Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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 1-25 of 51 citing articles:

CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage
Xianjing Zhao, Bijing Zhou, Yong Luo, et al.
European Radiology (2023) Vol. 34, Iss. 7, pp. 4417-4426
Closed Access | Times Cited: 16
Development and validation of a novel radiomics-clinical model for predicting post-stroke epilepsy after first-ever intracerebral haemorrhage
Ru Lin, Jiahe Lin, Yuchen Xu, et al.
European Radiology (2023) Vol. 33, Iss. 7, pp. 4526-4536
Closed Access | Times Cited: 13
Application of deep learning and radiomics in the prediction of hematoma expansion in intracerebral hemorrhage: a fully automated hybrid approach
Mengtian Lu, Yaqi Wang, Jiaqiang Tian, et al.
Diagnostic and Interventional Radiology (2024)
Open Access | Times Cited: 5
Machine learning-based radiomics for predicting outcomes in cervical cancer patients undergoing concurrent chemoradiotherapy
Xin Wang, Su Rixin, Linrui Li, et al.
Computers in Biology and Medicine (2024) Vol. 177, pp. 108593-108593
Closed Access | Times Cited: 5
Prognostic value of CT scan-based radiomics in intracerebral hemorrhage patients: A systematic review and meta-analysis
Zanyar HajiEsmailPoor, Zana Kargar, Mansoureh Baradaran, et al.
European Journal of Radiology (2024) Vol. 178, pp. 111652-111652
Closed Access | Times Cited: 5
Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning
Yong En Kok, Stefan Pszczółkowski, Zhe Kang Law, et al.
Radiology Artificial Intelligence (2022) Vol. 4, Iss. 6
Open Access | Times Cited: 20
Perihematomal edema-based CT-radiomics model to predict functional outcome in patients with intracerebral hemorrhage
Xiaoyu Huang, Dan Wang, Yaqiong Ma, et al.
Diagnostic and Interventional Imaging (2023) Vol. 104, Iss. 9, pp. 391-400
Open Access | Times Cited: 12
Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markers
Stefan P. Haider, Adnan I. Qureshi, Abhi Jain, et al.
Frontiers in Neuroscience (2023) Vol. 17
Open Access | Times Cited: 12
Application of radiomics in acute and severe non-neoplastic diseases: A literature review
Fang Yu, Qiannan Zhang, Junwei Yan, et al.
Journal of Critical Care (2025) Vol. 87, pp. 155027-155027
Open Access
Predicting hematoma expansion after intracerebral hemorrhage: a comparison of clinician prediction with deep learning radiomics models
Boyang Yu, Kara Melmed, Jennifer Frontera, et al.
Neurocritical Care (2025)
Closed Access
Comment on Radiation Lobectomy in Adjunct to Double Vein Embolization to Reach Sufficient Future Liver Remnant in Patients with Colorectal Cancer Liver Metastases: A Case Series
Ulrik Carling
CardioVascular and Interventional Radiology (2025)
Closed Access
Machine-Learning-Based Computed Tomography Radiomics Regression Model for Predicting Pulmonary Function
Wenfang Wang, Yingli Sun, Ruoyu Wu, et al.
Academic Radiology (2025)
Open Access
Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model
Dong‐yue Wen, Jiamin Chen, Zhiping Tang, et al.
BioMedical Engineering OnLine (2024) Vol. 23, Iss. 1
Open Access | Times Cited: 3
CT radiomics combined with clinical and radiological factors predict hematoma expansion in hypertensive intracerebral hemorrhage
Fei Yu, Mingguang Yang, Cheng He, et al.
European Radiology (2024) Vol. 35, Iss. 1, pp. 6-19
Open Access | Times Cited: 3
Collinearity and Dimensionality Reduction in Radiomics: Effect of Preprocessing Parameters in Hypertrophic Cardiomyopathy Magnetic Resonance T1 and T2 Mapping
Chiara Marzi, Daniela Marfisi, Andrea Barucci, et al.
Bioengineering (2023) Vol. 10, Iss. 1, pp. 80-80
Open Access | Times Cited: 9
Prediction of Intraparenchymal Hemorrhage Progression and Neurologic Outcome in Traumatic Brain Injury Patients Using Radiomics Score and Clinical Parameters
Yun‐Ju Shih, Yan-Lin Liu, Jeon‐Hor Chen, et al.
Diagnostics (2022) Vol. 12, Iss. 7, pp. 1677-1677
Open Access | Times Cited: 13
Radiomics for predicting revised hematoma expansion with the inclusion of intraventricular hemorrhage growth in patients with supratentorial spontaneous intraparenchymal hematomas
Xiaona Xia, Qingguo Ren, Jiufa Cui, et al.
Annals of Translational Medicine (2022) Vol. 10, Iss. 1, pp. 8-8
Open Access | Times Cited: 12
Radiomics for prediction of intracerebral hemorrhage outcomes: A retrospective multicenter study
Xiaoyu Huang, Dan Wang, Qiaoying Zhang, et al.
NeuroImage Clinical (2022) Vol. 36, pp. 103242-103242
Open Access | Times Cited: 12
Enhancing Outcome Prediction in Intracerebral Hemorrhage Through Deep Learning: A Retrospective Multicenter Study
Dan Wang, Jing Zhang, Hao Dong, et al.
Academic Radiology (2024)
Open Access | Times Cited: 2
Recent and future advances in intracerebral hemorrhage
Laurent Puy, Nils Jensen Boe, Marc Maillard, et al.
Journal of the Neurological Sciences (2024) Vol. 467, pp. 123329-123329
Closed Access | Times Cited: 2
A nomogram incorporating CT-based peri-hematoma radiomics features to predict functional outcome in patients with intracerebral hemorrhage
Xiaona Xia, Jieqiong Liu, Jiufa Cui, et al.
European Journal of Radiology (2024) Vol. 183, pp. 111871-111871
Closed Access | Times Cited: 2
Prediction of short-term prognosis of patients with hypertensive intracerebral hemorrhage by radiomic-clinical nomogram
Jing Wang, Lu Zhou, Yuanyuan Chen, et al.
Frontiers in Neurology (2023) Vol. 14
Open Access | Times Cited: 6
Outcome Prediction of Spontaneous Supratentorial Intracerebral Hemorrhage after Surgical Treatment Based on Non-Contrast Computed Tomography: A Multicenter Study
Kangwei Zhang, Xiang Zhou, Qian Xi, et al.
Journal of Clinical Medicine (2023) Vol. 12, Iss. 4, pp. 1580-1580
Open Access | Times Cited: 6
Editorial comment on “Development and validation of a novel radiomics-clinical model for predicting post-stroke epilepsy after first-ever intracerebral haemorrhage”
Stefan Pszczółkowski, Zhe Kang Law
European Radiology (2023) Vol. 33, Iss. 7, pp. 4524-4525
Closed Access | Times Cited: 5
Efficacy of non-enhanced computer tomography-based radiomics for predicting hematoma expansion: A meta-analysis
Yanwei Jiang, Xiong-Jei Xu, Rui Wang, et al.
Frontiers in Oncology (2023) Vol. 12
Open Access | Times Cited: 4
Page 1 - Next Page

Scroll to top