Lung-RADS分级在肺结节良恶性鉴别诊断中的应用价值研究
Study on the Application Value of Lung-RADS in the Differential Diagnosis of Benign and Malignant Lung Nodules
DOI: 10.12677/ACM.2023.13122842, PDF,   
作者: 依力夏提·马木提, 阿里甫·依马木*:新疆医科大学附属肿瘤医院,新疆 乌鲁木齐
关键词: 肺癌肺结节Lung-RADSLDCTLung Cancer Lung Nodules Lung-RADS LDCT
摘要: 在高危人群中开展肺癌筛查有益于发现早期肺癌,提高肺癌的生存率。早期肺癌最常见的表现即为肺结节,但肺结节并非肺癌所独有,而包括各种疾病,其良恶性质的鉴别是当今研究的热点与难点。2014年,美国放射学会(ACR)创建了肺部影像报告和数据系统(Lung-RADS 1.0)。该系统于1年更新至Lung- RADS 1.2019,随着更多数据的出现,预计将进一步更新。Lung-RADS为报告肺癌筛查(LCS)低剂量CT (LDCT)胸部检查时提供了通用词典和标准化结节随访管理范式,并可作为质量保证和结果监测工具。使用Lung-RADS旨在提高LCS性能并带来更好的患者预后。本文详细的介绍了Lung-RADS的发展史、应用价值以及其面临的挑战,并提出了展望。
文章引用:依力夏提·马木提, 阿里甫·依马木. Lung-RADS分级在肺结节良恶性鉴别诊断中的应用价值研究[J]. 临床医学进展, 2023, 13(12): 20190-20196. https://doi.org/10.12677/ACM.2023.13122842

参考文献

[1] Lin, S., Gao, K., Gu, S., et al. (2021) Worldwide Trends in Cervical Cancer Incidence and Mortality, with Predictions for the Next 15 Years. Cancer, 127, 4030-4039. [Google Scholar] [CrossRef] [PubMed]
[2] Zheng, R., Zeng, H., Zuo, T., et al. (2016) Lung Cancer Incidence and Mortality in China, 2011. Thoracic Cancer, 7, 94-99. [Google Scholar] [CrossRef] [PubMed]
[3] Yang, D., Liu, Y., Bai, C., et al. (2020) Epidemiology of Lung Cancer and Lung Cancer Screening Programs in China and the United States. Cancer Letters, 468, 82-87. [Google Scholar] [CrossRef] [PubMed]
[4] Ni, Y., Huang G., Yang, X., et al. (2022) Microwave Ablation Treatment for Medically Inoperable Stage I Non-Small Cell Lung Cancers: Long-Term Results. European Radiology, 32, 5616-5622. [Google Scholar] [CrossRef] [PubMed]
[5] Goldstraw, P., Chansky, K., Crowley, J., et al. (2016) The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. Journal of Thoracic Oncology, 11, 39-51. [Google Scholar] [CrossRef] [PubMed]
[6] Osarogiagbon, R.U., Liao, W., Faris, N.R., et al. (2022) Lung Cancer Diagnosed through Screening, Lung Nodule, and Neither Program: A Prospective Observational Study of the Detecting Early Lung Cancer (DELUGE) in the Mississippi Delta Cohort. Journal of Clinical Oncology, 40, 2094-2105. [Google Scholar] [CrossRef
[7] Bhende, M., Thakare, A., Saravanan, V., et al. (2022) Attention Lay-er-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer. BioMed Research International, 2022, Ar-ticle ID: 3947434. [Google Scholar] [CrossRef] [PubMed]
[8] Gu, Y., Lu, X., Yang, L., et al. (2018) Automatic Lung Nodule Detec-tion Using a 3D Deep Convolutional Neural Network Combined with a Multi-Scale Prediction Strategy in Chest CTs. Computers in Biology and Medicine, 103, 220-231. [Google Scholar] [CrossRef] [PubMed]
[9] Sathyakumar, K., Munoz, M., Singh, J., et al. (2020) Au-tomated Lung Cancer Detection Using Artificial Intelligence (AI) Deep Convolutional Neural Networks: A Narrative Lit-erature Review. Cureus, 12, e10017. [Google Scholar] [CrossRef] [PubMed]
[10] Kim, S.H., Choi, Y.H., Cho, H.H., et al. (2016) Comparison of Image Quality and Radiation Dose between High-Pitch Mode and Low-Pitch Mode Spiral Chest CT in Small Uncooperative Children: The Effect of Respiratory Rate. European Radiology, 26, 1149-1158. [Google Scholar] [CrossRef] [PubMed]
[11] Balata, H., Evison, M., Sharman, A., et al. (2019) CT Screening for Lung Cancer: Are We Ready to Implement in Europe? Lung Cancer, 134, 25-33. [Google Scholar] [CrossRef] [PubMed]
[12] Prosper, A.E., Inoue, K., Brown, K., et al. (2021) Association of Inclusion of More Black Individuals in Lung Cancer Screening with Reduced Mortality. JAMA Network Open, 4, e2119629. [Google Scholar] [CrossRef] [PubMed]
[13] Roberts, H., Walker-Dilks, C., Sivjee, K., et al. (2013) Screening High-Risk Populations for Lung Cancer: Guideline Recommendations. Journal of Thoracic Oncology, 8, 1232-1237. [Google Scholar] [CrossRef
[14] Lee, J.W., Kim, H.Y., Goo, J.M., et al. (2018) Radiological Report of Pilot Study for the Korean Lung Cancer Screening (K-LUCAS) Project: Feasibility of Imple-menting Lung Imaging Reporting and Data System. Korean Journal of Radiology, 19, 803-808. [Google Scholar] [CrossRef] [PubMed]
[15] 樊荣荣, 刘凯, 夏晨, 等. AI对非门控胸部LDCT平扫冠状动脉钙化积分危险分层的预测价值[J]. 国际医学放射学杂志, 2022, 45(1): 21-26. [Google Scholar] [CrossRef
[16] Pedersen, J.H., Ashraf, H., Dirksen, A., et al. (2009) The Danish Randomized Lung Cancer CT Screening Trial—Overall Design and Results of the Prevalence Round. Journal of Tho-racic Oncology, 4, 608-614. [Google Scholar] [CrossRef
[17] Walter, J.E., Heuvelmans, M.A. and Oudkerk, M. (2017) Small Pulmonary Nodules in Baseline and Incidence Screening Rounds of Low-Dose CT Lung Cancer Screening. Translational Lung Cancer Research, 6, 42-51. [Google Scholar] [CrossRef] [PubMed]
[18] Liu, Y., Luo, H., Qing, H., et al. (2019) Screening Baseline Charac-teristics of Early Lung Cancer on Low-Dose Computed Tomography with Computer-Aided Detection in a Chinese Pop-ulation. Cancer Epidemiology, 62, Article ID: 101567. [Google Scholar] [CrossRef] [PubMed]
[19] Wilson, D.O., Weissfeld, J.L., Fuhrman, C.R., et al. (2008) The Pittsburgh Lung Screening Study (PLuSS): Outcomes within 3 Years of a First Computed Tomography Scan. American Journal of Respiratory and Critical Care Medicine, 178, 956-961. [Google Scholar] [CrossRef
[20] Zhou, L., Zhou, Z., Liu, F., et al. (2022) Establishment and Validation of a Clinical Model for Diagnosing Solitary Pulmonary Nodules. Journal of Surgical Oncology, 126, 1316-1329. [Google Scholar] [CrossRef] [PubMed]
[21] Horeweg, N., Nackaerts, K., Oudkerk, M., et al. (2013) Low-Dose Computed Tomography Screening for Lung Cancer: Results of the First Screening Round. Journal of Com-parative Effectiveness Research, 2, 433-436. [Google Scholar] [CrossRef] [PubMed]
[22] De Koning, H.J., Van Der Aalst, C.M., De Jong, P.A., et al. (2020) Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial. The New England Journal of Medicine, 382, 503-513. [Google Scholar] [CrossRef
[23] Horeweg, N., Scholten, E.T., De Jong, P.A., et al. (2014) Detection of Lung Cancer through Low-Dose CT Screening (NELSON): A Prespecified Analysis of Screening Test Performance and Interval Cancers. The Lancet Oncology, 15, 1342-1350. [Google Scholar] [CrossRef
[24] Henschke, C.I., Yip, R., Yankelevitz, D.F., et al. (2013) Definition of a Positive Test Result in Computed Tomography Screening for Lung Cancer: A Cohort Study. Annals of Internal Medicine, 158, 246-252. [Google Scholar] [CrossRef] [PubMed]
[25] Kastner, J., Hossain, R., Jeudy, J., et al. (2021) Lung-RADS Version 1.0 versus Lung-RADS Version 1.1: Comparison of Categories Using Nodules from the National Lung Screening Trial. Radiology, 300, 199-206. [Google Scholar] [CrossRef] [PubMed]
[26] Pinsky, P.F., Gierada, D.S., Black, W., et al. (2015) Performance of Lung-RADS in the National Lung Screening Trial: A Retrospective Assessment. Annals of Internal Medicine, 162, 485-491. [Google Scholar] [CrossRef
[27] Lee, J., Lim, J., Kim, Y., et al. (2019) Development of Protocol for Korean Lung Cancer Screening Project (K-LUCAS) to Evaluate Effectiveness and Feasibility to Implement National Cancer Screening Program. Cancer Research and Treatment, 51, 1285-1294. [Google Scholar] [CrossRef] [PubMed]
[28] Obayashi, K., Shimizu, K., Nakazawa, S., et al. (2018) The Impact of Histology and Ground-Glass Opacity Component on Volume Doubling Time in Primary Lung Cancer. Journal of Tho-racic Disease, 10, 5428-5434. [Google Scholar] [CrossRef] [PubMed]
[29] Lee, C.T. (2015) What Do We Know about Ground-Glass Opacity Nodules in the Lung? Translational Lung Cancer Research, 4, 656-659.
[30] De Hoop, B., Gietema, H., Van De Vorst, S., et al. (2010) Pulmonary Ground-Glass Nodules: Increase in Mass as an Early Indicator of Growth. Radiology, 255, 199-206. [Google Scholar] [CrossRef] [PubMed]
[31] Gulati, C.M., Schreiner, A.M., Libby, D.M., et al. (2014) Outcomes of Unresected Ground-Glass Nodules with Cytology Suspicious for Adenocarcinoma. Journal of Thoracic Oncology, 9, 685-691. [Google Scholar] [CrossRef
[32] Yankelevitz, D.F., Yip, R., Smith, J.P., et al. (2015) CT Screening for Lung Cancer: Nonsolid Nodules in Baseline and Annual Repeat Rounds. Radiology, 277, 555-564. [Google Scholar] [CrossRef] [PubMed]
[33] Veronesi, G., Travaini, L.L., Maisonneuve, P., et al. (2015) Posi-tron Emission Tomography in the Diagnostic Work- Up of Screening-Detected Lung Nodules. European Respiratory Journal, 45, 501-510. [Google Scholar] [CrossRef] [PubMed]
[34] Ahn, M.I., Gleeson, T.G., Chan, I.H., et al. (2010) Perifissural Nodules Seen at CT Screening for Lung Cancer. Radiology, 254, 949-956. [Google Scholar] [CrossRef] [PubMed]
[35] De Hoop, B., Van Ginneken, B., Gietema, H. and Prokop, M. (2012) Pulmonary Perifissural Nodules on CT Scans: Rapid Growth Is Not a Predictor of Malignancy. Radiology, 265, 611-616. [Google Scholar] [CrossRef] [PubMed]
[36] Schreuder, A., Van Ginneken, B., Scholten, E.T., et al. (2018) Classi-fication of CT Pulmonary Opacities as Perifissural Nodules: Reader Variability. Radiology, 288, 867-875. [Google Scholar] [CrossRef] [PubMed]
[37] Macmahon, H., Naidich, D.P., Goo, J.M., et al. (2017) Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. Radiology, 284, 228-243. [Google Scholar] [CrossRef] [PubMed]
[38] Xu, D.M., Gietema, H., De Koning, H., et al. (2006) Nodule Management Protocol of the NELSON Randomised Lung Cancer Screening Trial. Lung Cancer, 54, 177-184. [Google Scholar] [CrossRef] [PubMed]
[39] Mcwilliams, A., Tammemagi, M.C., Mayo, J.R., et al. (2013) Probability of Cancer in Pulmonary Nodules Detected on First Screening CT. The New England Journal of Medicine, 369, 910-919. [Google Scholar] [CrossRef
[40] Shlomi, D., Ben-Avi, R., Balmor, G.R., et al. (2014) Screening for Lung Cancer: Time for Large-Scale Screening by Chest Computed Tomography. European Respiratory Journal, 44, 217-238. [Google Scholar] [CrossRef] [PubMed]
[41] Li, K., Yip, R., Avila, R., et al. (2017) Size and Growth Assess-ment of Pulmonary Nodules: Consequences of the Rounding. Journal of Thoracic Oncology, 12, 657-662. [Google Scholar] [CrossRef] [PubMed]
[42] Chung, K., Jacobs, C., Scholten, E.T., et al. (2017) Malignancy Es-timation of Lung-RADS Criteria for Subsolid Nodules on CT: Accuracy of Low and High Risk Spectrum When Using NLST Nodules. European Radiology, 27, 4672- 4679. [Google Scholar] [CrossRef] [PubMed]
[43] Barbosa Jr., E.J.M., Yang, R. and Hershman, M. (2021) Real-World Lung Cancer CT Screening Performance, Smoking Behavior, and Adherence to Recommendations: Lung-RADS Category and Smoking Status Predict Adherence. American Journal of Roentgenology, 216, 919-926. [Google Scholar] [CrossRef
[44] Dyer, S.C., Bartholmai, B.J. and Koo, C.W. (2020) Implications of the Updated Lung CT Screening Reporting and Data System (Lung-RADS version 1.1) for Lung Cancer Screening. Journal of Thoracic Disease, 12, 6966-6977. [Google Scholar] [CrossRef] [PubMed]
[45] Choi, J.H., Ahn, M.J., Rhim, H.C., et al. (2005) Comparison of WHO and RECIST Criteria for Response in Metastatic Colorectal Carcinoma. Cancer Research and Treatment, 37, 290-293. [Google Scholar] [CrossRef] [PubMed]
[46] Van Persijn Van Meerten, E.L., Gelderblom, H. and Bloem, J.L. (2010) RECIST Revised: Implications for the Radiologist. A Review Article on the Modified RECIST Guideline. European Radiology, 20, 1456-1467. [Google Scholar] [CrossRef] [PubMed]
[47] Nishino, M., Jagannathan, J.P., Ramaiya, N.H., et al. (2010) Re-vised RECIST Guideline Version 1.1: What Oncologists Want to Know and What Radiologists Need to Know. American Journal of Roentgenology, 195, 281-289. [Google Scholar] [CrossRef
[48] Revel, M.P., Bissery, A., Bienvenu, M., et al. (2004) Are Two-Dimensional CT Measurements of Small Noncalcified Pulmonary Nodules Reliable? Radiology, 231, 453-458. [Google Scholar] [CrossRef] [PubMed]
[49] Heuvelmans, M.A., Walter, J.E., Vliegenthart, R., et al. (2018) Disagreement of Diameter and Volume Measurements for Pulmonary Nodule Size Estimation in CT Lung Cancer Screening. Thorax, 73, 779-781. [Google Scholar] [CrossRef] [PubMed]
[50] Van Riel, S.J., Sanchez, C.I., Bankier, A.A., et al. (2015) Ob-server Variability for Classification of Pulmonary Nodules on Low-Dose CT Images and Its Effect on Nodule Manage-ment. Radiology, 277, 863-871. [Google Scholar] [CrossRef] [PubMed]
[51] Oxnard, G.R., Zhao, B., Sima, C.S., et al. (2011) Variability of Lung Tumor Measurements on Repeat Computed Tomography Scans Taken within 15 Minutes. Journal of Clinical On-cology, 29, 3114-3119. [Google Scholar] [CrossRef
[52] Petrick, N., Kim, H.J., Clunie, D., et al. (2014) Comparison of 1D, 2D, and 3D Nodule Sizing Methods by Radiologists for Spherical and Complex Nodules on Thoracic CT Phantom Im-ages. Academic Radiology, 21, 30-40. [Google Scholar] [CrossRef] [PubMed]
[53] Bankier, A.A., Macmahon, H., Goo, J.M., et al. (2017) Recom-mendations for Measuring Pulmonary Nodules at CT: A Statement from the Fleischner Society. Radiology, 285, 584-600. [Google Scholar] [CrossRef] [PubMed]
[54] Wang, Y., Van Klaveren, R.J., Van Der Zaag-Loonen, H.J., et al. (2008) Effect of Nodule Characteristics on Variability of Semiautomated Volume Measurements in Pulmonary Nodules Detected in a Lung Cancer Screening Program. Radiology, 248, 625-631. [Google Scholar] [CrossRef] [PubMed]
[55] Gietema, H.A., Wang, Y., Xu, D., et al. (2006) Pulmonary Nod-ules Detected at Lung Cancer Screening: Interobserver Variability of Semiautomated Volume Measurements. Radiology, 241, 251-257. [Google Scholar] [CrossRef] [PubMed]