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,

#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,

#4492 Utility of NETest 2.0 as a biomarker for neuroendocrine tumours: The Swiss experience – Real-world data

Introduction: The NETest 2.0 is a PCR assay of circulating mRNA, scored 0-100. It has been developed as a biomarker to diagnose neuroendocrine neoplasia (NEN) and to provide tumour prognostic information.

Conference:

Presenting Author: Chirindel A

Authors: Chirindel A, Wild D, Voegeli M, de Dosso S, Siebenhüner A,

Keywords: NETest, tumour biomarker, NEN, disease presence, disease progression,

#4459 Sequential alkylating therapy and pembrolizumab induce near-complete response in high tumour mutational burden pancreatic neuroendocrine tumour: A case report

Introduction: Traditional treatment for neuroendocrine tumours (NETs), including surgery, somatostatin therapy, and targeted therapy, often fails in advanced disease. Alkylating therapy can increase tumour mutational burden (TMB), and the recent KEYNOTE-158 study approved pembrolizumab for advanced solid tumours with a high TMB.

Conference:

Presenting Author:

Authors: Paranjpe I, Hornbacker K, Vadde S, Fisher G, Shaheen S,

Keywords: pancreatic neuroendocrine tumour, immunotherapy, pembrolizumab, tumour mutational burden, TMB,

#4380 Cell-free DNA concentration correlates with copy number variant (CNV)-count, describes tumour progression and nuclear instability in GEP-NETs

Introduction: Cell-free DNA (cfDNA) levels depend on various factors related to the shedding of cells and might provide valuable information on tumour state and treatment success. In a genomic profiling pilot study conducted as part of the COMPOSE Phase III multicentre open-labelled clinical trial we assessed multiple factors and their impact on cfDNA levels in 14 GEP-NET patients.

Conference:

Presenting Author: Srirajaskanthan R

Authors: Srirajaskanthan R, Capdevila J, Smutna V, Weckwerth W, Erdoe M,

Keywords: CNV, cfDNA, liquid biopsy, quantitative marker, cfDNA concentration, longitudinal, GEP-NET, genomic profiling,