Biodistribution Assessment of a Novel 68Ga-Labeled Radiopharmaceutical in a Cancer Overexpressing CCK2R Mouse Model: Conventional and Radiomics Methods for Analysis
Anna Maria Pavone, Viviana Benfante, Paolo Giaccone, Alessandro Stefano, Filippo Torrisi, Vincenzo Russo, Davide Serafini, Selene Richiusa, Marco Pometti, Fabrizio Scopelliti, Massimo Ippolito, Antonino Giulio Giannone, Daniela Cabibi, Mattia Asti, Elisa Vettorato, Luca Morselli, Mario Merone, Marcello Lunardon, Alberto Andrighetto, Antonino Tuttolomondo, Francesco Paolo Cammarata, Marco Verona, Giovanni Marzaro, Francesca Mastrotto, Rosalba Parenti, Giorgio Russo, Albert Comelli- Paleontology
- Space and Planetary Science
- General Biochemistry, Genetics and Molecular Biology
- Ecology, Evolution, Behavior and Systematics
The aim of the present study consists of the evaluation of the biodistribution of a novel 68Ga-labeled radiopharmaceutical, [68Ga]Ga-NODAGA-Z360, injected into Balb/c nude mice through histopathological analysis on bioptic samples and radiomics analysis of positron emission tomography/computed tomography (PET/CT) images. The 68Ga-labeled radiopharmaceutical was designed to specifically bind to the cholecystokinin receptor (CCK2R). This receptor, naturally present in healthy tissues such as the stomach, is a biomarker for numerous tumors when overexpressed. In this experiment, Balb/c nude mice were xenografted with a human epidermoid carcinoma A431 cell line (A431 WT) and overexpressing CCK2R (A431 CCK2R+), while controls received a wild-type cell line. PET images were processed, segmented after atlas-based co-registration and, consequently, 112 radiomics features were extracted for each investigated organ / tissue. To confirm the histopathology at the tissue level and correlate it with the degree of PET uptake, the studies were supported by digital pathology. As a result of the analyses, the differences in radiomics features in different body districts confirmed the correct targeting of the radiopharmaceutical. In preclinical imaging, the methodology confirms the importance of a decision-support system based on artificial intelligence algorithms for the assessment of radiopharmaceutical biodistribution.