MRI Texture Analysis for Tumor Recurrence Prediction
A study with pituitary macroadenomas
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The present work proposes the usage of texture features computationally extracted from MRI as imaging biomarkers in the prediction of tumor recurrence in patients with non-functioning pituitary adenomas (NFPA). With this purpose, this study analyzed MR images from patients of NFPA retrospectively separated in groups: the recurrent patient group, formed by patients who exhibited tumor recurrence after the first surgical approach, and the stable patient group formed by patients with lesions considered stable. The preoperative MR images were used to extract numerical textural features. Clinical f...
The present work proposes the usage of texture features computationally extracted from MRI as imaging biomarkers in the prediction of tumor recurrence in patients with non-functioning pituitary adenomas (NFPA). With this purpose, this study analyzed MR images from patients of NFPA retrospectively separated in groups: the recurrent patient group, formed by patients who exhibited tumor recurrence after the first surgical approach, and the stable patient group formed by patients with lesions considered stable. The preoperative MR images were used to extract numerical textural features. Clinical features were also considered in the study. The features were tested through conventional univariate statistical tests and were used to build machine learning prediction models. The findings of the study imply that textural features are useful in the prediction of tumor recurrence after first surgery in NFPA patients. And that the prediction power of those features can be observed with both conventional univariate statistical tests and multivariate analyses through machine learning algorithms.
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