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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 87 citing articles:

Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies
Ronrick Da‐ano, Ingrid Masson, François Lucia, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 156
Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization
Panagiotis Papadimitroulas, Lennart Brocki, Neo Christopher Chung, et al.
Physica Medica (2021) Vol. 83, pp. 108-121
Open Access | Times Cited: 152
Introduction to radiomics for a clinical audience
Cathal McCague, Syafiq Ramlee, Marika Reinius, et al.
Clinical Radiology (2023) Vol. 78, Iss. 2, pp. 83-98
Open Access | Times Cited: 63
Harmonization strategies for multicenter radiomics investigations
Ronrick Da‐ano, Dimitris Visvikis, Mathieu Hatt
Physics in Medicine and Biology (2020) Vol. 65, Iss. 24, pp. 24TR02-24TR02
Closed Access | Times Cited: 134
Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma
Yihuai Hu, Chenyi Xie, Hong Yang, et al.
JAMA Network Open (2020) Vol. 3, Iss. 9, pp. e2015927-e2015927
Open Access | Times Cited: 121
Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer
Fang Jin, Bin Zhang, Shuo Wang, et al.
Theranostics (2020) Vol. 10, Iss. 5, pp. 2284-2292
Open Access | Times Cited: 75
Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer
Qingxia Wu, Shuo Wang, Shuixing Zhang, et al.
JAMA Network Open (2020) Vol. 3, Iss. 7, pp. e2011625-e2011625
Open Access | Times Cited: 71
Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review
Sorayya Rezayi, Sharareh Rostam Niakan Kalhori, Soheila Saeedi
BioMed Research International (2022) Vol. 2022, pp. 1-34
Open Access | Times Cited: 50
High-dimensional role of AI and machine learning in cancer research
Enrico Capobianco
British Journal of Cancer (2022) Vol. 126, Iss. 4, pp. 523-532
Open Access | Times Cited: 40
Systematic review and meta-analysis of prediction models used in cervical cancer
Ashish Kumar Jha, Sneha Mithun, Umesh B. Sherkhane, et al.
Artificial Intelligence in Medicine (2023) Vol. 139, pp. 102549-102549
Open Access | Times Cited: 25
Noninvasive prediction of perineural invasion in intrahepatic cholangiocarcinoma by clinicoradiological features and computed tomography radiomics based on interpretable machine learning: a multicenter cohort study
Ziwei Liu, Chun Luo, Xinjie Chen, et al.
International Journal of Surgery (2023)
Open Access | Times Cited: 25
Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer
Lina Hou, Wei Zhou, Jialiang Ren, et al.
Frontiers in Oncology (2020) Vol. 10
Open Access | Times Cited: 54
MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study
Haimei Chen, Xiao Zhang, Xiaohong Wang, et al.
European Radiology (2021) Vol. 31, Iss. 10, pp. 7913-7924
Closed Access | Times Cited: 48
Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images
Alessandro Stefano, Albert Comelli
Journal of Imaging (2021) Vol. 7, Iss. 8, pp. 131-131
Open Access | Times Cited: 44
Artificial Intelligence-based Radiomics in the Era of Immuno-oncology
Cyra Y. Kang, Samantha Duarte, Hye Sung Kim, et al.
The Oncologist (2022) Vol. 27, Iss. 6, pp. e471-e483
Open Access | Times Cited: 28
Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology
Yuting Jiang, Chengdi Wang, Shengtao Zhou
Seminars in Cancer Biology (2023) Vol. 96, pp. 82-99
Open Access | Times Cited: 17
Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study
Qingxia Wu, Kuan Yao, Zhenyu Liu, et al.
EBioMedicine (2019) Vol. 50, pp. 355-365
Open Access | Times Cited: 50
A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method
Alessandro Stefano, Albert Comelli, Valentina Bravatà, et al.
BMC Bioinformatics (2020) Vol. 21, Iss. S8
Open Access | Times Cited: 44
A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets
Ronrick Da‐ano, François Lucia, Ingrid Masson, et al.
PLoS ONE (2021) Vol. 16, Iss. 7, pp. e0253653-e0253653
Open Access | Times Cited: 36
Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications
Damiano Caruso, Michela Polici, Marta Zerunian, et al.
Cancers (2021) Vol. 13, Iss. 11, pp. 2681-2681
Open Access | Times Cited: 34
A meta-analysis of MRI-based radiomic features for predicting lymph node metastasis in patients with cervical cancer
Longchao Li, Jing Zhang, Xia Zhe, et al.
European Journal of Radiology (2022) Vol. 151, pp. 110243-110243
Open Access | Times Cited: 27
Radiomics systematic review in cervical cancer: gynecological oncologists’ perspective
Nicolò Bizzarri, Luca Russo, Miriam Dolciami, et al.
International Journal of Gynecological Cancer (2023) Vol. 33, Iss. 10, pp. 1522-1541
Closed Access | Times Cited: 14
Radiomics in cervical and endometrial cancer
Lucia Manganaro, Gabriele Maria Nicolino, Miriam Dolciami, et al.
British Journal of Radiology (2021) Vol. 94, Iss. 1125
Open Access | Times Cited: 30
Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients With Cervical Cancer
Handong Li, Miaochen Zhu, Lian Jian, et al.
Frontiers in Oncology (2021) Vol. 11
Open Access | Times Cited: 27
The pathological risk score: A new deep learning‐based signature for predicting survival in cervical cancer
Chi Chen, Yuye Cao, Weili Li, et al.
Cancer Medicine (2022) Vol. 12, Iss. 2, pp. 1051-1063
Open Access | Times Cited: 22
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