A 2026 study by Jessica V. Eberle analyzes dominant error types in breast diffusion-weighted MRI for lesion characterization. The work includes a literature review of 24 published studies and an in-house analysis of a cohort of 171 patients from a university hospital. It determines the median sample size at which finite N errors and precision errors are equal to be n=932.
Use Cases
- Estimating required sample sizes for breast DWI studies based on the analysis of finite N versus precision errors.
- Assessing the statistical rigor of apparent diffusion coefficient (ADC)-based lesion characterization using the reported ROC analysis function.
- Comparing error type dominance across different study cohorts, as performed between the in-house data and the 24 reviewed studies.
- Informing study design optimization by incorporating dominant error type assessments as discussed in the paper.
Strengths
- Includes a literature review of 24 published breast DWI studies.
- Analyzes a specific in-house cohort of 171 patients with suspicious breast lesions.
- Provides a concrete median sample size estimate of n=932 for error type equilibrium.
Limitations
- The dataset is very small at 1.1 KB, suggesting limited raw data.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
Provenance
- Source
- Jessica V. Eberle via figshare.
- Collection Method
- Combines a literature review with analysis of a local university hospital patient cohort.
- Freshness
- Last updated 2026-06-04 17:32:03.