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,

#4631 Correlations between dosimetry and imaging in patients with neuroendocrine tumour treated with 177Lu-DOTATATE

Introduction: Tumour dose may be an important predictor of clinical response, but estimation of a clinically relevant tumour-absorbed dose without high risk of toxicity is challenging.

Conference:

Presenting Author: Gomez Sanchez D

Authors: Gomez Sanchez D, Ribelles M, Fernandez Iglesias A, Paruta Araez L, Mata E,

Keywords: dosimetry, 177Lu-DOTATATE,

#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.

Conference:

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,

#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,

#4587 Exploring the splicing landscape to identify survival-related biomarkers in small intestine neuroendocrine neoplasms

Introduction: The study of small intestine neuroendocrine neoplasms (siNENs) is challenging due to their rarity and complexity. While transcriptomic subtypes have been identified, the mechanisms behind their progression are still unclear. The process of RNA splicing is often altered in cancer, and our group has described that such dysregulation is also present in various NENs.

Conference:

Presenting Author: Ibáñez Costa A

Authors: Ibáñez-Costa A, García Vioque V, Pedraza-Arévalo S, Hernando Cubero J, García A,

Keywords: small intestine neuroendocrine tumour, splicing, biomarker, survival, ki-67, RNA,