Radiation-Induced Lymphopenia: From Mathematical Modeling Toward Mechanistic Learning.

Area of Science:

• Radiation oncology
• Immunology
• Computational modeling

Background:

• Radiation-induced lymphopenia (RIL) is a significant adverse effect of radiation therapy, negatively impacting patient prognosis.
• Understanding RIL mechanisms is crucial for patient risk stratification and outcome improvement.
• Existing mechanistic models vary in complexity, from dosimetry to those incorporating biological factors.

Purpose of the Study:

• To provide a comprehensive scoping review of mechanistic models for radiation-induced lymphopenia.
• To critically analyze the components, assumptions, and data supporting these models.
• To identify overlooked indirect irradiation effects and propose novel modeling approaches.

Main Methods:

• Scoping review methodology to identify and synthesize relevant literature on RIL models.
• Critical analysis of model components, including dosimetry, dose-response, lymphocyte dynamics, and tumor interactions.
• Identification and discussion of indirect irradiation effects (cytokines, MDSCs, lymphocyte recirculation).

Main Results:

• A wide spectrum of RIL models exists, from basic to complex, incorporating various biological factors.
• Indirect irradiation effects, such as cytokine modulation and impaired lymphocyte recirculation, are often overlooked but critical.
• Current models have limitations in accurately predicting RIL due to unaddressed indirect effects.

Conclusions:

• Mechanistic models are essential for understanding and predicting radiation-induced lymphopenia.
• Incorporating indirect irradiation effects and leveraging advanced computational approaches like mechanistic learning can improve model accuracy.
• Future models should aim for clinical translatability and mechanistic insight to better manage RIL in cancer patients.