Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features.
Authors
de Farias, Erick Costa
di Noia, Christian
Han, Changhee
Castelli, Mauro
Rundo, Leonardo
Publication Date
2021-11-01Journal Title
Sci Rep
ISSN
2045-2322
Publisher
Springer Science and Business Media LLC
Volume
11
Issue
1
Pages
21361
Language
eng
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
de Farias, E. C., di Noia, C., Han, C., Sala, E., Castelli, M., & Rundo, L. (2021). Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features.. Sci Rep, 11 (1), 21361. https://doi.org/10.1038/s41598-021-00898-z
Abstract
Robust machine learning models based on radiomic features might allow for accurate diagnosis, prognosis, and medical decision-making. Unfortunately, the lack of standardized radiomic feature extraction has hampered their clinical use. Since the radiomic features tend to be affected by low voxel statistics in regions of interest, increasing the sample size would improve their robustness in clinical studies. Therefore, we propose a Generative Adversarial Network (GAN)-based lesion-focused framework for Computed Tomography (CT) image Super-Resolution (SR); for the lesion (i.e., cancer) patch-focused training, we incorporate Spatial Pyramid Pooling (SPP) into GAN-Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE). At [Formula: see text] SR, the proposed model achieved better perceptual quality with less blurring than the other considered state-of-the-art SR methods, while producing comparable results at [Formula: see text] SR. We also evaluated the robustness of our model's radiomic feature in terms of quantization on a different lung cancer CT dataset using Principal Component Analysis (PCA). Intriguingly, the most important radiomic features in our PCA-based analysis were the most robust features extracted on the GAN-super-resolved images. These achievements pave the way for the application of GAN-based image Super-Resolution techniques for studies of radiomics for robust biomarker discovery.
Keywords
Algorithms, Humans, Image Processing, Computer-Assisted, Lung, Lung Neoplasms, Machine Learning, Tomography, X-Ray Computed
Sponsorship
Wellcome Trust (215733/Z/19/Z)
Cancer Research UK (C96/A25177)
National Institute for Health Research (IS-BRC-1215-20014)
EPSRC (EP/T017961/1)
Embargo Lift Date
2100-01-01
Identifiers
External DOI: https://doi.org/10.1038/s41598-021-00898-z
This record's URL: https://www.repository.cam.ac.uk/handle/1810/329582
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