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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Quantitative CT radiomics-based models for prediction of haematoma expansion and poor functional outcome in primary intracerebral haemorrhage
Stefan Pszczółkowski, J.P Manzano-Patron, Zhe Kang Law, et al.
European Radiology (2021) Vol. 31, Iss. 10, pp. 7945-7959
Open Access | Times Cited: 51

Showing 26-50 of 51 citing articles:

Machine-learning-based performance comparison of two-dimensional (2D) and three-dimensional (3D) CT radiomics features for intracerebral haemorrhage expansion
Qian Chen, Caixia Fu, Xuefeng Qiu, et al.
Clinical Radiology (2023) Vol. 79, Iss. 1, pp. e26-e33
Closed Access | Times Cited: 4
Quantitative imaging for predicting hematoma expansion in intracerebral hemorrhage: A multimodel comparison
Wen‐Song Yang, Jia-Yang Liu, Yiqing Shen, et al.
Journal of Stroke and Cerebrovascular Diseases (2024) Vol. 33, Iss. 7, pp. 107731-107731
Closed Access | Times Cited: 1
Research advances in predicting the expansion of hypertensive intracerebral hemorrhage based on CT images: an overview
Min Ai, Hanghang Zhang, Junbang Feng, et al.
PeerJ (2024) Vol. 12, pp. e17556-e17556
Open Access | Times Cited: 1
Hybrid Clinical-Radiomics Model Based on Fully Automatic Segmentation for Predicting the Early Expansion of Spontaneous Intracerebral Hemorrhage: A Multi-Center Study
Menghui Wang, Yi Liang, Hui Li, et al.
Journal of Stroke and Cerebrovascular Diseases (2024) Vol. 33, Iss. 11, pp. 107979-107979
Open Access | Times Cited: 1
Development and Validation of a Clinical-Based Signature to Predict the 90-Day Functional Outcome for Spontaneous Intracerebral Hemorrhage
Xiaoyu Huang, Dan Wang, Qiaoying Zhang, et al.
Frontiers in Aging Neuroscience (2022) Vol. 14
Open Access | Times Cited: 6
A non-contrast computed tomography-based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhage
Wei Xu, Hongquan Guo, Huiping Li, et al.
Frontiers in Neurology (2022) Vol. 13
Open Access | Times Cited: 6
Difference of mean Hounsfield units (dHU) between follow-up and initial noncontrast CT scan predicts 90-day poor outcome in spontaneous supratentorial acute intracerebral hemorrhage with deep convolutional neural networks
Xiaona Xia, Xiaoqian Zhang, Jiufa Cui, et al.
NeuroImage Clinical (2023) Vol. 38, pp. 103378-103378
Open Access | Times Cited: 3
Radiomics Outperforms Clinical and Radiologic Signs in Predicting Spontaneous Basal Ganglia Hematoma Expansion: A Pilot Study
Houman Sotoudeh, Ali Rezaei, Ryan C. Godwin, et al.
Cureus (2023)
Open Access | Times Cited: 3
Development and validation of a novel clinical prediction model to predict the 90-day functional outcome of spontaneous intracerebral hemorrhage
Zhi Geng, Tao Guo, Ziwei Cao, et al.
Frontiers in Neurology (2023) Vol. 14
Open Access | Times Cited: 3
Multi-Stage Harmonization for Robust AI across Breast MR Databases
Heather M. Whitney, Hui Li, Yu Ji, et al.
Cancers (2021) Vol. 13, Iss. 19, pp. 4809-4809
Open Access | Times Cited: 6
Clasificadores de aprendizaje supervisado no lineales basados en radiómica de la TC cerebral sin contraste para predecir el pronóstico funcional en pacientes con hematoma intracerebral espontáneo
Elena Serrano, Javier Moreno, Laura Llull, et al.
Radiología (2023) Vol. 65, Iss. 6, pp. 519-530
Closed Access | Times Cited: 2
Radiomic-based nonlinear supervised learning classifiers on non-contrast CT to predict functional prognosis in patients with spontaneous intracerebral hematoma
Elena Serrano, Javier Moreno, Laura Llull, et al.
Radiología (English Edition) (2023) Vol. 65, Iss. 6, pp. 519-530
Closed Access | Times Cited: 2
Location-Specific Radiomics Score: Novel Imaging Marker for Predicting Poor Outcome of Deep and Lobar Spontaneous Intracerebral Hemorrhage
Zhiming Zhou, Hongli Zhou, Zuhua Song, et al.
Frontiers in Neuroscience (2021) Vol. 15
Open Access | Times Cited: 5
Advances in computed tomography-based prognostic methods for intracerebral hemorrhage
Xiaoyu Huang, Dan Wang, Shenglin Li, et al.
Neurosurgical Review (2022) Vol. 45, Iss. 3, pp. 2041-2050
Closed Access | Times Cited: 3
Can Artificial Intelligence Be Applied to Diagnose Intracerebral Hemorrhage under the Background of the Fourth Industrial Revolution? A Novel Systemic Review and Meta-Analysis
Kai Zhao, Qing Zhao, Ping Zhou, et al.
International Journal of Clinical Practice (2022) Vol. 2022, pp. 1-17
Open Access | Times Cited: 3
An Online Dynamic Radiomics–Clinical Nomogram to Predict Recurrence in Patients with Spontaneous Intracerebral Hemorrhage
Zhixian Luo, Ying Zhou, Mengying Yu, et al.
World Neurosurgery (2024) Vol. 183, pp. e638-e648
Closed Access
CT Texture-Based Nomogram in Ischemic Stroke to Differentiate Intracerebral Hemorrhage from Contrast Extravasation after Thrombectomy
Kun An, Chun Chen, Meijuan Dong, et al.
Cerebrovascular Diseases (2024) Vol. 53, Iss. 4, pp. 457-466
Closed Access
HE-Mind: A model for automatically predicting hematoma expansion after spontaneous intracerebral hemorrhage
Zhiming Zhou, Weidao Chen, Ruize Yu, et al.
European Journal of Radiology (2024) Vol. 176, pp. 111533-111533
Closed Access
Machine learning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis
Yihua Liu, Fengfeng Zhao, Enjing Niu, et al.
Neuroradiology (2024) Vol. 66, Iss. 9, pp. 1603-1616
Closed Access
Application of Radiomics in Intracerebral Hemorrhage
浩 郑
Advances in Clinical Medicine (2024) Vol. 14, Iss. 07, pp. 329-335
Closed Access
Computer tomography-based radiomics combined with machine learning for predicting the time since onset of epidural hematoma
Mingzhe Wu, Pengfei Wang, Hao Cheng, et al.
International Journal of Legal Medicine (2024)
Closed Access
The “SALPARE study” of spontaneous intracerebral haemorrhage—part 2-early CT predictors of outcome in ICH: keeping it simple
Renzo Manara, Ludovica De Rosa, Francesca Vodret, et al.
Neurological Research and Practice (2023) Vol. 5, Iss. 1
Open Access | Times Cited: 1
Clinical Features, Non-Contrast CT Radiomic and Radiological Signs in Models for the Prediction of Hematoma Expansion in Intracerebral Hemorrhage
Zejia Chen, Liying Zhang, André Carrington, et al.
Canadian Association of Radiologists Journal (2023) Vol. 74, Iss. 4, pp. 713-722
Open Access
Radiomikmodell zur Prädiktion einer intrazerebralen Hämatomexpansion

RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren (2022) Vol. 194, Iss. 02, pp. 138-138
Closed Access
Prediction of Spontaneous Basal Ganglia Hematoma Expansion; Clinical Data Versus Radiology Signs Versus Radiomics; A Pilot Study
Ali Rezaei, Ryan Godwin, Veeranjaneyulu Prattipati, et al.
Research Square (Research Square) (2022)
Open Access

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