Informatics, digital & computational pathology

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Last staff update: 4 October 2022

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PubMed Search: Digital hematopathology

Ugochukwu John Jonah, M.B.B.S.
Anil Parwani, M.D., Ph.D., M.B.A.
Page views in 2021: 32
Page views in 2022 to date: 322
Cite this page: Jonah UJ, Parwani A. Hematopathology. website. Accessed December 10th, 2022.
Definition / general
  • Digital pathology (DP) is an up and coming branch of pathology that does the following:
    • Converts histopathology glass slides into digital images using specialized computer scanners
    • Makes use of a computer workstation to access these digital images, instead of a microscope
    • Views digital slides / images using any magnification (like a microscope)
  • Hematopathology is a branch of pathology that studies the diseases and disorders affecting:
    • All blood cells and their production
    • Tissues and organs involved in hematopoiesis, such as:
      • Bone marrow
      • Spleen
      • Thymus
  • References: J Hepatol 2019;70:1016, University of Toronto: Hematopathology [Accessed 12 September 2022]
Essential features
  • Diagnostic application of digital microscopy on peripheral blood (PB) elements
  • Digital pathology application in the diagnosis of acute and chronic leukemias in peripheral blood and bone marrow
  • Applications of digital pathology in the diagnosis and grading of lymphomas
  • Limitations of the use of digital pathology in hematopathology
  • Emerging areas of digital and computational pathology involves the following:
    • Machine learning (ML): a branch of artificial intelligence (AI) that uses algorithms and statistical models to predict future data from previously archived data; machine learning makes use of the following tools:
      • Support vector machine (SVM)
      • Neural network (NN)
      • Logistic regression (LR)
      • Random forest (RF)
      • Na├»ve Bayes (NB)
    • Deep learning (DL) also belongs to the family of machine learning; however, it makes use of algorithms based on the artificial neural network principle, which closely resembles the structure and function of human brain neurons (Cancers (Basel) 2020;12:797):
      • Deep learning makes use of the following tools:
        • Convolutional neural networks (CNN or ConvNet)
        • Recursive neural networks
        • Deep belief networks
        • Convolutional deep belief networks
        • Boltzmann machines
        • Stacked autoencoders
        • Tensor deep stacking networks
        • Spike and slab RBMs
        • Compound hierarchical deep models
        • Deep coding networks
        • Deep q networks
        • Encoder - decoder networks
        • Multilayer kernel machine
      • Operating principle of deep learning:
        • Deep learning makes use of neural networks
        • Neural networks consist of several layers of neurons stacked on top of each other in a hierarchical manner
        • Inputs are then generated and transmitted from the bottom up
        • These inputs generated can be trained using supervised, semisupervised and unsupervised learning techniques, thus enabling these inputs to detect certain characteristics and pathognomonic features; the new features are then stacked together and fine tuned to generate new outputs that are then transmitted to the next / higher neural network layer until a final output is produced (see figure 1 and video 1 below) (Cancers (Basel) 2020;12:797)
    • Whole slide imaging (WSI) involves imaging whole slides and using artificial intelligence to:
      • Diagnose the conditions on the slide
      • Classify and grade lesions and tumors
      • Interpret stains on slides
Diagrams / tables

Images hosted on other servers:

Neural network

Lymphomas analyzed using deep learning

WSI images

Machine learning for hematologic malignancies

Machine learning for
mature lymphoproliferative
diagnosis, grading
and prognostication

Diagnostic application of digital microscopy on peripheral blood elements
  • Automated morphology based peripheral blood elements image analyzers do the following:
    • Reduce the analysis time of leukocytes (detection, counting and classification) on peripheral blood smears using convolutional neural networks with a precision rate of 93% for mononuclear cells and 88% for polynuclear cells
    • Quantify different red cell morphologies allowing quantitative based diagnoses of certain red cell disorders, such as hereditary hemolytic anemia
    • Improve the automatic classification of normal and mature B cell neoplasms like chronic cell leukemia (CLL), hairy cell leukemia (HCL), mantle cell lymphoma (MCL), reactive lymphocytes, normal lymphocytes and abnormal lymphoid cells
    • This involves the use of automated digital microscopy systems and support vector algorithms to produce results with an accuracy of 98% (Cancers (Basel) 2020;12:797)
    • Rapid real time detection of normal and abnormal leukocytes (e.g., blasts and mature cells using Single Shot Multibox Detector [SSD] and You Only Look Once [YOLO] pipelines with a mean average precision [mAP] of 93% and 92%, respectively)
    • Automated detection and classification of teardrop cells with digital microscopy has yielded good outcomes (Int J Lab Hematol 2015;37:e153)
    • 100% detection rate of Plasmodium and Babesia parasites using conventional microscopic scanning when parasitemia is ≥ 2.5% and detection rate of 63% when parasitemia is < 0.1% (J Clin Microbiol 2015;53:167)
    • Hematology analyzers were one of the first clinical applications of digital pathology to find wide use
Digital pathology applications in diagnosis of acute and chronic leukemias in peripheral blood and bone marrow
  • Several applications of digital pathology in the diagnosis of acute and chronic leukemias have emerged:
    • Bayesian based algorithm software has been successfully used in the quantitation of lymphocyte aggregates in Sjögren biopsies, with an accuracy of 100% (J Pathol Inform 2019;10;S2)
    • Machine learning algorithms are increasingly being applied in the diagnosis of acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia, acute myeloid leukemia (AML) and chronic myeloid leukemia (CML) (Int J Lab Hematol 2019;41:717)
    • Using generalized matrix relevance learning vector quantization and nonparametric Bayesian algorithms of deep learning have increased the detection of acute myeloid leukemia by flow cytometry with an accuracy of 98 - 100% (Cancers (Basel) 2020;12:797)
    • The same method was used for chronic lymphocytic leukemia detection with an accuracy of 99.6%
    • Support vector machine method has been used to identify the immunophenotypic patterns of malignant myeloid cells of chronic myeloid leukemia and the normal / reactive neutrophils (Comput Biol Med 2013;43:1192)
    • Deep convolutional neural network applied to peripheral blood smears demonstrated sensitivity and specificity between 97 - 100% for detection of acute lymphocytic leukemia and its subclassification into L1, L2, L3, and distinction from normal (Technol Cancer Res Treat 2018;17:1533033818802789)
    • Artificial intelligence algorithms using the phenotypic personalized medicine digital health platform have been used to carefully adjust the dose of combination therapy for acute lymphocytic leukemia and in so doing improve treatment and maintenance therapy outcomes as well as decrease side effects (SLAS Technol 2017;22:276)
    • Supervised algorithms, along with support vector machine classifiers, have been used to detect acute myeloid leukemia myeloblasts; the same algorithm could also distinguish the French American British (FAB) subtypes of acute myeloid leukemia and diagnose M2, M3 and M5 subtypes of acute myeloid leukemia with an accuracy of 100% (PLoS One 2013;8:e72932)
    • Recent advancements in digital blood image processing and analysis have been used with an accuracy of 98% to screen for normal lymphocytes, abnormal lymphoid cells and reactive lymphocytes
      • The same process was also used for the classification of abnormal lymphoid cells into specific disease entities with an accuracy of 91% (Am J Clin Pathol 2015;143:168)
Digital pathology applications in the diagnosis and grading of lymphomas
  • Digital whole slide viewing with selected high power fields (HPFs) has been shown to have significantly decreased interobserver variations among pathologists, from ~41% to 6% (J Pathol Inform 2013;4:30)
  • Using iterative watershed, automated segmentation of follicles in follicular lymphoma (FL) showed an accuracy of 87% when compared to manual segmentation
  • Computer aided analysis tools have been used to improve the detection and grading of follicular lymphomas in malignant follicles
    • This system is automated and detects follicles in virtual slide images (VSI) of lymphoid tissues
    • For high accuracy, the system works on low resolution CD20 images and maps the follicle boundaries on high resolution H&E images (Comput Med Imaging Graph 2012;36:442)
  • Follicular lymphoma grading system (FLAGS) has been developed to correctly identify multiple (> 10) candidate fields in tissue sections of follicular lymphoma suitable for grading using a combination of H&E and CD20 stains
    • Identified field was further categorized into high or low grade based on the number of centroblasts detected
    • Overall accuracy of FLAGS was 80% (BMC Med Inform Decis Mak 2015;15:115)
  • Using Model Based Intermediate Representation (MBIR) and color texture for histopathological image analysis, researchers have been able to correctly identify the most aggressive follicular lymphoma (grade 3) with ~99% sensitivity and ~99% specificity; and an overall accuracy of ~86% for the classification of follicular lymphoma (J Signal Process Syst 2008;55:169)
  • Using machine learning method J48 and an IHC automated classification algorithm, IHC based classification of diffuse large B cell lymphoma (DLBCL) can correctly delineate cases into germinal center based (GCB) and non-germinal center based (non-GCB) with an accuracy of ~92%
    • This method also has a high concordance rate with gene expression profiles (GEP) and is of significant prognostic value
    • In addition, machine learning based methods of classifying diffuse large B cell lymphoma into GCB and non-GCB subtypes provides avenues for exploring the efficacy of different chemotherapy regimens for each subcategory
  • 2D and 3D computed tomography (CT) radiographic analysis with machine learning techniques based on random forest and support vector machine have been used to successfully provide high prediction accuracy for treatment resistance to R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone) (Blood 2019;134:4136)
    • Thus, artificial intelligence based techniques can now identify diffuse large B cell lymphoma patients that might have treatment resistance and help them avoid toxicity from ineffective drugs
  • Convolutional neural network algorithms have lymphoma diagnostic models capable of classifying lymphoma into categories such as benign lymph nodes, diffuse large B cell lymphoma, Burkitt lymphoma (BL) and small lymphocytic lymphoma (SLL), all based on H&E images
  • Clear and neat staining and scanning techniques are of utmost importance for better analysis of tissue specimens (J Cancer Treatment Diagn 2017;2:53)
    • This is even more applicable to small core biopsies
  • Table 2 presents a summary of artificial intelligence / digital pathology / machine learning applications in lymphoma diagnosis
  • Numerous automated scanning machines abound in the market, including the following:
Case reports
Limitations of digital pathology use in hematopathology
  • Some limitations of using digital pathology in hematopathology include
    • Most of the studies on digital pathology application in hematopathology are limited
    • Challenges of z stacking have impeded digital pathology application to bone marrow smears, a major modality in hematopathology
    • However, attempts have been successfully made to overcome these limitations using image alignments, Gaussian smoothing and Laplacian filtering to construct a final super resolution image from multiple images
    • The above methodology yielded a 24% increase in the sharpness and focus of images (J Pathol Inform 2018;9:48)
    • Most of the applications are in their infancy and needs more development
    • Automated interpretation of flow cytometry needs large numbers of abnormal cases
    • Digital microscopy can detect schistocytes with high sensitivity but low specificity
    • Digital microscopy can misclassify atypical cells as plasma cells, myelocytes or blasts, leading to the risk of misdiagnosis (Am J Clin Pathol 2018;150;S98)
Future directions
  • Future of digital pathology in hematopathology practice looks promising
    • Many studies done on digital pathology application in hematopathology achieved high accuracy results
    • Outcomes of any digitization systems will still require supervision by a pathologist
    • Primary aim of digital pathology should be to facilitate and standardize the diagnostic process
    • Digital pathology used in hematopathology creates potential methods for telecommunication between different institutions for:

Neural networks

OLYMPUS cellSens tutorial: real time panoramic imaging

Board review style question #1

Which of the following impacts the diagnostic accuracy of whole slide imaging (WSI) and analysis of small biopsies the most?

  1. Clear, neat staining and scanning techniques of slides
  2. Convolutional neural network based algorithms integrated into digital pathology machines
  3. Manual confirmation of the diagnosis by a pathologist
  4. Use of recursive neural networks by digital pathology machines
Board review style answer #1
A. Clear, neat staining and scanning techniques of slides. Clear and neat staining and scanning techniques is of utmost importance for a better analysis of tissue specimen, particularly when the available specimen is limited to a small core biopsy (J Cancer Treatment Diagn 2017;2:53).

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Reference: Informatics, digital & computational pathology - Hematopathology
Board review style question #2
What is the current best use of digital pathology in the diagnosis and grading of hematologic malignancies?

  1. For French American British (FAB) based classification of acute myeloid leukemia
  2. To decrease interobserver bias in the grading of lymphomas
  3. To enable the classification and grading of lymphomas with a high degree of accuracy
  4. To enhance telecommunication between institutions for second opinions
Board review style answer #2
D. To enhance telecommunication between institutions for second opinions. Overall, the efforts of optimizing the automation of hematologic malignancies grading serve, at least in the current state, as potential methods for teleconsultation between different institutions for second opinions and better patient care (Cancers (Basel) 2020;12:797, Arch Pathol Lab Med 2018;142:369).

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Reference: Informatics, digital & computational pathology - Hematopathology
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