Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks

Ortega-Martorell, Sandra ORCID: 0000-0001-9927-3209, Olier, Ivan, Hernandez, Orlando, Restrepo-Galvis, Paula D, Bellfield, Ryan AA and Candiota, Ana Paula (2023) Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks. CANCERS, 15 (15). 4002-.

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Official URL: http://dx.doi.org/10.3390/cancers15154002

Abstract

<h4>Background</h4>Glioblastoma (GB) is a malignant brain tumour that is challenging to treat, often relapsing even after aggressive therapy. Evaluating therapy response relies on magnetic resonance imaging (MRI) following the Response Assessment in Neuro-Oncology (RANO) criteria. However, early assessment is hindered by phenomena such as pseudoprogression and pseudoresponse. Magnetic resonance spectroscopy (MRS/MRSI) provides metabolomics information but is underutilised due to a lack of familiarity and standardisation.<h4>Methods</h4>This study explores the potential of spectroscopic imaging (MRSI) in combination with several machine learning approaches, including one-dimensional convolutional neural networks (1D-CNNs), to improve therapy response assessment. Preclinical GB (GL261-bearing mice) were studied for method optimisation and validation.<h4>Results</h4>The proposed 1D-CNN models successfully identify different regions of tumours sampled by MRSI, i.e., normal brain (N), control/unresponsive tumour (T), and tumour responding to treatment (R). Class activation maps using Grad-CAM enabled the study of the key areas relevant to the models, providing model explainability. The generated colour-coded maps showing the N, T and R regions were highly accurate (according to Dice scores) when compared against ground truth and outperformed our previous method.<h4>Conclusions</h4>The proposed methodology may provide new and better opportunities for therapy response assessment, potentially providing earlier hints of tumour relapsing stages.

Item Type:	Article
Uncontrolled Keywords:	class activation mapping, convolutional neural networks, deep learning, glioblastoma, Grad-CAM, magnetic resonance spectroscopy, preclinical models, temozolomide, therapy response
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	06 Feb 2024 11:10
Last Modified:	06 Feb 2024 11:10
DOI:	10.3390/cancers15154002
Open Access URL:	https://doi.org/10.3390/cancers15154002
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3178498