Breast Cancer Treatment Protocol Simulations with Fractional-Order Models

5.5 KB of data compares five breast cancer treatment protocols across four patient populations using a fractional-order computational framework. The dataset, created by Irshad Sikandar Jamadar and last updated in May 2026, likely contains simulation results evaluating protocol efficacy, immune response, and resistance mechanisms. It models memory effects in biological systems, with efficacy scores ranging up to 32.38 for specific patient-protocol combinations.

Use Cases

• Comparing the efficacy of Continuous, Adaptive, and other treatment protocols based on simulated patient outcomes.
• Analyzing the impact of fractional-order parameters (α from 0.75 to 1.0) on tumor dynamics and treatment predictions.
• Investigating patient-specific treatment optimization for younger, older, and compromised patient populations.
• Performing sensitivity analysis on model parameters, such as immune cytotoxic killing rate and treatment resistance parameters.

Strengths

• Includes results for five distinct treatment protocols and four patient populations.
• Quantifies context-specific improvements, with certain patient-protocol combinations showing up to a 3.68% benefit over integer-order models.
• Provides specific efficacy scores, such as a maximum benefit of 32.38 for younger patients under Continuous therapy at α = 0.80.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Computational simulation using a fractional-order modeling framework.

Freshness

Last updated 2026-05-14 17:38:10; freshness should be verified.