ArticleChest2023
Diagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With PET With CT Imaging and Dynamic Contrast-Enhanced CT Imaging Using Unenhanced and Contrast-Enhanced CT Imaging.
Article in Chest, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02013063 (Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules), which is not on this map. Cited by 7 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.
Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Use of methylene blue and a spring microcoil in the preoperative localization of small pulmonary nodules under CT guidance: a meta-analysis.The Journal of international medical research · 2023Pooled it
- Urinary Cell-Free DNA detection platform for bacterial cystitis diagnosis via spot-based machine learning.Journal of advanced research · 2026Article
- Scientific evidence of commercial artificial intelligence products for pulmonary nodule assessment on CT scans: a systematic review.European radiology · 2026Article
- Differentiation of pulmonary tuberculosis from non-tuberculous solid lung lesions using radiomics and clinical-semantic features on contrast-enhanced CT.Frontiers in medicine · 2026Article
- The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Integrating Machine Learning in Clinical Practice for Characterizing the Malignancy of Solitary Pulmonary Nodules in PET/CT Screening.Diseases (Basel, Switzerland) · 2024Article
- Value for combination of TZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2023Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundSolitary pulmonary nodules (SPNs) measuring 8 to 30 mm in diameter require further workup to determine the likelihood of malignancy. RESEARCH QUESTION: What is the diagnostic performance of a lung cancer prediction convolutional neural network (LCP-CNN) in SPNs using unenhanced and contrast-enhanced CT imaging compared with the current clinical workup? STUDY DESIGN AND
methodsThis was a post hoc analysis of the Single Pulmonary Nodule Investigation: Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules trial, a prospective multicenter study comparing the diagnostic accuracy of dynamic contrast-enhanced (DCE) CT imaging with PET imaging in SPNs. The LCP-CNN was designed and validated in an external cohort. LCP-CNN-generated risk scores were created from the noncontrast and contrast-enhanced CT scan images from the DCE CT imaging. The gold standard was histologic analysis or 2 years of follow-up. The area under the receiver operating characteristic curves (AUC) were calculated using LCP-CNN score, maximum standardized uptake value, and DCE CT scan maximum enhancement and were compared using the DeLong test.
resultsTwo hundred seventy participants (mean ± SD age, 68.3 ± 8.8 years; 49% women) underwent PET with CT scan imaging and DCE CT imaging with CT scan data available centrally for LCP-CNN analysis. The accuracy of the LCP-CNN on the noncontrast images (AUC, 0.83; 95% CI, 0.79-0.88) was superior to that of DCE CT imaging (AUC, 0.76; 95% CI, 0.69-0.82; P = .03) and equal to that of PET with CT scan imaging (AUC, 0.86; 95% CI, 0.81-0.90; P = .35). The presence of contrast resulted in a small reduction in diagnostic accuracy, with the AUC falling from 0.83 (95% CI, 0.79-0.88) on the noncontrast images to 0.80 to 0.83 after contrast (P < .05 for 240 s after contrast only).
interpretationAn LCP-CNN algorithm provides an AUC equivalent to PET with CT scan imaging in the diagnosis of solitary pulmonary nodules.
trial registrationClinicalTrials.gov Identifier; No.: NCT02013063.
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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.