Cancer Res Treat.  2025 Jan;57(1):186-197. 10.4143/crt.2024.333.

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

• Kim DY
• Jang BS
• Kim E
• Chie EK

• KMID: 2564593
• DOI: http://doi.org/10.4143/crt.2024.333

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.
Materials and Methods
We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data.
Results
The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the planning target volume (PTV) and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG. It provided a potential framework for selecting the optimal radiation therapy (RT) system based on individual patient characteristics.
Conclusion
We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.

Keyword

Deep learning; Upper GI cancer; Linear accelerator-based treatment plan; MR-guided treatment plan; Bayesian network; Decision-supporting algorithm

Figure

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  Fig. 1. The overall network architecture to construct the dose distribution model of computed tomography (CT)–based high dose rate stereotactic radiotherapy (TBX) (A), magnetic resonance (MR)–guided radiotherapy (MRG) (B), and the Bayesian network model schema (C).

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  Fig. 2. The actuarial dose-volume histogram (DVHs) of ground truth and the predicted values by developed 3D U-Net deep learning model of each of computed tomography–based high dose rate stereotactic radiotherapy (TBX) (A) and magnetic resonance–guided radiotherapy (MRG) (B). Solid lines depict in DVHs the ground truth values of planning target volume (PTV) and organs at risk, and the dashed lines are the predicted dose by the model. DUO, duodenum; KID, kidney; LIV, liver; PTV, planning target volume; STO, stomach.

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  Fig. 3. Statistical significance for maximum (A)/mean (B) normalized dose difference between ground truth and predicted value for computed tomography–based high dose rate stereotactic radiotherapy (TBX) and magnetic resonance–guided radiotherapy (MRG) model. DUO, duodenum; KID, kidney; LIV, liver; PTV, planning target volume; STO, stomach.

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  Fig. 4. The decision tree based on Bayesian Network model to decide optimal radiation therapy plan modality (computed tomography–based high dose rate stereotactic radiotherapy [TBX] vs. magnetic resonance–guided radiotherapy [MRG]) based on clinical factors and generated dose-volume histogram profiles by a 3D U-Net deep learning. The score next to the clinical variables in each box indicates the likelihood of the evidence. The higher the score, the more likely the explanation is when ranked.

Reference

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