ArticleExploration of targeted anti-tumor therapy2023
Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer.
Article in Exploration of targeted anti-tumor therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers, 4 of them syntheses that pooled it.
What it found
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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
7 citing papers in PubMed, 4 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Pooled it
- A Bayesian meta-analysis on MRI-based radiomics for predicting EGFR mutation in brain metastasis of lung cancer.BMC medical imaging · 2025Pooled it
- AI models for the identification of prognostic and predictive biomarkers in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- Comprehensive Guide to Randomized Controlled Trials in Radiology: Everything You Need to Know.The Indian journal of radiology & imaging · 2025Article
- Comparison of clinical and MRI features of brain metastases between ALK+ and ALK- NSCLC.Cancer medicine · 2024Article
- Deep-Learning-Based Predictive Imaging Biomarker Model for EGFR Mutation Status in Non-Small Cell Lung Cancer from CT Imaging.Cancers · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
17 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aim: The aim of this study was to investigate the feasibility of developing a deep learning (DL) algorithm for classifying brain metastases from non-small cell lung cancer (NSCLC) into epidermal growth factor receptor ( Methods: Data set of 117 patients was analysed from 2014 to 2018 out of which 33 patients were Results: In this study of 117 patients, the analysis by semantic method showed 79.2% of the patients belonged to Conclusions: Both semantic features and DL model showed comparable accuracy in classifying
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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 Socratic graph.