Diagnosis and Surgical Management of Insulinomas—A 23-Year Single-Center Experience

Hoskovec, David; Krška, Z; Škrha, J; Klobušický, Pavol; Dytrych, Petr

doi:10.3390/medicina59081423

Cited by 2 publications

(1 citation statement)

References 19 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A considerable number of patients diagnosed with F-PNETs, which frequently occur in conjunction with MEN1, commonly exhibit the presence of multiple small pancreatic lesions. Two-thirds of insulinomas are smaller than 2 cm and 30% are smaller than 1 cm ( 50 ). Numerous studies in the literature have demonstrated the superiority of EUS over CT and MRI in detecting small pancreatic lesions ( 27 , 47 ).…”

Section: Discussionmentioning

confidence: 99%

Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors

Mo,

Huang,

Wang

et al. 2024

Front. Endocrinol.

View full text Add to dashboard Cite

ObjectivesTo develop and validate radiomics models utilizing endoscopic ultrasonography (EUS) images to distinguish insulinomas from non-functional pancreatic neuroendocrine tumors (NF-PNETs).MethodsA total of 106 patients, comprising 61 with insulinomas and 45 with NF-PNETs, were included in this study. The patients were randomly assigned to either the training or test cohort. Radiomics features were extracted from both the intratumoral and peritumoral regions, respectively. Six machine learning algorithms were utilized to train intratumoral prediction models, using only the nonzero coefficient features. The researchers identified the most effective intratumoral radiomics model and subsequently employed it to develop peritumoral and combined radiomics models. Finally, a predictive nomogram for insulinomas was constructed and assessed.ResultsA total of 107 radiomics features were extracted based on EUS, and only features with nonzero coefficients were retained. Among the six intratumoral radiomics models, the light gradient boosting machine (LightGBM) model demonstrated superior performance. Furthermore, a peritumoral radiomics model was established and evaluated. The combined model, integrating both the intratumoral and peritumoral radiomics features, exhibited a comparable performance in the training cohort (AUC=0.876) and achieved the highest accuracy in predicting outcomes in the test cohorts (AUC=0.835). The Delong test, calibration curves, and decision curve analysis (DCA) were employed to validate these findings. Insulinomas exhibited a significantly smaller diameter compared to NF-PNETs. Finally, the nomogram, incorporating diameter and radiomics signature, was constructed and assessed, which owned superior performance in both the training (AUC=0.929) and test (AUC=0.913) cohorts.ConclusionA novel and impactful radiomics model and nomogram were developed and validated for the accurate differentiation of NF-PNETs and insulinomas utilizing EUS images.

show abstract

Section: Discussionmentioning

confidence: 99%

Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors

Mo,

Huang,

Wang

et al. 2024

Front. Endocrinol.

View full text Add to dashboard Cite

show abstract

Endoscopic radiofrequency ablation of pancreatic neuroendocrine tumors

Parfenchikova,

Orlova,

Melehina

et al. 2024

Ann. hir. gepatol.

View full text Add to dashboard Cite

Aim. To present the first experience of EUS-guided radiofrequency ablation in the treatment of patients with functioning neuroendocrine tumors of the pancreas. Materials and methods. Endoscopic radiofrequency ablation was performed in 2 patients with insulin-producing neuroendocrine tumor of the pancreatic head and clinical picture of organic hyperinsulinism. The radiofrequency ablation system includes a 19-gauge needle electrode to be conducted through the forceps aperture, a generator and an intraelectrode cooling system. Results. The efficacy of ultrasound-guided radiofrequency ablation in the cases presented above appears to be clinically proven. The outcomes include stabilization of the blood glucose level, a complete elimination of hypoglycemia-associated clinical symptoms. Both observations reveal no complications after the procedure. Conclusion. EUS-guided radiofrequency ablation appears to possess a potential for wider application as a minimally invasive intervention, thus introducing an alternative to surgical treatment of patients with neuroendocrine tumors of the pancreas. An experience of EUS-guided radiofrequency ablation is worth to be accumulated and outcomes of the method application to be analyzed on a larger number of clinical observations.

show abstract

Diagnosis and Surgical Management of Insulinomas—A 23-Year Single-Center Experience

Cited by 2 publications

References 19 publications

Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors

Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors

Endoscopic radiofrequency ablation of pancreatic neuroendocrine tumors

Contact Info

Product

Resources

About