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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:

Delta-Radiomics Predicts Response to First-Line Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients with Liver Metastases
Valentina Giannini, Laura Pusceddu, Arianna Defeudis, et al.
Cancers (2022) Vol. 14, Iss. 1, pp. 241-241
Open Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

Radiomics in colorectal cancer patients
Riccardo Inchingolo, Cesare Maino, Roberto Cannella, et al.
World Journal of Gastroenterology (2023) Vol. 29, Iss. 19, pp. 2888-2904
Open Access | Times Cited: 26
Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review
Jiaqing Yang, Jing Huang, Deqian Han, et al.
Clinical Medicine Insights Oncology (2024) Vol. 18
Open Access | Times Cited: 10
Clinical Validation of a Machine Learning-Based Biomarker Signature to Predict Response to Cytotoxic Chemotherapy Alone or Combined with Targeted Therapy in Metastatic Colorectal Cancer Patients: A Study Protocol and Review
Duilio Pagano, Vincenza Barresi, Alessandro Tropea, et al.
Life (2025) Vol. 15, Iss. 2, pp. 320-320
Open Access | Times Cited: 1
Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis
Valentina Russo, Eleonora Lallo, Armelle Munnia, et al.
Cancers (2022) Vol. 14, Iss. 16, pp. 4012-4012
Open Access | Times Cited: 26
Digital Medical X-ray Imaging, CAD in Lung Cancer and Radiomics in Colorectal Cancer: Past, Present and Future
Jacobo Porto-Álvarez, Gary T. Barnes, Alex Villanueva, et al.
Applied Sciences (2023) Vol. 13, Iss. 4, pp. 2218-2218
Open Access | Times Cited: 14
Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment
Vincenza Granata, Roberta Fusco, Federica De Muzio, et al.
La radiologia medica (2023) Vol. 128, Iss. 11, pp. 1310-1332
Closed Access | Times Cited: 13
Prognostication of colorectal cancer liver metastasis by CE-based radiomics and machine learning
Xijun Luo, Hui Deng, Fei Xie, et al.
Translational Oncology (2024) Vol. 47, pp. 101997-101997
Open Access | Times Cited: 4
Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases
Y. Miyamoto, Takeshi Nakaura, Mayuko Ohuchi, et al.
Journal of the Anus Rectum and Colon (2025) Vol. 9, Iss. 1, pp. 117-126
Open Access
Delta Radiomics and Tumor Size: A New Predictive Radiomics Model for Chemotherapy Response in Liver Metastases from Breast and Colorectal Cancer
Nicolò Gennaro, Moataz Soliman, Amir A. Borhani, et al.
Tomography (2025) Vol. 11, Iss. 3, pp. 20-20
Open Access
A Case Study on Complete Pathological Response in Advanced Rectal Cancer Patient with Oxaliplatin-based Chemotherapy without Cumulative Neurotoxicity
Rehab A. M Jawad, Bahir Abdul Razzaq Mshimesh, Qasim S. Al‐Mayah, et al.
Journal of Gastrointestinal Cancer (2025) Vol. 56, Iss. 1
Closed Access
Improving prediction of treatment response and prognosis in colorectal cancer with AI-based medical image analysis
Xiangyu Liu, Song Zhang, Lizhi Shao, et al.
The Innovation Medicine (2024) Vol. 2, Iss. 2, pp. 100069-100069
Open Access | Times Cited: 3
Delta radiomics analysis for prediction of intermediary- and high-risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapy
Rongrong Wu, Yimin Zhou, Xingyun Xie, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 7
Radiomics Based on Dynamic Contrast-Enhanced MRI to Early Predict Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Therapy
Qiao Zeng, Mengmeng Ke, Linhua Zhong, et al.
Academic Radiology (2022) Vol. 30, Iss. 8, pp. 1638-1647
Closed Access | Times Cited: 11
Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics
Vincenza Granata, Roberta Fusco, Sergio Venanzio Setola, et al.
Infectious Agents and Cancer (2023) Vol. 18, Iss. 1
Open Access | Times Cited: 5
Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors
Liuji Sheng, Chongtu Yang, Yidi Chen, et al.
Biomedicines (2023) Vol. 12, Iss. 1, pp. 58-58
Open Access | Times Cited: 5
MRI-Based Radiomics and Delta-Radiomics Models of the Patella Predict the Radiographic Progression of Osteoarthritis: Data From the FNIH OA Biomarkers Consortium
Hai Jiang, Peng Yi, Siyu Qin, et al.
Academic Radiology (2023) Vol. 31, Iss. 4, pp. 1508-1517
Closed Access | Times Cited: 4
Radiomics in precision medicine for colorectal cancer: a bibliometric analysis (2013–2023)
Hao Li, Yu-Pei Zhuang, Weichen Yuan, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 1
Artificial Intelligence in Oncology: A Topical Collection in 2022
Andreas Stadlbauer, Anke Meyer‐Baese
Cancers (2023) Vol. 15, Iss. 4, pp. 1065-1065
Open Access | Times Cited: 2
Imaging standardisation in metastatic colorectal cancer: A joint EORTC-ESOI-ESGAR expert consensus recommendation
Marcus Unterrainer, Christophe M. Deroose, Ken Herrmann, et al.
European Journal of Cancer (2022) Vol. 176, pp. 193-206
Open Access | Times Cited: 4
A novel ALG10/TGF-β positive regulatory loop contributes to the stemness of colorectal cancer
Xiaotian Xu, Huideng Wang, Xinhui Li, et al.
Aging (2022) Vol. 14, Iss. 11, pp. 4858-4873
Open Access | Times Cited: 3
Radiomics in Precision Medicine for Colorectal Cancer: A Bibliometric Analysis (2013-2023)
Hao Li, Yu-Pei Zhuang, Weichen Yuan, et al.
(2024)
Closed Access
Possibilities and prospects of artificial intelligence in the treatment of colorectal cancer (review)
A. Yu. Kravchenko, Е. В. Семина, В. В. Какоткин, et al.
Koloproktologia (2024) Vol. 23, Iss. 2, pp. 184-193
Open Access
A CT-based radiomics tumor quality and quantity model to predict early recurrence after radical surgery for colorectal liver metastases
Sunya Fu, Dawei Chen, Yuqin Zhang, et al.
Clinical & Translational Oncology (2024) Vol. 27, Iss. 3, pp. 1198-1210
Closed Access
Artificial intelligence and colorectal cancer drug resistance
Sikhumbuzo Z. Mbatha, Rupert Ecker, Zodwa Dlamini
Elsevier eBooks (2024), pp. 323-347
Closed Access
CT-based delta-radiomics for predicting pathological response to neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma: a multicenter study
Yuting Zheng, Peiyuan Mei, Mingliang Wang, et al.
BMC Medical Imaging (2024) Vol. 24, Iss. 1
Open Access
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