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Abstract #4686

Texture analysis of multiparametric MRI: interobserver variability of texture features and associations with nodal status.

Jose Angelo Udal Perucho1, Elaine Yuen Phin Lee1, Richard Du1, Varut Vardhanabhuti1, and Queenie Chan2

1Diagnostic Radiology, The University of Hong Kong, Hong Kong, Hong Kong, 2Philips Healthcare, Hong Kong, Hong Kong

Texture analysis of pre-treatment multiparametric MRI (mpMRI) consisting of diffusion-weighted MRI (DWI) and T2-weighted (T2W) texture features could be a promising and reproducible quantitative approach in assessing tumor heterogeneity in cervical cancer. We retrospectively studied forty treatment-naïve patients who had mpMRI examinations. We observed that around 30% of texture features had low interobserver variability, and that most of these features were from the Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run Length Matrix (GLRLM). Furthermore, T2W features had moderate associations with pelvic lymph node (PLN) status.

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