ArticleScientific reports2022
Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 42 citations in OpenAlex.
- Machine learning for the prediction of three-year survival in locally advanced breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound imaging.Scientific reports · 2026Article
- AI-enabled RF data synthesis for breast ultrasound: efficacy in quantitative ultrasound tissue characterization.Scientific reports · 2026Article
- Quantitative Ultrasound Radiomics for Predicting and Monitoring Neoadjuvant Chemotherapy Response in Breast Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Prediction of pathological complete response to neoadjuvant therapy in breast cancer using deep learning with multi-modal radiological image and biopsy whole slide images: a two-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Predicting pathological complete response to breast cancer neoadjuvant therapy using multi-combination machine learning models based on vision transformer features.Scientific reports · 2025Article
- The Role of Quantitative Ultrasound in Monitoring Neoadjuvant Chemotherapy in Breast Cancer: A Narrative Review.Cancers · 2025Review
- Migration of Deep Learning Models Across Ultrasound Scanners.IEEE transactions on bio-medical engineering · 2025Article
- The Clinical Application of Artificial Intelligence in Breast Imaging: Current Insights, Challenges, and Future Directions.Journal of breast cancer · 2025Review
- Artificial Intelligence in Breast Cancer Diagnosis and Surgical Decision-Making: An Updated and Comprehensive Overview of Precision and Personalization in Current Evidence.Cancer management and research · 2025Review
- Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based onJournal of cancer research and clinical oncology · 2024Article
- Development of a multiparametric model for predicting the response to neoadjuvant chemotherapy in breast cancer.Translational cancer research · 2024Article
- Transfer learning of pre-treatment quantitative ultrasound multi-parametric images for the prediction of breast cancer response to neoadjuvant chemotherapy.Scientific reports · 2024Article
- Deep learning based ultrasound analysis facilitates precise distinction between parotid pleomorphic adenoma and Warthin tumor.Frontiers in oncology · 2024Article
- Review
- Review
- Predicting head and neck cancer treatment outcomes with pre-treatment quantitative ultrasound texture features and optimising machine learning classifiers with texture-of-texture features.Frontiers in oncology · 2023Article
- A review on deep learning applications in highly multiplexed tissue imaging data analysis.Frontiers in bioinformatics · 2023Review
- Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis-A Rapid Review.Diagnostics (Basel, Switzerland) · 2022Review
- Review
- Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning.Machine learning: science and technology · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
In this study, a novel deep learning-based methodology was investigated to predict breast cancer response to neo-adjuvant chemotherapy (NAC) using the quantitative ultrasound (QUS) multi-parametric imaging at pre-treatment. QUS multi-parametric images of breast tumors were generated using the data acquired from 181 patients diagnosed with locally advanced breast cancer and planned for NAC followed by surgery. The ground truth response to NAC was identified for each patient after the surgery using the standard clinical and pathological criteria. Two deep convolutional neural network (DCNN) architectures including the residual network and residual attention network (RAN) were explored for extracting optimal feature maps from the parametric images, with a fully connected network for response prediction. In different experiments, the features maps were derived from the tumor core only, as well as the core and its margin. Evaluation results on an independent test set demonstrate that the developed model with the RAN architecture to extract feature maps from the expanded parametric images of the tumor core and margin had the best performance in response prediction with an accuracy of 88% and an area under the receiver operating characteristic curve of 0.86. Ten-year survival analyses indicate statistically significant differences between the survival of the responders and non-responders identified based on the model prediction at pre-treatment and the standard criteria at post-treatment. The results of this study demonstrate the promising capability of DCNNs with attention mechanisms in predicting breast cancer response to NAC prior to the start of treatment using QUS multi-parametric images.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.