Zhang Kevin Sun, Neelsen Christian Jan Oliver, Wennmann Markus, Hielscher Thomas, Kovacs Balint, Glemser Philip Alexander, Görtz Magdalena, Stenzinger Albrecht, Maier-Hein Klaus H, Huber Johannes, Schlemmer Heinz-Peter, Bonekamp David
German Cancer Research Center (DKFZ), Division of Radiology, Heidelberg, Germany.
German Cancer Research Center (DKFZ), Division of Biostatistics, Heidelberg, Germany.
Sci Rep. 2025 Aug 13;15(1):29703. doi: 10.1038/s41598-025-09989-7.
Despite academic success, radiomics-based machine learning algorithms have not reached clinical practice, partially due to limited repeatability/reproducibility. To address this issue, this work aims to identify a stable subset of radiomics features in prostate MRI for radiomics modelling. A prospective study was conducted in 43 patients who received a clinical MRI examination and a research exam with repetition of T2-weighted and two different diffusion-weighted imaging (DWI) sequences with repositioning in between. Radiomics feature (RF) extraction was performed from MRI segmentations accounting for intra-rater and inter-rater effects, and three different image normalization methods were compared. Stability of RFs was assessed using the concordance correlation coefficient (CCC) for different comparisons: rater effects, inter-scan (before and after repositioning) and inter-sequence (between the two diffusion-weighted sequences) variability. In total, only 64 out of 321 (~ 20%) extracted features demonstrated stability, defined as CCC ≥ 0.75 in all settings (5 high-b value, 7 ADC- and 52 T2-derived features). For DWI, primarily intensity-based features proved stable with no shape feature passing the CCC threshold. T2-weighted images possessed the largest number of stable features with multiple shape (7), intensity-based (7) and texture features (28). Z-score normalization for high-b value images and muscle-normalization for T2-weighted images were identified as suitable.
尽管基于影像组学的机器学习算法在学术上取得了成功，但尚未应用于临床实践，部分原因是其可重复性有限。为了解决这个问题，本研究旨在识别前列腺MRI中用于影像组学建模的稳定影像组学特征子集。对43例接受临床MRI检查和研究性检查的患者进行了一项前瞻性研究，检查重复进行了T2加权成像和两种不同的扩散加权成像（DWI）序列，中间进行了重新定位。从MRI分割中提取影像组学特征（RF），并考虑了评分者内和评分者间的影响，比较了三种不同的图像归一化方法。使用一致性相关系数（CCC）评估RF在不同比较中的稳定性：评分者效应、扫描间（重新定位前后）和序列间（两种扩散加权序列之间）的变异性。总共，在321个提取的特征中，只有64个（约20%）表现出稳定性，即在所有设置下（5个高b值、7个表观扩散系数[ADC]和52个T2衍生特征）CCC≥0.75。对于DWI，主要基于强度的特征被证明是稳定的，没有形状特征超过CCC阈值。T2加权图像具有最多的稳定特征，包括多个形状特征（7个）、基于强度的特征（7个）和纹理特征（28个）。高b值图像的Z分数归一化和T2加权图像的肌肉归一化被认为是合适的。
