68GaDOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing 177LuDOTATOC 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 68GaDOTATOC PET/CT images pre-PRRT in metastatic GEP NET. We retrospectively analyzed the predictive value of radiomics in 324 SSTR-2-positive lesions from 38 metastatic GEP-NET patients (nine G1, 27 G2, and two G3) who underwent restaging 68GaDOTATOC PET/CT before complete PRRT with 177LuDOTATOC. Clinical, laboratory, and radiological follow-up data were collected for at least six months after the last cycle. Through LifeX, we extracted 65 PET features for each lesion. Grading, PRRT number of cycles, and cumulative activity, pre- and post-PRRT CgA values were also considered as additional clinical features. 68GaDOTATOC PET/CT follow-up with the same scanner for each patient determined the disease status (progression vs. response in terms of stability/reduction/disappearance) for each lesion. All features (PET and clinical) were also correlated with follow-up data in a per-site analysis (liver, lymph nodes, and bone), and for features significantly associated with response, the Δradiomics for each lesion was assessed on follow-up 68GaDOTATOC PET/CT performed until nine months post-PRRT. A statistical system based on the point-biserial correlation and logistic regression analysis was used for the reduction and selection of the features. Discriminant analysis was used, instead, to obtain the predictive model using the k-fold strategy to split data into training and validation sets. From the reduction and selection process, HISTOSkewness and HISTOKurtosis were able to predict response with an area under the receiver operating characteristics curve (AUC ROC), sensitivity, and specificity of 0. 745, 80. 6%, 67. 2% and 0. 722, 61. 2%, 75. 9%, respectively. Moreover, a combination of three features (HISTOSkewness; HISTOKurtosis, and Grading) did not improve the AUC significantly with 0. 744. SUVmax, however, could not predict the response to PRRT (p = 0. 49, AUC 0. 523). The presented preliminary “theragnomics” model proved to be superior to conventional quantitative parameters to predict the response of GEP-NET lesions in patients treated with complete 177LuDOTATOC PRRT, regardless of the lesion site.