An LSTM-Assisted Markov Model for Predicting Cancer Stage Transitions: A Simulation Study

Abstract

Traditional Markov chain models are commonly used to represent cancer progression but are limited by the memoryless assumption and reduced ability to capture nonlinear temporal dependencies. This study proposes a hybrid LSTM-assisted Markov chain framework that integrates sequential deep learning with probabilistic state-transition modeling. A clinically informed simulation framework generated 30,000 synthetic longitudinal patient trajectories across five disease states (Carcinoma in situ, Early/Localized, Locally Advanced, Regionally Advanced, and Metastatic). Transition probabilities were parameterized to reflect realistic progression dynamics, including intensified forward transitions, rare backward transitions (<1.5%), and an absorbing metastatic state. Each sequence contained 5–15 time steps with 17 clinical features. Due to irreversible progression, advanced stages were moderately overrepresented. Data were stratified into training (64%, n=19,200), validation (16%, n=4,800), and independent test (20%, n=6,000) sets while preserving class proportions. Hyperparameters were tuned on the validation set. Performance uncertainty was estimated using 1,000 bootstrap resamples to compute 95% confidence intervals (CIs). On the independent test set, the hybrid model achieved an accuracy of 0.919 (95% CI: 0.912–0.926), precision of 0.883 (95% CI: 0.874–0.892), recall of 0.919 (95% CI: 0.912–0.926), and F1-score of 0.896 (95% CI: 0.887–0.905). Compared with a traditional Markov baseline, the hybrid framework demonstrated improved predictive stability and better discrimination of advanced disease states.These findings demonstrate methodological robustness within a simulation-based environment encoding realistic cancer progression patterns. However, results are derived solely from synthetic data and require validation on real-world longitudinal clinical datasets before clinical applicability can be established.