Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features

Erick Costa de Farias, Christian Di Noia, Changhee Han, Evis Sala, Mauro Castelli, Leonardo Rundo

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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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 2× 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 4× 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.
Original languageEnglish
Article number21361
Pages (from-to)1-12
Number of pages12
JournalScientific Reports
Volume11
Issue number1
Early online date1 Nov 2021
DOIs
Publication statusPublished - 1 Dec 2021

Keywords

  • radiomic features
  • Clinica studies
  • Generative Adversarial Network (GAN)
  • Computed tomography (CT)

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