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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:

Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram
Jingyu Zhong, Chengxiu Zhang, Yangfan Hu, et al.
European Radiology (2022) Vol. 32, Iss. 9, pp. 6196-6206
Closed Access | Times Cited: 33

Showing 1-25 of 33 citing articles:

Reproducibility of radiomics quality score: an intra- and inter-rater reliability study
Tugba Akinci D’Antonoli, Armando Ugo Cavallo, Federica Vernuccio, et al.
European Radiology (2023) Vol. 34, Iss. 4, pp. 2791-2804
Open Access | Times Cited: 30
Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study
Zijing Lin, Ting Wang, Qiong Li, et al.
European Radiology (2023) Vol. 33, Iss. 8, pp. 5814-5824
Closed Access | Times Cited: 23
Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application
Yichen Meng, Yue Yang, Miao Hu, et al.
Seminars in Cancer Biology (2023) Vol. 95, pp. 75-87
Closed Access | Times Cited: 23
Self-supervised tumor segmentation and prognosis prediction in osteosarcoma using multiparametric MRI and clinical characteristics
Zhixun Zhou, Peng Xie, Zhehao Dai, et al.
Computer Methods and Programs in Biomedicine (2023) Vol. 244, pp. 107974-107974
Closed Access | Times Cited: 17
CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies
Salvatore Gitto, Renato Cuocolo, Merel Huisman, et al.
Insights into Imaging (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 8
Deep learning model based on multi-lesion and time series CT images for predicting the benefits from anti-HER2 targeted therapy in stage IV gastric cancer
Meng He, Zifan Chen, Song Liu, et al.
Insights into Imaging (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 6
Self-reporting with checklists in artificial intelligence research on medical imaging: a systematic review based on citations of CLAIM
Burak Koçak, Ali Keleş, Tugba Akinci D’Antonoli
European Radiology (2023) Vol. 34, Iss. 4, pp. 2805-2815
Closed Access | Times Cited: 14
Deep learning-based automated lesion segmentation on pediatric focal cortical dysplasia II preoperative MRI: a reliable approach
Siqi Zhang, Yijiang Zhuang, Yi Luo, et al.
Insights into Imaging (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 5
End-to-End Deep Learning Prediction of Neoadjuvant Chemotherapy Response in Osteosarcoma Patients Using Routine MRI
Ping Yin, Xinyu Zhang, Ying Liu, et al.
Deleted Journal (2025)
Closed Access
Multimodal Imaging of Osteosarcoma: From First Diagnosis to Radiomics
Maurizio Cè, Michaela Cellina, Thirapapha Ueanukul, et al.
Cancers (2025) Vol. 17, Iss. 4, pp. 599-599
Open Access
Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications
Maxime Lacroix, Théodore Aouad, Jean Feydy, et al.
Diagnostic and Interventional Imaging (2022) Vol. 104, Iss. 1, pp. 18-23
Open Access | Times Cited: 18
3D vs. 2D MRI radiomics in skeletal Ewing sarcoma: Feature reproducibility and preliminary machine learning analysis on neoadjuvant chemotherapy response prediction
Salvatore Gitto, Valentina Corino, Alessio Annovazzi, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 18
An artificial neural network-based radiomics model for predicting the radiotherapy response of advanced esophageal squamous cell carcinoma patients: a multicenter study
Yuchen Xie, Qiang Liu, Chao Ji, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 8
An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics
Jingyu Zhong, Yangfan Hu, Guangcheng Zhang, et al.
Insights into Imaging (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 14
Imaging Assessment of the Efficacy of Chemotherapy in Primary Malignant Bone Tumors: Recent Advances in Qualitative and Quantitative Magnetic Resonance Imaging and Radiomics
X. Liu, Zhiqing Duan, Shaobo Fang, et al.
Journal of Magnetic Resonance Imaging (2023) Vol. 59, Iss. 1, pp. 7-31
Closed Access | Times Cited: 7
Magnetic Resonance Imaging Radiomics Predicts Histological Response to Neoadjuvant Chemotherapy in Localized High-grade Osteosarcoma of the Extremities
Yun Zhang, Lanlan Zhi, Jiao Li, et al.
Academic Radiology (2024) Vol. 31, Iss. 12, pp. 5100-5107
Closed Access | Times Cited: 2
Imaging of Osteosarcoma: Presenting Findings, Metastatic Patterns, and Features Related to Prognosis
Amandine Crombé, Mario Simonetti, Alessandra Longhi, et al.
Journal of Clinical Medicine (2024) Vol. 13, Iss. 19, pp. 5710-5710
Open Access | Times Cited: 2
Multiparametric MRI-Based Radiomics Signature with Machine Learning for Preoperative Prediction of Prognosis Stratification in Pediatric Medulloblastoma
Yi Luo, Yijiang Zhuang, Siqi Zhang, et al.
Academic Radiology (2023) Vol. 31, Iss. 4, pp. 1629-1642
Open Access | Times Cited: 6
Fusion Radiomics-Based Prediction of Response to Neoadjuvant Chemotherapy for Osteosarcoma
Fei Zheng, Ping Yin, Kewei Liang, et al.
Academic Radiology (2023) Vol. 31, Iss. 6, pp. 2444-2455
Closed Access | Times Cited: 6
DECIDE: A decoupled semantic and boundary learning network for precise osteosarcoma segmentation by integrating multi-modality MRI
Yinhao Wu, Jianqi Li, Xinxin Wang, et al.
Computers in Biology and Medicine (2024) Vol. 174, pp. 108308-108308
Closed Access | Times Cited: 1
Evaluation of the neoadjuvant chemotherapy response in osteosarcoma using the MRI DWI-based machine learning radiomics nomogram
Lu Zhang, Qiuru Gao, Yincong Dou, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 1
Research status and progress of radiomics in bone and soft tissue tumors: A review
Xiaohan Zhang, Jie Peng, Guanghai Ji, et al.
Medicine (2023) Vol. 102, Iss. 47, pp. e36196-e36196
Open Access | Times Cited: 3
Automated Osteosarcoma Detection and Classification Using Advanced Deep Learning with Remora Optimization Algorithm
Mohammed Obaid, Hanaa Ali Abed, Salima Baji Abdullah, et al.
2022 5th International Conference on Engineering Technology and its Applications (IICETA) (2023), pp. 122-128
Closed Access | Times Cited: 2
An MRI-Based Deep Learning Radiomics Nomogram Analysis for Prediction of Postoperative Lymph Node Metastasis in Patients with Locally Advanced Cervical Cancer Receiving Neoadjuvant Therapy
Rongrong Wu, Xingyun Xie, Yimin Zhou
Research Square (Research Square) (2024)
Open Access
CT-derived Radiomics Predicts the Efficacy of Tyrosine Kinase Inhibitors in Osteosarcoma Patients with Pulmonary Metastasis
Shanshui Zhou, Qi Liu, Yucheng Fu, et al.
Translational Oncology (2024) Vol. 45, pp. 101993-101993
Open Access
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