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Identifying Bone Marrow Microenvironmental Populations in Myelodysplastic Syndrome and Acute Myeloid Leukemia
Published on: November 10, 2023
[Characteristics of "pre-ALIP" in bone marrow sections of patients with acute myeloid leukemia]
Ye-Hua Yu1, Ying-Hua Yuan, Ling Li
1Department of Hematology, Shanghai Jiaotong University Sixth People Hospital, Shanghai, China.
Abstract:
To detect the characteristics of "pre-ALIP" and to investigate their relevance with the development of acute myeloid leukemia (AML) by computer image procession technology, bone marrow (BM) was collected by aspiration/trephine biopsy from AML patients during the complete remission (CR). BM sections were stained by HGF (haematoxylin-Giemsa-acid fuchsin) and photographed by optical microscope imaging system. 4 kinds of computer image segmentation technologies were compared to select the best one for detecting the localization and quantitation of the precursor cells. Planimetry was combined with morphology to segment bone trabeculae. The number of single and double-cluster precursor cells and their distance from bone trabeculae was detected with Euclidean distance change method in BM images of AML patients, and compared with the normal controls. Moreover, the morphological characteristics of "pre-ALIP" were investigated, and the correlation with the development of AML was analyzed. The results showed that the computer image segmentation method based on morphology could identify the precursor cells and bone trabeculae more exactly in BM image, as compared with the methods of 8-Sobel operater. Canny operator and watershed algorithm. Bone trabeculae could be segmented with combinative methods of morphology and planimetry. The number of single precursor cells (19.27 ± 11.60)/mm(2) and double-cluster precursor cells (1.77 ± 1.76)/mm(2) in CR group were higher than that in normal controls (p < 0.05). The distance of single precursor cells from bone trabeculae in CR group were closer to bone trabeculae than that in controls [(230.12 ± 97.68) µm vs (260.92 ± 99.88 µm)] (p < 0.05), but the distance of double-cluster precursor cells from bone trabeculae in AML patients was (274.56 ± 139.48) µm, which showed no statistically significant different from controls (p > 0.05), while the double-cluster precursor cells showed the tendency of migrating to the intermediate zone of bone trabeculae compared with the single precursor cells in CR group (p < 0.05). It is concluded that the structure of "pre-ALIP" in BM tissue exists before the occurrence of ALIP. The characteristics of "pre-ALIP" are single and double-cluster precursor cells with abnormal localization or quantitation, which showed correlation with the development of AML.
Insights
Computer image analysis identified "pre-leukemic" precursor cells in bone marrow of acute myeloid leukemia (AML) patients in remission. These precursor cells, with abnormal quantity and location near bone trabeculae, correlate with AML development.
Area of Science:
- Hematology
- Biomedical Engineering
- Computational Pathology
Context:
- Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy.
- Identifying pre-leukemic changes is crucial for understanding disease progression and relapse.
- Current methods for assessing bone marrow microenvironment in remission are limited.
Purpose:
- To characterize "pre-ALIP" (pre-Acute Leukemia Intravascular Proliferation) structures in bone marrow (BM) of AML patients in complete remission (CR).
- To investigate the correlation between "pre-ALIP" characteristics and AML development using computer image processing.
- To compare the efficacy of different image segmentation techniques for precursor cell and bone trabeculae detection.
Summary:
- A novel computer image segmentation method based on morphology accurately identified precursor cells and bone trabeculae in BM images.
- Patients in CR exhibited a higher number of single and double-cluster precursor cells compared to normal controls.
- Abnormal localization and quantitation of these precursor cells, termed "pre-ALIP", were observed and found to correlate with AML development.
Impact:
- Establishes a quantitative method for identifying pre-leukemic changes in AML remission.
- Provides insights into the bone marrow microenvironment's role in AML pathogenesis and relapse.
- Highlights the potential of computer image analysis in early detection and risk stratification of AML.
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