Decision curve analysis apropos of choice of preferable treatment positioning during breast irradiation

Ferenc Rárosi, Krisztina Boda, Zsuzsanna Kahán, Zoltán Varga

Research output: Article

Abstract

Background: Radiotherapy is a standard treatment option for breast cancer, but it may lead to significant late morbidity, including radiation heart damage. Breast irradiation performed individually in the supine or prone position may aid in minimizing the irradiation dose to the heart and LAD coronary artery. A series of CT scans and therapy plans are needed in both positions for the 'gold standard' decision on the preferable treatment position. This method is expensive with respect to technology and physician workload. Our ultimate goal is to develop a predictive tool to identify the preferable treatment position using easily measurable patient characteristics. In this article, we describe the details of how model building and consequently validation of the best model are done. Methods: Different models were used: both logistic regression and multiple linear regressions were used to estimate the LAD mean dose difference (the difference between the mean dose to the LAD in the supine position versus prone position); predicted dose differences were analysed compared to the 'gold standard' values, and the best model was selected accordingly. The final model was checked by random cross-validation. In addition to generally used measures (ROC and Brier score), decision curves were employed to evaluate the performance of the models. Results: ROC analysis demonstrated that none of the predictors alone was satisfactory. Multiple logistic regression models and the linear regression model lead to high values of net benefit for a wide range of threshold probabilities. Multiple linear regression seemed to be the most useful model. We also present the results of the random cross-validation for this model (i.e. sensitivity of 80.7% and specificity of 87.5%). Conclusions: Decision curves proved to be useful to evaluate our models. Our results indicate that any of the models could be implemented in clinical practice, but the linear regression model is the most useful model to facilitate the radiation treatment decision. In addition, it is in use in everyday practice in the Department of Oncotherapy, University of Szeged, Hungary.

Original languageEnglish
Article number204
JournalBMC Medical Informatics and Decision Making
Volume19
Issue number1
DOIs
Publication statusPublished - okt. 29 2019

Fingerprint

Decision Support Techniques
Linear Models
Breast
Prone Position
Logistic Models
Supine Position
Therapeutics
Radiation
Hungary
Workload
ROC Curve
Coronary Vessels
Radiotherapy
Breast Neoplasms
Technology
Morbidity
Physicians
Sensitivity and Specificity

ASJC Scopus subject areas

  • Health Policy
  • Health Informatics

Cite this

@article{2f0b7bfc2f54457d8c8f164b87419e3e,
title = "Decision curve analysis apropos of choice of preferable treatment positioning during breast irradiation",
abstract = "Background: Radiotherapy is a standard treatment option for breast cancer, but it may lead to significant late morbidity, including radiation heart damage. Breast irradiation performed individually in the supine or prone position may aid in minimizing the irradiation dose to the heart and LAD coronary artery. A series of CT scans and therapy plans are needed in both positions for the 'gold standard' decision on the preferable treatment position. This method is expensive with respect to technology and physician workload. Our ultimate goal is to develop a predictive tool to identify the preferable treatment position using easily measurable patient characteristics. In this article, we describe the details of how model building and consequently validation of the best model are done. Methods: Different models were used: both logistic regression and multiple linear regressions were used to estimate the LAD mean dose difference (the difference between the mean dose to the LAD in the supine position versus prone position); predicted dose differences were analysed compared to the 'gold standard' values, and the best model was selected accordingly. The final model was checked by random cross-validation. In addition to generally used measures (ROC and Brier score), decision curves were employed to evaluate the performance of the models. Results: ROC analysis demonstrated that none of the predictors alone was satisfactory. Multiple logistic regression models and the linear regression model lead to high values of net benefit for a wide range of threshold probabilities. Multiple linear regression seemed to be the most useful model. We also present the results of the random cross-validation for this model (i.e. sensitivity of 80.7{\%} and specificity of 87.5{\%}). Conclusions: Decision curves proved to be useful to evaluate our models. Our results indicate that any of the models could be implemented in clinical practice, but the linear regression model is the most useful model to facilitate the radiation treatment decision. In addition, it is in use in everyday practice in the Department of Oncotherapy, University of Szeged, Hungary.",
keywords = "Decision curve, LAD mean dose, Left-sided breast radiotherapy, Prediction, Regression model, Validation",
author = "Ferenc R{\'a}rosi and Krisztina Boda and Zsuzsanna Kah{\'a}n and Zolt{\'a}n Varga",
year = "2019",
month = "10",
day = "29",
doi = "10.1186/s12911-019-0927-4",
language = "English",
volume = "19",
journal = "BMC Medical Informatics and Decision Making",
issn = "1472-6947",
publisher = "BioMed Central",
number = "1",

}

TY - JOUR

T1 - Decision curve analysis apropos of choice of preferable treatment positioning during breast irradiation

AU - Rárosi, Ferenc

AU - Boda, Krisztina

AU - Kahán, Zsuzsanna

AU - Varga, Zoltán

PY - 2019/10/29

Y1 - 2019/10/29

N2 - Background: Radiotherapy is a standard treatment option for breast cancer, but it may lead to significant late morbidity, including radiation heart damage. Breast irradiation performed individually in the supine or prone position may aid in minimizing the irradiation dose to the heart and LAD coronary artery. A series of CT scans and therapy plans are needed in both positions for the 'gold standard' decision on the preferable treatment position. This method is expensive with respect to technology and physician workload. Our ultimate goal is to develop a predictive tool to identify the preferable treatment position using easily measurable patient characteristics. In this article, we describe the details of how model building and consequently validation of the best model are done. Methods: Different models were used: both logistic regression and multiple linear regressions were used to estimate the LAD mean dose difference (the difference between the mean dose to the LAD in the supine position versus prone position); predicted dose differences were analysed compared to the 'gold standard' values, and the best model was selected accordingly. The final model was checked by random cross-validation. In addition to generally used measures (ROC and Brier score), decision curves were employed to evaluate the performance of the models. Results: ROC analysis demonstrated that none of the predictors alone was satisfactory. Multiple logistic regression models and the linear regression model lead to high values of net benefit for a wide range of threshold probabilities. Multiple linear regression seemed to be the most useful model. We also present the results of the random cross-validation for this model (i.e. sensitivity of 80.7% and specificity of 87.5%). Conclusions: Decision curves proved to be useful to evaluate our models. Our results indicate that any of the models could be implemented in clinical practice, but the linear regression model is the most useful model to facilitate the radiation treatment decision. In addition, it is in use in everyday practice in the Department of Oncotherapy, University of Szeged, Hungary.

AB - Background: Radiotherapy is a standard treatment option for breast cancer, but it may lead to significant late morbidity, including radiation heart damage. Breast irradiation performed individually in the supine or prone position may aid in minimizing the irradiation dose to the heart and LAD coronary artery. A series of CT scans and therapy plans are needed in both positions for the 'gold standard' decision on the preferable treatment position. This method is expensive with respect to technology and physician workload. Our ultimate goal is to develop a predictive tool to identify the preferable treatment position using easily measurable patient characteristics. In this article, we describe the details of how model building and consequently validation of the best model are done. Methods: Different models were used: both logistic regression and multiple linear regressions were used to estimate the LAD mean dose difference (the difference between the mean dose to the LAD in the supine position versus prone position); predicted dose differences were analysed compared to the 'gold standard' values, and the best model was selected accordingly. The final model was checked by random cross-validation. In addition to generally used measures (ROC and Brier score), decision curves were employed to evaluate the performance of the models. Results: ROC analysis demonstrated that none of the predictors alone was satisfactory. Multiple logistic regression models and the linear regression model lead to high values of net benefit for a wide range of threshold probabilities. Multiple linear regression seemed to be the most useful model. We also present the results of the random cross-validation for this model (i.e. sensitivity of 80.7% and specificity of 87.5%). Conclusions: Decision curves proved to be useful to evaluate our models. Our results indicate that any of the models could be implemented in clinical practice, but the linear regression model is the most useful model to facilitate the radiation treatment decision. In addition, it is in use in everyday practice in the Department of Oncotherapy, University of Szeged, Hungary.

KW - Decision curve

KW - LAD mean dose

KW - Left-sided breast radiotherapy

KW - Prediction

KW - Regression model

KW - Validation

UR - http://www.scopus.com/inward/record.url?scp=85074328829&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85074328829&partnerID=8YFLogxK

U2 - 10.1186/s12911-019-0927-4

DO - 10.1186/s12911-019-0927-4

M3 - Article

C2 - 31664991

AN - SCOPUS:85074328829

VL - 19

JO - BMC Medical Informatics and Decision Making

JF - BMC Medical Informatics and Decision Making

SN - 1472-6947

IS - 1

M1 - 204

ER -