Clinical utility of urinary mulberry bodies/cells testing in the diagnosis of Fabry disease.

Introduction: Variants in the galactosidase alpha (GLA) gene cause Fabry disease (FD), an X-linked lysosomal storage disorder caused by α-galactosidase A (α-GAL) deficiency. Recently, disease-modifying therapies have been developed, and simple diagnostic biomarkers for FD are required to initiate these therapies in the early stages of the disease. Detection of urinary mulberry bodies and cells (MBs/MCs) is beneficial for diagnosing FD. However, few studies have evaluated the diagnostic accuracy of urinary MBs/MCs in FD. Herein, we retrospectively evaluated the diagnostic ability of urinary MBs/MCs for FD. Methods: We analyzed the medical records of 189 consecutive patients (125 males and 64 females) who underwent MBs/MCs testing. Out of these, two female patients had already been diagnosed with FD at the time of testing, and the remaining 187 patients were suspected of having FD and underwent both GLA gene sequencing and/or α-GalA enzymatic testing. Results: Genetic testing did not confirm the diagnosis in 50 females (26.5%); hence, they were excluded from the evaluation. Two patients were previously diagnosed with FD, and sixteen were newly diagnosed. Among these 18 patients, 15, including two who had already developed HCM at diagnosis, remained undiagnosed until targeted genetic screening of at-risk family members of patients with FD was performed. The accuracy of urinary MBs/MCs testing exhibited a sensitivity of 0.944, specificity of 1, positive predictive value of 1, and negative predictive value of 0.992. Conclusions: MBs/MCs testing is highly accurate in diagnosing FD and should be considered during the initial evaluation prior to genetic testing, particularly in female patients.

Quantitative distinction of the morphological characteristic of erythrocyte precursor cells with texture analysis using gray level co-occurrence matrix.

BACKGROUND: Morphological characteristics of blood cells are still qualitatively defined. So a texture analysis (Tx) method using gray level co-occurrence matrices (GLCMs; CM-Tx method) was applied to images of erythrocyte precursor cells (EPCs) for quantitatively distinguishing four types of EPC stages: proerythroblast, basophilic erythroblast, polychromatic erythroblast, and orthochromatic erythroblast. METHODS: Fifty-five images of four types of EPCs were downloaded from an atlas uploaded by the Blood Cell Morphology Standardization Subcommittee (BCMSS) of the Japanese Society of Laboratory Hematology (JSLH). Using in-house programs, two types of GLCMs-(R: d=1, θ=0°) and (U: d=1, θ=270°)-and nine types of texture distinction index (TDI) were calculated with images removed outer part of cell. RESULTS: Three binary decision trees were sequentially divided among four types of EPC with the sum average of GLCM (U), the contrast of GLCM (R), and the sum average of GLCM (U). The average concordance rate (sensitivity) of CM-Tx method with the judgments of eleven experts in the BCMSS of the JSLH was 95.8% (87.5-100.0), and the average specificity was 97.6% (92.5-100.0). CONCLUSIONS: The CM-Tx method is an effective tool for quantitative distinction of EPC with their morphological features.

Quantitative Distinction of Morphological Characteristics of White Blood Cells in Peripheral Blood Through Texture Analysis Using Gray Level Co-Occurrence Matrix.

BACKGROUND: Texture features are valuable clues for skilled technicians to differentiate peripheral blood (PB) white blood cells (WBCs). Some studies have tried to distinguish WBCs automatically by using texture analysis. However, no study so far has applied a gray level co-occurrence matrix (GLCM) to images of PB WBCs. Here, we developed a new GLCM method, called the CM-Tx method, for automatically distinguishing PB WBCs. METHODS: We used a total of 199 images of six different types of PB WBCs, taken from PB smears of 12 healthy volunteers, as objective standard images for the analysis. The six types were band form neutrophil, segmented form neutrophil, eosinophil, basophil, lymphocyte, and monocyte. Using in-house FORTRAN programs, three types of GLCM (R: distance (d) = 1, direction (θ) = 0°), (U: d = 1, θ = 270°) and (AE: d = 1, θ = 15° x q: q = 0, ..., 23), the mean intensity (MI) of each image and nine different texture distinction indexes (TDIs) for each GLCM were calculated. Then, a threshold value (TV) for distinguishing the type of PB WBC was selected from the dot plots of all TDIs and the MI. RESULTS: In total, we made 1,194 GLCMs. Using the selected TVs of the TDI, four sequential binary divisions could distinguish five types of PB WBCs. First, monocytes were distinguished (sensitivity 100%, specificity 100%, p < 0.0001) with the TV of the inverse difference moment of the GLCM (U). Then, segmented and band form neutrophils were distinguished from the remaining (100%, 99%, p < 0.0001) with the TV of the contrast of the GLCM (AE). Next, lymphocytes were distinguished (100%, 98%, p < 0.0001) with the TV of the entropy of the GLCM (AE). Finally, basophils were distinguished (82.4%, 100%, p < 0.0001) from eosinophils with the TV of the summed entropy of the GLCM (R). Band form neutrophils could not be distinguished from segmented form neutrophils. The average sensitivity of the CM-Tx method for the five types was 95.6%, and its average specificity was 99.3%. CONCLUSIONS: The CM-Tx method can distinguish five types of PB WBCs by using numerical differences only in texture futures quantified with GLCM. However, some other method was needed to distinguish the band and segmented form neutrophils from each other.