ReviewSeminars in respiratory and critical care medicine2016
The Pursuit of Noninvasive Diagnosis of Lung Cancer.
Review in Seminars in respiratory and critical care medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
14 citing papers in PubMed.
- Liquid biopsy biomarkers for accurate detection of malignant pulmonary nodules: a meta-analytic approach.Discover oncology · 2026Article
- Automatic and accurate auxiliary detection of lung cancer pathological classification based on novel lightweight deep learning model.Discover oncology · 2026Article
- An Indicator Cell Assay-based Multivariate Blood Test for Early Detection of Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2025Article
- Biomarkers in lung cancer diagnosis and bronchoscopy: Current landscape and future directions.Cancer biomarkers : section A of Disease markers · 2025Review
- Metal-Organic Frameworks in Surface Enhanced Raman Spectroscopy-Based Analysis of Volatile Organic Compounds.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Biomarkers of lung cancer for screening and in never-smokers-a narrative review.Translational lung cancer research · 2023Review
- Biomarkers in Lung Cancer Screening: a Narrative Review.Current challenges in thoracic surgery · 2023Article
- Non-Invasive Biomarkers for Early Lung Cancer Detection.Cancers · 2022Review
- The emergence of new trends in clinical laboratory diagnosis.Saudi medical journal · 2020Review
- Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification.Journal of digital imaging · 2020Article
- Tumor-derived Autoantibodies Identify Malignant Pulmonary Nodules.American journal of respiratory and critical care medicine · 2019Article
- 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
- Highly accurate model for prediction of lung nodule malignancy with CT scans.Scientific reports · 2018Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The noninvasive diagnosis of lung cancer remains a formidable challenge. Although tissue diagnosis will remain the gold standard for the foreseeable future, questions pertaining to the risks and costs associated with invasive diagnostic procedures are of prime relevance. This review addresses new modalities for improving the noninvasive evaluation of suspicious lung nodules. Ultimately, the goal is to translate early diagnosis into early treatment. We discuss how biomarkers could assist in distinguishing benign from malignant nodules and aggressive from indolent tumors. The field of biomarkers is rapidly expanding and progressing, and efforts are well underway to apply molecular diagnostics to address the shortcomings of current lung cancer diagnostic tools.
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What OpenQuestion holds
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.