ArticleScientific reports2017
Diagnostic classification of solitary pulmonary nodules using dual time
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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
38 citing papers in PubMed, 71 citations in OpenAlex.
- Radiomics based onCancer medicine · 2020Trial
- The Role of F-18 FDG PET/CT in the Evaluation of Solitary Pulmonary Nodules.Saudi medical journal · 2026Article
- A narrative review of imaging intratumor heterogeneity in non-small cell lung cancer: current advances.Translational lung cancer research · 2025Review
- The study of dual-phaseScientific reports · 2025Article
- Differentiating second primary lung cancer from pulmonary metastasis in patients of single solitary pulmonary lesion with extrapulmonary tumor using multiparametric analysis of FDG PET/CT.Annals of nuclear medicine · 2025Article
- Integrating Machine Learning in Clinical Practice for Characterizing the Malignancy of Solitary Pulmonary Nodules in PET/CT Screening.Diseases (Basel, Switzerland) · 2024Article
- Dual-layer detector spectral CT-based machine learning models in the differential diagnosis of solitary pulmonary nodules.Scientific reports · 2024Article
- Classification of Pulmonary Nodules in 2-[Nuclear medicine and molecular imaging · 2024Article
- PET/CT radiomics and deep learning in the diagnosis of benign and malignant pulmonary nodules: progress and challenges.Frontiers in oncology · 2024Review
- Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT.Cancer management and research · 2024Review
- Performance ofPhenomics (Cham, Switzerland) · 2023Article
- Radiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [Insights into imaging · 2023Article
- The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges.Frontiers in oncology · 2023Review
- Magnetic resonance radiomic feature performance in pulmonary nodule classification and impact of segmentation variability on radiomics.The British journal of radiology · 2022Article
- State of the Art: Lung Cancer Staging Using Updated Imaging Modalities.Bioengineering (Basel, Switzerland) · 2022Review
- Dopamine dysfunction in depression: application of texture analysis to dopamine transporter single-photon emission computed tomography imaging.Translational psychiatry · 2022Article
- Dopamine transporter single-photon emission computed tomography-derived radiomics signature for detecting Parkinson's disease.EJNMMI research · 2022Article
- Diagnostic Performance of Machine Learning Models Based onMolecular imaging and radionuclide therapy · 2022Article
- Effects of Tracer Uptake Time in Non-Small Cell Lung CancerJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
Lung cancer, the most commonly diagnosed cancer worldwide, usually presents as solid pulmonary nodules (SPNs) on early diagnostic images. Classification of malignant disease at this early timepoint is critical for improving the success of surgical resection and increasing 5-year survival rates.
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.