Radiation-induced fibrosis in the boost area after three-dimensional conformal radiotherapy with a simultaneous integrated boost technique for early-stage breast cancer: A multivariable prediction model

C. Hammer; J. H. Maduro; E. J. Bantema-Joppe; A. van der Schaaf; H. P. van der Laan; J. A. Langendijk; A. P. G. Crijns

doi:10.1016/j.radonc.2016.10.006

Radiation-induced fibrosis in the boost area after three-dimensional conformal radiotherapy with a simultaneous integrated boost technique for early-stage breast cancer: A multivariable prediction model

C. Hammer^*, J. H. Maduro, E. J. Bantema-Joppe, A. van der Schaaf, H. P. van der Laan, J. A. Langendijk, A. P. G. Crijns

^*Corresponding author for this work

Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE)

Research output: Contribution to journal › Article › Academic › peer-review

22 Citations (Scopus)

Abstract

Background and purpose: To develop a multivariable prediction model for the risk of grade >= 2 fibrosis in the boost area after breast conserving surgery (BCS) followed by three-dimensional conformal radiotherapy (RT) with a simultaneous integrated photon boost (3D-CRT-SIB), five years after RT.

Material and methods: This prospective cohort study included 1,030 patients treated with RT for breast cancer (stage 0-III), after BCS. Data regarding physician-rated fibrosis and dose-volume parameters were available in 546 patients. A multivariable logistic regression model for grade >= 2 fibrosis was generated.

Results: At 5 years, grade >= 2 fibrosis was observed in 13.4% of the patients. The multivariable analysis resulted in a prediction model for grade a >= 2 fibrosis in the boost area including three independent variables: patient age, breast volume receiving >= 55 Gy (V55 CTV breast) and the maximum radiation dose in the breast (D-max).

Conclusions: A multivariable prediction model was developed including age, V55 CTV breast and D-max for grade >= 2 fibrosis in the boost area after breast cancer RT using a 3D-CRT-SIB technique. This model can be used to estimate the risk of fibrosis and to optimize dose distributions aiming at reducing this risk. (C) 2016 Elsevier Ireland Ltd. All rights reserved.

Original language	English
Pages (from-to)	45-49
Number of pages	5
Journal	Radiotherapy and Oncology
Volume	122
Issue number	1
Early online date	25-Oct-2016
DOIs	https://doi.org/10.1016/j.radonc.2016.10.006
Publication status	Published - Jan-2017

Keywords

Multivariable prediction model
Fibrosis
Breast cancer
Radiotherapy
NTCP
CONSERVING THERAPY
FOLLOW-UP
NO BOOST
TRIAL
HYPOFRACTIONATION
COMPLICATIONS
IRRADIATION
OUTCOMES

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Hammer, C., Maduro, J. H., Bantema-Joppe, E. J., van der Schaaf, A., van der Laan, H. P., Langendijk, J. A., & Crijns, A. P. G. (2017). Radiation-induced fibrosis in the boost area after three-dimensional conformal radiotherapy with a simultaneous integrated boost technique for early-stage breast cancer: A multivariable prediction model. Radiotherapy and Oncology, 122(1), 45-49. https://doi.org/10.1016/j.radonc.2016.10.006

@article{9e64ef36ca5d49de8faf22e1af918049,

title = "Radiation-induced fibrosis in the boost area after three-dimensional conformal radiotherapy with a simultaneous integrated boost technique for early-stage breast cancer: A multivariable prediction model",

abstract = "Background and purpose: To develop a multivariable prediction model for the risk of grade >= 2 fibrosis in the boost area after breast conserving surgery (BCS) followed by three-dimensional conformal radiotherapy (RT) with a simultaneous integrated photon boost (3D-CRT-SIB), five years after RT.Material and methods: This prospective cohort study included 1,030 patients treated with RT for breast cancer (stage 0-III), after BCS. Data regarding physician-rated fibrosis and dose-volume parameters were available in 546 patients. A multivariable logistic regression model for grade >= 2 fibrosis was generated.Results: At 5 years, grade >= 2 fibrosis was observed in 13.4% of the patients. The multivariable analysis resulted in a prediction model for grade a >= 2 fibrosis in the boost area including three independent variables: patient age, breast volume receiving >= 55 Gy (V55 CTV breast) and the maximum radiation dose in the breast (D-max).Conclusions: A multivariable prediction model was developed including age, V55 CTV breast and D-max for grade >= 2 fibrosis in the boost area after breast cancer RT using a 3D-CRT-SIB technique. This model can be used to estimate the risk of fibrosis and to optimize dose distributions aiming at reducing this risk. (C) 2016 Elsevier Ireland Ltd. All rights reserved.",

keywords = "Multivariable prediction model, Fibrosis, Breast cancer, Radiotherapy, NTCP, CONSERVING THERAPY, FOLLOW-UP, NO BOOST, TRIAL, HYPOFRACTIONATION, COMPLICATIONS, IRRADIATION, OUTCOMES",

author = "C. Hammer and Maduro, {J. H.} and Bantema-Joppe, {E. J.} and {van der Schaaf}, A. and {van der Laan}, {H. P.} and Langendijk, {J. A.} and Crijns, {A. P. G.}",

year = "2017",

month = jan,

doi = "10.1016/j.radonc.2016.10.006",

language = "English",

volume = "122",

pages = "45--49",

journal = "Radiotherapy and Oncology",

issn = "0167-8140",

publisher = "ELSEVIER IRELAND LTD",

number = "1",

}

Hammer, C , Maduro, JH, Bantema-Joppe, EJ, van der Schaaf, A , van der Laan, HP , Langendijk, JA & Crijns, APG 2017, 'Radiation-induced fibrosis in the boost area after three-dimensional conformal radiotherapy with a simultaneous integrated boost technique for early-stage breast cancer: A multivariable prediction model', Radiotherapy and Oncology, vol. 122, no. 1, pp. 45-49. https://doi.org/10.1016/j.radonc.2016.10.006

Radiation-induced fibrosis in the boost area after three-dimensional conformal radiotherapy with a simultaneous integrated boost technique for early-stage breast cancer: A multivariable prediction model. / Hammer, C.; Maduro, J. H.; Bantema-Joppe, E. J. et al.
In: Radiotherapy and Oncology, Vol. 122, No. 1, 01.2017, p. 45-49.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - Radiation-induced fibrosis in the boost area after three-dimensional conformal radiotherapy with a simultaneous integrated boost technique for early-stage breast cancer

T2 - A multivariable prediction model

AU - Hammer, C.

AU - Maduro, J. H.

AU - Bantema-Joppe, E. J.

AU - van der Schaaf, A.

AU - van der Laan, H. P.

AU - Langendijk, J. A.

AU - Crijns, A. P. G.

PY - 2017/1

Y1 - 2017/1

N2 - Background and purpose: To develop a multivariable prediction model for the risk of grade >= 2 fibrosis in the boost area after breast conserving surgery (BCS) followed by three-dimensional conformal radiotherapy (RT) with a simultaneous integrated photon boost (3D-CRT-SIB), five years after RT.Material and methods: This prospective cohort study included 1,030 patients treated with RT for breast cancer (stage 0-III), after BCS. Data regarding physician-rated fibrosis and dose-volume parameters were available in 546 patients. A multivariable logistic regression model for grade >= 2 fibrosis was generated.Results: At 5 years, grade >= 2 fibrosis was observed in 13.4% of the patients. The multivariable analysis resulted in a prediction model for grade a >= 2 fibrosis in the boost area including three independent variables: patient age, breast volume receiving >= 55 Gy (V55 CTV breast) and the maximum radiation dose in the breast (D-max).Conclusions: A multivariable prediction model was developed including age, V55 CTV breast and D-max for grade >= 2 fibrosis in the boost area after breast cancer RT using a 3D-CRT-SIB technique. This model can be used to estimate the risk of fibrosis and to optimize dose distributions aiming at reducing this risk. (C) 2016 Elsevier Ireland Ltd. All rights reserved.

AB - Background and purpose: To develop a multivariable prediction model for the risk of grade >= 2 fibrosis in the boost area after breast conserving surgery (BCS) followed by three-dimensional conformal radiotherapy (RT) with a simultaneous integrated photon boost (3D-CRT-SIB), five years after RT.Material and methods: This prospective cohort study included 1,030 patients treated with RT for breast cancer (stage 0-III), after BCS. Data regarding physician-rated fibrosis and dose-volume parameters were available in 546 patients. A multivariable logistic regression model for grade >= 2 fibrosis was generated.Results: At 5 years, grade >= 2 fibrosis was observed in 13.4% of the patients. The multivariable analysis resulted in a prediction model for grade a >= 2 fibrosis in the boost area including three independent variables: patient age, breast volume receiving >= 55 Gy (V55 CTV breast) and the maximum radiation dose in the breast (D-max).Conclusions: A multivariable prediction model was developed including age, V55 CTV breast and D-max for grade >= 2 fibrosis in the boost area after breast cancer RT using a 3D-CRT-SIB technique. This model can be used to estimate the risk of fibrosis and to optimize dose distributions aiming at reducing this risk. (C) 2016 Elsevier Ireland Ltd. All rights reserved.

KW - Multivariable prediction model

KW - Fibrosis

KW - Breast cancer

KW - Radiotherapy

KW - NTCP

KW - CONSERVING THERAPY

KW - FOLLOW-UP

KW - NO BOOST

KW - TRIAL

KW - HYPOFRACTIONATION

KW - COMPLICATIONS

KW - IRRADIATION

KW - OUTCOMES

U2 - 10.1016/j.radonc.2016.10.006

DO - 10.1016/j.radonc.2016.10.006

M3 - Article

C2 - 27793444

SN - 0167-8140

VL - 122

SP - 45

EP - 49

JO - Radiotherapy and Oncology

JF - Radiotherapy and Oncology

IS - 1

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Hammer C , Maduro JH, Bantema-Joppe EJ, van der Schaaf A , van der Laan HP , Langendijk JA et al. Radiation-induced fibrosis in the boost area after three-dimensional conformal radiotherapy with a simultaneous integrated boost technique for early-stage breast cancer: A multivariable prediction model. Radiotherapy and Oncology. 2017 Jan;122(1):45-49. Epub 2016 Oct 25. doi: 10.1016/j.radonc.2016.10.006

Radiation-induced fibrosis in the boost area after three-dimensional conformal radiotherapy with a simultaneous integrated boost technique for early-stage breast cancer: A multivariable prediction model

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