Radiomics Based Differentiation of Glioblastoma and Metastatic Disease: Impact of Different T1-Contrast Enhanced Sequences on Radiomic Features and Model Performance.

Girish Bathla, Camila G Zamboni, Nicholas Larson, Yanan Liu, Honghai Zhang, Nam H Lee, Amit K Agarwal, Neetu Soni, Milan Sonka
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Abstract

Background and purpose: To evaluate the radiomics-based model performance for differentiation between glioblastoma (GB) and brain metastases (BM) using magnetization prepared rapid gradient echo (MPRAGE) and volumetric interpolated breath-hold examination (VIBE) T1-contrast enhanced sequences.

Materials and methods: T1-CE MPRAGE and VIBE sequences acquired in 108 patients (31 GBs and 77 BM) during the same MRI session were retrospectively evaluated. Post standardized image pre-processing and segmentation, radiomics features were extracted from necrotic and enhancing tumor components. Pearson correlation analysis of radiomics features from tumor subcomponents was also performed. A total of 90 machine learning (ML) pipelines were evaluated using a five-fold cross validation. Performance was measured by mean AUC-ROC, Log-loss and Brier scores.

Results: A feature-wise comparison showed that the radiomic features between sequences were strongly correlated, with the highest correlation for shape-based features. The mean AUC across the top-ten pipelines ranged between 0.851-0.890 with T1-CE MPRAGE and between 0.869-0.907 with T1-CE VIBE sequence. Top performing models for the MPRAGE sequence commonly used support vector machines, while those for VIBE sequence used either support vector machines or random forest. Common feature reduction methods for top-performing models included linear combination filter and least absolute shrinkage and selection operator (LASSO) for both sequences. For the same ML-feature reduction pipeline, model performances were comparable (AUC-ROC difference range: [-0.078, 0.046]).

Conclusions: Radiomic features derived from T1-CE MPRAGE and VIBE sequences are strongly correlated and may have similar overall classification performance for differentiating GB from BM.

Abbreviations: BM: Brain metastases, GB: glioblastoma, T1-CE: T1 contrast enhanced sequence, MPRAGE: magnetization prepared rapid gradient echo, ML: machine learning, RF: random forest, VIBE: volumetric interpolated breath-hold examination.

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基于放射组学的胶质母细胞瘤和转移性疾病的区分:不同的 T1 对比增强序列对放射组学特征和模型性能的影响
背景和目的:使用磁化准备快速梯度回波(MPRAGE)和容积插值屏气检查(VIBE)T1对比增强序列,评估基于放射组学的模型在区分胶质母细胞瘤(GB)和脑转移瘤(BM)方面的性能:对108例患者(31例GB和77例BM)在同一次磁共振成像检查中获得的T1-CE MPRAGE和VIBE序列进行回顾性评估。经过标准化图像预处理和分割后,从坏死和增强的肿瘤成分中提取放射组学特征。还对肿瘤亚组分的放射组学特征进行了皮尔逊相关分析。使用五倍交叉验证对总共 90 个机器学习(ML)管道进行了评估。通过平均 AUC-ROC、Log-loss 和 Brier 分数来衡量性能:特征比较显示,序列间的放射学特征具有很强的相关性,其中基于形状的特征相关性最高。T1-CE MPRAGE 和 T1-CE VIBE 序列的平均 AUC 分别为 0.851-0.890 和 0.869-0.907 之间。MPRAGE 序列中表现最好的模型通常使用支持向量机,而 VIBE 序列中表现最好的模型则使用支持向量机或随机森林。对于两种序列,表现最好的模型常用的特征缩减方法包括线性组合滤波器和最小绝对收缩和选择算子(LASSO)。对于相同的 ML 特征缩减管道,模型性能相当(AUC-ROC 差异范围:[-0.078, 0.046]):结论:从 T1-CE MPRAGE 和 VIBE 序列得出的放射学特征具有很强的相关性,在区分 GB 和 BM 时可能具有相似的整体分类性能:缩写:BM:脑转移瘤;GB:胶质母细胞瘤;T1-CE:T1对比增强序列;MPRAGE:磁化准备快速梯度回波;ML:机器学习;RF:随机森林;VIBE:容积插值屏气检查。
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