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:

Multiparametric 18F-FDG PET/MRI-Based Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer
Lale Umutlu, Julian Kirchner, Nils Martin Bruckmann, et al.
Cancers (2022) Vol. 14, Iss. 7, pp. 1727-1727
Open Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

18F-FDG PET/CT radiomic analysis and artificial intelligence to predict pathological complete response after neoadjuvant chemotherapy in breast cancer patients
Luca Urso, Luigi Manco, Corrado Cittanti, et al.
La radiologia medica (2025)
Open Access | Times Cited: 2
AI in Breast Cancer Imaging: An Update and Future Trends
Yizhou Chen, Xiaoliang Shao, Kuangyu Shi, et al.
Seminars in Nuclear Medicine (2025)
Open Access | Times Cited: 2
PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review
Luca Urso, Luigi Manco, Angelo Castello, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 21, pp. 13409-13409
Open Access | Times Cited: 40
Artificial intelligence for tumor [18F]FDG-PET imaging: Advancement and future trends—part I
Alireza Safarian, Seyed Ali Mirshahvalad, Abolfazl Farbod, et al.
Seminars in Nuclear Medicine (2025)
Open Access | Times Cited: 1
AI Applications to Breast MRI: Today and Tomorrow
Roberto Lo Gullo, Joren Brunekreef, Eric Marcus, et al.
Journal of Magnetic Resonance Imaging (2024) Vol. 60, Iss. 6, pp. 2290-2308
Closed Access | Times Cited: 5
A Novel Machine Learning Approach for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer: Integration of Multimodal Radiomics With Clinical and Molecular Subtype Markers
Abdelrahman Gamal, Ahmed Sharafeldeen, Eman Alnaghy, et al.
IEEE Access (2024) Vol. 12, pp. 104983-105003
Open Access | Times Cited: 5
Prediction of pathological response after neoadjuvant chemotherapy using baseline FDG PET heterogeneity features in breast cancer
Carla Oliveira, Francisco P. M. Oliveira, Sofia C. Vaz, et al.
British Journal of Radiology (2023) Vol. 96, Iss. 1146
Open Access | Times Cited: 11
FDG-PET/CT and Multimodal Machine Learning Model Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer
David Groheux, Loïc Ferrer, Jennifer Harford Vargas, et al.
Cancers (2025) Vol. 17, Iss. 7, pp. 1249-1249
Open Access
The Value of Semiquantitative Parameters Derived from 18F-FDG PET/CT for Predicting Response to Neoadjuvant Chemotherapy in a Cohort of Patients with Different Molecular Subtypes of Breast Cancer
Luca Urso, Laura Evangelista, Pierpaolo Alongi, et al.
Cancers (2022) Vol. 14, Iss. 23, pp. 5869-5869
Open Access | Times Cited: 17
AI-Enhanced PET and MR Imaging for Patients with Breast Cancer
Valeria Romeo, Linda Moy, Katja Pinker
PET Clinics (2023) Vol. 18, Iss. 4, pp. 567-575
Closed Access | Times Cited: 10
Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms
Guangying Zheng, Jiaxuan Peng, Zhenyu Shu, et al.
Journal of Cancer Research and Clinical Oncology (2024) Vol. 150, Iss. 3
Open Access | Times Cited: 3
Artificial intelligence in breast imaging: potentials and challenges
Jia-wei Li, Danli Sheng, Jiangang Chen, et al.
Physics in Medicine and Biology (2023) Vol. 68, Iss. 23, pp. 23TR01-23TR01
Open Access | Times Cited: 9
Artificial Intelligence-Enhanced Breast MRI
Roberto Lo Gullo, Eric Marcus, Jorge Huayanay, et al.
Investigative Radiology (2023) Vol. 59, Iss. 3, pp. 230-242
Open Access | Times Cited: 8
Hyperpolarised 13C-MRI using 13C-pyruvate in breast cancer: A review
Otso Arponen, Pascal Wodtke, Ferdia A. Gallagher, et al.
European Journal of Radiology (2023) Vol. 167, pp. 111058-111058
Closed Access | Times Cited: 6
Towards a fast PET/MRI protocol for breast cancer imaging: maintaining diagnostic confidence while reducing PET and MRI acquisition times
Kai Jannusch, Maike E. Lindemann, Nils Martin Bruckmann, et al.
European Radiology (2023) Vol. 33, Iss. 9, pp. 6179-6188
Open Access | Times Cited: 5
Deep Learning of Multimodal Ultrasound: Stratifying the Response to Neoadjuvant Chemotherapy in Breast Cancer Before Treatment
Jionghui Gu, Xian Zhong, Chengyu Fang, et al.
The Oncologist (2023) Vol. 29, Iss. 2, pp. e187-e197
Open Access | Times Cited: 5
Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features
Jiwoo Park, Min Jung Kim, Jong–Hyun Yoon, et al.
Diagnostics (2023) Vol. 13, Iss. 19, pp. 3031-3031
Open Access | Times Cited: 5
Radiomic Nomogram for Predicting Axillary Lymph Node Metastasis in Patients with Breast Cancer
Yusi Chen, Jinping Li, Jin Zhang, et al.
Academic Radiology (2023) Vol. 31, Iss. 3, pp. 788-799
Open Access | Times Cited: 5
Exploring Neoadjuvant Chemotherapy, Predictive Models, Radiomic, and Pathological Markers in Breast Cancer: A Comprehensive Review
Basma B ELSayed, Ahmed Alksas, Mohamed Shehata, et al.
Cancers (2023) Vol. 15, Iss. 21, pp. 5288-5288
Open Access | Times Cited: 5
Breast PET/MRI Hybrid Imaging and Targeted Tracers
Valeria Romeo, Thomas H. Helbich, Katja Pinker
Journal of Magnetic Resonance Imaging (2022) Vol. 57, Iss. 2, pp. 370-386
Open Access | Times Cited: 8
PET/MRI and Novel Targets for Breast Cancer
Hyun Woo Chung, Kyoung Sik Park, Ilhan Lim, et al.
Biomedicines (2024) Vol. 12, Iss. 1, pp. 172-172
Open Access | Times Cited: 1
Radiomics based on 18F-FDG PET/CT for prediction of pathological complete response to neoadjuvant therapy in non-small cell lung cancer
Jianjing Liu, Chunxiao Sui, Haiman Bian, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 1
The role of 18F-FDG PET/MRI in assessing pathological complete response to neoadjuvant chemotherapy in patients with breast cancer: a systematic review and meta-analysis
Milad Ghanikolahloo, Hayder Jasim Taher, Ayoob Dinar Abdullah, et al.
Radiation Oncology (2024) Vol. 19, Iss. 1
Open Access | Times Cited: 1
Methodological issues in radiomics: impact on accuracy of MRI for predicting response to neoadjuvant chemotherapy in breast cancer
Sofia Netti, Oriana D’Ecclesiis, Federica Corso, et al.
European Radiology (2024)
Closed Access | Times Cited: 1
Artificial Intelligence in Oncology: A Topical Collection in 2022
Andreas Stadlbauer, Anke Meyer‐Baese
Cancers (2023) Vol. 15, Iss. 4, pp. 1065-1065
Open Access | Times Cited: 2
Page 1 - Next Page

Scroll to top