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Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks

Ortega Martorell, S, Olier, I, Hernandez, O, Restrepo-Galvis, PD, Bellfield, RAA and Candiota, AP (2023) Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks. Cancers, 15 (15). ISSN 2072-6694

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Background: 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. Methods: 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. Results: 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. Conclusions: 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: therapy response; glioblastoma; temozolomide; preclinical models; magnetic resonance spectroscopy; class activation mapping; Grad-CAM; convolutional neural networks; deep learning; 1112 Oncology and Carcinogenesis
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions: Computer Science & Mathematics
Publisher: MDPI
SWORD Depositor: A Symplectic
Date Deposited: 07 Aug 2023 10:34
Last Modified: 07 Aug 2023 10:45
DOI or Identification number: 10.3390/cancers15154002
URI: https://researchonline.ljmu.ac.uk/id/eprint/20661

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