The Role of Texture Analysis of MRI in Prediction of Local Recurrence and Distant Metastasis in Locally Advanced Rectal Cancer: A retrospective Cohort study

Author(s): M. Alrahawy, M. Aker, B. Ganeshan, A. Zeinaldin, T. Arulampalam

Purpose: Locally advanced rectal cancer (LARC) is treated by neoadjuvant chemoradiotherapy (NCRT) followed by surgery after restaging with magnetic resonance imaging (MRI). Texture analysis (TA) is a novel imaging biomarker that can assess heterogeneity in MRIs by measuring grey-level intensities distribution. This study hypothesizes that TA of MRI is an imaging biomarker that can predict local recurrence and distant metastasis.

Method: This is a retrospective analysis of all patients diagnosed with LARC who received NCRT and had MRI scans between 2003-2014 at Colchester University Hospital. Region of interest was drawn around the tumor or its location on T2 MRI images. Six texture parameters were systematically extracted from Textural histograms of post-treatment scans. These parameters were examined to determine their ability to predict local recurrence and distant metastases through Kaplan-Meier survival curves and log-rank tests.

Results: 113 patients with LARC were included. Two texture parameters were significantly able to predict local recurrence: Entropy (p=0.033) and mean of positive pixels (MPP) (p=0.045). Five parameters were able to predict distant metastases: SD(p=0.015), entropy(p=0.017), MPP(p=0.005), skewness (p=0.046), and Kurtosis (P=0.019). Upon dichotomizing by the optimal cut-off values, Kaplan-Meier Log rank test showed that entropy and skewness significantly predicted distant metastases.

Conclusions: MRI textural features are potentially significant imaging biomarkers in predicting local recurrence and distant metastases in LARC.