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

医学 无线电技术 随机森林 特征(语言学) 核医学 胶质母细胞瘤 特征选择 支持向量机 人工智能 接收机工作特性 模式识别(心理学) 放射科 计算机科学 内科学 癌症研究 语言学 哲学
作者
Girish Bathla,Camila Gadens Zamboni,Nicholas B. Larson,Yanan Liu,Honghai Zhang,Nam H Lee,Amit Agarwal,Neetu Soni,Milan Sonka
出处
期刊:American Journal of Neuroradiology [American Society of Neuroradiology]
卷期号:: ajnr.A8470-ajnr.A8470
标识
DOI:10.3174/ajnr.a8470
摘要

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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