Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

Parameters of Dual-layer Spectral Detector CT Could be Used to Differentiate Non-Small Cell Lung Cancer from Small Cell Lung Cancer

Author(s): Ronghua Mu, Zhuoni Meng, Xiaodi Zhang, Zixuan Guo, Wei Zheng, Zeyu Zhuang and Xiqi Zhu*

Volume 18, Issue 10, 2022

Published on: 22 April, 2022

Article ID: e080322201867 Pages: 9

DOI: 10.2174/1573405618666220308105359

Price: $65

Abstract

Background and Objective: Differentiating non-small cell lung cancer (NSCLC) from small cell lung cancer (SCLC) remains a substantial challenge. This study aimed at evaluating the performance of dual-layer spectral detector CT (DLCT) in differentiating NSCLC from SCLC.

Methods: Spectral images of 247 cancer patients confirmed by pathology were retrospectively analyzed in both the arterial phase (AP) and the venous phase (VP), including 197 cases of NSCLC and 50 cases of SCLC. Effective atomic number (Z-eff), Spectral CT-Mono Energetic (MonoE [40keV~90keV]), iodine density (ID) and thoracic aorta iodine density (IDaorta) in contrast-enhanced images were measured and compared between the SCLC and NSCLC subgroups of tumors. The slope of the spectral curve (λ, interval of 10 keV) and normalized iodine density (NID) were also calculated between the SCLC and NSCLC. Through the statistical analysis, the diagnostic efficiency of each spectral parameter was calculated, and the difference in their efficiency was analyzed.

Results: Both in NSCLS and SCLC, all parameters in VP were significantly higher than those in AP (p<0.001), except for λ90. There were significant differences in all spectral parameters between NSCLS and SCLC, both in AP and VP (p < 0.001). Except for VP-λ90, there was no significant difference in ROC curves of all spectral parameters. VP-NID exhibited the best diagnostic performance with an AUC value of 0.917 (95%[CI]: 0.870~0.965), sensitivity and specificity of 92.9% and 80%, and a diagnostic threshold of 0.217.

Conclusion: All parameters of DLCT have high diagnostic efficiency in differentiating NSCLC from SCLC except for VP-λ90, and VP-NID has the highest diagnostic efficiency.

Keywords: Non-small cell lung cancer, small cell lung cancer, pathological classification, dual-layer detector, X-ray computed tomography, pathology.

[1]
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019; 69(1): 7-34.
[http://dx.doi.org/10.3322/caac.21551] [PMID: 30620402]
[2]
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71(3): 209-49.
[http://dx.doi.org/10.3322/caac.21660] [PMID: 33538338]
[3]
Blandin Knight S, Crosbie PA, Balata H, Chudziak J, Hussell T, Dive C. Progress and prospects of early detection in lung cancer. Open Biol 2017; 7(9): 170070.
[http://dx.doi.org/10.1098/rsob.170070] [PMID: 28878044]
[4]
Zheng XQ, Huang JF, Lin JL, et al. Incidence, prognostic factors, and a nomogram of lung cancer with bone metastasis at initial diagnosis: A population-based study. Transl Lung Cancer Res 2019; 8(4): 367-79.
[http://dx.doi.org/10.21037/tlcr.2019.08.16] [PMID: 31555512]
[5]
Iams WT, Porter J, Horn L. Immunotherapeutic approaches for small-cell lung cancer. Nat Rev Clin Oncol 2020; 17(5): 300-12.
[http://dx.doi.org/10.1038/s41571-019-0316-z] [PMID: 32055013]
[6]
Wang S, Zimmermann S, Parikh K, Mansfield AS, Adjei AA. Current diagnosis and management of small-cell lung cancer. Mayo Clin Proc 2019; 94(8): 1599-622.
[http://dx.doi.org/10.1016/j.mayocp.2019.01.034] [PMID: 31378235]
[7]
Deniffel D, Sauter A, Dangelmaier J, Fingerle A, Rummeny EJ, Pfeiffer D. Differentiating intrapulmonary metastases from different primary tumors via quantitative dual-energy CT based iodine concentration and conventional CT attenuation. Eur J Radiol 2019; 111: 6-13.
[http://dx.doi.org/10.1016/j.ejrad.2018.12.015] [PMID: 30691666]
[8]
Wen Q, Yue Y, Shang J, Lu X, Gao L, Hou Y. The application of dual-layer spectral detector computed tomography in solitary pulmonary nodule identification. Quant Imaging Med Surg 2021; 11(2): 521-32.
[http://dx.doi.org/10.21037/qims-20-2] [PMID: 33532253]
[9]
Große Hokamp N, Gupta A, Gilkeson RC. Stratification of pulmonary nodules using quantitative iodine maps from dual-energy computed tomography. Am J Respir Crit Care Med 2019; 199(2): e3-4.
[http://dx.doi.org/10.1164/rccm.201803-0506IM] [PMID: 30199642]
[10]
Kim J, Lee KH, Kim J, Shin YJ, Lee KW. Improved repeatability of subsolid nodule measurement in low-dose lung screening with monoen-ergetic images: A phantom study. Quant Imaging Med Surg 2019; 9(2): 171-9.
[http://dx.doi.org/10.21037/qims.2018.10.06] [PMID: 30976541]
[11]
Gao L, Lu X, Wen Q, Hou Y. Added value of spectral parameters for the assessment of lymph node metastasis of lung cancer with dual-layer spectral detector computed tomography. Quant Imaging Med Surg 2021; 11(6): 2622-33.
[http://dx.doi.org/10.21037/qims-20-1045] [PMID: 34079728]
[12]
Xu X, Sui X, Zhong W, et al. Clinical utility of quantitative dual-energy CT iodine maps and CT morphological features in distinguishing small-cell from non-small-cell lung cancer. Clin Radiol 2019; 74(4): 268-77.
[http://dx.doi.org/10.1016/j.crad.2018.10.012] [PMID: 30691731]
[13]
Zheng M. Classification and pathology of lung cancer. Surg Oncol Clin N Am 2016; 25(3): 447-68.
[http://dx.doi.org/10.1016/j.soc.2016.02.003] [PMID: 27261908]
[14]
Travis WD. Lung cancer pathology: Current concepts. Clin Chest Med 2020; 41(1): 67-85.
[http://dx.doi.org/10.1016/j.ccm.2019.11.001] [PMID: 32008630]
[15]
Zhang M, Kono M. Solitary pulmonary nodules: Evaluation of blood flow patterns with dynamic CT. Radiology 1997; 205(2): 471-8.
[http://dx.doi.org/10.1148/radiology.205.2.9356631] [PMID: 9356631]
[16]
Fehrenbach U, Kahn J, Böning G, et al. Spectral CT and its specific values in the staging of patients with non-small cell lung cancer: Tech-nical possibilities and clinical impact. Clin Radiol 2019; 74(6): 456-66.
[http://dx.doi.org/10.1016/j.crad.2019.02.010] [PMID: 30905380]
[17]
Wu F, Zhou H, Li F, et al. Spectral CT imaging of lung cancer: Quantitative analysis of spectral parameters and their correlation with tumor characterist. Acad Radiol 2018; 25(11): 1398-404.
[http://dx.doi.org/10.1016/j.acra.2018.04.017]
[18]
Lin LY, Zhang Y, Suo ST, Zhang F, Cheng JJ, Wu HW. Correlation between dual-energy spectral CT imaging parameters and pathological grades of non-small cell lung cancer. Clin Radiol 2018; 73(4): 412.e1-7.
[http://dx.doi.org/10.1016/j.crad.2017.11.004] [PMID: 29221718]
[19]
Zhang Y, Cheng J, Hua X, et al. Can spectral CT imaging improve the differentiation between malignant and benign solitary pulmonary nod-ules? PLoS One 2016; 11(2): e0147537.
[http://dx.doi.org/10.1371/journal.pone.0147537] [PMID: 26840459]
[20]
Doerner J, Hauger M, Hickethier T, et al. Image quality evaluation of dual-layer spectral detector CT of the chest and comparison with con-ventional CT imaging. Eur J Radiol 2017; 93: 52-8.
[http://dx.doi.org/10.1016/j.ejrad.2017.05.016] [PMID: 28668431]
[21]
Liu YH, Zhu SC, Shi DP, et al. Clinical value of spectral CT imaging in preoperative evaluation of pathological grading of esophageal squa-mous cell carcinoma. Zhonghua Yi Xue Za Zhi 2017; 97(43): 3406-11.
[http://dx.doi.org/10.3760/cma.j.issn.0376-2491.2017.43.010]
[22]
Kulpe S, Dierolf M, Günther B, et al. K-edge subtraction computed tomography with a compact synchrotron X-ray source. Sci Rep 2019; 9(1): 13332.
[http://dx.doi.org/10.1038/s41598-019-49899-z] [PMID: 31527643]
[23]
Goodsitt MM, Christodoulou EG, Larson SC. Accuracies of the synthesized monochromatic CT numbers and effective atomic numbers obtained with a rapid kVp switching dual energy CT scanner. Med Phys 2011; 38(4): 2222-32.
[http://dx.doi.org/10.1118/1.3567509] [PMID: 21626956]
[24]
Mairinger T. Histology, cytology and molecular diagnostics of lung cancer. Pathologe 2019; 40(6): 649-61.
[http://dx.doi.org/10.1007/s00292-019-00677-8]
[25]
Yu Y, Wang X, Shi C, Hu S, Zhu H, Hu C. Spectral computed tomography imaging in the differential diagnosis of lung cancer and inflam-matory myofibroblastic tumor. J Comput Assist Tomogr 2019; 43(2): 338-44.
[http://dx.doi.org/10.1097/RCT.0000000000000840] [PMID: 30762653]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy