ArticleInsights into imaging2023
Efficient pulmonary nodules classification using radiomics and different artificial intelligence strategies.
Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in pulmonary nodule detection and segmentation: a systematic review.European radiology · 2025Pooled it
- Diagnostic performance of deep learning models in differentiating benign and malignant pulmonary nodules: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2026Article
- Stacked CT radiomics, deep learning and clinical feature models for differentiating benign and malignant solitary pulmonary nodules.Scientific reports · 2026Article
- Multicenter peritumoral radiomics in oncology: advances, challenges, and future directions.Radiation oncology (London, England) · 2026Review
- LNMSNet: a multi-task deep learning network for pulmonary nodules segmentation and malignancy classification.Frontiers in medicine · 2026Article
- Research on Cancer Prediction Based on Feature Optimization and Multimodal Fusion.Health care science · 2025Article
- Classifiers Combined with DenseNet Models for Lung Cancer Computed Tomography Image Classification: A Comparative Analysis.Current medical imaging · 2025Article
- CT delta-radiomics predicts the risks of blood transfusion and massive bleeding during spinal tumor surgery.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Radiomic features add incremental benefit to conventional radiological feature-based differential diagnosis of lung nodules.European radiology · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Toward Robust Lung Cancer Diagnosis: Integrating Multiple CT Datasets, Curriculum Learning, and Explainable AI.Diagnostics (Basel, Switzerland) · 2024Article
- Clinically applicable optimized periprosthetic joint infection diagnosis via AI based pathology.NPJ digital medicine · 2024Article
- Advances in artificial intelligence applications in the field of lung cancer.Frontiers in oncology · 2024Review
- Pulmonary sclerosing pneumocytoma - approaching a solitary pulmonary nodule and the limitations of risk prediction models.BMJ case reports · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to explore and develop artificial intelligence approaches for efficient classification of pulmonary nodules based on CT scans. MATERIALS AND
methodsA number of 1007 nodules were obtained from 551 patients of LIDC-IDRI dataset. All nodules were cropped into 64 × 64 PNG images , and preprocessing was carried out to clean the image from surrounding non-nodular structure. In machine learning method, texture Haralick and local binary pattern features were extracted. Four features were selected using principal component analysis (PCA) algorithm before running classifiers. In deep learning, a simple CNN model was constructed and transfer learning was applied using VGG-16 and VGG-19, DenseNet-121 and DenseNet-169 and ResNet as pre-trained models with fine tuning.
resultsIn statistical machine learning method, the optimal AUROC was 0.885 ± 0.024 with random forest classifier and the best accuracy was 0.819 ± 0.016 with support vector machine. In deep learning, the best accuracy reached 90.39% with DenseNet-121 model and the best AUROC was 96.0%, 95.39% and 95.69% with simple CNN, VGG-16 and VGG-19, respectively. The best sensitivity reached 90.32% using DenseNet-169 and the best specificity attained was 93.65% when applying the DenseNet-121 and ResNet-152V2.
conclusionDeep learning methods with transfer learning showed several benefits over statistical learning in terms of nodule prediction performance and saving efforts and time in training large datasets. SVM and DenseNet-121 showed the best performance when compared with their counterparts. There is still more room for improvement, especially when more data can be trained and lesion volume is represented in 3D. CLINICAL RELEVANCE STATEMENT: Machine learning methods offer unique opportunities and open new venues in clinical diagnosis of lung cancer. The deep learning approach has been more accurate than statistical learning methods. SVM and DenseNet-121 showed superior performance in pulmonary nodule classification.
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