Effect of magnetic field strength and segmentation variability on the reproducibility and repeatability of radiomic texture features in cardiovascular magnetic resonance parametric mapping

TL;DR

This study examines how changes in magnetic field strength and segmentation affect the consistency of radiomic texture features in heart imaging, finding that some features are more reliable than others.

Contribution

The study identifies specific radiomic texture features and preprocessing filters that are robust to segmentation variability and scanner differences.

Findings

Only 7% of radiomic texture features showed good reproducibility across 1.5T and 3T scanners.

Up to 31% of features had excellent repeatability when segmentation variability was low.

Certain preprocessing filters and feature classes were less sensitive to variations in field strength and segmentation.

Abstract

Our study aims to assess the robustness of myocardial radiomic texture features (RTF) to segmentation variability and variations across scanners with different field strengths, addressing concerns about reliability in clinical practices. We conducted a retrospective analysis on 45 pairs of CMR T1 maps from 15 healthy volunteers using 1.5 T and 3 T Siemens scanners. Manual left ventricular myocardium segmentation and a deep learning-based model with Monte Carlo Dropout generated masks with different levels of variability and 1023 RTFs extracted from each region of interest (ROI). Reproducibility: the extent to which RTFs extracted from 1.5 T and 3 T images are consistent, and repeatability: the extent to which RTFs extracted from multiple segmentation runs at the same field strength agree with each other, were measured by the intraclass correlation coefficient (ICC). We categorized ICC values as excellent, good, moderate, and poor. We reported the proportion of RTFs that fell in each category. The proportion of RTFs with excellent repeatability decreased as the proportion of ROI pixels in congruence across segmentation runs decreased. Up to 31% of RTFs showed excellent repeatability, while 35% showed good repeatability across segmentation runs from the manually generated masks. Across scanners (i.e., 1.5 T vs 3 T), only 7% exhibited good reproducibility. While our results demonstrate RTF sensitivity to differences in field strength and segmentation variability, we identified certain preprocessing filters and feature classes that are less sensitive to these variations and, as such, may be good candidates for imaging biomarkers or building machine-learning models.

The online version contains supplementary material available at 10.1007/s10554-024-03312-7.

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