Abstract Library

#4127 A CT-based radiomics and deep learning signature for evaluating the somatostatin receptor 2 in non-functional pancreatic neuroendocrine tumors: A multicohort, retrospective study

Introduction: The diagnosis and treatment of Non-functional Pancreatic Neuroendocrine Tumor (NF-panNET) with unlabeled or labeled somatostatin analogues necessitate high expression of the somatostatin receptor subtype 2 (SSTR2), typically identified using PET or SPECT imaging. However, the application of PET or SPECT imaging is limited due to its low spatial resolution and unavailability in all units.

Conference:

Presenting Author:

Authors: Tang W, Wenchao G, Yinli C, Jie C,

Keywords: Non-functional Pancreatic Neuroendocrine Tumor, SSTR2, Radiomics, Deep-learning, CT,

#3972 Machine-learning identified optimised classification models for the diagnosis of typical and atypical lung carcinoids based on the genomic variance

Introduction: The current diagnosis of lung typical carcinoids (TCs) and atypical carcinoids (ACs) can be ambiguous for small biopsies containing limited tissues.

Conference:

Presenting Author: Guo Y

Authors: Guo Y, Su F, Hu S, Chen R, Chen Q,

Keywords: lung carcinoid, typical carcinoid, atypical carcinoid, machine-learning, diagnosis, genomic features,

#3911 Development and validation of CT-based radiomics deep learning signatures to preoperatively predict lymph node metastasis in non-functional pancreatic neuroendocrine tumor: A multi-cohort study

Introduction: Lymph node status is an important factor for the patients with non-functional pancreatic neuroendocrine tumors (NF-PanNETs) with respect to surgical methods, prognosis, recurrence.

Conference:

Presenting Author: Tang W

Authors: Tang W, Chen J, Gu W,

Keywords: Non-functional pancreatic neuroendocrine tumor, Radiomics, Deep learning, Lymph node metastasis,

#3183 Clinical stratification of pancreatic neuroendocrine tumors by systematically integrating multimodal and clinical data

Introduction: Non-functional pancreatic neuroendocrine tumors (PanNETs) are heterogeneous with at least two transcriptome subtypes with differential biology, immune mechanisms and prognosis. However, it is challenging to understand how multimodal data interact with clinical variables and contribute to the disease heterogeneity and phenotypes.

Conference: 18th Annual ENETS Concerence (2021)

Presenting Author: Sadanandam A

Authors: Sadanandam A, Lawlor R, Mafficini A, Luchini C, Nyamundanda G,

Keywords: pancreatic neuroendocrine tumor, multimodal data integration, multiomics, clinical data integration, machine learning, artificial intelligence, subtypes, pancreatic cancer, phenotypes, PhenMap, mutations, gene expression, microRNA,

#3146 Transcriptomic deconvolution of neuroendocrine neoplasms predicts clinically relevant characteristics

Introduction: Therapeutic decisions in oncology depend on a precise pathological classification of individual neoplasms. However, a comprehensive training of machine-learning models requires sufficiently large numbers of training samples, which are usually not available for rare cancer types.

Conference: 18th Annual ENETS Concerence (2021)

Presenting Author: Otto R

Authors: Otto R, Detjen K, Riemer P, Grötzinger C, Rindi G,

Keywords: bioinformatics, NET, PanNEN, deconvolution, transcriptomics, RNA-seq, neuroendocrine cancer,