Construction of a nomogram prediction model for the pathological complete response after neoadjuvant chemotherapy in breast cancer: a study based on ultrasound and clinicopathological features
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.Ultrasound effectively predicts breast cancer neoadjuvant chemotherapy response. Early evaluation after two chemotherapy cycles identifies patients likely to achieve pathological complete response (pCR), aiding treatment decisions.
Area Of Science
- Oncology
- Medical Imaging
- Radiology
Background
- Neoadjuvant chemotherapy (NAC) is crucial for breast cancer treatment.
- Accurate evaluation of NAC efficacy is essential for personalized treatment strategies.
- Ultrasound offers a non-invasive method for monitoring treatment response.
Purpose Of The Study
- To assess the utility of ultrasound in predicting pathological complete response (pCR) to NAC in breast cancer.
- To develop and validate a nomogram model for pCR prediction using ultrasound and clinicopathological data.
- To determine the optimal timing for predicting pCR during NAC treatment.
Main Methods
- A cohort of 249 breast cancer patients receiving NAC was studied.
- Ultrasound assessments were conducted at multiple time points during NAC (pre-NAC, NAC2, NAC4, NAC6).
- A nomogram model was constructed using training data and validated on a separate set, incorporating ultrasound features and clinicopathological variables.
Main Results
- 28.5% of patients achieved pCR.
- Ultrasound tumor size after NAC6 correlated significantly with pathological tumor size.
- Tumor size, posterior echo, RECIST, and PR status were significant predictors of pCR across different NAC cycles.
Conclusions
- Ultrasound parameters, alongside clinicopathological features, can accurately predict pCR to NAC.
- A nomogram model incorporating these factors demonstrates high predictive performance.
- Early ultrasound evaluation after NAC2 provides valuable predictive insights for clinical application.
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
