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Title: Reduction in Tumor Volume by Cone Beam Computed Tomography Predicts Overall Survival in Non-Small Cell Lung Cancer Treated With Chemoradiation Therapy

Abstract

Purpose: We sought to evaluate whether tumor response using cone beam computed tomography (CBCT) performed as part of the routine care during chemoradiation therapy (CRT) could forecast the outcome of unresectable, locally advanced, non-small cell lung cancer (NSCLC). Methods and Materials: We manually delineated primary tumor volumes (TV) of patients with NSCLC who were treated with radical CRT on days 1, 8, 15, 22, 29, 36, and 43 on CBCTs obtained as part of the standard radiation treatment course. Percentage reductions in TV were calculated and then correlated to survival and pattern of recurrence using Cox proportional hazard models. Clinical information including histologic subtype was also considered in the study of such associations. Results: We evaluated 38 patients with a median follow-up time of 23.4 months. The median TV reduction was 39.3% (range, 7.3%-69.3%) from day 1 (D1) to day 43 (D43) CBCTs. Overall survival was associated with TV reduction from D1 to D43 (hazard ratio [HR] 0.557, 95% CI 0.39-0.79, P=.0009). For every 10% decrease in TV from D1 to D43, the risk of death decreased by 44.3%. For patients whose TV decreased ≥39.3 or <39.3%, log-rank test demonstrated a separation in survival (P=.02), with median survivals of 31 months versusmore » 10 months, respectively. Neither local recurrence (HR 0.791, 95% CI 0.51-1.23, P=.29), nor distant recurrence (HR 0.78, 95% CI 0.57-1.08, P=.137) correlated with TV decrease from D1 to D43. Histologic subtype showed no impact on our findings. Conclusions: TV reduction as determined by CBCT during CRT as part of routine care predicts post-CRT survival. Such knowledge may justify intensification of RT or application of additional therapies. Assessment of genomic characteristics of these tumors may permit a better understanding of behavior or prediction of therapeutic outcomes.« less

Authors:
 [1];  [2];  [2];  [2]; ;  [3];  [4]; ; ;  [2]
  1. Division of Biometrics, Rutgers Cancer Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, Rutgers The State University of New Jersey, New Brunswick, New Jersey (United States)
  2. Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, Rutgers The State University of New Jersey, New Brunswick, New Jersey (United States)
  3. Division of Medical Oncology, Rutgers Cancer Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, Rutgers The State University of New Jersey, New Brunswick, New Jersey (United States)
  4. Division of Surgery, Rutgers Cancer Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, Rutgers The State University of New Jersey, New Brunswick, New Jersey (United States)
Publication Date:
OSTI Identifier:
22462357
Resource Type:
Journal Article
Journal Name:
International Journal of Radiation Oncology, Biology and Physics
Additional Journal Information:
Journal Volume: 92; Journal Issue: 3; Other Information: Copyright (c) 2015 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0360-3016
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; COMPUTERIZED TOMOGRAPHY; DEATH; FORECASTING; HAZARDS; LUNGS; NEOPLASMS; PATIENTS; REDUCTION; STANDARDS; THERAPY

Citation Formats

Jabbour, Salma K., E-mail: jabbousk@cinj.rutgers.edu, Kim, Sinae, Department of Biostatistics, School of Public Health, Rutgers University, New Brunswick, New Jersey, Haider, Syed A., Xu, Xiaoting, Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Soochow, Wu, Alson, Surakanti, Sujani, Aisner, Joseph, Langenfeld, John, Yue, Ning J., Haffty, Bruce G., and Zou, Wei. Reduction in Tumor Volume by Cone Beam Computed Tomography Predicts Overall Survival in Non-Small Cell Lung Cancer Treated With Chemoradiation Therapy. United States: N. p., 2015. Web. doi:10.1016/J.IJROBP.2015.02.017.
Jabbour, Salma K., E-mail: jabbousk@cinj.rutgers.edu, Kim, Sinae, Department of Biostatistics, School of Public Health, Rutgers University, New Brunswick, New Jersey, Haider, Syed A., Xu, Xiaoting, Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Soochow, Wu, Alson, Surakanti, Sujani, Aisner, Joseph, Langenfeld, John, Yue, Ning J., Haffty, Bruce G., & Zou, Wei. Reduction in Tumor Volume by Cone Beam Computed Tomography Predicts Overall Survival in Non-Small Cell Lung Cancer Treated With Chemoradiation Therapy. United States. https://doi.org/10.1016/J.IJROBP.2015.02.017
Jabbour, Salma K., E-mail: jabbousk@cinj.rutgers.edu, Kim, Sinae, Department of Biostatistics, School of Public Health, Rutgers University, New Brunswick, New Jersey, Haider, Syed A., Xu, Xiaoting, Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Soochow, Wu, Alson, Surakanti, Sujani, Aisner, Joseph, Langenfeld, John, Yue, Ning J., Haffty, Bruce G., and Zou, Wei. 2015. "Reduction in Tumor Volume by Cone Beam Computed Tomography Predicts Overall Survival in Non-Small Cell Lung Cancer Treated With Chemoradiation Therapy". United States. https://doi.org/10.1016/J.IJROBP.2015.02.017.
@article{osti_22462357,
title = {Reduction in Tumor Volume by Cone Beam Computed Tomography Predicts Overall Survival in Non-Small Cell Lung Cancer Treated With Chemoradiation Therapy},
author = {Jabbour, Salma K., E-mail: jabbousk@cinj.rutgers.edu and Kim, Sinae and Department of Biostatistics, School of Public Health, Rutgers University, New Brunswick, New Jersey and Haider, Syed A. and Xu, Xiaoting and Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Soochow and Wu, Alson and Surakanti, Sujani and Aisner, Joseph and Langenfeld, John and Yue, Ning J. and Haffty, Bruce G. and Zou, Wei},
abstractNote = {Purpose: We sought to evaluate whether tumor response using cone beam computed tomography (CBCT) performed as part of the routine care during chemoradiation therapy (CRT) could forecast the outcome of unresectable, locally advanced, non-small cell lung cancer (NSCLC). Methods and Materials: We manually delineated primary tumor volumes (TV) of patients with NSCLC who were treated with radical CRT on days 1, 8, 15, 22, 29, 36, and 43 on CBCTs obtained as part of the standard radiation treatment course. Percentage reductions in TV were calculated and then correlated to survival and pattern of recurrence using Cox proportional hazard models. Clinical information including histologic subtype was also considered in the study of such associations. Results: We evaluated 38 patients with a median follow-up time of 23.4 months. The median TV reduction was 39.3% (range, 7.3%-69.3%) from day 1 (D1) to day 43 (D43) CBCTs. Overall survival was associated with TV reduction from D1 to D43 (hazard ratio [HR] 0.557, 95% CI 0.39-0.79, P=.0009). For every 10% decrease in TV from D1 to D43, the risk of death decreased by 44.3%. For patients whose TV decreased ≥39.3 or <39.3%, log-rank test demonstrated a separation in survival (P=.02), with median survivals of 31 months versus 10 months, respectively. Neither local recurrence (HR 0.791, 95% CI 0.51-1.23, P=.29), nor distant recurrence (HR 0.78, 95% CI 0.57-1.08, P=.137) correlated with TV decrease from D1 to D43. Histologic subtype showed no impact on our findings. Conclusions: TV reduction as determined by CBCT during CRT as part of routine care predicts post-CRT survival. Such knowledge may justify intensification of RT or application of additional therapies. Assessment of genomic characteristics of these tumors may permit a better understanding of behavior or prediction of therapeutic outcomes.},
doi = {10.1016/J.IJROBP.2015.02.017},
url = {https://www.osti.gov/biblio/22462357}, journal = {International Journal of Radiation Oncology, Biology and Physics},
issn = {0360-3016},
number = 3,
volume = 92,
place = {United States},
year = {Wed Jul 01 00:00:00 EDT 2015},
month = {Wed Jul 01 00:00:00 EDT 2015}
}