ArticleBMC medical informatics and decision making2022
Discriminating TB lung nodules from early lung cancers using deep learning.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Revolutionizing diagnosis of pulmonaryFrontiers in microbiology · 2024Pooled it
- LungGANDetectAI: a GAN-augmented and attention-guided deep learning framework for accurate and explainable lung cancer detection.Scientific reports · 2026Article
- Optimized deep learning approach for lung cancer detection using flying fox optimization and bidirectional generative adversarial networks.PeerJ. Computer science · 2025Article
- Performance of radial endobronchial ultrasound for peripheral pulmonary lesions without automation technology in tuberculous endemic region: real-world experience in a single institution over 6 years.Journal of thoracic disease · 2023Article
- A hybrid algorithm for clinical decision support in precision medicine based on machine learning.BMC bioinformatics · 2023Article
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 at 1 institution in 1 country.
Funding
Abstract
backgroundIn developing countries where both high rates of smoking and endemic tuberculosis (TB) are often present, identification of early lung cancer can be significantly confounded by the presence of nodules such as those due to latent TB (LTB). It is very challenging to distinguish lung cancer and LTB without invasive procedures, which have their own risks of morbidity and even mortality.
methodsOur method uses a customized VGG16-based 15-layer 2-dimensional deep convolutional neural network (DNN) architecture with transfer learning. The DNN was trained and tested on sets of CT images set extracted from the National Lung Screening Trial and the National Institute of Allergy and Infectious Disease TB Portals. Performance of the DNN was evaluated under locked and step-wise unlocked pretrained weight conditions.
resultsThe DNN with unlocked pretrained weights achieved an accuracy of 90.4% with an F score of 90.1%.
conclusionsOur findings support the potential for a DNN to serve as a noninvasive screening tool capable of reliably detecting and distinguishing between lung cancer and LTB.
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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.