An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI

Elham Karami,Ali Sadeghi-Naini,Leedan Murray,Greg J Stanisz,Margaret M Koletar,Wendy Oakden,Stanley K Liu,Wilfred W Lam

doi:10.1038/s41598-020-64912-6

Abstract

Saturation transfer MRI can be useful in the characterization of different tumour types. It is sensitive to tumour metabolism, microstructure, and microenvironment. This study aimed to use saturation transfer to differentiate between intratumoural regions, demarcate tumour boundaries, and reduce data acquisition times by identifying the imaging scheme with the most impact on segmentation accuracy. Saturation transfer-weighted images were acquired over a wide range of saturation amplitudes and frequency offsets along with T1 and T2 maps for 34 tumour xenografts in mice. Independent component analysis and Gaussian mixture modelling were used to segment the images and identify intratumoural regions. Comparison between the segmented regions and histopathology indicated five distinct clusters: three corresponding to intratumoural regions (active tumour, necrosis/apoptosis, and blood/edema) and two extratumoural (muscle and a mix of muscle and connective tissue). The fraction of tumour voxels segmented as necrosis/apoptosis quantitatively matched those calculated from TUNEL histopathological assays. An optimal protocol was identified providing reasonable qualitative agreement between MRI and histopathology and consisting of T1 and T2 maps and 22 magnetization transfer (MT)-weighted images. A three-image subset was identified that resulted in a greater than 90% match in positive and negative predictive value of tumour voxels compared to those found using the entire 24-image dataset. The proposed algorithm can potentially be used to develop a robust intratumoural segmentation method.

Highlights

There is a diagnostic advantage to the segmentation of heterogeneous tumours prior to further analysis
Saturation transfer Magnetic resonance imaging (MRI) is sensitive to differences in tumour metabolism, which differ between intratumoural regions[1], and can be useful in the characterization of different tumour types[23], since it does offer superb tissue contrast, in comparison to other methods, without a need for exogenous contrast agents
An automatic framework was developed for segmenting intratumoural regions using T1 and T2 maps and saturation transfer-weighted images

Summary

Introduction

There is a diagnostic advantage to the segmentation of heterogeneous tumours prior to further analysis. Manual segmentation is certainly possible[2,10] It is time consuming, subjective, and typically based on a single image contrast. The goal of the present study was to develop an automated algorithm to segment intratumoural regions as well as the surrounding tissue in a xenograft model of prostate cancer using only saturation transfer MRI data and T1 and T2 maps. This would allow a secondary use of this data, which originally was intended for studying metabolism in tumours[23]. The MT and CEST effects in active tumour vs. necrosis/ apoptosis were quantitatively compared

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Conclusion