Abstract 3351: Overall survival prediction of glioblastoma patients combining clinical factors with texture features extracted from 3-D convolutional neural networks
Authors: Zong Weiwei , Lee Joon , Liu Chang , Snyder James , Wen Ning 
Published: August 2019

Objective:

We aim to predict overall survival (OS) in Glioblastoma treated with gross total resection (GTR) using pre-operative MRI images.

Methods:

A cohort of 87 GBM patients (59 patients for training and 28 patients for validation) who underwent GTR was analyzed using multi-institutional data from brain tumor segmentation (BraTS) challenge 2018 (Menze BH, et al. TMI 2015; Bakas S, et al. NSD 2017).

Each patient consisted of a series of pre-surgical MR images including T1 pre contrast, T1-Contrast Enhanced, T2 and Flair images. A group of experienced radiologists delineated edema, tumor core and enhanced tumor for each testing patient using these image sequences. A 2D U-net was trained to segment these structures on the validation cohort.

A 3D CNN model with orthogonalized random filters was used to learn images features from the three segmented subregions including texture, size, location, etc. Global maximum pooling was performed on intermediate convolutional layers to obtain representative image features for each patient.

Since mid-term survivors (6-18 months) outnumbered short (&lt;6 months) and long term (&gt;18 months) survivors for a large margin, the MR images from both short and long term survivors were augmented

with random rotations to balance the number of patients among three cohorts of patients. The extracted image features was then fed into an RBF-kernel based L-2 norm regression algorithm (Huang GB, IEEE SMC, 2012) to predict patient’s OS.

Results:

The average [standard deviation] of dice similarity coefficient (DSC) for the whole tumor, enhanced tumor, and tumor core contours were 0.882[0.080], 0.712 [0.294], and 0.769 [0.263], respectively for the validation cohort. Parameters of regression algorithm was optimized using leave one out cross validation.

One convolutional layer was used in the CNN archietecture due to limited training samples. The model performance deteriorated when using deeper layers. The best architecture to classify patients into short, mid and long term survivors was one convolutional layer with 30 filters. The prediction accuracy was 64.3%, and the spearman’r sank correlation was 0.395.

The model performance was slightly improved by including clinical factors such as age, tumor location, ratio of the whole tumor size to the entire brain etc. The Spearman’s rank correlation coefficient was increased to 0.432 while the accuracy maintained the same.

Conclusions:

We developed a 3D CNN model followd with kernel regression to extract image features from pre-operative MR images and predict OS of GBM patients after GTR. These signatures have shown potential values as biomarkers to predict OS.

Citation Format: Weiwei Zong, Joon Lee, Chang Liu, James Snyder, Ning Wen. Overall survival prediction of glioblastoma patients combining clinical factors with texture features extracted from 3-D convolutional neural networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3351.