Automated Classification of Breast Cancer Stroma Maturity from Histological Images

OBJECTIVE: The tumour microenvironment plays a crucial role in regulating tumour progression by a number of different mechanisms, in particular the remodelling of collagen fibres in tumour-associated stroma, which has been reported to be related to patient survival. The underlying motivation of this work is that remodelling of collagen fibres gives rise to observable patterns in Hematoxylin and Eosin (H&E) stained slides from clinical cases of invasive breast carcinoma that the pathologist can label as mature or immature stroma. The aim of this paper is to categorise and automatically classify stromal regions according to their maturity and show that this classification agrees with that of skilled observers, hence providing a repeatable and quantitative measure for prognostic studies. METHODS: We use multi-scale Basic Image Features (BIF) and Local Binary Patterns (LBP), in combination with a random decision trees classifier for classification of breast cancer stroma regions-ofinterest (ROI). RESULTS: We present results from a cohort of 55 patients with analysis of 169 ROI. Our multi-scale approach achieved a classification accuracy of 84%. CONCLUSION: This work demonstrates the ability of texture-based image analysis to differentiate breast cancer stroma maturity in clinically acquired H&E stained slides at least as well as skilled observers

The application of artificial intelligence and image analysis to novel prognostic classification systems of colorectal cancer

Summary: pp. IV-V

Medical vision: web and mobile medical image retrieval system based on google cloud vision

The application of information technology is rapidly utilized in the medical system. There is also a massive development in the automatic method for recognizing and detecting objects in the real world. In this study, we present a system called Medical Vision which is designed for people who has no expertise in medical. Medical Vision is a web and mobile-based application to give an initial knowledge in a medical image. This system has 5 features; object detection, web detection, object labeling, safe search, and image properties. These features are run by embedding Google Vision API in the system. We evaluate this system by observing the result of some medical images which inputted into the system. The results showed that our system presents a promising performance and able to give relevant information related to the given image

Clinical relevance of biomarkers in cholangiocarcinoma: critical revision and future directions

Cholangiocarcinoma (CCA) is a malignant tumour arising from the biliary system. In Europe, this tumour frequently presents as a sporadic cancer in patients without defined risk factors and is usually diagnosed at advanced stages with a consequent poor prognosis. Therefore, the identification of biomarkers represents an utmost need for patients with CCA. Numerous studies proposed a wide spectrum of biomarkers at tissue and molecular levels. With the present paper, a multidisciplinary group of experts within the European Network for the Study of Cholangiocarcinoma discusses the clinical role of tissue biomarkers and provides a selection based on their current relevance and potential applications in the framework of CCA. Recent advances are proposed by dividing biomarkers based on their potential role in diagnosis, prognosis and therapy response. Limitations of current biomarkers are also identified, together with specific promising areas (ie, artificial intelligence, patient-derived organoids, targeted therapy) where research should be focused to develop future biomarkers

Elemental and phase composition of breast calcifications

Despite the importance of calcifications in early detection of breast cancer, and their proposed association with tumour growth, remarkably little detail is known about their chemical composition, or how this relates to pathology. One reason for this gap is the difficulty of systematically and precisely locating calcifications for analysis, particularly in sections taken from diagnostic archives. Two simple methods were developed which can achieve this in sections cut from wax embedded breast tissue. These are based on micro-CT and x-ray fluoroscopy mapping, and were used to locate calcifications for further study. The elemental composition of calcifications in histological sections was measured using energy-dispersive x-ray spectroscopy in an environmental scanning electron microscope. Variations in Ca:P ratio could in principle be detected non-invasively by dual energy absorptiometry, as demonstrated in a proof of principle experiment. However, the Ca:P ratio was found to lie in a narrow range similar to bone, with no significant difference between benign and malignant. In contrast, a substantial and significant difference in Na:Ca ratio was found between benign and malignant specimens. This has potential for revealing malignant changes in the vicinity of a core needle biopsy. The phase composition and crystallographic parameters within calcifications was measured using synchrotron x-ray diffraction. This is the first time crystallite size and lattice parameters have been measured in breast calcifications, and it was found that these both parallel closely the changes in these parameters with age observed in foetal bone. It was also discovered that these calcifications contain a small proportion of magnesium whitlockite, and that this proportion increases from benign, to carcinoma in-situ, to invasive cancer. When combined with other recent evidence on the effect of magnesium on hydroxyapatite precipitation, this suggests a mechanism explaining observations that carbonate levels within breast calcifications are lower in malignant specimens

A novel approach to jointly address localization and classification of breast cancer using bio-inspired approach

Localization of the cancerous region as well as classification of the type of the cancer is highly inter-linked with each other. However, investigation towards existing approaches depicts that these problems are always iindividually solved where there is still a big research gap for a generalized solution towards addressing both the problems. Therefore, the proposed manuscript presents a simple, novel, and less-iterative computational model that jointly address the localization-classification problems taking the case study of early diagnosis of breast cancer. The proposed study harnesses the potential of simple bio-inspired optimization technique in order to obtained better local and global best outcome to confirm the accuracy of the outcome. The study outcome of the proposed system exhibits that proposed system offers higher accuracy and lower response time in contrast with other existing classifiers that are freqently witnessed in existing approaches of classification in medical image process

High-resolution fluorescence endomicroscopy for rapid evaluation of breast cancer margins

Breast cancer is a major public health problem world-wide and the second leading cause of cancer-related female deaths. Breast conserving surgery (BCS), in the form of wide local excision (WLE), allows complete tumour resection while maintaining acceptable cosmesis. It is the recommended treatment for a large number of patients with early stage disease or, in more advanced cases, following neoadjuvant chemotherapy. About 30% of patients undergoing BCS require one or more re-operative interventions, mainly due to the presence of positive margins. The standard of care for surgical margin assessment is post-operative examination of histopathological tissue sections. However, this process is invasive, introduces sampling errors and does not provide real-time assessment of the tumour status of radial margins. The objective of this thesis is to improve intra-operative assessment of margin status by performing optical biopsy in breast tissue. This thesis presents several technical and clinical developments related to confocal fluorescence endomicroscopy systems for real-time characterisation of different breast morphologies. The imaging systems discussed employ flexible fibre-bundle based imaging probes coupled to high-speed line-scan confocal microscope set-up. A preliminary study on 43 unfixed breast specimens describes the development and testing of line-scan confocal laser endomicroscope (LS-CLE) to image and classify different breast pathologies. LS-CLE is also demonstrated to assess the intra-operative tumour status of whole WLE specimens and surgical excisions with high diagnostic accuracy. A third study demonstrates the development and testing of a bespoke LS-CLE system with methylene blue (MB), an US Food and Drug Administration (FDA) approved fluorescent agent, and integration with robotic scanner to enable large-area in vivo imaging of breast cancer. The work also addresses three technical issues which limit existing fibre-bundle based fluorescence endomicroscopy systems: i) Restriction to use single fluorescence agent due to low-speed, single excitation and single fluorescence spectral band imaging systems; ii) Limited Field of view (FOV) of fibre-bundle endomicroscopes due to small size of the fibre tip and iii) Limited spatial resolution of fibre-bundle endomicroscopes due to the spacing between the individual fibres leading to fibre-pixelation effects. Details of design and development of a high-speed dual-wavelength LS-CLE system suitable for high-resolution multiplexed imaging are presented. Dual-wavelength imaging is achieved by sequentially switching between 488 nm and 660 nm laser sources for alternate frames, avoiding spectral bleed-through, and providing an effective frame rate of 60 Hz. A combination of hand-held or robotic scanning with real-time video mosaicking, is demonstrated to enable large-area imaging while still maintaining microscopic resolution. Finally, a miniaturised piezoelectric transducer-based fibre-shifting endomicroscope is developed to enhance the resolution over conventional fibre-bundle based imaging systems. The fibre-shifting endomicroscope provides a two-fold improvement in resolution and coupled to a high-speed LS-CLE scanning system, provides real-time imaging of biological samples at 30 fps. These investigations furthered the utility and applications of the fibre-bundle based fluorescence systems for rapid imaging and diagnosis of cancer margins.Open Acces

Automated analysis of colorectal cancer

Colorectal cancer (CRC) is the second largest cause of cancer deaths in the UK, with approximately 16,000 per year. Over 41,000 people are diagnosed annually, and 43% of those will die within ten years of diagnosis. The treatment of CRC patients relies on pathological examination of the disease to identify visual features that predict growth and spread, and response to chemoradiotherapy. These prognostic features are identified manually, and are subject to inter and intra-scorer variability. This variability stems from the subjectivity in interpreting large images which can have very varied appearances, as well as the time consuming and laborious methodology of visually inspecting cancer cells. The work in this thesis presents a systematic approach to developing a solution to address this problem for one such prognostic indicator, the Tumour:Stroma Ratio (TSR). The steps taken are presented sequentially through the chapters, in order of the work carried out. These specifically involve the acquisition and assessment of a dataset of 2.4 million expert-classified images of CRC, and multiple iterations of algorithm development, to automate the process of generating TSRs for patient cases. The algorithm improvements are made using conclusions from observer studies, conducted on a psychophysics experiment platform developed as part of this work, and further work is undertaken to identify issues of image quality that affect automated solutions. The developed algorithm is then applied to a clinical trial dataset with survival data, meaning that the algorithm is validated against two separate pathologist-scored, clinical trial datasets, as well as being able to test its suitability for generating independent prognostic markers