Results 121 to 130 of about 82,903 (262)

Radiomics: the facts and the challenges of image analysis

European Radiology Experimental, 2018
Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and ...
S. Rizzo, F. Botta, S. Raimondi, D. Origgi, Cristiana Fanciullo, A. Morganti, M. Bellomi   +6 more
semanticscholar   +1 more source

Dissimilarity-based representation for radiomics applications

, 2018
Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognostic information.
Bernard, Simon, Cao, Hongliu, Heutte, Laurent, Sabourin, Robert   +3 more
core  

Machine learning-based radiomics prognostic model for patients with proximal esophageal cancer after definitive chemoradiotherapy

Insights into Imaging
Objectives To explore the role of radiomics in predicting the prognosis of proximal esophageal cancer and to investigate the biological underpinning of radiomics in identifying different prognoses.
Linrui Li, Zhihui Qin, Juan Bo, Jiaru Hu, Yu Zhang, Liting Qian, Jiangning Dong   +6 more
doaj   +1 more source

The Evolving Status of Radiomics [PDF]

JNCI: Journal of the National Cancer Institute, 2020
Philip O Alderson, Ronald M Summers
openaire   +3 more sources

Prediction of lymphovascular invasion in esophageal squamous cell carcinoma by computed tomography-based radiomics analysis: 2D or 3D ?

Cancer Imaging
Background To compare the performance between one-slice two-dimensional (2D) and whole-volume three-dimensional (3D) computed tomography (CT)-based radiomics models in the prediction of lymphovascular invasion (LVI) status in esophageal squamous cell ...
Yang Li, Xiaolong Gu, Li Yang, Xiangming Wang, Qi Wang, Xiaosheng Xu, Andu Zhang, Meng Yue, Mingbo Wang, Mengdi Cong, Jialiang Ren, Wei Ren, Gaofeng Shi   +12 more
doaj   +1 more source

Correction: Corrigendum: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach [PDF]

, 2014
Hugo J.W.L. Aerts, Emmanuel Rios Velazquez, Ralph T. H. Leijenaar, Chintan Parmar, Patrick Großmann, Sara Carvalho, Johan Bussink, René Monshouwer, Benjamin Haibe‐Kains, D. Rietveld, Frank Hoebers, Michelle M. Rietbergen, C. René Leemans, André Dekker, John Quackenbush, Robert J. Gillies, Philippe Lambin   +16 more
openalex   +1 more source

Radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy

iScience, 2023
Summary: To guide individualized intensity-modulated radiotherapy (IMRT), we developed and prospectively validated a multiview radiomics risk model for predicting radiation-induced hypothyroidism in patients with nasopharyngeal carcinoma.
Shan-Shan Yang, Qing-He Peng, Ai-Qian Wu, Bao-Yu Zhang, Zhi-Qiao Liu, En-Ni Chen, Fang-Yun Xie, Pu-Yun OuYang, Chun-Yan Chen   +8 more
doaj  

Multifactorial decision support systems in radiation oncology : clinical predictors and radiomics [PDF]

, 2014
Emmanuel Rios Velazquez
openalex   +1 more source

Editorial: Imaging assessment of response to immunotherapy

Frontiers in Oncology, 2023
Mariaelena Occhipinti, Andrea R. Filippi, Andrea R. Filippi, Sean Walsh, Jan P. van Meerbeeck   +4 more
doaj   +1 more source

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