Results 231 to 240 of about 43,566 (284)

Predicting pulmonary nodule growth from a single time point: a fusion model of radiomics and deep learning to optimize follow-up strategies. [PDF]

J Thorac Dis
Xu Z, Wang X, Cao Z, Pan X, Zhang J, Li F, Chen L, Xiao Y, Liu S.   +8 more
europepmc   +1 more source

Novel Unsupervised Machine Learning Model Using Multi-Parametric Radiomics for Prognostic Stratification of Bifocal Hepatocellular Carcinoma. [PDF]

J Hepatocell Carcinoma
Jia X, Wu F, Dai H, Xiao Y, Yang C, Zeng M.   +5 more
europepmc   +1 more source

Preoperative prediction of early recurrence and microvascular invasion in hepatocellular carcinoma using conventional ultrasound, elastography, and clinical nutritional indicators: a radiomics-based study. [PDF]

Am J Transl Res
Jiang D, Chen D, Li Y, Mo F.
europepmc   +1 more source

13. Radiomics

, 2019
Ke Nie, Min-Ying Lydia Su
openaire   +1 more source

Prediction of the efficacy after the first transarterial chemoembolization in hepatocellular carcinoma using CT radiomics combined with inflammatory composite indicators. [PDF]

Front Med (Lausanne)
Sun JH, Zhang YX, Luo BS, Wang JP.
europepmc   +1 more source

Development and validation of a multiparametric MRI-based radiomics nomogram for the tripartite discrimination of primary benign, primary malignant, and metastatic lumbar spinal tumors. [PDF]

Front Oncol
Shen C, Yang S, Chen X, Feng Y, Song Y, Zhou J, Sun Y.   +6 more
europepmc   +1 more source

Clinical study of ¹⁸F-FDG PET/CT radiomics in differentiating pulmonary solitary solid adenocarcinoma nodules and inflammatory nodules. [PDF]

PeerJ
Liao YF, Zhang S, Wan BY, Li JX, Deng J, Hu J, Qin XL.   +6 more
europepmc   +1 more source

Radiomics in laryngeal squamous cell carcinoma: state of the art. [PDF]

Acta Otorhinolaryngol Ital
Lucchese S, Laganaro F, Petruzzi G, Vidiri A.   +3 more
europepmc   +1 more source

Development of a CT radiomics nomogram for preoperative prediction of differentiation grading in pancreatic ductal adenocarcinoma: a two-center retrospective study. [PDF]

BMC Gastroenterol
Wu J, Xu S, Tang S, He J.
europepmc   +1 more source

Predicting resistance to neoadjuvant chemotherapy in osteosarcoma using machine learning with clinical data and T2-weighted MRI radiomics. [PDF]

Eur Radiol Exp
Inkeaw P, Pruksakorn D, Angkurawaranon S, Morakote W, Boonsri P, Phettom R, Kanthawang T.   +6 more
europepmc   +1 more source