Quantitative-Morphological and Cytological Analyses in Leukemia

Leukemia, a blood cancer originating in the bone marrow, presents as a heterogeneous disease with highly variable survival rates. Leukemia is classified into major types based on the rate of cancerous cell growth and cell lineage: chronic or acute and myeloid or lymphoid leukemia. Histological and cytological analysis of the peripheral blood and the bone marrow can classify these major leukemia categories. However, histological analyses of patient biopsies and cytological microscopic assessment of blood and bone marrow smears are insufficient to diagnose leukemia subtypes and to direct therapy. Hence, more expensive and time-consuming diagnostic tools routinely complement histological-cytological analysis during a patient’s diagnosis. To extract more accurate and detailed information from patient tissue samples, digital pathology is emerging as a powerful tool to enhance biopsy- and smear-based decisions. Furthermore, digital pathology methods integrated with advances in machine learning enable new diagnostic features from leukemia patients’ histological and cytological slides and optimize patient classification, thus providing a cheaper, more robust, and faster diagnostic tool than current standards. This review summarizes emerging approaches to automatically diagnose leukemia from morphological and cytological-histological analyses

Learning Deep Neural Networks for Enhanced Prostate Histological Image Analysis

In recent years, deep convolutional neural networks (CNNs) have shown promise for improving prostate cancer diagnosis by enabling quantitative histopathology through digital pathology. However, there are a number of factors that limit the widespread adoption and clinical utility of deep learning for digital pathology. One of these limitations is the requirement for large labelled training datasets which are expensive to construct due to limited availability of the requisite expertise. Additionally, digital pathology applications typically require the digitisation of histological slides at high magnifications. This process can be challenging especially when digitising large histological slides such as prostatectomies. This work studies and addresses these issues in two important applications of digital pathology: prostate nuclei detection and cell type classification. We study the performance of CNNs at different magnifications and demonstrate that it is possible to perform nuclei detection in low magnification prostate histopathology using CNNs with minimal loss in accuracy. We then study the training of prostate nuclei detectors in the small data setting and demonstrate that although it is possible to train nuclei detectors with minimal data, the models will be sensitive to hyperparameter choice and therefore may not generalise well. Instead, we show that pre-training the CNNs with colon histology data makes them more robust to hyperparameter choice. We then study the CNN performance for prostate cell type classification using supervised, transfer and semi-supervised learning in the small data setting. Our results show that transfer learning can be detrimental to performance but semi-supervised learning is able to provide significant improvements to the learning curve, allowing the training of neural networks with modest amounts of labelled data. We then propose a novel semi-supervised learning method called Deeply-supervised Exemplar CNNs and demonstrate their ability to improve the cell type classifier learning curves at a much better rate than previous semi-supervised neural network methods

Quantitative interpretation of bone marrow biopsies in MPN—what's the point in a molecular age?

The diagnosis of myeloproliferative neoplasms (MPN) requires the integration of clinical, morphological, genetic and immunophenotypic findings. Recently, there has been a transformation in our understanding of the cellular and molecular mechanisms underlying disease initiation and progression in MPN. This has been accompanied by the widespread application of high-resolution quantitative molecular techniques. By contrast, microscopic interpretation of bone marrow biopsies by haematologists/haematopathologists remains subjective and qualitative. However, advances in tissue image analysis and artificial intelligence (AI) promise to transform haematopathology. Pioneering studies in bone marrow image analysis offer to refine our understanding of the boundaries between reactive samples and MPN subtypes and better capture the morphological correlates of high-risk disease. They also demonstrate potential to improve the evaluation of current and novel therapeutics for MPN and other blood cancers. With increased therapeutic targeting of diverse molecular, cellular and extra-cellular components of the marrow, these approaches can address the unmet need for improved objective and quantitative measures of disease modification in the context of clinical trials. This review focuses on the state-of-the-art in image analysis/AI of bone marrow tissue, with an emphasis on its potential to complement and inform future clinical studies and research in MPN

Simultaneous cell detection and classification in bone marrow histology images

Recently, deep learning frameworks have been shown to be successful and efficient in processing digital histology images for various detection and classification tasks. Among these tasks, cell detection and classification are key steps in many computer-assisted diagnosis systems. Traditionally, cell detection and classification is performed as a sequence of two consecutive steps by using two separate deep learning networks, one for detection and the other for classification. This strategy inevitably increases the computational complexity of the training stage. In this paper, we propose a synchronized deep autoencoder network for simultaneous detection and classification of cells in bone marrow histology images. The proposed network uses a single architecture to detect the positions of cells and classify the detected cells, in parallel. It uses a curve-support Gaussian model to compute probability maps that allow detecting irregularly-shape cells precisely. Moreover, the network includes a novel neighborhood selection mechanism to boost the classification accuracy. We show that the performance of the proposed network is superior than traditional deep learning detection methods and very competitive compared to traditional deep learning classification networks. Runtime comparison also shows that our network requires less time to be trained

Artificial intelligence in bone metastases: an MRI and CT imaging review

Background: The purpose of this review is to study the role of radiomics as a supporting tool in predicting bone disease status, differentiating benign from malignant bone lesions, and characterizing malignant bone lesions. (2) Methods: Two reviewers conducted the literature search independently. Thirteen articles on radiomics as a decision support tool for bone lesions were selected. The quality of the methodology was evaluated according to the radiomics quality score (RQS). (3) Results: All studies were published between 2018 and 2021 and were retrospective in design. Eleven (85%) studies were MRI-based, and two (15%) were CT-based. The sample size was <200 patients for all studies. There is significant heterogeneity in the literature, as evidenced by the relatively low RQS value (average score = 22.6%). There is not a homogeneous protocol used for MRI sequences among the different studies, although the highest predictive ability was always obtained in T2W-FS. Six articles (46%) reported on the potential application of the model in a clinical setting with a decision curve analysis (DCA). (4) Conclusions: Despite the variability in the radiomics method application, the similarity of results and conclusions observed is encouraging. Substantial limits were found; prospective and multicentric studies are needed to affirm the role of radiomics as a supporting tool

Computational Pathology: A Survey Review and The Way Forward

Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath (https://github.com/AtlasAnalyticsLab/CPath_Survey).Comment: Accepted in Elsevier Journal of Pathology Informatics (JPI) 202