Artificial Intelligence-based Motion Tracking in Cancer Radiotherapy: A Review

TL;DR

This review discusses how artificial intelligence enhances real-time tumor motion tracking in advanced cancer radiotherapy techniques, highlighting current algorithms, challenges, and future improvements.

Contribution

It provides a comprehensive summary of AI algorithms for tumor motion management in radiotherapy and discusses their limitations and potential enhancements.

Findings

AI enables real-time tumor tracking during radiotherapy.

Challenges include data bias, transparency, and limited samples.

The review suggests directions for future algorithm improvements.

Abstract

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Recently, artificial intelligence (AI) has demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review serves to present the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provide a literature summary on the topic. We will also discuss the limitations of these algorithms and propose potential improvements.