Learning Algorithms for Fat Quantification and Tumor Characterization

Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice

Cloud-Based Benchmarking of Medical Image Analysis

Medical imagin

Analyse der Körperzusammensetzung: Messung der Skelettmuskulatur mit Computertomographie und Implikationen für die Patientenversorgung

Objective: This thesis aims to evaluate the relationship between the skeletal muscle index derived from computed tomography (CT) images and patient outcomes, as well as its implications for patient care. This goal was pursued in five individual studies: Studies A and B evaluated the relationship between the lumbar skeletal muscle index (L3SMI) and patient outcomes in the intensive care unit (ICU) and oncology setting, respectively. Studies C and D evaluated the effect of CT acquisition parameters on body composition measures. Study E proposed a novel technique to automate the segmentation of skeletal muscle using a fully automated deep learning system. Material and methods: In total, 1328 axial CT images were included in the five studies. Patients in studies A and B were part of the clinical trials NCT01967056 and NCT01401907 at Massachusetts General Hospital (MGH), respectively. Body composition indices were computed using semi-automated segmentation. Multivariable regression models with a priori defined covariates were used to analyze clinical outcomes. To evaluate whether CT acquisition parameters influence segmentation, the Bland-Altman approach was used. In study E, a fully convolutional neural network was implemented for deep learning-based automatic segmentation. Results: Study A found lower L3SMI to be a predictor of increased mortality within 30 days of extubation (p = 0.033), increased rate of pneumonia within 30 days of extubation (p = 0.002), increased adverse discharge disposition (p = 0.044), longer hospital stays post-extubation (p = 0.048), and higher total hospital costs (p = 0.043). In study B, low L3SMI was associated with worse quality of life (p = 0.048) and increased depression symptoms (p = 0.005). Threshold-based segmentation of skeletal muscle in study C and adipose tissue compartments in study D were significantly affected by CT acquisition parameters. The proposed deep learning system in study E provided automatic segmentation of skeletal muscle cross-sectional area and achieved a high congruence to segmentations performed by domain experts (average Dice coefficient of 0.93). Conclusion: L3SMI is a useful tool for the assessment of muscle mass in clinical practice. In critically ill patients, L3SMI can provide clinically useful information about patient outcomes at the time of extubation. Patients with advanced cancer who suffered from low muscle mass reported worse quality of life and increased depression symptoms. This highlights the clinical relevance of addressing muscle loss early on as part of a multimodal treatment plan. Importantly, indices utilized in body composition analysis are significantly affected by CT acquisition parameters. These effects should be considered when body composition analysis is used in clinical practice or research studies. Finally, our fully automated deep learning system enabled instantaneous segmentation of skeletal muscle.Zielsetzung: Das Ziel der vorliegenden Dissertation war es, den Einfluss des auf CT-Bildern berechneten Skelettmuskelindexes auf klinische Ergebnisse von Patienten und die daraus resultierenden Implikationen für die Patientenversorgung zu evaluieren. Dieses Ziel wurde in fünf Einzelstudien verfolgt: In den Studien A und B wurde der Einfluss des lumbalen Skelettmuskelindex (L3SMI) auf klinische Endpunkte von Patienten auf der Intensivstation sowie in der Onkologie untersucht. Die Studien C und D evaluierten die Auswirkungen von CT-Akquisitionsparametern auf Indizes der Körperzusammensetzung. Studie E stellte eine neuartige Technik der automatisierten Segmentierung von Skelettmuskulatur vor, die durch maschinelles Lernen ermöglicht wurde. Material und Methoden: Insgesamt wurden 1328 axiale CT-Bilder in die fünf Studien eingeschlossen. Die Patienten der Studien A und B waren Teilnehmer der klinischen Studien NCT01967056 und NCT01401907 am Massachusetts General Hospital. Die Indizes der Körperzusammensetzung wurden mithilfe halbautomatischer Segmentierung berechnet. Die klinischen Endpunkte wurden in multivariablen Regressionsmodellen mit a priori definierten Kovariaten analysiert. Um zu evaluieren, ob CT-Akquisitionsparameter die Segmentierung beeinflussen, wurde der Bland-Altman-Ansatz verwendet. In Studie E wurden ein künstliches neuronales Netzwerk sowie maschinelles Lernen für die automatische Segmentierung eingesetzt. Ergebnisse: In Studie A war ein niedriger L3SMI ein Prädiktor für eine höhere Mortalität (p = 0.033) und Pneumonierate (p = 0.002) innerhalb von 30 Tagen nach der Extubation sowie für mehr ungünstige Entlassungen (p = 0.044) und höhere Behandlungskosten für den gesamten Krankenhausaufenthalt (p = 0.043). Ein niedriger L3SMI war in Studie B mit einer schlechteren Lebensqualität (p = 0.048) und stärkeren depressiven Symptomen (p = 0.005) assoziiert. Die schwellenwertbasierte Segmentierung der Skelettmuskulatur in Studie C und der Fettgewebekompartimente in Studie D wurde durch CT-Akquisitionsparameter signifikant beeinflusst. Das in Studie E vorgestellte vollautomatische Segmentierungssystem erreichte eine hohe Übereinstimmung mit den durch Experten erstellten Segmentationen (durchschnittlicher Dice-Koeffizient von 0.93). Fazit: Der L3SMI ist ein Werkzeug zur Beurteilung von Muskelmasse. Bei Intensivpatienten kann L3SMI zum Zeitpunkt der Extubation nützliche klinische Informationen liefern. Patienten mit fortgeschrittener Krebserkrankung, die zudem eine geringere Muskelmasse hatten, berichteten über eine schlechtere Lebensqualität und stärkere depressive Symptome. Dies unterstreicht die Notwendigkeit, die Muskulatur frühzeitig als Teil eines multimodalen Behandlungskonzeptes zu adressieren. Die Indizes der Körperzusammensetzung werden signifikant von CT-Akquisitionsparametern beeinflusst. Darüber hinaus ermöglichte unser vollautomatisiertes System dank maschinellen Lernens die verzögerungsfreie Segmentierung von Skelettmuskulatur

DEVELOPING MEDICAL IMAGE SEGMENTATION AND COMPUTER-AIDED DIAGNOSIS SYSTEMS USING DEEP NEURAL NETWORKS

Diagnostic medical imaging is an important non-invasive tool in medicine. It provides doctors (i.e., radiologists) with rich diagnostic information in clinical practice. Computer-aided diagnosis (CAD) schemes aim to provide a tool to assist the doctors for reading and interpreting medical images. Traditional CAD schemes are based on hand-crafted features and shallow supervised learning algorithms. They are greatly limited by the difficulties of accurate region segmentation and effective feature extraction. In this dissertation, our motivation is to apply deep learning techniques to address these challenges. We comprehensively investigated the feasibilities of applying deep learning technique to develop medical image segmentation and computer-aided diagnosis schemes for different imaging modalities and different tasks. First, we applied a two-step convolutional neural network architecture for selection of abdomen part and segmentation of subtypes of adipose tissue from abdominal CT images. We demonstrated high agreement between the segmentation generated by human and by our proposed deep learning models. Second, we explored to combine transfer learning technique with traditional hand-crafted features to improve the accuracy of breast mass classification from digital mammograms. Our results show that the ensemble of hand-crafted features and transferred features yields improvement of prediction performances. Third, we proposed a 3D fully convolutional network architecture with a novel coarse-to-fine residual module for prostate segmentation from MRI. State-of-art segmentation accuracy was obtained by using this model. We also investigated the feasibilities of applying fully convolutional network for prostate cancer detection based on multi-parametric MRI and obtained promising detection accuracy. Last, we proposed a novel cascaded neural network architecture with post-processing steps for nuclear segmentation from histology images. Superiority of the model was demonstrated by experiments. In summary, these study results demonstrated that deep learning is a very promising technology to help significantly improve efficacy of developing computer-aided diagnosis schemes of medical images and achieve higher performance

Quantitative Magnetic Resonance Imaging Techniques for the Measurement of Organ Fat and Body Composition - Validation and Initial Clinical Utility

Ectopic fat is defined by excess deposition of triglycerides in non-adipose tissues that normally contain only small amounts of fat. Measuring the distribution of ectopic fat is important for understanding the pathogenesis of diseases such as obesity and type 2 diabetes mellitus (T2DM) and understanding variation in treatment response amongst patients. Body composition (the proportion of fat and lean mass in the body) is thought to influence both the development of T2DM and outcomes for treatments such as weight-loss surgery. It can also affect clinical outcomes in chronic diseases and malignancy. Quantitative magnetic resonance imaging (qMRI) enables objective measurements of tissue characteristics to be made directly from acquired data. In this thesis, a qMRI protocol based on chemical shift-encoded (CSE)-MRI, specifically the derived proton density fat fraction (PDFF) measurements, was validated against phantoms, and in volunteers and patients with obesity. A new, semi-automated tool for measurement of body composition from CSE-MRI images was developed and validated. CSE-MRI was used to quantify ectopic organ fat depots and body composition in diseases including obesity, T2DM and cancer. Specifically, differences in organ fat between patients with and without remission of T2DM after bariatric surgery was explored. Body composition was investigated in T2DM remission and it was also compared between patients with colorectal and lung cancer undergoing whole body MRI staging. Data from the pilot phase of a study investigating a new duodenal surfacing procedural treatment for T2DM (Revita-2) is presented, demonstrating the utility of hepatic fat content measured using PDFF as an endpoint in an international, multi-centre clinical trial. Finally, I describe the development of a novel technique for quantification of bone mineral density (BMD) using CSE-MRI techniques. The methodology and tools described in this thesis could be used to measure ectopic fat and body composition in future studies and have the potential for integration into clinical care pathways

Multimodality Treatments in Metastatic Gastric Cancer

Gastric cancer represents one of the most frequent and lethal tumors worldwide today, finding itself in the fifth place in incidence and the third in mortality. Surgery remains the only curative treatment for localized tumors, but only 20% of patients are suitable for surgery due to the lack of specific symptoms and the late diagnosis, especially in Western countries. Additionally, even in patients who receive curative treatment, rates of locoregional relapse and distant metastasis remain high. Palliative chemotherapy is the principal treatment in cases of metastatic disease even if the prognosis of patients receiving chemotherapy is still poor. Therefore, a multidisciplinary evaluation is important in order to improve the efficacy of active treatments. In this context, there is an unmet need for a better understanding of genetic alterations and prognostic and predictive factors in order to choose the best tailored therapy for each patient. The aim of this Special Issue is to focus on the results and problems of multimodality treatment in metastatic gastric cancer, the search for prognostic and predictive factors, and the evaluation of novel strategies for individualized treatment. We are inviting relevant original research, systematic reviews, meta-analyses, and short communications covering the above-mentioned topics

Advanced CRISPR-Cas9 techniques for modulation of non-coding disease-associated genetic variants

The search for genetic explanations of individual susceptibility to complex polygenic diseases has greatly intensified in recent years, with GWAS studies having successfully linked several hundred thousand genomic loci to complex diseases. However, the disease mechanisms underlying these associations remain largely unknown due to several limitations of GWAS. In particular, since 90 % of identified disease-associated SNPs are located outside of protein-coding regions, their biological effects are largely unclear, and although they likely affect gene regulatory functions via altered DNA motifs for specific transcription factors, the target genes need to be identified. Furthermore, each locus contains up to several hundred SNPs in linkage disequilibrium, and thus the causal SNPs are unknown. Recent progress in addressing the problem has been made through the development of a more systematic approach involving several bioinformatic and experimental advances, that specifically tackles the mechanistic limitations of GWAS. The approach applies a series of methods to systematically narrow down the number of candidate causal SNPs, and ultimately identify the causal SNP and affected cell types, enhancers and gene regulatory mechanisms.. To experimentally validate causal SNPs in cellulo and establish downstream target genes and phenotypes, genome editing of the causal SNP must be performed. While regular CRISPR/Cas9 theoretically can be used for this purpose, it is highly inefficient and introduces several issues such as double-stranded breaks and potential off-target effects. In contrast, the newly developed Prime Editing (PE) technology may prove to be ideal for this type of precise genome editing. Furthermore, a second method, CRISPR/Cas9-mediated enhancer modulation (CA/I), may be used to strengthen the findings through direct epigenetic activation or repression of the enhancer in which the SNP resides. As a proof-of-concept study of these recent advances, this thesis builds upon previous unpublished work from our group, which identified a likely causal SNP (rs1799993) in an enhancer associated with visceral obesity, a particularly harmful type of fat accumulation. Epigenetic data suggested that the SNP affects an enhancer element that is active in adipose-derived mesenchymal stem cells (AD-MSCs). Thus, the current study has focused on establishing the genome editing tool PE for in situ editing of the SNP, as well as the epigenetic modulation system CA/I for modulation of the surrounding predicted enhancer element, for use in AD-MSCs. Spacers for sgRNAs and pegRNAs (the latter in PE) targeting the enhancer region and the SNP, respectively, were designed by in silico analysis, and evaluated in vitro for on-target efficiency. Extensions for pegRNAs were designed for editing the SNP from risk to protective allele and vice versa, and a plasmid library of the sgRNAs and pegRNAs was then prepared and sequence-validated. Furthermore, an appropriate mesenchymal stem cell (MSC) model was obtained and genotyped for the SNP in question. Because the MSCs are notoriously hard to transfect, comprehensive testing of transfection methods in these cells was performed, including a variety of chemical and physical methods of gene delivery. While the required plasmids for both PE and CA/I were successfully made, no transfection method proved successful in MSCs using the large-size PE plasmids. Consequently, the SNP rs1799993 was not edited. However, nucleofection was identified as the method that gave the best results in this cell type using smaller plasmids, thus suggesting that optimizations should be directed toward reducing the PE-plasmid size for successful use of this method is MSCs. For similar reasons, a pilot lentiviral transduction of plasmids for CRISPR activation/repression did not result in stably transduced MSCs. In summary, the work of this thesis has laid the groundwork for utilizing the PE and CA/I methods as tools to help translate GWAS association signals into causal gene regulatory mechanisms. Once optimized, the techniques should be able to determine whether rs1799993 is a causal SNP in the visceral obesity associated locus of interest, as well as identify the target genes of the enhancer where the SNP resides and alters transcription factor binding.Masteroppgave i molekylærbiologiMOL399MAMN-MO

Characterisation and mechanisms of altered body composition and tissue wasting in cancer cachexia

Cancer cachexia has been defined as a multifactorial syndrome characterised by an ongoing loss of skeletal muscle that cannot be fully reversed by conventional nutritional support. Cachexia affects most patients with advanced cancer and is associated with reductions in treatment tolerance, response to therapy, quality of life, and survival. Thus, amelioration of cachexia would improve both quality of life and clinical outcome. However, the aetiology of cachexia is poorly understood, and there are no agreed diagnostic biomarkers or management strategy for patients with cancer cachexia. Recent advances in the field of cachexia research include the development of diagnostic criteria for cachexia, as well as computed tomography (CT) body composition analysis software, making the ability to detect clinically significant muscle wasting in obese patients in particular more accurate. Although muscle loss appears to be the most important and physiologically relevant event in cachexia, the importance of fat wasting is less understood. During cachexia, different adipose depots around the body demonstrate differential rates of wasting. Furthermore, recent studies from animal models have suggested that adipose tissue may be a key driver of muscle wasting through fat-muscle crosstalk. However, human studies in this area are lacking. The molecular mechanisms driving muscle loss in humans are also poorly understood, and the relationships between muscle and fat wasting, functional impairment and reduced survival are largely unknown. The prognostic significance of adipose wasting and investigations in tissue cross-talk therefore are now becoming more important whilst the quest for a cachexia related biomarker remains at the fore. The main aim of this thesis was to investigate specific mediators, mechanisms and biomarkers of cachexia in robustly phenotyped patients with upper gastrointestinal cancer (UGI) in whom cachexia is known to be prevalent. This thesis is comprised of several projects designed to investigate various areas of cachexia pathophysiology, diagnosis and staging. In order to recruit patients to clinical trials, drive cachexia research and identify those who would benefit from early intervention, it is important to understand how to screen and diagnose patients with cachexia. Many patients present to clinicians with unintentional weight loss (UWL). This can occur in patients with cachexia, sarcopenia and malnutrition. With increasing rates of obesity worldwide, as well as an ageing population, differentiating causes of UWL is difficult. Firstly therefore, in order to investigate the feasibility of screening for UWL a systematic review was undertaken in chapter 3 to determine which screening tools were able to assess cachexia, sarcopenia and malnutrition according to the consensus definitions for each. Each tool was judged against a reference method and psychometric evaluation carried out. No one tool was able to assess all three conditions simultaneously, and out of the 22 tools assessed, only 3 had been validated against the gold standard of CT cross-sectional imaging. Thus, the development of a novel tool that encompasses the consensus definition criteria and directs clinicians towards the underlying diagnosis would likely improve detection and outcomes. Secondly, building upon screening and methods for diagnosing low muscularity, chapter 4 uses CT body composition analysis to determine any age and sex-related variations in patients with UGI cancer. CT-based cut-offs for determining low skeletal muscle volume are sex and body mass index (BMI) specific and have been driven in order to predict mortality in these patients. As discussed above, the prevalence of obesity is increasing and the population is ageing therefore, many patients may be sarcopenic at diagnosis, making the assessment of clinically significant muscle wasting difficult. A retrospective, observational study was carried out on patients who had undergone potentially curative oncological and surgical treatment for oesophageal cancer. Analysis of both staging and post neoadjuvant chemotherapy (NAC) CT was performed in order to assess baseline characteristics and dynamic changes in body composition. Males had higher baseline muscle and visceral fat volume whereas females had higher subcutaneous fat volume. Patients of all ages and both sexes lost muscle volume though there was no difference in rates of wasting between groups. Older patients and females lost significantly more total fat during chemotherapy. This chapter therefore highlights the need for further investigation to define differences in adipose depots during cancer progression and their prognostic value. Chapter 5 showcases the main biological assessment of cancer associated muscle wasting in this thesis. As shown in chapter 4 all patients demonstrated some evidence of muscle wasting. A potential mechanism of this was therefore investigated further by looking at the role of the neuromuscular junction (NMJ). The NMJ provides the link between myelinated motor nerves and skeletal muscle. Very little is known about the structure of the NMJ in human health or in disease. Experimental denervation is a recognised model for studying muscle wasting in vivo, and as a result experimental evidence for the role of the NMJ in cachexia is dependent upon animal models. Recent data, however have shown that rodent and human NMJs are markedly different. NMJ morph, an imageJ-based package was used for morphometric analysis of the NMJ in UGI cancer patients with or without cachexia and non-cancer controls. No significant differences were found between groups in any of the major pre- or post-synaptic variables measured suggesting that the NMJ remains structurally intact in cancer cachexia, and thus, the denervation of skeletal muscle is not a major driver of the disease. Whilst it is recognised that muscle mass plays a significant role in the syndrome of cancer cachexia, as shown in chapter 4 through body composition analysis the importance of fat wasting and the effect of metabolic mediators on fat volume requires attention. In murine tumour models, loss of fat volume may predate the loss of muscle volume. Fatty acids, leptines, cytokines and other adipokines may cause lipotoxic effects in skeletal muscle. Adipokines have been reported to induce insulin resistance, impair muscle development, alter muscle lipid amino acid metabolism and modify signalling thus affecting skeletal muscle volume. Clinical studies have shown that adipokines from murine models are also measurable in patients with cancer cachexia. In chapter 6 through the use of transcriptomics, subcutaneous (SAT) and visceral adipose tissue (VAT) depots were analysed from UGI cancer patients with and without cachexia and healthy controls to elucidate the biochemistry of fat wasting in cancer cachexia. Over 2000 genes differed between cachexia VAT and SAT. The gene that showed the largest difference in expression between cancer VAT and control was Intelectin-1 (ITLN1), a novel adipocytokine. Genes involving inflammation were upregulated in cancer whereas genes involved in energy metabolism and fat browning were down regulated. VAT, therefore, may be a target for therapeutic manipulation in cancer. Further investigation is required in to the role of Intelectin-1 as a biomarker in cachexia. Finally, in previous searches for biomarkers of cancer, likely responsiveness to treatment and the presence of cachexia, plasma has been used as a readily available biofluid for investigation. However, no robust cachexia biomarker has been found. Although as work continues it seems that individual biomarker targets should be replaced by an array of markers. Chapter 7 used liquid chromatography mass spectrometry (LC/MS)-based metabolomics to investigate the metabolic profile of weight loss from plasma samples taken at the time of anaesthesia from patients under-going UGI resectional surgery. This showed two distinct profiles based on percentage weight loss in accordance with the consensus definition. There were 40 metabolites associated with cachexia with six of those being highly discriminative of weight loss. Specifically, a combination profile of LysoPC 18.2, Hexadecanoic acid, Octadecanoic acid, Phenylalanine and LysoPC 16.1 showed close correlation for eight weight-losing samples (≥5% weight loss) and nine weight stable samples (<5% weight loss). In particular ,many of the metabolites discovered were involved in lipid metabolism, lending credence again to the importance of understanding adipose wasting in cachexia. In summary, the role of adipose wasting as investigated through imaging and biochemical results has been shown to be important in the aetiology of cancer cachexia. It has been demonstrated that there is currently no adequate way to screen for conditions which present with UWL and that the adaptation of a tool in order to do this would drive further research and the content of complex interventions. Previously published CT-derived cut points are BMI and sex specific, however, it has been shown that there is a growing need to develop these in order to define patients by age also. In doing so this would define stricter criteria for clinical trials and lead to improved end points. Potential novel biomarkers of lipid wasting have been discovered in this thesis. ITLN1 which has corresponded with weight loss previously in other groups warrants further investigation as it may be a target for future therapeutic manipulation. Those biomarkers discovered in the metabolomics study show that it is possible to separate patients based on weight loss alone. Although this was a pilot study after further investigation and development, biomarkers of lipid wasting may be useful as inclusion criteria or outcome measures in clinical trials. The discoveries of the lack of fat browning and the stability of the NMJ in cachectic patients also importantly highlights the need for patient rather than animal based research