Background: Integrating molecular markers with clinical parameters into prognostic models enhances the prediction of therapy response, recurrence, and mortality. However, improved prognostication in breast cancer has not been well established in Sudan. This study aims to construct and validate a data-driven prognostic index (PI) to inform chemotherapy decisions for breast cancer patients in Sudan.
Methods: A prospective cohort of 257 breast cancer patients treated with neoadjuvant chemotherapy at Khartoum Oncology Hospital was followed to identify factors associated with early relapse (≤18 months). Variables included clinical variables (age, tumour stage, lymph node (LN) status and neoadjuvant tumour response), as well as immunohistochemical markers (Human Epidermal Growth Factor Receptor 2, Ki-67 and Topoisomerase 2A). The PI was constructed using Cox Proportional Hazards regression. Model performance was assessed using Kaplan–Meier survival analysis, receiver operating characteristic curve and calibration test. The clinical utility of the model was evaluated using the net reclassification improvement (NRI), and performance was compared with the Nottingham Prognostic Index (NPI).
Results: Initial LN involvement was the only factor statistically associated with early relapse (p = 0.004; HR = 3.79, 95% CI: 1.53–9.37). A seven-factor PI was developed, stratifying patients into five distinct risk groups. Kaplan–Meier survival analysis revealed a significant difference between the groups (log-rank p < 0.0001). The PI demonstrated good calibration (p > 0.05) and higher predictive accuracy than the NPI, with an area under the curve of 0.81 (95% CI: 0.717–0.895) compared to 0.74 (95% CI: 0.643–0.834) for the NPI. NRI was 0.13.
Conclusion: The developed PI demonstrated strong predictive performance and robust internal validation for predicting early relapse in Sudanese breast cancer patients. It offers a promising framework for personalised treatment decisions in resource-limited healthcare settings.
