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Clinical and Translational Oncology
Published in: Clinical and Translational Oncology 9/2017

01-09-2017 | Research Article

The TNM 8 M1b and M1c classification for non-small cell lung cancer in a cohort of patients with brain metastases

Authors: C. Nieder, M. Hintz, O. Oehlke, A. Bilger, A. L. Grosu

Published in: Clinical and Translational Oncology | Issue 9/2017

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Abstract

Background and purpose

According to the recent TNM 8 classification, patients with metastatic non-small cell lung cancer (NSCLC) and single extrathoracic metastasis should be classified as stage M1b, while those with 2 or more metastases comprise stage M1c. The purpose of this study was to analyze the impact of this classification in patients with brain metastases.

Materials and methods

This retrospective study included 172 patients treated with individualized approaches. Actuarial survival was calculated. Uni- and multivariate analyses were performed.

Results

Thirty patients (17%) were staged as M1b. Those with squamous cell cancer were more likely to harbor M1b disease (29%, adenocarcinoma 14%, other histology 17%, p = 0.16). Median survival was 5.4 months (8.0 months in case of M1b disease and 4.5 months in case of M1c disease, p = 0.001). Multivariate analysis confirmed the role of M1b stage. M1b patients managed with upfront surgery or radiosurgery had significantly longer median survival than those who received whole-brain irradiation (21.0 vs. 3.5 months, p = 0.0001) and the potential to survive beyond 5 years.

Conclusions

We found the M1b classification to provide clinically relevant information. The multivariate analysis suggested that patients with M1b disease, better performance status and younger age have better survival.
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Metadata
Title
The TNM 8 M1b and M1c classification for non-small cell lung cancer in a cohort of patients with brain metastases
Authors
C. Nieder
M. Hintz
O. Oehlke
A. Bilger
A. L. Grosu
Publication date
01-09-2017
Publisher
Springer International Publishing
Published in
Clinical and Translational Oncology / Issue 9/2017
Print ISSN: 1699-048X
Electronic ISSN: 1699-3055
DOI
https://doi.org/10.1007/s12094-017-1651-0

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