Results 81 to 90 of about 52,953 (291)

Foundation Model‐Enabled Pathology‐Informed MRI Model for Preoperative Prediction of Vessels Encapsulating Tumor Clusters in Resectable Hepatocellular Carcinoma: A Multicenter Retrospective Study

Advanced Science, EarlyView.
PIVOT uses pathology‐derived morphologic and vascular representations from postoperative H&E and CD34 whole‐slide images to guide model development, while clinical inference requires only preoperative multiparametric MRI. Across five centers, PIVOT enabled accurate preoperative assessment of vessels encapsulating tumor clusters, stratified patients by ...
Zhehan Shen, Wanmin Liu, Xiaoming Li, Wenjie Zou, Yiman Li, Jie Yuan, Yasen Yimit, Shihang Wang, Linhao Qu, Hui Dong, Wei Chen, Ningyang Jia, Peijun Wang   +12 more
wiley   +1 more source

Differentiation of retroperitoneal paragangliomas and schwannomas based on computed tomography radiomics

Scientific Reports, 2023
The purpose of this study was to differentiate the retroperitoneal paragangliomas and schwannomas using computed tomography (CT) radiomics. This study included 112 patients from two centers who pathologically confirmed retroperitoneal pheochromocytomas ...
Yuntai Cao, Zhan Wang, Jialiang Ren, Wencun Liu, Huiwen Da, Xiaotong Yang, Haihua Bao   +6 more
doaj   +1 more source

Cardiac Computed Tomography Radiomics [PDF]

Journal of Thoracic Imaging, 2018
Radiologic images are vast three-dimensional data sets in which each voxel of the underlying volume represents distinct physical measurements of a tissue-dependent characteristic. Advances in technology allow radiologists to image pathologies with unforeseen detail, thereby further increasing the amount of information to be processed.
Kolossváry, Márton József, Kellermayer, Miklós, Merkely, Béla Péter, Maurovich-Horvat, Pál   +3 more
openaire   +3 more sources

A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?

npj Precision Oncology
The search for understanding immunotherapy response has sparked interest in diverse areas of oncology, with artificial intelligence (AI) and radiomics emerging as promising tools, capable of gathering large amounts of information to identify suitable ...
Marta Ligero, Bente Gielen, Victor Navarro, Pablo Cresta Morgado, Olivia Prior, Rodrigo Dienstmann, Paolo Nuciforo, Stefano Trebeschi, Regina Beets-Tan, Evis Sala, Elena Garralda, Raquel Perez-Lopez   +11 more
doaj   +1 more source

A Study on Energy Consumption in AI-Driven Medical Image Segmentation

Journal of Imaging
As artificial intelligence advances in medical image analysis, its environmental impact remains largely overlooked. This study analyzes the energy demands of AI workflows for medical image segmentation using the popular Kidney Tumor Segmentation-2019 ...
R. Prajwal, S. J. Pawan, Shahin Nazarian, Nicholas Heller, Christopher J. Weight, Vinay Duddalwar, C.-C. Jay Kuo   +6 more
doaj   +1 more source

QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research

European Radiology Experimental, 2023
Background Radiomics, the field of image-based computational medical biomarker research, has experienced rapid growth over the past decade due to its potential to revolutionize the development of personalized decision support models. However, despite its
Daniel Abler, Roger Schaer, Valentin Oreiller, Himanshu Verma, Julien Reichenbach, Orfeas Aidonopoulos, Florian Evéquoz, Mario Jreige, John O. Prior, Adrien Depeursinge   +9 more
doaj   +1 more source

TristramFeng/cSCI_radiomics: Radiomics Code

<p>Radiomics code</p ...
TristramFeng
core   +1 more source

A CT Lesion Foundation Model for Non‐Invasive Assessment of Hepatocellular Carcinoma Aggressiveness and Prediction of Recurrence Risk

Advanced Science, EarlyView.
A computed tomography lesion foundation model, built by sequential self‐supervised pretraining on generic and hepatocellular carcinoma‐specific lesion images, non‐invasively assesses microvascular invasion and pathological grade from contrast‐enhanced CT and stratifies postoperative recurrence risk.
Wei Xia, Zhehan Shen, Mengsi Li, Jianxin Lu, Ji Xia, Yang Cheng, Wenzheng Li, Shunjie Dong, Lin Deng, Ruokun Li   +9 more
wiley   +1 more source

Radiomics and Radiogenomics [PDF]

, 2021
In the recent past, medicine has not had much choice in treating patients or predicting the course of the disease. Today, modern methods are being developed that will improve the daily life of patients in the form of better diagnostics and therapy.
Karić, Maja, Blažetić, Karlo
core   +1 more source

AI‐Powered 3D Liver Fat Volumetric Map for Clinical‐Grade Steatosis Assessment From Cross‐Modality MRI

Advanced Intelligent Systems, EarlyView.
An automated, AI‐powered system generates a 3D liver fat volumetric map for precise fat fraction quantification from MRI. By overcoming manual analysis constraints, it provides high‐fidelity spatial characterization of steatosis. This graphical abstract highlights a clinically robust framework that enables efficient, anatomically informed assessment ...
Bincan Deng, Yingyun Gong, Xiao Wang, Xinxi Wang, Dingding Chen, Chen Xuan, Qing Xu, Hongwen Zhou, Yuwen Cui, Manuel Doblare   +9 more
wiley   +1 more source