A nomogram based on inflammation and nutritional biomarkers for predicting the survival of breast cancer patients
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View abstract on PubMed
Summary
This summary is machine-generated.A new prognostic model integrating inflammation, nutrition, and clinical data accurately predicts breast cancer survival. This tool enhances personalized treatment strategies for breast cancer (BC) patients.
Area Of Science
- Oncology
- Biostatistics
Background
- Breast cancer (BC) prognosis requires improved predictive models.
- Current models may not fully integrate inflammatory and nutritional markers.
Purpose Of The Study
- To develop and validate a novel prognostic nomogram for breast cancer (BC).
- To incorporate inflammation, nutritional parameters, and clinicopathological features for predicting overall survival (OS) and disease-free survival (DFS).
Main Methods
- Utilized data from 2857 BC patients (2013-2021), divided into training (n=2001) and validation (n=856) cohorts.
- Developed a nomogram using multivariate Cox regression analysis.
- Evaluated predictive accuracy and discrimination using concordance index (C-index) and calibration curves.
- Assessed clinical utility via decision curve analysis (DCA).
Main Results
- The developed nomogram incorporates lymphocyte count, platelet count, hemoglobin, albumin-to-globulin ratio, prealbumin, subtype, and TNM staging.
- The nomogram demonstrated a statistically superior C-index for predicting OS and DFS compared to TNM staging alone.
- Time-dependent AUC exceeded 0.7, indicating satisfactory discriminative performance.
- DCA showed greater overall net benefit for the nomogram compared to TNM staging.
Conclusions
- The nomogram integrating inflammation, nutritional, and clinicopathological variables shows excellent discrimination.
- This tool is promising for predicting BC patient outcomes.
- It can aid in defining personalized treatment strategies for breast cancer.
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