Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! By
Ultrasound-Based Machine Learning Models for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer: A Meta-Analysis Publisher Pubmed



P Valizadeh PARYA ; P Jannatdoust PAYAM ; N Moradi NILOOFAR ; S Yaghoobpoor SHIRIN ; S Toofani SAJJAD ; N Rafiei NAZANIN ; F Moodi FARZAN ; H Ghorani HAMED ; A Arian ARVIN
Authors

Source: Clinical Imaging Published:2025


Abstract

Background and aims: Breast cancer remains the most common cancer among women globally, with neoadjuvant chemotherapy (NAC) serving as a critical pre-surgical intervention. Ultrasound-based radiomics and machine learning (ML) models offer potential for early prediction of NAC response, aiding personalized treatment strategies. This study systematically reviews the efficacy of ultrasound-based ML models in predicting NAC response in breast cancer patients. Methods: We conducted a systematic review and meta-analysis following PRISMA-DTA guidelines, searching PubMed, Scopus, Web of Science, and Embase up to August 30, 2023. Studies developing ultrasound-based radiomics or deep learning (DL) models to predict NAC response were include. Models for complete and partial response were analyzed separately. Results: Twenty-two studies were included. For models predicting complete response, pooled sensitivity, specificity, and AUC were 85.1 % (95 % CI: 79.2–89.6 %), 85.8 % (95 % CI: 76.7–91.8 %), and 86 % (95 % CI: 82 %–94 %), respectively for internal validation and 82.9 % (95 % CI: 76.2 % - 88.1 %), 89.4 % (95 % CI: 84.7 %–92.9 %), and 93 % (95 % CI: 82 %–94 %), respectively for external validation. For partial response, analysis could only be performed on internal validation and the pooled sensitivity was 87.5 % (95 % CI: 85.1–89.6 %) with pooled specificity of 82.3 % (95 % CI: 75.6–87.5 %), and pooled AUC of 88 % (95 % CI: 85 %–92 %). Conclusion: Ultrasound-based ML models show strong potential for predicting NAC response in breast cancer, with delta radiomics enhancing predictive accuracy. Further research is needed to develop clinically generalizable models. © 2025 Elsevier B.V., All rights reserved.
Related Docs
View other Related Docs
1. A Comprehensive Framework for Multi-Class Breast Lesion Classification Using a Gaussian Process-Optimized Vgg16-Capsule Hybrid Model on Ultrasound Radio Frequency B-Mode Images, AUT Journal of Electrical Engineering (2025)
2. Diagnostic Accuracy of Ct-Based Radiomics and Deep Learning for Predicting Lymph Node Metastasis in Esophageal Cancer, Clinical Imaging (2024)
3. Implementation of Artificial Intelligence in Detection, Classification, and Prognostication of Osteosarcoma Utilizing Different Assessment Techniques: A Systematic Review, Intelligence-Based Medicine (2025)
Experts (# of related papers)
View all Related Experts
Nasrin Ahmadinejad (2)
Sedigheh Marjaneh Hejazi (2)
Other Related Docs
4. Endorectal Ultrasound Radiomics in Locally Advanced Rectal Cancer Patients: Despeckling and Radiotherapy Response Prediction Using Machine Learning, Abdominal Radiology (2022)
5. Predicting Lymph Node Metastasis in Thyroid Cancer: Systematic Review and Meta-Analysis on the Ct/Mri-Based Radiomics and Deep Learning Models, Clinical Imaging (2025)
6. Artificial Intelligence in Breast Cancer Survival Prediction: A Comprehensive Systematic Review and Meta-Analysis, Frontiers in Oncology (2024)
7. A Novel Deep Learning Model for Breast Lesion Classification Using Ultrasound Images: A Multicenter Data Evaluation, Physica Medica (2023)
8. Performance of Radiomics and Deep Learning Models in Predicting Distant Metastases in Soft Tissue Sarcomas: A Systematic Review and Meta-Analysis, Academic Radiology (2025)
9. Machine Learning Approaches in the Prediction of Positive Axillary Lymph Nodes Post Neoadjuvant Chemotherapy Using Mri, Ct, or Ultrasound: A Systematic Review, European Journal of Radiology Open (2024)
10. Prediction of Sentinel Lymph Node Metastasis in Breast Cancer Patients Based on Preoperative Features: A Deep Machine Learning Approach, Scientific Reports (2024)
11. Machine Learning Applications in Placenta Accreta Spectrum Disorders, European Journal of Obstetrics and Gynecology and Reproductive Biology: X (2025)
12. Applications of Machine-Learning Algorithms for Prediction of Benign and Malignant Breast Lesions Using Ultrasound Radiomics Signatures: A Multi-Center Study, Biocybernetics and Biomedical Engineering (2022)
13. Diagnostic Performance of Deep Learning for Predicting Glioma Isocitrate Dehydrogenase and 1P/19Q Co-Deletion in Mri: A Systematic Review and Meta-Analysis, European Radiology (2025)
14. Methodological Insights Into Chatgpt’S Screening Performance in Systematic Reviews, BMC Medical Research Methodology (2024)
15. Predicting the Early Detection of Breast Cancer Using Hybrid Machine Learning Systems and Thermographic Imaging, International Journal of Imaging Systems and Technology (2024)
16. Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis, Diagnostics (2025)
17. Research Advancements in the Use of Artificial Intelligence for Prenatal Diagnosis of Neural Tube Defects, Frontiers in Pediatrics (2025)
18. Machine Learning Models for Predicting Sudden Sensorineural Hearing Loss Outcome: A Systematic Review, Annals of Otology# Rhinology and Laryngology (2024)
19. The Value of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (Dce-Mri) in the Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: A Meta-Analysis, International Journal of Radiation Research (2024)
20. Cultivating Diagnostic Clarity: The Importance of Reporting Artificial Intelligence Confidence Levels in Radiologic Diagnoses, Clinical Imaging (2025)