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:

Validation analysis of the novel imaging-based prognostic radiomic signature in patients undergoing primary surgery for advanced high-grade serous ovarian cancer (HGSOC)
Christina Fotopoulou, Andrea Rockall, Haonan Lu, et al.
British Journal of Cancer (2021) Vol. 126, Iss. 7, pp. 1047-1054
Open Access | Times Cited: 25

Showing 25 citing articles:

CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric Dataset
Giacomo Avesani, Huong Elena Tran, Giulio Cammarata, et al.
Cancers (2022) Vol. 14, Iss. 11, pp. 2739-2739
Open Access | Times Cited: 38
Tumor and local lymphoid tissue interaction determines prognosis in high-grade serous ovarian cancer
Haonan Lu, Hantao Lou, Georg Wengert, et al.
Cell Reports Medicine (2023) Vol. 4, Iss. 7, pp. 101092-101092
Open Access | Times Cited: 25
Ovarian cancer staging and follow-up: updated guidelines from the European Society of Urogenital Radiology female pelvic imaging working group
Stefania Rizzo, Giacomo Avesani, Camilla Panico, et al.
European Radiology (2025)
Open Access | Times Cited: 1
Predicting Response to Treatment and Survival in Advanced Ovarian Cancer Using Machine Learning and Radiomics: A Systematic Review
Sabrina Piedimonte, Mariam Mohamed, Gabriela Silveira da Rosa, et al.
Cancers (2025) Vol. 17, Iss. 3, pp. 336-336
Open Access | Times Cited: 1
Comparative analysis of active contour random walker and watershed algorithms in segmentation of ovarian cancer
P J Ruchitha, Richitha Y Sai, Ashwini Kodipalli, et al.
(2022), pp. 234-238
Closed Access | Times Cited: 34
A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility
Meng-Lin Huang, Jing Ren, Zhengyu Jin, et al.
Insights into Imaging (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 16
Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma
Leonardo Rundo, Lucian Beer, Lorena Escudero Sánchez, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 19
Radiomics and Radiogenomics of Ovarian Cancer
Camilla Panico, Giacomo Avesani, Konstantinos Zormpas‐Petridis, et al.
Radiologic Clinics of North America (2023) Vol. 61, Iss. 4, pp. 749-760
Closed Access | Times Cited: 12
Predicting progression-free survival in patients with epithelial ovarian cancer using an interpretable random forest model
Lian Jian, Xiaoyan Chen, Pingsheng Hu, et al.
Heliyon (2024) Vol. 10, Iss. 15, pp. e35344-e35344
Open Access | Times Cited: 4
Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer
Song Zeng, Xinlu Wang, Hua Yang
Military Medical Research (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 4
ROLE OF LYMPHADENECTOMY IN ADVANCED OVARIAN CANCER–A SUBGROUP ANALYSIS OF THE PATIENTS EXCLUDED FROM THE LION TRIAL (THE CHARITÉ COHORT)
Robert Armbrust, Christina Fotopoulou, Dario Zocholl, et al.
International Journal of Gynecological Cancer (2025), pp. 101683-101683
Closed Access
Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning
Yimin Wu, Daojing Xu, Zhengang Zha, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Machine learning based radiomic models outperform clinical biomarkers in predicting outcomes after immunotherapy for hepatocellular carcinoma
Mathew Vithayathil, Deniz Koku, Claudia Campani, et al.
Journal of Hepatology (2025)
Closed Access
Machine learning and radiomics for segmentation and classification of adnexal masses on ultrasound
J. Barcroft, Kristofer Linton‐Reid, C. Landolfo, et al.
npj Precision Oncology (2024) Vol. 8, Iss. 1
Open Access | Times Cited: 3
Clinical and Biological Aspects of Disseminated Tumor Cells and Dormancy in Breast Cancer
Alexander Ring, Maria Spataro, Andreas Wicki, et al.
Frontiers in Cell and Developmental Biology (2022) Vol. 10
Open Access | Times Cited: 15
Gross tumour volume radiomics for prognostication of recurrence & death following radical radiotherapy for NSCLC
Sumeet Hindocha, Thomas Charlton, Kristofer Linton‐Reid, et al.
npj Precision Oncology (2022) Vol. 6, Iss. 1
Open Access | Times Cited: 10
Intrathoracic surgery as part of primary cytoreduction for advanced ovarian cancer: The evolution of a “pelvic” surgeon
Christina Fotopoulou
Gynecologic Oncology (2023) Vol. 170, pp. A1-A3
Closed Access | Times Cited: 5
Tumor biology and impact on timing of surgery in advanced epithelial ovarian cancer
Irina Tsibulak, Christina Fotopoulou
International Journal of Gynecological Cancer (2023) Vol. 33, Iss. 10, pp. 1627-1632
Closed Access | Times Cited: 4
HCK Promotes High-Grade Serous Ovarian Cancer Tumorigenesis through CD44 and NOTCH3 Signaling
Christen A. Khella, Lucyann Franciosa, Lorna Rodirguez-Rodriguez, et al.
Molecular Cancer Research (2023) Vol. 21, Iss. 10, pp. 1037-1049
Open Access | Times Cited: 2
CT-based radiomic prognostic vector (RPV) predicts survival and stromal histology in high-grade serous ovarian cancer: an external validation study
Georg Wengert, Haonan Lu, Eric O. Aboagye, et al.
European Radiology (2024)
Open Access
The Use of Machine Learning Models and Radiomics for Segmentation and Classification of Adnexal Masses on Ultrasound: A multi-cohort retrospective study
J. Barcroft, Kristofer Linton‐Reid, C. Landolfo, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 1
End-to-End Integrative Segmentation and Radiomics Prognostic Models Improve Risk Stratification of High-Grade Serous Ovarian Cancer: A Retrospective Multi-Cohort Study
Kristofer Linton‐Reid, Georg Wengert, Haonan Lu, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access
Radiomics in Gynaecological Imaging: A State-of-the-Art Review
Paolo Niccolò Franco, Federica Vernuccio, Cesare Maino, et al.
Applied Sciences (2023) Vol. 13, Iss. 21, pp. 11839-11839
Open Access
Integration of Imaging and Pathology in the Multidisciplinary Process
S. Swift, M.J. Weston, Mahfooz Basha Mohamed, et al.
Springer eBooks (2023), pp. 115-144
Closed Access
CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-stage Malignant Ovarian Tumors: A Multi-Class Classification and Multi-Model Comparation Study
Jia Chen, Lei Liu, Ziying He, et al.
Research Square (Research Square) (2022)
Open Access
Page 1

Scroll to top