Clinical Scorecard: Identification and Immunohistochemical Confirmation of a Combined Immune-Inflammatory Profile for Stratifying Disease-Free Survival in Breast Cancer Using Machine Learning Techniques

At a Glance

Key Highlights

• RSF model achieved the best prognostic performance with time-dependent AUCs reaching 0.911 at 60 months.
• 21.3% of patients experienced a disease-free survival (DFS) event during follow-up.
• 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.
• Combining machine learning with immune-inflammatory markers may enhance recurrence risk stratification.

Guideline-Based Recommendations

Diagnosis

• Assessment of stromal tumor-infiltrating lymphocytes (TILs) and systemic immune-inflammation index (SII) for prognostic evaluation.

Management

• Utilization of machine learning models like RSF for improved DFS prediction.

Monitoring & Follow-up

• Regular follow-up and assessment of immune-related factors in breast cancer patients.

Risks

• Higher SII and poor integrated immune phenotype associated with increased risk of recurrence.

Patient & Prescribing Data

503 patients with surgically treated breast cancer

Integration of immune markers with clinical data may inform treatment decisions.

Clinical Best Practices

• Incorporate immune-inflammatory markers in routine prognostic assessments.
• Utilize advanced survival modeling techniques for better risk stratification.

Related Resources & Content

• GLOBOCAN 2020