Clustering of MRI Radiomics Features for Glioblastoma Multiforme: An Initial Study

Li, Zhicheng; Li, Qihua; Song, Bolin; Chen, Yinsheng; Sun, Qinjun; Xie, Yaoqin; Wang, Lei

doi:10.1007/978-3-319-43775-0_28

Cited by 1 publication

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“…A representative feature was selected from each of the k clusters of the optimum number previously established by one of two methods: medoid or univariate prognostic power. With the medoid selection process, the Spearman correlation among cluster members was calculated, and the feature with the highest average correlation was selected (Parmar et al 2015, Li et al 2016. For the univariate prognostic power selection, a log likelihood ratio test was performed comparing the model with each individual feature to the model containing only the intercept, and the feature with the most significant log likelihood p -value was selected as the representative feature (Fave et al 2017).…”

Section: Feature Selection and Reductionmentioning

confidence: 99%

Repeatability of texture features derived from magnetic resonance and computed tomography imaging and use in predictive models for non-small cell lung cancer outcome

2019

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To evaluate the repeatability of MRI and CT derived texture features and to investigate the feasibility of use in predictive single and multi-modality models for radiotherapy of non-small cell lung cancer, 59 texture features were extracted from unfiltered and wavelet filtered images. Repeatability of test-retest features from helical 4D CT scans, true fast MRI with steady state precession (TRUFISP), and volumetric interpolation breath-hold examination (VIBE) was determined by the concordance correlation coefficient (CCC). A workflow was developed to predict overall survival at 12, 18, and 24 months and tumour response at end of treatment for tumour features, and normal muscle tissue features as a control. Texture features were reduced to repeatable and stable features before clustering. Cluster representative feature selection was performed by univariate or medoid analysis before model selection. P-values were corrected for false discovery rate. Repeatable (CCC ⩾ 0.9) features were found for both tumour and normal muscle tissue: CT: 54.4% for tumour and 78.5% for normal tissue, TRUFISP: 64.4% for tumour and 67.8% for normal tissue, and VIBE: 52.6% for tumour and 72.9% for normal muscle tissue. Muscle tissue control analysis found seven significant models with six of seven models utilizing the univariate representative feature selection technique. Tumour analysis revealed 12 significant models for overall survival and none for tumour response at end of treatment. The accuracy of significant single modality was about the same for MR and CT. Multi-modality tumour models had comparable performance to single modality models. MR derived texture features may add value to predictive models and should be investigated in a larger cohort. Control analysis demonstrated that the medoid representative feature selection method may result in more robust models.

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Section: Feature Selection and Reductionmentioning

confidence: 99%