Treatment-wise glioblastoma survival Inference with multi-parametric preoperative MRI

Xiaofeng Liu; Nadya Shusharina; Helen A. Shih; C.-C. Jay Kuo; Georges El Fakhri; Jonghye Woo

doi:10.1117/12.3006897

3 April 2024 Treatment-wise glioblastoma survival Inference with multi-parametric preoperative MRI

Xiaofeng Liu, Nadya Shusharina, Helen A. Shih, C.-C. Jay Kuo, Georges El Fakhri, Jonghye Woo

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129272H (2024) https://doi.org/10.1117/12.3006897
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

In this work, we aim to predict the survival time (ST) of glioblastoma (GBM) patients undergoing different treatments based on preoperative magnetic resonance (MR) scans. The personalized and precise treatment planning can be achieved by comparing the ST of different treatments. It is well established that both the current status of the patient (as represented by the MR scans) and the choice of treatment are the cause of ST. While previous related MR-based glioblastoma ST studies have focused only on the direct mapping of MR scans to ST, they have not included the underlying causal relationship between treatments and ST. To address this limitation, we propose a treatment-conditioned regression model for glioblastoma ST that incorporates treatment information in addition to MR scans. Our approach allows us to effectively utilize the data from all of the treatments in a unified manner, rather than having to train separate models for each of the treatments. Furthermore, treatment can be effectively injected into each convolutional layer through the adaptive instance normalization we employ. We evaluate our framework on the BraTS20 ST prediction task. Three treatment options are considered: Gross Total Resection (GTR), Subtotal Resection (STR), and no resection. The evaluation results demonstrate the effectiveness of injecting the treatment for estimating GBM survival.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaofeng Liu, Nadya Shusharina, Helen A. Shih, C.-C. Jay Kuo, Georges El Fakhri, and Jonghye Woo "Treatment-wise glioblastoma survival Inference with multi-parametric preoperative MRI", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129272H (3 April 2024); https://doi.org/10.1117/12.3006897

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