[<sup>68</sup>Ga]DOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing [<sup>177</sup>Lu]DOTATOC PRRT: The “Theragnomics” Concept

[<sup>68</sup>Ga]DOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing [<sup>177</sup>Lu]DOTATOC PRRT: The “Theragnomics” Concept

Despite impressive results, almost 30% of NET do not respond to PRRT and no well-established criteria are suitable to predict response. Therefore, we assessed the predictive value of radiomics [<sup>68</sup>Ga]DOTATOC PET/CT images pre-PRRT in metastatic GEP NET. We retrospectively analy...

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Bibliographic Details
Main Authors: Riccardo Laudicella, Albert Comelli, Virginia Liberini, Antonio Vento, Alessandro Stefano, Alessandro Spataro, Ludovica Crocè, Sara Baldari, Michelangelo Bambaci, Desiree Deandreis, Demetrio Arico’, Massimo Ippolito, Michele Gaeta, Pierpaolo Alongi, Fabio Minutoli, Irene A. Burger, Sergio Baldari
Format: Article
Language:English
Published: MDPI AG 2022-02-01
Series:Cancers
Subjects:
<sup>177</sup>Lu
artificial intelligence
[<sup>68</sup>Ga]DOTATOC PET
GEP NET
machine-learning
PRRT
Online Access:https://www.mdpi.com/2072-6694/14/4/984

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