Integrating Deep Learning-Based Dose Distribution Prediction with Bayesian Networks for Decision Support in Radiotherapy for Upper Gastrointestinal Cancer.

Journal: Cancer research and treatment

Published Date: Aug 2, 2024

Abstract

PURPOSE: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or magnetic resonance-guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data.

Authors

Dong-Yun Kim

Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Korea.
Bum-Sup Jang

Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea.
Eunji Kim

Samsung Advanced Institute of Technology, Samsung Electronics Co., Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon 16678, Republic of Korea.
Eui Kyu Chie

Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.

Keywords

Aged Aged, 80 and over Bayes Theorem Deep Learning Female Gastrointestinal Neoplasms Humans Liver Neoplasms Male Middle Aged Pancreatic Neoplasms Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (39091147)

Integrating Deep Learning-Based Dose Distribution Prediction with Bayesian Networks for Decision Support in Radiotherapy for Upper Gastrointestinal Cancer.

Abstract

Authors

Keywords

External Resources

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