Abstract Library

#4626 Comparison of two prognostic models for the treatment of pancreatic NET with PRRT

Introduction: The efficacy and time to progression (TTP) following peptide receptor radionuclide therapy (PRRT) for pancreatic neuroendocrine tumours varies widely.

Conference:

Presenting Author: Papantoniou D

Authors: Tiensuu Janson E, Fröss-Baron K, Grönberg M, Ziolkowska B,

Keywords: prrt, pancreatic neuroendocrine tumour, predictive model,

#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,

#3772 R-ache-L score – A nomogram model for predicting prognosis of lung neuroendocrine tumors according to primary tumor side

Introduction: The prognosis of atypical carcinoids (AC) is worse than that of typical carcinoids (TC). Molecular analyses have suggested a more heterogeneous disease for the first ones. However, no previous study has established an individualized predictive model for the prognosis of lung neuroendocrine tumos (Lu-NETs) considering the location of the primary tumor in the right versus left lung parenchyma.

Conference:

Presenting Author: La Salvia A

Authors: La Salvia A, Marcozzi B, Manai C, Mazzilli R, Pallocca M,

Keywords: Lung neuroendocrine tumor, Tumor location (left vs right parenchyma), Prognostic factor, Nomogram, R-ache-L Prognostic Score,

#3531 Modified TGR: A new strong radiological marker to accurately predict early response to PRRT in GEP-NETs

Introduction: .

Conference:

Presenting Author:

Authors: Scalorbi F, Calareso G, Garanzini E, Argiroffi G, Fuoco V,

Keywords: gep-net, prrt, tgr, progression, predictive model,

#3511 Predictive factors of adverse events onset in GEP-NET patients treated with LRT

Introduction: Since metastatic GEP-NET are low-growing neoplasms, characterised by stability followed by progression, multiple lines of treatment have to be adopted to control distant localisations and symptoms. In this scenario, there are no precise models to assess the occurrence of adverse events during LRT (Ligand Radiotherapy).

Conference:

Presenting Author: Scalorbi F

Authors: Scalorbi F, Argiroffi G, Baccini M, Gherardini L, Fuoco V,

Keywords: lrt, toxicity, logistic regression, predictive model, gep-net,