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

Diagnostic and prognostic biomarkers of malignant pleural mesothelioma

Malignant Pleural Mesothelioma (MPM) is an aggressive intrathoracic malignancy with an overall poor prognosis. MPM is associated with asbestos exposure but has a long latency period between exposure and disease development. Incidence of MPM in the UK is therefore still rising, predicted to reach a peak in 2020. The majority of patients with MPM present with breathlessness, frequently due to a pleural effusion and/or chest pain. Diagnosis of MPM can be difficult. Radiological detection of early stage MPM in particular can be challenging, as pleural tumour, nodularity or significant pleural thickening may not be evident. Diagnosis is further complicated by the low yield of pleural fluid cytology examination in MPM and pleural biopsy is therefore usually required to allow definitive diagnosis. This can be achieved under image guidance, at surgical thoracoscopy or at local anaesthetic thoracoscopy (LAT). A significant number of patients are either elderly or have co-morbidity precluding general anaesthesia and surgical thoracoscopy. Image-guided pleural biopsy is not always feasible, particularly in the absence of significant pleural thickening. LAT remains a limited resource in the UK. A non-invasive biomarker of MPM, which could be performed early in the patient’s presentation, and that could be available to most hospitals, would therefore be a major clinical advance, allowing clinicians to direct appropriate patients to specialist centres with access to LAT and specialist MDT input where MPM appears likely. There have been several potential blood biomarkers identified in the mesothelioma literature, including the most widely studied, Mesothelin, and more recently Fibulin-3 and SOMAscan™. Unfortunately study results have been variably limited by retrospective study design, inconsistent sampling time points, inconsistent results and lack of external validation, therefore despite initial promising results, none of these biomarkers have entered routine clinical practice for diagnosis. Similarly, utility of imaging biomarkers such as perfusion Computed Tomography (CT), Positron Emission Tomography (PET) and Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has been limited by high radiation dose, limited availability, and requirement for bulky (and therefore late stage) disease for assessment respectively. In chapter 2, study design, recruitment and preliminary results of the DIAPHRAGM (Diagnostic and Prognostic Biomarkers in the Rational Assessment of Mesothelioma) study are reported. A prospective, multi-centre study was designed, recruiting patients with suspected pleural malignancy (SPM) at initial presentation to secondary care services, from a mixture of academic and more clinical units in the UK and Ireland, in addition to asbestos-exposed control subjects. In one of the largest biomarker studies in mesothelioma to date, 639 patients with SPM and 113 asbestos-exposed control subjects were recruited over three years. Data cleaning is being finalised by the Cancer Research UK Clinical Trials Unit Glasgow at the time of writing. Preliminary results reveal that 26% (n=154) patients recruited to the SPM cohort were diagnosed with MPM, 33% (n=209) had secondary pleural malignancy and 34% (n=218) were diagnosed with benign pleural disease. A final diagnosis is awaited in 7% (n=47) at the time of writing. SOMAscan™ and Fibulin-3 biomarker analyses are ongoing and DIAPHRAGM will definitively answer the question of diagnostic utility of these blood biomarkers in routine clinical practice, in a ‘real-life’ MPM population, relative to that of Mesothelin. In chapter 3, contrast-enhanced MRI was performed in patients with suspected MPM and a novel MRI biomarker of pleural malignancy defined (Early Contrast Enhancement – ECE). ECE was defined as a peak in pleural signal intensity at or before 4.5 minutes after intravenous Gadobutrol administration. ECE assessment was successfully performed in all patients who underwent contrast-enhanced MRI. This included patients with pleural thickening 0.533AU/s), indicative of high tumour vascularity, was associated with poor median overall survival (12 months vs. 20 months, p=0.047). Staging of MPM represents an additional challenge to clinicians. This is due to the complex morphology and often rind-like growth pattern of MPM. In addition, delineation of pleural disease from adjacent structures such as intercostal muscle and diaphragm can be difficult to assess, particularly at CT, which is the most commonly used imaging modality for diagnostic and staging assessment in MPM. Current clinical staging frequently underestimates extent of disease, with a significant proportion of patients being upstaged at time of surgery, and is limited by high inter-observer variability. Recent studies have reported the prognostic significance of CT-derived tumour volume; however, many of these studies have been limited by the laborious or complex nature of tumour segmentation, significant inter-observer variability or challenges encountered in separating pleural tumour from adjacent structures, which are often of similar density. MRI is superior to CT in the detection of invasion of the chest wall and diaphragm in MPM. In Chapter 4, MRI was used to quantitatively assess pleural tumour volume in 31 patients with MPM using novel semi-automated segmentation methodology. Four different segmentation methodologies, using Myrian® segmentation software were developed and examined. Optimum methodology was defined, based on the accuracy of volume estimates of an MRI phantom, visual-based analysis, intra-observer agreement and analysis time. Using the optimum methodology, there was acceptable error around the MRI phantom volume (3.6%), a reasonable analysis time (approximately 14 minutes), good intra-observer agreement (intra-class correlation coefficient (ICC) 0.875) and excellent inter-observer agreement (ICC 0.962). Patients with a high MRI-estimated tumour volume (≥300cm3) had a significantly poorer median overall survival (8.5 months vs. 20 months) and was a statistically significant prognostic variable on univariate (HR 2.273 (95% CI 1.162 – 4.446), p=0.016) and multi-variate Cox proportional hazards model (HR 2.114 (95% CI 1.046 – 4.270), p=0.037)

Deep learning for lung cancer analysis

This thesis describes the development and evaluation of two novel deep learning applications that tackle two cancers that affect the lungs. The first, lung cancer, is the largest cause of cancer-related deaths in the United Kingdom. It accounts for more than 1 in 5 cancer deaths; around 35,000 people every year. Lung cancer is curable providing it is detected very early. Computed tomography (CT) X-ray imaging has been shown to be effective for screening. However, the resulting 3D medical images are laborious for humans to read, and widespread adoption would put a huge strain on already stretched radiology departments. I developed a novel deep learning based approach for the automatic detection of lung nodules, potential early lung cancer, that has potential to reduce human workloads. It was evaluated on two independent datasets, and achieves performance competitive with published state-of-the-art tools, with average sensitivity of 84% to 92% at 8 false positives per scan. I developed a related invention which allows hierarchical relationships to be leveraged to improve the performance of CAD tools like this for detection and segmentation tasks. The second cancer is malignant pleural mesothelioma. It is very different from lung cancer: rather than growing within the lung, mesothelioma grows around the outside of the lung in the pleural cavity, like the rind on an orange. It is a rare cancer, caused by exposure to asbestos fibres. It can take decades from exposure to symptoms developing. In Glasgow many mesothelioma patients worked in the ship-building industry, which relied heavily on asbestos. Although asbestos has been banned in the UK since 1999, its presence in buildings and equipment built before then mean that mesothelioma will remain a problem for years to come. Sadly, asbestos is still being mined and many countries, including the United States, have still not instigated a complete ban. For mesothelioma the main challenge is not detection, but accurate measurement —- without the ability to measure tumour size it is difficult to evaluate potential treatments. We therefore developed a fully automated volumetric assessment of malignant pleural mesothelioma. Performance of the algorithm is shown on a multi-centre test set, where volumetric predictions are highly correlated with an expert annotator (r=0.851, p<0.0001). Region overlap scores between the automated method and an expert annotator exceed those for inter-annotator agreement across a subset of cases. Dice overlap scores of 0.64 and 0.55, by cross-validation and independent testing respectively, were achieved. Future work will progress this algorithm towards clinical deployment for the automated assessment of longitudinal tumour development

Non-invasive measures of regional lung function and their clinical application in thoracic surgery

The decision to operate, or not operate, is a critical step in the care of patients with a thoracic condition that potentially requires surgery. Weighing the risks and benefits of a thoracic operation involves careful assessment of the patient’s lung function and how this may be altered by surgery. This thesis will describe the application of multiple different methods to assess regional lung function in the context of assessing patients who may benefit from thoracic surgery. The patients are those with resectable Non-Small Cell Lung Cancer (NSCLC), interstitial lung disease (ILD), severe emphysema, and pleural thickening. Prediction of postoperative lung function for NSCLC was reported to be most accurate and precise using CT based on a systematic review and meta-analysis but using density and volume changes was found to be unfeasible in a cohort study. The Lobar Segmentation and Parenchymal Analysis modules of the open access Chest Imaging Platform were found not to give reproducible results for lobar lung volume and density. Heterogeneity of specific volume of gas on CT in 2D did not help to discriminate between Usual Interstitial Pneumonia and other types of ILD, but heterogeneity was higher in ILD compared to reported values in health. Measurement of chest wall movement did not show a clinically useful difference between patients with mesothelioma compared to benign pleural thickening. Chest wall motion did not have an association with prognosis in patients with mesothelioma; data provided external validation for the Brims decision tree prognostication. Early dynamic hyperinflation may be associated with symptomatic benefit from lung volume reduction but further study of this is required to confirm this. Key limitations exist in the available technologies; the lack of normal reference ranges is particularly important

Topology polymorphism graph for lung tumor segmentation in PET-CT images

Accurate lung tumor segmentation is problematic when the tumor boundary or edge, which reflects the advancing edge of the tumor, is difficult to discern on chest CT or PET. We propose a ‘topo-poly’ graph model to improve identification of the tumor extent. Our model incorporates an intensity graph and a topology graph. The intensity graph provides the joint PET-CT foreground similarity to differentiate the tumor from surrounding tissues. The topology graph is defined on the basis of contour tree to reflect the inclusion and exclusion relationship of regions. By taking into account different topology relations, the edges in our model exhibit topological polymorphism. These polymorphic edges in turn affect the energy cost when crossing different topology regions under a random walk framework, and hence contribute to appropriate tumor delineation. We validated our method on 40 patients with non-small cell lung cancer where the tumors were manually delineated by a clinical expert. The studies were separated into an ‘isolated’ group (n = 20) where the lung tumor was located in the lung parenchyma and away from associated structures / tissues in the thorax and a ‘complex’ group (n = 20) where the tumor abutted / involved a variety of adjacent structures and had heterogeneous FDG uptake. The methods were validated using Dice’s similarity coefficient (DSC) to measure the spatial volume overlap and Hausdorff distance (HD) to compare shape similarity calculated as the maximum surface distance between the segmentation results and the manual delineations. Our method achieved an average DSC of 0.881  ±  0.046 and HD of 5.311  ±  3.022 mm for the isolated cases and DSC of 0.870  ±  0.038 and HD of 9.370  ±  3.169 mm for the complex cases. Student’s t-test showed that our model outperformed the other methods (p-values <0.05)

Advanced machine learning methods for oncological image analysis

Cancer is a major public health problem, accounting for an estimated 10 million deaths worldwide in 2020 alone. Rapid advances in the field of image acquisition and hardware development over the past three decades have resulted in the development of modern medical imaging modalities that can capture high-resolution anatomical, physiological, functional, and metabolic quantitative information from cancerous organs. Therefore, the applications of medical imaging have become increasingly crucial in the clinical routines of oncology, providing screening, diagnosis, treatment monitoring, and non/minimally- invasive evaluation of disease prognosis. The essential need for medical images, however, has resulted in the acquisition of a tremendous number of imaging scans. Considering the growing role of medical imaging data on one side and the challenges of manually examining such an abundance of data on the other side, the development of computerized tools to automatically or semi-automatically examine the image data has attracted considerable interest. Hence, a variety of machine learning tools have been developed for oncological image analysis, aiming to assist clinicians with repetitive tasks in their workflow. This thesis aims to contribute to the field of oncological image analysis by proposing new ways of quantifying tumor characteristics from medical image data. Specifically, this thesis consists of six studies, the first two of which focus on introducing novel methods for tumor segmentation. The last four studies aim to develop quantitative imaging biomarkers for cancer diagnosis and prognosis. The main objective of Study I is to develop a deep learning pipeline capable of capturing the appearance of lung pathologies, including lung tumors, and integrating this pipeline into the segmentation networks to leverage the segmentation accuracy. The proposed pipeline was tested on several comprehensive datasets, and the numerical quantifications show the superiority of the proposed prior-aware DL framework compared to the state of the art. Study II aims to address a crucial challenge faced by supervised segmentation models: dependency on the large-scale labeled dataset. In this study, an unsupervised segmentation approach is proposed based on the concept of image inpainting to segment lung and head- neck tumors in images from single and multiple modalities. The proposed autoinpainting pipeline shows great potential in synthesizing high-quality tumor-free images and outperforms a family of well-established unsupervised models in terms of segmentation accuracy. Studies III and IV aim to automatically discriminate the benign from the malignant pulmonary nodules by analyzing the low-dose computed tomography (LDCT) scans. In Study III, a dual-pathway deep classification framework is proposed to simultaneously take into account the local intra-nodule heterogeneities and the global contextual information. Study IV seeks to compare the discriminative power of a series of carefully selected conventional radiomics methods, end-to-end Deep Learning (DL) models, and deep features-based radiomics analysis on the same dataset. The numerical analyses show the potential of fusing the learned deep features into radiomic features for boosting the classification power. Study V focuses on the early assessment of lung tumor response to the applied treatments by proposing a novel feature set that can be interpreted physiologically. This feature set was employed to quantify the changes in the tumor characteristics from longitudinal PET-CT scans in order to predict the overall survival status of the patients two years after the last session of treatments. The discriminative power of the introduced imaging biomarkers was compared against the conventional radiomics, and the quantitative evaluations verified the superiority of the proposed feature set. Whereas Study V focuses on a binary survival prediction task, Study VI addresses the prediction of survival rate in patients diagnosed with lung and head-neck cancer by investigating the potential of spherical convolutional neural networks and comparing their performance against other types of features, including radiomics. While comparable results were achieved in intra- dataset analyses, the proposed spherical-based features show more predictive power in inter-dataset analyses. In summary, the six studies incorporate different imaging modalities and a wide range of image processing and machine-learning techniques in the methods developed for the quantitative assessment of tumor characteristics and contribute to the essential procedures of cancer diagnosis and prognosis