Prognostic nomograms for predicting overall and cancer-specific survival in acute erythroid leukemia: a SEER-based...

Acute erythroid leukemia (AEL) is a rare and highly aggressive subtype of acute myeloid leukemia with a poor prognosis and no standardized treatment strategy. Its rarity and molecular complexity mean that conventional staging systems fail to predict its outcomes accurately. This study aimed to develop and validate nomogram models for predicting overall survival (OS) and cancer-specific survival (CSS) in patients with AEL.

We retrospectively analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database (2000-2021) and identified 778 patients with AEL (ICD-O-3 code 9840/3). Patients were divided randomly into training (n = 544) and validation (n = 234) cohorts. Independent prognostic factors for OS and CSS were identified using Cox regression. Nomograms were developed and validated using concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Risk stratification was performed based on nomogram-derived scores.

Multivariate Cox regression identified age, chemotherapy, marital status, and first primary tumor status as independent prognostic factors for both OS and CSS. The nomograms demonstrated good discrimination, with C-index values of 0.669 (OS) and 0.665 (CSS) in the training cohort, and 0.654 (OS) and 0.661 (CSS) in the validation cohort. ROC analysis confirmed good predictive accuracy, and calibration plots showed good agreement between predicted and observed survival. DCA confirmed the clinical utility of the nomograms. Risk stratification based on median nomogram scores effectively distinguished between high- and low-risk patients (P < 0.001).

We developed and validated novel SEER-based nomograms for predicting OS and CSS in patients with AEL, which demonstrated reliable performance in internal validation. However, the lack of molecular data (e.g., TP53 mutations) limits the biological interpretability of the models. External validation is required before clinical implementation.