provides an approach to assess the efficacy of NSCLC patients with first-line EGFR-TKI treatment. Software is developed to support clinical decisions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.