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.

Radiomics in Oncological PET Imaging: A Systematic Review—Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

“…Seven studies were found to be conducted on neuroendocrine tumors [ 114 , 115 , 116 , 117 , 118 , 119 , 120 ], half of which on pancreatic neuroendocrine tumors. The radiotracers used were 68Ga-DOTA-peptides (6/7) and 18F-FDG (1/7).…”

“…Four studies aimed at predicting prognosis and four were conducted for diagnostic purposes, particularly for Ki67 prediction. Bevilacqua et al [ 114 ] developed a model to predict grade 1 and grade 2 pancreatic neuroendocrine tumors, obtaining an AUC > 0.8.…”

“…Only few studies evaluated PET radiomics and AI analysis in neuroendocrine tumors [ 114 , 115 , 116 , 117 , 118 , 119 , 120 ]. In low-grade disease, 68Ga-labelled somatostatin analogues are used for staging and restaging purposes: radiomics and AI analysis could provide more information in metastatic stable disease treated with cold somatostatin analogues.…”

Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications

“…The radiomic models developed on a training cohort achieved performances much superior to the clinical model on a validation cohort from a different institution (AUCs of 0.88–0.96 vs. 0.65). Bevilacqua et al used [68Ga]Ga-DOTANOC PET/CT radiomic features to detect grade 1 and 2 pancreatic neuroendocrine tumors [ 49 ].…”

Radiolabeled Somatostatin Analogues for Diagnosis and Treatment of Neuroendocrine Tumors

“…It is currently difficult to generalize the results obtained in published papers due to excessive data heterogeneity; indeed, the application of AI in clinical practice requires absolute methodological harmonization [ 81 ]. Thus far, few papers have addressed the use of PET/CT-derived features for the assessment of NENs ( Table 2 ) [ 82 , 83 , 84 , 85 , 86 , 87 , 88 ].…”