Abstract Library

#4662 A novel hormone based anti-SSTR anti-CD3 T-cell engager for the treatment of neuroendocrine tumours

Introduction: Somatostatin receptor 2 (SSTR2) is overexpressed in well-differentiated NETs.

Conference:

Presenting Author: Pelle E

Authors: Pelle E, Cives M, Chaoul N, d'Angelo G, Medina E,

Keywords: T-cell engager, immunotherapy, tumouroids,

#4655 A novel nonpeptide drug conjugate (NDC) for the treatment of somatostatin receptor 2-expressing tumours

Introduction: Somatostatin receptor 2 (SST2) is an established target for the treatment of NETs and potentially other solid tumours.

Conference:

Presenting Author:

Authors: Zhao J, Sturchler E, Yang B, Chen M, Tang Y,

Keywords: somatostatin, solid tumour, drug conjugate, internalisation, cytotoxicity,

#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,

#4554 Base on deep learning-integrated multimodal diagnostic framework for enhanced early detection of neuroendocrine tumours

Introduction: Neuroendocrine tumours (NETs) are a heterogeneous group of malignant neoplasms originating from neuroendocrine cells, commonly found in the digestive system, lungs, pancreas, and other organs. Due to their diverse clinical presentations and overlap with common diseases, the early diagnosis of NETs is challenging, often leading to misdiagnosis or delayed diagnosis at advanced stages.

Conference:

Presenting Author:

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

Keywords: Neuroendocrine Tumour, Early Disease Screening, Multimodal Medical Data Analysis,