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,

#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,

#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,

#4512 A 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. While current imaging techniques and biomarkers are useful in certain cases, their diagnostic sensitivity and specificity remain limited when used in isolation.

Conference:

Presenting Author:

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

Keywords: Neuroendocrine Tumour, Multimodal Diagnostic algorithm, Deep Learning, Early Disease Screening,

#4369 The predictive value of 68Ga-DOTANOC, 18F-DOPA, and 18F-FDG PET/CT in patients with paragangliomas

Introduction: Paragangliomas (PGLs) are rare neuroendocrine tumours with variable clinical outcomes. Early identification of prognostic markers is essential for effective disease management. PET/CT imaging with 68Ga-DOTANOC, 18F-DOPA, and 18F-FDG offers unique insights into tumour biology, respectively.

Conference:

Presenting Author: Xu J

Authors: Gao J, Xu J, Liang Y, Chen J, Song S,

Keywords: PET/CT, paraganglioma, prognosis,