Jamadar, Irshad Sikandar and Kumar, Krishna and Khan, Ambareen and Khan, Sher Afghan and Alahmadi, Ahmad Aziz and Alwetaishi, Mamdooh and Hui, Liew Tze (2026) Optimizing breast cancer treatment strategies through fractional-order dynamics: a computational modeling approach. PLOS ONE, 21 (5). pp. 1-36. E-ISSN 1932-6203
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Abstract
Breast cancer treatment optimization is hindered by heterogeneity, resistance development, and interindividual variability. Most existing traditional mathematical models do not generally consider memory effects in biological systems. This may somewhat limit their predictive capability. Therefore, this study develops a fractional-order computational framework to capture tumor dynamics, immune responses, resistance mechanisms, and the effects of thermal therapy, with a focus on memory effects and their significance for treatment predictions. We considered the values of the fractional order parameter (), which varied from 0.75 to 1.0 across five treatment protocols, and the analysis also included four patient populations. Efficacy was highest (32.26) with Continuous protocols at = 0.75. Specifically-optimized, patient-specific input yielded context-dependent patterns: Younger patients realized the maximum benefit (32.38) with Continuous therapy at = 0.80, while compromised patients had an optimum response (32.36) to Adaptive treatment performed at = 0.75. For older patients, the better result (31.82) was achieved with Continuous protocols, with a = 0.93. Parameter sensitivity analyses show that the immune cytotoxic killing rate is the most effective parameter. In addition, treatment resistance parameters are among the five most sensitive. While aggregate differences between fractional-order and integer-order models remain small, context-specific improvements witnessed in certain patient-protocol combinations were as much as 3.68%. Fractional-order modeling thus provides a framework for investigating memory effects in cancer treatment, while clinical validation must establish whether these theoretical improvements indeed yield a discernible increase in predictive accuracy in practice.
| Item Type: | Article (Journal) |
|---|---|
| Subjects: | R Medicine > RC Internal medicine R Medicine > RC Internal medicine > RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid |
| Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Engineering > Department of Mechanical Engineering |
| Depositing User: | Prof. Dr. Sher Afghan Khan |
| Date Deposited: | 19 May 2026 11:12 |
| Last Modified: | 19 May 2026 11:12 |
| Queue Number: | 2026-05-Q3421 |
| URI: | http://irep.iium.edu.my/id/eprint/129028 |
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