Quantitative imaging in radiation oncology

Artificially intelligent eyes, built on machine and deep learning technologies, can empower our capability of analysing patients’ images. By revealing information invisible at our eyes, we can build decision aids that help our clinicians to provide more effective treatment, while reducing side effects. The power of these decision aids is to be based on patient tumour biologically unique properties, referred to as biomarkers. To fully translate this technology into the clinic we need to overcome barriers related to the reliability of image-derived biomarkers, trustiness in AI algorithms and privacy-related issues that hamper the validation of the biomarkers. This thesis developed methodologies to solve the presented issues, defining a road map for the responsible usage of quantitative imaging into the clinic as decision support system for better patient care

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book

Potential Impacts of Artificial Intelligence on Spine Imaging Interpretation and Diagnosis

Spine and related disorders represent one of the most common causes of pain and disability in the United States. Imaging represents an important diagnostic procedure in spine care. Imaging studies contain actionable data and insights undetectable through routine visual analysis. Convergent advances in imaging, artificial intelligence (AI), and radiomic methods has revealed the potential of multiscale in vivo interrogation to improve the assessment and monitoring of pathology. AI offers various types of decision support through the analysis of structured and unstructured data. The primary purpose of this qualitative exploratory case study was to identify the potential impacts of AI solutions on spine imaging interpretation and diagnosis. Selected constructs from the diffusion of innovations theory and the technology acceptance model provided the conceptual framework. Data were acquired from 4 consensus-based white papers, researcher reflective journaling, and 2 homogenous focus group sessions comprising radiologists and AI experts. Content and thematic analyses of acquired data were performed with ATLAS.ti. Three primary themes emerged from qualitative analysis: patient-based decision support, population-based decision support, and application-based decision support. Subthemes include multiscale in vivo analysis, naturally language processing, change analysis, prioritization, and ground truth. The results suggest how further development of AI could fundamentally alter how spine pathology is detected, characterized, and classified. The study also addresses the potential impact of AI on in vivo tissue analysis, the differential diagnosis, and imaging workflow. This includes introducing the concept of the virtual biopsy and its use in spine imaging

Biomedical image analysis of brain tumours through the use of artificial intelligence

Thesis (MCom)--Stellenbosch University, 2022.ENGLISH SUMMARY: Cancer is one of the leading causes of morbidity and mortality on a global scale. More specifically, cancer of the brain, which is one of the rarest forms. One of the major challenges is that of timely diagnoses. In the ongoing fight against cancer early and accurate detection in combination with effective treatment strategy planning remains one of the best tools for improved patient outcomes and success. Emphasis has been placed on the identification and classification of brain lesions in patients - that is, either the absence or presence of brain tumours. In the case of malignant brain tumours it is critical to classify patients into either high-grade or low-grade brain lesion groups: different gradings of brain tumours have different prognoses, thus different survival rates. The growth in the availability and accessibility of big data due to digitisation has led individuals in the area of bioinformatics in both academia and industry to apply and evaluate artificial intelligence techniques. However, one of the most important challenges, not only in the field of bioinformatics but also in other realms, is transforming the raw data into valuable insights and knowledge. In this research thesis artificial intelligence techniques that can detect vital and fundamental underlying patterns in the data are reviewed. The models may provide significant predictive performance to assist with decision making. Much artificial intelligence has been applied to brain tumour classification and segmentation in the research literature. However, in this study the theoretical background of two more traditional machine learning methods, namely -nearest neighbours and support vector machines, is discussed. In recent years, deep learning (artificial neural networks) has gained prominence due to its ability to handle copious amounts of data. The specialised version of the artificial neural network that is reviewed is convolutional neural networks. The rationale behind this particular technique is that it is applied to visual imagery. In addition to making use of the convolutional neural network architecture, the study reviews the training of neural networks that involves the use of optimisation techniques, considered to be one of the most difficult parts. Utilising only one learning algorithm (optimisation technique) in the architecture of convolutional neural network models for classification tasks may be regarded as insufficient unless there is strong support in the design of the analysis for using a particular technique. Nine state-of-the-art optimisation techniques formed part of a comparative study to determine if there was any improvement in the classification and segmentation of high-grade or low-grade brain tumours. These machine learning and deep learning techniques have proved to be successful in image classification and - more relevant to this research – brain tumours. To supplement the theoretical knowledge, these artificial intelligence methodologies (models) are applied through the exploration of magnetic resonance imaging scans of brain lesions.AFRIKAANSE OPSOMMING: Kanker is wêreldwyd een van die hoofoorsake van morbiditeit en sterftes; veral breinkanker, wat een van die mees seldsame soorte is. Een van die groot uitdagings is om dit betyds te diagnoseer. In die voortgesette stryd teen kanker is vroeë en akkurate opsporing, in kombinasie met doeltreffende beplanning van die behandelingstrategie, een van die beste hulpmiddels vir verbeterde pasiëntuitkomste en sukses. Klem word geplaas op die identifikasie en klassifikasie van breinletsels in pasiënte – dit wil sê, die teenwoordigheid of afwesigheid van breingewasse. In die geval van kwaadaardige breingewasse is dit noodsaaklik om pasiënte in groepe as hetsy hoëgraad- of laegraadbreingewasse te klassifiseer: verskillende graderings van breingewasse het verskillende prognoses, en dus verskillende oorlewingskoerse. Die toename in die beskikbaarheid en toeganklikheid van groot data danksy digitalisering, het daartoe gelei dat individue op die gebied van bio-informatika in die akademie en die bedryf begin het om kunsmatige-intelligensie-tegnieke toe te pas en te evalueer. Een van die belangrikste uitdagings, nie slegs op die gebied van bio-informatika nie, maar ook op ander terreine, is egter die omskakeling van rou data na waardevolle insigte en kennis. Hierdie navorsingstesis hersien die kunsmatige-intelligensie-tegnieke wat lewensbelangrike en grondliggende onderliggende patrone in die data kan opspoor. Die modelle kan beduidende voorspellende prestasie bied om met besluitneming te help. Die navorsingsliteratuur dek heelwat toepassings van kunsmatige intelligensie op breingewasklassifikasie en -segmentasie. In hierdie studie word die teoretiese agtergrond van meer tradisionele masjienleermetodes, naamlik die -naaste-bure-algoritme (-nearest neighbour algorithm) en steunvektormasjiene, bespreek. Diep leer (kunsmatige neurale netwerke) het onlangs op die voorgrond getree weens die vermoë daarvan om groot hoeveelhede data te kan hanteer. Die gespesialiseerde weergawe van die kunsmatige neurale netwerk wat hersien word, is konvolusionele neurale netwerkargitektuur. Die rasionaal vir hierdie spesifieke tegniek is dat dit op visuele beelde toegepas word. Buiten dat dit van konvolusionele neurale netwerkargitektuur gebruik maak, hersien die studie ook die afrigting van neurale netwerke met behulp van optimaliseringstegnieke, wat as een van die moeilikste dele beskou word. Die aanwending van slegs een leeralgoritme (optimaliseringstegniek) in die argitektuur van konvolusionele neurale netwerkmodelle vir klassifikasietake, kan as onvoldoende beskou word, tensy daar sterk steun vir die gebruik van ʼn spesifieke tegniek in die ontwerp van die ontleding is. Nege van die jongste optimaliseringstegnieke was deel van ʼn vergelykende studie om vas te stel of daar enige verbetering in die klassifikasie en segmentasie van hoëgraad- en laegraadbreingewasse was. Hierdie masjienleer- en diep-leertegnieke was suksesvol met beeldklassifikasie en – meer relevant vir hierdie navorsing – breingewasklassifikasie. Ter aanvulling van die teoretiese kennis, word hierdie kunsmatige-intelligensie-metodologieë (-modelle) deur die verkenning van magnetiese resonansbeelding van breingewasse toegepas.Master

Measurement of treatment response and survival prediction in malignant pleural mesothelioma

Malignant pleural mesothelioma (MPM) is a rare cancer of the mesothelial cells of the visceral and parietal pleurae that is heterogeneous in terms of biology, prognosis and response to systemic anti-cancer therapy (SACT). The primary tumour forms an unusual, complex shape which makes survival prediction and response measurement uniquely challenging. Computed tomography (CT) imaging is the bedrock of radiological quantification and response assessment, but it has major limitations that translate into low sensitivity and high inter-observer variation when classifying response using Response Evaluation Classification In Solid Tumours (mRECIST) criteria. Magnetic resonance imaging (MRI) tools have been developed that overcome some of these problems but cost and availability of MRI mean that optimisation of CT and better use for data acquired by this method are important priorities in the short term. In this thesis, I conducted 3 studies focused on, 1) development of a semi-automated volumetric segmentation method for CT based on recently positive studies in MRI, 2) training and external validation of a deep learning artificial intelligence (AI) tool for fully automated volumetric segmentation based on CT data, and, 3) use of non-tumour imaging features available from CT related to altered body composition for development of new prognostic models, which could assist in selection of patients for treatment and improving tolerance to treatment by targeting the systemic consequences of MPM. The aim of Chapter 3 is to develop a semi-automated MPM tumour volume segmentation method that would serve as the ground truth for the training of a fully automated AI algorithm. A semi-automated approach to pleural tumour segmentation has been developed using MRI scans which calculated volumetric measurements from seed points - defined by differential tumour enhancement - placed within a pre-defined volume of pleural tumour. I extrapolated this MRI method using contrast-enhanced CT scans in 23 patients with MPM. Radiodensity values – defined by Hounsfield units (HU) - were calculated for the different thoracic tissues by placing regions of interest (ROI) on visible areas of pleural tumour with similar ROIs placed on other thoracic tissues. Pleural volume contours were drawn on axial CT slices and propagated throughout the volume by linear interpolation using volumetric software (Myrian Intrasense® software v2.4.3 (Paris, France)). Seed points based on the radiodensity range of pleural tumour were placed on representative areas of tumour with regions grown. There were similarities in median thoracic tissue HU values: pleural tumour, 52 [IQR 46 to 60] HU; intercostal muscle, 20.4 [IQR 11.9 to 32.3] HU; diaphragm, 40.4 [IQR 26.4 to 56.4] HU and pleural fluid, 11.8 [IQR 8.3 to 17.8] HU. There was also reduced definition between MPM tumour and neighbouring structures. The mean time taken to complete semi-automated volumetric segmentations for the 8 CT scans examined was 25 (SD 7) minutes. The semi-automated CT volumes were larger than the MRI volumes with a mean difference between MRI and CT volumes of -457.6 cm3 (95% limits of agreement -2741 to +1826 cm3). The complex shape of MPM tumour and overlapping thoracic tissue HU values precluded HU threshold-based region growing and meant that semi-automated volumetry using CT was not possible in this thesis. Chapter 4 describes a multicentre retrospective cohort study that developed and validated an automated AI algorithm – termed a deep learning Convolutional Neural Network (CNN) - for volumetric MPM tumour segmentation. Due to the limitations of the semi-automated approach described in Chapter 3, manually annotated tumour volumes were used to train the CNN. The manual segmentation method ensured that all the parietal pleural tumour was included in the respective volumes. Although the manual CT volumes were consistently smaller than semi-automated MRI volumes (average difference between AI and human volumes 74.8 cm3), they were moderately correlated (Pearson’s r=0.524, p=0.0103). There was strong correlation (external validation set r=0.851, p<0.0001) and agreement (external validation set mean AI minus human volume difference of +31 cm3 between human and AI tumour volumes). AI segmentation errors (4/60 external validation set cases) were associated with complex anatomical features. There was agreement between human and AI volumetric responses in 20/30 (67%) cases. There was agreement between AI volumetric and mRECIST classification responses in 16/30 (55%) cases. Overall survival (OS) was shorter in patients with higher AI-defined pre-chemotherapy tumour volumes (HR=2.40, 95% CI 1.07 to 5.41, p=0.0114). Survival prediction in MPM is difficult due to the heterogeneity of the disease. Previous survival prediction models have not included measures of body composition which are prognostic in other solid organ cancers. In Chapter 5, I explore the impact of loss of skeletal muscle and adipose tissue at the level of the third lumbar vertebra (L3) and the loss of skeletal muscle at the fourth thoracic (T4) vertebrae on survival and response to treatment in patients with MPM receiving chemotherapy. Skeletal and adipose muscle areas at L3 and T4 were quantified by manual delineation of relevant muscle and fat groups using ImageJ software (U.S. National Institutes of Health, Bethesda, MD) on pre-chemotherapy and response assessment CT scans, with normalisation for height. Sarcopenia at L3 was not associated with shorter OS at the pre-chemotherapy (HR 1.49, 95% CI 0.95 to 2.52, p=0.077) or response assessment time points (HR 1.48, 95% CI 0.97 to 2.26, p=0.0536). A higher visceral adipose tissue index (VFI) measured at L3 was associated with shorter OS (HR 1.95, 95% CI 1.05 to 3.62, p=0.0067). In multivariate analysis, obesity was associated with improved OS (HR 0.36, 95% CI 0.20 to 0.65, p<0.001) while interval VFI loss (HR 1.81, 95% CI 1.04 to 3.13, p=0.035) was associated with reduced OS. Overall loss of skeletal muscle index at the fourth thoracic vertebra (T4SMI) during treatment was associated with poorer OS (HR 2.79, 95% CI 1.22 to 6.40, p<0.0001). Skeletal muscle index on the ipsilateral side of the tumour at the fourth thoracic vertebra (Ipsilateral T4SMI) loss was also associated with shorter OS (HR 2.91, 95% CI 1.28 to 6.59, p<0.0001). In separate multivariate models, overall T4SMI muscle loss (HR 2.15, 95% CI 102 to 4.54, p=0.045) and ipsilateral T4SMI muscle loss (HR 2.85, 95% CI 1.17 to 6.94, p=0.021) were independent predictors of OS. Response to chemotherapy was not associated with decreasing skeletal muscle or adipose tissue indices