ArticleEuropean radiology2022
Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.
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64 citing papers in PubMed, 1 synthesis or guideline pooled it, 128 citations in OpenAlex.
- Ultrasound-based radiomics for early predicting response to neoadjuvant chemotherapy in patients with breast cancer: a systematic review with meta-analysis.La Radiologia medica · 2024Pooled it
- Development and evaluation of clinical models based on ultrasound, serum CA153 and molecular subtype in predicting the efficacy of neoadjuvant chemotherapy in breast cancer.Oncology letters · 2026Article
- Article
- Deep learning-based multimodal radiopathomics for preoperative prediction of lymph node metastasis in papillary thyroid carcinoma.Scientific reports · 2026Article
- Early predictive value of calcification on ultrasound and mammography for pathological complete response to neoadjuvant chemotherapy in breast cancer.BMC women's health · 2026Article
- From Standard Establishment to Precision Intelligence: Research Progress in Quality Control of Mammography.Diagnostics (Basel, Switzerland) · 2026Review
- Development and validation of an integrated model combining deep learning, radiomics, and clinical and breast ultrasound features for Breast Imaging Reporting and Data System 4A lesion malignancy classification.Quantitative imaging in medicine and surgery · 2025Article
- The Role of Quantitative Ultrasound in Monitoring Neoadjuvant Chemotherapy in Breast Cancer: A Narrative Review.Cancers · 2025Review
- Predicting breast cancer response to neoadjuvant chemotherapy with ultrasound-based deep learning radiomics models -- dual-center study.BMC cancer · 2025Article
- Early prediction of neoadjuvant chemotherapy efficacy among patients with triple-negative breast cancer using an ultrasound-based radiomics nomogram.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Predicting lymphovascular invasion in stage IA lung adenocarcinoma: a CT-based classification and regression tree model.European radiology · 2025Article
- Development and validation of deep learning models for qualitative classification of benign and malignant enlarged cervical lymph nodes based on ultrasound images.Gland surgery · 2025Article
- Deep learning and radiomics integration of photoacoustic/ultrasound imaging for non-invasive prediction of luminal and non-luminal breast cancer subtypes.Breast cancer research : BCR · 2025Article
- Enhanced HER-2 prediction in breast cancer through synergistic integration of deep learning, ultrasound radiomics, and clinical data.Scientific reports · 2025Article
- An ultrasound-based model for predicting the response to neoadjuvant chemotherapy in early stage triple negative breast cancer patients.BMC medical imaging · 2025Article
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer.Science advances · 2025Article
- DCE-MRI radiomics of primary breast lesions combined with ipsilateral axillary lymph nodes for predicting efficacy of NAT.BMC cancer · 2025Article
- Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model.Breast cancer research : BCR · 2025Article
- A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images.BMC medical imaging · 2025Article
4 more citing papers are in PubMed but not listed here.
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8 authors at 3 institutions in 1 country.
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Abstract
objectivesBreast cancer (BC) is the most common cancer in women worldwide, and neoadjuvant chemotherapy (NAC) is considered the standard of treatment for most patients with BC. However, response rates to NAC vary among patients, which leads to delays in appropriate treatment and affects the prognosis for patients who ineffectively respond to NAC. This study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage.
methodsIn total, 168 patients with clinicopathologically confirmed BC were enrolled in this prospective study, from March 2016 to December 2020. All patients completed NAC treatment and underwent ultrasonography (US) at three time points (before NAC, after the second course, and after the fourth course). We developed two DLR models, DLR-2 and DLR-4, for predicting responses after the second and fourth courses of NAC. Furthermore, a novel deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response at different time points of NAC administration.
resultsIn the validation cohort, DLR-2 achieved an AUC of 0.812 (95% CI: 0.770-0.851) with an NPV of 83.3% (95% CI: 76.5-89.6). DLR-4 achieved an AUC of 0.937 (95% CI: 0.913-0.955) with a specificity of 90.5% (95% CI: 86.3-94.2). Moreover, 19 of 21 non-response patients were successfully identified by DLRP, suggesting that they could benefit from treatment strategy adjustment at an early stage of NAC.
conclusionsThe proposed DLRP strategy holds promise for effectively predicting NAC response at its early stage for BC patients. KEY POINTS: • We proposed two novel deep learning radiomics (DLR) models to predict response to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on US images at different NAC time points. • Combining two DLR models, a deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response to NAC. • The DLRP may provide BC patients and physicians with an effective and feasible tool to predict response to NAC at an early stage and to determine further personalized treatment options.
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