Molecular imaging of pancreatic neoplasms

Duan, Heying; Baratto, Lucia; Laudicella, Riccardo; Stracuzzi, Federica; Baldari, Sergio; Iagaru, Andrei

doi:10.1007/s40336-020-00408-7

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“…Gastroenteropancreatic (GEP) NETs represent the most common subtype, covering up to 70% of all NET. Functional GEP NET release hormones that can cause symptoms, being consequently diagnosed earlier, while non-functional GEP NETs are more frequent and usually diagnosed at an advanced stage [ 1 , 2 ]. According to the recent 2019 WHO classification, GEP NET can be divided into G1, G2, G3, small-cell type (SCNEC), large-cell type (LCNEC), and mixed NET (MiNET) based on differentiation, grade, mitotic rate, and ki-67 index [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

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

Laudicella

Comelli

Liberini

et al. 2022

Cancers

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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 [68Ga]DOTATOC 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 [68Ga]DOTATOC PET/CT before complete PRRT with [177Lu]DOTATOC. 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. [68Ga]DOTATOC 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 [68Ga]DOTATOC 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, HISTO_Skewness and HISTO_Kurtosis 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 (HISTO_Skewness; HISTO_Kurtosis, and Grading) did not improve the AUC significantly with 0.744. SUVmax. However, it could not predict 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 [177Lu]DOTATOC PRRT, regardless of the lesion site.

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Section: Introductionmentioning

confidence: 99%