Computer-Aided Diagnosis Systems in the Classification of Neuroblastoma Histological Images

University of Technology Sydney. Faculty of Engineering and Information Technology.Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathological classification by a human histopathologist is considered the gold standard, computers can help to extract many more features, some of which may not be recognisable by the human eye. Neuroblastoma histological images have a complex texture with complicated features which are different from appearance-based features. Computer-aided diagnosis (CAD) systems facilitate the analysis and classification of neuroblastoma histological images which are non-trivial tasks due to the differences in staining, intensity, and instrumentation. This motivates the thesis to work on the classification of neuroblastoma histological images. In the past, a small number of methods were proposed by previous studies for the classification of neuroblastoma histological images. These methods are based on the geometry and appearance of the different cells. However, there is a high intra-class variation of intensity and size of the neuroblast cells within the same classification group. Therefore, these methods are not applicable to neuroblastoma histological images. This research proposes a solution based on traditional machine learning approaches and deep learning approaches to extract non-appearance-based features in small regions. This thesis will investigate two research areas of feature extraction: low-level feature extraction and high-level feature extraction. Low-level features are minor details of the image such as lines, curves and edges. However, high-level features are on top of the low-level features to detect object and larger shape in the image. Feature extraction is aggregated with the classifier in this research to classify neuroblastoma histological images into five categories. This thesis makes four contributions. Contribution 1 is the construction of a dataset comprising neuroblastoma histological images which are labeled by an expert histopathologist. Contribution 2 is the proposal of a local feature extraction method which can extract local features which are robust to high intra-class variations of intensity. Contribution 3 is the extraction of discriminative features which are robust to high intra-class variation of scale of the neuroblast cells within the same class. Contribution 4 is the proposal of deep networks to extract high-level features which are difficult for the human eye to recognise. The performance of all the proposed methods in this research is evaluated on a dataset collected from The Children's Hospital at Westmead, Sydney, Australia. As there was no publicly available dataset in this field, the proposed algorithms were evaluated on the second dataset of neuroblastoma provided by the University of Bristol and the public breast cancer dataset. All the results are compared with state-of-the-art methods. The results indicate the effectiveness of the proposed algorithms. This is the first time that neuroblastoma histological images have been classified into five subtypes using low-level and high-level features. However, there are limitations in this research. The specificity is not 100% compared with the gold standard. Moreover, the proposed algorithms are confused in the distinction between poorly-differentiated and differentiating neuroblastoma, a distinction that human pathologists also find difficult in limited fields of view

Convolutional deep belief network with feature encoding for classification of neuroblastoma histological images

© 2018 Journal of Pathology Informatics. Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification. Subjects and Methods: We apply a combination of convolutional deep belief network (CDBN) with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier. Data: We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors. Results: The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods. Conclusion: The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images

Patched completed local binary pattern is an effective method for neuroblastoma histological image classification

© Springer Nature Singapore Pte Ltd. 2018. Neuroblastoma is the most common extra cranial solid tumour in children. The histology of neuroblastoma has high intra-class variation, which misleads existing computer-aided histological image classification methods that use global features. To tackle this problem, we propose a new Patched Completed Local Binary Pattern (PCLBP) method combining Sign Binary Pattern (SBP) and Magnitude Binary Pattern (MBP) within local patches to build feature vectors which are classified by k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) classifiers. The advantage of our method is extracting local features which are more robust to intra-class variation compared to global ones. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our experiments show the proposed method improves the weighted average F-measure by 1.89% and 0.81% with k-NN and SVM classifiers, respectively

A comparative study of three different chloride currents in rat sympathetic neurones

Cl- ions are the most abundant anion in cells. Cl- conducting channels have been proposed to be involved in several cellular processes. These include the modulation of electrical excitability and synaptic inhibition, the regulation of intracellular pH and the regulation of cell volume. In this thesis three types of Cl- channels in rat superior cervical ganglion neurones have been investigated. These are a Ca2+ and protein kinase C-dependent Cl- current (ICl(m)), a hyperpolarization-activated Cl- current (ICL(HP)) and a swelling-activated Cl- current (Icl(swell))- These three currents differ in several properties including biophysical characteristics, sensitivity to pharmacological agents, anion permeability, Ca2+-dependence and involvement of second messenger pathways. Icl(m) is induced synergistically by Ca2+ and diacylglycerol. Consequently it can be activated by the actions of the excitatory neurotransmitter, acetylcholine, which causes a rise in intracellular Ca2+ via the activation of Ca2+ -permeable nicotinic ionotropic receptors, and generates diacylglycerol by stimulating muscarinic receptors. The hyperpolarization-activated Cl- current, (ICL(HP)), shows strong voltage- and time-dependence, is Ca2+-independent and is sensitive to block by the divalent cations, Cd2+ and Zn2+. Icl(swell) is activated in response to hypotonicity-induced cell swelling. The movement of Cl- ions out of the cell is accompanied by water which thus leads to a reversal of cell swelling (Regulatory Volume Decrease mechanism). I have shown that Icl(swell) is different to previously identified Cl- currents in these cells but shares several properties with swelling-activated Cl- currents in other cell types. To conclude, I have studied three types of neuronal Cl- currents which are distinct from one another. Icl(m) and ICL(HP) may have a physiological role in the regulation of electrical excitability, and Icl(swell) in the response of cells to osmolarity changes under both physiological and pathological conditions

STEM Undergraduate Research Symposium 2016 Full Program

Full Program of the 2016 LSSF STEM Undergraduate Research Conference

Genetic-epigenetic interactions in medulloblastoma development

PhD ThesisMedulloblastoma is the most common malignant brain tumour of childhood. Transcriptomic profiling has revealed the existence of four core molecular subgroups (SHH, WNT, Group 3 and Group 4) with distinct clinical, pathologic and molecular characteristics. However, the specific molecular events associated with tumour development in these groups are poorly understood. DNA methylation plays a key role in epigenetic transcriptional regulation, and promoter hypermethylation leading to gene silencing is a common feature of medulloblastoma. DNA methylation profiling has identified distinct methylomic profiles associated with the four subgroups of medulloblastoma, and the wider role of DNA methylation in medulloblastoma now requires investigation. Using two high-throughput screening approaches, this project therefore undertook a comprehensive investigation into the potential role of specific DNA methylation events in the development of the distinct subgroups of medulloblastoma. Using DNA methylation profiles, which were generated for 216 medulloblastomas using the GoldenGate methylation array, the first approach identified 73 CpG methylation markers (encompassing 63 genes) which significantly distinguished Group 3 and/or Group 4 medulloblastomas. Subgroup-specific differential gene expression analysis showed that, for the majority of the methylation markers identified, there was no clear inverse association between methylation and gene expression. One gene (RHOH) was identified which showed strong evidence of epigenetic dysregulation in medulloblastomas. RHOH methylation represented a potential epigenetic event in Group 4 tumours; 51% of Group 4 medulloblastomas showed aberrant hypomethylation of multiple RHOH promoter-associated CpG residues, which was associated with upregulated RHOH expression in Group 4 tumours. RHOH was re-expressed in 4 out of 6 methylated cell lines following treatment with the demethylating agent 5’-aza-2’-deoxycytidine (5-azaCdR). This study has thus identified a novel putative oncogenic role for RHOH in Group 4 medulloblastoma development. In the second approach, a functional epigenomics screen identified 283 genes which were upregulated in 2 or more cell lines investigated (n=10) following 5-azaCdR treatment. Assessment of DNA methylation using the Illumina 450K methylation array identified 160 CpG residues (encompassing 21 of the 283 genes) whose methylation status was consistent with expression alterations observed after 5-azaCDR, and methylation-dependent gene regulation, in cell lines. 9/160 CpG residues (6%) showed evidence of subgroup-specific differential methylation which was concordant with differential gene expression and potential epigenetic gene regulation in medulloblastoma subgroups. These 9 sites represented 5 candidate genes (ACTC1, ANXA2, FAM46A, PRPH and S100A4). Aberrant hypermethylation of multiple gene body CpG residues was associated with FAM46A silencing in non-SHH tumours, while aberrant hypermethylation of multiple promoter-associated residues was associated with ACTC1 silencing in Group 3 and Group 4 medulloblastomas. Single site v hypomethylation events associated with upregulated expression in WNT tumours were identified for ANXA2, PRPH and S100A4. This study has identified six genes with putative oncogenic or tumour suppressor roles in the development of distinct subgroups of medulloblastoma through their epigenetic dysregulation. Further work is now required to validate these findings and to assess their functional significance in medulloblastoma subgroups, as well as their potential relevance in medulloblastoma sub-classification and prognostication

Investigations into the Ulnar Response to Mechanical Stimuli Activating Lamellar and Woven Bone Formation

Woven and lamellar bone formation can be stimulated using mechanical loading. Woven bone forms rapidly in response to damaging loading in a disorganized manner with low mineral density. In contrast, lamellar bone formation can be induced in the absence of damage, and is characterized by its slow, organized deposition and high density. In this dissertation, we first examined the molecular response to woven and lamellar bone formation using damaging and non-damaging dynamic loading protocols, respectively. We observed a significant increase in gene expression related to angiogenesis, cell proliferation and osteogenesis prior to woven bone formation, with significantly lower levels of expression associated with lamellar bone formation. To fully characterize the molecular responses of woven and lamellar bone we used a whole genome microarray. The micorarray results brought to light many inflammatory factors not previously investigated in our model, expanded previous findings about angiogenesis, and strengthened our understanding of the role of osteogenic pathways. Our investigations suggested that angiogenesis is required for successful woven bone formation. We used several angiogenic inhibitors, but were unable to prove the dependence of woven bone formation on angiogenesis. Finally, we sought to separate the effects of static and dynamic strains on bone formation. These findings demonstrate that in the absence dynamic strain, bone damage triggers a woven bone response that leads to a functional repair of whole-bone strength. Overall, the work done in this thesis has enhanced our understanding of bone formation. Future studies will expand on the microarray findings and clarify the role of angiogenesis in woven bone formation

Biological mechanisms of disease relapse in childhood medulloblastoma

PhD ThesisOver 30% of patients diagnosed with a medulloblastoma experience disease recurrence. Relapse is almost universally fatal, only infants who receive delayed radiotherapy at disease recurrence typically survive long term. Consequently relapse is the single leading cause of mortality disease-wide. Improved understanding of medulloblastoma at diagnosis has led to the identification of four distinct molecular subgroups with differing biology and outcome. These comprise of medulloblastomas associated with WNT and SHH pathway disruption (MBWNT and MBSHH respectively), and Group 3 and Group 4 tumours (MBGroup3 and MBGroup4). In contrast, very little is understood about the disease at recurrence, and at present there are only two published studies interrogating the biology of relapsed medulloblastoma. However, improved understanding of the biology at relapse is critical to improving treatment. Events at disease relapse could be explored as therapeutic targets or, if predictive of disease recurrence, provide an opportunity to escalate upfront therapy with the aim of preventing relapse. This study compiled a cohort of medulloblastoma tumours sampled at relapse (n=29), paired with their diagnostic counterparts. All clinicopathological and molecular features, with an established relationship to disease prognosis at diagnosis, were interrogated in this paired relapse cohort. With the exception of molecular subgroup, all features investigated displayed evidence of alteration and predominantly acquisition at recurrence. Most strikingly, the emergence of combined p53-MYC defects was commonly observed at relapse and these features were associated with locally aggressive, rapidly progressive disease following relapse. Through collaborative work, this discovery was explored further, with the development of a novel GTML/Trp53 KI/KI mouse model which faithfully recapitulated the clinicopathological and molecular features of the p53-MYC human tumours, and demonstrated the dependency of tumourigenesis and maintenance on this genetic interaction. Moreover, therapeutic inhibition of Aurora A kinase using MLN8237 in these mouse tumours led to degradation of MYCN, tumour reduction and prolonged survival. v A novel genome-wide DNA methylation analysis was next undertaken in the paired relapse cohort, focusing on MBGroup4 tumours, to interrogate maintained and acquired DNA methylation events between diagnosis and relapse, which may play a role in tumour development. Individual CpG sites on the Infinium DNA methylation 450K array were assessed for changes in their DNA methylation status between diagnosis and relapse. Fifteen candidate genes demonstrated tumour-specific methylation states that emerged at relapse and correlated with gene expression. The T-box and Homeobox gene families accounted for 8/15 (53%) candidates identified. Both these families are reportedly important for tumour development in other cancers. In addition, several studies suggest that epigenetic mechanisms, such as DNA methylation, play a regulatory role in their gene expression. Finally, a large cohort of medulloblastoma tumours (n=206), sampled at diagnosis, from patients who are known to go on and recur, was assembled to investigate any subgroup-specific patterns and timings of relapse. MBWNT rarely relapsed, whereas MBSHH frequently relapsed at both local and distant sites, but were the tumour subgroup most readily salvaged by radiotherapy in patients who were not treated with craniospinal irradiation (CSI) at diagnosis (8/12, 67%). Both MBGroup3 and MBGroup4 were widely metastatic at recurrence (34/41 (83%) and 52/61 (85%)) but contrastingly MBGroup3 relapsed quickly (p=0.0022), whereas MBGroup4 relapsed more slowly (p=0.0008). In patients who did not receive upfront CSI, MYC amplification at diagnosis was associated with rapid disease progression after relapse (p=0.0003). No diagnostic feature was significantly associated with time to death following relapse in the cohort of patients who received upfront CSI. This finding was supported by data from the paired relapse cohort where, in patients who received upfront CSI, it was the biological features of the tumour at relapse and not diagnosis, which were associated with disease course. In summary, this study has discovered emergent combined p53-MYC defects at medulloblastoma relapse which are associated with disease behaviour, identified potentially epigenetically regulated candidate genes in relapsed MBGroup4 tumours, and shown that the patterns of disease relapse are associated with radiotherapy and molecular subgroup. Together these findings demonstrate that medulloblastoma tumour biology is significantly different at relapse and that the timings and location of vi disease recurrence should be considered in the context of molecular subgroup and treatment. Biopsy at disease recurrence is now essential to validate and expand on these novel findings, interrogate all molecular subgroups at disease recurrence, and translate these discoveries into improved outcomes for the patients suffering from this devastating diagnosis.Action Medical Researc

Brain Tumors

Brain tumors comprise a spectrum of histological patterns. Their presentation and management depend on their location, size, and grade of lesions. This book is a collection of high-quality research work from global experts on brain tumors, including meningiomas, and their treatment