Artificial intelligence in radiotherapy: a technological review

doi:10.1007/s11684-020-0761-1

Front. Med.

2020, Vol. 14

Issue (4) : 431-449 https://doi.org/10.1007/s11684-020-0761-1

REVIEW

Artificial intelligence in radiotherapy: a technological review

Ke Sheng(

)

Department of Radiation Oncology, University of California, Los Angeles, CA 90095, USA

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Abstract

Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.

Keywords artificial intelligence radiation therapy medical imaging treatment planning quality assurance outcome prediction

Corresponding Author(s): Ke Sheng

Just Accepted Date: 28 June 2020 Online First Date: 28 July 2020 Issue Date: 26 August 2020

Cite this article:

Ke Sheng. Artificial intelligence in radiotherapy: a technological review[J]. Front. Med., 2020, 14(4): 431-449.

URL:

https://academic.hep.com.cn/fmd/EN/10.1007/s11684-020-0761-1
https://academic.hep.com.cn/fmd/EN/Y2020/V14/I4/431

Fig.1 Typical RT workflow.

Tab.1 Overview of the opportunities for AI in each step of RT

Fig.2 Architecture of deep residual CNN for CBCT scatter correction [⁹²]. Black solid rectangles denote residual blocks.

Fig.3 Example of a patient showing various registrations with and without synthetic bridge images. (A−E) Registration of non-aligned CT (green) to a MR image (red). (D) The directly registered non-aligned CT (purple). (E) Registration of the corresponding CTsynth to the nonaligned CT. (F–I) Sagittal view. The non-deformed volumes. (J–N) The results for various registrations. Arrows denote unanatomical deformation in direct multimodel registration [¹³⁵].

Fig.4 Examples of H&N CT segmentation results by GAN, SC-DenseNet, SC-GAN-ResNet, and SC-GAN-DenseNet. The first column shows the ground truth; the second, third, fourth, and fifth columns present the segmentation results by GAN, SC-DenseNet, SC-GAN-ResNet, and SC-GAN-DenseNet, respectively. Brainstem (purple), optical chiasm (dark green), mandible (green), left and right optical nerves (orange and light orange), left and right parotid glands (blue and yellow), left and right submandibular glands (pink and light green). Single, double, and blunt arrows denote false-positive islands, under-segmentations, and mis-segmentations, respectively [¹⁵⁵].

Tab.2 Comparison of segmentation accuracy between the conventional segmentation and deep-learning segmentation for H&N CT

Fig.6 Scatter plots illustrating error classification with the deep-learning and handcrafted approaches on the training data set.

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