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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:

MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer
Andrea Delli Pizzi, Antonio Maria Chiarelli, Piero Chiacchiaretta, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 79

Showing 1-25 of 79 citing articles:

Artificial intelligence and machine learning in cancer imaging
Dow‐Mu Koh, Nikolaos Papanikolaou, Ulrich Bick, et al.
Communications Medicine (2022) Vol. 2, Iss. 1
Open Access | Times Cited: 176
Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect
Zugang Yin, Chenhui Yao, Limin Zhang, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 42
Direct capacity regeneration for spent Li-ion batteries
Nobuhiro Ogihara, Katsuhiko Nagaya, Hiroyuki Yamaguchi, et al.
Joule (2024) Vol. 8, Iss. 5, pp. 1364-1379
Open Access | Times Cited: 19
An automated deep learning pipeline for EMVI classification and response prediction of rectal cancer using baseline MRI: a multi-centre study
Lishan Cai, Doenja M. J. Lambregts, Geerard L. Beets, et al.
npj Precision Oncology (2024) Vol. 8, Iss. 1
Open Access | Times Cited: 9
Contemporary Management of Locally Advanced and Recurrent Rectal Cancer: Views from the PelvEx Collaborative
PelvEx Collaborative PelvEx Collaborative
Cancers (2022) Vol. 14, Iss. 5, pp. 1161-1161
Open Access | Times Cited: 37
MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer
Maxiaowei Song, Shuai Li, Hongzhi Wang, et al.
British Journal of Cancer (2022) Vol. 127, Iss. 2, pp. 249-257
Open Access | Times Cited: 31
MRI-based multiregional radiomics for predicting lymph nodes status and prognosis in patients with resectable rectal cancer
Hang Li, Xiaoli Chen, Huan Liu, et al.
Frontiers in Oncology (2023) Vol. 12
Open Access | Times Cited: 20
MRI-based multiregional radiomics for preoperative prediction of tumor deposit and prognosis in resectable rectal cancer: a bicenter study
Hang Li, Xiaoli Chen, Huan Liu, et al.
European Radiology (2023) Vol. 33, Iss. 11, pp. 7561-7572
Closed Access | Times Cited: 20
Radiomics Approaches for the Prediction of Pathological Complete Response after Neoadjuvant Treatment in Locally Advanced Rectal Cancer: Ready for Prime Time?
Vincent Bourbonne, Ulrike Schick, Olivier Pradier, et al.
Cancers (2023) Vol. 15, Iss. 2, pp. 432-432
Open Access | Times Cited: 17
MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy
Likun Wang, Xueliang Wu, Ruoxi Tian, et al.
Frontiers in Oncology (2023) Vol. 13
Open Access | Times Cited: 16
Scalable Swin Transformer network for brain tumor segmentation from incomplete MRI modalities
Dongsong Zhang, Changjian Wang, Tianhua Chen, et al.
Artificial Intelligence in Medicine (2024) Vol. 149, pp. 102788-102788
Closed Access | Times Cited: 7
T2WI-based MRI radiomics for the prediction of preoperative extranodal extension and prognosis in resectable rectal cancer
Hang Li, Li Chai, Hong Pu, et al.
Insights into Imaging (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 6
Multi-omics staging of locally advanced rectal cancer predicts treatment response: a pilot study
Ilaria Cicalini, Antonio Maria Chiarelli, Piero Chiacchiaretta, et al.
La radiologia medica (2024) Vol. 129, Iss. 5, pp. 712-726
Open Access | Times Cited: 5
MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study
Arianna Defeudis, Simone Mazzetti, Jovana Panić, et al.
European Radiology Experimental (2022) Vol. 6, Iss. 1
Open Access | Times Cited: 26
Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning
Samira Abbaspour, Hamid Abdollahi, Hossein Arabalibeik, et al.
Abdominal Radiology (2022) Vol. 47, Iss. 11, pp. 3645-3659
Closed Access | Times Cited: 24
The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer
Sebastian Curcean, Andra Curcean, Daniela Martin, et al.
Cancers (2024) Vol. 16, Iss. 17, pp. 3111-3111
Open Access | Times Cited: 4
Deep learning algorithms for predicting pathological complete response in MRI of rectal cancer patients undergoing neoadjuvant chemoradiotherapy: a systematic review
Bor‐Kang Jong, Zhen-Hao Yu, Yu-Jen Hsu, et al.
International Journal of Colorectal Disease (2025) Vol. 40, Iss. 1
Open Access
Beyond the tumor region: Peritumoral radiomics enhances prognostic accuracy in locally advanced rectal cancer
Zhiying Liang, Ming‐Lung Yu, Hui Yang, et al.
World Journal of Gastroenterology (2025) Vol. 31, Iss. 8
Closed Access
Inclusion of tumor periphery in radiomics analysis of magnetic resonance images does not improve predictions of preoperative therapy response in patients with rectal cancer
Nafsika Korsavidou Hult, Sambit Tarai, Klara Hammarström, et al.
Abdominal Radiology (2025)
Open Access
Computed tomography–based radiomics modeling to predict patient overall survival in cervical cancer with intensity-modulated radiotherapy combined with concurrent chemotherapy
Lihong Xiao, Youhua Wang, Xiangxiang Shi, et al.
Journal of International Medical Research (2025) Vol. 53, Iss. 3
Open Access
Preoperative prediction of extramural venous invasion in rectal cancer by dynamic contrast-enhanced and diffusion weighted MRI: a preliminary study
Weiqun Ao, Xian Zhang, Xiuzhen Yao, et al.
BMC Medical Imaging (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 19
Radiomics-based machine learning differentiates “ground-glass” opacities due to COVID-19 from acute non-COVID-19 lung disease
Andrea Delli Pizzi, Antonio Maria Chiarelli, Piero Chiacchiaretta, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 26
Utility of contrast-enhanced MRI radiomics features combined with clinical indicators for predicting induction chemotherapy response in primary central nervous system lymphoma
Xiaochen Wang, Litao Zhao, Sihui Wang, et al.
Journal of Neuro-Oncology (2024) Vol. 166, Iss. 3, pp. 451-460
Open Access | Times Cited: 3
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
MRI-Based Radiomics Features to Predict Treatment Response to Neoadjuvant Chemotherapy in Locally Advanced Rectal Cancer: A Single Center, Prospective Study
Biyun Chen, Hui Xie, Yuan Li, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 14
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