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Multi-center Dose Prediction Using Attention-aware Deep learning Algorithm Based on Transformers for Cervical Cancer

Z Wu1, X Jia2, L Lu3

  • 1Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University, Chongqing, PR China; Department of Radiotherapy, Zigong First People's Hospital, Sichuan, PR China; Yu-Yue Pathology Research Center, Jinfeng Laboratory, Chongqing, PR China.

Clinical Oncology (Royal College of Radiologists (Great Britain))
|April 17, 2024
PubMed
Summary

A new deep learning algorithm, AtTranNet, accurately predicts radiation doses for cervical cancer volumetric modulated arc therapy (VMAT) across multiple centers. It also shows promise for endometrial cancer VMAT without retraining.

Keywords:
Cervical cancerdeep learningdose predictionmulti-centervolumetric modulated arc therapy

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Artificial Intelligence in Medicine

Background:

  • Accurate radiation dose delivery is critical for effective cervical cancer treatment using volumetric modulated arc therapy (VMAT).
  • Existing dose prediction methods may face challenges with multicenter data and varying treatment prescriptions.

Purpose of the Study:

  • To develop and validate a deep learning (DL) algorithm, AtTranNet, for rapid and precise 3D dose prediction in cervical cancer VMAT.
  • To assess the generalizability of the AtTranNet algorithm for endometrial cancer VMAT with diverse dose prescriptions.

Main Methods:

  • The AtTranNet algorithm was developed for 3D dose prediction.
  • A multicenter dataset of 367 cervical cancer patients was used for training, validation, and testing.
  • External validation included 45 cervical cancer patients and 70 endometrial cancer patients.

Main Results:

  • AtTranNet demonstrated clinically acceptable dose prediction accuracy for cervical cancer VMAT.
  • Mean absolute error within the body was 0.66 ± 0.63% in internal testing.
  • The algorithm showed feasibility for endometrial cancer VMAT without requiring transfer learning.

Conclusions:

  • The AtTranNet algorithm is a feasible and accurate tool for cervical cancer VMAT dose prediction across multiple institutions.
  • The DL model effectively generalizes to endometrial cancer VMAT, highlighting its adaptability to different prescriptions.