目的 探讨磨玻璃结节(GGN)型肺腺癌的HRCT特征与脏层胸膜侵犯(VPI)的相关性。方法 收集2019年1至12月解放军总医院第四医学中心手术切除直径≤3 cm的GGN[0≤实性肿瘤比(CTR)≤0.5]且病理证实为早期肺腺癌的患者资料219例。病变与非叶间胸膜关系(RLP)分为5型。单因素分析VPI(-)组与VPI(+)组临床和HRCT特征,进一步多因素分析有统计学意义变量,确定预测 VPI的独立危险因素。结果 219例GGN中,VPI(-)组134例,VPI(+)组85例。单因素分析中,GGN类型、直径、CT值、RLP差异有统计学意义(P< 0.05),年龄、性别、位置差异无统计学意义(P> 0.05)。RLP中Ⅰ~Ⅲ型的VPI发生率分别为0、0、5.6%,而Ⅳ、Ⅴ型为45.2%、78.7%。多因素分析显示Ⅳ型和Ⅴ型是预测VPI的独立危险因素[P<0.001,OR=72.449,95%CI(8.743~600.333);P=0.003,OR=4.576,95%CI(1.688~12.409)]。结论 HRCT下RLP对GGN型肺腺癌中VPI的发生有很高的诊断价值,Ⅰ~Ⅱ型不发生VPI,Ⅲ型VPI罕见,而与胸膜宽基底接触的Ⅳ型和Ⅴ型容易发生VPI,尤其出现胸膜凹陷征更能提示VPI的发生。
Abstract
Objective To investigate the correlation between HRCT features and visceral pleural invasion (VPI) in lung adenocarcinoma with ground glass nodules (GGN).Methods Data of 219 patients with GGN [0≤CTR≤0.5] with diameter ≤3 cm and pathologically confirmed early lung adenocarcinoma were collected from the Fourth Medical Center of Chinese PLA General Hospital from January to December 2019. The relation between lesion and non-interlobular pleura (RLP) was divided into 5 types. Clinical and HRCT features of VPI(-) group and the VPI(+) group were analyzed by univariate analysis, and statistically significant variables were analyzed by multivariate analysis to determine the independent risk factors for predicting VPI.Results Among 219 GGNs, there were 134 cases in VPI(-) group and 85 cases in VPI(+) group. In univariate analysis, there was significant difference in the type of GGN, diameter, CT value, and RLP (P<0.05), while there was no significant difference in age, gender or location (P>0.05). The incidence of VPI in RLP types Ⅰ-Ⅲ was 0, 0, 5.6%, while the incidence of VPI in RLP types Ⅳ and Ⅴ was 45.2%, 78.7%. respectively. Multivariate analysis showed that type Ⅳ and type Ⅴ were independent risk factors for predicting VPI [P<0.001, OR=72.449, 95% confidence interval(8.743-600.333); P=0.003, OR=4.576, 95%confidence interval (1.688-12.409) ].Conclusions RLP under HRCT has a high diagnostic value for the occurrence of VPI in GGN lung adenocarcinoma. VPI does not occur in type Ⅰ and type Ⅱ, and is rare in type Ⅲ. VPI is easy to occur in type Ⅳ and type Ⅴ, which is in contact with the broad pleural base, especially the occurrence of pleural indentation sign can indicate the occurrence of VPI.
关键词
肺肿瘤 /
磨玻璃结节 /
脏层胸膜侵犯 /
HRCT
Key words
lung neoplasms /
ground glass nodule /
visceral pleural invasion /
HRCT
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Sung H, Ferlay J, Siegel R L, et al. Global cancer statistics2020:GLOBOCAN estimates of incidence and mortality worldwidefor 36 cancers in 185 countries [J]. CA Cancer J Clin, 2021, 71(3): 209-249.
[2] MacMahon H, Naidich D P, Goo J M, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the fleischner society 2017[J]. Radiology, 2017, 284(1): 228-243.
[3] Nicholson A G, Tsao M S, Beasley M B, et al. The 2021 WHO classification of lung tumors: impact of advances since 2015[J]. J Thorac Oncol, 2022, 17(3):362-387.
[4] Zuo Z, Li Y, Peng K, et al. CT texture analysis-based nomogram for the preoperative prediction of visceral pleural invasion in cT1N0M0 lung adenocarcinoma: an external validation cohort study[J]. Clin Radiol, 2022, 77(3):e215-e221.
[5] Wo Y, Zhao Y, Qiu T, et al. Impact of visceral pleural invasion on the association of extent of lymphadenectomy and survival in stage I nonsmall cell lung cancer[J]. Cancer Med, 2019, 8(2):669-678.
[6] Zeng Y, Mayne N, Yang C J, et al. A nomogram for predicting cancerspecific survival of TNM 8th edition stage I non-small-cell lung cancer[J]. Ann Surg Oncol, 2019, 26(7):2053-2062.
[7] Feng S H, Yang S T. The new 8th TNM staging system of lung cancer and its potential imaging interpretation pitfalls and limitations with CT image demonstrations[J]. Diagn Interv Radiol, 2019, 25(4): 270-279.
[8] Okada S, Hattori A, Matsunaga T, et al. Prognostic value of visceral pleural invasion in pure-solid and part-solid lung cancer patients[J]. Gen Thorac Cardiovasc Surg, 2021, 69(2):303-310.
[9] Kim H J, Cho J Y, Lee Y J, et al. Clinical significance of pleural attachment and indentation of subsolid nodule lung cancer[J]. J Cancer Research, 2019, 51(4): 1540-1548.
[10] Zhao L L, Xie H K, Zhang L P, et al. Visceral pleural invasion in lung adenocarcinoma ≤3 cm with ground-glass opacity: a clinical, pathological and radiological study[J]. J Thorac Dis, 2016, 8(7): 1788-1797.
[11] 符程皓,蒋以恒,葛佳云,等. 混合磨玻璃结节型肺腺癌脏层胸膜侵犯的临床特征和危险因素分析 [J]. 中国肺癌杂志,2022,25(4): 236-244.
[12] 刘宝东. 磨玻璃结节型肺癌胸膜改变与脏层胸膜侵犯的相关性研究进展 [J]. 中国肺癌杂志,2022,25(12): 895-900.
[13] 李 洪,陈 远,王 俊,等. 小结节型ⅠA期肺腺癌胸膜侵犯危险因素分析 [J]. 中国胸心血管外科临床杂志,2019,26(10): 979-983.
[14] Shen G, Dong J, Xiang Z, et al. Double staining of elastic fibre and immunohistochemistry is helpful to differentiate pleural invasion of lung cancer[J]. J Clin Pathol, 2022, 75(3):215-216.
[15] Ahn S Y, Park C M, Jeon Y K, et al. Predictive CT features of visceral pleural invasion by T1-sized peripheral pulmonary adenocarcinomas manifesting as subsolid nodules[J]. AM J Roentgenol, 2017, 209(3): 561-566.
[16] Yang S, Yang L, Teng L, et al. Visceral pleural invasion by pulmonary adenocarcinoma ≤3 cm: the pathological correlation with pleural signs on computed tomography[J]. J Thorac Dis, 2018, 10(7):3992-3999.
[17] Qi L P, Li X T, Yang Y, et al. Multivariate analysis of pleural invasion of peripheral non-small cell lung cancer-based computed tomography features[J]. J Comput Assist Tomogr, 2016, 40(5):757-762.
[18] 浦学慧,袁 梅,陈天羽,等. 多层螺旋 CT 特征评估 [J]. 实用放射学杂志,2019,35(4): 549-545.
[19] 汤 敏,孙丹丹,尹 柯,等. 胸膜下肺腺癌脏层胸膜侵犯CT及临床风险因素 [J]. 放射学实践,2020, 35(10): 1243-1248.
[20] Ebara K, Takashima S, Jiang B, et al. Pleural invasion by peripheral lung cancer: prediction with three-dimensional CT[J]. Acad Radiol, 2015, 22(3):310-319.
[21] 杨建丽,牛海亚,于 静,等.人工智能辅助诊断系统对肺腺癌亚型的预测价值[J].武警医学,2022,33(12):1038-1041.
[22] Wang S, Liu G, Fu Z, et al. Predicting pathological invasiveness of lung adenocarcinoma manifesting as GGO-predominant nodules: a combined prediction model generated from DECT[J]. Acad Radiol, 2021, 28(4):509-516.
[23] Qiu T, Ru X, Yin K, et al. Two nomograms based on CT features to predict tumor invasiveness of pulmonary adenocarcinoma and growth in pure GGN: a retrospective analysis[J]. JPN J Radiol, 2020, 38(8):761-770.
PDF(1045 KB)
