Abstract Library

#4613 Correlation of serum cytokeratin 18, ferritin, cancer antigen 125, and beta-2 microglobulin with tumour grade and stage in pancreatic neuroendocrine tumours

Introduction: Pancreatic neuroendocrine neoplasms (PNENs) remain challenging to diagnose, with limited reliable serum biomarkers available for early detection and prognostication.

Conference:

Presenting Author: Janas K

Authors: Janas K, Rosiek V, Kos-Kudła B,

Keywords: NEN, biomarker, analysis, diagnostic, pancreatic tumour,

#4612 Intraoperative beta-probe for pathological tissue detection in GEP-NET patients: A prospective phase II trial

Introduction: Accurate localisation of gastroenteropancreatic neuroendocrine tumours (GEP-NET) is essential for successful radical surgery. Radio guided surgery (RGS) is an innovative technique that may enhance lesion detection.

Conference:

Presenting Author: Bertani E

Authors: Bertani E, Fumagalli Romario U, Collamati F, Ferrari M, Mattana F,

Keywords: radio guided surgery, surgery, neuroendocrine tumour, GEP-NET, gastrointestinal tumour,

#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,

#4586 Base on neuroendocrine tumour classification in CT imaging using pretrained vision transformers and data augmentation

Introduction: Neuroendocrine tumours (NETs) are a group of cancers that can be difficult to distinguish from other malignancies in CT imaging. Their diverse appearance often leads to diagnostic challenges, requiring advanced techniques to improve classification accuracy. Pretrained Vision Transformers (ViTs) have shown promise in medical image analysis, but additional methods are needed to enhance model robustness, particularly when dealing with complex and heterogeneous tumour characteristics.

Conference:

Presenting Author:

Authors: Li H, Tang Z, Ding Z, Chen Y, Li H,

Keywords: Vision transformers, Cancer Detection, Transfer Learning, Medical Image Analysis,

#4580 Improving diagnosis of neuroendocrine tumours using large language models

Introduction: Neuroendocrine tumours (NETs) are a heterogeneous group of cancers that can be difficult to diagnose due to their variable clinical presentation and non-specific symptoms. Accurate diagnosis often requires integrating a wide range of clinical, radiological, and pathological information. Large language models (LLMs) have shown promise in processing and understanding complex medical text, making them a valuable tool for improving diagnostic accuracy in oncology.

Conference:

Presenting Author:

Authors: Tang Z, Chen P, Tang J, Li H, Chen Y,

Keywords: Neuroendocrine Tumour (NET), Large Language Model (LLM), Medical Natural Language Processing (NLP), Clinical Decision Support,