Results 31 to 40 of about 70,067 (222)

MRI radiomic features are independently associated with overall survival in soft tissue sarcoma [PDF]

, 2019
Purpose: Soft tissue sarcomas (STS) represent a heterogeneous group of diseases, and selection of individualized treatments remains a challenge. The goal of this study was to determine whether radiomic features extracted from magnetic resonance (MR ...
Ball, Kevin C, Chapman, Tobias R, Combs, Stephanie E, Hippe, Daniel S, Hoff, Michael N, Kim, Edward Y, Macomber, Meghan W, Nyflot, Matthew J, Peeken, Jan C, Pollack, Seth M, Spraker, Matthew B, Wootton, Landon S   +11 more
core   +2 more sources

Integrated Hyperparameter Optimization with Dimensionality Reduction and Clustering for Radiomics: A Bootstrapped Approach

Multimodal Technologies and Interaction
Radiomics involves extracting quantitative features from medical images, resulting in high-dimensional data. Unsupervised clustering has been used to discover patterns in radiomic features, potentially yielding hidden biological insights.
S. J. Pawan, Matthew Muellner, Xiaomeng Lei, Mihir Desai, Bino Varghese, Vinay Duddalwar, Steven Y. Cen   +6 more
doaj   +1 more source

An Improvement of Survival Stratification in Glioblastoma Patients via Combining Subregional Radiomics Signatures

Frontiers in Neuroscience, 2021
PurposeTo investigate whether combining multiple radiomics signatures derived from the subregions of glioblastoma (GBM) can improve survival prediction of patients with GBM.MethodsIn total, 129 patients were included in this study and split into training
Yang Yang, Yang Yang, Yu Han, Xintao Hu, Wen Wang, Guangbin Cui, Lei Guo, Lei Guo, Xin Zhang   +8 more
doaj   +1 more source

Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography

, 2016
We propose new methods for the prediction of 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and ...
Bradley, Andrew P., Carneiro, Gustavo, Nascimento, Jacinto, Oakden-Rayner, Luke, Palmer, Lyle   +4 more
core   +2 more sources

The Bionic Radiologist: avoiding blurry pictures and providing greater insights [PDF]

, 2019
Radiology images and reports have long been digitalized. However, the potential of the more than 3.6 billion radiology examinations performed annually worldwide has largely gone unused in the effort to digitally transform health care.
Dewey, Marc, Wilkens, Uta
core   +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   +1 more source

Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection [PDF]

, 2017
While skin cancer is the most diagnosed form of cancer in men and women, with more cases diagnosed each year than all other cancers combined, sufficiently early diagnosis results in very good prognosis and as such makes early detection crucial.
Shafiee, Mohammad Javad, Wong, Alexander
core   +3 more sources

A CT-based radiomics approach to predict intra-tumoral tertiary lymphoid structures and recurrence of intrahepatic cholangiocarcinoma

Insights into Imaging, 2023
Purpose To predict the tertiary lymphoid structures (TLSs) status and recurrence-free survival (RFS) of intrahepatic cholangiocarcinoma (ICC) patients using preoperative CT radiomics.
Ying Xu, Zhuo Li, Yi Yang, Lu Li, Yanzhao Zhou, Jingzhong Ouyang, Zhen Huang, Sicong Wang, Lizhi Xie, Feng Ye, Jinxue Zhou, Jianming Ying, Hong Zhao, Xinming Zhao   +13 more
doaj   +1 more source

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

, 2018
Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size.
A Jungo, AW Awad, B Fischl, BH Menze, CH Lampert, DN Louis, DR Cox, G Wang, J Lao, JJ Griethuysen van, Julián Pérez-Beteta, N Sanai, R Meier, R Tibshirani, RJ Gillies, S Bakas, S Pereira, S Rathore, Spyridon Bakas, Spyridon Bakas, T Hastie, TC Steed, Y Li   +22 more
core   +1 more source

Highly accurate model for prediction of lung nodule malignancy with CT scans [PDF]

, 2018
Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer.
Causey, Jason L., Huang, Xiuzhen, Jiang, Bo, Ma, Shiqian, Politte, David G., Prior, Fred, Qualls, Jake A., Zhang, Junyu, Zhang, Shuzhong   +8 more
core   +3 more sources