Mind the tributary of the canal: Are stents necessary for insulinoma enucleation in proximity to a prominent Duct of Santorini: A case report and literature review

Liu, Tiantong; Xu, Qiang; Zou, Xi; Zhu, Liang; Zhao, Yupei

doi:10.1097/md.0000000000031211

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…However, guidelines from the European Neuroendocrine Tumor Society and the National Comprehensive Cancer Network suggest that asymptomatic NF-PNETs measuring less than 2cm may be safely observed without active surgical intervention ( 17 , 18 ). In contrast, current guidelines generally advocate for surgical intervention in the case of insulinomas, while somatostatin analogs are increasingly being utilized for the treatment of well-differentiated, low-grade F-PNETs ( 19 , 20 ). Consequently, the timely and precise diagnosis and prognostication of both NF-PNETs and insulinomas are of paramount importance in guiding treatment strategies.…”

Section: Introductionmentioning

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.

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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.

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Section: Introductionmentioning

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.

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show abstract

“…However, guidelines from the European Neuroendocrine Tumor Society and the National Comprehensive Cancer Network suggest that asymptomatic NF-PNETs measuring less than 2cm may be safely observed without active surgical intervention (17,18). In contrast, current guidelines generally advocate for surgical intervention in the case of insulinomas, while somatostatin analogs are increasingly being utilized for the treatment of welldifferentiated, low-grade F-PNETs (19,20). Consequently, the timely and precise diagnosis and prognostication of both NF-PNETs and insulinomas are of paramount importance in guiding treatment strategies.…”

Section: Introductionmentioning

confidence: 99%

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An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer

Yi,

Mo,

Zhang

et al. 2024

Preprint

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Objectives To retrospectively validate and develop an interpretable deep learning model and nomogram using EUS images to predict pancreatic neuroendocrine tumors (pNETs). Methods After pathological confirmation, a retrospective analysis of 266 patients (115 with pNETs and 151 with pancreatic cancer) was conducted. Patients were randomly divided into training and test groups (7:3 ratio). The least absolute shrinkage and selection operator algorithm reduced DL feature dimensions from pre-standardized EUS images. Nonzero features developed eight predictive DL models using different machine learning algorithms. The best model established a clinical signature for a nomogram. Grad-CAM and SHAP were used to interpret and visualize model outputs. Results Out of 2048 DL features, 27 with nonzero coefficients were retained. The SVM DL model achieved AUCs of 0.948 (training) and 0.795 (test). A nomogram combining DL and clinical signatures was developed, and calibration curves, DCA plots, and CICs confirmed high accuracy. Grad-CAM and SHAP enhanced model interpretability, benefiting clinical decision-making. Conclusions The novel interpretable DL model and nomogram, validated with EUS images and machine learning, show promise for enhancing EUS's ability to predict pNETs from pancreatic cancer, providing valuable insights for future research and application.

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Mind the tributary of the canal: Are stents necessary for insulinoma enucleation in proximity to a prominent Duct of Santorini: A case report and literature review

Cited by 4 publications

References 21 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

DataSheet_1.pdf

An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer

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