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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:

Assessment of Primary Colorectal Cancer CT Radiomics to Predict Metachronous Liver Metastasis
Yue Li, Jing Gong, Xigang Shen, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 21

Showing 21 citing articles:

5‐hydroxymethylcytosine features of portal venous blood predict metachronous liver metastases of colorectal cancer and reveal phosphodiesterase 4 as a therapeutic target
Nuo Xu, Zhaoya Gao, Deyan Wu, et al.
Clinical and Translational Medicine (2025) Vol. 15, Iss. 2
Open Access | Times Cited: 2
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
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
Exploring tumor heterogeneity in colorectal liver metastases by imaging: Unsupervised machine learning of preoperative CT radiomics features for prognostic stratification
Qiang Wang, Henrik Nilsson, Keyang Xu, et al.
European Journal of Radiology (2024) Vol. 175, pp. 111459-111459
Closed Access | Times Cited: 4
The role of artificial intelligence and image processing in the diagnosis, treatment, and prognosis of liver cancer: a narrative-review
Platon Dimopoulos, Admir Mulita, Andreas Antzoulas, et al.
Gastroenterology Review (2024) Vol. 19, Iss. 3, pp. 221-230
Open Access | Times Cited: 4
Complete blood counts as potential risk factors of early dissemination to liver and lungs in resected colorectal cancer: a retrospective cohort study
Marta Popęda, Jolanta Żok, Bartłomiej Tomasik, et al.
International Journal of Colorectal Disease (2025) Vol. 40, Iss. 1
Open Access
Prediction of metachronous liver metastasis in mid-low rectal cancer using quantitative perirectal fat content from high-resolution MRI
Jiaming Qin, Wen Dong, Fengshu Zhao, et al.
Magnetic Resonance Imaging (2025) Vol. 118, pp. 110338-110338
Closed Access
Radiomic imaging: Basic principles and applications
Francesco Pisu, Luca Saba
Elsevier eBooks (2025), pp. 225-248
Closed Access
A nomogram prediction model based on clinicopathological combined radiological features for metachronous liver metastasis of colorectal cancer
Zhihua Lü, Jinbing Sun, Mi Wang, et al.
Journal of Cancer (2024) Vol. 15, Iss. 4, pp. 916-925
Open Access | Times Cited: 3
Role of radiomics in the diagnosis and treatment of gastrointestinal cancer
Qi Mao, Mao-Ting Zhou, Zhang-Ping Zhao, et al.
World Journal of Gastroenterology (2022) Vol. 28, Iss. 42, pp. 6002-6016
Open Access | Times Cited: 14
Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives
Pasquale Avella, Micaela Cappuccio, Teresa Cappuccio, et al.
Life (2023) Vol. 13, Iss. 10, pp. 2027-2027
Open Access | Times Cited: 7
Radiomics based on preoperative rectal cancer MRI to predict the metachronous liver metastasis
Zhuofu Li, Liqing Kang, Fenghai Liu, et al.
Abdominal Radiology (2022)
Closed Access | Times Cited: 9
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
A nomogram was developed using clinicopathological features to predict postoperative liver metastasis in patients with colorectal cancer
Xinyu Dou, Jiaona Xi, Gaozan Zheng, et al.
Journal of Cancer Research and Clinical Oncology (2023) Vol. 149, Iss. 15, pp. 14045-14056
Open Access | Times Cited: 3
Mono+ algorithm assessment of the diagnostic value of dual-energy CT for high-risk factors for colorectal cancer: a preliminary study
Junfan Chen, Jing Yang, Weijuan Chen, et al.
Quantitative Imaging in Medicine and Surgery (2023) Vol. 14, Iss. 1, pp. 432-446
Open Access | Times Cited: 2
Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery
Meng Liang, Xiaohong Ma, Leyao Wang, et al.
Cancer Imaging (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 3
Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: a network-based feature selection approach
S. Dalmonte, Maria Adriana Cocozza, Dajana Cuicchi, et al.
Heliyon (2024) Vol. 11, Iss. 1, pp. e41404-e41404
Open Access
Prediction of Colorectal Cancer Liver Metastasis through an MRI Radiomic Model
Yaokun Wu, Ning Liu, Yun-Yun Tao, et al.
(2023)
Open Access | Times Cited: 1
5-hydroxymethylcytosine features of portal venous blood predict metachronous liver metastases of colorectal cancer and reveal phosphodiesterase 4 as a therapeutic target
Jian Lin, Nuo Xu, Zhaoya Gao, et al.
Research Square (Research Square) (2023)
Open Access
Machine learning-based radiomic models for predicting metachronous liver metastases in colorectal cancer patients: a multimodal study
Jianping Wang, Ze-Ning Zhang, Ding-Bo Shu, et al.
Research Square (Research Square) (2023)
Open Access
Fish Erythrocytes Nuclear Abnormalities Classification using Machine Learning
Newton Loebens, Bruno do Amaral Crispim, Nathalya Alice de Lima, et al.
(2023), pp. 96-101
Open Access
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