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Review
. 2023 Jul 28;13(8):1647.
doi: 10.3390/life13081647.

Applications of Artificial Intelligence and Radiomics in Molecular Hybrid Imaging and Theragnostics for Neuro-Endocrine Neoplasms (NENs)

Michele Balma  1 , Riccardo Laudicella  2 , Elena Gallio  3 , Sara Gusella  4 , Leda Lorenzon  5 , Simona Peano  1 , Renato P Costa  2 , Osvaldo Rampado  3 , Mohsen Farsad  4 , Laura Evangelista  6 , Desiree Deandreis  7 , Alberto Papaleo  1 , Virginia Liberini  1
Affiliations
Review

Applications of Artificial Intelligence and Radiomics in Molecular Hybrid Imaging and Theragnostics for Neuro-Endocrine Neoplasms (NENs)

Michele Balma et al. Life (Basel). .

Abstract

Nuclear medicine has acquired a crucial role in the management of patients with neuroendocrine neoplasms (NENs) by improving the accuracy of diagnosis and staging as well as their risk stratification and personalized therapies, including radioligand therapies (RLT). Artificial intelligence (AI) and radiomics can enable physicians to further improve the overall efficiency and accuracy of the use of these tools in both diagnostic and therapeutic settings by improving the prediction of the tumor grade, differential diagnosis from other malignancies, assessment of tumor behavior and aggressiveness, and prediction of treatment response. This systematic review aims to describe the state-of-the-art AI and radiomics applications in the molecular imaging of NENs.

Keywords: DOTA PET; NET; PET; machine learning; neuroendocrine tumor; nuclear medicine; radiomics; theragnostics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic representation of the performed literature search and the review strategy.
Figure 2
Figure 2
A graphic summary of technical factors affecting the stability of PET radiomics and potentially reducing its robustness, reproducibility, and performance.
See this image and copyright information in PMC

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