AIIMS Mantle Cell Lymphoma Overall Survival (AMOS) & AIIMS Mantle Cell Lymphoma Event-Free Survival (AMES) scores: Development of novel indigenous survival prediction models & risk stratification for mantle cell lymphoma in the Indian populat

Background & objectives Mantle cell lymphoma (MCL) is an aggressive subtype of non-Hodgkin lymphoma (NHL) with considerable heterogeneity in clinical outcomes. Existing prognostic models, such as the MCL International Prognostic Index (MIPI), demonstrate limited applicability in the Indian population due to differences in demographic and clinical profiles. This study was designed to develop indigenous prognostic tools to predict the survival of MCL patients from India. Methods We conducted a retrospective analysis of 92 patients with MCL diagnosed and treated over the past decade. Clinical, laboratory, and treatment parameters were reviewed to identify predictors of survival outcomes. Two prognostic models, the AIIMS MCL Overall Survival (AMOS) and Event-Free Survival (AMES) scores, were developed using multivariate logistic regression. Model performance was evaluated using receiver operator characteristic (ROC) curve analysis, with stratification into risk categories based on empiric score cut-offs. Results AMOS and AMES models demonstrated robust predictive performance with area under ROC curve values of 0.75 and 0.82, respectively. The AMOS score incorporated Eastern Co-operative Oncology Group performance status (ECOG PS) and splenomegaly to predict two yr overall survival, while the AMES score included ECOG PS, B-symptoms, advanced stage, and rituximab maintenance (RM) to predict two yr event-free survival. Risk stratification revealed significant survival differences, with a median overall survival of nine months in the high-risk AMOS group compared to 75.5 months in the low-risk group. Similar trends were observed for event-free survival. Interpretation & conclusions AMOS and AMES scores are novel indigenous MCL survival prediction models. They are effective, pragmatic tools for risk stratification of MCL patients.