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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.
Methods: Fifty-nine 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.
Results: Repeatable (CCC ≥ 0.9) features were found for both tumour and normal muscle tissue:
54.4% for tumour and 78.5% for normal tissue,
64.4% for tumour and 67.8% for normal tissue, and
52.6% for tumour and 72.9% for normal muscle tissue. Muscle tissue control analysis found 7 significant models with 6 of 7 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. Conclusion: 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. .
This article was published in the following journal.
Name: Physics in medicine and biology
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