Finite sample size errors in the context of multiple error sources in quantitative medical imaging: An evaluation for breast magnetic resonance diffusion-weighted imaging - Jessica V. Eberle; Sebastian Bickelhaupt; Lorenz A. Kapsner; Sabine Ohlmeyer; Evelyn Wenkel; Michael Uder; Dominika Skwierawska; Katharina Tkotz; Dominique Hadler; Tristan A. Kuder; Frederik B. Laun (PLoS ONE, 2026)
Background Selecting appropriate sample sizes in magnetic resonance imaging studies is a complex process that requires to balance statistical rigor with the practical challenges of measuring a large patient population. In this Institutional Review Board approved study, we evaluate the dominant error types (“finite N” errors versus precision errors) for apparent diffusion coefficient (ADC)-based lesion characterization in diffusion-weighted magnetic resonance imaging (DWI) of the female breast in a local dataset and compare our results with current literature. Methods First, in a literature review including 24 published breast DWI studies, the standard error of the area under the receiver operating characteristic curve as a measure of sample size-related errors (finite N errors) was estimated for the reported ADC values and compared to the values, derived from expert readings of a university hospital’s cohort of 171 patients with suspicious breast lesions. Second, precision errors were assessed based on published analyses of the coefficient of variation of ADC values, measured in breast DWI exams. Results Finite N errors were dominant in the in-house study and most of the 24 reviewed studies. The median sample size at which finite N errors and precision errors were equal was determined to be n = 932. Discussion This analysis of dominant error types shows that the required sample sizes for the considered use case are not unreasonably large and that reducing sample sizes may not be justified based on the merits of the conducted analysis. Nonetheless, incorporating dominant error type assessments into future studies may provide valuable insights for optimizing study design and improving methodological rigor.