From plan to delivery: Machine learning based positional accuracy prediction of multi-leaf collimator and estimation of delivery effect in volumetric modulated arc therapy.

Journal: Journal of applied clinical medical physics

PMID: 39031794

Abstract

PURPOSE: The positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that may affect MLC positional accuracy in VMAT, and constructed a model to predict MLC positional deviation and estimate planning delivery quality according to the VMAT plans before delivery.

Authors

Minmin Qiu

Department of Radiation Oncology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Jiajian Zhong

Department of Radiation Oncology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Zhenhua Xiao

Department of Radiation Oncology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Yongjin Deng

Department of Radiation Oncology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.

Keywords

Algorithms Humans Machine Learning Neoplasms Organs at Risk Particle Accelerators Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated

External Resources

View on PubMed Access via DOI PubMed (39031794)

From plan to delivery: Machine learning based positional accuracy prediction of multi-leaf collimator and estimation of delivery effect in volumetric modulated arc therapy.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals