ReviewJournal of thoracic disease2025
Advantages of integrating artificial intelligence and spectral CT for lung nodule classification and prognostic judgment: a narrative review.
Review in Journal of thoracic disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Application and Development of Spectral CT in Target Delineation for Lung Cancer Radiotherapy.Diagnostics (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: The accurate diagnosis of lung nodules remains a significant challenge in clinical practice due to their diverse and often nonspecific imaging characteristics. This limitation underscores the need for more advanced analytical approaches. The present review aims to summarise and discuss the advancements and applications of integrating artificial intelligence (AI) with spectral computed tomography (CT) for diagnosing lung nodules with diverse characteristics. Methods: This narrative review sourced literature from PubMed/MEDLINE, Web of Science, and Google Scholar (2010-2025) using keywords "spectral CT", "pulmonary nodule", and "artificial intelligence". Inclusion criteria focused on studies applying spectral CT and/or AI to lung nodule characterization. Two reviewers independently screened and selected studies, with a third resolving discrepancies. A total of 25 studies were included for analysis. Key Content and Finding: This review highlights the advances in applying this dual strategy to the multiparametric analysis of pulmonary nodules. Studies indicate that combining the rich parametric information provided by spectral CT [e.g., iodine concentration (IC), spectral curves] with AI's powerful pattern recognition and quantitative analysis capabilities can significantly enhance diagnostic efficacy for pulmonary nodules exhibiting diverse characteristics (e.g., varying sizes, densities, locations). This integrated approach demonstrates considerable potential for improving diagnostic accuracy in lung nodules. It significantly enhances diagnostic efficacy for nodules exhibiting diverse characteristics (e.g., varying size, density, and location). This combined methodology shows significant promise in improving the accuracy of benign-malignant differentiation and prognosis prediction. Conclusions: The synergistic application of AI and energy-spectrum CT is recognized as an emerging frontier in pulmonary nodule diagnosis. This dual-strategy approach overcomes the limitations of traditional imaging and single-technology methods, providing a more comprehensive and reliable tool for the precise identification, qualitative diagnosis, and prognostic assessment of pulmonary nodules. It demonstrates significant clinical value and broad application prospects.
Indexed as
Identifiers
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.