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UNIVERSIDAD COMPLUTENSE DE MADRID FACULTAD DE CIENCIAS FÍSICAS TESIS DOCTORAL Heterogeneidad tumoral en imágenes PET-CT Tumor heterogeneity in PET-CT images MEMORIA PARA OPTAR AL GRADO DE DOCTOR PRESENTADA POR Ober Van Gómez López DIRECTORES José Manuel Udías Moinelo Joaquín López Herraiz Madrid © Ober Van Gómez López, 2021 UNIVERSIDAD COMPLUTENSE DE MADRID FACULTAD DE CIENCIAS FÍSICAS DEPARTAMENTO DE ESTRUCTURA DE LA MATERIA, FÍSICA TÉRMICA Y ELECTRÓNICA TUMOR HETEROGENEITY IN PET/CT IMAGES TESIS DOCTORAL HETEROGENEIDAD TUMORAL EN IMÁGENES PET-CT TUMOR HETEROGENEITY IN PET/CT IMAGES MEMORIA PARA OPTAR AL GRADO DE DOCTOR PRESENTADA POR Ober Van Gómez López DIRECTORES Dr. José Manuel Udías Moinelo Dr. Joaquín López Herraiz Madrid, 2020 A mis amados padres, esposa e hijas, por su apoyo incondicional. iv AGRADECIMIENTOS Mi gratitud, principalmente está dirigida a mis Directores de Tesis: El Prof. Dr. José Manuel Udías Moinelo y el Prof. Dr. Joaquín López Herraiz, quienes me apoyaron y señalaron siempre el camino a seguir. Gracias por su confianza y por estar siempre que los necesité. Quiero nuevamente extender mis más profundos agradecimientos a mi director de tesis el Prof. Dr. José Manuel Udías Moinelo. Él es el único responsable de que esta tesis vea la luz. Siempre confió en que lo podíamos hacer, y trasladó esa confianza en apoyo constante y efectivo, para que pudiera dedicarme a la elaboración de esta tesis. Así mismo, quiero agradecer a mi director de tesis el Prof. Dr. Joaquín López Herraiz, quien siempre me proveyó con valiosas y muy asertivas recomendaciones procedimentales. Muchas gracias a la Facultad de Ciencias Físicas de la Universidad Complutense de Madrid, especialmente al Departamento de Estructura de la Materia, Física Térmica y Electrónica, por haberme dado la oportunidad de ingresar y cumplir este gran sueño. Agradezco inmensamente el apoyo y tutoría permanente del Dr. Ángel Soriano Castrejón, jefe del servicio de Medicina Nuclear del Hospital Universitario de Ciudad Real y la Dra. Ana María García Vicente adjunta adscrita al mismo servicio, asicomo al Dr. Antonio Francisco Honguero Martínez, del servicio del Servicio de Cirugía Torácica del Complejo Hospitalario Universitario de Albacete. Igualmente, quiero agradecer a la División de Medicina Nuclear, Departamento de Imagen Biomédica y Terapia Guiada por Imagen de la Universidad Médica de Viena. Especialmente al Univ.-Prof. Dr. Marcus Hacker y al Assoc. -Prof. Univ. -Doz. Dr. Alexander Haug por haberme permitido realizar una estancia formativa (“Fellow”) en sus instalaciones y permitirme trabajar con algunas de sus bases de datos. Finalmente, a todos aquellos que directa o indirectamente han colocado un granito de arena para el logro de esta Tesis Doctoral, agradezco de forma sincera su valiosa colaboración. “In memoriam” a mi abuela Flor Contents Table of contents vi List of figures xi List of tables x iv Summary x vii General overview and motivation of this thesis 1 Chapter 1. Medical Background 14 1.1 Introduction 15 1.2 Clinical and biological background 15 1.2.1. Epidemiology of cancer 15 1.2.2. Hallmarks of cancer 16 1.2.3. Heterogeneity of cancer 21 1.3. Cancer staging 23 1.4. Cancer treatment 24 1.5 Some specific types of cancer 25 1.5.1 Non-Small Cell Lung Cancer NSCLC) 25 1.5.2 Breast cancer 27 1.6 Medical imaging methods in Oncology 29 1.6.1 X-ray computed tomography (CT) 30 1.6.2 Positron emission tomography 33 1.7 Clinical applications of PET/CT 37 1.7.1 Cancer staging 37 1.7.2 Response assessment 37 Chapter 2. PET/CT image biomarkers and Radiomics 42 2.1. Introduction 43 2.2. Imaging biomarkers (IB) 43 2.3. PET/CT radiomic methodology and workflow 47 2.3.1 Acquisition 47 2.3.2. Tumor segmentation and preprocessing 48 2.3.3. Features extraction 52 2.3.4. Post-processing (features selection) 57 2.3.4.1 Analysis of variance (ANOVA) with F-value 58 2.3.4.2 Mutual information (MI) 58 2.3.4.3 Principal component analysis (PCA) 58 2.3.4.4 Independent component analysis (ICA) 59 2.3.4.5 Least absolute shrinkage and selection operator (Lasso) 59 2.3.4.6 Clustering 59 2.3.4.7 Wilcoxon test 60 2.3.5. Analysis of radiomic Data 60 2.4. Some important issues in radiomic analysis 60 2.5. Examples of PET/CT radiomic application 62 Chapter 3. Machine learning in Medical Images and model construction 66 3.1 Introduction 67 3.2. Machine learning methods 67 3.2.1. Supervised learning 68 3.2.2. Unsupervised learning 69 3.2.3. Reinforcement learning method 69 3.3. Supervised machine learning algorithms 70 3.3.1. Logistic regression 70 3.3.2. Naive Bayes 72 3.3.3. k-Nearest neighbors classifier 72 3.3.4. Support vector machine 73 3.3.5. Decision tree 74 3.3.5.1 Random forests 76 3.4. Unsupervised machine learning algorithm 77 3.4.1. k-Means clustering algorithm 77 3.4.2. Hierarchical clustering 78 3.4.3. Principal component analysis 79 3.5. Artificial neural networks and deep learning 79 3.5.1. Deep learning 81 3.6 Machine learning workflows 82 3.6.1 Data preprocessing 84 3.6.2 Data splitting 84 3.6.3 Feature selection and dimensionality reduction 85 3.6.4. Model performance evaluation 86 3.6.4.1 Confusion Matrix 86 3.6.4.2 Receiver operating characteristic curve (ROC curve) 87 3.7. Application of ML for biomarkers development 88 Chapter 4. Heterogeneity in 18F-Fluorodeoxyglucose Positron Emission 91 Tomography/Computed Tomography of Non–Small Cell Lung Carcinoma and Its Relationship to Metabolic Parameters and Pathologic Staging Summary 92 4.1. Introduction 92 4.2. Methods 94 4.2.1. Patients 94 4.2.2. PET/CT image acquisition 95 4.2.3. Lesion segmentation 95 4.2.4. Metabolic parameters 95 4.2.5. Texture analysis 96 4.2.6. Statistical analysis 96 4.3. Results 97 4.3.1. Pathologic characteristics and metabolic parameters 97 4.3.2. Correlation between texture and metabolic parameters 98 4.3.3. Correlation between textural parameters and tumor stage 98 4.4. Discussion 101 4.5. Conclusion 107 Chapter 5. 18F-FDG-PET/CT in the assessment of pulmonary solitary nodules: 109 comparison of different analysis methods and risk variables in the prediction of malignancy Summary 110 5.1. Introduction 110 5.2. Materials and methods 112 5.2.1. Patients 112 5.2.2. PET/CT image acquisition and interpretation 112 5.2.3. Final diagnosis 113 5.2.4. Statistical analysis 113 5.3. Results 114 5.4. Discussion 117 5.5. Conclusion 120 Chapter 6. Comparison of cross-combinations between feature selection and 122 machine-learning classifier methods based on 18F-PET/CT radiomic features for prediction of the metabolic response in metastatic breast cancer Summary 123 6.1. Introduction 124 6.2. Methods 126 6.2.1. Patient cohort 126 6.2.2. PET/CT image acquisition 126 6.2.3. ROI delineation 128 6.2.4. Image preprocessing 128 6.2.5. Metabolic parameters extraction 128 6.2.6. PET/CT response assessment 128 6.2.7. Radiomic features extraction 129 6.2.7.1. Texture features 129 6.2.8. Univariate statistical analysis 130 6.2.9 Machine learning model 131 6.2.9.1. Feature selection 131 6.2.9.2. Classification methods 131 6.2.9.3. Model construction 132 6.2.9.4. Model performance metrics and validation 133 6.3. Results 133 6.3.1. Clinical characteristics 133 6.3.2. Feature extraction and correlation 134 6.3.3. Feature reduction 134 6.3.4. Performance of feature selection methods and classifiers 136 6.3.5. Cross-validation 136 6.3.6. Prediction performance (validation) 137 6.4. Discussion 137 6.5. Conclusion 141 Conclusions of this thesis 143 Bibliography 147 Appendix A. Publications derived from this thesis 174 A.1. Published articles 174 A.2. Articles pending to be published 174 Appendix B. 175 Table B1. summary of the radiomic features 175 Table B2. Patient´s treatment and affectation places 177 Table B3. Univariate analysis 178 Figure B1. Calibration of the best model (Random Forest) 180 Appendix C. 181 Table C1. Formulas and description of some image features 181 Appendix D. Resumen en castellano 185 xi List of Figures General overview and motivation of this thesis 3 1. 18F-FDG PET/CT images in lung cancer (adapted from [59]) Chapter 1. Medical Background 1.1. Cancer incidence and mortality worldwide in 2018, according to WHO (from [1]) 15 1.2. The natural history of progression toward cancer (from [54]) 16 1.3. Warburg effect in tumor cells and its exploitation to obtain PET image (from [111]) 18 1.4. Schematic for the metabolic trapping of 18F-FDG (from [118]) 19 1.5 Non-small-cell lung cancer showing spatial variation in staining for angiogenesis (CD34), 21 pimonidazole (hypoxia), and glucose transporter protein expression (Glut-1) (from [55]) 1.6. Images of an NSCLC patient having both an FDG-PET/CT scan (left) and a hypoxia 22 HX4-PET/CT scan (from [131]) 1.7. Depicting a patient with an IIB stage NSCLC (from [136]) 26 1.8. Patient with no metastatic breast cancer (from [139]) 29 1.9. A fan-beam projection (from [144]) 31 1.10. Reconstruction matrix (from [145]) 32 1.11. Thorax CT slice, where a lung tumor can be appreciated on the left side. In integrated 33 PET/CT (from the personal library) 1.12. The basic principle of a PET system (from [147]) 34 1.13.
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    2 Radiomics consists of a wide range of (partially intercon- nected) research areas with each individual branch possibly worth a complete exploration, the purpose of this article is to provide an inclusive introduction to Radiomics for the signal processing community, as such, we will focus on the progression of signal processing algorithms within the context of Radiomics. In brief, we aim to present an overview of the current state, opportunities, and challenges of Radiomics from signal processing perspective to facilitate further advancement and innovation in this critical multidisciplinary research area. As such, the article will cover the following four main areas: (i) Hand-crafted Radiomics, where we introduce and in- Fig. 1: Increasing interest in Radiomics based on data from Google vestigate different feature extraction, feature reduction, Scholar (“Radiomics” is used as the keyword). It is observed that and classification approaches used within the context there is an increasing interest in both types of the Radiomics. of Radiomics. Most of the techniques utilized in any as cancer stage. This clinical study has illustrated/validated of the aforementioned steps lie within the broad area effectiveness of Radiomics for tumor related predictions and of “Machine Learning,” where the goal is to improve showed that Radiomics has the capability to identify lung the performance of different computational models using and head-and-neck cancers from a single-time point CT scan. past experiences (data) [17]. In other words, the underly- Consequently, there has been a recent surge of interest [8]– ing models are capable of learning from past data, lead- [12] on this multidisciplinary research area as Radiomics has ing to the automatic process of prediction and diagno- the potential to provide significant assistance for assessing sis.
  • An Exploratory Study on the Stable Radiomics Features of Metastatic Small Pulmonary Nodules in Colorectal Cancer Patients

    An Exploratory Study on the Stable Radiomics Features of Metastatic Small Pulmonary Nodules in Colorectal Cancer Patients

    ORIGINAL RESEARCH published: 16 July 2021 doi: 10.3389/fonc.2021.661763 An Exploratory Study on the Stable Radiomics Features of Metastatic Small Pulmonary Nodules in Colorectal Cancer Patients Caiyin Liu 1, Qiuhua Meng 1*, Qingsi Zeng 1, Huai Chen 1, Yilian Shen 1, Biaoda Li 2, Renli Cen 1, Jiongqiang Huang 3, Guangqiu Li 4, Yuting Liao 5 and Tingfan Wu 5 1 Department of Radiology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China, 2 Department of Radiology, Shenzhen Hospital, University of Hong Kong, Shenzhen, China, 3 Department of Gastrointestinal Surgery, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China, 4 Department of Pathology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China, 5 Department of Pharmaceutical Diagnostics, GE Healthcare (China), Shanghai, China Objectives: To identify the relatively invariable radiomics features as essential Edited by: characteristics during the growth process of metastatic pulmonary nodules with a Miguel Angel Gonzalez Ballester, diameter of 1 cm or smaller from colorectal cancer (CRC). Pompeu Fabra University, Spain Reviewed by: Methods: Three hundred and twenty lung nodules were enrolled in this study (200 CRC Arnav Bhavsar, metastatic nodules in the training cohort, 60 benign nodules in the verification cohort 1, 60 Indian Institute of Technology Mandi, fi India CRC metastatic nodules in the veri cation cohort 2). All the nodules were divided into four Sang Hyun Park, groups according to the maximum diameter: 0 to 0.25 cm, 0.26 to 0.50 cm, 0.51 to Daegu Gyeongbuk Institute of Science 0.75 cm, 0.76 to 1.0 cm. These pulmonary nodules were manually outlined in computed and Technology (DGIST), South Korea *Correspondence: tomography (CT) images with ITK-SNAP software, and 1724 radiomics features were Qiuhua Meng extracted.