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

Journal: Journal of applied clinical medical physics

PMID: 39641663

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.

Authors

Shriram Ashok Rajurkar

DHR-ICMR JRF, Department of Radiation Oncology, KGMU, Lucknow, Uttar Pradesh, India.
Teerthraj Verma

Department of Radiation Oncology, KGMU, Lucknow, Uttar Pradesh, India.
Rajeev Gupta

Department of Radiation Oncology, KGMU, Lucknow, Uttar Pradesh, India.

Keywords

Adult Algorithms Breast Neoplasms Breath Holding Feasibility Studies Female Heart Humans Middle Aged Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Supervised Machine Learning Unilateral Breast Neoplasms

External Resources

View on PubMed Access via DOI PubMed (39641663)

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

Abstract

Authors

Keywords

External Resources

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