Synthetic MRI improves radiomics-based glioblastoma survival prediction

Abstract

Glioblastoma is an aggressive and fast-growing brain tumor with poor prognosis. Predicting the expected survival of patients with glioblastoma is a key task for efficient treatment and surgery planning. Survival predictions could be enhanced by means of a radiomic system. However, these systems demand high numbers of multicontrast images, the acquisitions of which are time consuming, giving rise to patient discomfort and low healthcare system efficiency. Synthetic MRI could favor deployment of radiomic systems in the clinic by allowing practitioners not only to reduce acquisition time, but also to retrospectively complete databases or to replace artifacted images. In this work we analyze the replacement of an actually acquired MR weighted image by a synthesized version to predict survival of glioblastoma patients with a radiomic system. Each synthesized version was realistically generated from two acquired images with a deep learning synthetic MRI approach based on a convolutional neural network. Specifically, two weighted images were considered for the replacement one at a time, a T2w and a FLAIR, which were synthesized from the pairs T1w and FLAIR, and T1w and T2w, respectively. Furthermore, a radiomic system for survival prediction, which can classify patients into two groups (survival >480 days and $\le$ 480 days), was built. Results show that the radiomic system fed with the synthesized image achieves similar performance compared with using the acquired one, and better performance than a model that does not include this image. Hence, our results confirm that synthetic MRI does add to glioblastoma survival prediction within a radiomics-based approach.

Keywords: glioblastoma; radiomics; survival prediction; synthetic MRI.

FIGURE 1

Workflow of the proposed approach. Initially, patients are divided into training (175 patients) and testing (24 patients) sets. The preprocessing pipeline segments tumors and normalizes the contrast intensity. Features are retrieved from the segmented regions of interest (ROIs). After feature selection, relevant features are retained. Five machine learning models for SP were examined. Three different experiment configurations (referred to throughout the text as Experiments/Results I, II, and III) were defined for comparative performance assessment

FIGURE 2

Preprocessing pipeline. Initial images are first co‐registered and skull‐stripped following the CaPTk pipeline. Afterwards, the different contrast images are denoised and bias‐corrected before obtaining the white matter and tumor segmentations. Finally, skull‐stripped images are bias‐corrected and intensity normalized using the segmentations

FIGURE 3

Overview of the self‐supervised CNN. The inputs of the network are T1w and T2w for the synthesis of FLAIR, and T1w and FLAIR for the synthesis of T2w. Note that all the switches change depending on the weighting we want to synthesize. The lambda layers implement the ideal equations indicated in Appendix B

FIGURE 4

A representative axial slice of the images synthesized by the self‐supervised CNN for different test patients of Dataset24. A, Synthesized FLAIR images. B, Corresponding actually acquired FLAIR images. C, Synthesized T2w images. D, Corresponding actually acquired T2w images

FIGURE 5

AUC, accuracy, precision, recall, and $F_1$‐score of the RS tested with Dataset24 when FLAIR (A) and T2w (B) are acquired, synthesized, or ignored in the whole pipeline

FIGURE 6

Scatter plots of the predicted probabilities obtained at the output of the RS for the experiment with the acquired versus the synthesized images (top row) and versus an RS trained from scratch without considering this weighted image as input (bottom row). The plots are shown for FLAIR (A) and T2w (B). Dashed lines represent the threshold fixed in the RS to classify survival (>480 days). Each point represents one glioblastoma patient and its color corresponds to the ground‐truth labels. $R^2$ values from the identity linear regressions are provided

FIGURE 7

Boxplots of the differences between the probabilities obtained at the output of the RS for Experiments I–II and I–III. Each point represents the probability difference for each glioblastoma patient and the dashed line corresponds to zero difference