Literature-Based Parameters for Fractional-Order Breast Cancer Treatment Modeling

A collection of 19 literature-based parameters for a computational framework modeling breast cancer tumor dynamics, immune responses, and resistance mechanisms. The dataset supports analysis of fractional-order models across five treatment protocols and four patient populations, with efficacy scores up to 32.38. It was authored by Irshad Sikandar Jamadar and last updated on May 14, 2026.

Use Cases

• Calibrating fractional-order computational models based on the described literature parameters
• Comparing treatment protocol efficacy (Continuous, Adaptive) across different patient populations mentioned in the description
• Performing parameter sensitivity analysis, focusing on immune cytotoxic killing rate and treatment resistance parameters
• Investigating the impact of memory effects in biological systems on cancer treatment predictions

Strengths

• Contains 19 parameters derived from literature for model calibration
• Analysis includes five distinct treatment protocols and four patient populations
• Specific efficacy results are provided, such as a maximum benefit of 32.38 for younger patients

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment
• The dataset is very small at 9.5 KB, indicating limited scope

Provenance

Source

figshare

Collection Method

Literature-derived parameters compiled for a computational modeling study.

Freshness

Last updated 2026-05-14 17:38:14; freshness should be verified