Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability.

Radiomic features derived from medical imaging hold significant promise for enhancing diagnostic and prognostic accuracy in clinical settings. However, the reliability of these quantitative metrics remains a concern for their integration into decision support tools. No prior work had resolved the specific stability of these features across different breast magnetic resonance imaging sequences. That uncertainty drove the need for rigorous test-retest assessments to validate their utility as clinical biomarkers. Prior research has shown that image acquisition parameters and post-processing steps can introduce substantial variability in extracted data. This gap motivated a systematic investigation into how various preprocessing techniques influence the consistency of radiomic measurements. Establishing a baseline for repeatability is a prerequisite for moving these computational tools into routine patient care. Researchers must confirm that observed variations reflect biological differences rather than technical noise or artifacts.

The aim of this study is to identify repeatable radiomic features within breast tissue using prospectively collected magnetic resonance imaging exams. Researchers seek to determine which quantitative metrics remain stable across multiple test-retest measurements in healthy volunteers. This investigation addresses the critical need for reliable biomarkers before integrating such data into clinical decision support systems. The team focuses on evaluating how various image preprocessing procedures influence the consistency of these extracted features. By comparing different sequences, the study clarifies the impact of technical protocols on the reproducibility of quantitative imaging data. No prior work had systematically assessed these specific variables across T1-weighted, T2-weighted, and diffusion-weighted imaging modalities. That uncertainty drove the researchers to establish a baseline for feature stability in a controlled clinical environment. This work ultimately intends to provide a foundation for the future development of robust diagnostic and prognostic tools in breast oncology.

The research team conducted a prospective study involving 11 healthy female volunteers to evaluate feature stability. Review approach involved performing 18 MRI exams per participant across three distinct test-retest settings over two days. All scans utilized an identical 1.5 Tesla scanner to maintain consistency throughout the data collection phase. Experts manually segmented the right breast in three dimensions to define the region of interest for analysis. The team extracted 91 quantitative metrics using the Pyradiomics platform before and after applying various image enhancement techniques. Preprocessing steps included bias field correction, z-score normalization, and grayscale discretization using 32 or 64 bins. The investigators compared each scan against the remaining 17 images to calculate the concordance correlation coefficient for every feature. This rigorous comparison approach allowed for the identification of stable metrics across different sequences and processing pipelines.

Key findings from the literature indicate that raw images without preprocessing yielded the highest number of repeatable features for T1-weighted sequences. Specifically, 15 of 91 features, or 16.5%, demonstrated stability in these unprocessed T1-weighted images. For apparent diffusion coefficient maps, raw data also performed best, with 8 of 91 features, or 8.8%, meeting the repeatability threshold. Preprocessed images for T1-weighted sequences showed lower stability, ranging between 4.4% and 15.4% of features. Similarly, preprocessed apparent diffusion coefficient maps yielded between 6.6% and 7.7% repeatable metrics. Z-score normalization significantly improved stability in T2-weighted sequences, producing 26 of 91 repeatable features, or 28.6%. In contrast, unprocessed T2-weighted images only exhibited 11 of 91, or 12.1%, repeatable features. These results demonstrate that the impact of preprocessing on feature consistency is highly dependent on the specific imaging sequence.

The authors conclude that the number of stable radiomic features varies significantly depending on the specific imaging sequence employed. Synthesis and implications suggest that image preprocessing procedures do not universally improve the consistency of extracted data across all modalities. The researchers propose that T2-weighted sequences benefit most from z-score normalization to enhance the count of reliable metrics. Conversely, T1-weighted sequences and apparent diffusion coefficient maps demonstrate superior stability without any additional image processing steps. These findings imply that clinical protocols must be tailored to the specific sequence to maximize the utility of radiomic biomarkers. The authors highlight that repeatability is highly sequence-dependent, necessitating careful selection of processing pipelines for each imaging type. Future applications of these features in decision support systems should account for these observed variations in stability. This work provides a framework for identifying robust features that can be reliably utilized in prospective clinical studies.