Results 41 to 50 of about 70,067 (222)

Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis. [PDF]

, 2018
Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization ...
Arasu, Vignesh A, Behr, Spencer C, Copeland, Timothy P, Esserman, Laura, Franc, Benjamin L, Harnish, Roy J, Huang, Shih-Ying, Hylton, Nola M, Jones, Ella F, Kornak, John, Liu, Gengbo, Mitra, Debasis, Price, Elissa R, Seo, Youngho   +13 more
core  

Heterogeneity of Rheumatoid Arthritis–Associated Interstitial Lung Disease by Longitudinal Forced Vital Capacity Trajectory and Associations With Disease Outcomes

Arthritis Care &Research, EarlyView.
Objective We aimed to identify unique disease trajectories within rheumatoid arthritis–associated interstitial lung disease (RA‐ILD) based on longitudinal forced vital capacity (FVC) values and their associated clinical outcomes. Methods We performed a cohort study of RA‐ILD within the Veterans Health Administration from 1999 to 2021.
Bryant R. England, Yangyuna Yang, Punyasha Roul, Tate M. Johnson, Scott M. Matson, Grant W. Cannon, Brian C. Sauer, Jeffrey R. Curtis, Joshua F. Baker, Ted R. Mikuls   +9 more
wiley   +1 more source

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

Predicting Immunotherapy Outcomes in NSCLC Using RNA and Pathology from Multicenter Clinical Trials

Advanced Science, EarlyView.
LIRA, a machine learning‐based model, is developed using transcriptomic data from 891 NSCLC patients in the OAK and POPLAR cohorts. Its predictive performance is validated in multiple external cohorts. Patients stratified by LIRA‐score exhibit distinct clinical characteristics and tumor microenvironment profiles.
Zhaojun Wang, Yiran Fang, Xiatong Huang, Guichuang Ma, Qianqian Mao, Xiansheng Lu, Guangda Rong, Yunfang Yu, Yuanyuan Wang, Zhenhua Huang, Huiying Sun, Jiani Wu, Wenchao Gu, Na Huang, Jianhua Wu, Rui Zhou, Xiaoxiang Rong, Siting Zheng, Shaowei Li, Gaofeng Wang, Ling Wang, Wenjun Qiu, Luyang Jiang, Peng Luo, Yonggang Liu, Jianping Bin, Yulin Liao, Min Shi, Zuqiang Wu, Jiguang Wang, Wangjun Liao, Gang Chen, Dongqiang Zeng   +32 more
wiley   +1 more source

Prediction of PD-L1 and CD68 in Clear Cell Renal Cell Carcinoma with Green Learning

Journal of Imaging
Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer. Extensive efforts have been made to utilize radiomics from computed tomography (CT) imaging to predict tumor immune microenvironment (TIME) measurements. This study proposes
Yixing Wu, Alexander Shieh, Steven Cen, Darryl Hwang, Xiaomeng Lei, S. J. Pawan, Manju Aron, Inderbir Gill, William D. Wallace, C.-C. Jay Kuo, Vinay Duddalwar   +10 more
doaj   +1 more source

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

Leveraging Compressed Sensing and Radiomics for Robust Feature Selection for Outcome Prediction in Personalized Ultra‐Fractionated Stereotactic Adaptive Radiotherapy

Advanced Intelligent Systems, EarlyView.
A compressed sensing (CS)‐based feature selection method is proposed to select the most informative elements in the radiomic features extracted from medical images of personalized ultra‐fractionated stereotactic adaptive treatment. The CS‐based approach is able to simplify the feature selection process and enhance the accuracy and robustness of a ...
Yajun Yu, Steve Jiang, Robert Timmerman, Hao Peng   +3 more
wiley   +1 more source

Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancerResearch in context

EBioMedicine, 2018
To develop and validate a radiomics signature for the prediction of gastric cancer (GC) survival and chemotherapeutic benefits. In this multicenter retrospective analysis, we analyzed the radiomics features of portal venous-phase computed tomography in ...
Yuming Jiang, Chuanli Chen, Jingjing Xie, Wei Wang, Xuefan Zha, Wenbing Lv, Hao Chen, Yanfeng Hu, Tuanjie Li, Jiang Yu, Zhiwei Zhou, Yikai Xu, Guoxin Li   +12 more
doaj   +1 more source

Clinlabomics‐Enabled Blending Ensemble Learning for Low‐Cost Pan‐Cancer Detection and Classification Using Routine Clinical Laboratory Data

Advanced Intelligent Systems, EarlyView.
Researchers develop clinlabomics assisted for cancer identification, an artificial intelligence‐powered system using routine clinical lab data to detect and identify 10 cancer types. Tested on 19 199 individuals, it achieves 90.39% sensitivity and 82.41% specificity in cancer detection, with 72.57% accuracy in identifying specific cancer types ...
Bowen Zhang, Lin Li, Long Cheng, Yujie Liu, Zhaoqing Liu, Jiangpeng Wu, Pengyi Zhang, Jiarui Wang, Dekui Zhang, Shuyan Li   +9 more
wiley   +1 more source

Machine Learning Model Integrating CT Radiomics of the Lung to Predict Checkpoint Inhibitor Pneumonitis in Patients with Advanced Cancer

Technology in Cancer Research & Treatment
Objective Checkpoint inhibitor pneumonitis (CIP) is a potentially life-threatening immune-related adverse event. Efficient strategies to select patients at risk are still required.
François Cousin MD, PhD, Thomas Louis MS, PhD, Pierre Frères MD, PhD, Julien Guiot MD, PhD, Mariaelena Occhipinti MD, Fabio Bottari MS, Wim Vos MS, Roland Hustinx MD, PhD   +7 more
doaj   +1 more source