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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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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 |
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author | Xu, Hang Zhu, Na Yue, Yong Guo, Yan Wen, Qingyun Gao, Lu Hou, Yang Shang, Jin |
author_facet | Xu, Hang Zhu, Na Yue, Yong Guo, Yan Wen, Qingyun Gao, Lu Hou, Yang Shang, Jin |
author_sort | Xu, Hang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9878920 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-98789202023-01-27 Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign Xu, Hang Zhu, Na Yue, Yong Guo, Yan Wen, Qingyun Gao, Lu Hou, Yang Shang, Jin BMC Cancer Research 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. BioMed Central 2023-01-26 /pmc/articles/PMC9878920/ /pubmed/36703132 http://dx.doi.org/10.1186/s12885-023-10572-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Xu, Hang Zhu, Na Yue, Yong Guo, Yan Wen, Qingyun Gao, Lu Hou, Yang Shang, Jin Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign |
title | Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign |
title_full | Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign |
title_fullStr | Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign |
title_full_unstemmed | Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign |
title_short | Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign |
title_sort | spectral ct-based radiomics signature for distinguishing malignant pulmonary nodules from benign |
topic | Research |
url | 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 |
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