Deep learning for adaptive chemotherapy: A DDPG-based approach to optimizing tumor-immune dynamics.

Area of Science:

• Computational Biology
• Artificial Intelligence in Medicine
• Oncology

Background:

• Cancer treatment requires dynamic and personalized strategies to balance efficacy and toxicity.
• Current chemotherapy optimization methods often lack adaptability to individual patient responses and tumor microenvironment dynamics.

Purpose of the Study:

• To develop a deep reinforcement learning (DRL) framework for personalized and dynamic chemotherapy optimization.
• To model the tumor microenvironment and optimize drug dosing strategies for improved cancer treatment outcomes.

Main Methods:

• A nonlinear dynamic system was employed to model tumor, normal, and immune cell interactions within the tumor microenvironment.
• The Deep Deterministic Policy Gradient (DDPG) algorithm was utilized for optimal dosing strategy learning in a continuous action space.
• Gaussian noise was incorporated to simulate physiological uncertainties and enhance strategy stability.

Main Results:

• The DRL framework demonstrated effective control over tumor growth across various initial conditions.
• The proposed method achieved flexible and safe management of drug concentration accumulation.
• The system showed high adaptability in inhibiting tumor progression while minimizing damage to normal tissues.

Conclusions:

• Deep reinforcement learning offers a feasible technical approach for precision, low-toxicity adaptive chemotherapy.
• The DDPG-based framework enables dynamic optimization of cancer treatment for enhanced patient outcomes.
• This method provides a robust strategy for personalized cancer therapy management.