Zobrazeno 1 - 10
of 326
pro vyhledávání: '"radiomics features"'
Autor:
Mazen M. Yassin, Jiaxi Lu, Asim Zaman, Huihui Yang, Anbo Cao, Xueqiang Zeng, Haseeb Hassan, Taiyu Han, Xiaoqiang Miao, Yongkang Shi, Yingwei Guo, Yu Luo, Yan Kang
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract Ischemic stroke is a leading global cause of death and disability and is expected to rise in the future. The present diagnostic techniques, like CT and MRI, have some limitations in distinguishing acute from chronic ischemia and in early isc
Externí odkaz:
https://doaj.org/article/f05cad84c4ff4cc896b1cf8bac7a2700
Autor:
Yanhui Liu, Wenxiu Zhang, Mengzhou Sun, Xiaoyun Liang, Lu Wang, Jiaqi Zhao, Yongquan Hou, Haina Li, Xiaoguang Yang
Publikováno v:
BMC Pulmonary Medicine, Vol 24, Iss 1, Pp 1-12 (2024)
Abstract Background Previous studies have shown that patients with pre-existing chronic obstructive pulmonary diseases (COPD) were more likely to be infected with coronavirus disease (COVID-19) and lead to more severe lung lesions. However, few studi
Externí odkaz:
https://doaj.org/article/c85621ddb92d4eec8ff802180705648f
Autor:
Jia Jiang, Siqin Chen, Shaofeng Zhang, Yaling Zeng, Jiayi Liu, Wei lei, Xiang Liu, Xin Chen, Qiang Xiao
Publikováno v:
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-11 (2024)
Abstract Background Community-Acquired Pneumonia (CAP) remains a significant global health concern, with a subset of cases progressing to Severe Community-Acquired Pneumonia (SCAP). This study aims to develop and validate a CT-based radiomics model f
Externí odkaz:
https://doaj.org/article/397379f5b3664ec0883d79676a5caca7
Publikováno v:
Frontiers in Neuroscience, Vol 18 (2024)
ObjectivesThis study attempted to determine potential predictors among radiomics features for poor prognosis in aneurysmal subarachnoid hemorrhage (aSAH), develop models for prediction, and verify their predictive power.MethodsIn total, 252 patients
Externí odkaz:
https://doaj.org/article/7128ec1eabb34763ba03aadb67cf0869
Publikováno v:
Frontiers in Oncology, Vol 14 (2024)
IntroductionEsophageal sarcomatoid carcinoma (ESC) is a rare pathological subtype of esophageal carcinomas, wherein its epithelial component typically demonstrates squamous cell carcinoma (SCC). However, the clinicopathological features and prognosis
Externí odkaz:
https://doaj.org/article/2368fb40387a4b56b8183de35cb9e118
Publikováno v:
Frontiers in Endocrinology, Vol 15 (2024)
ObjectiveThis study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors.Methods242 pancreatic tumor patients who were hospita
Externí odkaz:
https://doaj.org/article/d79ea97718e04887afbfdd3f7b1825e0
Publikováno v:
Frontiers in Endocrinology, Vol 15 (2024)
ObjectivesTo apply machine learning to extract radiomics features from thyroid two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) images to classify and predict benign and malignant thyroid nodules, classified accord
Externí odkaz:
https://doaj.org/article/3e530fd3eef940219a4763bd67d091e8
Autor:
Hasan Khanfari, Saeed Mehranfar, Mohsen Cheki, Mahmoud Mohammadi Sadr, Samir Moniri, Sahel Heydarheydari, Seyed Masoud Rezaeijo
Publikováno v:
BMC Medical Imaging, Vol 23, Iss 1, Pp 1-13 (2023)
Abstract Background The purpose of this study is to investigate the use of radiomics and deep features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading prostate cancer. We propose a novel approach called multi-flavored fea
Externí odkaz:
https://doaj.org/article/4b7273404f2c4cbdb4690c9b760b4736
Autor:
Salar Bijari, Sahar Sayfollahi, Shiwa Mardokh-Rouhani, Sahar Bijari, Sadegh Moradian, Ziba Zahiri, Seyed Masoud Rezaeijo
Publikováno v:
Bioengineering, Vol 11, Iss 7, p 643 (2024)
This study evaluates the reproducibility of machine learning models that integrate radiomics and deep features (features extracted from a 3D autoencoder neural network) to classify various brain hemorrhages effectively. Using a dataset of 720 patient
Externí odkaz:
https://doaj.org/article/cfbda9ff25e84c69ace4c0c0b800d45c
Autor:
Warid Islam, Neman Abdoli, Tasfiq E. Alam, Meredith Jones, Bornface M. Mutembei, Feng Yan, Qinggong Tang
Publikováno v:
Diagnostics, Vol 14, Iss 9, p 954 (2024)
Background: At the time of cancer diagnosis, it is crucial to accurately classify malignant gastric tumors and the possibility that patients will survive. Objective: This study aims to investigate the feasibility of identifying and applying a new fea
Externí odkaz:
https://doaj.org/article/3317aff004f54d6c8da5dc45b9c5d15e