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 comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling

Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco- regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e.g., MSR-RF: C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints

Definition and validation of a radiomics signature for loco-regional tumour control in patients with locally advanced head and neck squamous cell carcinoma

Purpose: To develop and validate a CT-based radiomics signature for the prognosis of loco-regional tumour control (LRC) in patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated by primary radiochemotherapy (RCTx) based on retrospective data from 6 partner sites of the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Material and methods: Pre-treatment CT images of 318 patients with locally advanced HNSCC were col-lected. Four-hundred forty-six features were extracted from each primary tumour volume and then fil-tered through stability analysis and clustering. First, a baseline signature was developed from demographic and tumour-associated clinical parameters. This signature was then supplemented by CT imaging features. A final signature was derived using repeated 3-fold cross-validation on the discovery cohort. Performance in external validation was assessed by the concordance index (C-Index). Furthermore, calibration and patient stratification in groups with low and high risk for loco-regional recurrence were analysed. Results: For the clinical baseline signature, only the primary tumour volume was selected. The final sig-nature combined the tumour volume with two independent radiomics features. It achieved moderatel

Gender and race influence metabolic benefits of fitness in children: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Increasing obesity and poor cardiovascular fitness (CVF) contribute to higher rates of type 2 diabetes mellitus (T2DM) in children. While the relative contributions of fitness and body fat on development of insulin resistance (IR) in children and adolescents remains unresolved, gender- and race-specific differences likely exist in the degree to which CVF influences IR and risk for T2DM. Better understanding of how gender and race affect interactions between body fat, CVF, and metabolic health would be helpful in designing effective and targeted strategies to reduce obesity-associated disease risk. We evaluated whether metabolic benefits of fitness on reducing inflammation and insulin resistance (IR) are affected by gender and race.</p> <p>Methods</p> <p>This cross-sectional study included 203 healthy children (mean age 12.2 y, 50% male, 46% non-Hispanic white (NHW), 54% racially diverse (RD)). Fasting insulin, glucose, hsCRP, and adiponectin were measured; race was self-reported; cardiovascular fitness (CVF) was evaluated by the Progressive Aerobic Cardiovascular Endurance Run. Associations between inflammation and gender, race, and CVF were evaluated using analysis of covariance. Multivariate regression analysis identified independent predictors of IR.</p> <p>Results</p> <p>Fitness and inflammation were inversely related in both males and females (p < 0.01); this effect was marginally stronger in RD children (p = 0.06) and non-overweight males (p = 0.07). High BMI (p < 0.001), low fitness (p = 0.006), and (female) gender (p = 0.003) were independently associated with higher HOMA-IR. In males, BMI and fitness, but not race independently predicted HOMA-IR. In females, BMI and race, but not fitness independently predicted HOMA-IR.</p> <p>Conclusions</p> <p>In middle school children, the beneficial effects of fitness vary based on gender and race. High CVF has an enhanced anti-inflammatory effect in male and RD children. While BMI is the strongest predictor of IR in the study group as a whole, fitness is a significant predictor of IR only in males, and race is a significant predictor of IR only in females.</p

Reactivation from the Ni-B state in [NiFe] hydrogenase of Ralstonia eutropha is controlled by reduction of the superoxidised proximal cluster

The tolerance towards oxic conditions of O2-tolerant [NiFe] hydrogenases has been attributed to an unusual [4Fe–3S] cluster that lies proximal to the [NiFe] active site. Upon exposure to oxygen, this cluster converts to a superoxidised (5+) state, which is believed to secure the formation of the so-called Ni–B state that is rapidly reactivated under reducing conditions. Here, the reductive reactivation of the membrane-bound [NiFe]-hydrogenase (MBH) from Ralstonia eutropha in a native-like lipid membrane was characterised and compared to a variant that instead carries a typical [4Fe–4S] proximal cluster. Reactivation from the Ni–B state was faster in the [4Fe–4S] variant, suggesting that the reactivation rate in MBH is limited by the reduction of the superoxidised [4Fe–3S] cluster. We propose that the [4Fe–3S] cluster plays a major role in protecting MBH by blocking the reversal of electron transfer to the [NiFe] active site, which would produce damaging radical oxygen species

Enhanced oxygen-tolerance of the full heterotrimeric membrane-bound [NiFe]-hydrogenase of ralstonia eutropha.

Hydrogenases are oxygen-sensitive enzymes that catalyze the conversion between protons and hydrogen. Water-soluble subcomplexes of membrane-bound [NiFe]-hydrogenases (MBH) have been extensively studied for applications in hydrogen-oxygen fuel cells as they are relatively tolerant to oxygen, although even these catalysts are still inactivated in oxidative conditions. Here, the full heterotrimeric MBH of Ralstonia eutropha, including the membrane-integral cytochrome b subunit, was investigated electrochemically using electrodes modified with planar tethered bilayer lipid membranes (tBLM). Cyclic voltammetry and chronoamperometry experiments show that MBH, in equilibrium with the quinone pool in the tBLM, does not anaerobically inactivate under oxidative redox conditions. In aerobic environments, the MBH is reversibly inactivated by O2, but reactivation was found to be fast even under oxidative redox conditions. This enhanced resistance to inactivation is ascribed to the oligomeric state of MBH in the lipid membrane

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead