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,

#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,

#4369 The predictive value of 68Ga-DOTANOC, 18F-DOPA, and 18F-FDG PET/CT in patients with paragangliomas

Introduction: Paragangliomas (PGLs) are rare neuroendocrine tumours with variable clinical outcomes. Early identification of prognostic markers is essential for effective disease management. PET/CT imaging with 68Ga-DOTANOC, 18F-DOPA, and 18F-FDG offers unique insights into tumour biology, respectively.

Conference:

Presenting Author: Xu J

Authors: Gao J, Xu J, Liang Y, Chen J, Song S,

Keywords: PET/CT, paraganglioma, prognosis,