<abstract xmlns="http://www.w3.org/1999/xhtml">Purpose: To develop a multiple logistic regression model as normal tissue complication probability model by least absolute shrinkage and selection operator (LASSO) technique in breast cancer patients treated with three-dimensional conformal radiation therapy (3D-CRT), we focused on the changes of pulmonary function tests to achieve the optimal predictive parameters for the occurrence of symptomatic radiation pneumonitis (SRP).Materials and methods: Dosimetric and spirometry data of 60 breast cancer patients were analyzed. Pulmonary function tests were done before RT, after completion of RT, 3, and 6 months after RT. Multiple logistic regression model was used to obtain the effective predictive parameters. Forward selection method was applied in NTCP model to determine the effective risk factors from obtained different parameters.Results: Symptomatic radiation pneumonitis was observed in five patients. Significant changes in pulmonary parameters have been observed at six months after RT. The parameters of mean lung dose (MLD), bridge separation (BS), mean irradiated lung volume (ILVmean), and the percentage of the ipsilateral lung volume that received dose of 20 Gy (IV20) introduced as risk factors using the LASSO technique for SRP in a multiple normal tissue complication probability model in breast cancer patients treated with 3D-CRT. The BS, central lung distance (CLD) and ILV in tangential field have obtained as 23.5 (20.9-26.0) cm, 2.4 (1.5-3.3) cm, and 12.4 (10.6-14.3) % of lung volume in radiation field in patients without pulmonary complication, respectively.Conclusion: The results showed that if BS, CLD, and ILV are more than 23 cm, 2 cm, and 12%, respectively, so incidence of SRP in the patients will be considerable. Our multiple NTCP LASSO model for breast cancer patients treated with 3D-CRT showed that in order to have minimum probability of SRP occurrence, parameters of BS, IV20, ILV and especially MLD would be kept in minimum levels. Considering dose-volume histogram, the mean lung dose factor is most important parameter which minimizing it in treatment planning, minimizes the probability of SRP and consequently improves the quality of life in breast cancer patients.</abstract>

Purpose: To develop a multiple logistic regression model as normal tissue complication probability model by least absolute shrinkage and selection operator (LASSO) technique in breast cancer patients treated with three-dimensional conformal radiation therapy (3D-CRT), we focused on the changes of pulmonary function tests to achieve the optimal predictive parameters for the occurrence of symptomatic radiation pneumonitis (SRP).Materials and methods: Dosimetric and spirometry data of 60 breast cancer patients were analyzed. Pulmonary function tests were done before RT, after completion of RT, 3, and 6 months after RT. Multiple logistic regression model was used to obtain the effective predictive parameters. Forward selection method was applied in NTCP model to determine the effective risk factors from obtained different parameters.Results: Symptomatic radiation pneumonitis was observed in five patients. Significant changes in pulmonary parameters have been observed at six months after RT. The parameters of mean lung dose (MLD), bridge separation (BS), mean irradiated lung volume (ILVmean), and the percentage of the ipsilateral lung volume that received dose of 20 Gy (IV20) introduced as risk factors using the LASSO technique for SRP in a multiple normal tissue complication probability model in breast cancer patients treated with 3D-CRT. The BS, central lung distance (CLD) and ILV in tangential field have obtained as 23.5 (20.9-26.0) cm, 2.4 (1.5-3.3) cm, and 12.4 (10.6-14.3) % of lung volume in radiation field in patients without pulmonary complication, respectively.Conclusion: The results showed that if BS, CLD, and ILV are more than 23 cm, 2 cm, and 12%, respectively, so incidence of SRP in the patients will be considerable. Our multiple NTCP LASSO model for breast cancer patients treated with 3D-CRT showed that in order to have minimum probability of SRP occurrence, parameters of BS, IV20, ILV and especially MLD would be kept in minimum levels. Considering dose-volume histogram, the mean lung dose factor is most important parameter which minimizing it in treatment planning, minimizes the probability of SRP and consequently improves the quality of life in breast cancer patients.

Developing of predictive models for pneumonitis with forward variable selection and LASSO logistic model for breast cancer patients treated with 3D-CRT

Kermanshah University of Medical Sciences

Department of Medical Physics, School of Medicine

Department of Radiation Oncology, Mahdieh Radiotherapy and Oncology Charity Center

Polish Journal of Medical Physics and Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Purpose: To develop a multiple logistic regression model as normal tissue complication probability model by least absolute shrinkage and selection operator...

Developing of predictive models for pneumonitis with forward variable selection and LASSO logistic model for breast cancer patients treated with 3D-CRT

Published Online: Dec 24, 2018

Page range: 149 - 156

Received: Nov 03, 2017

Accepted: Oct 31, 2018

DOI: https://doi.org/10.2478/pjmpe-2018-0021

Keywords
radiation therapy, radiobiological model, breast cancer, pneumonitis

© 2018 Maede Hasan Abdali et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Developing of predictive models for pneumonitis with forward variable selection and LASSO logistic model for breast cancer patients treated with 3D-CRT

Published Online: Dec 24, 2018

Page range: 149 - 156

Received: Nov 03, 2017

Accepted: Oct 31, 2018

DOI: https://doi.org/10.2478/pjmpe-2018-0021

Keywordsradiation therapy, radiobiological model, breast cancer, pneumonitis

© 2018 Maede Hasan Abdali et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
radiation therapy, radiobiological model, breast cancer, pneumonitis