ArticleEuropean radiology2021
Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Artificial intelligence model boosts lung nodule diagnostic accuracy in clinical trial.Nature cancer · 2026Article
- AI-MDT: an automatic and intelligent multidisciplinary team consultations platform for lung cancer diagnosis.Journal of cancer research and clinical oncology · 2026Article
- Impact of CT acquisition settings on the stability of radiomic features and the performance of pulmonary nodule classification models.Insights into imaging · 2026Article
- A CT-based radiomics nomogram incorporating adipose tissue to differentiate invasive adenocarcinomas among part-solid pulmonary nodules.BMC cancer · 2025Article
- The effectiveness of deep learning model in differentiating benign and malignant pulmonary nodules on spiral CT.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- A deep learning algorithm to identify carotid plaques and assess their stability.Frontiers in artificial intelligence · 2024Article
- Diagnostic performance of a deep learning-based method in differentiating malignant from benign subcentimeter (≤10 mm) solid pulmonary nodules.Journal of thoracic disease · 2023Article
- Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case-control study.Frontiers in oncology · 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
7 authors.
Funding
Abstract
objectiveTo compare the performance of a deep learning (DL)-based method for diagnosing pulmonary nodules compared with radiologists' diagnostic approach in computed tomography (CT) of the chest. MATERIALS AND
methodsA total of 150 pathologically confirmed pulmonary nodules (60% malignant) assessed and reported by radiologists were included. CT images were processed by the proposed DL-based method to generate the probability of malignancy (0-100%), and the nodules were divided into the groups of benign (0-39.9%), indeterminate (40.0-59.9%), and malignant (60.0-100%). Taking the pathological results as the gold standard, we compared the diagnostic performance of the proposed DL-based method with the radiologists' diagnostic approach using the McNemar-Bowker test.
resultsThere was a statistically significant difference between the diagnosis results of the proposed DL-based method and the radiologists' diagnostic approach (p < 0.001). Moreover, there was no statistically significant difference in the composition of the diagnosis results between the proposed DL-based method and the radiologists' diagnostic approach (all p > 0.05). The difference in diagnostic accuracy between the proposed DL-based method (70%) and radiologists' diagnostic performance (64%) was not statistically significant (p = 0.243).
conclusionsThe proposed DL-based method achieved an accuracy comparable with the radiologists' diagnostic approach in clinical practice. Furthermore, its advantage in improving diagnostic certainty may raise the radiologists' confidence in diagnosing pulmonary nodules and may help clinical management. Therefore, the proposed DL-based method showed great potential in a certain clinical application. KEY POINTS: • Deep learning-based method for diagnosing the pulmonary nodules in computed tomography provides a higher diagnostic certainty.
Indexed as
Identifiers
33956178What 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.