Dynamical Framework for Detecting Harmful Stabilization in Cancer and Chronic Conditions

This work introduces the concept of maladaptive plateaus: stable, low-variation states in biological systems that are not beneficial, but pathological. Contrary to the traditional assumption that stability reflects health, this framework shows that systems such as tumors, immune responses, or chronic inflammatory processes can become “locked” into harmful stable regimes. The paper defines a plateau using three simultaneous conditions—bounded drift, bounded variation, and persistence—and introduces the cumulative stability integral O(t) as a quantitative measure of how much “stable order” a system accumulates over time. While O(t) captures the strength and duration of stability, it must be interpreted alongside the mean system level to distinguish healthy from maladaptive states. Through examples including cancer therapy resistance, long COVID, chronic inflammation, and fibrosis, the framework demonstrates how dangerous stability can remain undetected by traditional metrics focused on variability or change. The proposed approach provides a simple, interpretable, and model-free method for identifying regime shifts toward pathological stabilization in real time. This work reframes stability as a directional and diagnostic property rather than an inherently positive one, offering a new perspective for early detection and intervention in complex diseases and dynamical systems.