Radiogenomics in non-small-cell lung cancer

Ο μη μικροκυτταρικός καρκίνος του πνεύμονα είναι ο πιο συχνά συναντώμενος υποτύπος καρκίνου του πνεύμονα, ο οποίος αποτελείται από ένα φάσμα υποτύπων. Το NSCLC είναι ένας θανατηφόρος, ετερογενής συμπαγής όγκος με μια εκτεταμένη σειρά μοριακών χαρακτηριστικών. Η πάθηση έχει γίνει ένα αξιοσημείωτο παράδειγμα ιατρικής ακριβείας καθώς το ενδιαφέρον για το θέμα συνεχίζει να επεκτείνεται. Ο απώτερος στόχος της τρέχουσας έρευνας είναι να χρησιμοποιήσει συγκεκριμένα γονίδια ως βιοδείκτες για την πρόγνωση, την έγκαιρη διάγνωση και την εξατομικευμένη θεραπεία, τα οποία διευκολύνονται από τη χρήση εξελισσόμενων τεχνικών αλληλούχισης επόμενης γενιάς που επιτρέπουν την ταυτόχρονη ανίχνευση μεγάλου αριθμού γενετικές ανωμαλίες. Γνωστές μεταλλάξεις ενός αριθμού γονιδίων, όπως τα EGFR, ALK και KRAS, επηρεάζουν ήδη τις αποφάσεις θεραπείας και νέα βασικά γονίδια και μοριακές υπογραφές διερευνώνται για την προγνωστική τους αξία καθώς και για την πιθανή συμβολή τους στην ανοσοθεραπεία και τη θεραπεία της υποτροπής στην αντίσταση στις υπάρχουσες θεραπείες. Οι τύποι δειγμάτων που χρησιμοποιούνται για μελέτες NGS, όπως αναρροφήσεις με λεπτή βελόνα, ιστός ενσωματωμένος σε παραφίνη σταθεροποιημένος με φορμαλίνη και DNA χωρίς κύτταρα, έχουν ο καθένας τα δικά του πλεονεκτήματα και μειονεκτήματα που πρέπει να ληφθούν υπόψηNon-small cell lung cancer is the most often encountered subtype of lung cancer, which consists of a spectrum of subtypes. NSCLC is a lethal, heterogeneous solid tumor with an extensive array of molecular features. The condition has become a notable example of precision medicine as interest in the topic continues to expand. The ultimate goal of the current research is to use specific genes as biomarkers for its prognosis, timely diagnosis, and personalized therapy, all of which are facilitated by the use of evolving next-generation sequencing techniques that permit the simultaneous detection of a large number of genetic abnormalities. Known mutations of a number of genes, such as EGFR, ALK, and KRAS, already influence treatment decisions, and new key genes and molecular signatures are being investigated for their prognostic value as well as their potential contribution to immunotherapy and the treatment of recurrence due to resistance to existing therapies. The sample types utilized for NGS studies, such as fine-needle aspirates, formalin-fixed paraffin-embedded tissue, and cell-free DNA, each have their own advantages and disadvantages that must be taken into accoun

Texture Analysis Platform for Imaging Biomarker Research

abstract: The rate of progress in improving survival of patients with solid tumors is slow due to late stage diagnosis and poor tumor characterization processes that fail to effectively reflect the nature of tumor before treatment or the subsequent change in its dynamics because of treatment. Further advancement of targeted therapies relies on advancements in biomarker research. In the context of solid tumors, bio-specimen samples such as biopsies serve as the main source of biomarkers used in the treatment and monitoring of cancer, even though biopsy samples are susceptible to sampling error and more importantly, are local and offer a narrow temporal scope. Because of its established role in cancer care and its non-invasive nature imaging offers the potential to complement the findings of cancer biology. Over the past decade, a compelling body of literature has emerged suggesting a more pivotal role for imaging in the diagnosis, prognosis, and monitoring of diseases. These advances have facilitated the rise of an emerging practice known as Radiomics: the extraction and analysis of large numbers of quantitative features from medical images to improve disease characterization and prediction of outcome. It has been suggested that radiomics can contribute to biomarker discovery by detecting imaging traits that are complementary or interchangeable with other markers. This thesis seeks further advancement of imaging biomarker discovery. This research unfolds over two aims: I) developing a comprehensive methodological pipeline for converting diagnostic imaging data into mineable sources of information, and II) investigating the utility of imaging data in clinical diagnostic applications. Four validation studies were conducted using the radiomics pipeline developed in aim I. These studies had the following goals: (1 distinguishing between benign and malignant head and neck lesions (2) differentiating benign and malignant breast cancers, (3) predicting the status of Human Papillomavirus in head and neck cancers, and (4) predicting neuropsychological performances as they relate to Alzheimer’s disease progression. The long-term objective of this thesis is to improve patient outcome and survival by facilitating incorporation of routine care imaging data into decision making processes.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Radiomics risk modelling using machine learning algorithms for personalised radiation oncology

One major objective in radiation oncology is the personalisation of cancer treatment. The implementation of this concept requires the identification of biomarkers, which precisely predict therapy outcome. Besides molecular characterisation of tumours, a new approach known as radiomics aims to characterise tumours using imaging data. In the context of the presented thesis, radiomics was established at OncoRay to improve the performance of imaging-based risk models. Two software-based frameworks were developed for image feature computation and risk model construction. A novel data-driven approach for the correction of intensity non-uniformity in magnetic resonance imaging data was evolved to improve image quality prior to feature computation. Further, different feature selection methods and machine learning algorithms for time-to-event survival data were evaluated to identify suitable algorithms for radiomics risk modelling. An improved model performance could be demonstrated using computed tomography data, which were acquired during the course of treatment. Subsequently tumour sub-volumes were analysed and it was shown that the tumour rim contains the most relevant prognostic information compared to the corresponding core. The incorporation of such spatial diversity information is a promising way to improve the performance of risk models.:1. Introduction 2. Theoretical background 2.1. Basic physical principles of image modalities 2.1.1. Computed tomography 2.1.2. Magnetic resonance imaging 2.2. Basic principles of survival analyses 2.2.1. Semi-parametric survival models 2.2.2. Full-parametric survival models 2.3. Radiomics risk modelling 2.3.1. Feature computation framework 2.3.2. Risk modelling framework 2.4. Performance assessments 2.5. Feature selection methods and machine learning algorithms 2.5.1. Feature selection methods 2.5.2. Machine learning algorithms 3. A physical correction model for automatic correction of intensity non-uniformity in magnetic resonance imaging 3.1. Intensity non-uniformity correction methods 3.2. Physical correction model 3.2.1. Correction strategy and model definition 3.2.2. Model parameter constraints 3.3. Experiments 3.3.1. Phantom and simulated brain data set 3.3.2. Clinical brain data set 3.3.3. Abdominal data set 3.4. Summary and discussion 4. Comparison of feature selection methods and machine learning algorithms for radiomics time-to-event survival models 4.1. Motivation 4.2. Patient cohort and experimental design 4.2.1. Characteristics of patient cohort 4.2.2. Experimental design 4.3. Results of feature selection methods and machine learning algorithms evaluation 4.4. Summary and discussion 5. Characterisation of tumour phenotype using computed tomography imaging during treatment 5.1. Motivation 5.2. Patient cohort and experimental design 5.2.1. Characteristics of patient cohort 5.2.2. Experimental design 5.3. Results of computed tomography imaging during treatment 5.4. Summary and discussion 6. Tumour phenotype characterisation using tumour sub-volumes 6.1. Motivation 6.2. Patient cohort and experimental design 6.2.1. Characteristics of patient cohorts 6.2.2. Experimental design 6.3. Results of tumour sub-volumes evaluation 6.4. Summary and discussion 7. Summary and further perspectives 8. Zusammenfassun

A Modular Approach to Lung Nodule Detection from Computed Tomography Images Using Artificial Neural Networks and Content Based Image Representation

Lung cancer is one of the most lethal cancer types. Research in computer aided detection (CAD) and diagnosis for lung cancer aims at providing effective tools to assist physicians in cancer diagnosis and treatment to save lives. In this dissertation, we focus on developing a CAD framework for automated lung cancer nodule detection from 3D lung computed tomography (CT) images. Nodule detection is a challenging task that no machine intelligence can surpass human capability to date. In contrast, human recognition power is limited by vision capacity and may suffer from work overload and fatigue, whereas automated nodule detection systems can complement expert’s efforts to achieve better detection performance. The proposed CAD framework encompasses several desirable properties such as mimicking physicians by means of geometric multi-perspective analysis, computational efficiency, and the most importantly producing high performance in detection accuracy. As the central part of the framework, we develop a novel hierarchical modular decision engine implemented by Artificial Neural Networks. One advantage of this decision engine is that it supports the combination of spatial-level and feature-level information analysis in an efficient way. Our methodology overcomes some of the limitations of current lung nodule detection techniques by combining geometric multi-perspective analysis with global and local feature analysis. The proposed modular decision engine design is flexible to modifications in the decision modules; the engine structure can adopt the modifications without having to re-design the entire system. The engine can easily accommodate multi-learning scheme and parallel implementation so that each information type can be processed (in parallel) by the most adequate learning technique of its own. We have also developed a novel shape representation technique that is invariant under rigid-body transformation and we derived new features based on this shape representation for nodule detection. We implemented a prototype nodule detection system as a demonstration of the proposed framework. Experiments have been conducted to assess the performance of the proposed methodologies using real-world lung CT data. Several performance measures for detection accuracy are used in the assessment. The results show that the decision engine is able to classify patterns efficiently with very good classification performance

The impact of arterial input function determination variations on prostate dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic modeling: a multicenter data analysis challenge, part II

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study

Radiogenomics Framework for Associating Medical Image Features with Tumour Genetic Characteristics

Significant progress has been made in the understanding of human cancers at the molecular genetics level and it is providing new insights into their underlying pathophysiology. This progress has enabled the subclassification of the disease and the development of targeted therapies that address specific biological pathways. However, obtaining genetic information remains invasive and costly. Medical imaging is a non-invasive technique that captures important visual characteristics (i.e. image features) of abnormalities and plays an important role in routine clinical practice. Advancements in computerised medical image analysis have enabled quantitative approaches to extract image features that can reflect tumour genetic characteristics, leading to the emergence of ‘radiogenomics’. Radiogenomics investigates the relationships between medical imaging features and tumour molecular characteristics, and enables the derivation of imaging surrogates (radiogenomics features) to genetic biomarkers that can provide alternative approaches to non-invasive and accurate cancer diagnosis. This thesis presents a new framework that combines several novel methods for radiogenomics analysis that associates medical image features with tumour genetic characteristics, with the main objectives being: i) a comprehensive characterisation of tumour image features that reflect underlying genetic information; ii) a method that identifies radiogenomics features encoding common pathophysiological information across different diseases, overcoming the dependence on large annotated datasets; and iii) a method that quantifies radiogenomics features from multi-modal imaging data and accounts for unique information encoded in tumour heterogeneity sub-regions. The present radiogenomics methods advance radiogenomics analysis and contribute to improving research in computerised medical image analysis