[Establishment of 14-Color Panel for Determining Leukocyte Subsets in Human Peripheral Blood with Flow Cytometry].

OBJECTIVE: To establish a 14-color flow cytometry protocol for the examination of leukocyte subsets in human peripheral blood. METHODS: We used cell membrane surface antibodies CD45, CD3, CD4, CD8, CD19, CD56, CD16, CD14, CD25, CD127, HLA-DR, CD123, CD11c and nucleus staining dye DAPI to establish a 14-color flow cytometry assay to determine the major cell subsets in human peripheral blood. We collected peripheral blood specimens from healthy volunteers to test for antibody titers and optimal photomultiplier tube (PMT) voltage, and to conduct single-color staining and fluorescence minus one control staining. After determining the test method and test conditions, the peripheral blood samples of 18 healthy volunteers were analyzed. RESULTS: According to the cell classification and staining index, optimal antibody mass concentrations selected were as follows: CD25 and CD127 at 8.0 µg/mL, CD45, CD3, CD14 and CD123 at 4.0 µg/mL, CD8, CD19, CD56, CD16, HLA-DR and CD11c at 2.0 µg/mL, CD4 at 1.0 µg/mL and DAPI at 0.1 µg/mL. The detection voltages for CD45, CD3, CD4, CD8, CD19, CD56, CD16, CD14, CD25, CD127, HLA-DR, CD123, CD11c and DAPI were 450 V, 410 V, 400 V, 550 V, 405 V, 500 V, 520 V, 550 V, 550 V, 400 V, 450 V, 400 V, 580 V, and 300 V, respectively. The appropriate fluorescence compensation was determined by single-color staining and fluorescence minus one controls. The 14-color flow cytometry panel was established to analyze the main subsets of leukocytes in human peripheral blood, and peripheral blood samples from 18 healthy adults were examined, obtaining the percentages of each subset of peripheral blood leukocytes and the immunophenotypes of the main subsets. CONCLUSION: We established a 14-color panel for determining leukocyte subsets in human peripheral blood by flow cytometry, which produced stable and reliable results and was easy to operate.

[Prediction of Response to Neoadjuvant Chemotherapy for Breast Cancer Based on Histomorphology Analysis of Needle Biopsy Images].

OBJECTIVE: The deep learning method was used to automatically segment the tumor area and the cell nucleus based on needle biopsy images of breast cancer patients prior to receiving neoadjuvant chemotherapy (NAC), and then, the features of the cell clusters in the tumor area were identified to predict the level of pathological remission of breast cancer after NAC. METHODS: 68 breast cancer patients who were to receive NAC at Jiangsu Province Hospital were recruited and the hematoxylin-eosin (HE) stained preoperative biopsy sections of these patients were collected. Unet++ was used to establish a segmentation model and the tumor area and nucleus of the needle biopsy images were automatically segmented accordingly. Then, according to the nuclei in the automatically segmented tumor area, the features of the cells in the tumor were constructed. After that, effective features were selected through the feature selection method and the classifier model was constructed and trained with five-fold cross validation to predict the degree of post-NAC pathological remission. RESULTS: Predictions were made based on the needle biopsy images of the 68 patients. The model that combined the 10-dimensional features selected with the minimal redundancy-maximum-relevancy approach (mRMR) and training with the random forest (RF) classifier had the highest prediction accuracy, reaching 82.35%, and an area under curve ( AUC) value of 0.908 2. CONCLUSION: This model automatically segments tumor areas and cell nucleus on the biopsy images. The features of the cell clusters which are analyzed and identified in the tumor area can be used to predict the pathological response of the patient to NAC. The method is reliable and replicable. In addition, we found that the textural features of cells in the tumor area was a useful predictor of patient response to NAC, which further confirmed that cell cluster in the tumor area is of great significance to the prediction of treatment outcome.

Diagnostic performance of DCE-MRI, multiparametric MRI and multimodality imaging for discrimination of breast non-mass-like enhancement lesions.

OBJECTIVE: The aim of this study was to investigate and compare the diagnostic performance of dynamic contrast-enhanced (DCE)-MRI, multiparametric MRI (mpMRI), and multimodality imaging (MMI) combining mpMRI and mammography (MG) for discriminating breast non-mass-like enhancement (NME) lesions. METHODS: This retrospective study enrolled 193 patients with 199 lesions who underwent 3.0 T MRI and MG from January 2017 to December 2019. The features of DCE-MRI, turbo inversion recovery magnitude (TIRM), and diffusion-weighted imaging (DWI) were assessed by two breast radiologists. Then, all lesions were divided into microcalcification and non-microcalcification groups to assess the features of MG. Comparisons were performed between groups using univariate analyses. Then, multivariate analyses were performed to construct diagnostic models for distinguishing NME lesions. Diagnostic performance was evaluated by using the area under the curve (AUC) and the differences between AUCs were evaluated by using the DeLong test. RESULTS: Overall (n = 199), mpMRI outperformed DCE-MRI alone (AUCmpMRI = 0.924 vs. AUCDCE-MRI = 0.884; p = 0.007). Furthermore, MMI outperformed both mpMRI and MG (the microcalcification group [n = 140]: AUCMMI = 0.997 vs. AUCmpMRI = 0.978, p = 0.018 and AUCMMI = 0.997 vs. AUCMG = 0.912, p < 0.001; the non-microcalcification group [n = 59]: AUCMMI = 0.857 vs. AUCmpMRI = 0.768, p = 0.044 and AUCMMI = 0.857 vs. AUCMG = 0.759, p = 0.039). CONCLUSION & ADVANCES IN KNOWLEDGE: DCE-MRI combined with DWI and TIRM information could improve the diagnostic performance for discriminating NME lesions compared with DCE-MRI alone. Furthermore, MMI combining mpMRI and MG showed better discrimination than both mpMRI and MG.

Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI.

BACKGROUND: Triple-negative breast cancers generally occur in young women with remarkable potential to be aggressive. It will be of great help to detect this subtype of tumor early. To retrospectively evaluate the performance of histogram analysis of apparent diffusion coefficient (ADC) maps in distinguishing triple-negative breast cancer (TNBC) from other subtypes of breast cancer (non-TNBC), when combined with magnetic resonance imaging (MRI) features. MATERIALS AND METHODS: From February 2014 to December 2018, 192 patients were included in this study taking preoperative standard MRI (s-MRI) and DWI. Seventy-six of them were pathologically confirmed with TNBC and rest 116 with other subtypes. First, their clinical-pathological features and morphological characteristics on MRI were assessed, including tumor size, foci quantity, tumor shape, margin, internal enhancement, and time-signal intensity curve types, in addition to the signal intensity on T2-weighted images. Second, whole-lesion apparent diffusion coefficient (ADC) histogram analysis was executed. Finally, both univariate and multivariate regression analyses were applied to identify the most useful variables in separating TNBCs from non-TNBCs, and then their effects were evaluated following receiver operating characteristic curve analysis. RESULT: Multivariate regression analysis indicated that circumscribed margin, rim enhancement, and ADC90 were important predictors for TNBC. Increased area under curve (AUC) and improved specificity can be obtained when combined s-MRI and DWI (circumscribed margin+rim enhancement+ADC90>1.47×10-3 mm2/s) is taken as the criterion, other than s-MRI (circumscribed margin+rim enhancement) alone (s-MRI+DWI vs s-MRI; AUC, 0.833 vs 0.797; specificity, 98.3% vs 89.7%; sensitivity, 68.4% vs 69.7%). CONCLUSION: Circumscribed margin and rim enhancement on s-MRI and ADC90 are three important elements in detecting TNBC, while ADC histogram analysis can provide additional value in this detection.

Preoperative predicting malignancy in breast mass-like lesions: value of adding histogram analysis of apparent diffusion coefficient maps to dynamic contrast-enhanced magnetic resonance imaging for improving confidence level.

OBJECTIVE: This study aims to find out the benefits of adding histogram analysis of apparent diffusion coefficient (ADC) maps onto dynamic contrast-enhanced MRI (DCE-MRI) in predicting breast malignancy. METHODS: This study included 95 patients who were found with breast mass-like lesions from January 2014 to March 2016 (47 benign and 48 malignant). These patients were estimated by both DCE-MRI and diffusion-weighted imaging (DWI) and classified into two groups, namely, the benign and the malignant. Between these groups, the DCE-MRI parameters, including morphology, enhancement homogeneity, maximum slope of increase (MSI) and time-signal intensity curve (TIC) type, as well as histogram parameters generated from ADC maps were compared. Then, univariate and multivariate logistic regression analyses were conducted to determine the most valuable variables in predicting malignancy. Receiver operating characteristic curve analyses were taken to assess their clinical values. RESULTS: The lesion morphology, MSI and TIC Type (p < 0.05) were significantly different between the two groups. Multivariate logistic regression analyses revealed that irregular morphology, TIC Type II/III and ADC10 were important predictors for breast malignancy. Increased area under curve (AUC) and specificity can be achieved with Model 2 (irregular morphology + TIC Type II/III + ADC10 < 1.047 ×10-3 mm2 s-1) as the criterion than Model 1 (irregular morphology + TIC Type II/III) only (Model 2 vs Model 1; AUC, 0.822 vs 0.705; sensitivity, 68.8 vs 75.0%; specificity, 95.7 vs 66.0%). CONCLUSION: Irregular morphology, TIC Type II/III and ADC10 are indicators for predicting breast malignancy. Histogram analysis of ADC maps can provide additional value in predicting breast malignancy. Advances in knowledge: The morphology, MSI and TIC types in DCE-MRI examination have significant difference between the benign and malignant groups. A higher AUC can be achieved by using ADC10 as the diagnostic index than other ADC parameters, and the difference in AUC based on ADC10 and ADCmean was statistically significant. The irregular morphology, TIC Type II/III and ADC10 were significant predictors for malignant lesions.