Abstract Library

#4600 Artificial intelligence in predicting neuroendocrine tumour (NET) outcomes: A model-based prognostic approach

Introduction: Neuroendocrine tumours (NETs) vary widely in clinical behaviour, complicating prognosis. Traditional models often struggle with accuracy due to the complexity of NETs. Artificial intelligence (AI) offers tools for enhanced prognostic precision by analysing complex datasets. This study explores an AI-driven approach to predict outcomes in NET patients at Cantonment General Hospital, Pakistan.

Conference:

Presenting Author: Fatima A

Authors: Fatima A,

Keywords: artificial intelligence, machine learning, Precision medicine, Personalised treatment,

#4281 Automated (artificial intelligence) vs. manual evaluation of the somatostatin-receptor 2A and proliferation marker Ki-67 in neuroendocrine neoplasms: A pilot study

Introduction: Digital image analysis methods are currently being equipped with artificial intelligence (AI). Various AI applications are already in use to determine Ki-67. To date, there is no specialised AI application available to determine the somatostatin-receptor 2A (SSTR2A).

Conference:

Presenting Author:

Authors: Kaemmerer D, Lupp A, Klöppel G, Ayako I, Kasajima A,

Keywords: artificial intelligence, Ki-67, SSTR2A, usability, neuroendocrine neoplasm,

#4076 Machine learning model: Predicting prognosis in neuroendocrine tumors

Introduction: Neuroendocrine tumors (NETs) have a heterogeneous clinical course. Even with similar Ki-67 proliferative index/morphology, the course varies in patients with similar pathological characterization.

Conference:

Presenting Author:

Authors: Varghese D, Naimian A, Yazdian P, Lokre O, Perk T,

Keywords: Neuroendocrine tumor, artificial intelligence, DOTATATE scan,

#3981 Identifying patients with undiagnosed small intestinal neuroendocrine tumors using statistical and machine learning: Model development and validation study

Introduction: Diagnosis of small intestinal neuroendocrine tumors (SI-NETs) is often delayed due to non-specific symptoms and lack of awareness among primary and secondary care physicians. Earlier diagnosis may lead to improved clinical and patient outcomes. Clinical prediction models using machine learning could present novel opportunities for expedited diagnosis in primary care.

Conference:

Presenting Author:

Authors: Clift A, Mahon H, Khan G, Boardman-Pretty F, Worker A,

Keywords: machine learning, artificial intelligence, diagnosis, neuroendocrine tumor,

#3857 The use of quantitative contrast-enhanced endoscopic ultrasound in the evaluation of pancreatic neuroendocrine tumors – Can we move from quality to quantity? A proof-of-concept study

Introduction: Contrast-enhanced EUS (CE-EUS) is a helpful tool for the diagnosis of pancreatic focal lesions, as pancreatic cancer (PC) and pancreatic neuroendocrine tumor (pNEN). The enhancement of the lesion after contrast medium injection is usually assessed qualitatively and arbitrarily by the endosonographer. Dedicated software has recently been developed to obtain an unbiased quantitative assessment using perfusion parameters.

Conference:

Presenting Author: Tacelli M

Authors: Tacelli M, Bina N, Capurso G, Zaccari P, Petrone M,

Keywords: CE-EUS, pNEN, artificial intelligence, pancreatic cancer, prediction,