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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:

Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma
Yihuai Hu, Chenyi Xie, Hong Yang, et al.
Radiotherapy and Oncology (2020) Vol. 154, pp. 6-13
Closed Access | Times Cited: 111

Showing 1-25 of 111 citing articles:

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential
Xingping Zhang, Yanchun Zhang, Guijuan Zhang, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 115
A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study
Yanfen Cui, Jiayi Zhang, Zhenhui Li, et al.
EClinicalMedicine (2022) Vol. 46, pp. 101348-101348
Open Access | Times Cited: 105
Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling
Yuanpeng Zhang, Xinyun Zhang, Yu‐Ting Cheng, et al.
Military Medical Research (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 94
Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer
Reza Mohammadi, Iman Shokatian, Mohammad Salehi, et al.
Radiotherapy and Oncology (2021) Vol. 159, pp. 231-240
Open Access | Times Cited: 72
A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning
Sema Atasever, Nuh Azgınoglu, Duygu Sinanç Terzi, et al.
Clinical Imaging (2022) Vol. 94, pp. 18-41
Closed Access | Times Cited: 65
Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence
Tong Li, Wenqi Shi, Monica Isgut, et al.
IEEE Reviews in Biomedical Engineering (2023) Vol. 17, pp. 80-97
Open Access | Times Cited: 32
Multi-omics approaches for biomarker discovery in predicting the response of esophageal cancer to neoadjuvant therapy: A multidimensional perspective
Zhi Yang, Fada Guan, Lawrence F. Bronk, et al.
Pharmacology & Therapeutics (2024) Vol. 254, pp. 108591-108591
Closed Access | Times Cited: 16
Use of MRI-based deep learning radiomics to diagnose sacroiliitis related to axial spondyloarthritis
Ke Zhang, Chaoran Liu, Jielin Pan, et al.
European Journal of Radiology (2024) Vol. 172, pp. 111347-111347
Closed Access | Times Cited: 9
CT-based delta-radiomics nomogram to predict pathological complete response after neoadjuvant chemoradiotherapy in esophageal squamous cell carcinoma patients
L. T. Fan, Zhe Yang, Ming‐Hui Chang, et al.
Journal of Translational Medicine (2024) Vol. 22, Iss. 1
Open Access | Times Cited: 9
Multiscale deep learning radiomics for predicting recurrence-free survival in pancreatic cancer: A multicenter study
Qianbiao Gu, Huiling Sun, Peng Liu, et al.
Radiotherapy and Oncology (2025) Vol. 205, pp. 110770-110770
Closed Access | Times Cited: 1
Pretreatment DCE-MRI-Based Deep Learning Outperforms Radiomics Analysis in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer
Yunsong Peng, Ziliang Cheng, Chang Gong, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 31
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: 29
Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions
Quan-Hao He, Jia-Jun Feng, Fajin Lv, et al.
Insights into Imaging (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 20
Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques
Hari Mohan, Joon Yoo, Abdul Razaque
Expert Systems with Applications (2024) Vol. 255, pp. 124838-124838
Closed Access | Times Cited: 8
Radiomics for clinical decision support in radiation oncology
Luca Russo, Diepriye Charles-Davies, Silvia Bottazzi, et al.
Clinical Oncology (2024) Vol. 36, Iss. 8, pp. e269-e281
Open Access | Times Cited: 7
Deep learning radiomics based on contrast enhanced computed tomography predicts microvascular invasion and survival outcome in early stage hepatocellular carcinoma
Yuhan Yang, Yin Zhou, Chen Zhou, et al.
European Journal of Surgical Oncology (2021) Vol. 48, Iss. 5, pp. 1068-1077
Closed Access | Times Cited: 36
3D Deep Learning Model for the Pretreatment Evaluation of Treatment Response in Esophageal Carcinoma: A Prospective Study (ChiCTR2000039279)
Xiaoqin Li, Han Gao, Jian Zhu, et al.
International Journal of Radiation Oncology*Biology*Physics (2021) Vol. 111, Iss. 4, pp. 926-935
Open Access | Times Cited: 34
A Meta-Analysis for Using Radiomics to Predict Complete Pathological Response in Esophageal Cancer Patients Receiving Neoadjuvant Chemoradiation
Yung‐Shuo Kao, Yen Hsu
In Vivo (2021) Vol. 35, Iss. 3, pp. 1857-1863
Open Access | Times Cited: 33
Novel computer aided diagnostic models on multimodality medical images to differentiate well differentiated liposarcomas from lipomas approached by deep learning methods
Yuhan Yang, Yin Zhou, Zhou Chen, et al.
Orphanet Journal of Rare Diseases (2022) Vol. 17, Iss. 1
Open Access | Times Cited: 28
The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis
Zhi Yang, Jie Gong, Jie Li, et al.
International Journal of Surgery (2023) Vol. 109, Iss. 8, pp. 2451-2466
Open Access | Times Cited: 16
A machine learning approach using 18F-FDG PET and enhanced CT scan-based radiomics combined with clinical model to predict pathological complete response in ESCC patients after neoadjuvant chemoradiotherapy and anti-PD-1 inhibitors
Wei‐Xiang Qi, Shuyan Li, Ji-Feng Xiao, et al.
Frontiers in Immunology (2024) Vol. 15
Open Access | Times Cited: 5
Classification and prediction of chemoradiotherapy response and survival from esophageal carcinoma histopathology images
Yu Chen, Ruihuan Gao, Di Jing, et al.
Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (2024) Vol. 312, pp. 124030-124030
Open Access | Times Cited: 5
A combined nomogram based on radiomics and hematology to predict the pathological complete response of neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma
Yang Yu, Yan Yi, Zhongtang Wang, et al.
BMC Cancer (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 5
A deep learning radiomics model based on CT images for predicting the biological activity of hepatic cystic echinococcosis
Mayidili Nijiati, Mireayi Tuerdi, Maihemitijiang Damola, et al.
Frontiers in Physiology (2024) Vol. 15
Open Access | Times Cited: 5
Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature
Chenyi Xie, Chun Pang, Benjamin Chan, et al.
Cancers (2021) Vol. 13, Iss. 10, pp. 2469-2469
Open Access | Times Cited: 29
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