Real-time survival assessment in breast cancer with liver metastasis.

BACKGROUND

The heterogeneity in outcomes of breast cancer liver metastasis (BCLM) complicates prognosis assessment. This study conducted conditional survival (CS) analysis and develop a CS-nomogram model for BCLM using SEER database data, providing individualized and adaptive prognostic predictions.

METHODS

Data were extracted from the SEER 18 database, encompassing clinical records of BCLM patients diagnosed between 2010 and 2021. CS was calculated using the formula CS(t∣s) = S(t + s)/S(s), allowing for the dynamic assessment of survival probabilities. Annual hazard rate (AHR) analysis was performed to evaluate the risk of mortality at specific time intervals. A two-stage feature selection process was used to identify prognostic factors. We then developed a CS-nomogram, validated through calibration curves, time-dependent receiver operating characteristic curve (ROC) analysis, and decision curve analysis (DCA).

RESULTS

The study cohort comprised 4,702 BCLM patients. The CS analysis and AHR analysis demonstrated that survival probabilities improved progressively for patients who survived beyond the high-risk period, particularly during the first year post-diagnosis. The CS-nomogram, developed using Cox regression, incorporated 14 variables, including patient characteristics, tumor features, and treatment information. It effectively predicted overall survival and CS at 3, 5, and 10 years. The model's clinical utility was confirmed through calibrations, ROC with area under the curve values, and DCA, offering valuable insights for individualized treatment decisions.

CONCLUSION

By incorporating CS analysis, this study provided a dynamic, adaptable approach to predict prognosis for BCLMs. The CS-nomogram model transformed survival probabilities into a continuously adjustable process, supporting more precise clinical decision-making and offering hope to patients with a historically poor prognosis.