ArticleTranslational oncology2017
A Prediction Model Based on Biomarkers and Clinical Characteristics for Detection of Lung Cancer in Pulmonary Nodules.
Article in Translational oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Incidence and risk factors for malignancy in patients with incidental solitary pulmonary nodules: a systematic review and meta-analysis.Annals of medicine · 2026Pooled it
- Pulmonary nodules: classification, biomarkers, signaling pathways, diseases, early diagnosis and management.Signal transduction and targeted therapy · 2026Review
- Emerging Strategies in Lung Cancer Screening: Blood and Beyond.Clinical chemistry · 2024Review
- Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges?Cancers · 2023Review
- Liquid biopsies to distinguish malignant from benign pulmonary nodules.Thoracic cancer · 2021Review
- A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules.BMC cancer · 2021Observational
- Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning.Journal of healthcare engineering · 2021Article
- [Research Progress on Lung Cancer Screening].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2020Review
- Upregulation of microRNA-1303 is a potential prognostic marker of non-small cell lung cancer.Cancer biomarkers : section A of Disease markers · 2020Observational
- Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2019Review
- A Direct Plasma miRNA Assay for Early Detection and Histological Classification of Lung Cancer.Translational oncology · 2018Article
- An integromic signature for lung cancer early detection.Oncotarget · 2018Article
- Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer.Medical physics · 2018Article
- Integrating Circulating Immunological and Sputum Biomarkers for the Early Detection of Lung Cancer.Biomarkers in cancer · 2018Article
- The clinical use of circulating microRNAs as non-invasive diagnostic biomarkers for lung cancers.Oncotarget · 2017Article
- A prediction model for distinguishing lung squamous cell carcinoma from adenocarcinoma.Oncotarget · 2017Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Lung cancer early detection by low-dose computed tomography (LDCT) can reduce the mortality. However, LDCT increases the number of indeterminate pulmonary nodules (PNs), whereas 95% of the PNs are ultimately false positives. Modalities for specifically distinguishing between malignant and benign PNs are urgently needed. We previously identified a panel of peripheral blood mononucleated cell (PBMC)-miRNA (miRs-19b-3p and -29b-3p) biomarkers for lung cancer. This study aimed to evaluate efficacy of integrating biomarkers and clinical and radiological characteristics of smokers for differentiating malignant from benign PNs. We analyzed expression of 2 miRNAs (miRs-19b-3p and -29b-3p) in PBMCs of a training set of 137 individuals with PNs. We used multivariate logistic regression analysis to develop a prediction model based on the biomarkers, radiographic features of PNs, and clinical characteristics of smokers for identifying malignant PNs. The performance of the prediction model was validated in a testing set of 111 subjects with PNs. A prediction model comprising the two biomarkers, spiculation of PNs and smoking pack-year, was developed that had 0.91 area under the curve of the receiver operating characteristic for distinguishing malignant from benign PNs. The prediction model yielded higher sensitivity (80.3% vs 72.6%) and specificity (89.4% vs 81.9%) compared with the biomarkers used alone (all P<.05). The performance of the prediction model for malignant PNs was confirmed in the validation set. We have for the first time demonstrated that the integration of biomarkers and clinical and radiological characteristics could efficiently identify lung cancer among indeterminate PNs.
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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 OpenQuestion graph.