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

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

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 afected 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 2x 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 4x SR. We also evaluated the robustness of our mode's radiomic feature in terms of quantization on a diferent 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.

• Jezik: Angleški jezik
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: EF - Ekonomska fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2021
• Št. strani: Str. 1-12
• Številčenje: Vol. 11, art. 21361
• PID: 20.500.12556/RUL-134022
• UDK: 659.2:004
• ISSN pri članku: 2045-2322
• DOI: 10.1038/s41598-021-00898-z
• COBISS.SI-ID: 83224579
• Datum objave v RUL: 22.12.2021

Gradivo je del revije

• Naslov: Scientific reports
• Skrajšan naslov: Sci. rep.
• Založnik: Nature Publishing Group
• ISSN: 2045-2322
• COBISS.SI-ID: 18727432

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 01.11.2021