Dataset Information

Study protocol for endoscopic ultrasonography-guided ethanol injection therapy for patients with pancreatic neuroendocrine neoplasm: a multicentre prospective study.

ABSTRACT:

Introduction

The management of small pancreatic neuroendocrine neoplasms (PNENs) remains controversial. The standard treatment for PNENs is surgical resection; however, invasiveness of surgical procedure remains higher and the incidence of postoperative adverse events is still high. Recently, the efficacy and safety of endoscopic ultrasonography (EUS)-guided ethanol injection for small PNENs has been preliminarily demonstrated. Thus, a multicentre prospective study is being conducted to evaluate the efficacy and safety of EUS-guided ethanol injection therapy for small PNENs.

Methods and analysis

The major eligibility criteria are the presence of pathologically diagnosed grade (G) 1 tumour, a tumour size of ≤15 mm and non-functional PNEN or insulinoma. For treatment, we will use a 25-gauge needle and pure ethanol. Contrast-enhanced CT (CE-CT) will be performed on postoperative day 3-5, and if enhanced areas of the tumour are still apparent, an additional session is scheduled during the same hospitalisation period. We set the total amount of ethanol per session to 2 mL. To evaluate the efficacy and safety, CE-CT will be performed at 1 and 6 months after treatment. The primary endpoint is the percentage of subjects who achieved all of the following evaluated points. Efficacy will be evaluated based on the achievement of complete ablation (defined as no enhanced area within the tumour on CE-CT) at 1 and 6 months. Safety will be evaluated based on the avoidance of severe adverse events within 1 month after treatment, continuing severe pancreatic fistula at 1 month after treatment and the incidence and/or exacerbation of diabetes mellitus at 6 months after treatment.

Ethics and dissemination

This protocol has been approved by Okayama University Certified Review Board (approval number. CRB19-007). The results will be submitted to peer-reviewed journals and will be presented at international conferences.

Trial registration number

jRCTs061200016.

SUBMITTER: Matsumoto K

PROVIDER: S-EPMC8276291 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.