Abstract Library

#4668 Predictive factors of postoperative recurrence in patients with neuroendocrine tumour of the lung: Prospective analysis of clinicopathologic features and radiomics

Introduction: In localised and locally advanced forms neuroendocrine tumours (NETs) of the lung radical surgical resection represents the standard of care, several clinicopathologic factors are potentially associated with recurrence of disease.

Conference:

Presenting Author: Paravani P

Authors: Paravani P, Russo F, Mazzilli R, Zamponi V, Mancini C,

Keywords: radiomics, predictive role, rfs, lung, lung net,

#4604 Germline predisposition to neuroendocrine tumours of the pancreas (pNEN) based on mutations in DNA repair genes – BRCA1, BRCA2, PALB2, CHEK2, MLH1, MSH2, MSH6, PMS2, EPCAM, APC, MUTYH, STK11

Introduction: Neuroendocrine tumours of the pancreas (pNEN) rank as the second most common epithelial neoplasms after pancreatic adenocarcinoma, with increasing prevalence and a mortality rate of 60%. Identifying germline mutations in DNA repair genes such as CHEK2, BRCA1/2, and MUTYH within pNEN cases may pave the way for personalised diagnostics and therapies.

Conference:

Presenting Author:

Authors: Jurecka Lubieniecka B, Ros-Mazurczyuk M, Oczko-Wojciechowska M, Cortez A, Handkiewicz-Junak D,

Keywords: pNEN, DNA repair genes,

#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,