Results 131 to 140 of about 455,909 (199)

No evidence of hypercoagulability, altered fibrinolysis, or increased fibrin(ogen) microaggregates in never‐hospitalized long COVID

HemaSphere, Volume 10, Issue 10, October 2026.
Emily Fraser, Kher Ng, Neda Hasan, Isobel Dunstan, Avianna Laws, Fraser Macrae, Jeries Abu‐Hanna, Robert Ariens, Victoria Harris, Jason Oke, Laura Saunders, James Grist, Guilhem Collier, Paul Hughes, Jim Wild, Roger Thompson, Fergus Gleeson, Susan Shapiro, the EXPLAIN Investigators   +18 more
wiley   +1 more source

Development of a nomogram for differentiation between pure mucinous breast carcinomas and fibroadenomas based on DCE-MRI features 

肿瘤影像学
Objective: To develop a nomogram to differentiate pure mucinous breast carcinoma (PMBC) from fibroadenoma (FA) with high signal intensity on T2-weighted imaging (T2WI) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features,
QU Ning, LUO Yahongnbsp
doaj  

DCE-MRI: Lesion Detection and Classification in Breast Cancer

, 2013
Dynamic contrast-enhanced MRI (DCE-MRI) is a well established, high-performance, imaging modality for the diagnosis and management of patients with solid tumors.
A. Petrillo, R. Fusco, SANSONE, MARIO
core  

Deep learning-based generation of DSC MRI parameter maps using DCE MRI data

BACKGROUND AND PURPOSE: Perfusion and perfusion-related parameter maps obtained using dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are both useful for clinical diagnosis and research.
Knutsson, Linda, Lambrecht, Sebastian, Feng, Li, Nyquist, Paul, van Zijl, Peter, Lyu, Yixuan, Lin, Doris, Pei, Haoyang, Liu, Fang, Xu, Xiang, Lin, Doris,, Pei, Haoyang,, Nyquist, Paul,, Lyu, Yixuan,, Liu, Fang,, Xu, Xiang,, van Zijl, Peter,, Feng, Li,, Lambrecht, Sebastian,, Knutsson, Linda,, Lund University.   +20 more
core   +1 more source

Exploiting machine-learning models with DCE-MRI radiomics for breast cancer molecular subtyping. [PDF]

Front Oncol
Lou Y, Zhao L.
europepmc   +1 more source

A radiomics-deep learning nomogram integrating intratumoral and peritumoral DCE-MRI features for pCR prediction in breast cancer. [PDF]

Front Oncol
Wang Y, Wang H, Cui J.
europepmc   +1 more source

Plasma volume fraction in drug-resistant epilepsy: A DCE-MRI marker of microvascular pathology. [PDF]

Neuroimage Clin
Talebanpour A, Mirloo S, Aleali A, Kamintsky L, Serlin Y, Cafri N, Goldberg I, Bowen C, Ben-Arie G, Rüber T, Benninger F, Whatley B, Friedman A.   +12 more
europepmc   +1 more source

Breast DCE-MRI morphological features and semi-quantitative parameters associated with upgrade to invasive carcinoma in biopsyproven DCIS. [PDF]

Radiol Oncol
Urban V, Milosevic Z, Bukumiric Z, Obradovic K, Adzic N, Pavlovic-Stankovic D, Petkovic I, Sovic I, Jovanic I, Spurnic I.   +9 more
europepmc   +1 more source

Predicting ki-67 expression in breast cancer via transformer and multiple instance learning on DCE-MRI. [PDF]

Cancer Imaging
Cao Y, Lin M, Ouyang Y, Wu Y, Zhao C, Wu Q, Teng D, He N, Zhong X, Yang J.   +9 more
europepmc   +1 more source

Hybrid Fusion of Time-Intensity Curve, Deep Learning, and Fractional Zernike-Caputo Features for Accurate Liver Lesion Classification in DCE-MRI. [PDF]

J Imaging
Hasan AM, Al-Waely NKN, Alfalluji WL, Ibrahim RW, Jalab HA, Meziane F.   +5 more
europepmc   +1 more source