Abstract Library

#4586 Base on neuroendocrine tumour classification in CT imaging using pretrained vision transformers and data augmentation

Introduction: Neuroendocrine tumours (NETs) are a group of cancers that can be difficult to distinguish from other malignancies in CT imaging. Their diverse appearance often leads to diagnostic challenges, requiring advanced techniques to improve classification accuracy. Pretrained Vision Transformers (ViTs) have shown promise in medical image analysis, but additional methods are needed to enhance model robustness, particularly when dealing with complex and heterogeneous tumour characteristics.

Conference:

Presenting Author:

Authors: Li H, Tang Z, Ding Z, Chen Y, Li H,

Keywords: Vision transformers, Cancer Detection, Transfer Learning, Medical Image Analysis,

#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,

#4540 Retrospective analysis of somatostatin receptors expression in cohort of thoracic neuroendocrine neoplasms with immunohistochemistry method or PET/CT Ga68 DOTA-TATE

Introduction: Somatostatin receptors (SSTRs) expression in cohort of thoracic neuroendocrine neoplasms (NENs) is still not fully described. We attempted to evaluate the SSTR expression by IHC and PET/CT with Ga68 DOTA-TATE, as well as the sensitivity of both methods.

Conference:

Presenting Author: Evdokimova E

Authors: Evdokimova E, Vorobeva M, Zhulikov Y, Markovich A, Gorbunova V,

Keywords: NEN, thoracic, SSTR, IHC, DOTATATE, lung, thymus, mediastinum, LCNC, NET,

#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,

#4398 The role of chromogranin A as a biomarker in neuroendocrine tumours: Diagnostic and prognostic implications

Introduction: Chromogranin A (CgA) is a widely used serum biomarker for neuroendocrine tumours (NETs), reflecting tumour burden and secretory activity. Despite its clinical utility, the diagnostic sensitivity and prognostic value of CgA remain a subject of debate, especially given its potential for false positives and its variation across different NET subtypes.

Conference:

Presenting Author:

Authors: Xu L, Ye M, Zeng X, Chen S, Ding Y,

Keywords: Chromogranin A, neuroendocrine tumour, diagnosis, prognosis, biomarker, Ki-67, metastatic disease,