ReviewCancers2019
Lung Cancer Screening, Towards a Multidimensional Approach: Why and How?
Review in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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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.
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Who cites it
18 citing papers in PubMed.
- Surgical Aspects of Treatment of the Lung Cancer Found in Low-Dose CT-Based Screenings.Journal of clinical medicine · 2026Article
- Plasma metabolites as biomarkers for screening and differential diagnosis of lung cancer.Journal of cardiothoracic surgery · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Toward a Computable Phenotype for Determining Eligibility of Lung Cancer Screening Using Electronic Health Records.JCO clinical cancer informatics · 2025Article
- Article
- Article
- Matched tissue and liquid biopsies for advanced non-small cell lung cancer patients A potentially indispensable complementary approach.Translational oncology · 2023Article
- Benign-malignant classification of pulmonary nodules by low-dose spiral computerized tomography and clinical data with machine learning in opportunistic screening.Cancer medicine · 2023Article
- The use of biomarkers in the diagnosis of lung cancer.La Tunisie medicale · 2023Article
- Patient-Derived Lung Tumoroids-An Emerging Technology in Drug Development and Precision Medicine.Biomedicines · 2022Review
- Assisted versus Manual Interpretation of Low-Dose CT Scans for Lung Cancer Screening: Impact on Lung-RADS Agreement.Radiology. Imaging cancer · 2021Article
- Artificial Intelligence Tools for Refining Lung Cancer Screening.Journal of clinical medicine · 2020Review
- Lung cancer increases HHeliyon · 2020Article
- Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19.Machine vision and applications · 2020Article
- Management of incidental nodules in lung cancer screening: ready for prime-time?Breathe (Sheffield, England) · 2019Article
- Article
- Article
- The OncoAge Consortium: Linking Aging and Oncology from Bench to Bedside and Back Again.Cancers · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Early-stage treatment improves prognosis of lung cancer and two large randomized controlled trials have shown that early detection with low-dose computed tomography (LDCT) reduces mortality. Despite this, lung cancer screening (LCS) remains challenging. In the context of a global shortage of radiologists, the high rate of false-positive LDCT results in overloading of existing lung cancer clinics and multidisciplinary teams. Thus, to provide patients with earlier access to life-saving surgical interventions, there is an urgent need to improve LDCT-based LCS and especially to reduce the false-positive rate that plagues the current detection technology. In this context, LCS can be improved in three ways: (1) by refining selection criteria (risk factor assessment), (2) by using Computer Aided Diagnosis (CAD) to make it easier to interpret chest CTs, and (3) by using biological blood signatures for early cancer detection, to both spot the optimal target population and help classify lung nodules. These three main ways of improving LCS are discussed in this review.
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Registered trials
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