Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions

Pistel M, Brock L, Laun FB, Erber R, Weiland E, Uder M, Wenkel E, Ohlmeyer S, Bickelhaupt S (2024)

Publication Language: English

Publication Type: Journal article

Publication year: 2024

Journal

Diagnostics MDPI

Book Volume: 14

Article Number: 1427

Journal Issue: 13

DOI: 10.3390/diagnostics14131427

Abstract

Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.

Authors with CRIS profile

Mona Pistel Professur für quantitative MR-Bildgebung (Heisenberg-Professur) Luise Brock Institute of Radiology Frederik Bernd Laun Professur für quantitative MR-Bildgebung (Heisenberg-Professur) Ramona Erber Institute of Pathology Michael Uder Lehrstuhl für Diagnostische Radiologie Evelyn Wenkel Lehrstuhl für Diagnostische Radiologie Sabine Ohlmeyer Institute of Radiology Sebastian Bickelhaupt Institute of Radiology

Involved external institutions

Siemens Healthineers DE Germany (DE)

How to cite

APA:

Pistel, M., Brock, L., Laun, F.B., Erber, R., Weiland, E., Uder, M.,... Bickelhaupt, S. (2024). Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions. Diagnostics, 14(13). https://doi.org/10.3390/diagnostics14131427

MLA:

Pistel, Mona, et al. "Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions." Diagnostics 14.13 (2024).

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