20.500.12556/RUL-134022 Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features 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. true false true Angleški jezik Angleški jezik Članek v reviji 2021-12-22 08:27:51 2021-12-22 08:55:33 2022-09-13 03:52:56 0000-00-00 00:00:00 2021 0 0 Str. 1-12 art. 21361 Vol. 11 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 659.2:004 2045-2322 10.1038/s41598-021-00898-z 83224579 RAZ_Costa_de_Farias_Erick_2021.pdf RAZ_Costa_de_Farias_Erick_2021.pdf 1 C7C49B3A145F92C7AA10375CE88DE5B7 2699ed4cc0bf7f6a363cd9daf176c65f341cb57facab8c3509465d31de66d755 43c7c4df-62fc-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=152326 https://www.nature.com/articles/s41598-021-00898-z 0 4d3011f3-62fc-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=152327 Ekonomska fakulteta 0 0 0