持续性亚实性肺结节的生长预测研究进展
Research Advances on Growth Prediction of Persistent Subsolid Nodules
DOI: 10.12677/ACM.2024.141180, PDF,   
作者: 王雯琦, 李长毅*：重庆医科大学附属第二医院，呼吸与危重症学科，重庆
关键词: 亚实性结节；生长；放射组学；人工智能；Subsolid Nodules； Growth； Radiomics； Artificial Intelligence
摘要: 随着全球肺癌筛查计划的实施，亚实性肺结节的高发现率越来越受到重视。持续存在的亚实性结节很有可能是前驱腺体病变或癌性病变。目前还不清楚需通过手术切除的持续性亚实性肺结节的特征，以及稳定的亚实性肺结节的随访年限。在过去的几年里，已经发布了许多肺结节的管理指南，但这些指南仍然存在争议。另外，随着放射组学、人工智能技术飞速发展，也建立了许多结节生长的预测模型。故本文就持续性亚实性结节的生长定义、生长影响因素、预测模型和分子生物学特征作一综述，旨在优化亚实性肺结节的随访管理和临床决策。
Abstract: With the implementation of the global lung cancer screening program, the high detection rate of pulmonary subsolid nodules has received more and more attention. Persistent subsolid nodules are likely to be prodromal or cancerous. The features of persistent subsolid pulmonary nodules requir-ing surgical resection and the follow-up period are unclear. Over the past few years, a number of guidelines have been issued for the management of pulmonary nodules, but these guidelines re-main controversial. In addition, with the rapid development of radiomics and artificial intelligence technology, many prediction models for nodule growth have been established. Therefore, this arti-cle reviews the growth definition, growth influencing factors, prediction models and molecular bi-ology characteristics of persistent subsolid pulmonary nodules, aiming to optimize the follow-up management and clinical decision-making of pulmonary subsolid nodules.
文章引用：王雯琦, 李长毅. 持续性亚实性肺结节的生长预测研究进展[J]. 临床医学进展, 2024, 14(1): 1244-1251. https://doi.org/10.12677/ACM.2024.141180

参考文献

[1] Liu, Y.-C., Liang, C.-H., Wu, Y.-J., et al. (2023) Managing Persistent Subsolid Nodules in Lung Cancer: Education, De-cision Making, and Impact of Interval Growth Patterns. Diagnostics, 13, Article No. 2674. [Google Scholar] [CrossRef] [PubMed]
[2] Borczuk, A.C. and Cooper, W.A. (2021) WHO Classification of Tumours. Thoracic Tumours. 5th Edition, World Health Organization, Geneva, 1-565.
[3] Lee, G.D., Park, C.H., Park, H.S., et al. (2018) Lung Adenocarcinoma Invasiveness Risk in Pure Ground-Glass Opacity Lung Nodules Smaller than 2 cm. The Thoracic and Cardiovascular Surgeon, 67, 321-328. [Google Scholar] [CrossRef] [PubMed]
[4] Hu, F., Huang, H., Jiang, Y., et al. (2021) Discriminating Invasive Adenocarcinoma among Lung Pure Ground-Glass Nodules: A Multi-Parameter Prediction Model. Journal of Thoracic Disease, 13, 5383-5394. [Google Scholar] [CrossRef] [PubMed]
[5] Kakinuma, R., Muramatsu, Y. and Kusumoto, M. (2015) Solitary Pure Ground-Glass Nodules 5 mm or Smaller: Frequency of Growth. Radiology, 276, 873-882. [Google Scholar] [CrossRef] [PubMed]
[6] Lee, J.H., Lim, W.H., Hong, J.H., et al. (2020) Growth and Clini-cal Impact of 6-mm or Larger Subsolid Nodules after 5 Years of Stability at Chest CT. Radiology, 295, 448-455. [Google Scholar] [CrossRef] [PubMed]
[7] Lee, H.W., Jin, K.-N., Lee, J.-K., et al. (2019) Long-Term Fol-low-Up of Ground-Glass Nodules after 5 Years of Stability. Journal of Thoracic Oncology, 14, 1370-1377. [Google Scholar] [CrossRef] [PubMed]
[8] 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]
[9] Callister, M.E.J., Baldwin, D.R., Akram, A.R., et al. (2015) British Thoracic Society Guidelines for the Investigation and Management of Pulmonary Nodules: Accredited by NICE. Thorax, 70, ii1-ii54. [Google Scholar] [CrossRef] [PubMed]
[10] Zhao, Y., Xie, X., De Koning, H.J., et al. (2011) NELSON Lung Cancer Screening Study. Cancer Imaging, 11, S79-S84. [Google Scholar] [CrossRef] [PubMed]
[11] Kim, H., Park, C.M., Hwang, E.J., et al. (2017) Pulmonary Subsolid Nodules: Value of Semi-Automatic Measurement in Diagnostic Accuracy, Diagnostic Reproducibility and Nodule Classification Agreement. European Radiology, 28, 2124-2133. [Google Scholar] [CrossRef] [PubMed]
[12] Kakinuma, R., Ashizawa, K., Kuriyama, K., et al. (2012) Meas-urement of Focal Ground-Glass Opacity Diameters on CT Images. Academic Radiology, 19, 389-394. [Google Scholar] [CrossRef] [PubMed]
[13] Tang, E.-K., Chen, C.-S., Wu, C.C., et al. (2019) Natural History of Persistent Pulmonary Subsolid Nodules: Long-Term Observation of Different Interval Growth. Heart, Lung and Circu-lation, 28, 1747-1754. [Google Scholar] [CrossRef] [PubMed]
[14] Gao, C., Li, J., Wu, L., et al. (2020) The Natural Growth of Subsolid Nodules Predicted by Quantitative Initial CT Features: A Systematic Review. Frontiers in Oncology, 10, Article No. 318. [Google Scholar] [CrossRef] [PubMed]
[15] Cronin, P., Lee, J.H., Hwang, E.J., et al. (2022) Determination of the Optimum Definition of Growth Evaluation for Indeterminate Pulmonary Nodules Detected in Lung Cancer Screening. PLOS ONE, 17, e0274583. [Google Scholar] [CrossRef] [PubMed]
[16] He, Y., Xiong, Z., Zhang, J., et al. (2023) Growth Assessment of Pure Ground-Glass Nodules on CT: Comparison of Density and Size Measurement Methods. Journal of Cancer Re-search and Clinical Oncology, 149, 9937-9946. [Google Scholar] [CrossRef] [PubMed]
[17] Guo, X., Jia, X., Zhang, D., et al. (2022) Indeterminate Pulmo-nary Subsolid Nodules in Patients with No History of Cancer: Growing Prediction, CT Pattern, and Pathological Diag-nosis. Diagnostic and Interventional Radiology, 28, 230-238. [Google Scholar] [CrossRef] [PubMed]
[18] Sun, Y., Ma, Z., Zhao, W., et al. (2023) Computed Tomography Radiomics in Growth Prediction of Pulmonary Ground-Glass Nodules. European Journal of Radiology, 159, Article ID: 110684. [Google Scholar] [CrossRef] [PubMed]
[19] Zhang, Z., Zhou, L., Min, X., et al. (2023) Long-Term Follow-Up of Persistent Pulmonary Subsolid Nodules: Natural Course of Pure, Heterogeneous, and Real Part-Solid Ground-Glass Nodules. Thoracic Cancer, 14, 1059-1070. [Google Scholar] [CrossRef] [PubMed]
[20] Yoon, H.Y., Bae, J.Y., Kim, Y., et al. (2019) Risk Factors Asso-ciated with an Increase in the Size of Ground‐Glass Lung Nodules on Chest Computed Tomography. Thoracic Cancer, 10, 1544-1551. [Google Scholar] [CrossRef] [PubMed]
[21] Liang, X., Liu, M., Li, M., et al. (2022) Clinical and CT Features of Subsolid Pulmonary Nodules with Interval Growth: A Systematic Review and Meta-Analysis. Frontiers in Oncology, 12, Article ID: 929174. [Google Scholar] [CrossRef] [PubMed]
[22] Wu, L., Gao, C., Kong, N., et al. (2022) The Long-Term Course of Subsolid Nodules and Predictors of Interval Growth on Chest CT: A Systematic Review and Meta-Analysis. European Radiology, 33, 2075-2088. [Google Scholar] [CrossRef] [PubMed]
[23] Shi, Z., Deng, J., She, Y., et al. (2019) Quantitative Features Can Predict Further Growth of Persistent Pure Ground-Glass Nodule. Quantitative Imaging in Medicine and Surgery, 9, 283-291. [Google Scholar] [CrossRef] [PubMed]
[24] Cho, J., Kim, E.S., Kim, S.J., et al. (2016) Long-Term Follow-Up of Small Pulmonary Ground-Plass Nodules Stable for 3 Years: Implications of the Proper Follow-Up Period and Risk Factors for Subsequent Growth. Journal of Thoracic Oncology, 11, 1453-1459. [Google Scholar] [CrossRef] [PubMed]
[25] Barletta, J.A., Yeap, B.Y. and Chirieac, L.R. (2009) Prognostic Significance of Grading in Lung Adenocarcinoma. Cancer, 116, 659-669. [Google Scholar] [CrossRef] [PubMed]
[26] Kato, T., Iwano, S., Hanamatsu, Y., et al. (2023) Prognostic Impact of Highly Solid Component in Early-Stage Solid Lung Adenocarcinoma. Quantitative Imaging in Medicine and Surgery, 13, 5641-5652. [Google Scholar] [CrossRef] [PubMed]
[27] Xi, J., Yin, J., Liang, J., et al. (2021) Prognostic Impact of Radiological Consolidation Tumor Ratio in Clinical Stage IA Pulmonary Ground Glass Opacities. Frontiers in Oncology, 11, Article ID: 616149. [Google Scholar] [CrossRef] [PubMed]
[28] Lee, J.H., Park, C.M., Lee, S.M., et al. (2015) Persistent Pulmonary Subsolid Nodules with Solid Portions of 5 mm or Smaller: Their Natural Course and Predictors of Interval Growth. Eu-ropean Radiology, 26, 1529-1537. [Google Scholar] [CrossRef] [PubMed]
[29] Song, Y.S., Park, C.M., Park, S.J., et al. (2014) Volume and Mass Doubling Times of Persistent Pulmonary Subsolid Nodules Detected in Patients without Known Malignancy. Ra-diology, 273, 276-284. [Google Scholar] [CrossRef] [PubMed]
[30] Xia, T., Cai, M., Zhuang, Y., et al. (2020) Risk Factors for the Growth of Residual Nodule in Surgical Patients with Adenocarcinoma Presenting as Multifocal Ground-Glass Godules. European Journal of Radiology, 133, Article ID: 109332. [Google Scholar] [CrossRef] [PubMed]
[31] Silva, M., Prokop, M., Jacobs, C., et al. (2018) Long-Term Active Surveillance of Screening Detected Subsolid Nodules Is a Safe Strategy to Reduce Overtreatment. Journal of Thoracic Oncology, 13, 1454-1463. [Google Scholar] [CrossRef] [PubMed]
[32] Lin, C.-Y., Guo, S.-M., Lien, J.-J.J., et al. (2023) Combined Model Integrating Deep Learning, Radiomics, and Clinical Data to Classify Lung Nodules at Chest CT. La Radiologia Medica. [Google Scholar] [CrossRef] [PubMed]
[33] Huang, W., Deng, H., Li, Z., et al. (2023) Baseline Whole-Lung CT Features Deriving from Deep Learning and Radiomics: Prediction of Benign and Malignant Pulmonary Ground-Glass Nodules. Frontiers in Oncology, 13, Article ID: 1255007. [Google Scholar] [CrossRef] [PubMed]
[34] Kao, T.-N., Hsieh, M.-S., Chen, L.-W., et al. (2022) CT-Based Radiomic Analysis for Preoperative Prediction of Tumor Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodule. Cancers, 14, Article No. 5888. [Google Scholar] [CrossRef] [PubMed]
[35] Ather, S., Kadir, T. and Gleeson, F. (2020) Artificial Intelligence and Radiomics in Pulmonary Nodule Management: Current Status and Future Applications. Clinical Radiology, 75, 13-19. [Google Scholar] [CrossRef] [PubMed]
[36] Gao, C., Yan, J., Luo, Y., et al. (2020) The Growth Trend Predic-tions in Pulmonary Ground Glass Nodules Based on Radiomic CT Features. Frontiers in Oncology, 10, Article ID: 580509. [Google Scholar] [CrossRef] [PubMed]
[37] Ma, Z.J., Ma, Z.X., Sun, Y.L., et al. (2023) Prediction of Subsolid Pulmonary Nodule Growth Rate Using Radiomics. BMC Medical Imaging, 23, Article No. 177. [Google Scholar] [CrossRef] [PubMed]
[38] Wu, Y.-J., Wu, F.-Z., Yang, S.-C., et al. (2022) Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education. Diagnostics, 12, Article No. 1064. [Google Scholar] [CrossRef] [PubMed]
[39] Kim, R.Y., Oke, J.L., Dotson, T.L., et al. (2023) Effect of an Artificial Intelligence Tool on Management Decisions for Indeterminate Pulmonary Nodules. Respirology, 28, 582-584. [Google Scholar] [CrossRef] [PubMed]
[40] Balagurunathan, Y., Beers, A., Mcnitt-Gray, M., et al. (2021) Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge. IEEE Transactions on Medical Imaging, 40, 3748-3761. [Google Scholar] [CrossRef] 
[41] Borghesi, A., Coviello, F.L., Scrimieri, A., et al. (2023) Soft-ware-Based Quantitative CT Analysis to Predict the Growth Trend of Persistent Nonsolid Pulmonary Nodules: A Retro-spective Study. La Radiologia Medica, 128, 734-743. [Google Scholar] [CrossRef] [PubMed]
[42] Qi, L.-L., Wang, J.-W., Yang, L., et al. (2020) Natural History of Pathologically Confirmed Pulmonary Subsolid Nodules with Deep Learning-Assisted Nodule Segmentation. European Radiology, 31, 3884-3897. [Google Scholar] [CrossRef] [PubMed]
[43] Tao, G., Zhu, L., Chen, Q., et al. (2022) Prediction of Future Imagery of Lung Nodule as Growth Modeling with Follow-Up Computed Tomography Scans Using Deep Learning: A Retrospective Cohort Study. Translational Lung Cancer Research, 11, 250-262. [Google Scholar] [CrossRef] [PubMed]
[44] Cai, Y., Chen, T., Zhang, S., et al. (2023) Correlation Exploration among CT Imaging, Pathology and Genotype of Pulmonary Ground-Glass Opacity. Journal of Cellular and Molecular Medicine, 27, 2021-2031. [Google Scholar] [CrossRef] [PubMed]
[45] Xu, X., Li, N., Zhao, R., et al. (2017) Targeted Next-Generation Se-quencing for Analyzing the Genetic Alterations in Atypical Adenomatous Hyperplasia and Adenocarcinoma in Situ. Journal of Cancer Research and Clinical Oncology, 143, 2447-2453. [Google Scholar] [CrossRef] [PubMed]
[46] Lu, Q., Ma, Y., An, Z., et al. (2018) Epidermal Growth Factor Receptor Mutation Accelerates Radiographic Progression in Lung Adenocarcinoma Presented as a Solitary Ground-Glass Opacity. Journal of Thoracic Disease, 10, 6030-6039. [Google Scholar] [CrossRef] [PubMed]
[47] Ren, J., Wang, Y., Liu, C., et al. (2023) Correlation Analysis of Clinical, Pathological, Imaging and Genetic Features of Ground-Glass Nodule Featured Lung Adenocarcinomas between High-Risk and Non-High-Risk Individuals. European Journal of Medical Research, 28, 1-14. [Google Scholar] [CrossRef] [PubMed]

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