ArticleCancers2020
Association of a CT-Based Clinical and Radiomics Score of Non-Small Cell Lung Cancer (NSCLC) with Lymph Node Status and Overall Survival.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis.Frontiers in public health · 2022Pooled it
- Lung disease in crisponi/cold-induced sweating syndrome type 1: expanding the clinical phenotype spectrum.European journal of pediatrics · 2026Article
- Ultrasound diagnosis of appendiceal tumor with synchronous liver metastasis mimicking acute appendicitis and liver abscess.Journal of ultrasound · 2026Article
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- CT radiomics-histopathological correlation for mediastinal lymph node staging in non-small cell lung cancer.PloS one · 2026Article
- Development and Validation of a Computed Tomography-based Radiomics Nomogram for Diagnosing Cervical Lymph Node Metastasis in Oropharyngeal Squamous Cell Carcinomas.Advances in radiation oncology · 2025Article
- CT-based machine learning model integrating intra- and peri-tumoral radiomics features for predicting occult lymph node metastasis in peripheral lung cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Predicting early stage lung cancer recurrence and survival from combined tumor motion amplitude and radiomics on free-breathing 4D-CT.Medical physics · 2025Article
- Radiomics models using machine learning algorithms to differentiate the primary focus of brain metastasis.Translational cancer research · 2025Article
- Preoperative prediction of multiple biological characteristics in colorectal cancer using MRI and machine learning.Heliyon · 2025Article
- Lung Cancers Associated with Cystic Airspaces.Cancers · 2025Review
- The Role of Sublobar Resection in Early-Stage Non-Small-Cell Lung Cancer.Journal of clinical medicine · 2024Review
- Dual-Region Computed Tomography Radiomics-Based Machine Learning Predicts Subcarinal Lymph Node Metastasis in Patients with Non-small Cell Lung Cancer.Annals of surgical oncology · 2024Article
- Prognostic Factors and Clinical Outcomes of Surgical Treatment of Major Thoracic Trauma.Healthcare (Basel, Switzerland) · 2024Article
- Assessment of pulmonary vascular anatomy: comparing augmented reality by holograms versus standard CT images/reconstructions using surgical findings as reference standard.European radiology experimental · 2024Article
- Review
- Segmentation method of magnetic resonance imaging brain tumor images based on improved UNet network.Translational cancer research · 2024Article
- Article
- Radiogenomics: a key component of precision cancer medicine.British journal of cancer · 2023Review
- Use of 3D ultrasound to characterise temporal changes in thyroid nodules: an in vitro study.Journal of ultrasound · 2023Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo evaluate whether a model based on radiomic and clinical features may be associated with lymph node (LN) status and overall survival (OS) in lung cancer (LC) patients; to evaluate whether CT reconstruction algorithms may influence the model performance.
methodspatients operated on for LC with a pathological stage up to T3N1 were retrospectively selected and divided into training and validation sets. For the prediction of positive LNs and OS, the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was used; univariable and multivariable logistic regression analysis assessed the association of clinical-radiomic variables and endpoints. All tests were repeated after dividing the groups according to the CT reconstruction algorithm.
results270 patients were included and divided into training (n = 180) and validation sets (n = 90). Transfissural extension was significantly associated with positive LNs. For OS prediction, high- and low-risk groups were different according to the radiomics score, also after dividing the two groups according to reconstruction algorithms.
conclusionsa combined clinical-radiomics model was not superior to a single clinical or single radiomics model to predict positive LNs. A radiomics model was able to separate high-risk and low-risk patients for OS; CTs reconstructed with Iterative Reconstructions (IR) algorithm showed the best model performance.
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