Develop and Validate a Nomogram Combining Contrast-Enhanced Spectral Mammography Deep Learning with Clinical-Pathological Features to Predict Neoadjuvant Chemotherapy Response in Patients with ER-Positive/HER2-Negative Breast Cancer
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.A new nomogram combining contrast-enhanced spectral mammography (CESM) deep learning and clinical data accurately predicts neoadjuvant chemotherapy (NAC) response in ER-positive/HER2-negative breast cancer patients. This tool offers improved prediction over existing models.
Area Of Science
- Oncology
- Radiology
- Artificial Intelligence
Background
- Accurate prediction of neoadjuvant chemotherapy (NAC) response is crucial for managing ER-positive/HER2-negative breast cancer.
- Current prediction methods may not fully leverage advanced imaging and clinical data.
Purpose Of The Study
- To develop and validate a nomogram integrating contrast-enhanced spectral mammography (CESM) deep learning features with clinical-pathological data.
- To predict NAC response (low vs. high Miller Payne grades) in ER-positive/HER2-negative breast cancer patients.
Main Methods
- A retrospective study of 265 patients with ER-positive/HER2-negative breast cancer.
- Deep learning models (ResNet34) trained on CESM images, combined with clinical-pathological features (age, PR, ER, Ki67, neutrophil-to-lymphocyte ratio).
- Nomogram development using logistic regression and stepwise selection; evaluation via ROC curves, precision-recall curves, and decision curve analysis (DCA).
Main Results
- The nomogram demonstrated superior predictive performance compared to deep learning-based and clinical models.
- Key metrics for the nomogram included high area under the ROC curve (0.95), accuracy (0.94), specificity (0.98), and positive predictive value (0.89).
- Decision curve analysis indicated significant clinical utility for the nomogram in treatment decision-making.
Conclusions
- The developed nomogram effectively predicts NAC response in ER-positive/HER2-negative breast cancer.
- Integrating CESM deep learning with clinical-pathological features enhances predictive accuracy.
- The nomogram shows promise as a valuable tool for personalized breast cancer treatment strategies.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.