[HTML][HTML] A primer on artificial intelligence in pancreatic imaging
Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data
contained in medical images. Deep learning and radiomics are the two main AI methods …
contained in medical images. Deep learning and radiomics are the two main AI methods …
Pancreatic cancer, radiomics and artificial intelligence
L Marti-Bonmati, L Cerdá-Alberich… - The British Journal of …, 2022 - academic.oup.com
Patients with pancreatic ductal adenocarcinoma (PDAC) are generally classified into four
categories based on contrast-enhanced CT at diagnosis: resectable, borderline resectable …
categories based on contrast-enhanced CT at diagnosis: resectable, borderline resectable …
Radiomics-based machine-learning models can detect pancreatic cancer on prediagnostic computed tomography scans at a substantial lead time before clinical …
Background & Aims Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC)
at the prediagnostic stage (3–36 months before clinical diagnosis) using radiomics-based …
at the prediagnostic stage (3–36 months before clinical diagnosis) using radiomics-based …
Liver metastases in pancreatic ductal adenocarcinoma: A predictive model based on CT texture analysis
Purpose To develop a predictive model for liver metastases in patients with pancreatic
ductal adenocarcinoma (PDAC) based on textural features of the primary tumor extracted by …
ductal adenocarcinoma (PDAC) based on textural features of the primary tumor extracted by …
A nomogram based on clinical information, conventional ultrasound and radiomics improves prediction of malignant parotid gland lesions
Although conventional ultrasound (CUS) allows for clear detection of parotid gland lesions
(PGLs), it fails to accurately provide benign-malignant differentiation due to overlap** …
(PGLs), it fails to accurately provide benign-malignant differentiation due to overlap** …
Volumetric visceral fat machine learning phenotype on CT for differential diagnosis of inflammatory bowel disease
Z Zhou, Z **ong, R Cheng, Q Luo, Y Li, Q **e… - European …, 2023 - Springer
Objectives To investigate whether volumetric visceral adipose tissue (VAT) features
extracted using radiomics and three-dimensional convolutional neural network (3D-CNN) …
extracted using radiomics and three-dimensional convolutional neural network (3D-CNN) …
A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability
J Zhong, Y Hu, Y **ng, X Ge, D Ding, H Zhang… - Insights into …, 2022 - Springer
Background Multiple tools have been applied to radiomics evaluation, while evidence rating
tools for this field are still lacking. This study aims to assess the quality of pancreatitis …
tools for this field are still lacking. This study aims to assess the quality of pancreatitis …
Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs
Objectives To develop a bounding-box-based 3D convolutional neural network (CNN) for
user-guided volumetric pancreas ductal adenocarcinoma (PDA) segmentation. Methods …
user-guided volumetric pancreas ductal adenocarcinoma (PDA) segmentation. Methods …
Pancreatic ductal adenocarcinoma staging: a narrative review of radiologic techniques and advances
Radiology plays an important role in the initial diagnosis and staging of patients with
pancreatic ductal adenocarcinoma (PDAC). CT is the preferred modality over MRI due to …
pancreatic ductal adenocarcinoma (PDAC). CT is the preferred modality over MRI due to …
Preoperative prediction of lymphovascular invasion in patients with T1 breast invasive ductal carcinoma based on radiomics nomogram using grayscale ultrasound
ML Xu, SE Zeng, F Li, XW Cui, GF Liu - Frontiers in Oncology, 2022 - frontiersin.org
Purpose The aim of this study was to develop a radiomics nomogram based on grayscale
ultrasound (US) for preoperatively predicting Lymphovascular invasion (LVI) in patients with …
ultrasound (US) for preoperatively predicting Lymphovascular invasion (LVI) in patients with …