Open Access

ARTICLE

Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

by Do Nang Toan^1,*, Lam Thanh Hien², Ha Manh Toan¹, Nguyen Trong Vinh², Pham Trung Hieu¹

1 Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi, 10072, Vietnam
2 Faculty of Information Technology, Lac Hong University, Bien Hoa, Dong Nai, 76120, Vietnam

* Corresponding Author: Do Nang Toan. Email:

(This article belongs to the Special Issue: Deep Learning, IoT, and Blockchain in Medical Data Processing )

Intelligent Automation & Soft Computing 2024, 39(2), 319-335. https://doi.org/10.32604/iasc.2024.046925

Received 19 October 2023; Accepted 20 December 2023; Issue published 21 May 2024

Abstract

Cancer is one of the most dangerous diseases with high mortality. One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians. In our study, we focused on the 3D dose prediction problem in radiotherapy by applying the deep-learning approach to computed tomography (CT) images of cancer patients. Medical image data has more complex characteristics than normal image data, and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem. We proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and augmentation. In strategies, we trained our custom convolutional neural network model which has a structure inspired by the U-net, and residual blocks were also applied to the architecture. The output of the network is added with a rectified linear unit (Re-Lu) function for each pixel to ensure there are no negative values, which are absurd with radiation doses. Our experiments were conducted on the dataset of the Open Knowledge-Based Planning Challenge which was collected from head and neck cancer patients treated with radiation therapy. The results of four strategies show that our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score (DVH-score). In the best training cases, the Dose-score is 3.08 and the DVH-score is 1.78. In addition, we also conducted a comparison with the results of another study in the same context of using the loss function.

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Keywords

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