A machine learning-based integrated clinical model for predicting prognosis in atypical meningioma patients.

Purpose: Atypical meningioma (AM) recurs in up to half of patients after surgical resection and may require adjuvant therapy to improve patient prognosis. Various clinicopathological features have been shown to have prognostic implications in AM, but an integrated prediction model is lacking. Thus, in this study, we aimed to develop and validate an integrated prognostic model for AM.

Methods: A retrospective cohort of 528 adult AM patients surgically treated at our institution were randomly assigned to a training or validation group in a 7:3 ratio. Sixteen baseline demographic, clinical, and pathological parameters, progression-free survival (PFS), and overall survival (OS) were analysed. Sixty-five combinations of machine learning (ML) algorithms were used for model training and validation to predict tumour recurrence and patient mortality.

Results: The random survival forest (RSF) model was the best model for predicting recurrence and death. Primary or secondary tumour, Ki-67 index, extent of resection, tumour size, brain involvement, tumour necrosis, and age contributed significantly to the model. The C-index value of the RSF recurrence prediction model reached 0.8080. The AUCs for 1-, 3-, and 5-year PFS were 0.83, 0.82, and 0.86, respectively. The C-index value of the RSF death prediction model reached 0.8890. The AUCs for 3-year and 5-year OS were 0.88 and 0.89, respectively.

Conclusion: A high-performing integrated RSF predictive model for AM recurrence and patient mortality was proposed that may guide therapeutic decision-making and long-term monitoring.