Results 101 to 110 of about 89,335 (330)

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

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

4D radiomics: impact of 4D-CBCT image quality on radiomic analysis

Physics in Medicine & Biology, 2021
To investigate the impact of 4D-CBCT image quality on radiomic analysis and the efficacy of using deep learning based image enhancement to improve the accuracy of radiomic features of 4D-CBCT.In this study, 4D-CT data from 16 lung cancer patients were obtained.
Jordan Torok, Lei Ren, Fang-Fang Yin, Zeyu Zhang, Zhuoran Jiang, Zhuoran Jiang, Yushi Chang, Mi Huang   +7 more
openaire   +3 more sources

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

Development of a MRI-Based Radiomics Nomogram for Prediction of Response of Patients With Muscle-Invasive Bladder Cancer to Neoadjuvant Chemotherapy

Frontiers in Oncology, 2022
ObjectiveTo develop and evaluate the performance of a magnetic resonance imaging (MRI)-based radiomics nomogram for prediction of response of patients with muscle-invasive bladder cancer (MIBC) to neoadjuvant chemotherapy (NAC).MethodsA total of 70 ...
Xinxin Zhang, Yichen Wang, Jin Zhang, Lianyu Zhang, Sicong Wang, Yan Chen   +5 more
doaj   +1 more source

Radiomics-Based Prognostication in Primary Sclerosing Cholangitis: A Proof-of-Concept Study. [PDF]

Liver Int
ABSTRACT Background and Aim Risk assessment in primary sclerosing cholangitis (PSC) by magnetic resonance imaging (MRI) relies on semi‐quantitative analysis, which can result in interpretation variability. Radiomics may offer a quantitative approach for risk stratification.
Cristoferi L, Maino C, Bernasconi DP, Ripamonti I, Scaravaglio M, Savino A, Pesatori E, Gerussi A, Nofit E, Falco O, D'Amato D, Gallivanone F, Alberzoni C, Viganò R, Mazzarelli C, Belli LS, Dinelli ME, Mutignani M, Valsecchi MG, Corso R, Invernizzi P, Carbone M, Ippolito D, Bernardi E.   +23 more
europepmc   +2 more sources

Machine and deep learning methods for radiomics.

Medical Physics (Lancaster), 2020
Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints.
M. Avanzo, Lise Wei, J. Stancanello, M. Vallières, A. Rao, O. Morin, S. Mattonen, I. E. El Naqa   +7 more
semanticscholar   +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

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

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