Automated MRI based pipeline for glioma segmentation and prediction of grade, IDH mutation and 1p19q co-deletion

In the WHO glioma classification guidelines grade, IDH mutation and 1p19q co-deletion play a central role as they are important markers for prognosis and optimal therapy planning. Therefore, we propose a fully automatic, MRI based, 3D pipeline for glioma segmentation and classification. The designed segmentation network was a 3D U-Net achieving an average whole tumor dice score of 90%. After segmentation, the 3D tumor ROI is extracted and fed into the multi-task classification network. The network was trained and evaluated on a large heterogeneous dataset of 628 patients, collected from The Cancer Imaging Archive and BraTS 2019 databases. Additionally, the network was validated on an independent dataset of 110 patients retrospectively acquired at the Ghent University Hospital (GUH). Classification AUC scores are 0.93, 0.94 and 0.82 on the TCIA test data and 0.94, 0.86 and 0.87 on the GUH data for grade, IDH and 1p19q status respectively

Deep learning applications in neuro-oncology

Deep learning (DL) is a relatively newer subdomain of machine learning (ML) with incredible potential for certain applications in the medical field. Given recent advances in its use in neuro-oncology, its role in diagnosing, prognosticating, and managing the care of cancer patients has been the subject of many research studies. The gamut of studies has shown that the landscape of algorithmic methods is constantly improving with each iteration from its inception. With the increase in the availability of high-quality data, more training sets will allow for higher fidelity models. However, logistical and ethical concerns over a prospective trial comparing prognostic abilities of DL and physicians severely limit the ability of this technology to be widely adopted. One of the medical tenets is judgment, a facet of medical decision making in DL that is often missing because of its inherent nature as a black box. A natural distrust for newer technology, combined with a lack of autonomy that is normally expected in our current medical practices, is just one of several important limitations in implementation. In our review, we will first define and outline the different types of artificial intelligence (AI) as well as the role of AI in the current advances of clinical medicine. We briefly highlight several of the salient studies using different methods of DL in the realm of neuroradiology and summarize the key findings and challenges faced when using this nascent technology, particularly ethical challenges that could be faced by users of DL

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of “radiomics and genomics” has been considered under the umbrella of “radiogenomics”. Furthermore, AI in a radiogenomics’ environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor’s characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them

The role of T2-FLAIR mismatch sign in the diagnosis of adult gliomas

O sinal de incompatibilidade T2-FLAIR é um sinal radiológico altamente específico para a mutação IDH, especificamente gliomas IDH-mutados 1p19q co-deletados. O objetivo deste estudo foi avaliar o desempenho deste sinal e a sua possível associação com outras características imagiológicas em gliomas. Selecionámos retrospetivamente 49 pacientes com diagnóstico histopatológico de glioma difuso e RM pré-operatória disponível. Incompatibilidade T2-FLAIR foi observada em 6 casos (12.24%): dois astrocitomas grau 2, dois astrocitomas grau 4 e dois oligodendrogliomas grau 2. O sinal de incompatibilidade T2-FLAIR foi um marcador de mutação IDH com 54.5% de sensibilidade, 100% de especificidade, 100% de valor preditivo positivo e 88% de valor preditivo negativo; O envolvimento da zona subventricular foi associado significativamente com incompatibilidade T2-FLAIR (p=0.010). Este estudo reforça o potencial diagnóstico e aplicabilidade clínica do sinal de incompatibilidade T2-FLAIR, especialmente em combinação com outras ferramentas radiogenómicas, e revela uma possível conexão entre gliomas positivos para incompatibilidade T2-FLAIR, envolvimento da zona subventricular e uma célula estaminal de origem diferente,T2-FLAIR mismatch sign is a highly specific radiological sign for IDH mutation, specifically IDH-mutated 1p19q non-codeleted gliomas. The aim of this study was to evaluate the diagnostic performance of this sign and possible association with other tumor imaging features in gliomas. We retrospectively selected 49 adult patients with histopathologic diagnosis of diffuse glioma and available pre-operative MRI. T2-FLAIR mismatch sign was observed in 6 cases (12,24%): two grade 2 astrocytomas, two grade 4 astrocytomas and two grade 2 oligodendrogliomas. T2-FLAIR mismatch sign was a marker for IDH mutation with 54.5% sensitivity, 100% specificity, 100% positive predictive value and 88% negative predictive value; Subventricular zone involvement was significantly associated with T2-FLAIR mismatch sign (p=0.010). This study reinforces the diagnostic potential and clinical applicability of the T2-FLAIR mismatch sign, especially in combination with other radiogenomic tools, and reveals a possible connection between T2-FLAIR mismatch-positive gliomas, subventricular zone involvement and a different origin stem cell

Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas

Brain tumors, such as low grade gliomas (LGG), are molecularly classified which require the surgical collection of tissue samples. The pre-surgical or non-operative identification of LGG molecular type could improve patient counseling and treatment decisions. However, radiographic approaches to LGG molecular classification are currently lacking, as clinicians are unable to reliably predict LGG molecular type using magnetic resonance imaging (MRI) studies. Machine learning approaches may improve the prediction of LGG molecular classification through MRI, however, the development of these techniques requires large annotated data sets. Merging clinical data from different hospitals to increase case numbers is needed, but the use of different scanners and settings can affect the results and simply combining them into a large dataset often have a significant negative impact on performance. This calls for efficient domain adaption methods. Despite some previous studies on domain adaptations, mapping MR images from different datasets to a common domain without affecting subtitle molecular-biomarker information has not been reported yet. In this paper, we propose an effective domain adaptation method based on Cycle Generative Adversarial Network (CycleGAN). The dataset is further enlarged by augmenting more MRIs using another GAN approach. Further, to tackle the issue of brain tumor segmentation that requires time and anatomical expertise to put exact boundary around the tumor, we have used a tight bounding box as a strategy. Finally, an efficient deep feature learning method, multi-stream convolutional autoencoder (CAE) and feature fusion, is proposed for the prediction of molecular subtypes (1p/19q-codeletion and IDH mutation). The experiments were conducted on a total of 161 patients consisting of FLAIR and T1 weighted with contrast enhanced (T1ce) MRIs from two different institutions in the USA and France. The proposed scheme is shown to achieve the test accuracy of\ua074.81%\ua0on 1p/19q codeletion and\ua081.19%\ua0on IDH mutation, with marked improvement over the results obtained without domain mapping. This approach is also shown to have comparable performance to several state-of-the-art methods

INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

Model-Based Approach for Diffuse Glioma Classification, Grading, and Patient Survival Prediction

The work in this dissertation proposes model-based approaches for molecular mutations classification of gliomas, grading based on radiomics features and genomics, and prediction of diffuse gliomas clinical outcome in overall patient survival. Diffuse gliomas are types of Central Nervous System (CNS) brain tumors that account for 25.5% of primary brain and CNS tumors and originate from the supportive glial cells. In the 2016 World Health Organization’s (WHO) criteria for CNS brain tumor, a major reclassification of the diffuse gliomas is presented based on gliomas molecular mutations and the growth behavior. Currently, the status of molecular mutations is determined by obtaining viable regions of tumor tissue samples. However, an increasing need to non-invasively analyze the clinical outcome of tumors requires careful modeling and co-analysis of radiomics (i.e., imaging features) and genomics (molecular and proteomics features). The variances in diffuse Lower-grade gliomas (LGG), which are demonstrated by their heterogeneity, can be exemplified by radiographic imaging features (i.e., radiomics). Therefore, radiomics may be suggested as a crucial non-invasive marker in the tumor diagnosis and prognosis. Consequently, we examine radiomics extracted from the multi-resolution fractal representations of the tumor in classifying the molecular mutations of diffuse LGG non-invasively. The proposed radiomics in the decision-tree-based ensemble machine learning molecular prediction model confirm the efficacy of these fractal features in glioma prediction. Furthermore, this dissertation proposes a novel non-invasive statistical model to classify and predict LGG molecular mutations based on radiomics and count-based genomics data. The performance results of the proposed statistical model indicate that fusing radiomics to count-based genomics improves the performance of mutations prediction. Furthermore, the radiomics-based glioblastoma survival prediction framework is proposed in this work. The survival prediction framework includes two survival prediction pipelines that combine different feature selection and regression approaches. The framework is evaluated using two recent widely used benchmark datasets from Brain Tumor Segmentation (BraTS) challenges in 2017 and 2018. The first survival prediction pipeline offered the best overall performance in the 2017 Challenge, and the second survival prediction pipeline offered the best performance using the validation dataset. In summary, in this work, we develop non-invasive computational and statistical models based on radiomics and genomics to investigate overall survival, tumor progression, and the molecular classification in diffuse gliomas. The methods discussed in our study are important steps towards a non-invasive approach to diffuse brain tumor classification, grading, and patient survival prediction that may be recommended prior to invasive tissue sampling in a clinical setting

Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning

Background: Accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. But previous studies on magnetic resonance imaging (MRI) images were not effective enough. According to the remarkable performance of convolutional neural network (CNN) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the World Health Organization (WHO) low grade and high grade gliomas.Methods: One hundred and thirteen glioma patients were retrospectively included. Tumor images were segmented with a rectangular region of interest (ROI), which contained about 80% of the tumor. Then, 20% data were randomly selected and leaved out at patient-level as test dataset. AlexNet and GoogLeNet were both trained from scratch and fine-tuned from models that pre-trained on the large scale natural image database, ImageNet, to magnetic resonance images. The classification task was evaluated with five-fold cross-validation (CV) on patient-level split.Results: The performance measures, including validation accuracy, test accuracy and test area under curve (AUC), averaged from five-fold CV of GoogLeNet which trained from scratch were 0.867, 0.909, and 0.939, respectively. With transfer learning and fine-tuning, better performances were obtained for both AlexNet and GoogLeNet, especially for AlexNet. Meanwhile, GoogLeNet performed better than AlexNet no matter trained from scratch or learned from pre-trained model.Conclusion: In conclusion, we demonstrated that the application of CNN, especially trained with transfer learning and fine-tuning, to preoperative glioma grading improves the performance, compared with either the performance of traditional machine learning method based on hand-crafted features, or even the CNNs trained from scratch