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:: Volume 21, Issue 3 (6-2023) ::
Int J Radiat Res 2023, 21(3): 369-375 Back to browse issues page
Value of volume-rendering computed tomography for diagnosing different solitary pulmonary nodules and invasion depth in lung adenocarcinoma
Y. Li , J. Liu , J. Zhou , L. Zhang , X. Li
Department of Critical Care Medicine, Minhang Hospital, Fudan University, Shanghai 201199, China
Abstract:   (1152 Views)
Background: We aimed to analyze the value of volume rendering (VR) in diagnosing different solitary pulmonary nodules (SPNs) with diameter less than 1.0 cm and assessing invasion depth in lung adenocarcinoma. Materials and Methods: In total, 908 patients with SPN that was confirmed by postoperative pathology were included, followed by an analysis of the imaging characteristics (including microvascular sign, vascular convergence, lobulation, and spiculation) of malignant and benign SPN based on VR. Moreover, the detection rates of imaging signs of three types of malignant SPNs (pure ground grass nodule, pGGN; part-solid nodule; and solid nodule) classified by SPN density and three invasion depths of adenocarcinoma (pre-invasion lesion, PIL; micro invasive adenocarcinoma, MIA; and invasive adenocarcinoma, IAC) were also analyzed. Results: The microvascular sign detection rate was higher while vascular convergence and spiculation detection rates were lower in malignant SPN than in benign SPN. The microvascular sign possessed high sensitivity (82%) and specificity (72%) in predicting malignant and benign SPNs. The microvascular sign detection rate decreased while vascular convergence, lobulation, and spiculation detection increased with the rising density of malignant SPN. Furthermore, the detection rates of the four imaging signs all increased with the adenocarcinoma invasion depth. Microvascular sign showed good detecting ability in low density SPNs pGGN (81.8%), part-solid nodules (95.8%), and in all three invasion depths of adenocarcinoma (PIL [68.2%], MIS [95.3%], and IAC [87.2%]). Conclusion: These imaging features distinguished by VR exhibited an excellent differential diagnostic ability of various SPNs as well as invasion depth of lung adenocarcinoma.
Keywords: Breast cancer, radiotherapy, lymphocytes, chromosomal aberration, bioindicator.
Full-Text [PDF 776 kb]   (644 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
1. 1. Smith RA, Andrews KS, Brooks D, et al. (2017) Cancer screening in the United States, 2017: a review of current American Cancer Society guidelines and current issues in cancer screening. CA: A Cancer Journal for Clinicians, 67(2): 100-121. [DOI:10.3322/caac.21392] [PMID]
2. Wyker A and Henderson WW (2021) Solitary pulmonary nodule, in StatPearls. 2021, StatPearls Publishing: Treasure Island (FL).
3. Bai C Choi C-M, Chu CM, et al. (2016) Evaluation of pulmonary nodules: clinical practice consensus guidelines for Asia. Chest, 150(4): 877-893. [DOI:10.1016/j.chest.2016.02.650] [PMID]
4. Wahidi MM, Govert JA, Goudar RK, et al. (2007) Evidence for the treatment of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines. Chest, 132(3): 94S-107S. [DOI:10.1378/chest.07-1352] [PMID]
5. Ettinger DS, Aisner DL, Wood DE, et al. (2018) NCCN Guidelines Insights: Non-Small Cell Lung Cancer, Version 5.2018. J Natl Compr Canc Netw, 16(7): 807-821. [DOI:10.6004/jnccn.2018.0062] [PMID]
6. Prakashini K, Babu S, Rajgopal K, Kokila KR (2016) Role of computer aided diagnosis (CAD) in the detection of pulmonary nodules on 64 row multi detector computed tomography. Lung India: official organ of Indian Chest Society, 33(4): 391. [DOI:10.4103/0970-2113.184872] [PMID] []
7. Lin J-z, Zhang L, Zhang C-y, Yang L, et al. (2016) Application of gemstone spectral computed tomography imaging in the characterization of solitary pulmonary nodules: Preliminary result. Journal of Computer Assisted Tomography, 40(6): 907-911. [DOI:10.1097/RCT.0000000000000469] [PMID]
8. Calheiros JLL, de Amorim LBV, de Lima LL, et al. (2021) The Effects of Perinodular Features on Solid Lung Nodule Classification. J Digit Imaging, 34(4): 798-810. [DOI:10.1007/s10278-021-00453-2] [PMID] []
9. Truong MT, Sabloff BS, Ko JP (2010) Multidetector CT of solitary pulmonary nodules. Thoracic Surgery Clinics, 20(1): 9-23. [DOI:10.1016/j.thorsurg.2009.12.002] [PMID]
10. Shi Z, Wang Y, He X (2016) Differential diagnosis of solitary pulmonary nodules with dual-source spiral computed tomography. Experimental and therapeutic medicine, 12(3): 1750-1754. [DOI:10.3892/etm.2016.3528] [PMID] []
11. Bankier AA, MacMahon H, Goo JM, et al. (2017) Recommendations for measuring pulmonary nodules at CT: a statement from the Fleischner Society. Radiology, 285(2): 584-600. [DOI:10.1148/radiol.2017162894] [PMID]
12. Naeem MQ, Darira J, Ahmed MS, et al. (2021) Comparison of maximum intensity projection and volume rendering in detecting pulmonary nodules on multidetector computed tomography. Cureus, 13(3): p. e14025. [DOI:10.7759/cureus.14025] [PMID] []
13. Han F, Wang H, Zhang G, et al. (2015) Texture feature analysis for computer-aided diagnosis on pulmonary nodules. Journal of Digital Imaging, 28(1): 99-115. [DOI:10.1007/s10278-014-9718-8] [PMID] []
14. Dhara AK, Mukhopadhyay S, Saha P, et al. (2016) Differential geometry-based techniques for characterization of boundary roughness of pulmonary nodules in CT images. Int J Computer Assisted Radiology and Surgery, 11(3): 337-349. [DOI:10.1007/s11548-015-1284-0] [PMID]
15. Buty M, Xu Z, Gao M, et al. (2016). Characterization of lung nodule malignancy using hybrid shape and appearance features. in International Conference on Medical Image Computing and Computer-Assisted Intervention. 2016. Springer. [DOI:10.1007/978-3-319-46720-7_77]
16. Yue X, Liu S, Yang G, Z, et al. (2018) HRCT morphological characteristics distinguishing minimally invasive pulmonary adenocarcinoma from invasive pulmonary adenocarcinoma appearing as subsolid nodules with a diameter of ≤ 3 cm. Clinical Radiology, 73(4): 411. e7-411. e15. [DOI:10.1016/j.crad.2017.11.014] [PMID]
17. Kim KH, Ryu S-Y, Lee HY, et al. (2019) Evaluating the tumor biology of lung adenocarcinoma: A multimodal analysis. Medicine, 98(29). [DOI:10.1097/MD.0000000000016313] [PMID] []
18. Travis WD, Brambilla E, Noguchi M, et al. (2011) International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society:International multidisciplinary classification of lung adenocarcinoma: executive summary. Proc Am Thorac Soc, 8(5): 381-5. [DOI:10.1513/pats.201107-042ST] [PMID]
19. Daković-Bjelaković M, Popović J, Antić M, (2017) Analysis of the anatomical variations of the supraorbital transcranial passage in Southeast Serbian population on volume rendered CT scans. Acta medica Medianae, 56(3): 81-87. [DOI:10.5633/amm.2017.0313]
20. Cruickshank A, Stieler G, Ameer F (2019) Evaluation of the solitary pulmonary nodule. Intern Med J, 49(3): 306-315. [DOI:10.1111/imj.14219] [PMID]
21. Hansell DM, Bankier AA, MacMahon H, et al. (2008) Fleischner Society: glossary of terms for thoracic imaging. Radiology, 246(3): 697-722. [DOI:10.1148/radiol.2462070712] [PMID]
22. Wu L, Cao G, Zhao L, et al. (2018) Spectral CT analysis of solitary pulmonary nodules for differentiating malignancy from benignancy: the value of iodine concentration spatial distribution difference. BioMed research international, 2018, https://www.hindawi.com/journals/bmri/2018/4830659/. [DOI:10.1155/2018/4830659] [PMID] []
23. Wang X, Leader JK, Wang R, et al. (2017) Vasculature surrounding a nodule: A novel lung cancer biomarker. Lung Cancer, 114: 38-43. [DOI:10.1016/j.lungcan.2017.10.008] [PMID] []
24. Sitartchouk I, Roberts HC, Pereira AM, et al. (2008) Computed tomography perfusion using first pass methods for lung nodule characterization. Investigative Radiology, 43(6): 349-358. [DOI:10.1097/RLI.0b013e3181690148] [PMID]
25. Gao F, Sun Y, Zhang G, et al. (2019) CT characterization of different pathological types of subcentimeter pulmonary ground-glass nodular lesions. The British Journal of Radiology, 92(1094): 20180204. [DOI:10.1259/bjr.20180204] [PMID] []
26. Snoeckx A, Reyntiens P, Desbuquoit D, et al. (2018) Evaluation of the solitary pulmonary nodule: size matters, but do not ignore the power of morphology. Insights into Imaging, 9(1): 73. [DOI:10.1007/s13244-017-0581-2] [PMID] []
27. Hu H, Wang Q, Tang H, et al. (2016) Multi‐slice computed tomography characteristics of solitary pulmonary ground‐glass nodules: Differences between malignant and benign. Thoracic cancer, 7(1): 80-87. [DOI:10.1111/1759-7714.12280] [PMID] []
28. Wang, X, Lv L, Zheng Q, et al. (2018) Differential diagnostic value of 64-slice spiral computed tomography in solitary pulmonary nodule. Experimental and Therapeutic Medicine, 15(6): 4703-4708. [DOI:10.3892/etm.2018.6041] [PMID] []
29. Wei Y, Bo Y, Liyun X, et al. (2016) Establishment of a clinical prediction model of solid solitary pulmonary nodules. Chinese Journal of Lung Cancer, 19(10): 705-710
30. Fan L, Liu S, Li Q, et al. (2012) Multidetector CT features of pulmonary focal ground-glass opacity: differences between benign and malignant. The British Journal of Radiology, 85(1015): 897-904. [DOI:10.1259/bjr/33150223] [PMID] []
31. Han G, Liu X, Soomro NQ, et al. (2017) Empirical driven automatic detection of lobulation imaging signs in lung CT. BioMed Research International, 2017, https://www.hindawi.com/journals/bmri/2017/3842659/. [DOI:10.1155/2017/3842659] [PMID] []
32. He Q, Fei Y, Ping D, et al. (2014) Radiology, and T. affiliated, comparative study of MSCT and pathological findings of solitary pulmonary nodules. Chongqing Medicine, 43(29):4.
33. Dhara AK, Mukhopadhyay S, Alam N, Khandelwal N (2013) Measurement of spiculation index in 3D for solitary pulmonary nodules in volumetric lung CT images. in Medical Imaging 2013: Computer-Aided Diagnosis. 2013. International Society for Optics and Photonics. 8670, DOI: 10.1117/12.2006970. [DOI:10.1117/12.2006970]
34. Yang D, Li Y, Liu J, et al. (2010) Study on solitary pulmonary nodules: Correlation between diameter and clinical manifestation and pathological features. Zhongguo fei ai za zhi= Chinese Journal of Lung Cancer, 13(6): 607-611.
35. Yang D, Li Y, Liu J, et al. (2010) Study on solitary pulmonary nodules: correlation between diameter and clinical manifestation and pathological features. Zhongguo Fei Ai Za Zhi, 13(6): 607-11.
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Li Y, Liu J, Zhou J, Zhang L, Li X. Value of volume-rendering computed tomography for diagnosing different solitary pulmonary nodules and invasion depth in lung adenocarcinoma. Int J Radiat Res 2023; 21 (3) :369-375
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Volume 21, Issue 3 (6-2023) Back to browse issues page
International Journal of Radiation Research
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