Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

T2-Weighted Image-Based Radiomics Signature for Discriminating Between Seminomas and Nonseminoma
Peipei Zhang, Zhaoyan Feng, Wei Cai, et al.
Frontiers in Oncology (2019) Vol. 9
Open Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

Testicular Germ Cell Tumors: Classification, Pathologic Features, Imaging Findings, and Management
Venkata S. Katabathina, Daniel Vargas-Zapata, Roberto A. Monge, et al.
Radiographics (2021) Vol. 41, Iss. 6, pp. 1698-1716
Closed Access | Times Cited: 42
The MRI radiomics signature can predict the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma
Shuang Lü, Chenglong Wang, Yun Liu, et al.
European Radiology (2023) Vol. 34, Iss. 1, pp. 485-494
Closed Access | Times Cited: 14
Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning
Liang Wang, Peipei Zhang, Yanhui Feng, et al.
BMC Cancer (2025) Vol. 25, Iss. 1
Open Access
Development of an Ultrasound‐Based Clinical‐Radiomic Nomogram for Predicting Histologic Subtypes in Focal Testicular Tumors
Lin Tuo, Shun‐Ping Chen, Shouliang Miao
Journal of Ultrasound in Medicine (2025)
Closed Access
Development and validation of an MRI-based radiomic nomogram to distinguish between good and poor responders in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy
Jia Wang, Xuejun Liu, Bin Hu, et al.
Abdominal Radiology (2020) Vol. 46, Iss. 5, pp. 1805-1815
Closed Access | Times Cited: 28
When to ask for an MRI of the scrotum
Athina C. Tsili, Maria I. Argyropoulou, Miriam Dolciami, et al.
Andrology (2021) Vol. 9, Iss. 5, pp. 1395-1409
Open Access | Times Cited: 25
A radiomics nomogram based on MSCT and clinical factors can stratify fibrosis in inflammatory bowel disease
Xu Zeng, Huijie Jiang, Yanmei Dai, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
Differentiation of testicular seminomas from nonseminomas based on multiphase CT radiomics combined with machine learning: A multicenter study
Fuxiang Fang, Linfeng Wu, Xing Luo, et al.
European Journal of Radiology (2024) Vol. 175, pp. 111416-111416
Closed Access | Times Cited: 3
A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings
Gang Wu, Shuchang Zhou, Yujin Wang, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 26
The potential role of MR based radiomic biomarkers in the characterization of focal testicular lesions
Giacomo Feliciani, Lorenzo Mellini, Aldo Carnevale, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 23
Discriminating malignant from benign testicular masses using machine-learning based radiomics signature of appearance diffusion coefficient maps: Comparing with conventional mean and minimum ADC values
Chanyuan Fan, Kailun Sun, Xiangde Min, et al.
European Journal of Radiology (2022) Vol. 148, pp. 110158-110158
Open Access | Times Cited: 15
An Overview of the Role of Multiparametric MRI in the Investigation of Testicular Tumors
Athina C. Tsili, Nikolaos Sofikitis, Ourania Pappa, et al.
Cancers (2022) Vol. 14, Iss. 16, pp. 3912-3912
Open Access | Times Cited: 15
A first look into radiomics application in testicular imaging: A systematic review
Salvatore Claudio Fanni, Maria Febi, Leonardo Colligiani, et al.
Frontiers in Radiology (2023) Vol. 3
Open Access | Times Cited: 7
A Framework for Deep Multitask Learning With Multiparametric Magnetic Resonance Imaging for the Joint Prediction of Histological Characteristics in Breast Cancer
Ming Fan, Chengcheng Yuan, Guangyao Huang, et al.
IEEE Journal of Biomedical and Health Informatics (2022) Vol. 26, Iss. 8, pp. 3884-3895
Open Access | Times Cited: 12
Multiparametric ultrasound for the assessment of testicular lesions with negative tumoral markers
Hui Liu, Lin Dong, Lihua Xiang, et al.
Asian Journal of Andrology (2022) Vol. 25, Iss. 1, pp. 50-57
Open Access | Times Cited: 10
Applications of radiomics in genitourinary tumors.
Longfei Liu, Xiaoping Yi, Can Lü, et al.
PubMed (2020) Vol. 10, Iss. 8, pp. 2293-2308
Closed Access | Times Cited: 15
Testicular Radiomics To Predict Pathology At Time of Postchemotherapy Retroperitoneal Lymph Node Dissection for Nonseminomatous Germ Cell Tumor
Nikit Venishetty, Jacob Taylor, Yin Xi, et al.
Clinical Genitourinary Cancer (2023) Vol. 22, Iss. 1, pp. 33-37
Closed Access | Times Cited: 5
Contemporary options and future perspectives: three examples highlighting the challenges in testicular cancer imaging
Gamal Anton Wakileh, Christian Ruf, Axel Heidenreich, et al.
World Journal of Urology (2021) Vol. 40, Iss. 2, pp. 307-315
Open Access | Times Cited: 12
Prediction of High-Risk Neuroblastoma Among Neuroblastic Tumors Using Radiomics Features Derived from Magnetic Resonance Imaging: A Pilot Study
Jisoo Kim, Young Hun Choi, Haesung Yoon, et al.
Yonsei Medical Journal (2024) Vol. 65, Iss. 5, pp. 293-293
Open Access | Times Cited: 1
Radiomics nomogram analysis of T2-fBLADE-TSE in pulmonary nodules evaluation
Shuyi Yang, Yida Wang, Yuxin Shi, et al.
Magnetic Resonance Imaging (2021) Vol. 85, pp. 80-86
Closed Access | Times Cited: 7
Comparison and analysis of multiple machine learning models for discriminating benign and malignant testicular lesions based on magnetic resonance imaging radiomics
Yanhui Feng, Zhaoyan Feng, Liang Wang, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 2
Magnetic Resonance Imaging-based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two-center Study
Ruixin Yan, Dapeng Hao, Jie Li, et al.
Research Square (Research Square) (2020)
Open Access | Times Cited: 4
Deep Learning-Based Super-Resolution Us Radiomics to Differentiate Testicular Seminoma and Non-Seminoma
Yafang Zhang, Shilin Lu, Chuan Peng, et al.
(2024)
Closed Access
Other genitourinary cancers and AI (penile, urethra, and testes)
Alireza Ghoreifi, Farshad Sheybaee Moghaddam, Hooman Djaladat
Elsevier eBooks (2024), pp. 157-168
Closed Access
Machine learning: a predication model of outcome of SARS-CoV-2 pneumonia
Gang Wu, Shuchang Zhou, Yujin Wang, et al.
Research Square (Research Square) (2020)
Open Access | Times Cited: 3
Page 1 - Next Page

Scroll to top