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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:

Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images
Xiangxue Wang, Andrew Janowczyk, Yu Zhou, et al.
Scientific Reports (2017) Vol. 7, Iss. 1
Open Access | Times Cited: 113

Showing 1-25 of 113 citing articles:

Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Kaustav Bera, Kurt A. Schalper, David L. Rimm, et al.
Nature Reviews Clinical Oncology (2019) Vol. 16, Iss. 11, pp. 703-715
Open Access | Times Cited: 1156
Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
Ahmed Hosny, Chintan Parmar, Thibaud Coroller, et al.
PLoS Medicine (2018) Vol. 15, Iss. 11, pp. e1002711-e1002711
Open Access | Times Cited: 514
Digital pathology and artificial intelligence in translational medicine and clinical practice
Vipul Baxi, Robin Edwards, Michael Montalto, et al.
Modern Pathology (2021) Vol. 35, Iss. 1, pp. 23-32
Open Access | Times Cited: 379
Artificial intelligence as the next step towards precision pathology
Balázs Ács, Mattias Rantalainen, Johan Hartman
Journal of Internal Medicine (2020) Vol. 288, Iss. 1, pp. 62-81
Open Access | Times Cited: 338
Emerging role of deep learning‐based artificial intelligence in tumor pathology
Yahui Jiang, Meng Yang, Shuhao Wang, et al.
Cancer Communications (2020) Vol. 40, Iss. 4, pp. 154-166
Open Access | Times Cited: 313
Artificial Intelligence in Lung Cancer Pathology Image Analysis
Shidan Wang, Donghan M. Yang, Ruichen Rong, et al.
Cancers (2019) Vol. 11, Iss. 11, pp. 1673-1673
Open Access | Times Cited: 239
Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer
Germán Corredor, Xiangxue Wang, Yu Zhou, et al.
Clinical Cancer Research (2018) Vol. 25, Iss. 5, pp. 1526-1534
Open Access | Times Cited: 213
Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes
James A. Diao, Jason K. Wang, Wan Fung Chui, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 186
Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks
Mesut Toğaçar, Burhan Ergen, Zafer Cömert
Journal of Applied Biomedicine (2019) Vol. 40, Iss. 1, pp. 23-39
Closed Access | Times Cited: 164
Artificial intelligence for digital and computational pathology
Andrew H. Song, Guillaume Jaume, Drew F. K. Williamson, et al.
Nature Reviews Bioengineering (2023) Vol. 1, Iss. 12, pp. 930-949
Closed Access | Times Cited: 89
Computational Pathology: A Survey Review and The Way Forward
Mahdi S. Hosseini, Babak Ehteshami Bejnordi, Vincent Quoc‐Huy Trinh, et al.
Journal of Pathology Informatics (2024), pp. 100357-100357
Open Access | Times Cited: 34
Deep learning in digital pathology image analysis: a survey
Shujian Deng, Xin Zhang, Yan Wen, et al.
Frontiers of Medicine (2020) Vol. 14, Iss. 4, pp. 470-487
Closed Access | Times Cited: 120
ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network
Shidan Wang, Tao Wang, Lin Yang, et al.
EBioMedicine (2019) Vol. 50, pp. 103-110
Open Access | Times Cited: 96
Automated classification of benign and malignant cells from lung cytological images using deep convolutional neural network
Atsushi Teramoto, Ayumi Yamada, Yuka Kiriyama, et al.
Informatics in Medicine Unlocked (2019) Vol. 16, pp. 100205-100205
Open Access | Times Cited: 83
A narrative review of digital pathology and artificial intelligence: focusing on lung cancer
Taro Sakamoto, T Furukawa, Kris Lami, et al.
Translational Lung Cancer Research (2020) Vol. 9, Iss. 5, pp. 2255-2276
Open Access | Times Cited: 82
The state of the art for artificial intelligence in lung digital pathology
Vidya Sankar Viswanathan, Paula Toro, Germán Corredor, et al.
The Journal of Pathology (2022) Vol. 257, Iss. 4, pp. 413-429
Open Access | Times Cited: 65
Deep Learning Approaches in Histopathology
Alhassan Ali Ahmed, Mohamed Abouzid, Elżbieta Kaczmarek
Cancers (2022) Vol. 14, Iss. 21, pp. 5264-5264
Open Access | Times Cited: 39
Digital Pathology for Better Clinical Practice
Assia Hijazi, Carlo Bifulco, Paméla Baldin, et al.
Cancers (2024) Vol. 16, Iss. 9, pp. 1686-1686
Open Access | Times Cited: 15
Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC
Oraianthi Fiste, Ioannis Gkiozos, Andriani Charpidou, et al.
Cancers (2024) Vol. 16, Iss. 4, pp. 831-831
Open Access | Times Cited: 14
Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review
Filippo Lococo, Galal Ghaly, Marco Chiappetta, et al.
Cancers (2024) Vol. 16, Iss. 10, pp. 1832-1832
Open Access | Times Cited: 12
Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics
William T. Tran, Katarzyna J. Jerzak, Fang-I Lu, et al.
Journal of medical imaging and radiation sciences (2019) Vol. 50, Iss. 4, pp. S32-S41
Open Access | Times Cited: 69
Topological Tumor Graphs: A Graph-Based Spatial Model to Infer Stromal Recruitment for Immunosuppression in Melanoma Histology
Henrik Failmezger, Sathya Muralidhar, Antonio Rullan, et al.
Cancer Research (2019) Vol. 80, Iss. 5, pp. 1199-1209
Open Access | Times Cited: 69
A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study
Cheng Lu, Kaustav Bera, Xiangxue Wang, et al.
The Lancet Digital Health (2020) Vol. 2, Iss. 11, pp. e594-e606
Open Access | Times Cited: 64
A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis
Neofytos Dimitriou, Ognjen Arandjelović, David J. Harrison, et al.
npj Digital Medicine (2018) Vol. 1, Iss. 1
Open Access | Times Cited: 63
Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm
Simon C. Williams, Hugo Layard Horsfall, Jonathan P. Funnell, et al.
Cancers (2021) Vol. 13, Iss. 19, pp. 5010-5010
Open Access | Times Cited: 55
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