Abstract Library

#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,

#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,

#4379 Fully automated segmentation and lymph node metastasis prediction of non-functional pancreatic neuroendocrine tumours using deep learning

Introduction: Lymph node status is an important factor for the patients with NF-PanNETs with respect to surgical methods, prognosis, and recurrence. Our model serves as a non-invasive tool that supports clinical decision-making for precision surgical treatment in patients with NF-PanNETs.

Conference:

Presenting Author: Tang W

Authors: Tang W, Chen J,

Keywords: Non-functional pancreatic neuroendocrine tumour, deep learning, fully automated segmentation, imaging Informatics,

#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,

#3960 A combined nomogram to predict liver metastasis of pancreatic neuroendocrine tumors: Integrating deep learning radiomics and computational pathology

Introduction: Pancreatic neuroendocrine tumors (panNETs) are a diverse group of tumors, and liver metastasis is an important prognostic factor.

Conference:

Presenting Author: Huang D

Authors: Huang D, Tang W, Ma M, Liang Y,

Keywords: Pancreatic neuroendocrine tumor, Liver metastasis, Deep learning radiomics, Computational pathology, Nomogram,