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Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign

OBJECTIVES: To evaluate the discriminatory capability of spectral CT-based radiomics to distinguish benign from malignant solitary pulmonary solid nodules (SPSNs). MATERIALS AND METHODS: A retrospective study was performed including 242 patients with SPSNs who underwent contrast-enhanced dual-layer...

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Detalles Bibliográficos
Autores principales: Xu, Hang, Zhu, Na, Yue, Yong, Guo, Yan, Wen, Qingyun, Gao, Lu, Hou, Yang, Shang, Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9878920/
https://www.ncbi.nlm.nih.gov/pubmed/36703132
http://dx.doi.org/10.1186/s12885-023-10572-4
Descripción
Sumario:OBJECTIVES: To evaluate the discriminatory capability of spectral CT-based radiomics to distinguish benign from malignant solitary pulmonary solid nodules (SPSNs). MATERIALS AND METHODS: A retrospective study was performed including 242 patients with SPSNs who underwent contrast-enhanced dual-layer Spectral Detector CT (SDCT) examination within one month before surgery in our hospital, which were randomly divided into training and testing datasets with a ratio of 7:3. Regions of interest (ROIs) based on 40-65 keV images of arterial phase (AP), venous phases (VP), and 120kVp of SDCT were delineated, and radiomics features were extracted. Then the optimal radiomics-based score in identifying SPSNs was calculated and selected for building radiomics-based model. The conventional model was developed based on significant clinical characteristics and spectral quantitative parameters, subsequently, the integrated model combining radiomics-based model and conventional model was established. The performance of three models was evaluated with discrimination, calibration, and clinical application. RESULTS: The 65 keV radiomics-based scores of AP and VP had the optimal performance in distinguishing benign from malignant SPSNs (AUC(65keV-AP) = 0.92, AUC(65keV-VP) = 0.88). The diagnostic efficiency of radiomics-based model (AUC = 0.96) based on 65 keV images of AP and VP outperformed conventional model (AUC = 0.86) in the identification of SPSNs, and that of integrated model (AUC = 0.97) was slightly further improved. Evaluation of three models showed the potential for generalizability. CONCLUSIONS: Among the 40-65 keV radiomics-based scores based on SDCT, 65 keV radiomics-based score had the optimal performance in distinguishing benign from malignant SPSNs. The integrated model combining radiomics-based model based on 65 keV images of AP and VP with Z(eff-AP) was significantly superior to conventional model in the discrimination of SPSNs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-023-10572-4.