Cyclic Distinction–Integration (CDIL):

Cyclic cancer therapies—including radiation fractionation, chemotherapy scheduling, pulsed electric fields, focused ultrasound, and photodynamic therapy—have emerged independently across clinical modalities, yet consistently employ alternating phases of therapeutic perturbation and recovery. This work proposes a unifying control-theoretic framework, Cyclic Distinction–Integration (CDIL), to explain this convergent temporal structure and to formalize how therapeutic selectivity arises.Within the CDIL framework, treatment cycles are modeled as two alternating phases: Distinction, in which controlled perturbations reduce cellular integration and increase molecular disorder in both tumor and normal tissue, and Integration, in which recovery is permitted through host-specific metabolic and repair mechanisms. Therapeutic selectivity is shown to emerge not from perturbation strength alone, but from differential recovery capacity between malignant and normal cells during integration phases. Mathematical analysis demonstrates that cyclic protocols generate cumulative advantage through buffering asymmetry, whereas continuous exposure drives both populations toward steady-state survival.The framework is shown to be consistent with empirically optimized protocols across multiple modalities, including radiation fractionation, tumor-treating fields, chemotherapy cycles, pulsed ultrasound, and fractionated photodynamic therapy. CDIL further yields testable predictions: (i) therapeutic efficacy should correlate more strongly with buffering differentials than absolute sensitivity; (ii) active support during integration phases should widen therapeutic windows; (iii) cycle timing personalized to individual recovery kinetics should outperform fixed schedules; and (iv) multimodal combinations should synergize when distinction mechanisms are orthogonal and integration phases are shared.By reframing cyclic cancer therapy as a control-theoretic optimization problem, CDIL provides a principled basis for rational schedule design, adaptive protocols, and cross-modal learning. Beyond oncology, the framework generalizes to other therapeutic contexts characterized by differential buffering capacity, including infectious disease, immune modulation, and aging-related interventions.