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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-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer
Alicja Rączkowska, Iwona Paśnik, Michał Kukiełka, et al.
BMC Cancer (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 22

Showing 22 citing articles:

Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review
Athena S. Davri, Effrosyni Birbas, Theofilos Kanavos, et al.
Cancers (2023) Vol. 15, Iss. 15, pp. 3981-3981
Open Access | Times Cited: 28
Application of digital pathology and machine learning in the liver, kidney and lung diseases
Benjamin M. Wu, Gilbert Moeckel
Journal of Pathology Informatics (2023) Vol. 14, pp. 100184-100184
Open Access | Times Cited: 19
Unleashing the potential of AI for pathology: challenges and recommendations
Amina Asif, Kashif Rajpoot, Simon Graham, et al.
The Journal of Pathology (2023) Vol. 260, Iss. 5, pp. 564-577
Open Access | Times Cited: 16
Prediction of pathological grade in prostate cancer: an ensemble deep learning-based whole slide image classification model
Gamze Korkmaz Erdem, Sevinç İlhan Omurca, Esra Betul Cakir, et al.
The European Physical Journal Special Topics (2025)
Open Access
Integration of Nuclear, Clinical, and Genetic Features for Lung Cancer Subtype Classification and Survival Prediction Based on Machine- and Deep-Learning Models
Bin Xie, M. Mo, Haidong Cui, et al.
Diagnostics (2025) Vol. 15, Iss. 7, pp. 872-872
Open Access
PROGNOSTIC SIGNIFICANCE OF FOXP3 IN RADICALLY TREATED NON-SMALL CELL LUNG CANCER PATIENTS
О. І. Vynnychenko, Yuliia Moskalenko, Artem Piddubnyi, et al.
Art of Medicine (2025), pp. 25-32
Closed Access
Prognostic value of the preoperative ratio of fibrinogen to prealbumin in patients undergoing radical resection for lung cancer
Zhining Huang, Gaoxiang Wang, Liangdong Xu, et al.
ANZ Journal of Surgery (2025)
Open Access
The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI
Ren Yuan Lee, Chan Way Ng, Menaka Priyadharsani Rajapakse, et al.
Frontiers in Oncology (2023) Vol. 13
Open Access | Times Cited: 9
Next-generation lung cancer pathology: Development and validation of diagnostic and prognostic algorithms
Carina Kludt, Yuan Wang, Waleed Ahmad, et al.
Cell Reports Medicine (2024) Vol. 5, Iss. 9, pp. 101697-101697
Open Access | Times Cited: 3
Survey of Recent Deep Neural Networks with Strong Annotated Supervision in Histopathology
Dominika Petríková, Ivan Cimrák
Computation (2023) Vol. 11, Iss. 4, pp. 81-81
Open Access | Times Cited: 5
Artificial Intelligence and Lung Pathology
Emanuel Caranfil, Kris Lami, Wataru Uegami, et al.
Advances in Anatomic Pathology (2024) Vol. 31, Iss. 5, pp. 344-351
Closed Access | Times Cited: 1
An AI-based cell segmentation pipeline for studying immune markers from brightfield microscopy images
Miriam Köhler, Yvonne Reiss, Jadranka Macas, et al.
Research Square (Research Square) (2024)
Open Access
Exploring the Use of Deep Learning Models in Predicting Genetic Mutations Associated with Non-Small Cell Lung Cancer: A Systematic Review (Preprint)
Abdel Rahman Alsaify, Laila Shafei, Sara Al Dali, et al.
(2024)
Closed Access
Leveraging immuno-fluorescence data to reduce pathologist annotation requirements in lung tumor segmentation using deep learning
Hatef Mehrabian, Jens Brodbeck, Peipei Lyu, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
Attention-Fusion Model for Multi-omics (AMMO) Data Integration in Lung Adenocarcinoma
Wentao Li, Amgad Muneer, Muhammad Waqas, et al.
Lecture notes in computer science (2024), pp. 52-60
Closed Access
Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer
Qiuyan Sun, Tan Li, Wei Zheng, et al.
Annals of Medicine (2024) Vol. 56, Iss. 1
Open Access
Research on Non-Small Cell Lung Cancer Segmentation Algorithm Based on LBSK-UNet
Jinyu Song, Yi Chen
(2024), pp. 99-103
Closed Access
Image-based Lung Analysis in the Context of Digital Pathology: a Brief Review
Somayeh Shahrabadi, João Carias, Emanuel Peres, et al.
Procedia Computer Science (2024) Vol. 239, pp. 2168-2175
Open Access
Development of Ex Vivo Analysis for Examining Cell Composition, Immunological Landscape, Tumor and Immune Related Markers in Non-Small-Cell Lung Cancer
Elena Ufimtseva, Margarita S. Gileva, Ruslan V. Kostenko, et al.
Cancers (2024) Vol. 16, Iss. 16, pp. 2886-2886
Open Access
Prognostic analysis of the plasma fibrinogen combined with neutrophil‐to‐lymphocyte ratio in patients with non–small cell lung cancer after radical resection
Gaoxiang Wang, Zhining Huang, Yingquan Ye, et al.
Thoracic Cancer (2023) Vol. 14, Iss. 15, pp. 1383-1391
Open Access | Times Cited: 1
Cycle-consistent Generative Adversarial Network for computational hematoxylin-and-eosin staining of fluorescence confocal microscopic images of basal cell carcinoma tissue
Mahmoud Bagheri, Alireza Ghanadan, Maryam Daneshpazhooh, et al.
Research Square (Research Square) (2023)
Open Access
Prognostic Value of the Preoperative Ratio of Fibrinogen to Prealbumin in Patients Undergoing Radical Resection for Lung Cancer
Zhining Huang, Gaoxiang Wang, Liangdong Xu, et al.
Research Square (Research Square) (2023)
Open Access
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