A nomogram to predict individual prognosis in node-negative breast carcinoma

Area of Science:

• Oncology
• Biostatistics

Background:

• The benefit of chemotherapy for node-negative breast carcinoma (NNBC) remains debated.
• Classical prognostic factors offer limited accuracy for individual patient outcomes.
• A statistical model was developed to enhance prognostic accuracy in NNBC.

Purpose of the Study:

• To develop and validate a statistical model for improved prognostic accuracy in node-negative breast carcinoma.
• To create a prognosis index (PI) for predicting individual patient outcomes.
• To compare the developed PI with the Adjuvant! Online program.

Main Methods:

• A cohort of 305 node-negative breast carcinomas without adjuvant treatment was analyzed.
• Prognostic factors including age, tumor size, ER, PgR, SBR grade, uPA, PAI-1, and TK were evaluated.
• Internal validation used Harrell's concordance index; a PI was derived from a nomogram.

Main Results:

• Age, pathological tumor size (pT), PgR, and PAI-1 were significant predictors of 5-year breast cancer-specific survival (BCSS).
• The model achieved a concordance index of 0.71 upon internal validation.
• The PI score demonstrated significant association with BCSS (HR: 4.1-8.8), outperforming the Adjuvant! Online score (HR: 1.4).

Conclusions:

• A nomogram-based approach can effectively predict survival probability curves for individual breast cancer patients.
• The developed PI offers a more accurate prognostic tool for node-negative breast cancer.
• This model aids in personalized treatment decisions for NNBC patients.