Mamba-Based Radiomics for Prognostic Prediction in Precision Oncology
Keywords:
radiomics; Mamba; state space models; precision oncology; prognostic modeling; clinical deployment; fairness; data infrastructureAbstract
Radiomics has transformed medical imaging into a source of quantitative biomarkers, enabling noninvasive characterization of tumor heterogeneity and biological aggressiveness. However, prognostic prediction in precision oncology remains difficult because imaging features, clinical variables, and molecular data interact over multiple spatial and temporal scales. Conventional deep learning architectures, including convolutional networks and transformers, have advanced radiomic modeling but often face computational, interpretability, and sequence-length constraints. Mamba-based selective state space models offer a promising alternative by capturing long-range dependencies with efficient sequence processing. From a systems perspective, the adoption of Mamba-based radiomics requires more than architectural innovation. It demands careful integration of image acquisition standards, feature harmonization, multi-institutional data governance, workflow deployment, and post-market surveillance. This paper examines the structural trade-offs in designing and governing Mamba-based radiomic prognostic models for oncology. It discusses architecture-level considerations, data infrastructure requirements, interoperability challenges, fairness and regulatory implications, and sustainability issues. The analysis emphasizes that clinical value depends on robust validation, transparent uncertainty communication, and alignment with existing oncology decision pathways. Rather than treating Mamba as an isolated model improvement, the paper argues for its placement within a socio-technical infrastructure that supports reliable, equitable, and auditable prognostic inference. Case illustrations and cross-domain comparisons are used to clarify deployment and policy challenges. The discussion concludes with forward-looking perspectives on how selective state space models may reshape prognostic modeling in precision oncology while cautioning against premature adoption without rigorous systems-level evidence.
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