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Open Access 01-12-2022 | Computed Tomography | Research

The value of radiomics based on dual-energy CT for differentiating benign from malignant solitary pulmonary nodules

Authors: Gao Liang, Wei Yu, Shu-qin Liu, Ming-guo Xie, Min Liu

Published in: BMC Medical Imaging | Issue 1/2022

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Abstract

Objective

To investigate the value of monochromatic dual-energy CT (DECT) images based on radiomics in differentiating benign from malignant solitary pulmonary nodules.

Materials and methods

This retrospective study was approved by the institutional review board, and informed consent was waived. Pathologically confirmed lung nodules smaller than 3 cm with integrated arterial phase and venous phase (AP and VP) gemstone spectral imaging were retrospectively identified. After extracting the radiomic features of each case, principal component analysis (PCA) was used for feature selection, and after training with the logistic regression method, three classification models (ModelAP, ModelVP and ModelCombination) were constructed. The performance was assessed by the area under the receiver operating curve (AUC), and the efficacy of the models was validated using an independent cohort.

Results

A total of 153 patients were included and divided into a training cohort (n = 107) and a validation cohort (n = 46). A total of 1130 radiomic features were extracted from each case. The PCA method selected 22, 25 and 35 principal components to construct the three models. The diagnostic accuracy of ModelAP, ModelVP and ModelCombination was 0.8043, 0.6739, and 0.7826 in the validation set, with AUCs of 0.8148 (95% CI 0.682–0.948), 0.7485 (95% CI 0.602–0.895), and 0.8772 (95% CI 0.780–0.974), respectively. The DeLong test showed that there were significant differences in the AUCs between ModelAP and ModelCombination (P = 0.0396) and between ModelVP and ModelCombination (P = 0.0465). However, the difference in AUCs between ModelAP and ModelVP was not significant (P = 0.5061). These results demonstrate that ModelCombination shows a better performance than the other models. Decision curve analysis proved the clinical utility of this model.

Conclusions

We developed a radiomics model based on monochromatic DECT images to identify solitary pulmonary nodules. This model could serve as an effective tool for discriminating benign from malignant pulmonary nodules in patients. The combination of arterial phase and venous phase imaging could significantly improve the model performance.
Literature
1.
go back to reference Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.PubMed Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.PubMed
2.
go back to reference Yao S, Fangyi X, Wenchao Z, et al. Multiclassifier fusion based on radiomics features for the prediction of benign and malignant primary pulmonary solid nodules. Ann Transl Med. 2020;8(5):171.CrossRef Yao S, Fangyi X, Wenchao Z, et al. Multiclassifier fusion based on radiomics features for the prediction of benign and malignant primary pulmonary solid nodules. Ann Transl Med. 2020;8(5):171.CrossRef
3.
go back to reference Ying Z, Jiejun C, Xiaolan H, et al. Can spectral CT imaging improve the differentiation between malignant and benign solitary pulmonary nodules? PLoS ONE. 2016;11(2):e147537. Ying Z, Jiejun C, Xiaolan H, et al. Can spectral CT imaging improve the differentiation between malignant and benign solitary pulmonary nodules? PLoS ONE. 2016;11(2):e147537.
4.
go back to reference Philippe L, Emmanuel R, Ralph L, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer (Oxf Engl 1990). 2012;48(4):441–6.CrossRef Philippe L, Emmanuel R, Ralph L, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer (Oxf Engl 1990). 2012;48(4):441–6.CrossRef
5.
go back to reference Cameron H, Bino AV, Jorge N, et al. Radiomics in pulmonary lesion imaging. Am J Roentgenol. 2019;212(3):497–504.CrossRef Cameron H, Bino AV, Jorge N, et al. Radiomics in pulmonary lesion imaging. Am J Roentgenol. 2019;212(3):497–504.CrossRef
6.
go back to reference Limkin EJ, Sun R, Dercle L, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol Off J Eur Soc Med Oncol. 2017;28(6):1191–206.CrossRef Limkin EJ, Sun R, Dercle L, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol Off J Eur Soc Med Oncol. 2017;28(6):1191–206.CrossRef
7.
go back to reference Wookjin C, Jung Hun O, Sadegh R, et al. Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer. Med Phys. 2018;45(4):1537–49.CrossRef Wookjin C, Jung Hun O, Sadegh R, et al. Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer. Med Phys. 2018;45(4):1537–49.CrossRef
8.
go back to reference Mengdi C, Hui F, Jia-Liang R, et al. Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer. Lung Cancer (Amst Neth). 2020;139:73–9.CrossRef Mengdi C, Hui F, Jia-Liang R, et al. Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer. Lung Cancer (Amst Neth). 2020;139:73–9.CrossRef
9.
go back to reference Linning E, Lin L, Li L, et al. Radiomics for classification of lung cancer histological subtypes based on nonenhanced computed tomography. Acad Radiol. 2019;26(9):1245–52.CrossRef Linning E, Lin L, Li L, et al. Radiomics for classification of lung cancer histological subtypes based on nonenhanced computed tomography. Acad Radiol. 2019;26(9):1245–52.CrossRef
10.
go back to reference Paul F, Philipp F, Clemens K, et al. Radiomic analysis using density threshold for FDG-PET/CT-based N-staging in lung cancer patients. Mol Imaging Biol. 2017;19(2):315–22.CrossRef Paul F, Philipp F, Clemens K, et al. Radiomic analysis using density threshold for FDG-PET/CT-based N-staging in lung cancer patients. Mol Imaging Biol. 2017;19(2):315–22.CrossRef
11.
go back to reference Thorsten RCJ, Bernhard K, Martin S, et al. Material differentiation by dual energy CT: initial experience. Eur Radiol. 2007;17(6):1510–7.CrossRef Thorsten RCJ, Bernhard K, Martin S, et al. Material differentiation by dual energy CT: initial experience. Eur Radiol. 2007;17(6):1510–7.CrossRef
12.
go back to reference Chae EJ, Song JW, Seo JB, et al. Clinical utility of dual-energy CT in the evaluation of solitary pulmonary nodules: initial experience. Radiology. 2008;249(2):671–81.CrossRef Chae EJ, Song JW, Seo JB, et al. Clinical utility of dual-energy CT in the evaluation of solitary pulmonary nodules: initial experience. Radiology. 2008;249(2):671–81.CrossRef
13.
go back to reference Silva AC, Morse BG, Hara AK, et al. Dual-energy (spectral) CT: applications in abdominal imaging. Radiographics. 2011;31(4):1031–46.CrossRef Silva AC, Morse BG, Hara AK, et al. Dual-energy (spectral) CT: applications in abdominal imaging. Radiographics. 2011;31(4):1031–46.CrossRef
14.
go back to reference Lin J, Zhang L, Zhang C, et al. Application of gemstone spectral computed tomography imaging in the characterization of solitary pulmonary nodules: preliminary result. J Comput Assist Tomogr. 2016;40(6):907–11.CrossRef Lin J, Zhang L, Zhang C, et al. Application of gemstone spectral computed tomography imaging in the characterization of solitary pulmonary nodules: preliminary result. J Comput Assist Tomogr. 2016;40(6):907–11.CrossRef
15.
go back to reference Beig N, Khorrami M, Alilou M, et al. Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas. Radiology. 2019;290(3):783–92.CrossRef Beig N, Khorrami M, Alilou M, et al. Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas. Radiology. 2019;290(3):783–92.CrossRef
16.
go back to reference Chae HD, Park CM, Park SJ, et al. Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas. Radiology. 2014;273(1):285–93.CrossRef Chae HD, Park CM, Park SJ, et al. Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas. Radiology. 2014;273(1):285–93.CrossRef
17.
go back to reference DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45.CrossRef DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45.CrossRef
18.
go back to reference Kaikai W, Huifang S, Guofeng Z, et al. Potential application of radiomics for differentiating solitary pulmonary nodules. OMICS J Radiol. 2016;5(2):1000218. Kaikai W, Huifang S, Guofeng Z, et al. Potential application of radiomics for differentiating solitary pulmonary nodules. OMICS J Radiol. 2016;5(2):1000218.
19.
go back to reference Zegers CML, Boellard R, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer Off J Eur Organ Res Treat Cancer Eur Assoc Cancer Res. 2012;48(4):441–6. Zegers CML, Boellard R, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer Off J Eur Organ Res Treat Cancer Eur Assoc Cancer Res. 2012;48(4):441–6.
20.
go back to reference Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77.CrossRef Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77.CrossRef
21.
go back to reference Ailing L, Zhiheng W, Yachao Y, et al. Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram. Cancer Commun (Lond Engl). 2020;40(1):16–24.CrossRef Ailing L, Zhiheng W, Yachao Y, et al. Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram. Cancer Commun (Lond Engl). 2020;40(1):16–24.CrossRef
22.
go back to reference Yunlang S, Lei Z, Huiyuan Z, et al. The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules. Eur Radiol. 2018;28(12):5121–8.CrossRef Yunlang S, Lei Z, Huiyuan Z, et al. The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules. Eur Radiol. 2018;28(12):5121–8.CrossRef
23.
go back to reference Shu-Ju T, Chih-Wei W, Kuang-Tse P, et al. Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening. Phys Med Biol. 2018;63(6):65005.CrossRef Shu-Ju T, Chih-Wei W, Kuang-Tse P, et al. Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening. Phys Med Biol. 2018;63(6):65005.CrossRef
24.
go back to reference Xu QQ, Shan WL, Zhu Y, et al. Prediction efficacy of feature classification of solitary pulmonary nodules based on CT radiomics. Eur J Radiol. 2021;139:109667.CrossRef Xu QQ, Shan WL, Zhu Y, et al. Prediction efficacy of feature classification of solitary pulmonary nodules based on CT radiomics. Eur J Radiol. 2021;139:109667.CrossRef
25.
go back to reference Shen Y, Xu F, Zhu W, et al. Multiclassifier fusion based on radiomics features for the prediction of benign and malignant primary pulmonary solid nodules. Ann Transl Med. 2020;8(5):171.CrossRef Shen Y, Xu F, Zhu W, et al. Multiclassifier fusion based on radiomics features for the prediction of benign and malignant primary pulmonary solid nodules. Ann Transl Med. 2020;8(5):171.CrossRef
26.
go back to reference Matsumoto K, Jinzaki M, Tanami Y, et al. Virtual monochromatic spectral imaging with fast kilovoltage switching: improved image quality as compared with that obtained with conventional 120-kVp CT. Radiology. 2011;259(1):257–62.CrossRef Matsumoto K, Jinzaki M, Tanami Y, et al. Virtual monochromatic spectral imaging with fast kilovoltage switching: improved image quality as compared with that obtained with conventional 120-kVp CT. Radiology. 2011;259(1):257–62.CrossRef
27.
go back to reference Yu L, Leng S, McCollough CH. Dual-energy CT-based monochromatic imaging. AJR Am J Roentgenol. 2012;199(5 Suppl):S9–15.CrossRef Yu L, Leng S, McCollough CH. Dual-energy CT-based monochromatic imaging. AJR Am J Roentgenol. 2012;199(5 Suppl):S9–15.CrossRef
28.
go back to reference Gordic S, Morsbach F, Schmidt B, et al. Ultralow-dose chest computed tomography for pulmonary nodule detection: first performance evaluation of single energy scanning with spectral shaping. Invest Radiol. 2014;49(7):465–73.CrossRef Gordic S, Morsbach F, Schmidt B, et al. Ultralow-dose chest computed tomography for pulmonary nodule detection: first performance evaluation of single energy scanning with spectral shaping. Invest Radiol. 2014;49(7):465–73.CrossRef
29.
go back to reference Hou WS, Wu HW, Yin Y, et al. Differentiation of lung cancers from inflammatory masses with dual-energy spectral CT imaging. Acad Radiol. 2015;22(3):337–44.CrossRef Hou WS, Wu HW, Yin Y, et al. Differentiation of lung cancers from inflammatory masses with dual-energy spectral CT imaging. Acad Radiol. 2015;22(3):337–44.CrossRef
30.
go back to reference Zegadło A, Żabicka M, Kania-Pudło M, et al. Assessment of solitary pulmonary nodules based on virtual monochrome images and iodine-dependent images using a single-source dual-energy CT with fast kVp switching. J Clin Med. 2020;9(8):2514.CrossRef Zegadło A, Żabicka M, Kania-Pudło M, et al. Assessment of solitary pulmonary nodules based on virtual monochrome images and iodine-dependent images using a single-source dual-energy CT with fast kVp switching. J Clin Med. 2020;9(8):2514.CrossRef
Metadata
Title
The value of radiomics based on dual-energy CT for differentiating benign from malignant solitary pulmonary nodules
Authors
Gao Liang
Wei Yu
Shu-qin Liu
Ming-guo Xie
Min Liu
Publication date
01-12-2022
Publisher
BioMed Central
Published in
BMC Medical Imaging / Issue 1/2022
Electronic ISSN: 1471-2342
DOI
https://doi.org/10.1186/s12880-022-00824-3