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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.

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Showing 1-25 of 30 citing articles:

Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions
Mohamed Khalifa, Mona Albadawy
Computer Methods and Programs in Biomedicine Update (2024) Vol. 5, pp. 100148-100148
Open Access | Times Cited: 40
Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma
Maria Chiara Brunese, Maria Rita Fantozzi, Roberta Fusco, et al.
Diagnostics (2023) Vol. 13, Iss. 8, pp. 1488-1488
Open Access | Times Cited: 19
Development and validation of a 18F-FDG PET/CT radiomics nomogram for predicting progression free survival in locally advanced cervical cancer: a retrospective multicenter study
Huiling Liu, Yongbin Cui, Cheng Chang, et al.
BMC Cancer (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 6
Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges
Alessia Mondello, Michele Dal Bo, Giuseppe Toffoli, et al.
Frontiers in Pharmacology (2024) Vol. 14
Open Access | Times Cited: 5
Orally Administered Drugs and Their Complicated Relationship with Our Gastrointestinal Tract
Stavros Bashiardes, Christina Christodoulou
Microorganisms (2024) Vol. 12, Iss. 2, pp. 242-242
Open Access | Times Cited: 5
Radiomics approaches to predict PD-L1 and PFS in advanced non-small cell lung patients treated with immunotherapy: a multi-institutional study
Sevinj Yolchuyeva, Elena Giacomazzi, Marion Tonneau, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 11
Multi-Centered Pre-Treatment CT-Based Radiomics Features to Predict Locoregional Recurrence of Locally Advanced Esophageal Cancer After Definitive Chemoradiotherapy
Nuo Yu, Xiaolin Ge, Lijing Zuo, et al.
Cancers (2025) Vol. 17, Iss. 1, pp. 126-126
Open Access
Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation
Isha Goel, Yogendra Bhaskar, Nand Kumar, et al.
Frontiers in Digital Health (2025) Vol. 7
Open Access
Current Role of Artificial Intelligence in the Management of Esophageal Cancer
Evgenia Mela, Dimitrios Tsapralis, Dimitrios Papakonstantinou, et al.
Journal of Clinical Medicine (2025) Vol. 14, Iss. 6, pp. 1845-1845
Open Access
Multimodal deep learning for predicting PD-L1 biomarker and clinical immunotherapy outcomes of esophageal cancer
Hui Liu, Yuchen Bai, Zhidong Wang, et al.
Frontiers in Immunology (2025) Vol. 16
Open Access
A machine learning model utilizing CT radiomics features and peripheral blood inflammatory markers predicts the prognosis of patients with unresectable esophageal squamous cell carcinoma undergoing PD-1 inhibitor combined with concurrent chemoradiotherapy
Xudong Liu, Fei Gao, Shusheng Wu, et al.
Journal of Cancer (2025) Vol. 16, Iss. 6, pp. 2001-2014
Open Access
Incorporating frequency domain features into radiomics for improved prognosis of esophageal cancer
Shuqing Chen, Shumin Zhou, Liyang Wu, et al.
Medical & Biological Engineering & Computing (2025)
Closed Access
Performance of radiomics-based artificial intelligence systems in the diagnosis and prediction of treatment response and survival in esophageal cancer: a systematic review and meta-analysis of diagnostic accuracy
Nainika Menon, Nadia Guidozzi, Swathikan Chidambaram, et al.
Diseases of the Esophagus (2023) Vol. 36, Iss. 6
Open Access | Times Cited: 10
Using clinical and radiomic feature–based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation
J. Wang, Xiang Zhu, Jian Zeng, et al.
European Radiology (2023) Vol. 33, Iss. 12, pp. 8554-8563
Closed Access | Times Cited: 9
Design of risk prediction model for esophageal cancer based on machine learning approach
Raoof Nopour
Heliyon (2024) Vol. 10, Iss. 2, pp. e24797-e24797
Open Access | Times Cited: 3
CT-based radiomics combined with hematologic parameters for survival prediction in locally advanced esophageal cancer patients receiving definitive chemoradiotherapy
Jinfeng Cui, Dexian Zhang, Yongsheng Gao, et al.
Insights into Imaging (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 3
Laboratory blood parameters and machine learning for the prognosis of esophageal squamous cell carcinoma
Feng Lu, Yang Lin-lan, Zhenglian Luo, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 3
Preliminary analysis of explainable machine learning methods for multiple myeloma chemotherapy treatment recognition
Nesma Settouti, Meryem Saidi
Evolutionary Intelligence (2023) Vol. 17, Iss. 1, pp. 513-533
Closed Access | Times Cited: 5
Individualized treatment decision model for inoperable elderly esophageal squamous cell carcinoma based on multi-modal data fusion
Yong Huang, Xiaoyu Huang, Anling Wang, et al.
BMC Medical Informatics and Decision Making (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 4
Cone-beam computed-tomography-based delta-radiomic analysis for investigating prognostic power for esophageal squamous cell cancer patients undergoing concurrent chemoradiotherapy
Takahiro Nakamoto, Hideomi Yamashita, Haruka Jinnouchi, et al.
Physica Medica (2023) Vol. 117, pp. 103182-103182
Open Access | Times Cited: 4
The role of spleen radiomics model for predicting prognosis in esophageal squamous cell carcinoma patients receiving definitive radiotherapy
Longxiang Guo, Ao Liu, Xiaotao Geng, et al.
Thoracic Cancer (2024) Vol. 15, Iss. 12, pp. 947-964
Open Access | Times Cited: 1
Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era
Wan-Yue Zhang, Yong-Jian Chang, Ruihua Shi
World Journal of Gastroenterology (2024) Vol. 30, Iss. 39, pp. 4267-4280
Closed Access | Times Cited: 1
CT radiomics to predict pathologic complete response after neoadjuvant immunotherapy plus chemoradiotherapy in locally advanced esophageal squamous cell carcinoma
Liqiang Shi, Chengqiang Li, Yaya Bai, et al.
European Radiology (2024)
Closed Access | Times Cited: 1
A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study
Camelia Alexandra Coadă, Miriam Santoro, Vladislav Zybin, et al.
Cancers (2023) Vol. 15, Iss. 18, pp. 4534-4534
Open Access | Times Cited: 3
Revolutionizing healthcare by use of artificial intelligence in esophageal carcinoma – a narrative review
Anmol Mohan, Zoha Asghar, Rabia Abid, et al.
Annals of Medicine and Surgery (2023) Vol. 85, Iss. 10, pp. 4920-4927
Open Access | Times Cited: 2
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