Microscale Histology-Informed Minipig Salivary Gland Model for Preclinical Radiopharmaceutical Dosimetry
Abstract
Purpose
In prostate-specific membrane antigen (PSMA)-targeted radiopharmaceutical therapy for metastatic castration-resistant prostate cancer, salivary glands often receive unintended high absorbed doses and are considered dose-limiting organs. For radiopharmaceuticals emitting short-range particles, such as alpha particles, tissue-level geometry is critical for accurately estimating dose heterogeneity. In the preclinical phase, minipigs are frequently used as experimental models due to their biological similarity to humans. Therefore, this study aims to develop a three-dimensional microscale model of minipig salivary glands for preclinical dosimetry.
Methods
Serially sectioned H&E-stained histology slides were obtained from fixed parotid gland samples of 6-month-old male and female micro-Yucatan minipigs (200 slides per sex). The section thickness is 5 μm with a pixel resolution of 0.27 μm. Using 3D Slicer software, tissue structures—including ducts, blood vessels, and acini—were segmented and exported in polygonal mesh format. The exported models were subsequently refined using Blender software to remove mesh defects such as self-intersections.
Results
The present study developed a microscale minipig salivary gland model with a volume of 1 x 1 x 1 mm3. The model represents fine tissue structures, including interlobular and intercalated ducts, blood vessels, and acini. The final model is watertight and free of mesh defects, enabling conversion to a tetrahedral mesh format suitable for Monte Carlo dose calculations.
Conclusion
By providing a detailed representation of salivary gland microanatomy, the developed microscale minipig model is expected to improve preclinical dosimetry for short-range radiation. In future studies, this model will be used to establish a comprehensive set of S values for clinically relevant radionuclides, enabling more accurate prediction of salivary gland toxicity and supporting informed clinical decision-making.