Skip to main content
ADVANCED SEARCH
Log in
Top
European Radiology
Published in:

08-06-2024 | Metastasis | Chest

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

Authors: 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

Published in: European Radiology | Issue 12/2024

Login to get access

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.
Appendix
Available only for authorised users
Literature
1.
Fritscher-Ravens A, Bohuslavizki KH, Brandt L et al (2003) Mediastinal lymph node involvement in potentially resectable lung cancer: comparison of CT, positron emission tomography, and endoscopic ultrasonography with and without fine-needle aspiration. Chest 123:442–451PubMedCrossRef
2.
Lv YL, Yuan DM, Wang K et al (2011) Diagnostic performance of integrated positron emission tomography/computed tomography for mediastinal lymph node staging in non-small cell lung cancer: a bivariate systematic review and meta-analysis. J Thorac Oncol 6:1350–1358PubMedCrossRef
3.
Maeyashiki T, Suzuki K, Hattori A, Matsunaga T, Takamochi K, Oh S (2013) The size of consolidation on thin-section computed tomography is a better predictor of survival than the maximum tumour dimension in resectable lung cancer. Eur J Cardiothorac Surg 43:915–918PubMedCrossRef
4.
Seok Y, Yang HC, Kim TJ et al (2014) Frequency of lymph node metastasis according to the size of tumors in resected pulmonary adenocarcinoma with a size of 30 mm or smaller. J Thorac Oncol 9:818–824PubMedPubMedCentralCrossRef
5.
Ye B, Cheng M, Li W et al (2014) Predictive factors for lymph node metastasis in clinical stage IA lung adenocarcinoma. Ann Thorac Surg 98:217–223PubMedCrossRef
6.
Ettinger DS, Wood DE, Aggarwal C et al (2019) NCCN guidelines insights: non-small cell lung cancer, version 1.2020. J Natl Compr Canc Netw 17:1464–1472PubMedCrossRef
7.
Silvestri GA, Gonzalez AV, Jantz MA et al (2013) Methods for staging non-small cell lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest 143:e211S–e250SPubMedCrossRef
8.
Kiyono K, Sone S, Sakai F et al (1988) The number and size of normal mediastinal lymph nodes: a postmortem study. AJR Am J Roentgenol 150:771–776PubMedCrossRef
9.
Guhlmann A, Storck M, Kotzerke J, Moog F, Sunder-Plassmann L, Reske SN (1997) Lymph node staging in non-small cell lung cancer: evaluation by [18F]FDG positron emission tomography (PET). Thorax 52:438–441PubMedPubMedCentralCrossRef
10.
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. J Thorac Oncol 6:244–285PubMedPubMedCentralCrossRef
11.
Tsutani Y, Miyata Y, Nakayama H et al (2013) Solid tumor size on high-resolution computed tomography and maximum standardized uptake on positron emission tomography for new clinical T descriptors with T1 lung adenocarcinoma. Ann Oncol 24:2376–2381PubMedCrossRef
12.
Aokage K, Miyoshi T, Ishii G et al (2017) Clinical and pathological staging validation in the eighth edition of the TNM classification for lung cancer: correlation between solid size on thin-section computed tomography and invasive size in pathological findings in the new T classification. J Thorac Oncol 12:1403–1412PubMedCrossRef
13.
Koike T, Koike T, Yamato Y, Yoshiya K, Toyabe S (2012) Predictive risk factors for mediastinal lymph node metastasis in clinical stage IA non-small-cell lung cancer patients. J Thorac Oncol 7:1246–1251PubMedCrossRef
14.
Lee PC, Port JL, Korst RJ, Liss Y, Meherally DN, Altorki NK (2007) Risk factors for occult mediastinal metastases in clinical stage I non-small cell lung cancer. Ann Thorac Surg 84:177–181PubMedCrossRef
15.
Farjah F, Lou F, Sima C, Rusch VW, Rizk NP (2013) A prediction model for pathologic N2 disease in lung cancer patients with a negative mediastinum by positron emission tomography. J Thorac Oncol 8:1170–1180PubMedCrossRef
16.
Cho S, Song IH, Yang HC, Kim K, Jheon S (2013) Predictive factors for node metastasis in patients with clinical stage I non-small cell lung cancer. Ann Thorac Surg 96:239–245PubMedCrossRef
17.
O’Connell OJ, Almeida FA, Simoff MJ et al (2017) A prediction model to help with the assessment of adenopathy in lung cancer: HAL. Am J Respir Crit Care Med 195:1651–1660PubMedPubMedCentralCrossRef
18.
Li M, Wu N, Zhang L et al (2017) Pathologic N0 status in clinical T1N0M0 lung adenocarcinoma is predictable by the solid component proportion with quantitative CT number analysis. Sci Rep 7:16810PubMedPubMedCentralCrossRef
19.
Lee H, Kim HK, Kang D et al (2020) Prognostic value of 6-min walk test to predict postoperative cardiopulmonary complications in patients with non-small cell lung cancer. Chest 157:1665–1673PubMedCrossRef
20.
Song SH, Park H, Lee G et al (2017) Imaging phenotyping using radiomics to predict micropapillary pattern within lung adenocarcinoma. J Thorac Oncol 12:624–632PubMedCrossRef
21.
Lee G, Park H, Lee HY et al (2021) Tumor margin contains prognostic information: radiomic margin characteristics analysis in lung adenocarcinoma patients. Cancers (Basel) 13:1676PubMedCrossRef
22.
Jakobsson U, Westergren A (2005) Statistical methods for assessing agreement for ordinal data. Scand J Caring Sci 19:427–431PubMedCrossRef
23.
Hansell DM, Bankier AA, MacMahon H, McLoud TC, Muller NL, Remy J (2008) Fleischner Society: glossary of terms for thoracic imaging. Radiology 246:697–722PubMedCrossRef
24.
Chu ZG, Li WJ, Fu BJ, Lv FJ (2020) CT characteristics for predicting invasiveness in pulmonary pure ground-glass nodules. AJR Am J Roentgenol 215:351–358PubMedCrossRef
25.
Gu K, Lee HY, Lee K et al (2019) Integrated evaluation of clinical, pathological and radiological prognostic factors in squamous cell carcinoma of the lung. PLoS One 14:e0223298PubMedPubMedCentralCrossRef
26.
Lee HY, Choi YL, Lee KS et al (2014) Pure ground-glass opacity neoplastic lung nodules: histopathology, imaging, and management. AJR Am J Roentgenol 202:W224–W233PubMedCrossRef
27.
Liu Y, Kim J, Qu F et al (2016) CT features associated with epidermal growth factor receptor mutation status in patients with lung adenocarcinoma. Radiology 280:271–280PubMedCrossRef
28.
Bankier AA, MacMahon H, Colby T et al (2024) Fleischner Society: glossary of terms for thoracic imaging. Radiology 310:e232558PubMedCrossRef
29.
DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845PubMedCrossRef
30.
Pencina MJ, D’Agostino Sr RB, D’Agostino Jr RB, Vasan RS (2008) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 27:157–172. discussion 207-112PubMedCrossRef
31.
Ketchedjian A, Daly BD, Fernando HC et al (2006) Location as an important predictor of lymph node involvement for pulmonary adenocarcinoma. J Thorac Cardiovasc Surg 132:544–548PubMedCrossRef
32.
Decaluwe H, Stanzi A, Dooms C et al (2016) Central tumour location should be considered when comparing N1 upstaging between thoracoscopic and open surgery for clinical stage I non-small-cell lung cancer. Eur J Cardiothorac Surg 50:110–117PubMedCrossRef
33.
Fintelmann FJ, Brinkmann JK, Jeck WR et al (2017) Lung cancers associated with cystic airspaces: natural history, pathologic correlation, and mutational analysis. J Thorac Imaging 32:176–188PubMedCrossRef
34.
Sheard S, Moser J, Sayer C, Stefanidis K, Devaraj A, Vlahos I (2018) Lung cancers associated with cystic airspaces: underrecognized features of early disease. Radiographics 38:704–717PubMedCrossRef
35.
Tan Y, Gao J, Wu C et al (2019) CT characteristics and pathologic basis of solitary cystic lung cancer. Radiology 291:495–501PubMedCrossRef
36.
Hattori A, Hirayama S, Matsunaga T et al (2019) Distinct clinicopathologic characteristics and prognosis based on the presence of ground glass opacity component in clinical stage IA lung adenocarcinoma. J Thorac Oncol 14:265–275PubMedCrossRef
37.
Travis WD, Asamura H, Bankier AA et al (2016) The IASLC Lung Cancer Staging Project: proposals for coding T categories for subsolid nodules and assessment of tumor size in part-solid tumors in the forthcoming eighth edition of the TNM classification of lung cancer. J Thorac Oncol 11:1204–1223PubMedCrossRef
38.
Rami-Porta R, Asamura H, Travis WD, Rusch VW (2017) LungAJCC Cancer Staging Manual. Springer, pp 431–456
39.
40.
Shimosato Y, Suzuki A, Hashimoto T et al (1980) Prognostic implications of fibrotic focus (scar) in small peripheral lung cancers. Am J Surg Pathol 4:365–373PubMedCrossRef
41.
Aoki T, Tomoda Y, Watanabe H et al (2001) Peripheral lung adenocarcinoma: correlation of thin-section CT findings with histologic prognostic factors and survival. Radiology 220:803–809PubMedCrossRef
42.
Lee HY, Jeong JY, Lee KS et al (2013) Histopathology of lung adenocarcinoma based on new IASLC/ATS/ERS classification: prognostic stratification with functional and metabolic imaging biomarkers. J Magn Reson Imaging 38:905–913PubMedCrossRef
43.
Balkwill FR, Capasso M, Hagemann T (2012) The tumor microenvironment at a glance. J Cell Sci 125:5591–5596PubMedCrossRef
44.
Parra ER, Behrens C, Rodriguez-Canales J et al (2016) Image analysis-based assessment of PD-L1 and tumor-associated immune cells density supports distinct intratumoral microenvironment groups in non-small cell lung carcinoma patients. Clin Cancer Res 22:6278–6289PubMedPubMedCentralCrossRef
45.
Fu T, Dai LJ, Wu SY et al (2021) Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response. J Hematol Oncol 14:98PubMedPubMedCentralCrossRef
46.
Xia C, Liu M, Li X et al (2021) Prediction model for lung cancer in high-risk nodules being considered for resection: development and validation in a Chinese population. Front Oncol 11:700179PubMedPubMedCentralCrossRef
47.
Wang JC, Sone S, Feng L et al (2000) Rapidly growing small peripheral lung cancers detected by screening CT: correlation between radiological appearance and pathological features. Br J Radiol 73:930–937PubMedCrossRef
48.
Yue JY, Chen J, Zhou FM et al (2018) CT-pathologic correlation in lung adenocarcinoma and squamous cell carcinoma. Medicine (Baltimore) 97:e13362PubMedCrossRef
49.
Jiang B, Takashima S, Miyake C et al (2014) Thin-section CT findings in peripheral lung cancer of 3 cm or smaller: are there any characteristic features for predicting tumor histology or do they depend only on tumor size? Acta Radiol 55:302–308PubMedCrossRef
50.
Lee HY, Lee SW, Lee KS et al (2015) Role of CT and PET imaging in predicting tumor recurrence and survival in patients with lung adenocarcinoma: a comparison with the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society Classification of Lung Adenocarcinoma. J Thorac Oncol 10:1785–1794PubMedCrossRef
Metadata
Title
Computed tomography characteristics of cN0 primary non-small cell lung cancer predict occult lymph node metastasis
Authors
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
Publication date
08-06-2024
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 12/2024
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-024-10835-z

Other articles of this Issue 12/2024

In vitro blood sample assessment: investigating correlation of laboratory hemoglobin and spectral properties of dual-energy CT measurements (ρ/Z)

Reply to Letter to the Editor: Nodal infiltration in endometrial cancer: a prediction model using best subset regression

Dorsal wrist ganglion: clinical and imaging correlation in symptomatic population based on high-field MRI

Image quality of whole-body diffusion MR images comparing deep-learning accelerated and conventional sequences

  • Magnetic Resonance

Serum tumor marker and CT body composition scoring system predicts outcomes in colorectal cancer surgical patients

Cerebral collaterals are associated with pre-treatment brain–blood barrier permeability in acute ischemic stroke patients