Patient-specific microdosimetry: a proof of concept

Area of Science:

• Medical physics
• Radiation oncology
• Computational biology

Background:

• Microscopic energy deposition from ionizing radiation varies with biological target size, influencing biological effects.
• Cell and nucleus sizes, critical for microdosimetry, vary significantly between patients due to tissue type, cell cycle, and malignancy.
• Accurate prediction of radiation's biological effects requires understanding these patient-specific variations.

Purpose of the Study:

• To develop and evaluate methods for patient-specific microdosimetry, calculating dose distributions in patient-representative cell and nucleus volumes.
• To compare the accuracy and efficiency of fixed radius models versus a novel summation method for personalized microdosimetric calculations.
• To assess the suitability of these methods for determining patient-specific relative biological effectiveness in treatment planning.

Main Methods:

• A 3D tissue model was generated from histopathological data of a lung adenocarcinoma patient using pouring simulations.
• Microdosimetric distributions (f(y) and d(y)) were calculated for various radioisotopes (C60o, I192r, Y169b, I125) within the patient-specific model.
• The patient-specific model was compared against fixed radius models and a summation method for accuracy and computational efficiency.

Main Results:

• Fixed radius models showed poor approximation for lower energy sources (Y169b, I125) compared to the full patient-specific model.
• Higher energy sources (C60o, I192r) were less sensitive to target size variations.
• The summation method provided the most accurate approximation of d(y) across all investigated radioisotopes and allowed for computation on a personal computer.

Conclusions:

• The summation method offers an accurate and computationally feasible approach for patient-specific microdosimetry.
• This method enables the calculation of patient-specific microdosimetric distributions, crucial for personalized treatment planning.
• Integrating these patient-specific calculations can lead to more accurate relative biological effectiveness estimations for improved radiotherapy outcomes.