Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

Abstract

Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentation and test whether these characteristics are predictive of tumor genomic subtypes.We used preoperative imaging and genomic data of 110 patients from 5 institutions with lower-grade gliomas from The Cancer Genome Atlas. Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors. Genomic data for the analyzed cohort of patients consisted of previously identified genomic clusters based on IDH mutation and 1p/19q co-deletion, DNA methylation, gene expression, DNA copy number, and microRNA expression. To analyze the relationship between the imaging features and genomic clusters, we conducted the Fisher exact test for 10 hypotheses for each pair of imaging feature and genomic subtype. To account for multiple hypothesis testing, we applied a Bonferroni correction. P-values lower than 0.005 were considered statistically significant.We found the strongest association between RNASeq clusters and the bounding ellipsoid volume ratio (p < 0.0002) and between RNASeq clusters and margin fluctuation (p < 0.005).In addition, we identified associations between bounding ellipsoid volume ratio and all tested molecular subtypes (p < 0.02) as well as between angular standard deviation and RNASeq cluster (p < 0.02). In terms of automatic tumor segmentation that was used to generate the quantitative image characteristics, our deep learning algorithm achieved a mean Dice coefficient of 82% which is comparable to human performance.

Enhancing U-Net with Spatial-Channel Attention Gate for Abnormal Tissue Segmentation in Medical Imaging

“…The images were obtained from The Cancer Imaging Archive (TCIA). They correspond to 110 patients included in The Cancer Genome Atlas (TCGA) lower-grade glioma collection with at least FLAIR sequence and genomic cluster data available [31]. This dataset contains 1373 brain images with a resolution of 256 × 256 pixels.…”

A deep learning-based approach for high-throughput hypocotyl phenotyping

“…Central to our approach is the U-Net deep convolutional neural network for segmentation, which is particularly excellent for finding thin objects. It has been applied with success on various problems such as detecting cell nuclei in microscopic images or identifying subparts of the brain on MRI scans (Ronneberger et al, 2015;Buda et al, 2019). On images for plant phenotyping, in addition to separating the plant from the background, U-Net is able to identify the specific parts of the plants.…”

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“…Genomic data, either alone or in conjunction with neuroimaging and histopathology, has provided cancer researchers a wealth of data on which to perform cancer-related predictive tasks [77,79,80]. Deep learning offers several advantages when working simultaneously with multiple data modalities, removing subjective interpretations of histological images, accurately predicting time-to-event outcomes, and even surpassing gold standard clinical paradigms for glioma patient survival [80].…”