ArticleBiomedicines2024
Deep Learning Radiomics Features of Mediastinal Fat and Pulmonary Nodules on Lung CT Images Distinguish Benignancy and Malignancy.
Article in Biomedicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Ultrasound-based deep learning radiomics for the differential diagnosis of benign and malignant subpleural pulmonary lesions.Frontiers in oncology · 2026Article
- A CT-based radiomics nomogram incorporating adipose tissue to differentiate invasive adenocarcinomas among part-solid pulmonary nodules.BMC cancer · 2025Article
- Development and validation of a deep learning-based automated computed tomography image segmentation and diagnostic model for infectious hydronephrosis: a retrospective multicentre cohort study.EClinicalMedicine · 2025Article
- Hybrid feature fusion in cervical cancer cytology: a novel dual-module approach framework for lesion detection and classification using radiomics, deep learning, and reproducibility.Frontiers in oncology · 2025Article
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9 authors.
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Abstract
This study investigated the relationship between mediastinal fat and pulmonary nodule status, aiming to develop a deep learning-based radiomics model for diagnosing benign and malignant pulmonary nodules. We proposed a combined model using CT images of both pulmonary nodules and the fat around the chest (mediastinal fat). Patients from three centers were divided into training, validation, internal testing, and external testing sets. Quantitative radiomics and deep learning features from CT images served as predictive factors. A logistic regression model was used to combine data from both pulmonary nodules and mediastinal adipose regions, and personalized nomograms were created to evaluate the predictive performance. The model incorporating mediastinal fat outperformed the nodule-only model, with C-indexes of 0.917 (training), 0.903 (internal testing), 0.942 (external testing set 1), and 0.880 (external testing set 2). The inclusion of mediastinal fat significantly improved predictive performance (NRI = 0.243,
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