2. Morphological diagnosis in leukemia

proliferation of blood cells in the bone marrow (BM). Blood cells differentiate in the BM and, then, when mature, spread out to the peripheral blood (PB) system. In normal circumstances, the multipotent progenitor hematopoietic stem cells in the bone marrow reproduce and commit to differentiate into common myeloid or lymphoid progenitor cells. Myeloid and lymphoid progenitor cells differentiate into two main cell lineages containing unipotential precursor cells. Each precursor matures through multiple stages to become a red blood cell (RBC), a platelet, or a white blood cell (WBC) type. Myeloid cells consist of RBCs, platelets, segmented neutrophils, monocytes, eosinophils, basophils, and mast cells; lymphoid cells are

Malignant proliferation in the myeloid or lymphoid cell linage causes myeloid or lymphoid leukemia. The diseased cells stop maturing, halt differentiation, and then accumulate, hence blocking the development of healthy progenitor cells. Cell maturation in chronic leukemia is blocked at a later stage, and it has a longer course of development compared to acute leukemia, where lineage proliferation is arrested at an early stage of differentiation leading to a very

Based on these two major differences, myeloid or lymphoid and chronic or acute, four major leukemia types are distinguished: acute myeloid leukemia (AML), acute lymphoid leukemia (ALL), chronic myeloid leukemia (CML), and chronic lymphoid leukemia (CLL). Each type has a distinguishable morphology, and diagnosis is based on histological analysis of each patient's bone marrow biopsy and cytological microscopic assessment of bone marrow smear or periph-

However, full classification requires more refined categories than the four major leukemia types, and modern classification also includes mutation analysis, cytogenetics, and flow cytometry data. Therefore, older morphological-based classification systems (French-American-British (FAB)) cannot be fully matched with the World Health Organization (WHO) scheme, which utilizes all of these features. The FAB classification system is predominantly used for the 30– 40% of AML cases that are not otherwise specified, while in special cases, a morphological

T and B lymphocytes, dendritic cells, or natural killer (NK) cells (Figure 1) [1, 4].

aggressive, fast-growing disease [4, 5].

96 Hematology - Latest Research and Clinical Advances

eral blood smear [5, 6].

Figure 1. Schematic chart of hematopoiesis.

The current major standard diagnostic test in leukemia is histological-cytological analysis. This includes basic light microscopy of routinely stained bone marrow biopsy, bone marrow smear, and peripheral blood smear.

Diagnosis from the smear is established based on the complete blood count and differential count (the proportion of specific cell types in the specimen). A biopsy can confirm the percentage of the specific cell types in the smear. Normal PB smear contains mature cells and up to 1–2% immature cells. The presence of immature cells at a significantly higher percentage leads to the diagnosis of leukemia. Leukemia in the BM smear is detected based on the irregular proportion of specific immature cells and their morphological alterations [1].

Based on abnormally high proportions of specific blood cells and morphological dysplasia in the biopsy and smear specimen, the French-American-British (FAB) system describes a morphologically based classification for acute leukemia. AML subtypes are divided into eight different groups (M0–M7) and ALL subtypes into three different groups (L1–L3). Such classification system for chronic leukemia is less precise, where the subtypes are overlapping [5, 9].

Although the FAB classification system is based on cellular appearance, some immature cells do not have distinguishable morphological characteristics. Immunophenotyping confirms the diagnosis, especially in ALL T- and B-cell lineage and AML minimally differentiated (M0) and AML megakaryoblastic (M7) subtypes [5].

As a result, histology and cytology are major diagnostic tools: however, their current prognostic potential is limited, as the majority of genetic events do not have known, defining morphological characteristics [5, 10]. Thanks to emerging computer technologies, a pathologist's qualitative decision can be supported by an automated quantitative decision tool. Morphometrics of the pathological slides can both provide new diagnostic information not visible to the naked eye and improve the prognostic ability of histological-cytological analyses [15, 16].

normalization, color or stain correction for image enhancement, contrast enhancement and

Quantitative-Morphological and Cytological Analyses in Leukemia

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Leukemia is detected based on the number, type, and proportion of various cell types in the blood. Segmentation algorithms enable identification of individual cells from smear images (Figure 2b); these algorithms can distinguish overlapping cells from individual cells in order to extract cell-based features and can also divide each WBC into its components: cell membrane, nucleus, and cytoplasm (Figure 2c) [19, 21–37]. Following segmentation, metrics can be extracted from the WBCs and their subcellular components (cell size, nuclear size, etc.).

To computationally classify tissue types from smear images, identified cells and tissues in the images have to be transformed into a vector of features. Conventional machine learning algorithms typically utilize a domain-specific approach to classify cell and tissue types based on a series of handcrafted features. These algorithms extract metrics from images based on a

Features of the smear sample can be extracted from an individual cell in the image or across the entire slide. Once a WBC is segmented within the image, features are extracted either from the whole WBC or separately from the nucleus and cytoplasm. The major discriminating cellular characteristics to classify WBCs are (a) geometric features such as shape (e.g., roundness) and size (e.g., nucleus-cytoplasm size ratio); (b) color features; (c) texture features such as density, granularity, and Fourier descriptors for texture quantification calculated by the twodimensional Fourier transform; and (d) irregularity or boundary roughness measured by fractal dimension [10, 23, 33, 35, 40–49]. Although the analysis at the single cell level provides useful information, it is not sufficient for the diagnosis of a very heterogeneous disorder such as leukemia. In addition to single cell data, characteristics of multicellular groups need to be studied [1]. New studies have extended cell-based morphometric analysis to distinguish major

The common characteristics in these studies are general steps of the image processing pipeline: preprocessing, segmentation, feature engineering, and supervised classification (Table 1). They discriminate cancerous vs. healthy tissue, AML vs. ALL, CL vs. AL, or AML and ALL subtypes. The main differences across the various studies are the choice of the specific engineered

Most of the digital pathology studies of leukemia analyze PB. A healthy blood smear is distinguished from a leukemic smear if one or more immature cells are present. This can be determined from the nucleus structure or from whole cell characteristics. Discriminating features that classify healthy tissue, AML and ALL in the PB are extracted from the cell nucleus. BM is more heterogeneous than PB, and features of BM images are extracted from the whole cells or separately from the nuclei and the cytoplasm. Commonly used features include texture-based metrics and morphology. Texture is based on the spatial variation of the

smoothing, contrast stretching, and histogram equalization [19–21].

4. Quantification of cytology using machine learning

human engineering process that requires domain knowledge [38, 39].

features and the choice of the classification method as illustrated below.

leukemia types and subtypes (Table 1).
