Enhanced dose prediction for head and neck cancer artificial intelligence-driven radiotherapy based on transfer learning with limited training data.

Journal: Journal of applied clinical medical physics

PMID: 40087841

Abstract

PURPOSE: Training deep learning dose prediction models for the latest cutting-edge radiotherapy techniques, such as AI-based nodal radiotherapy (AINRT) and Daily Adaptive AI-based nodal radiotherapy (DA-AINRT), is challenging due to limited data. This study aims to investigate the impact of transfer learning on the predictive performance of an existing clinical dose prediction model and its potential to enhance emerging radiotherapy approaches for head and neck cancer patients.

Authors

Hui-Ju Wang

Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Austen Maniscalco

Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
David Sher

Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.
Mu-Han Lin

Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.
Steve Jiang
Dan Nguyen

University of Massachusetts Chan Medical School, Worcester, Massachusetts.

Keywords

Algorithms Artificial Intelligence Deep Learning Head and Neck Neoplasms Humans Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated

External Resources

View on PubMed Access via DOI PubMed (40087841)

Enhanced dose prediction for head and neck cancer artificial intelligence-driven radiotherapy based on transfer learning with limited training data.

Abstract

Authors

Keywords

External Resources

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