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Thorac Cardiovasc Surg 2024; 72(03): 217-226
DOI: 10.1055/a-2146-6879
Original Thoracic

The Incidence and Effect of Different Organ Metastasis on the Prognosis of NSCLC

Shenhai Wei 
1   Department of Thoracic Surgery, The First Hospital of Tsinghua University, Beijing, China
,
Wenchao Wei
2   School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
,
Bingqun Wu
3   Department of Thoracic Surgery, The First Hospital of Tsinghua University, Beijing, China
,
Jintao Tian
3   Department of Thoracic Surgery, The First Hospital of Tsinghua University, Beijing, China
,
Pengcheng Hu
3   Department of Thoracic Surgery, The First Hospital of Tsinghua University, Beijing, China
,
Shouqiang Pan
3   Department of Thoracic Surgery, The First Hospital of Tsinghua University, Beijing, China
,
Xiaoping Song
3   Department of Thoracic Surgery, The First Hospital of Tsinghua University, Beijing, China
› Author Affiliations Funding This study was supported by the Leading Foundation of The First Hospital of Tsinghua University.
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Abstract

Objective The aim of this study was to explore the effect of different organ metastasis on the prognosis of non-small cell lung cancer (NSCLC).

Methods Patients with distant metastatic NSCLC were selected from Surveillance, Epidemiology, and End Results database during 2016 to 2019. The incidence of different organ metastasis and their association with clinicopathological factors were explored. Overall survival (OS) and lung cancer-specific survival (LCSS) for metastatic NSCLC were calculated, and multivariate Cox regression analysis was performed with a nomogram for OS being constructed based on Cox regression.

Results Total 26,210 patients with distant metastatic NSCLC were included in this study. Around 48.9% of the metastatic NSCLC were multiple-organ metastasis and bone was the most commonly involved organ (44.4%). For patients with single-organ metastasis, the prognosis for lung or distant lymph nodes (LNs) metastasis was better than others (with median OS of 15 and 16 months for lung and distant LNs metastasis, respectively), and liver metastasis resulted in the worst prognosis with median OS of 8 months. A nomogram was constructed to visualize Cox regression model, along with the receiver operating characteristic (ROC) curve demonstrated good discrimination for the predictive model with 1- and 2-year area under the curve of ROC of 0.687 and 0.702, respectively.

Conclusion The prognosis of NSCLC patients with distant metastasis was poor. Liver metastasis results in the worst prognosis among the single-organ metastasis. The nomogram developed based on the Cox regression model has provided a useful tool to estimate the probability of OS of the metastatic NSCLC.

Keywords

lung cancer - treatment - metastases - lung cancer - diagnosis


Publication History

Received: 01 April 2023

Accepted: 29 July 2023

Accepted Manuscript online:
02 August 2023

Article published online:
11 September 2023

© 2023. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin 2022; 72 (01) 7-33
    Google Scholar
  • 2 Bethesda M. SEER Cancer Stat Facts: Lung and Bronchus Cancer 2022 . Accessed March 20, 2023, at: https://seer.cancer.gov/statfacts/html/lungb.html
  • 3 NCC Network. NCCN Guidelines Version 2.2023 Non-Small Cell Lung Cancer 2023. Accessed March 20, 2023, at: https://www.nccn.org/login?ReturnURL=https://www.nccn.org/professionals/physician_gls/pdf/nscl.pdf
  • 4 Hernandez RK, Wade SW, Reich A, Pirolli M, Liede A, Lyman GH. Incidence of bone metastases in patients with solid tumors: analysis of oncology electronic medical records in the United States. BMC Cancer 2018; 18 (01) 44
    Google Scholar
  • 5 Tsuya A, Kurata T, Tamura K, Fukuoka M. Skeletal metastases in non-small cell lung cancer: a retrospective study. Lung Cancer 2007; 57 (02) 229-232
    Google Scholar
  • 6 Goksel S, Ozcelik N. Distant metastasis patterns of lung cancer on positron emission tomography/computed tomography association with age and histological subtype. J Coll Physicians Surg Pak 2021; 31 (12) 1438-1444
    Google Scholar
  • 7 Ali A, Goffin JR, Arnold A, Ellis PM. Survival of patients with non-small-cell lung cancer after a diagnosis of brain metastases. Curr Oncol 2013; 20 (04) e300-e306
    Google Scholar
  • 8 Chu X, Gong J, Yang X, Ni J, Gu Y, Zhu Z. A “seed-and-soil” radiomics model predicts brain metastasis development in lung cancer: implications for risk-stratified prophylactic cranial irradiation. Cancers (Basel) 2023; 15 (01) 15
    Google Scholar
  • 9 Abdel-Rahman O. Predictors of skeletal-related events among cancer patients with bone metastases treated with zoledronic acid: a secondary analysis of a randomized study. Expert Opin Drug Saf 2018; 17 (08) 757-761
    Google Scholar
  • 10 Goeckeritz J, Cerillo J, Sanghadia C. et al. Principles of lung cancer metastasis to brain. J Skelet Syst 2022; 1 (01) 1
    Google Scholar
  • 11 Peng L, Bin Y, Ding P. et al. Integrated circulating tumor DNA and T cell repertoire predict radiotherapeutic response and outcome in non-small cell lung cancer patients with brain metastasis. Cancer Commun (Lond) 2023; 43 (05) 625-629
    Google Scholar
  • 12 Souza VGP, de Araújo RP, Santesso MR. et al. Advances in the molecular landscape of lung cancer brain metastasis. Cancers (Basel) 2023; 15 (03) 15
    Google Scholar
  • 13 Xu M, Song K, Zhou Z, Yu Z, Lv Y, Xu H. Survival and prognostic factors in patients undergoing the resection of solitary brain metastasis from non-small cell lung cancer: a retrospective cohort study. J Thorac Dis 2022; 14 (10) 4113-4124
    Google Scholar
  • 14 Cai Y, Wang JY, Liu H. Clinical observation of whole brain radiotherapy concomitant with targeted therapy for brain metastasis in non-small cell lung cancer patients with chemotherapy failure. Asian Pac J Cancer Prev 2013; 14 (10) 5699-5703
    Google Scholar
  • 15 Lee SW, Kim YS, Sung SY. et al. Upfront radiosurgery plus targeted agents followed by active brain control using radiosurgery delays neurological death in non-small cell lung cancer with brain metastasis. Clin Exp Metastasis 2020; 37 (02) 353-363
    Google Scholar
  • 16 Muhammet Hacioglu B, Kostek O, Erdogan B, Uzunoglu S, Cicin I. Targeted therapy with anaplastic lymphoma kinase inhibitors in non-small cell lung cancer even with brain metastasis. J Balkan Union Oncol 2017; 22 (03) 586-591
    Google Scholar
  • 17 Sun Y, Guo X, Zhang L, Zhang W, Zuo Y. Evaluation of radiotherapy combined with targeted therapy and concurrent radiotherapy, chemotherapy in the treatment of non-small cell lung cancer with brain metastasis. Pak J Med Sci 2020; 36 (03) 322-326
    Google Scholar
  • 18 Tang N, Guo J, Zhang Q, Wang Y, Wang Z. Greater efficacy of chemotherapy plus bevacizumab compared to chemo- and targeted therapy alone on non-small cell lung cancer patients with brain metastasis. Oncotarget 2016; 7 (03) 3635-3644
    Google Scholar
  • 19 Wang X, Mao W, Wang Z. et al. Enhanced anti-brain metastasis from non-small cell lung cancer of osimertinib and doxorubicin co-delivery targeted nanocarrier. Int J Nanomedicine 2020; 15: 5491-5501
    Google Scholar
  • 20 Ren Y, Dai C, Zheng H. et al. Prognostic effect of liver metastasis in lung cancer patients with distant metastasis. Oncotarget 2016; 7 (33) 53245-53253
    Google Scholar
  • 21 Wang L, Shi T, Feng L. et al. The prognostic value of baseline distant metastasis in icotinib-treated patients with EGFR-mutated stage IV non-small cell lung cancer. Cancer Manag Res 2021; 13: 2613-2622
    Google Scholar
  • 22 Ying X, Ma N, Zhang X. et al. Research progress on the molecular mechanisms of hepatic metastasis in lung cancer: a narrative review. Ann Palliat Med 2021; 10 (04) 4806-4822
    Google Scholar
  • 23 Watanabe H, Okauchi S, Miyazaki K, Satoh H, Hizawa N. Factors associated with distant metastasis in EGFR-mutated non-small cell lung cancer patients: logistic analysis. In Vivo 2019; 33 (04) 1369-1372
    Google Scholar
  • 24 Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol 2008; 26 (08) 1364-1370
    Google Scholar