Literature DB >> 24361250

Assessing response to treatment in non--small-cell lung cancer: role of tumor volume evaluated by computed tomography.

Friedrich D Knollmann1, Rohan Kumthekar2, David Fetzer2, Mark A Socinski3.   

Abstract

INTRODUCTION: We set out to investigate whether volumetric tumor measurements allow for a prediction of treatment response, as measured by patient survival, in patients with advanced non-small-cell lung cancer (NSCLC).
MATERIALS AND METHODS: Patients with nonresectable NSCLC (stage III or IV, n = 100) who were repeatedly evaluated for treatment response by computed tomography (CT) were included in a Health Insurance Portability and Accountability Act (HIPAA)-compliant retrospective study. Tumor response was measured by comparing tumor volumes over time. Patient survival was compared with Response Evaluation Criteria in Solid Tumors (RECIST) using Kaplan-Meier survival statistics and Cox regression analysis.
RESULTS: The median overall patient survival was 553 days (standard error, 146 days); for patients with stage III NSCLC, it was 822 days, and for patients with stage IV disease, 479 days. The survival differences were not statistically significant (P = .09). According to RECIST, 5 patients demonstrated complete response, 39 partial response, 44 stable disease, and 12 progressive disease. Patient survival was not significantly associated with RECIST class, the change of the sum of tumor diameters (P = .98), nor the change of the sum of volumetric tumor dimensions (P = .17).
CONCLUSION: In a group of 100 patients with advanced-stage NSCLC, neither volumetric CT measurements of changes in tumor size nor RECIST class significantly predicted patient survival. This observation suggests that size response may not be a sufficiently precise surrogate marker of success to steer treatment decisions in individual patients.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  CT; Carcinoma; Imaging; Outcome; Survival

Mesh:

Year:  2013        PMID: 24361250     DOI: 10.1016/j.cllc.2013.11.001

Source DB:  PubMed          Journal:  Clin Lung Cancer        ISSN: 1525-7304            Impact factor:   4.785


  3 in total

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Authors:  Iain Phillips; Mazhar Ajaz; Veni Ezhil; Vineet Prakash; Sheaka Alobaidli; Sarah J McQuaid; Christopher South; James Scuffham; Andrew Nisbet; Philip Evans
Journal:  Br J Radiol       Date:  2017-10-27       Impact factor: 3.039

2.  CT perfusion imaging of lung cancer: benefit of motion correction for blood flow estimates.

Authors:  Lisa L Chu; Robert J Knebel; Aryan D Shay; Jonathan Santos; Ramsey D Badawi; David R Gandara; Friedrich D Knollmann
Journal:  Eur Radiol       Date:  2018-06-04       Impact factor: 5.315

Review 3.  Radiomics: the facts and the challenges of image analysis.

Authors:  Stefania Rizzo; Francesca Botta; Sara Raimondi; Daniela Origgi; Cristiana Fanciullo; Alessio Giuseppe Morganti; Massimo Bellomi
Journal:  Eur Radiol Exp       Date:  2018-11-14
  3 in total

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