Results 71 to 80 of about 89,335 (330)

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

Nature Communications, 2020
Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications.
Xueyi Zheng, Z. Yao, Yini Huang, Yanyan Yu, Yun Wang, Yubo Liu, Rushuang Mao, Fei Li, Yang Xiao, Yuanyuan Wang, Yixin Hu, Jinhua Yu, Jianhua Zhou   +12 more
semanticscholar   +1 more source

Radiomics in cervical and endometrial cancer

The British Journal of Radiology, 2021
Radiomics is an emerging field of research that aims to find associations between quantitative information extracted from imaging examinations and clinical data to support the best clinical decision. In the last few years, some papers have been evaluating the role of radiomics in gynecological malignancies, mainly focusing on ovarian cancer ...
Manganaro L., Nicolino G. M., Dolciami M., Martorana F., Stathis A., Colombo I., Rizzo S.   +6 more
openaire   +3 more sources

Differential Diagnosis of Type 1 and Type 2 Papillary Renal Cell Carcinoma Based on Enhanced CT Radiomics Nomogram

Frontiers in Oncology, 2022
ObjectivesTo construct a contrast-enhanced CT-based radiomics nomogram that combines clinical factors and a radiomics signature to distinguish papillary renal cell carcinoma (pRCC) type 1 from pRCC type 2 tumours.MethodsA total of 131 patients with 60 in
Yankun Gao, Xingwei Wang, Shihui Wang, Yingying Miao, Chao Zhu, Cuiping Li, Guoquan Huang, Yan Jiang, Jianying Li, Xiaoying Zhao, Xingwang Wu   +10 more
doaj   +1 more source

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

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

Phantom-based radiomics feature test–retest stability analysis on photon-counting detector CT

European Radiology, 2023
Objectives Radiomics image data analysis offers promising approaches in research but has not been implemented in clinical practice yet, partly due to the instability of many parameters.
Alexander Hertel, Hishan Tharmaseelan, Lukas T. Rotkopf, Dominik Nörenberg, P. Riffel, K. Nikolaou, Jakob Weiss, F. Bamberg, S. O. Schoenberg, M. Froelich, Isabelle Ayx   +10 more
semanticscholar   +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

Criteria for the translation of radiomics into clinically useful tests

Nature Reviews Clinical Oncology, 2022
Despite tens of thousands of radiomic studies, the number of settings in which radiomics is used to guide clinical decision-making is limited, in part owing to a lack of standardization of the radiomic measurement extraction processes and the lack of ...
Erich P Huang, J. O’Connor, L. McShane, M. Giger, P. Lambin, Paul Kinahan, E. Siegel, L. Shankar   +7 more
semanticscholar   +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

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Frontiers in Oncology, 2022
The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest ...
Xing-wei Zhang, Yanchun Zhang, Guijuan Zhang, Xingting Qiu, Wenjun Tan, Xiaoxia Yin, Liefa Liao   +6 more
semanticscholar   +1 more source