Highlights
- •Metastatic lung cancer holds a poor prognosis with high morbidity and mortality.
- •No significance was found in primary cancer type nor number of vertebral levels.
- •Non-operative treatments may have a significant survival advantage over surgery.
- •Less aggressive approaches may be beneficial for lung cancer spinal metastases.
Abstract
Seventy percent of cancer patients will have metastatic bone disease, most commonly
in the vertebra. Prognosis of metastatic lung cancer is poor and treatment is mostly
palliative. To-date, there is no systematic review on the ideal treatment for lung
cancer with spinal metastases in regards to mortality. Literature searches were performed
based on PRISMA guidelines for systematic review. Thirty-nine studies comprising 1925
patients treated for spinal metastases of lung cancer met inclusion criteria. All
analyses were performed using SAS and SPSS. Data were analyzed for meaningful comparisons
of baseline patient characteristics, primary cancer type, metastatic lesion characteristics,
treatment modality, and clinical and radiologic outcomes. Significantly greater mean
survival length was seen in the non-surgical group (8.5 months, SD 6.6, SEM 0.17)
compared to the surgical group (7.5 months, SD 4.5, SEM 0.25; p = 0.013). There was
no statistically significant survival difference between different types of primary
lung cancer: NSCLC (8.3 months, SD 13.8, SEM 0.91) and SCLC (7.0 months, SD 4.6, SEM
0.46; p = 0.36). Number of vertebral levels involved per lesion also did not exhibit
significant difference: single lesion (11.3 months, SD 6.8, SEM 2.2) and multiple
lesions (13.8 months, SD 15.7, SEM 3.6; p = 0.64). For patients with symptomatic spinal
metastases from lung cancer, non-operative approaches experience significantly better
survival outcomes (p = 0.013). Future clinical studies are needed to determine the
best treatment algorithm to help maximize outcomes and minimize mortality in metastatic
lung cancer.
Keywords
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Article info
Publication history
Published online: January 14, 2019
Accepted:
January 3,
2019
Received:
April 16,
2018
Identification
Copyright
© 2019 Elsevier Ltd. All rights reserved.
