Trial reportBMC cancer2022
The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images.
Trial report in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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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.
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Who cites it
20 citing papers in PubMed, 35 citations in OpenAlex.
- Revolutionizing lung cancer screening: the rise of artificial intelligence integrating circulating tumor markers.World journal of surgical oncology · 2026Review
- Reproducible meningioma grading across multi-center MRI protocols via hybrid radiomic and deep learning features.Neuroradiology · 2025Article
- Radiomics analysis for the early diagnosis of common sexually transmitted infections and skin lesions.PLOS digital health · 2025Article
- Non-Invasive Procedure in Differential Diagnosis of Sarcoidosis and Tuberculosis Lymph Nodes: Radiomic Model of 18F-FDG PET-CT.Sarcoidosis, vasculitis, and diffuse lung diseases : official journal of WASOG · 2025Article
- Bridging surgical oncology and personalized medicine: the role of artificial intelligence and machine learning in thoracic surgery.Annals of medicine and surgery (2012) · 2025Review
- Deep learning radiomics analysis for prediction of survival in patients with unresectable gastric cancer receiving immunotherapy.European journal of radiology open · 2025Article
- Prediction model for the early recurrence of stage IA-IIA non-small cell lung cancer based on hematological indexes and imaging features.Discover oncology · 2025Article
- New Perspectives on Lung Cancer Screening and Artificial Intelligence.Life (Basel, Switzerland) · 2025Review
- Assessments of lung nodules by an artificial intelligence chatbot using longitudinal CT images.Cell reports. Medicine · 2025Article
- Exploring the Potentials of Artificial Intelligence in Sepsis Management in the Intensive Care Unit.Critical care research and practice · 2025Review
- Evaluation of deep learning tools in medical diagnosis and treatment of cancer: research analysis of clinical and randomized clinical trials.Frontiers in network physiology · 2025Review
- Clinic, CT radiomics, and deep learning combined model for the prediction of invasive pulmonary aspergillosis.BMC medical imaging · 2024Article
- Multi-modality multi-task model for mRS prediction using diffusion-weighted resonance imaging.Scientific reports · 2024Article
- Prediction of the Benign or Malignant Nature of Pulmonary Pure Ground-Glass Nodules Based on Radiomics Analysis of High-Resolution Computed Tomography Images.Tomography (Ann Arbor, Mich.) · 2024Article
- Review
- Future implications of artificial intelligence in lung cancer screening: a systematic review.BJR open · 2024Article
- Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules.Biomedical engineering online · 2023Article
- Preoperative CT-based radiomics combined with tumour spread through air spaces can accurately predict early recurrence of stage I lung adenocarcinoma: a multicentre retrospective cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Development and Validation of a Deep Learning Predictive Model Combining Clinical and Radiomic Features for Short-Term Postoperative Facial Nerve Function in Acoustic Neuroma Patients.Current medical science · 2023Article
- Quantitative Analysis of TP53-Related Lung Cancer Based on Radiomics.International journal of general medicine · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundSolid pulmonary nodules are different from subsolid nodules and the diagnosis is much more challenging. We intended to evaluate the diagnostic and prognostic value of radiomics and deep learning technologies for solid pulmonary nodules.
methodsRetrospectively enroll patients with pathologically-confirmed solid pulmonary nodules and collect clinical data. Obtain pre-treatment high-resolution thoracic CT and manually delineate the nodule in 3D. Then, all patients were randomly divided into training and testing sets at a ratio of 7:3, and convolutional neural networks (CNN) models and random forest (RF) models were established. Survival analyses were performed for patients with solid adenocarcinomas.
resultsTotally 720 solid pulmonary nodules were enrolled, 348 benign and 372 malignant. The CNN model with clinical features achieved the highest AUC [0.819, 95% confidence interval (CI): 0.760-0.877] with a sensitivity of 0.778, specificity of 0.788 and accuracy of 0.783. No significant differences were observed between the CNN and radiomics models. There were 295 solid adenocarcinomas in survival analysis. Different disease-free survival was observed between the low-risk and high-risk groups divided according to the radiomics Rad-score. However, the groups based on deep learning signatures showed similar survival. Cox regression analysis indicated that the radiomics Rad-score (hazard ratio: 5.08, 95% CI: 2.61-9.90) was an independent predictor of recurrence.
conclusionsThe radiomics and deep learning models can well predict the malignancy of solid pulmonary nodules. Radiomics signatures also demonstrate prognostic value in solid adenocarcinomas.
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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.