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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:

Multimodal Prediction of Five-Year Breast Cancer Recurrence in Women Who Receive Neoadjuvant Chemotherapy
Simona Rabinovici‐Cohen, Xosé M. Fernández, Beatriz Grandal Rejo, et al.
Cancers (2022) Vol. 14, Iss. 16, pp. 3848-3848
Open Access | Times Cited: 21

Showing 21 citing articles:

A comprehensive investigation of multimodal deep learning fusion strategies for breast cancer classification
Fatima-Zahrae Nakach, Ali Idri, Evgin Göçeri
Artificial Intelligence Review (2024) Vol. 57, Iss. 12
Open Access | Times Cited: 10
From Pixels to Diagnosis: Algorithmic Analysis of Clinical Oral Photos for Early Detection of Oral Squamous Cell Carcinoma
Simona Rabinovici‐Cohen, Naomi Fridman, Michal Weinbaum, et al.
Cancers (2024) Vol. 16, Iss. 5, pp. 1019-1019
Open Access | Times Cited: 5
Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data
José Alcides Sarmento da Silveira, Antônio Rafael da Silva, Mariana Zuliani Theodoro de Lima
Discover Oncology (2025) Vol. 16, Iss. 1
Open Access
Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning
Jiaye Sun, Shijun Shao, Hua Wan, et al.
BMC Medical Informatics and Decision Making (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 4
Role of radiomics in predicting early disease recurrence in locally advanced breast cancer patients: integration of radiomic features and RECIST criteria
Charlotte Marguerite Lucille Trombadori, Edda Boccia, Huong Elena Tran, et al.
La radiologia medica (2025)
Closed Access
Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database
Ruiyang Wu, Jing Luo, Hangyu Wan, et al.
PLoS ONE (2023) Vol. 18, Iss. 1, pp. e0280340-e0280340
Open Access | Times Cited: 8
FuseMedML: a framework for accelerated discovery in machine learning based biomedicine
Alex Golts, Moshe Raboh, Yoel Shoshan, et al.
The Journal of Open Source Software (2023) Vol. 8, Iss. 81, pp. 4943-4943
Open Access | Times Cited: 5
A Multiparametric MRI-based Radiomics Model for Stratifying Postoperative Recurrence in Luminal B Breast Cancer
Kepei Xu, Meiqi Hua, Ting Mai, et al.
Deleted Journal (2024)
Open Access | Times Cited: 1
Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models
Ghanashyam Sahoo, Ajit Kumar Nayak, Pradyumna Kumar Tripathy, et al.
Current Oncology (2024) Vol. 31, Iss. 11, pp. 6577-6597
Open Access | Times Cited: 1
The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI
Tomoyuki Fujioka, Shohei Fujita, Daiju Ueda, et al.
Magnetic Resonance in Medical Sciences (2024)
Open Access | Times Cited: 1
Multimodal deep learning approaches for precision oncology: a comprehensive review
Huan Yang, Minglei Yang, Jiani Chen, et al.
Briefings in Bioinformatics (2024) Vol. 26, Iss. 1
Open Access | Times Cited: 1
Breast cancer relapse disease prediction improvements with ensemble learning approaches
Ghanashyam Sahoo, Ajit Kumar Nayak, Pradyumna Kumar Tripathy, et al.
Indonesian Journal of Electrical Engineering and Computer Science (2024) Vol. 35, Iss. 1, pp. 335-335
Open Access
Research progress of artificial intelligence in evaluating the efficacy of neoadjuvant chemotherapy for breast cancer
Wei Wei, Menghang Ma, Zhenyu Liu
Deleted Journal (2024) Vol. 1, Iss. 2, pp. 100024-100024
Open Access
Accessible Machine Learning and Deep Learning Models Predict Response and Survival in Early Stage Hormone Receptor-Positive/HER2-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
Giovanna Garufi, Luca Mastrantoni, Giulia Giordano, et al.
Research Square (Research Square) (2024)
Open Access
Multimodal BEHRT: Transformers for Multimodal Electronic Health Records to predict breast cancer prognosis
Ndèye Maguette Mbaye, Michael M. Danziger, Aullène Toussaint, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Closed Access
Multimodal predictions of end stage chronic kidney disease from asymptomatic individuals for discovery of genomic biomarkers
Simona Rabinovici‐Cohen, Daniel E. Platt, Toshiya Iwamori, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Editorial: Explainable multimodal AI in cancer patient care: how can we reduce the gap between technology and practice?
Michal Rosen‐Zvi, Lisa A. Mullen, Robertus Jan Lukas, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 1
Optimized Deep Convolutional Neural Network for the Prediction of Breast Cancer Recurrence
A. I., V. Mary Amala Bai
Journal of Applied Engineering and Technological Science (JAETS) (2023) Vol. 5, Iss. 1, pp. 495-514
Open Access | Times Cited: 1
Classification of breast cancer recurrence based on imputed data: a simulation study
Rahibu A. Abassi, Amina S. Msengwa
BioData Mining (2022) Vol. 15, Iss. 1
Open Access | Times Cited: 2
Diagnostics and Therapeutics in Early Stage Breast Cancer Receiving Neoadjuvant Systemic Therapy
Paolo Belli, Simone Palma, Melania Costantini
Cancers (2023) Vol. 15, Iss. 19, pp. 4874-4874
Open Access
Enhanced breast Cancer Relapse Prediction Based on Ensemble Learning Approaches
Ghanashyam Sahoo
International Journal on Recent and Innovation Trends in Computing and Communication (2023) Vol. 11, Iss. 10, pp. 999-1007
Open Access
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