ArticleThorax2019
Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal models.
Article in Thorax, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 68 citations in OpenAlex.
- EarlyCDT Lung blood test for risk classification of solid pulmonary nodules: systematic review and economic evaluation.Health technology assessment (Winchester, England) · 2022Pooled it
- Lung cancer risk prediction models based on pulmonary nodules: A systematic review.Thoracic cancer · 2022Pooled it
- The Physiology Of the WEight Reduced State (POWERS) study: overview and study design.International journal of obesity (2005) · 2026Review
- miR-4513 promotes the proliferation and metastasis of non-small cell lung cancer cells by targeting VIPR1.Hereditas · 2026Article
- Performance of a screening-trained DL model for pulmonary nodule malignancy estimation of incidental clinical nodules.European radiology · 2026Article
- Clinical applications of deep learning in distinguishing benign from malignant pulmonary nodules in computed tomography scans.Archives of medical science : AMS · 2026Article
- Beyond nodules: body composition as a biomarker for future lung cancer.European radiology · 2025Article
- Prevalence and influencing factors of pulmonary nodules in hospitalized patients with diabetes.Journal of thoracic disease · 2024Article
- Development and validation of a mortality risk prediction model for chronic obstructive pulmonary disease: a cross-sectional study using probabilistic graphical modelling.EClinicalMedicine · 2024Article
- Identification of factors directly linked to incident chronic obstructive pulmonary disease: A causal graph modeling study.PLoS medicine · 2024Article
- A plasma peptidomic signature reveals extracellular matrix remodeling and predicts prognosis in alcohol-associated hepatitis.Hepatology communications · 2024Article
- A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images.Diagnostics (Basel, Switzerland) · 2023Review
- Identification of Potent Inhibitors Targeting EGFR and HER3 for Effective Treatment of Chemoresistance in Non-Small Cell Lung Cancer.Molecules (Basel, Switzerland) · 2023Article
- Single Nucleotide Polymorphisms' Causal Structure Robustness within Coronary Artery Disease Patients.Biology · 2023Article
- Radiomic phenotyping of the lung parenchyma in a lung cancer screening cohort.Scientific reports · 2023Article
- Article
- Development and validation of a nomogram model for lung cancer based on radiomics artificial intelligence score and clinical blood test data.Frontiers in oncology · 2023Article
- Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets.Patterns (New York, N.Y.) · 2022Article
- Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method.Scientific reports · 2022Article
- Interventions of Advanced Lung Cancer Patient Receiving Chemotherapy by Computed Tomography Image Information Data Analysis-Based Soothing Care Plans.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
introductionLow-dose CT (LDCT) is currently used in lung cancer screening of high-risk populations for early lung cancer diagnosis. However, 96% of individuals with detected nodules are false positives.
methodsIn order to develop an efficient early lung cancer predictor from clinical, demographic and LDCT features, we studied a total of 218 subjects with lung cancer or benign nodules. Probabilistic graphical models (PGMs) were used to integrate demographics, clinical data and LDCT features from 92 subjects (training cohort) from the Pittsburgh Lung Screening Study cohort.
resultsLearnt PGMs identified three variables directly (causally) linked to malignant nodules and the largest benign nodule and used them to build the Lung Cancer Causal Model (LCCM), which was validated in a separate cohort of 126 subjects. Nodule and vessel numbers and years since the subject quit smoking were sufficient to discriminate malignant from benign nodules. Comparison with existing predictors in the training and validation cohorts showed that (1) incorporating LDCT scan features greatly enhances predictive accuracy; and (2) LCCM improves cancer detection over existing methods, including the Brock parsimonious model (p DISCUSSION: LCCM shows promise as a lung cancer predictor as it is significantly improved over existing models. Validated in a larger, prospective study, it may help reduce unnecessary follow-up visits and procedures.
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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.