Direct comparison of three different mathematical models in two independent datasets of EUSOMA certified centers to predict recurrence and survival in estrogen receptor-positive breast cancer: impact on clinical practice.

Purpose: Prediction algorithms estimating survival rates for breast cancer (BC) based upon risk factors and treatment could give a help to the clinicians during multidisciplinary meetings. The aim of this study was to evaluate accuracy and clinical utility of three different scores: the Clinical Treatment Score Post-5 Years (CTS5), the PREDICT Score, and the Nottingham Prognostic Index (NPI).
Methods: This is a retrospective cohort analysis conducted on prospectively recorded databases of two EUSOMA certified centers (Breast Unit of Trieste Academic Hospital and of Cremona Hospital, Italy). We included patients with Luminal BC undergone to breast surgery between 2010 and 2015, and subsequent endocrine therapy for 5 years for curative intent.
Results: A total of 473 patients were enrolled in this study. ROC analysis showed fair accuracy for NPI, good accuracy for PREDICT, and optimal accuracy for CTS5 (AUC 0.7, 0.76, and 0.83, respectively). The three scores seemed strongly correlated in Spearman's rank correlation coefficient analysis. PREDICT partially overestimated OS in patients undergone to mastectomy, and in pT3-4, G3 tumors. Considering DRFS as a surrogate of OS, CTS5 showed women in intermediate and high risk class had shorter OS too (respectively p = 0.001 and p < 0.001). Combining scores does not improve prognostication power.
Conclusion: Mathematical models may help clinicians in decision making (adjuvant therapies, CDK4/6i, genomic test's gray zones). CTS5 has the higher prognostic accuracy in predicting recurrence, while score predicting OS did not show substantial advances, proving that pN, G, and pT are still the most important variables in predicting OS.