Current role of machine learning and radiogenomics in precision neuro-oncology

In the past few years, artificial intelligence (AI) has been increasingly used to create tools that can enhance workflow in medicine. In particular, neuro-oncology has benefited from the use of AI and especially machine learning (ML) and radiogenomics, which are subfields of AI. ML can be used to develop algorithms that dynamically learn from available medical data in order to automatically do specific tasks. On the other hand, radiogenomics can identify relationships between tumor genetics and imaging features, thus possibly giving new insights into the pathophysiology of tumors. Therefore, ML and radiogenomics could help treatment tailoring, which is crucial in personalized neuro-oncology. The aim of this review is to illustrate current and possible future applications of ML and radiomics in neuro-oncology

Artificial Intelligence in the Radiomic Analysis of Glioblastomas: A Review, Taxonomy, and Perspective

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area

Artificial Intelligence in the Radiomic Analysis of Glioblastomas: A Review, Taxonomy, and Perspective

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area

Computational models for stuctural analysis of retinal images

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe evaluation of retina structures has been of great interest because it could be used as a non-intrusive diagnosis in modern ophthalmology to detect many important eye diseases as well as cardiovascular disorders. A variety of retinal image analysis tools have been developed to assist ophthalmologists and eye diseases experts by reducing the time required in eye screening, optimising the costs as well as providing efficient disease treatment and management systems. A key component in these tools is the segmentation and quantification of retina structures. However, the imaging artefacts such as noise, intensity homogeneity and the overlapping tissue of retina structures can cause significant degradations to the performance of these automated image analysis tools. This thesis aims to provide robust and reliable automated retinal image analysis technique to allow for early detection of various retinal and other diseases. In particular, four innovative segmentation methods have been proposed, including two for retinal vessel network segmentation, two for optic disc segmentation and one for retina nerve fibre layers detection. First, three pre-processing operations are combined in the segmentation method to remove noise and enhance the appearance of the blood vessel in the image, and a Mixture of Gaussians is used to extract the blood vessel tree. Second, a graph cut segmentation approach is introduced, which incorporates the mechanism of vectors flux into the graph formulation to allow for the segmentation of very narrow blood vessels. Third, the optic disc segmentation is performed using two alternative methods: the Markov random field image reconstruction approach detects the optic disc by removing the blood vessels from the optic disc area, and the graph cut with compensation factor method achieves that using prior information of the blood vessels. Fourth, the boundaries of the retinal nerve fibre layer (RNFL) are detected by adapting a graph cut segmentation technique that includes a kernel-induced space and a continuous multiplier based max-flow algorithm. The strong experimental results of our retinal blood vessel segmentation methods including Mixture of Gaussian, Graph Cut achieved an average accuracy of 94:33%, 94:27% respectively. Our optic disc segmentation methods including Markov Random Field and Compensation Factor also achieved an average sensitivity of 92:85% and 85:70% respectively. These results obtained on several public datasets and compared with existing methods have shown that our proposed methods are robust and efficient in the segmenting retinal structures such the blood vessels and the optic disc.Brunel University Londonhttp://bura.brunel.ac.uk/bitstream/2438/10387/1/FulltextThesis.pd

A Review on Segmentation of Knee Articular Cartilage: from Conventional Methods Towards Deep Learning

In this paper, we review the state-of-the-art approaches for knee articular cartilage segmentation from conventional techniques to deep learning (DL) based techniques. Knee articular cartilage segmentation on magnetic resonance (MR) images is of great importance in early diagnosis of osteoarthritis (OA). Besides, segmentation allows estimating the articular cartilage loss rate which is utilised in clinical practice for assessing the disease progression and morphological changes. Topics covered include various image processing algorithms and major features of different segmentation techniques, feature computations and the performance evaluation metrics. This paper is intended to provide researchers with a broad overview of the currently existing methods in the field, as well as to highlight the shortcomings and potential considerations in the application at clinical practice. The survey showed that the state-of-the-art techniques based on DL outperforms the other segmentation methods. The analysis of the existing methods reveals that integration of DL-based algorithms with other traditional model-based approaches have achieved the best results (mean Dice similarity cofficient (DSC) between 85:8% and 90%)

Radiogenomics Framework for Associating Medical Image Features with Tumour Genetic Characteristics

Significant progress has been made in the understanding of human cancers at the molecular genetics level and it is providing new insights into their underlying pathophysiology. This progress has enabled the subclassification of the disease and the development of targeted therapies that address specific biological pathways. However, obtaining genetic information remains invasive and costly. Medical imaging is a non-invasive technique that captures important visual characteristics (i.e. image features) of abnormalities and plays an important role in routine clinical practice. Advancements in computerised medical image analysis have enabled quantitative approaches to extract image features that can reflect tumour genetic characteristics, leading to the emergence of ‘radiogenomics’. Radiogenomics investigates the relationships between medical imaging features and tumour molecular characteristics, and enables the derivation of imaging surrogates (radiogenomics features) to genetic biomarkers that can provide alternative approaches to non-invasive and accurate cancer diagnosis. This thesis presents a new framework that combines several novel methods for radiogenomics analysis that associates medical image features with tumour genetic characteristics, with the main objectives being: i) a comprehensive characterisation of tumour image features that reflect underlying genetic information; ii) a method that identifies radiogenomics features encoding common pathophysiological information across different diseases, overcoming the dependence on large annotated datasets; and iii) a method that quantifies radiogenomics features from multi-modal imaging data and accounts for unique information encoded in tumour heterogeneity sub-regions. The present radiogenomics methods advance radiogenomics analysis and contribute to improving research in computerised medical image analysis

UNCERTAINTY MITIGATION IN IMAGE-BASED MACHINE LEARNING MODELS FOR PRECISION MEDICINE

Machine learning (ML) algorithms have been developed to build predictive models in medicine and healthcare. In most cases, the performance of ML models/algorithms is measured by predictive accuracy or accuracy-related measures only. In medicine, the model results are intended to guide physicians to make critical decisions regarding patient care. This means that quantifying and mitigating the uncertainty of the output is also very important as it will allow decision makers to know how much they can rely on the model output. My dissertation focuses on studying model uncertainty of image-based ML in the context of precision medicine of brain cancer. Specifically, I focus on developing ML models to predict intra-tumor heterogeneity of genomic and molecular markers based on multi-contrast magnetic resonance imaging (MRI) data for glioblastoma (GBM) – the most aggressive type of brain cancer. Intra-tumor heterogeneity has been found to be a leading cause of treatment failure of GBM. Devising a non-invasive approach to map out the molecular/genomic distribution using MRI helps develop treatment with high precision. My dissertation research addresses the model uncertainties due to high-dimensional and noisy features, sparsity of labeled data, and utility of domain knowledge. In the first study, we developed a Semi-supervised Gaussian Process with Uncertainty-minimizing Feature-selection (SGP-UF), which can incorporate selected unlabeled samples (i.e. unbiopsied regions of a tumor) in the model training, and integrate feature selection with a new criterion of seeking features that minimize the prediction uncertainty. In the second study, we developed a Knowledge-infused Global-Local data fusion (KGL) framework, which optimally fuses three sources of data/information including biopsy samples (labeled data, local/sparse), images (unlabeled data, global), and knowledge-driven mechanistic models. In the third study, we developed a Weakly Supervised Ordinal Support Vector Machine (WSO-SVM), which aims to leverage a combination of data sources including biopsy/labeled samples and unlabeled samples from the tumor and image data from the normal brain, as well as their intrinsic ordinal relationship. We demonstrate that these novel methods significantly reduce prediction uncertainty while at the same time achieving higher accuracy in precision medicine, which can inform personalized targeted treatment decisions that potentially improve clinical outcome.Ph.D

Quantitative MR Image Analysis - a Useful Tool in Differentiating Glioblastoma from Solitary Brain Metastasis

Cilj: Prikaz glioblastoma i metastaza na konvencionalnom MRI je često jako sličan, ali se terapijski pristup i prognoza bitno razlikuju. Čak i primenom naprednih MR tehnika, u nekim slučajevima dijagnoza ostaje nejasna. Glavni cilj disertacije bio je da utvrdi da li fraktalna ili teksturna, ili obe kvantitativne analize MR slike mogu doprineti diferencijaciji glioblastoma od solitarne metastaze mozga. Metod: Studija je sprovedena na ukupno 96 pacijenata sa dokazanim dijagnozama glioblastoma (50 pacijenata), odnosno solitarne metastaze (46 pacijenata). Izdvojene su slike sa najinformativnijim prikazom lezije (jedan isti presek u tri različite sekvence: CET1, T2 i SWI), a zatim je učinjena njihova kompjuterska analiza, primenom fraktalne metode brojanja kvadrata i teksturne metode bazirane na matrici zajedničke pojave istog nivoa sive boje (GLCM). Rezultati: Analizom sive skale celog tumora i binarne slike unutrašnjosti tumora sa T2 sekvence dobijen je najveći broj parametara koji značajno razlikuju dve vrste tumora (drugi ugaoni moment SASM, inverzni moment razlike SIDM, kontrast SCON, korelacija SCOR, diferencijalna fraktalna dimenzija DDIFF, odnosno binarna fraktalna dimenzija unutrašnjosti DBIN2, normirana fraktalna dimenzija DNORM, lakunarnost Ʌ2), dok su se druge dve sekvence (CET1 i SWI) pokazale manje pogodnim za kvantifikaciju. Kombinacijom parametara povećala se tačnost testiranja (AUC 0,838±0,041, senzitivnost 78% i specifičnost 76% za kombinaciju SASM i SIDM sa CET1 i T2 + SASM sa SWI + DBIN2 i DNORM sa T2). Zaključak: Kvantifikacija MR slike može doprineti diferencijalno dijagnostičkoj odluci između glioblastoma i solitarne metastaze mozga i potencijalno može postati deo svakodnevne radiološke prakse.Purpose: Presentation of glioblastomas and metastases on conventional MRI is quite similar, however treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of dissertation was to determine whether fractal, texture, or both quantitative MR image analysis could aid in differentiating glioblastoma from solitary brain metastasis. Method: Study embraced 96 patients with proven diagnosis of glioblastoma (50 patients), respectively solitary metastasis (46 patients). Images with the most representative lesion (one same slice on three different sequences: CET1, T2 and SWI) were selected, and computer analysis was done by fractal box-counting and texture gray level co-occurrence matrix (GLCM) methods. Results: Gray scale analysis of whole tumor and binary image analysis of tumor´s inner structures, both derived on T2 sequence, obtained the most significantly different parameters between two types of tumors (angular second moment SASM, inverse difference moment SIDM, contrast SCON, correlation SCOR, differential box dimension DDIFF, respectively binary box dimension DBIN2, normalized box dimension DNORM, lacunarity Ʌ2), while the other two sequences (CET1 and SWI) showed less suitable for quantification. The combinations of parameters yielded better results (AUC-0.838±0.041, sensitivity 78% and specificity 76% for next combination SASM and SIDM from CET1 and T2 + SASM from SWI + DBIN2 and DNORM from T2). Conclusions: MR image quantification may aid in differentiation between glioblastoma and solitary brain metastasis, and potentially could become a part of daily radiology practice