Deep learning for clinical decision support in oncology

In den letzten Jahrzehnten sind medizinische Bildgebungsverfahren wie die Computertomographie (CT) zu einem unersetzbaren Werkzeug moderner Medizin geworden, welche eine zeitnahe, nicht-invasive Begutachtung von Organen und Geweben ermöglichen. Die Menge an anfallenden Daten ist dabei rapide gestiegen, allein innerhalb der letzten Jahre um den Faktor 15, und aktuell verantwortlich für 30 % des weltweiten Datenvolumens. Die Anzahl ausgebildeter Radiologen ist weitestgehend stabil, wodurch die medizinische Bildanalyse, angesiedelt zwischen Medizin und Ingenieurwissenschaften, zu einem schnell wachsenden Feld geworden ist. Eine erfolgreiche Anwendung verspricht Zeitersparnisse, und kann zu einer höheren diagnostischen Qualität beitragen. Viele Arbeiten fokussieren sich auf „Radiomics“, die Extraktion und Analyse von manuell konstruierten Features. Diese sind jedoch anfällig gegenüber externen Faktoren wie dem Bildgebungsprotokoll, woraus Implikationen für Reproduzierbarkeit und klinische Anwendbarkeit resultieren. In jüngster Zeit sind Methoden des „Deep Learning“ zu einer häufig verwendeten Lösung algorithmischer Problemstellungen geworden. Durch Anwendungen in Bereichen wie Robotik, Physik, Mathematik und Wirtschaft, wurde die Forschung im Bereich maschinellen Lernens wesentlich verändert. Ein Kriterium für den Erfolg stellt die Verfügbarkeit großer Datenmengen dar. Diese sind im medizinischen Bereich rar, da die Bilddaten strengen Anforderungen bezüglich Datenschutz und Datensicherheit unterliegen, und oft heterogene Qualität, sowie ungleichmäßige oder fehlerhafte Annotationen aufweisen, wodurch ein bedeutender Teil der Methoden keine Anwendung finden kann. Angesiedelt im Bereich onkologischer Bildgebung zeigt diese Arbeit Wege zur erfolgreichen Nutzung von Deep Learning für medizinische Bilddaten auf. Mittels neuer Methoden für klinisch relevante Anwendungen wie die Schätzung von Läsionswachtum, Überleben, und Entscheidungkonfidenz, sowie Meta-Learning, Klassifikator-Ensembling, und Entscheidungsvisualisierung, werden Wege zur Verbesserungen gegenüber State-of-the-Art-Algorithmen aufgezeigt, welche ein breites Anwendungsfeld haben. Hierdurch leistet die Arbeit einen wesentlichen Beitrag in Richtung einer klinischen Anwendung von Deep Learning, zielt auf eine verbesserte Diagnose, und damit letztlich eine verbesserte Gesundheitsversorgung insgesamt.Over the last decades, medical imaging methods, such as computed tomography (CT), have become an indispensable tool of modern medicine, allowing for a fast, non-invasive inspection of organs and tissue. Thus, the amount of acquired healthcare data has rapidly grown, increased 15-fold within the last years, and accounts for more than 30 % of the world's generated data volume. In contrast, the number of trained radiologists remains largely stable. Thus, medical image analysis, settled between medicine and engineering, has become a rapidly growing research field. Its successful application may result in remarkable time savings and lead to a significantly improved diagnostic performance. Many of the work within medical image analysis focuses on radiomics, i. e. the extraction and analysis of hand-crafted imaging features. Radiomics, however, has been shown to be highly sensitive to external factors, such as the acquisition protocol, having major implications for reproducibility and clinical applicability. Lately, deep learning has become one of the most employed methods for solving computational problems. With successful applications in diverse fields, such as robotics, physics, mathematics, and economy, deep learning has revolutionized the process of machine learning research. Having large amounts of training data is a key criterion for its successful application. These data, however, are rare within medicine, as medical imaging is subject to a variety of data security and data privacy regulations. Moreover, medical imaging data often suffer from heterogeneous quality, label imbalance, and label noise, rendering a considerable fraction of deep learning-based algorithms inapplicable. Settled in the field of CT oncology, this work addresses these issues, showing up ways to successfully handle medical imaging data using deep learning. It proposes novel methods for clinically relevant tasks, such as lesion growth and patient survival prediction, confidence estimation, meta-learning and classifier ensembling, and finally deep decision explanation, yielding superior performance in comparison to state-of-the-art approaches, and being applicable to a wide variety of applications. With this, the work contributes towards a clinical translation of deep learning-based algorithms, aiming for an improved diagnosis, and ultimately overall improved patient healthcare

The Technome - a predictive internal calibration approach for quantitative imaging biomarker research

The goal of radiomics is to convert medical images into a minable data space by extraction of quantitative imaging features for clinically relevant analyses, e.g. survival time prediction of a patient. One problem of radiomics from computed tomography is the impact of technical variation such as reconstruction kernel variation within a study. Additionally, what is often neglected is the impact of inter-patient technical variation, resulting from patient characteristics, even when scan and reconstruction parameters are constant. In our approach, measurements within 3D regions-of-interests (ROI) are calibrated by further ROIs such as air, adipose tissue, liver, etc. that are used as control regions (CR). Our goal is to derive general rules for an automated internal calibration that enhance prediction, based on the analysed features and a set of CRs. We define qualification criteria motivated by status-quo radiomics stability analysis techniques to only collect information from the CRs which is relevant given a respective task. These criteria are used in an optimisation to automatically derive a suitable internal calibration for prediction tasks based on the CRs. Our calibration enhanced the performance for centrilobular emphysema prediction in a COPD study and prediction of patients’ one-year-survival in an oncological study

Sequential treatment of ADHD in mother and child (AIMAC study): importance of the treatment phases for intervention success in a randomized trial

Background: The efficacy of parent-child training (PCT) regarding child symptoms may be reduced if the mother has attention-deficit/hyperactivity disorder (ADHD). The AIMAC study (ADHD in Mothers and Children) aimed to compensate for the deteriorating effect of parental psychopathology by treating the mother (Step 1) before the beginning of PCT (Step 2). This secondary analysis was particularly concerned with the additional effect of the Step 2 PCT on child symptoms after the Step 1 treatment. Methods: The analysis included 143 mothers and children (aged 6–12 years) both diagnosed with ADHD. The study design was a two-stage, two-arm parallel group trial (Step 1 treatment group [TG]: intensive treatment of the mother including psychotherapy and pharmacotherapy; Step 1 control group [CG]: supportive counseling only for mother; Step 2 TG and CG: PCT). Single- and multi-group analyses with piecewise linear latent growth curve models were applied to test for the effects of group and phase. Child symptoms (e.g., ADHD symptoms, disruptive behavior) were rated by three informants (blinded clinician, mother, teacher). Results: Children in the TG showed a stronger improvement of their disruptive behavior as rated by mothers than those in the CG during Step 1 (Step 1: TG vs. CG). In the CG, according to reports of the blinded clinician and the mother, the reduction of children’s disruptive behavior was stronger during Step 2 than during Step 1 (CG: Step 1 vs. Step 2). In the TG, improvement of child outcome did not differ across treatment steps (TG: Step 1 vs. Step 2). Conclusions: Intensive treatment of the mother including pharmacotherapy and psychotherapy may have small positive effects on the child’s disruptive behavior. PCT may be a valid treatment option for children with ADHD regarding disruptive behavior, even if mothers are not intensively treated beforehand. Trial registration: ISRCTN registry ISRCTN73911400. Registered: 29 March 2007

Regulating E-Cigarettes: Why Policies Diverge

This paper, part of a festschrift in honor of Professor Malcolm Feeley, explores the landscape of e-cigarette policy globally by looking at three jurisdictions that have taken starkly different approaches to regulating e-cigarettes—the US, Japan, and China. Each of those countries has a robust tobacco industry, government agencies entrusted with protecting public health, an active and sophisticated scientific and medical community, and a regulatory structure for managing new pharmaceutical, tobacco, and consumer products. All three are signatories of the World Health Organization’s Framework Convention on Tobacco Control, all are signatories of the Agreement on Trade-Related Aspects of Intellectual Property Rights, and all are members of the World Trade Organization. Which legal, economic, social and political differences between the three countries explain their diverse approaches to regulating e-cigarettes? Why have they embraced such dramatically different postures toward e-cigarettes? In seeking to answer those questions, the paper builds on Feeley\u27s legacy of comparative scholarship, policy analysis, and focus on law in action

Mobile Assessment Tools

This paper describes the concept for a Web-based, database supported e-Learning Test, Examination and Assessment System, called TEASE, that can be used via the Internet and is therefore suitable for both local and remote examination preparation as well as for examination within lab courses (entry test) or during lectures (big online exams in lecture halls). Another topic of this contribution is the improvement of the mandatory authentication for a multitude of students during written examinations in large lecture halls – the usage of barcodes to register both the student as well as the issued exercise sheet of the exam

Study on CerAMfacturing of Novel Alumina Aerospike Nozzles by Lithography-Based Ceramic Vat Photopolymerization (CerAM VPP)

Advanced ceramics are recognized as key enabling materials possessing combinations of properties not achievable in other material classes. They provide very high thermal, chemical and mechanical resistance and typically exhibit lower densities than metals. These properties predestine ceramics for many different applications, especially those in space. Aerospike nozzles promise an increased performance compared to classic bell nozzles but are also inherently more complex to manufacture due to their shape. Additive manufacturing (AM) drastically simplifies or even enables the fabrication of very complex structures while minimizing the number of individual parts. The applicability of ceramic AM (“CerAMfacturing”) on rocket engines and especially nozzles is consequently investigated in the frame of the “MACARONIS” project, a cooperation of the Institute of Aerospace Engineering at Technische Universität Dresden and the Fraunhofer Institute for Ceramic Technologies and Systems (IKTS) in Dresden. The goal is to develop novel filigree aerospike nozzles with 2.5 N and 10 N thrust. For this purpose, CerAM VPP (ceramic AM via Vat Photopolymerization) using photoreactive and highly particle-filled suspensions was utilized. This contribution gives an overview of the component development starting from CAD modeling, suspension development based on alumina AES-11C, heat treatment and investigation of the microstructure of the sintered components. It could be shown that modifying the suspension composition significantly reduced the formation of cracks during processing, resulting in defect-free filigree aerospike nozzles for application in space

Genetic Variation in Sodium‐glucose Cotransporter 2 and Heart Failure

Inhibition of sodium‐glucose cotransporter 2 (SGLT2) represents an emerging pharmaceutical approach for the treatment of heart failure. The mechanisms by which SGLT2 inhibitors reduce the risk of heart failure are not well understood. The objective of this study was to investigate the association between single nucleotide polymorphisms (SNPs) in the SLC5A2 gene, encoding SGLT2, and heart failure, and to assess potential mediators of this association. Regression and mediation analyses were conducted with individual participant data of the UK Biobank (n = 416,737) and validated in the cardiovascular high‐risk cohort of the LUdwigshafen RIsk and Cardiovascular Health study (LURIC; n = 3316). Two intronic SNPs associated with SLC5A2 expression were included in a genetic score, which was associated with lower risk of heart failure in UK Biobank (odds ratio 0.97, 95% confidence interval, 0.95−0.99, P = 0.016). This association was also present in participants without type 2 diabetes or coronary artery disease (CAD). The associations of the genetic score with HbA1c, high‐density lipoprotein cholesterol, uric acid, systolic blood pressure, waist circumference, and body composition mediated 35% of the effect of the score on heart failure risk. No associations of the genetic SGLT2 score with atherosclerotic cardiovascular disease outcomes or markers of volume status were observed, which was confirmed in the LURIC study. Variations in the gene encoding SGLT2 were associated with the risk of prevalent or incident heart failure. This association was mediated by several mechanisms and did not depend on the presence of type 2 diabetes or previous CAD events

Radiomics features of the spleen as surrogates for CT-based lymphoma diagnosis and subtype differentiation

The spleen is often involved in malignant lymphoma, which manifests on CT as either splenomegaly or focal, hypodense lymphoma lesions. This study aimed to investigate the diagnostic value of radiomics features of the spleen in classifying malignant lymphoma against non-lymphoma as well as the determination of malignant lymphoma subtypes in the case of disease presence—in particular Hodgkin lymphoma (HL), diffuse large B-cell lymphoma (DLBCL), mantle-cell lymphoma (MCL), and follicular lymphoma (FL). Spleen segmentations of 326 patients (139 female, median age 54.1 +/ 18.7 years) were generated and 1317 radiomics features per patient were extracted. For subtype classification, we created four different binary differentiation tasks and addressed them with a Random Forest classifier using 10-fold cross-validation. To detect the most relevant features, permutation importance was analyzed. Classifier results using all features were: malignant lymphoma vs. non-lymphoma AUC = 0.86 (p < 0.01); HL vs. NHL AUC = 0.75 (p < 0.01); DLBCL vs. other NHL AUC = 0.65 (p < 0.01); MCL vs. FL AUC = 0.67 (p < 0.01). Classifying malignant lymphoma vs. non-lymphoma was also possible using only shape features AUC = 0.77 (p < 0.01), with the most important feature being sphericity. Based on only shape features, a significant AUC could be achieved for all tasks, however, best results were achieved combining shape and textural features. This study demonstrates the value of splenic imaging and radiomic analysis in the diagnostic process in malignant lymphoma detection and subtype classification.peer-reviewe

Quantitative Imaging Biomarkers of the Whole Liver Tumor Burden Improve Survival Prediction in Metastatic Pancreatic Cancer

Finding prognostic biomarkers with high accuracy in patients with pancreatic cancer (PC) remains a challenging problem. To improve the prediction of survival and to investigate the relevance of quantitative imaging biomarkers (QIB) we combined QIB with established clinical parameters. In this retrospective study a total of 75 patients with metastatic PC and liver metastases were analyzed. Segmentations of whole liver tumor burden (WLTB) from baseline contrast-enhanced CT images were used to derive QIBs. The benefits of QIBs in multivariable Cox models were analyzed in comparison with two clinical prognostic models from the literature. To discriminate survival, the two clinical models had concordance indices of 0.61 and 0.62 in a statistical setting. Combined clinical and imaging-based models achieved concordance indices of 0.74 and 0.70 with WLTB volume, tumor burden score (TBS), and bilobar disease being the three WLTB parameters that were kept by backward elimination. These combined clinical and imaging-based models have significantly higher predictive performance in discriminating survival than the underlying clinical models alone (p &lt; 0.003). Radiomics and geometric WLTB analysis of patients with metastatic PC with liver metastases enhances the modeling of survival compared with models based on clinical parameters alone