Results 91 to 100 of about 89,335 (330)
Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey
European Radiology, 2023 Abstract Objectives Radiomics is the high-throughput extraction of mineable and—possibly—reproducible quantitative imaging features from medical imaging. The aim of this work is to perform an unbiased bibliometric analysis on Radiomics 10 years after the first work became available, to highlight its status, pitfalls, and
Volpe S. +3 more
openaire +2 more sources
CRCFound: A Colorectal Cancer CT Image Foundation Model Based on Self‐Supervised Learning
Advanced Science, EarlyView.CRCFound is a self‐supervised learning‐based CT image foundation model for colorectal cancer (CRC). Pretrained on 5137 unlabeled CRC CT images, it learns universal feature representations, enabling efficient adaptation to various clinical tasks. The model demonstrates outstanding performance and generalization across multiple diagnostic and prognosis ...
Jing Yang +13 more
wiley +1 more source
Scientific Reports, 2023 The purpose of this study was to differentiate the retroperitoneal paragangliomas and schwannomas using computed tomography (CT) radiomics. This study included 112 patients from two centers who pathologically confirmed retroperitoneal pheochromocytomas ...
Yuntai Cao +6 more
doaj +1 more source
Radiomics: A primer for the radiation oncologist
Cancer/Radiothérapie, 2020 Radiomics are a set of methods used to leverage medical imaging and extract quantitative features that can characterize a patient's phenotype. All modalities can be used with several different software packages. Specific informatics methods can then be used to create meaningful predictive models.
Anita Burgun +7 more
openaire +6 more sources
Radiomics in esophageal and gastric cancer [PDF]
Abdominal Radiology, 2018 Esophageal, esophago-gastric, and gastric cancers are major causes of cancer morbidity and cancer death. For patients with potentially resectable disease, multi-modality treatment is recommended as it provides the best chance of survival. However, quality of life may be adversely affected by therapy, and with a wide variation in outcome despite multi ...
Gary Cook +6 more
openaire +4 more sources
Interpretable Multimodal Fusion Model Enhances Postoperative Recurrence Prediction in Gastric Cancer
Advanced Science, EarlyView.An interpretable multimodal fusion model (RSA) integrating clinical, radiomic, and pathomic features is developed and validated to predict early postoperative recurrence in gastric cancer. The RSA model demonstrates superior predictive performance and holds potential for guiding adjuvant chemotherapy decisions and improving individualized patient ...
Ping'an Ding +16 more
wiley +1 more source
European Radiology Experimental, 2023 Background Radiomics, the field of image-based computational medical biomarker research, has experienced rapid growth over the past decade due to its potential to revolutionize the development of personalized decision support models. However, despite its
Daniel Abler +9 more
doaj +1 more source
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 +13 more
core
Radiomics: is it time to compose the puzzle? [PDF]
Clinical and Translational Imaging, 2018 -
Castiglioni I., Gilardi M. C.
openaire +5 more sources
Aggregate, EarlyView.Rationally engineered AIE nanoparticles can unite deep‐tissue imaging, photodynamic therapy, photothermal therapy, and immune microenvironment reprogramming. Through precise photophysical tuning and targeted delivery, these intelligent nanoplatforms enable real‐time monitoring, potent tumor eradication, and immune activation, offering a versatile and ...
Quazi T. H. Shubhra +5 more
wiley +1 more source