Estimation of heart dose in left breast cancer radiotherapy: Assessment of vDIBH feasibility using the supervised machine learning algorithm.

Abstract

Background and objective: The volunteer deep inspiration breath hold (vDIBH) technique is used to reduce the heart dose in left breast cancer radiotherapy. Many times, it is faced that despite rigorous exercise and training, not all patients get benefited as expected. The primary objective of this study was to develop a machine learning program for prediction of mean heart dose before left breast radiotherapy under vDIBH.

Methods: The present work is based on the dosimetric parameters of eighty-two left breast cancer patients, who have undergone modified radical mastectomy, enrolled for their radiation treatment. The trained machine learning algorithm employed linear regression to establish a correlation between Haller Index and heart mean dose (HMD) received during the ca left breast cancer radiotherapy. Subsequently, HMD values were used to model the regression relationship with maximum heart distance (MHD).

Results: The method adopted is beneficial in patient selection and assessment for suitability of patients' radiotherapy planning under vDIBH treatment technique. For data from 21 test patients, the mean of HMD obtained from the treatment planning system (TPS) and the mean of predicted HMD by developed program were found to be 468.76 cGy and 464.66 cGy, respectively.

Conclusion: The present work facilitates precise HMD prediction in left breast cancer radiation therapy even before starting the treatment planning process. Additionally, this program offers suggestions in terms of modifications in treatment settings for even better results of vDIBH techniques if not matches with the anticipated results.