ArticleLa Radiologia medica2024
Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT.
Article in La Radiologia medica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
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Who cites it
53 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparative diagnostic accuracy of radiomics, deep learning, and hybrid AI for invasiveness stratification of pulmonary ground-glass nodules: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Combined Model Integrating Clinical-Imaging and Deep Learning to Discriminate Between Persistent Inflammatory and Malignant Subsolid Pulmonary Nodules.Journal of imaging informatics in medicine · 2026Article
- Fusion of Radiomics and Gated Graph Attention Network for Pulmonary Nodule Malignancy Classification.Journal of imaging · 2026Article
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- MRI-based deep learning radiomics model for automated classification of disc degeneration in the lumbar spine.Scientific reports · 2026Article
- Systematic evaluation of machine learning models for clinical risk prediction on real-world hospital datasets.iScience · 2026Article
- A multicenter study of automatic segmentation-based multimodal fusion integrating radiomics, deep learning, and clinical parameters for prostate cancer detection.Abdominal radiology (New York) · 2026Article
- Advancing Lung Cancer Imaging with Self-Supervised Foundation Models.Radiology. Imaging cancer · 2026Article
- Grayscale ultrasound radiomics for characterizing subpleural pulmonary lesions: a multicenter prospective study.Insights into imaging · 2026Article
- Integration of radiomics, deep learning, transcriptomics, and metabolomics reveals prognostic risk stratification and underlying biological mechanisms in colorectal cancer.NPJ precision oncology · 2026Article
- A Self-Supervised Foundation Model Based on Three-Dimensional Chest CT Scans for Lung Cancer Diagnosis and Prognosis Prediction.Radiology. Imaging cancer · 2026Article
- Integrating central-slice anatomy and MRF-mapped radiomics dynamics for lung adenocarcinoma subtyping.Quantitative imaging in medicine and surgery · 2026Article
- The intersection of artificial intelligence and lung nodule research: current applications and future prospects.International journal of surgery (London, England) · 2026Article
- Enhancing Lung Nodule Classification with Triple Union Efficient Attention and Neural Architecture Search.Journal of imaging informatics in medicine · 2026Article
- Identification of KRAS mutation in rectal cancer based on a 2.5D deep learning model.Frontiers in oncology · 2026Article
- Differential diagnosis model for tuberculous and malignant pleural effusion combining U-Net automatic segmentation and deep learning.Frontiers in medicine · 2026Article
- Combining computed tomography features and histogram analysis facilitates differentiation between solitary pulmonary invasive mucinous and non-mucinous adenocarcinomas.Frontiers in radiology · 2026Article
- A deep learning and radiomics fusion model enhances endoscopic ultrasonography diagnosis of gastric tumors.Frontiers in oncology · 2026Article
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Authors and funding
9 authors.
Funding
Abstract
objectivesThe study aimed to develop a combined model that integrates deep learning (DL), radiomics, and clinical data to classify lung nodules into benign or malignant categories, and to further classify lung nodules into different pathological subtypes and Lung Imaging Reporting and Data System (Lung-RADS) scores. MATERIALS AND
methodsThe proposed model was trained, validated, and tested using three datasets: one public dataset, the Lung Nodule Analysis 2016 (LUNA16) Grand challenge dataset (n = 1004), and two private datasets, the Lung Nodule Received Operation (LNOP) dataset (n = 1027) and the Lung Nodule in Health Examination (LNHE) dataset (n = 1525). The proposed model used a stacked ensemble model by employing a machine learning (ML) approach with an AutoGluon-Tabular classifier. The input variables were modified 3D convolutional neural network (CNN) features, radiomics features, and clinical features. Three classification tasks were performed: Task 1: Classification of lung nodules into benign or malignant in the LUNA16 dataset; Task 2: Classification of lung nodules into different pathological subtypes; and Task 3: Classification of Lung-RADS score. Classification performance was determined based on accuracy, recall, precision, and F1-score. Ten-fold cross-validation was applied to each task.
resultsThe proposed model achieved high accuracy in classifying lung nodules into benign or malignant categories in LUNA 16 with an accuracy of 92.8%, as well as in classifying lung nodules into different pathological subtypes with an F1-score of 75.5% and Lung-RADS scores with an F1-score of 80.4%.
conclusionOur proposed model provides an accurate classification of lung nodules based on the benign/malignant, different pathological subtypes, and Lung-RADS system.
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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.