ArticlePloS one2018
Radiomic features analysis in computed tomography images of lung nodule classification.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 1 of them a synthesis that pooled it.
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Who cites it
83 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images.Journal of medical systems · 2019Pooled it
- Trial
- The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images.BMC cancer · 2022Trial
- Trial
- Radiomics machine learning models for lung cancer early diagnosis in heterogenous multicentre chest CT data: LIBRA study results.European radiology experimental · 2026Article
- An intelligent lung nodule classification model using 3D Trans-DenseUnet++-based lung nodule segmentation.Scientific reports · 2026Article
- Impact of CT acquisition settings on the stability of radiomic features and the performance of pulmonary nodule classification models.Insights into imaging · 2026Article
- Evaluation of unified harmonization of CT images across multiple tasks: A step towards AI generalizability.Medical physics · 2025Article
- Predictive radiomics based ensemble machine learning approach in CT lung nodule diagnosis.Journal of the Egyptian National Cancer Institute · 2025Article
- Pilot Exploratory Study of a CT Radiomics Model for the Classification of Small Cell Lung Cancer and Non-Small-Cell Lung Cancer in the Moscow Population: A Step Toward Virtual Biopsy.Journal of imaging · 2025Article
- An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules.BMC medical imaging · 2025Article
- Precision Medicine in Lung Cancer Screening: A Paradigm Shift in Early Detection-Precision Screening for Lung Cancer.Diagnostics (Basel, Switzerland) · 2025Review
- Radiomic features add incremental benefit to conventional radiological feature-based differential diagnosis of lung nodules.European radiology · 2025Article
- Significance of Image Reconstruction Parameters for Future Lung Cancer Risk Prediction Using Low-Dose Chest Computed Tomography and the Open-Access Sybil Algorithm.Investigative radiology · 2025Article
- Differentiation of Minute Pulmonary Meningothelial-Like Nodules and Adenocarcinoma In situ with CT RadiomicsCurrent medical imaging · 2025Article
- Real-World and Clinical Trial Validation of a Deep Learning Radiomic Biomarker for PD-(L)1 Immune Checkpoint Inhibitor Response in Advanced Non-Small Cell Lung Cancer.JCO clinical cancer informatics · 2024Article
- Evaluation of Pulmonary Nodules by Radiologists vs. Radiomics in Stand-Alone and Complementary CT and MRI.Diagnostics (Basel, Switzerland) · 2024Article
- Observational
- Article
- The value of computed tomography-based radiomics for predicting malignant pleural effusions.Frontiers in oncology · 2024Article
23 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeRadiomics, which extract large amount of quantification image features from diagnostic medical images had been widely used for prognostication, treatment response prediction and cancer detection. The treatment options for lung nodules depend on their diagnosis, benign or malignant. Conventionally, lung nodule diagnosis is based on invasive biopsy. Recently, radiomics features, a non-invasive method based on clinical images, have shown high potential in lesion classification, treatment outcome prediction.
methodsLung nodule classification using radiomics based on Computed Tomography (CT) image data was investigated and a 4-feature signature was introduced for lung nodule classification. Retrospectively, 72 patients with 75 pulmonary nodules were collected. Radiomics feature extraction was performed on non-enhanced CT images with contours which were delineated by an experienced radiation oncologist.
resultAmong the 750 image features in each case, 76 features were found to have significant differences between benign and malignant lesions. A radiomics signature was composed of the best 4 features which included Laws_LSL_min, Laws_SLL_energy, Laws_SSL_skewness and Laws_EEL_uniformity. The accuracy using the signature in benign or malignant classification was 84% with the sensitivity of 92.85% and the specificity of 72.73%.
conclusionThe classification signature based on radiomics features demonstrated very good accuracy and high potential in clinical application.
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