ArticleThoracic cancer2018
Development and validation of a model to predict tyrosine kinase inhibitor-sensitive EGFR mutations of non-small cell lung cancer based on multi-institutional data.
Article in Thoracic cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Predicting epidermal growth factor receptor mutations in non-small cell lung cancer through dual-layer spectral CT: a prospective study.Insights into imaging · 2024Article
- Article
- Accounting for EGFR Mutations in Epidemiologic Analyses of Non-Small Cell Lung Cancers: Examples Based on the International Lung Cancer Consortium Data.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022Article
- Establishment and Evaluation of EGFR Mutation Prediction Model Based on Tumor Markers and CT Features in NSCLC.Journal of healthcare engineering · 2022Article
- Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNon-small cell lung cancer (NSCLC) with different EGFR mutation types shows distinct sensitivity to tyrosine kinase inhibitors (TKIs). This study developed a patho-clinical profile-based prediction model of TKI-sensitive EGFR mutations.
methodsThe records of 1121 Chinese patients diagnosed with NSCLC from November 2008 to October 2014 (the development set) were reviewed. Multivariate logistic regression was conducted to identify any association between potential predictors and the classic sensitive EGFR mutations (exon 19 deletion and exon 21 L858R point mutation). A prediction index was created by assigning weighted scores to each factor proportional to a regression coefficient. Validation was made in an independent cohort consisting of 864 patients who were consecutively enrolled between November 2014 and January 2017 (the validation set).
resultsSeven independent predictors were identified: gender (female vs. male), adenocarcinoma (yes vs. no), smoking history (no vs. yes), N stage (N+ vs. N0), M stage (M1 vs. M0), brain metastasis (yes vs. no), and elevated Cyfra 21-1 (no vs. yes). Each was assigned a number of points. In the validation set, the area under curve of the prediction index appeared as 0.698 (95% confidence interval 0.663-0.733). The sensitivity, specificity, positive and negative predictive values, and concordance were 95.0%, 32.3%, 61.4%, 85.1%, and 65.6%, respectively.
conclusionWe developed a patho-clinical profile-based model for predicting TKI-sensitive EGFR mutations. Our model may represent a noninvasive, economical choice for clinicians to inform TKI therapy.
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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.