Results 121 to 130 of about 71,655 (241)

Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma [PDF]

, 2015
Sebastian Echegaray, Olivier Gevaert, Rajesh P. Shah, Aya Kamaya, John D. Louie, Nishita Kothary, Sandy Napel   +6 more
openalex   +1 more source

Privacy-Preserving and Trustworthy Deep Learning for Medical Imaging [PDF]

arXiv
The shift towards efficient and automated data analysis through Machine Learning (ML) has notably impacted healthcare systems, particularly Radiomics. Radiomics leverages ML to analyze medical images accurately and efficiently for precision medicine. Current methods rely on Deep Learning (DL) to improve performance and accuracy (Deep Radiomics).
arxiv  

Repeatability of Radiomic Features in Non-Small-Cell Lung Cancer [18F]FDG-PET/CT Studies: Impact of Reconstruction and Delineation [PDF]

, 2016
Floris H. P. van Velden, G. Krämer, Virginie Frings, Ida A. Nissen, Emma R. Mulder, Adrianus J. de Langen, Otto S. Hoekstra, Egbert F. Smit, Ronald Boellaard   +8 more
openalex   +1 more source

Preoperative Noninvasive Radiomics Approach Predicts Tumor Consistency in Patients With Acromegaly: Development and Multicenter Prospective Validation

Frontiers in Endocrinology, 2019
Background: Prediction of tumor consistency before surgery is of vital importance to determine individualized therapeutic schemes for patients with acromegaly.
Yanghua Fan, Min Hua, Anna Mou, Miaojing Wu, Xiaohai Liu, Xinjie Bao, Renzhi Wang, Ming Feng   +7 more
doaj   +1 more source

OS4.6 Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response [PDF]

, 2016
Philipp Kickingereder, Michael Götz, Antje Wick, Ulf Neuberger, HP Schlemmer, Alexander Radbruch, Wolfgang Wick, Martin Bendszus, Klaus Maier‐Hein, David Bonekamp   +9 more
openalex   +1 more source

Development and optimisation of a preclinical cone beam computed tomography-based radiomics workflow for radiation oncology research

Physics and Imaging in Radiation Oncology, 2023
Background and purpose: Radiomics features derived from medical images have the potential to act as imaging biomarkers to improve diagnosis and predict treatment response in oncology.
Kathryn H. Brown, Neree Payan, Sarah Osman, Mihaela Ghita, Gerard M. Walls, Ileana Silvestre Patallo, Giuseppe Schettino, Kevin M. Prise, Conor K. McGarry, Karl T. Butterworth   +9 more
doaj  

Radiomics-based artificial intelligence (AI) models in colorectal cancer (CRC) diagnosis, metastasis detection, prognosis, and treatment response [PDF]

arXiv
With a high rate of morbidity and mortality, colorectal cancer (CRC) ranks third in mortality among cancers. By analyzing the texture properties of images and quantifying the heterogeneity of tumors, radiomics and radiogenomics are non-invasive methods to determine the biological properties of CRC.
arxiv  

Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer

Journal of digital imaging, 2023
V. Le, Q. Kha, Tran Nguyen Thu Minh, V. H. Nguyen, Van Long Le, N. Le   +5 more
semanticscholar   +1 more source

A Novel Clinical Radiomics Nomogram to Identify Crohn’s Disease from Intestinal Tuberculosis

Journal of Inflammation Research, 2021
Chao Zhu,1 Yongmei Yu,2 Shihui Wang,2 Xia Wang,1 Yankun Gao,1 Cuiping Li,1 Jianying Li,3 Yaqiong Ge,3 Xingwang Wu1 1Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People’s Republic of China; 2Department
Zhu C, Yu Y, Wang S, Wang X, Gao Y, Li C, Li J, Ge Y, Wu X   +8 more
doaj  

Impact of image preprocessing on the volume dependence and prognostic potential of radiomics features in non-small cell lung cancer [PDF]

, 2016
Xenia Fave, Lifei Zhang, Jinzhong Yang, Dennis Mackin, Peter Balter, Daniel de la Rosa Gómez, D Followill, A. Kyle Jones, Francesco C. Stingo, Laurence E. Court   +9 more
openalex   +1 more source