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

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

Impact of GAN-based Lesion-Focused Medical Image Super-Resolution on Radiomic Feature Robustness

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceRobust 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 GANConstrained 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 PCAbased 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 discoveryModelos de machine learning robustos baseados em atributos radiômicos possibilitam diagnósticos e decisões médicas mais precisas. Infelizmente, por causa da falta de padronização na extração de atributos radiômicos, sua utilização em contextos clínicos tem sido restrita. Considerando que atributos radiômics tendem a ser afetados pelas estatítiscas de voxels de baixo volume nas regiões de interesse, o aumento to tamanho da amostra tem o potencial de melhorar a robustez desses atributos em estudos clínicos. Portanto, esse trabalho propões um framework baseado numa rede neural generativa (GAN) focada na região de interesse para a super-resolução de imagens de Tomografia Computadorizada (CT). Para treinar a rede de forma concentrada na lesão (i.e. cancer), incorporamos a tecnica de Spatial Pyramid Pooling no framework da GAN-CIRCLE. Nos experimentos de super-resolução 2×, o modelo proposto alcançou melhor qualidade perceptual com menos embaçamento do que outros métodos estado-da-arte considerados. A robustez dos atributos radiômics das imagens super-resolvidas geradas pelo modelo também foram analizadas em termos de quantização em um banco de imagens diferente, contendo imagens de tomografia computadorizada de câncer de pulmão, usando anaálise de componentes principaiss (PCA). Intrigantemente, os atributos radiômicos mais importantes nessa análise foram também os atributos mais robustos extraídos das imagens super-resolvidas pelo método proposto. Esses resultados abrem caminho para a aplicação de técnicas de super-resolução baseadas em redes neurais generativas aplicadas a estudos de radômica para a descoberta de biomarcadores robustos.This work was partially supported by The Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre [C9685/A25177], the Wellcome Trust Innovator Award, UK [215733/Z/19/Z] and the CRUK National Cancer Imaging Translational Accelerator (NCITA) [C42780/A27066]. Additional support was also provided by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014). This works was also finantially supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the projects GADgET (DSAIPA/DS/0022/2018) and the Slovenian Research Agency (research core funding no. P5-0410).This work was partially supported by The Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre [C9685/A25177], the Wellcome Trust Innovator Award, UK [215733/Z/19/Z] and the CRUK National Cancer Imaging Translational Accelerator (NCITA) [C42780/A27066]. Additional support was also provided by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014). This works was also finantially supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the projects GADgET (DSAIPA/DS/0022/2018) and the Slovenian Research Agency (research core funding no. P5-0410)