Radiation-induced lung toxicity in non-small-cell lung cancer: Understanding the interactions of clinical factors and cytokines with the dose-toxicity relationship

Peter G. Hawkins, Philip S. Boonstra, Stephen T. Hobson, Jason W.D. Hearn, James A. Hayman, Randall K. Ten Haken, Martha M. Matuszak, Paul Stanton, Gregory P. Kalemkerian, Nithya Ramnath, Theodore S. Lawrence, Matthew J. Schipper, Feng Ming (Spring) Kong, Shruti Jolly

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Background and purpose Current methods to estimate risk of radiation-induced lung toxicity (RILT) rely on dosimetric parameters. We aimed to improve prognostication by incorporating clinical and cytokine data, and to investigate how these factors may interact with the effect of mean lung dose (MLD) on RILT. Materials and methods Data from 125 patients treated from 2004 to 2013 with definitive radiotherapy for stages I-III NSCLC on four prospective clinical trials were analyzed. Plasma levels of 30 cytokines were measured pretreatment, and at 2 and 4 weeks midtreatment. Penalized logistic regression models based on combinations of MLD, clinical factors, and cytokine levels were developed. Cross-validated estimates of log-likelihood and area under the receiver operating characteristic curve (AUC) were used to assess accuracy. Results In prognosticating grade 3 or greater RILT by MLD alone, cross-validated log-likelihood and AUC were −28.2 and 0.637, respectively. Incorporating clinical features and baseline cytokine levels increased log-likelihood to −27.6 and AUC to 0.669. Midtreatment cytokine data did not further increase log-likelihood or AUC. Of the 30 cytokines measured, higher levels of 13 decreased the effect of MLD on RILT, corresponding to a lower odds ratio for RILT per Gy MLD, while higher levels of 4 increased the association. Conclusions Although the added prognostic benefit from cytokine data in our model was modest, understanding how clinical and biologic factors interact with the MLD-RILT relationship represents a novel framework for understanding and investigating the multiple factors contributing to radiation-induced toxicity.

Original languageEnglish (US)
Pages (from-to)66-72
Number of pages7
JournalRadiotherapy and Oncology
Volume125
Issue number1
DOIs
StatePublished - Oct 2017

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Non-Small Cell Lung Carcinoma
Radiation
Cytokines
Lung
Area Under Curve
Logistic Models
Biological Factors
ROC Curve
Radiotherapy
Odds Ratio
Clinical Trials

Keywords

  • Biomarker
  • Cytokine
  • Lung toxicity
  • Non-small-cell lung cancer
  • Radiotherapy

ASJC Scopus subject areas

  • Hematology
  • Oncology
  • Radiology Nuclear Medicine and imaging

Cite this

Radiation-induced lung toxicity in non-small-cell lung cancer : Understanding the interactions of clinical factors and cytokines with the dose-toxicity relationship. / Hawkins, Peter G.; Boonstra, Philip S.; Hobson, Stephen T.; Hearn, Jason W.D.; Hayman, James A.; Ten Haken, Randall K.; Matuszak, Martha M.; Stanton, Paul; Kalemkerian, Gregory P.; Ramnath, Nithya; Lawrence, Theodore S.; Schipper, Matthew J.; (Spring) Kong, Feng Ming; Jolly, Shruti.

In: Radiotherapy and Oncology, Vol. 125, No. 1, 10.2017, p. 66-72.

Research output: Contribution to journalArticle

Hawkins, PG, Boonstra, PS, Hobson, ST, Hearn, JWD, Hayman, JA, Ten Haken, RK, Matuszak, MM, Stanton, P, Kalemkerian, GP, Ramnath, N, Lawrence, TS, Schipper, MJ, (Spring) Kong, FM & Jolly, S 2017, 'Radiation-induced lung toxicity in non-small-cell lung cancer: Understanding the interactions of clinical factors and cytokines with the dose-toxicity relationship', Radiotherapy and Oncology, vol. 125, no. 1, pp. 66-72. https://doi.org/10.1016/j.radonc.2017.09.005
Hawkins, Peter G. ; Boonstra, Philip S. ; Hobson, Stephen T. ; Hearn, Jason W.D. ; Hayman, James A. ; Ten Haken, Randall K. ; Matuszak, Martha M. ; Stanton, Paul ; Kalemkerian, Gregory P. ; Ramnath, Nithya ; Lawrence, Theodore S. ; Schipper, Matthew J. ; (Spring) Kong, Feng Ming ; Jolly, Shruti. / Radiation-induced lung toxicity in non-small-cell lung cancer : Understanding the interactions of clinical factors and cytokines with the dose-toxicity relationship. In: Radiotherapy and Oncology. 2017 ; Vol. 125, No. 1. pp. 66-72.
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abstract = "Background and purpose Current methods to estimate risk of radiation-induced lung toxicity (RILT) rely on dosimetric parameters. We aimed to improve prognostication by incorporating clinical and cytokine data, and to investigate how these factors may interact with the effect of mean lung dose (MLD) on RILT. Materials and methods Data from 125 patients treated from 2004 to 2013 with definitive radiotherapy for stages I-III NSCLC on four prospective clinical trials were analyzed. Plasma levels of 30 cytokines were measured pretreatment, and at 2 and 4 weeks midtreatment. Penalized logistic regression models based on combinations of MLD, clinical factors, and cytokine levels were developed. Cross-validated estimates of log-likelihood and area under the receiver operating characteristic curve (AUC) were used to assess accuracy. Results In prognosticating grade 3 or greater RILT by MLD alone, cross-validated log-likelihood and AUC were −28.2 and 0.637, respectively. Incorporating clinical features and baseline cytokine levels increased log-likelihood to −27.6 and AUC to 0.669. Midtreatment cytokine data did not further increase log-likelihood or AUC. Of the 30 cytokines measured, higher levels of 13 decreased the effect of MLD on RILT, corresponding to a lower odds ratio for RILT per Gy MLD, while higher levels of 4 increased the association. Conclusions Although the added prognostic benefit from cytokine data in our model was modest, understanding how clinical and biologic factors interact with the MLD-RILT relationship represents a novel framework for understanding and investigating the multiple factors contributing to radiation-induced toxicity.",
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T1 - Radiation-induced lung toxicity in non-small-cell lung cancer

T2 - Understanding the interactions of clinical factors and cytokines with the dose-toxicity relationship

AU - Hawkins, Peter G.

AU - Boonstra, Philip S.

AU - Hobson, Stephen T.

AU - Hearn, Jason W.D.

AU - Hayman, James A.

AU - Ten Haken, Randall K.

AU - Matuszak, Martha M.

AU - Stanton, Paul

AU - Kalemkerian, Gregory P.

AU - Ramnath, Nithya

AU - Lawrence, Theodore S.

AU - Schipper, Matthew J.

AU - (Spring) Kong, Feng Ming

AU - Jolly, Shruti

PY - 2017/10

Y1 - 2017/10

N2 - Background and purpose Current methods to estimate risk of radiation-induced lung toxicity (RILT) rely on dosimetric parameters. We aimed to improve prognostication by incorporating clinical and cytokine data, and to investigate how these factors may interact with the effect of mean lung dose (MLD) on RILT. Materials and methods Data from 125 patients treated from 2004 to 2013 with definitive radiotherapy for stages I-III NSCLC on four prospective clinical trials were analyzed. Plasma levels of 30 cytokines were measured pretreatment, and at 2 and 4 weeks midtreatment. Penalized logistic regression models based on combinations of MLD, clinical factors, and cytokine levels were developed. Cross-validated estimates of log-likelihood and area under the receiver operating characteristic curve (AUC) were used to assess accuracy. Results In prognosticating grade 3 or greater RILT by MLD alone, cross-validated log-likelihood and AUC were −28.2 and 0.637, respectively. Incorporating clinical features and baseline cytokine levels increased log-likelihood to −27.6 and AUC to 0.669. Midtreatment cytokine data did not further increase log-likelihood or AUC. Of the 30 cytokines measured, higher levels of 13 decreased the effect of MLD on RILT, corresponding to a lower odds ratio for RILT per Gy MLD, while higher levels of 4 increased the association. Conclusions Although the added prognostic benefit from cytokine data in our model was modest, understanding how clinical and biologic factors interact with the MLD-RILT relationship represents a novel framework for understanding and investigating the multiple factors contributing to radiation-induced toxicity.

AB - Background and purpose Current methods to estimate risk of radiation-induced lung toxicity (RILT) rely on dosimetric parameters. We aimed to improve prognostication by incorporating clinical and cytokine data, and to investigate how these factors may interact with the effect of mean lung dose (MLD) on RILT. Materials and methods Data from 125 patients treated from 2004 to 2013 with definitive radiotherapy for stages I-III NSCLC on four prospective clinical trials were analyzed. Plasma levels of 30 cytokines were measured pretreatment, and at 2 and 4 weeks midtreatment. Penalized logistic regression models based on combinations of MLD, clinical factors, and cytokine levels were developed. Cross-validated estimates of log-likelihood and area under the receiver operating characteristic curve (AUC) were used to assess accuracy. Results In prognosticating grade 3 or greater RILT by MLD alone, cross-validated log-likelihood and AUC were −28.2 and 0.637, respectively. Incorporating clinical features and baseline cytokine levels increased log-likelihood to −27.6 and AUC to 0.669. Midtreatment cytokine data did not further increase log-likelihood or AUC. Of the 30 cytokines measured, higher levels of 13 decreased the effect of MLD on RILT, corresponding to a lower odds ratio for RILT per Gy MLD, while higher levels of 4 increased the association. Conclusions Although the added prognostic benefit from cytokine data in our model was modest, understanding how clinical and biologic factors interact with the MLD-RILT relationship represents a novel framework for understanding and investigating the multiple factors contributing to radiation-induced toxicity.

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KW - Lung toxicity

KW - Non-small-cell lung cancer

KW - Radiotherapy

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