Abstract Library

#4646 Dysregulated miRNA in patients with GEP-NEN and potential role as circulating biomarker

Introduction: Neuroendocrine neoplasms (NENs) are a class of rare and molecularly extremely heterogeneous tumours. NENs arise predominantly in the gastrointestinal (GEP) and pulmonary tracts but can also involve thyroid and breast. NENs often present with non-specific symptoms and lack early specific biomarkers, leading to frequent metastatic diagnoses and primary site challenges.

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Presenting Author:

Authors: Di Mauro A, Clemente O, Cannella L, Della Vittoria G, Neri G,

Keywords: neuroendocrine tumour, miRNome profiling, biomarker, gep-net, molecular biology,

#4610 The last 10 years – Single centre experience of gastrointestinal NEN

Introduction: Neuroendocrine neoplasms (NEN) are rare and heterogeneous, comprising 2% of all malignancies, with progressively increasing incidence. Most arise sporadically and the most frequent primary sites are gastrointestinal and lung.

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Presenting Author:

Authors: Corrêa Figueira C, Alves H, Bento A, Oliveira M, Garrido R,

Keywords: Neuroendocrine neoplasm, Gastrointestinal neuroendocrine tumour, Multidisciplinary team meeting, Retrospective, Single centre,

#4609 External validation of Rachel score: A prognostic tool for lung neuroendocrine tumours

Introduction: Lung NETs are a heterogeneous group of tumours with variegated clinical presentation and course. The histological subtype (TC vs AC) and TNM stage are established prognostic factors. In a previous work by our group, we developed a prognostic model named Rachel score, with a satisfactory ability to predict OS and PFS in this population.

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Presenting Author: La Salvia A

Authors: La Salvia A, Marcozzi B, Lamberti G, Manai C, Mazzilli R,

Keywords: lung neuroendocrine tumour, prognostic tool, Rachel score, external cohort, validation,

#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.

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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,