Computed tomography characteristics of cN0 primary non-small cell lung cancer predict occult lymph node metastasis.

IF 4.7 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Radiology Pub Date : 2024-12-01 Epub Date: 2024-06-08 DOI:10.1007/s00330-024-10835-z
Dong Woog Yoon, Danbee Kang, Yeong Jeong Jeon, Junghee Lee, Sumin Shin, Jong Ho Cho, Yong Soo Choi, Jae Ill Zo, Jhingook Kim, Young Mog Shim, Juhee Cho, Hong Kwan Kim, Ho Yun Lee
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Abstract

Rationale: Occult lymph node metastasis (OLNM) is frequently found in patients with resectable non-small cell lung cancer (NSCLC), despite using diagnostic methods recommended by guidelines.

Objectives: To evaluate the risk of OLNM in NSCLC patients using the radiologic characteristics of the primary tumor on computed tomography (CT).

Methods: We retrospectively reviewed clinicopathologic features of 2042 clinical T1-4N0 NSCLC patients undergoing curative intent pulmonary resection. Unique radiological features (i.e., air-bronchogram throughout the whole tumor, heterogeneous ground-glass opacity (GGO), mainly cystic appearance, endobronchial location), percentage of solid portion, and shape of tumor margin were analyzed via a stepwise approach. We used multivariable logistic regression to assess the relationship between OLNM and tumor characteristics.

Results: Compared with the other unique features, endobronchial tumors were associated with the highest risk of OLNM (OR = 3.9, 95% confidence interval (CI) = 2.29-6.62), and heterogeneous GGO and mainly cystic tumors were associated with a low risk of OLNM. For tumors without unique features, the percentage of the solid portion was measured, and solid tumors were associated with OLNM (OR = 2.49, 95% CI = 1.86-3.35). Among part-solid tumors with solid proportion > 50%, spiculated margin, and peri-tumoral GGO were associated with OLNM.

Conclusions: The risk of OLNM could be assessed using radiologic characteristics on CT. This could allow us to adequately select optimal candidates for invasive nodal staging procedures (INSPs) and complete systematic lymph node dissection.

Clinical relevance statement: These data may be helpful for clinicians to select appropriate candidates for INSPs and complete surgical systematic lymph node dissection in NSCLC patients.

Key points: Lymph node metastasis status plays a key role in both prognostication and treatment planning. Solid tumors, particularly endobronchial tumors, were associated with occult lymph node metastasis (OLNM). The risk of OLNM can be assessed using radiologic characteristics acquired from CT images.

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cN0 原发性非小细胞肺癌的计算机断层扫描特征可预测隐匿性淋巴结转移。
理论依据:尽管使用了指南推荐的诊断方法,但在可切除的非小细胞肺癌(NSCLC)患者中仍经常发现隐匿性淋巴结转移(OLNM):根据计算机断层扫描(CT)显示的原发肿瘤放射学特征,评估 NSCLC 患者发生 OLNM 的风险:方法:我们回顾性分析了2042例接受根治性肺切除术的临床T1-4N0 NSCLC患者的临床病理学特征。我们通过逐步法分析了独特的放射学特征(即整个肿瘤的气胸图、异质性磨玻璃不透明(GGO)、主要为囊性外观、支气管内位置)、实性部分的比例以及肿瘤边缘的形状。我们使用多变量逻辑回归评估了 OLNM 与肿瘤特征之间的关系:结果:与其他独特特征相比,支气管内肿瘤发生 OLNM 的风险最高(OR = 3.9,95% 置信区间 (CI) = 2.29-6.62),而异质性 GGO 和以囊性为主的肿瘤发生 OLNM 的风险较低。对于没有独特特征的肿瘤,则测量实性部分的百分比,实性肿瘤与OLNM相关(OR = 2.49,95% CI = 1.86-3.35)。在实性比例大于50%的部分实性肿瘤中,棘状边缘和瘤周GGO与OLNM相关:结论:OLNM的风险可通过CT的放射学特征进行评估。结论:通过 CT 的放射学特征可以评估 OLNM 的风险,这可以让我们充分选择侵入性结节分期手术(INSP)和完整的系统性淋巴结清扫术的最佳候选者:这些数据可能有助于临床医生为 NSCLC 患者选择 INSPs 和完整的外科系统性淋巴结清扫术的合适人选:要点:淋巴结转移状态在预后和治疗计划中起着关键作用。实体瘤,尤其是支气管内肿瘤,与隐匿性淋巴结转移(OLNM)有关。隐匿性淋巴结转移的风险可通过 CT 图像获得的放射学特征进行评估。
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来源期刊
European Radiology
European Radiology 医学-核医学
CiteScore
11.60
自引率
8.50%
发文量
874
审稿时长
2-4 weeks
期刊介绍: European Radiology (ER) continuously updates scientific knowledge in radiology by publication of strong original articles and state-of-the-art reviews written by leading radiologists. A well balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes ER an indispensable source for current information in this field. This is the Journal of the European Society of Radiology, and the official journal of a number of societies. From 2004-2008 supplements to European Radiology were published under its companion, European Radiology Supplements, ISSN 1613-3749.
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