Life (Mar 2024)

Biodistribution Assessment of a Novel <sup>68</sup>Ga-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

DOI
https://doi.org/10.3390/life14030409
Journal volume & issue
Vol. 14, no. 3
p. 409

Abstract

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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.

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