TransAnaNet: Transformer-based anatomy change prediction network for head and neck cancer radiotherapy.

Meixu Chen¹, Kai Wang^1,2, Michael Dohopolski¹, Howard Morgan^1,3, David Sher¹, Jing Wang¹

¹Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas, USA.

²Department of Radiation Oncology, University of Maryland Medical Center, Baltimore, Maryland, USA.

³Department of Radiation Oncology, Central Arkansas Radiation Therapy Institute, Little Rock, Arkansas, USA.

PubMed PMC DOI PDF

Abstract

BACKGROUND: Adaptive radiotherapy (ART) can compensate for the dosimetric impact of anatomic change during radiotherapy of head-neck cancer (HNC) patients. However, implementing ART universally poses challenges in clinical workflow and resource allocation, given the variability in patient response and the constraints of available resources. Therefore, the prediction of anatomical change during rad…

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NIH Funding

Grant #11128695
A Multifaceted Radiomics Model to Predict Cervical Lymph Node Metastasis for Involved Nodal Radiation Therapy
UT SOUTHWESTERN MEDICAL CENTER
$542,556 (FY2025)
Grant #11372242
A Multifaceted Radiomics Model to Predict Cervical Lymph Node Metastasis for Involved Nodal Radiation Therapy
UT SOUTHWESTERN MEDICAL CENTER
$542,556 (FY2026)

Other Funding

NCI NIH HHS(R01 CA251792)
United States

MeSH Terms

Humans0Head and Neck Neoplasms*Neural Networks, Computer*Retrospective Studies0Radiotherapy Planning, Computer-Assisted0Image Processing, Computer-Assisted0

Keywords

adaptive radiation therapyanatomy change predictionhead and neck cancerstransformer network

Article Info

PMID: 39887473
PMCID: PMC12059511
DOI: 10.1002/mp.17655
Received: May 10, 2024
Accepted: January 14, 2025

How to Cite

Chen M et al. TransAnaNet: Transformer-based anatomy change prediction network for head and neck cancer radiotherapy. Med Phys2025;52(5):3015-3029. doi:10.1002/mp.17655

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