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Deep Learning Radiomics Features of Mediastinal Fat and Pulmonary Nodules on Lung CT Images Distinguish Benignancy and Malignancy.
Qi, Hongzhuo; Xuan, Qifan; Liu, Pingping; An, Yunfei; Huang, Wenjuan; Miao, Shidi; Wang, Qiujun; Liu, Zengyao; Wang, Ruitao.
Afiliación
  • Qi H; School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.
  • Xuan Q; School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.
  • Liu P; Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin 150081, China.
  • An Y; School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.
  • Huang W; Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin 150081, China.
  • Miao S; School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.
  • Wang Q; Department of General Practice, The Second Affiliated Hospital of Harbin Medical University, Harbin 150001, China.
  • Liu Z; Department of Interventional Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin 150086, China.
  • Wang R; Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin 150081, China.
Biomedicines ; 12(8)2024 Aug 15.
Article en En | MEDLINE | ID: mdl-39200329
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, p < 0.05). A decision curve analysis indicated that incorporating mediastinal fat features provided greater patient benefits. Mediastinal fat offered complementary information for distinguishing benign from malignant nodules, enhancing the diagnostic capability of this deep learning-based radiomics model. This model demonstrated strong diagnostic ability for benign and malignant pulmonary nodules, providing a more accurate and beneficial approach for patient care.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Biomedicines Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Biomedicines Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza