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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:

Intra‐ and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer
Wenyan Jiang, Ruiqing Meng, Yuan Cheng, et al.
Journal of Magnetic Resonance Imaging (2023) Vol. 59, Iss. 2, pp. 613-625
Closed Access | Times Cited: 25

Showing 25 citing articles:

Prior Clinico‐Radiological Features Informed Multi‐Modal MR Images Convolution Neural Network: A novel deep learning framework for prediction of lymphovascular invasion in breast cancer
Hong Zheng, Lian Jian, Li Li, et al.
Cancer Medicine (2024) Vol. 13, Iss. 3
Open Access | Times Cited: 8
Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer Based on Intratumoral and Peritumoral CT Radiomics Nomogram
Akun Li, Zhengguang Xiao, Zhaoli Zhou
Cancer Treatment and Research Communications (2025), pp. 100909-100909
Open Access
Assessment of Lymphovascular Invasion in Breast Cancer Using a Combined MRI Morphological Features, Radiomics, and Deep Learning Approach Based on Dynamic Contrast‐Enhanced MRI
Xiuqi Yang, Xiaohong Fan, Shanyue Lin, et al.
Journal of Magnetic Resonance Imaging (2023) Vol. 59, Iss. 6, pp. 2238-2249
Open Access | Times Cited: 15
Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer
Yihong Jiang, Ying Zeng, Zhichao Zuo, et al.
Heliyon (2023) Vol. 10, Iss. 1, pp. e23916-e23916
Open Access | Times Cited: 10
Exploring habitats-based spatial distributions: improving predictions of lymphovascular invasion in invasive breast cancer
Ge Wu, Xiaohong Fan, Ying Zeng, et al.
Academic Radiology (2024)
Closed Access | Times Cited: 3
MRI radiomics for the preoperative evaluation of lymphovascular invasion in breast cancer: A meta-analysis
Qinqin Ma, Zhifan Li, Wenjing Li, et al.
European Journal of Radiology (2023) Vol. 168, pp. 111127-111127
Closed Access | Times Cited: 8
Prognostic Implication of Lymphovascular Invasion in Early Gastric Cancer Meeting Endoscopic Submucosal Dissection Criteria: Insights from Radical Surgery Outcomes
Bo Sun, Huanhuan Li, Xiaodong Gu, et al.
Cancers (2024) Vol. 16, Iss. 5, pp. 979-979
Open Access | Times Cited: 2
Machine learning based on optimal VOI of multi-sequence MR images to predict lymphovascular invasion in invasive breast cancer
Dengke Jiang, Qiuqin Qian, Xiuqi Yang, et al.
Heliyon (2024) Vol. 10, Iss. 7, pp. e29267-e29267
Open Access | Times Cited: 2
Ultrasound-Based Deep Learning Radiomics Nomogram for the Assessment of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study
Di Zhang, Wang Zhou, Wenwu Lu, et al.
Academic Radiology (2024) Vol. 31, Iss. 10, pp. 3917-3928
Closed Access | Times Cited: 2
An unsupervised learning model based on CT radiomics features accurately predicts axillary lymph node metastasis in breast cancer patients—diagnostic study
Limeng Qu, Xilong Mei, Zixi Yi, et al.
International Journal of Surgery (2024) Vol. 110, Iss. 9, pp. 5363-5373
Open Access | Times Cited: 2
A preoperative radiogenomic model based on quantitative heterogeneity for predicting outcomes in triple-negative breast cancer patients who underwent neoadjuvant chemotherapy
Jiayin Zhou, Yansong Bai, Ying Zhang, et al.
Cancer Imaging (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 2
Development of an Intratumoral and Peritumoral Radiomics Nomogram Using Digital Breast Tomosynthesis for Preoperative Assessment of Lymphovascular Invasion in Invasive Breast Cancer
Maolin Xu, Hui-Min Yang, Jia Sun, et al.
Academic Radiology (2023) Vol. 31, Iss. 5, pp. 1748-1761
Closed Access | Times Cited: 4
A comprehensive approach for evaluating lymphovascular invasion in invasive breast cancer: Leveraging multimodal MRI findings, radiomics, and deep learning analysis of intra- and peritumoral regions
Wen Liu, Li Li, Jiao Deng, et al.
Computerized Medical Imaging and Graphics (2024) Vol. 116, pp. 102415-102415
Closed Access | Times Cited: 1
Predicting the Malignancy Grade of Soft Tissue Sarcomas on MRI Using Conventional Image Reading and Radiomics
Fabian Schmitz, Hendrik Voigtländer, Hyungseok Jang, et al.
Diagnostics (2024) Vol. 14, Iss. 19, pp. 2220-2220
Open Access | Times Cited: 1
Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI for Predicting Lymphovascular Invasion in Invasive Breast Cancer
Hong Zheng, Lian Jian, Li Li, et al.
Academic Radiology (2023) Vol. 31, Iss. 5, pp. 1762-1772
Closed Access | Times Cited: 3
Diagnosis of benign and malignant nodules with a radiomics model integrating features from nodules and mammary regions on DCE-MRI
Wei Fan, Wei Sun, Ming Xu, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access
Radiomics-based analysis of dynamic contrast-enhanced magnetic resonance image: A prediction nomogram for lymphovascular invasion in breast cancer
Xiuqi Yang, Xuefei Wang, Zhichao Zuo, et al.
Magnetic Resonance Imaging (2024) Vol. 112, pp. 89-99
Closed Access
Multiphases DCE-MRI Radiomics Nomogram for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer
Qinqin Ma, Xingru Lu, Qitian Chen, et al.
Academic Radiology (2024) Vol. 31, Iss. 12, pp. 4743-4758
Closed Access
The role of multiparametric MRI in predicting lymphovascular invasion in breast cancer patients
Jinhua Wang, Siqing Jing, Zhongxian Yang, et al.
Future Oncology (2024), pp. 1-10
Closed Access
Early prediction of local tumor progression after ablation of colorectal liver metastases based on MRI radiomics
Xiucong Zhu, Jinke Zhu, Chenwen Sun, et al.
Research Square (Research Square) (2024)
Open Access
Clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer
Yaxin Guo, Shunian Li, Jun Liao, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access
A Machine Learning Approach for Breast Cancer Risk Prediction in Digital Mammography
Francesca Angelone, Alfonso Maria Ponsiglione, Carlo Ricciardi, et al.
Applied Sciences (2024) Vol. 14, Iss. 22, pp. 10315-10315
Open Access
Automatic segmentation-based multi-modal radiomics analysis of US and MRI for predicting disease-free survival of breast cancer: a multicenter study
Lang Xiong, Xiaofeng Tang, Xinhua Jiang, et al.
Breast Cancer Research (2024) Vol. 26, Iss. 1
Open Access
A Non-Invasive Mri-Based Multimodal Fusion Deep Learning Model (MF-DLM) for Predicting Overall Survival in Bladder Cancer: A Multicenter Retrospective Study
Lingkai Cai, Rongjie Bai, Qiang Cao, et al.
(2024)
Closed Access
Differentiation of histological calcification classifications in breast cancer using ultrashort echo time and chemical shift-encoded imaging MRI
Yazan Ayoub, Sai Man Cheung, Boddor Maglan, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access
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