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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:

Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer
Yanhong Chen, Lijun Wang, Xue Dong, et al.
Journal of Digital Imaging (2023) Vol. 36, Iss. 4, pp. 1323-1331
Open Access | Times Cited: 21

Showing 21 citing articles:

Breast cancer detection using deep learning techniques: challenges and future directions
Muhammad Shahid, Azhar Imran
Multimedia Tools and Applications (2025)
Closed Access | Times Cited: 3
Nomograms Integrating MRI-derived Apparent Diffusion Coefficient and Clinicopathologic Features for Prediction of Axillary Lymph Node Metastasis in Breast Cancer
Huifang Chen, Xiaoxia Wang, Yao Huang, et al.
Radiology Imaging Cancer (2025) Vol. 7, Iss. 2
Open Access
Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer
Yunxia Wang, Yiyan Shang, Yaxin Guo, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 4
Photoacoustic Imaging with Attention-Guided Deep Learning for Predicting Axillary Lymph Node Status in Breast Cancer
Guoqiu Li, Shuzhen Tang, Zhibin Huang, et al.
Academic Radiology (2025)
Closed Access
Breast cancer staging with contrast-enhanced imaging. The benefits and drawbacks of MRI, CEM, and dedicated breast CT
Marialena I. Tsarouchi, Alma Hoxhaj, Antonio Portaluri, et al.
European Journal of Radiology (2025) Vol. 185, pp. 112013-112013
Open Access
Deep Learning-Assisted Diagnosis of Placenta Accreta Spectrum Using the DenseNet-121 Model: A Multicenter, Retrospective Study
Yurui Hu, Tianyu Liu, Shutong Pang, et al.
Deleted Journal (2025)
Closed Access
SC-Unext: A Lightweight Image Segmentation Model with Cellular Mechanism for Breast Ultrasound Tumor Diagnosis
Fenglin Cai, Jiaying Wen, Fangzhou He, et al.
Deleted Journal (2024)
Open Access | Times Cited: 3
MRI radiomics and biological correlations for predicting axillary lymph node burden in early-stage breast cancer
Minping Hong, Sijia Fan, Zeyan Xu, et al.
Journal of Translational Medicine (2024) Vol. 22, Iss. 1
Open Access | Times Cited: 3
Machine learning and radiomics for segmentation and classification of adnexal masses on ultrasound
J. Barcroft, Kristofer Linton‐Reid, C. Landolfo, et al.
npj Precision Oncology (2024) Vol. 8, Iss. 1
Open Access | Times Cited: 3
An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study
Hao Zhang, Yunfeng Yang, Xue-Lin Song, et al.
BMC Medical Imaging (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 2
Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer
Hongbing Luo, Zhe Chen, Hao Xu, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
Application of Deep Learning in Cancer Prognosis Prediction Model
Heng Zhang, Qianyi Xi, Fan Zhang, et al.
Technology in Cancer Research & Treatment (2023) Vol. 22
Open Access | Times Cited: 4
Cross-Modality Calibration in Multi-Input Network for Axillary Lymph Node Metastasis Evaluation
Michela Gravina, Domiziana Santucci, Ermanno Cordelli, et al.
IEEE Transactions on Artificial Intelligence (2024) Vol. 5, Iss. 9, pp. 4823-4836
Open Access | Times Cited: 1
Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours
Guoxiu Lu, Ronghui Tian, Wei Yang, et al.
Frontiers in Medicine (2024) Vol. 11
Open Access | Times Cited: 1
Current status and prospects of breast cancer imaging-based diagnosis using artificial intelligence
Chikako Sekine, Jun Horiguchi
International Journal of Clinical Oncology (2024)
Closed Access | Times Cited: 1
Nomogram Utilizing ABVS Radiomics and Clinical Factors for Predicting ≤ 3 Positive Axillary Lymph Nodes in HR+ /HER2- Breast Cancer with 1–2 Positive Sentinel Nodes
Bin Hu, Yanjun Xu, Huiling Gong, et al.
Academic Radiology (2024) Vol. 31, Iss. 7, pp. 2684-2694
Closed Access
MRI Radiomics and Biological Correlations for Predicting Axillary Lymph Node Burden in Early-Stage Breast Cancer
Minping Hong, Maosheng Xu, Sijia Fan, et al.
Research Square (Research Square) (2024)
Closed Access
Research Progress of Multiparametric MRI Radiomics in Breast Cancer
昕毓 钱
Advances in Clinical Medicine (2024) Vol. 14, Iss. 07, pp. 613-619
Closed Access
The Value of Radiomic Features of Primary Breast Tumor in STIR Sequences in Predicting Axilla Metastasis
Günay Rona, Meral Arifoğlu, Ahmet TEKİN, et al.
Archives of Breast Cancer (2024) Vol. 11, Iss. 3
Open Access
Prediction of axillary lymph node metastasis with the ultrasound and Magnetic Resonance Imaging features in breast cancer
Qiu Jing Shang, Li Yan, Man Lin Lang, et al.
Research Square (Research Square) (2024)
Open Access
Machine Learning Based on Digital Mammography to Reduce the Need for Invasive Biopsies of Benign Calcifications Classified in BI-RADS Category 4
Neng Wang, Wenjie Xu, Huogen Wang, et al.
Deleted Journal (2024)
Closed Access
Deep Learning Technique for Predicting Axillary Lymph Node Metastasis in Breast Cancer
Haonan Qiao, Wang Zhou, Laixiang Qiu, et al.
2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) (2023), pp. 1-4
Closed Access
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