ArticleEuropean radiology2015
Quantitative CT texture and shape analysis: can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer?
Article in European radiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 1 of them a synthesis that pooled it.
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Who cites it
93 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical applications of textural analysis in non-small cell lung cancer.The British journal of radiology · 2018Pooled it
- Deep learning combined with radiomics for the classification of enlarged cervical lymph nodes.Journal of cancer research and clinical oncology · 2022Trial
- Nodal-based radiomics analysis for identifying cervical lymph node metastasis at levels I and II in patients with oral squamous cell carcinoma using contrast-enhanced computed tomography.European radiology · 2021Trial
- CT texture analysis in histological classification of epithelial ovarian carcinoma.European radiology · 2021Trial
- Preoperative assessment of lymph node metastasis in Colon Cancer patients using machine learning: a pilot study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2020Trial
- Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness.Scientific reports · 2019Trial
- Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?Medical physics · 2015Trial
- Machine Learning-Driven Radiomics for an Early-Stage Predictive Model of Nodal Upstaging in Thoracic Oncology.Cancers · 2026Article
- A narrative review of imaging intratumor heterogeneity in non-small cell lung cancer: current advances.Translational lung cancer research · 2025Review
- Non-Hodgkin's lymphoma classification using 3D radiomics machine learning models for precision imaging in oncology.BMC medical imaging · 2025Article
- Preoperative identification of small metastatic lymph nodes in esophageal squamous cell carcinoma using CT radiomics of lymph nodes.Abdominal radiology (New York) · 2025Article
- Predictive value of radiomics modeling based on dynamic and staticFrontiers in oncology · 2025Article
- Diagnostic accuracy and reliability of CT-based Node-RADS for colon cancer.Abdominal radiology (New York) · 2025Article
- Article
- Radiomic approach to support multidisciplinary tumor board decision-making in locally advanced non-small cell lung cancer.Frontiers in oncology · 2025Article
- Predicting the immune therapy response of advanced non-small cell lung cancer based on primary tumor and lymph node radiomics features.Frontiers in medicine · 2025Article
- Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features.Journal of translational medicine · 2024Observational
- Prediction of metastases in confusing mediastinal lymph nodes based on flourine-18 fluorodeoxyglucose (Quantitative imaging in medicine and surgery · 2024Article
- CT texture analysis for differentiating between peritoneal carcinomatosis and peritoneal tuberculosis: a cross-sectional study.Abdominal radiology (New York) · 2024Article
- Radiomics-based quantitative contrast-enhanced CT analysis of abdominal lymphadenopathy to differentiate tuberculosis from lymphoma.Precision clinical medicine · 2024Article
33 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
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Abstract
objectiveTo assess the accuracy of CT texture and shape analysis in the differentiation of benign and malignant mediastinal nodes in lung cancer.
methodsForty-three patients with biopsy-proven primary lung malignancy with pathological mediastinal nodal staging and unenhanced CT of the thorax were studied retrospectively. Grey-level co-occurrence and run-length matrix textural features, as well as morphological features, were extracted from 72 nodes. Differences between benign and malignant features were assessed using Mann-Whitney U tests. Receiver operating characteristic (ROC) curves for each were constructed and the area under the curve (AUC) calculated with histopathology diagnosis as outcome. Combinations of features were also entered as predictors in logistic regression models and optimal threshold criteria were used to estimate sensitivity and specificity.
resultsUsing optimum-threshold criteria, the combined textural and shape features identified malignant mediastinal nodes with 81% sensitivity and 80% specificity (AUC = 0.87, P < 0.0001). Using this combination, 84% malignant and 71% benign nodes were correctly classified.
conclusionsQuantitative CT texture and shape analysis has the potential to accurately differentiate malignant and benign mediastinal nodes in lung cancer. KEY POINTS: • Mediastinal nodal staging is crucial in the management of lung cancer • Mediastinal nodal metastasis affects prognosis and suitability for surgical treatment • Computed tomography (CT) is limited for mediastinal nodal staging • Texture analysis measures tissue heterogeneity not perceptible to human vision • CT texture analysis may accurately differentiate malignant and benign mediastinal nodes.
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