Abstract Library

#4658 The nutritional status worsens over 5 years in patients with gastroenteropancreatic neuroendocrine tumours on treatment with somatostatin analogues

Introduction: Malnutrition is common in patients with gastroenteropancreatic neuroendocrine tumours (GEP-NETs) treated with monthly somatostatin analogues (SSAs). Ideally all malnourished patients are offered nutritional support. However, clinical guidelines and data on the development and progression of malnutrition is lacking.

Conference:

Presenting Author: Clement D

Authors: Soran V, Srirajaskanthan R, Cananea E, Martin W, Minott S,

Keywords: gastroenteropancreatic neuroendocrine tumour, malnutrition, somatostatin analogue,

#4601 Pembrolizumab combined with cisplatin or carboplatin and etoposide in treatment naïve advanced Merkel cell carcinoma (MCC): The phase II PANDORA Trial

Introduction: MCC is a rare and aggressive skin neuroendocrine tumour with high metastatic potential and mortality. Therapeutic options in metastatic/unresectable MCC are based on immunotherapy or platinum-based chemotherapy. However, a subgroup of patients (pts) has primary immunoresistance disease, so it would be important discover active upfront combinations. PANDORA trial [NCT 06086288] is an open-label, multicentre, single-arm phase II trial evaluating the activity and safety of pembrolizumab (PEM) combined with platinum-based chemotherapy as 1st line treatment in pts with metastatic, unresectable or recurrence MCC. Supported in part by a research grant from Investigator-Initiated Studies Program of MSD.

Conference:

Presenting Author: Oldani S

Authors: Oldani S, Morano F, Cingarlini S, Di Giacomo A, Borghesani M,

Keywords: Pembrolizumab, clinical trial, Merkel cell carcinoma, chemio-immunotherapy,

#4600 Artificial intelligence in predicting neuroendocrine tumour (NET) outcomes: A model-based prognostic approach

Introduction: Neuroendocrine tumours (NETs) vary widely in clinical behaviour, complicating prognosis. Traditional models often struggle with accuracy due to the complexity of NETs. Artificial intelligence (AI) offers tools for enhanced prognostic precision by analysing complex datasets. This study explores an AI-driven approach to predict outcomes in NET patients at Cantonment General Hospital, Pakistan.

Conference:

Presenting Author: Fatima A

Authors: Fatima A,

Keywords: artificial intelligence, machine learning, Precision medicine, Personalised treatment,

#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,

#4556 Optimising the establishment of patient-derived models for neuroendocrine neoplasms

Introduction: Neuroendocrine neoplasms (NENs) are clinically and molecularly diverse, with limited understanding of their tumour biology. Efforts to develop patient-derived models have been unsuccessful, highlighting an urgent need for accurate models to support fundamental research.

Conference:

Presenting Author: Hernández Llorens M

Authors: Hernández-Llorens M, Baena-Moreno M, Lamas-Paz A, Sarmentero J, Anton-Pascual B,

Keywords: patient-derived model, organoids, PDXs, Growth factor,