Machine learning-driven identification and immunohistochemical validation of an integrated immune-inflammatory phenotype for disease-free survival stratification in breast cancer - Report - MDSpire
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Machine learning-driven identification and immunohistochemical validation of an integrated immune-inflammatory phenotype for disease-free survival stratification in breast cancer
Clinical Report: Identification of Immune-Inflammatory Profile in Breast Cancer
Overview
This study identifies a combined immune-inflammatory profile that enhances disease-free survival (DFS) stratification in breast cancer patients. Machine learning techniques, particularly random survival forest (RSF), demonstrated superior prognostic performance compared to traditional models.
Background
Breast cancer is the most prevalent cancer among women, with significant variability in recurrence risk post-treatment. Traditional prognostic factors often fail to capture the complexity of tumor biology and immune interactions. Understanding immune-related factors, such as tumor-infiltrating lymphocytes (TILs) and systemic immune-inflammation index (SII), is crucial for improving patient outcomes.
Data Highlights
Time (months)
AUC
12
0.867
24
0.880
36
0.879
48
0.893
60
0.911
Key Findings
107 patients (21.3%) experienced a DFS event during follow-up.
RSF model achieved the highest time-dependent AUC of 0.911 at 60 months.
Pathological N stage was identified as the most significant predictor of DFS.
The poor integrated immune phenotype was independently associated with worse DFS (hazard ratio 2.53).
Immunohistochemical validation showed significant differences in CD8+ and CD163+ cell densities between phenotypes.
Clinical Implications
Incorporating immune-inflammatory markers like TILs and SII into prognostic models may enhance the stratification of recurrence risk in breast cancer. Clinicians should consider these factors when assessing patient prognosis and tailoring treatment strategies.
Conclusion
The integration of machine learning with immune-inflammatory markers offers a promising approach to improve DFS risk stratification in breast cancer. This study underscores the importance of a multifaceted view of tumor biology in clinical practice.
