Journal of Cancer Research and Pharmacology

Journal of Cancer Research and Pharmacology

Strategic management of cancer progression: Novel approaches and the role of AI and modeling in treatment and crisis management

Document Type : Review

Authors
Amin Ghalehnovi 1
Reza Akhavan-Sigari 2
1 Department of Civil Engineering, Ne.C., Islamic Azad University, Neyshabur, Iran
2 Dr. Schneiderhan GmbH and ISAR Klinikum Munich, Germany AND Department of Health Care Management and Clinical Research, Collegium Humanum Warsaw Management University Warsaw, Poland
10.22034/jcrph.2025.562496.1018
Abstract
Cancer progression is increasingly recognized as a complex, multilevel process shaped by biological heterogeneity, treatment-induced evolution, and constraints at the organizational and health-system levels. Conventional, episode-based decision-making struggles to keep pace with expanding therapeutic options, growing data volumes, and recurrent crises such as pandemics that disrupt cancer pathways. This narrative review proposes a strategic framework for managing cancer progression that integrates novel clinical approaches with artificial intelligence (AI) and modeling across the entire cancer care trajectory and across micro (patient), meso (service), and macro (system) decision levels. We first conceptualize cancer care as a longitudinal continuum, spanning prevention and early detection, diagnosis and staging, primary treatment, surveillance and survivorship, management of recurrence or advanced disease, and palliative and end-of-life care, and argue that progression management requires anticipatory planning across this continuum rather than reactive choices at isolated time points. Within this structure, three pillars are examined in detail: novel therapeutic and organizational approaches (including precision oncology, immunotherapy, advanced radiotherapy, and pathway-based care), AI methods for prediction and decision support, and mechanistic and simulation-based models, including digital twins, for treatment optimization and scenario analysis. We then highlight how AI and modeling can be combined to support crisis management, for example by forecasting backlogs, guiding triage, and testing mitigation strategies during system shocks. Finally, we discuss ethical, legal, and organizational challenges and outline a research agenda for hybrid AI–mechanistic platforms and dynamic digital twins that could shift cancer care from reactive to strategic, learning system management of progression.
Keywords
Cancer progression
Strategic management
Artificial intelligence
Mathematical modeling
Crisis management
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Journal of Cancer Research and Pharmacology
Volume 1, Issue 2
Autumn 2025
Pages 145-171

  • Receive Date 28 January 2026