Dataset Information

A [68Ga]Ga-DOTANOC PET/CT Radiomic Model for Non-Invasive Prediction of Tumour Grade in Pancreatic Neuroendocrine Tumours.

ABSTRACT: Predicting grade 1 (G1) and 2 (G2) primary pancreatic neuroendocrine tumour (panNET) is crucial to foresee panNET clinical behaviour. Fifty-one patients with G1-G2 primary panNET demonstrated by pre-surgical [68Ga]Ga-DOTANOC PET/CT and diagnostic conventional imaging were grouped according to the tumour grade assessment method: histology on the whole excised primary lesion (HS) or biopsy (BS). First-order and second-order radiomic features (RFs) were computed from SUV maps for the whole tumour volume on HS. The RFs showing the lowest p-values and the highest area under the curve (AUC) were selected. Three radiomic models were assessed: A (trained on HS, validated on BS), B (trained on BS, validated on HS), and C (using the cross-validation on the whole dataset). The second-order normalized homogeneity and entropy was the most effective RFs couple predicting G2 and G1. The best performance was achieved by model A (test AUC = 0.90, sensitivity = 0.88, specificity = 0.89), followed by model C (median test AUC = 0.87, sensitivity = 0.83, specificity = 0.82). Model B performed worse. Using HS to train a radiomic model leads to the best prediction, although a "hybrid" (HS+BS) population performs better than biopsy-only. The non-invasive prediction of panNET grading may be especially useful in lesions not amenable to biopsy while [68Ga]Ga-DOTANOC heterogeneity might recommend FDG PET/CT.

SUBMITTER: Bevilacqua A

PROVIDER: S-EPMC8150289 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Somatostatin receptors are overexpressed in GEP-NETs and can be visualized in vivo by radiolabeled somatostatin-analogs. During the last decades, conventional scintigraphy using 111In-DTPA-Octreotide (often named somatostatin receptor scintigraphy or SRS) was considered as the gold standard nuclear imaging technique in the evaluation of GEP-NETs. However, SRS may be suboptimal in this clinical setting because of the low intrinsic resolution of the technique and its selectivity for SST2 only. Its overall sensitivity is estimated to 60-70% (per lesion analysis), even when using the most recent SPECT-CT cameras. MRI have also a higher sensitivity than CT and SRS for the detection of liver metastases from GEP-NETs. In recent years, positron emission tomography (PET) imaging, a high resolution and sensitive technology, has gained an increasing role in oncology. It has also been evaluated in GEP-NETs with somatostatin agonists (SSTa) radiolabelled with Gallium-68 [68Ga], a positron emitter with very promising results. Its diagnostic sensitivity is clearly superior to SRS and many European centers have already replaced SRS by [68Ga]-PET-SSTa. Currently, three different [68Ga]-coupled peptides can be used in trials: DOTA-TOC, DOTA-TATE and DOTA-NOC with excellent affinities for SST2 (IC50: 2.5; 0.2 and 1.9 nM, respectively). Sensitivities of DOTA-TOC and DOTA-TATE PET/CT are quite similar. [68Ga]-DOTANOC which also binds to SST5 was recently found to detect significantly more lesions than the SST2-specific radiotracer [68Ga]-DOTATATE in patients with GEP-NETs but this requires further evaluation. It is therefore important to determine the interest of [68Ga]-DOTANOC combined with the standard diagnosis strategy in GEP-NETs and evaluate medicoeconomic impact of adding [68Ga]-DOTANOC in the work-up of patients. The investigators hypothesis is that [68Ga]-DOTANOC will modify the management in at least 20% of patients in a more adapted way according to the 2012 ENETS guidelines in comparison to the decision based on the standard imaging work up (multiphasic WB CT, liver MRI and SRS). 110 patients will be included prospectively in 5 different French experienced centers (Marseille, Bordeaux, Toulouse, Paris, Clermond-Ferrand).

Dataset Information

A [68Ga]Ga-DOTANOC PET/CT Radiomic Model for Non-Invasive Prediction of Tumour Grade in Pancreatic Neuroendocrine Tumours.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure