Breast cancer neoadjuvant therapy outcome prediction based on clinical patient and tumor features: A cross-sectional study
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View abstract on PubMed
Summary
This summary is machine-generated.This study identified key patient and tumor features impacting breast cancer neoadjuvant therapy outcomes. A predictive model using these factors achieved 80% accuracy, aiding personalized treatment strategies.
Area Of Science
- Oncology
- Biostatistics
- Machine Learning
Background
- Breast cancer is a leading cause of mortality in women globally.
- Numerous factors influencing breast cancer development and prognosis are under investigation.
- Neoadjuvant therapy is a critical treatment modality for breast cancer.
Purpose Of The Study
- To identify significant factors influencing neoadjuvant therapy outcomes in breast cancer patients.
- To develop a predictive model for breast cancer neoadjuvant therapy response.
- To enhance personalized treatment strategies through outcome prediction.
Main Methods
- Retrospective analysis of patient data from 2018-2022.
- Statistical association analysis (Spearman, Mann-Whitney U, ANOVA, Kruskal-Wallis) of patient/tumor features with RCB index.
- Development of a random forest machine learning model utilizing significant features.
Main Results
- Patient factors (age, BMI, prior malignancy) and tumor characteristics (focality, grade, immunophenotype, receptor status, Ki-67, lymphovascular invasion) significantly correlated with RCB index.
- A predictive model achieved 80% accuracy and 0.83 ROC-AUC.
- Identified factors align with existing breast cancer research.
Conclusions
- Established patient and tumor features are crucial for predicting neoadjuvant therapy response.
- The developed predictive model shows promise for personalized breast cancer treatment.
- Further research with larger datasets is needed to refine predictive models and improve treatment outcomes.
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