Cargando…

CT-based radiomics for differentiating intracranial contrast extravasation from intraparenchymal haemorrhage after mechanical thrombectomy

OBJECTIVE: To develop a nonenhanced CT-based radiomic signature for the differentiation of iodinated contrast extravasation from intraparenchymal haemorrhage (IPH) following mechanical thrombectomy. METHODS: Patients diagnosed with acute ischaemic stroke who underwent mechanical thrombectomy in 4 in...

Descripción completa

Detalles Bibliográficos
Autores principales:	Chen, Xiaojun, Li, Yuanzhe, Zhou, Yongjin, Yang, Yan, Yang, Jiansheng, Pang, Peipei, Wang, Yi, Cheng, Jianmin, Chen, Haibo, Guo, Yifan
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Springer Berlin Heidelberg 2022
Materias:	Imaging Informatics and Artificial Intelligence
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9213289/ https://www.ncbi.nlm.nih.gov/pubmed/35113213 http://dx.doi.org/10.1007/s00330-022-08541-9

Ejemplares similares

Quantitative CT radiomics-based models for prediction of haematoma expansion and poor functional outcome in primary intracerebral haemorrhage
por: Pszczolkowski, Stefan, et al.
Publicado: (2021)

Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey
por: Volpe, Stefania, et al.
Publicado: (2023)

Radiomics with 3-dimensional magnetic resonance fingerprinting: influence of dictionary design on repeatability and reproducibility of radiomic features
por: Fujita, Shohei, et al.
Publicado: (2022)

Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative
por: Spadarella, Gaia, et al.
Publicado: (2022)

Radiomics analysis based on lumbar spine CT to detect osteoporosis
por: Jiang, Yan-Wei, et al.
Publicado: (2022)

Dynamic 3D radiomics analysis using artificial intelligence to assess the stage of COVID-19 on CT images
por: Cai, Shengping, et al.
Publicado: (2022)

Predicting visual recovery in pituitary adenoma patients post-endoscopic endonasal transsphenoidal surgery: Harnessing delta-radiomics of the optic chiasm from MRI
por: Zhang, Yang, et al.
Publicado: (2023)

Advanced gastric cancer: CT radiomics prediction and early detection of downstaging with neoadjuvant chemotherapy
por: Xu, Qinmei, et al.
Publicado: (2021)

Prediction of incident cardiovascular events using machine learning and CMR radiomics
por: Pujadas, Esmeralda Ruiz, et al.
Publicado: (2022)

Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis
por: Xue, Li-Yun, et al.
Publicado: (2020)

Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility
por: Schurink, Niels W., et al.
Publicado: (2021)

Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform
por: Fornacon-Wood, Isabella, et al.
Publicado: (2020)

[(18)F]FDG-PET/CT radiomics for the identification of genetic clusters in pheochromocytomas and paragangliomas
por: Noortman, Wyanne A., et al.
Publicado: (2022)

Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2
por: Fang, Xu, et al.
Publicado: (2020)

Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning
por: Lin, Yu-Chun, et al.
Publicado: (2023)

Development and multicenter validation of a CT-based radiomics signature for predicting severe COVID-19 pneumonia
por: Li, Liang, et al.
Publicado: (2021)

Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma—a systematic review and meta-analysis
por: Ursprung, Stephan, et al.
Publicado: (2020)

Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network
por: Ye, Hai, et al.
Publicado: (2019)

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer
por: Bleker, Jeroen, et al.
Publicado: (2019)

A PET/CT nomogram incorporating SUVmax and CT radiomics for preoperative nodal staging in non-small cell lung cancer
por: Xie, Yunming, et al.
Publicado: (2021)

Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer
por: Hou, Min, et al.
Publicado: (2022)

Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers
por: Fournier, Laure, et al.
Publicado: (2021)

Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients
por: Schmitt, N., et al.
Publicado: (2021)

FDG PET/CT radiomics as a tool to differentiate between reactive axillary lymphadenopathy following COVID-19 vaccination and metastatic breast cancer axillary lymphadenopathy: a pilot study
por: Eifer, Michal, et al.
Publicado: (2022)

A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients
por: Gang, Yadong, et al.
Publicado: (2021)

Computed Tomography Radiomics Kinetics as Early Imaging Correlates of Osteoradionecrosis in Oropharyngeal Cancer Patients
por: Barua, Souptik, et al.
Publicado: (2021)

Prediction of carcinogenic human papillomavirus types in cervical cancer from multiparametric magnetic resonance images with machine learning-based radiomics models
por: İnce, Okan, et al.
Publicado: (2023)

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19
por: Liang, Hengrui, et al.
Publicado: (2022)

Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs
por: Cheng, Chi-Tung, et al.
Publicado: (2019)

A dynamic graph Hawkes process based on linear complexity self-attention for dynamic recommender systems
por: Hou, Zhiwen, et al.
Publicado: (2023)

Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening
por: Pan, Yaling, et al.
Publicado: (2020)

A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
por: Wang, Shuai, et al.
Publicado: (2021)

An automated surgical decision-making framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features in renal cell carcinoma
por: Yang, Huancheng, et al.
Publicado: (2023)

Automated segmentation of craniopharyngioma on MR images using U-Net-based deep convolutional neural network
por: Chen, Chaoyue, et al.
Publicado: (2022)

Automated lung cancer assessment on 18F-PET/CT using Retina U-Net and anatomical region segmentation
por: Weikert, T., et al.
Publicado: (2023)

Any unique image biomarkers associated with COVID-19?
por: Pu, Jiantao, et al.
Publicado: (2020)

RatUNet: residual U-Net based on attention mechanism for image denoising
por: Zhang, Huibin, et al.
Publicado: (2022)

Radiomics in Oncology: A Practical Guide
por: Shur, Joshua D., et al.
Publicado: (2021)

Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists
por: Antonelli, Michela, et al.
Publicado: (2019)

Deep learning in interstitial lung disease—how long until daily practice
por: Trusculescu, Ana Adriana, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_n6lm8vl116icen0j5lc3nqr1pv