Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI

We propose a new method for breast cancer screening from DCE-MRI based on a post-hoc approach that is trained using weakly annotated data (i.e., labels are available only at the image level without any lesion delineation). Our proposed post-hoc method automatically diagnosis the whole volume and, for positive cases, it localizes the malignant lesions that led to such diagnosis. Conversely, traditional approaches follow a pre-hoc approach that initially localises suspicious areas that are subsequently classified to establish the breast malignancy -- this approach is trained using strongly annotated data (i.e., it needs a delineation and classification of all lesions in an image). Another goal of this paper is to establish the advantages and disadvantages of both approaches when applied to breast screening from DCE-MRI. Relying on experiments on a breast DCE-MRI dataset that contains scans of 117 patients, our results show that the post-hoc method is more accurate for diagnosing the whole volume per patient, achieving an AUC of 0.91, while the pre-hoc method achieves an AUC of 0.81. However, the performance for localising the malignant lesions remains challenging for the post-hoc method due to the weakly labelled dataset employed during training.Comment: Submitted to Medical Image Analysi

Early Contrast Enhancement: a novel Magnetic Resonance Imaging biomarker of pleural malignancy

Introduction: Pleural Malignancy (PM) is often occult on subjective radiological assessment. We sought to define a novel, semi-objective Magnetic Resonance Imaging (MRI) biomarker of PM, targeted to increased tumour microvessel density (MVD) and applicable to minimal pleural thickening. Materials and methods: 60 consecutive patients with suspected PM underwent contrast-enhanced 3-T MRI then pleural biopsy. In 58/60, parietal pleura signal intensity (SI) was measured in multiple regions of interest (ROI) at multiple time-points, generating ROI SI/time curves and Mean SI gradient (MSIG: SI increment/time). The diagnostic performance of Early Contrast Enhancement (ECE; which was defined as a SI peak in at least one ROI at or before 4.5 min) was compared with subjective MRI and Computed Tomography (CT) morphology results. MSIG was correlated against tumour MVD (based on Factor VIII immunostain) in 31 patients with Mesothelioma. Results: 71% (41/58) patients had PM. Pleural thickening was <10 mm in 49/58 (84%). ECE sensitivity was 83% (95% CI 61–94%), specificity 83% (95% CI 68–91%), positive predictive value 68% (95% CI 47–84%), negative predictive value 92% (78–97%). ECE performance was similar or superior to subjective CT and MRI. MSIG correlated with MVD (r = 0.4258, p = .02). Discussion: ECE is a semi-objective, perfusion-based biomarker of PM, measurable in minimal pleural thickening. Further studies are warranted

Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification

Classification of malignancy for breast cancer and other cancer types is usually tackled as an object detection problem: Individual lesions are first localized and then classified with respect to malignancy. However, the drawback of this approach is that abstract features incorporating several lesions and areas that are not labelled as a lesion but contain global medically relevant information are thus disregarded: especially for dynamic contrast-enhanced breast MRI, criteria such as background parenchymal enhancement and location within the breast are important for diagnosis and cannot be captured by object detection approaches properly. In this work, we propose a 3D CNN and a multi scale curriculum learning strategy to classify malignancy globally based on an MRI of the whole breast. Thus, the global context of the whole breast rather than individual lesions is taken into account. Our proposed approach does not rely on lesion segmentations, which renders the annotation of training data much more effective than in current object detection approaches. Achieving an AUROC of 0.89, we compare the performance of our approach to Mask R-CNN and Retina U-Net as well as a radiologist. Our performance is on par with approaches that, in contrast to our method, rely on pixelwise segmentations of lesions.Comment: Accepted to MICCAI 201

Computer-aided detection and diagnosis of breast cancer in 2D and 3D medical imaging through multifractal analysis

This Thesis describes the research work performed in the scope of a doctoral research program and presents its conclusions and contributions. The research activities were carried on in the industry with Siemens S.A. Healthcare Sector, in integration with a research team. Siemens S.A. Healthcare Sector is one of the world biggest suppliers of products, services and complete solutions in the medical sector. The company offers a wide selection of diagnostic and therapeutic equipment and information systems. Siemens products for medical imaging and in vivo diagnostics include: ultrasound, computer tomography, mammography, digital breast tomosynthesis, magnetic resonance, equipment to angiography and coronary angiography, nuclear imaging, and many others. Siemens has a vast experience in Healthcare and at the beginning of this project it was strategically interested in solutions to improve the detection of Breast Cancer, to increase its competitiveness in the sector. The company owns several patents related with self-similarity analysis, which formed the background of this Thesis. Furthermore, Siemens intended to explore commercially the computer- aided automatic detection and diagnosis eld for portfolio integration. Therefore, with the high knowledge acquired by University of Beira Interior in this area together with this Thesis, will allow Siemens to apply the most recent scienti c progress in the detection of the breast cancer, and it is foreseeable that together we can develop a new technology with high potential. The project resulted in the submission of two invention disclosures for evaluation in Siemens A.G., two articles published in peer-reviewed journals indexed in ISI Science Citation Index, two other articles submitted in peer-reviewed journals, and several international conference papers. This work on computer-aided-diagnosis in breast led to innovative software and novel processes of research and development, for which the project received the Siemens Innovation Award in 2012. It was very rewarding to carry on such technological and innovative project in a socially sensitive area as Breast Cancer.No cancro da mama a deteção precoce e o diagnóstico correto são de extrema importância na prescrição terapêutica e caz e e ciente, que potencie o aumento da taxa de sobrevivência à doença. A teoria multifractal foi inicialmente introduzida no contexto da análise de sinal e a sua utilidade foi demonstrada na descrição de comportamentos siológicos de bio-sinais e até na deteção e predição de patologias. Nesta Tese, três métodos multifractais foram estendidos para imagens bi-dimensionais (2D) e comparados na deteção de microcalci cações em mamogramas. Um destes métodos foi também adaptado para a classi cação de massas da mama, em cortes transversais 2D obtidos por ressonância magnética (RM) de mama, em grupos de massas provavelmente benignas e com suspeição de malignidade. Um novo método de análise multifractal usando a lacunaridade tri-dimensional (3D) foi proposto para classi cação de massas da mama em imagens volumétricas 3D de RM de mama. A análise multifractal revelou diferenças na complexidade subjacente às localizações das microcalci cações em relação aos tecidos normais, permitindo uma boa exatidão da sua deteção em mamogramas. Adicionalmente, foram extraídas por análise multifractal características dos tecidos que permitiram identi car os casos tipicamente recomendados para biópsia em imagens 2D de RM de mama. A análise multifractal 3D foi e caz na classi cação de lesões mamárias benignas e malignas em imagens 3D de RM de mama. Este método foi mais exato para esta classi cação do que o método 2D ou o método padrão de análise de contraste cinético tumoral. Em conclusão, a análise multifractal fornece informação útil para deteção auxiliada por computador em mamogra a e diagnóstico auxiliado por computador em imagens 2D e 3D de RM de mama, tendo o potencial de complementar a interpretação dos radiologistas

Radiologic evaluation of breast disorders related to tuberculosis amongst women in Durban, KwaZulu-Natal, South Africa.

Doctor of Philosophy in Clinical Medicine, University of KwaZulu-Natal, Durban, 2016.Women in KwaZulu-Natal Province, South Africa, are at high risk of developing breast tuberculosis (BTB) due to the increased incidence of HIV. However, there is a general lack of knowledge regarding the various diseases that can affect the breast. This is compounded by lack of the national breast screening program. As a result, many patients with breast cancer (BCA) and BTB are initially misdiagnosed by clinicians. It was evident from the study that much still has to be done in educating the public and healthcare workers about breast diseases. This project endeavoured to compare the effectiveness of various radiological technologies to identify breast problems. The study consisted of three phases all based at Ethekwini Municipality tertiary referral hospitals. The first phase aimed to determine the prevalence of the BTB using retrospective data over a period of 13 years. The same data further provided information of the clinical and radiological manifestations of BTB. This study concluded that while BTB is not common, it shares the clinical and radiology features with BCA, and is difficult to diagnose with current pathology methods. The second phase was done prospectively by recruiting patients who were newly diagnosed with BTB. The aim was to evaluate the use of modern imaging techniques to further describe the radiology patterns of BTB and to determine the radiological parameters that may be used in disease monitoring. The results provided insight into disease extent, and showed that it is usually more severe than perceived with current diagnostic methods. The third phase was performed using retrospective image analysis of patients who had BCA and BTB by using modern radiology techniques. The purpose was to identify the salient features that can differentiate BTB from the BCA. Several radiology parameters were identified as possible biomarkers for differentiation between the two conditions. The knowledge of their respective features would aid in the timeous diagnosis of both conditions, particularly in cases where the pathology results are inconclusive for various reasons. Overall the study highlights the lack of evidence based information on BTB. Recommendations and conclusions are provided in the last chapter

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Innovations in Medical Image Analysis and Explainable AI for Transparent Clinical Decision Support Systems

This thesis explores innovative methods designed to assist clinicians in their everyday practice, with a particular emphasis on Medical Image Analysis and Explainability issues. The main challenge lies in interpreting the knowledge gained from machine learning algorithms, also called black-boxes, to provide transparent clinical decision support systems for real integration into clinical practice. For this reason, all work aims to exploit Explainable AI techniques to study and interpret the trained models. Given the countless open problems for the development of clinical decision support systems, the project includes the analysis of various data and pathologies. The main works are focused on the most threatening disease afflicting the female population: Breast Cancer. The works aim to diagnose and classify breast cancer through medical images by taking advantage of a first-level examination such as Mammography screening, Ultrasound images, and a more advanced examination such as MRI. Papers on Breast Cancer and Microcalcification Classification demonstrated the potential of shallow learning algorithms in terms of explainability and accuracy when intelligible radiomic features are used. Conversely, the union of deep learning and Explainable AI methods showed impressive results for Breast Cancer Detection. The local explanations provided via saliency maps were critical for model introspection, as well as increasing performance. To increase trust in these systems and aspire to their real use, a multi-level explanation was proposed. Three main stakeholders who need transparent models have been identified: developers, physicians, and patients. For this reason, guided by the enormous impact of COVID-19 in the world population, a fully Explainable machine learning model was proposed for COVID-19 Prognosis prediction exploiting the proposed multi-level explanation. It is assumed that such a system primarily requires two components: 1) inherently explainable inputs such as clinical, laboratory, and radiomic features; 2) Explainable methods capable of explaining globally and locally the trained model. The union of these two requirements allows the developer to detect any model bias, the doctor to verify the model findings with clinical evidence, and justify decisions to patients. These results were also confirmed for the study of coronary artery disease. In particular machine learning algorithms are trained using intelligible clinical and radiomic features extracted from pericoronaric adipose tissue to assess the condition of coronary arteries. Eventually, some important national and international collaborations led to the analysis of data for the development of predictive models for some neurological disorders. In particular, the predictivity of handwriting features for the prediction of depressed patients was explored. Using the training of neural networks constrained by first-order logic, it was possible to provide high-performance and explainable models, going beyond the trade-off between explainability and accuracy

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)

Morphological quantitation software in breast MRI: application to neoadjuvant chemotherapy patients

The work in this thesis examines the use of texture analysis techniques and shape descriptors to analyse MR images of the breast and their application as a potential quantitative tool for prognostic indication.Textural information is undoubtedly very heavily used in a radiologist’s decision making process. However, subtle variations in texture are often missed, thus by quantitatively analysing MR images the textural properties that would otherwise be impossible to discern by simply visually inspecting the image can be obtained. Texture analysis is commonly used in image classification of aerial and satellite photography, studies have also focussed on utilising texture in MRI especially in the brain. Recent research has focussed on other organs such as the breast wherein lesion morphology is known to be an important diagnostic and prognostic indicator. Recent work suggests benefits in assessing lesion texture in dynamic contrast-enhanced (DCE) images, especially with regards to changes during the initial enhancement and subsequent washout phases. The commonest form of analysis is the spatial grey-level dependence matrix method, but there is no direct evidence concerning the most appropriate pixel separation and number of grey levels to utilise in the required co-occurrence matrix calculations. The aim of this work is to systematically assess the efficacy of DCE-MRI based textural analysis in predicting response to chemotherapy in a cohort of breast cancer patients. In addition an attempt was made to use shape parameters in order to assess tumour surface irregularity, and as a predictor of response to chemotherapy.In further work this study aimed to texture map DCE MR images of breast patients utilising the co-occurrence method but on a pixel by pixel basis in order to determine threshold values for normal, benign and malignant tissue and ultimately creating functionality within the in house developed software to highlight hotspots outlining areas of interest (possible lesions). Benign and normal data was taken from MRI screening data and malignant data from patients referred with known malignancies.This work has highlighted that textural differences between groups (based on response, nodal status, triple negative and biopsy grade groupings) are apparent and appear to be most evident 1-3 minutes post-contrast administration. Whilst the large number of statistical tests undertaken necessitates a degree of caution in interpreting the results, the fact that significant differences for certain texture parameters and groupings are consistently observed is encouraging.With regards to shape analysis this thesis has highlighted that some differences between groups were seen in shape descriptors but that shape may be limited as a prognostic indicator. Using textural analysis gave a higher proportion of significant differences whilst shape analysis results showed inconsistency across time points.With regards to the mapping this work successfully analysed the texture maps for each case and established lesion detection is possible. The study successfully highlighted hotspots in the breast patients data post texture mapping, and has demonstrated the relationship between sensitivity and false positive rate via hotspot thresholding