Abstract Library

#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,

#4550 The relationship between MEN1 germline mutations and SSTR2 expression in neuroendocrine tumours

Introduction: Multiple endocrine neoplasia type 1 (MEN1) is a rare hereditary disease characterised by the development of multiglandular parathyroid disease, pituitary tumours, and duodenopancreatic neuroendocrine tumours (NETs). Germline mutations in the tumour suppressor gene MEN1 are the underlying cause. Somatostatin receptor 2 (SSTR2) is commonly expressed by NETs. However, the expression of SSTR2 in patients with MEN1 remains unclear.

Conference:

Presenting Author: Chi Y

Authors: Sun Y, Tan H, Wang H, Shi S, Dong L,

Keywords: multiple endocrine neoplasia type 1, somatostatin receptor 2, neuroendocrine tumour,

#4522 Leveraging large language models for enhanced diagnosis of neuroendocrine tumours

Introduction: Neuroendocrine tumours (NETs) are a heterogeneous group of cancers that are 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. However, clinicians may struggle to synthesise these diverse data sources effectively. 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, Tang J, Cheng F, Li H, Chen Y,

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

#4487 Fast pancreatic cancer diagnosis using lightweight Mamba framework

Introduction: Pancreatic cancer is a deadly malignancy often diagnosed too late for effective treatment. Fast and precise identification of pathological regions in histopathology images is essential but limited by current slow methods. We present a framework to accelerate this process and enhance diagnostic support.

Conference:

Presenting Author:

Authors: Shengzhe Y, Yan K,

Keywords: Pancreatic Cancer, Lightweight Mamba Architecture, Pathology Region Classification and Recognition,

#4420 Nab-paclitaxel plus bevacizumab for patients with previously treated, metastatic extrapulmonary neuroendocrine carcinomas (EP-NECs): A multicentre, open-label, phase II trial

Introduction: No standard therapies beyond first line are established for advanced EP-NECs. Paclitaxel is also active in NECs however there is no data on the role of nab-paclitaxel.

Conference:

Presenting Author: Zhang P

Authors: Zhang P, Li S, Li J, Shen L, Lu M,

Keywords: extrapulmonary neuroendocrine carcinoma, nab-paclitaxel, bevacizumab,