Breast Cancer Prognosis Factors Based on Glucose Metabolic Activity in Adipose Tissue

Data Sheet 2 contains clinical and body composition data for 74 breast cancer patients, collected by Jinci Mai. The dataset includes factors such as AJCC stage and the mean standard uptake value of subcutaneous adipose tissue (SUVmean_SAT) from PET imaging, used to analyze progression-free survival over a median follow-up of 18 months. It was last updated on March 17, 2026.

Use Cases

• Predicting breast cancer recurrence risk based on AJCC stage and SUVmean_SAT.
• Building Cox proportional hazards models for progression-free survival analysis.
• Assessing the confounding effects of adipose tissue metabolic activity on clinical stage.
• Comparing prognostic model performance using metrics like time-dependent AUC and net reclassification index.

Strengths

• Includes data from 74 breast cancer patients with a median follow-up of 18 months.
• Key prognostic factors (AJCC stage and SUVmean_SAT) are identified with statistical significance (p < 0.001, p = 0.001).
• Model comparison performed using time-dependent AUC and net reclassification index.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data may reflect temporal or source bias inherent to the single study on figshare.

Provenance

Source

Jinci Mai via figshare.

Collection Method

Body composition parameters and clinicopathological data were collected from patients.

Time Range

Median follow-up of 18 months.

Freshness

Last updated 2026-03-17 13:06:27; freshness should be verified.

Geography

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