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Updated: Apr 5, 2026

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

Reinforcement learning for real-time adaptive radiotherapy.

Kenneth Lau¹, Jana Tumova², David Broman²

¹EECS and Digital Futures, KTH Royal Institute of Technology, Stockholm, 100 44, Sweden; Elekta Instrument AB, Hagaesplanaden 4, Stockholm, 113 68, Sweden.

Artificial Intelligence in Medicine

|April 3, 2026

Summary

This summary is machine-generated.

This study introduces a novel reinforcement learning (RL) approach for real-time adaptive radiotherapy. It enables precise radiation delivery by adapting to patient motion, minimizing dose discrepancies.

Keywords:

Adaptive radiotherapy MLC tracking Reinforcement learning

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Last Updated: Apr 5, 2026

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Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

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Published on: February 6, 2019

Area of Science:

Medical Physics
Oncology
Artificial Intelligence

Background:

Magnetic Resonance-Linear Accelerators (MR-Linacs) enable real-time tumor tracking for precise radiotherapy.
Current adaptive radiotherapy relies on geometric tracking, not the delivered radiation dose.
Real-time dose-based adaptation is complex due to high dimensionality.

Purpose of the Study:

To develop a radiotherapy simulator and a reinforcement learning (RL) approach for real-time adaptive radiotherapy.
To utilize 2D fluence as a surrogate for 3D dose in adaptive radiotherapy.
To enable adaptive radiotherapy that closes the feedback loop by considering delivered dose.

Main Methods:

Development of a radiotherapy simulator.
Proposal of a novel reinforcement learning (RL)-based algorithm for adaptive radiotherapy.
Utilizing 2D fluence as a proxy for real-time dose monitoring and adaptation.

Main Results:

Demonstrated feasibility of RL for closing the feedback loop in adaptive radiotherapy.
Successful dynamic adaptation to patient motion during simulated treatment.
Minimized discrepancies between delivered and intended radiation dose in clinical scenarios.

Conclusions:

Reinforcement learning offers a viable solution for real-time adaptive radiotherapy.
The proposed method allows for dynamic treatment adjustments based on motion and reference fluence.
This approach represents a new paradigm in radiotherapy delivery, moving beyond predefined machine settings.