Application of multispectral imaging in quantitative immunohistochemistry study of breast cancer: a comparative study.

Multispectral imaging (MSI) based on imaging and spectroscopy, as relatively novel to the field of histopathology, has been used in biomedical multidisciplinary researches. We analyzed and compared the utility of multispectral (MS) versus conventional red-green-blue (RGB) images for immunohistochemistry (IHC) staining to explore the advantages of MSI in clinical-pathological diagnosis. The MS images acquired of IHC-stained membranous marker human epidermal growth factor receptor 2 (HER2), cytoplasmic marker cytokeratin5/6 (CK5/6), and nuclear marker estrogen receptor (ER) have higher resolution, stronger contrast, and more accurate segmentation than the RGB images. The total signal optical density (OD) values for each biomarker were higher in MS images than in RGB images (all P < 0.05). Moreover, receiver operator characteristic (ROC) analysis revealed that a greater area under the curve (AUC), higher sensitivity, and specificity in evaluation of HER2 gene were achieved by MS images (AUC = 0.91, 89.1 %, 83.2 %) than RGB images (AUC = 0.87, 84.5, and 81.8 %). There was no significant difference between quantitative results of RGB images and clinico-pathological characteristics (P > 0.05). However, by quantifying MS images, the total signal OD values of HER2 positive expression were correlated with lymph node status and histological grades (P = 0.02 and 0.04). Additionally, the consistency test results indicated the inter-observer agreement was more robust in MS images for HER2 (inter-class correlation coefficient (ICC) = 0.95, r s = 0.94), CK5/6 (ICC = 0.90, r s = 0.88), and ER (ICC = 0.94, r s = 0.94) (all P < 0.001) than that in RGB images for HER2 (ICC = 0.91, r s = 0.89), CK5/6 (ICC = 0.85, r s = 0.84), and ER (ICC = 0.90, r s = 0.89) (all P < 0.001). Our results suggest that the application of MS images in quantitative IHC analysis could obtain higher accuracy, reliability, and more information of protein expression in relation to clinico-pathological characteristics versus conventional RGB images. It may become an optimal IHC digital imaging system used in quantitative pathology.

Quantum dots-based double imaging combined with organic dye imaging to establish an automatic computerized method for cancer Ki67 measurement.

As a widely used proliferative marker, Ki67 has important impacts on cancer prognosis, especially for breast cancer (BC). However, variations in analytical practice make it difficult for pathologists to manually measure Ki67 index. This study is to establish quantum dots (QDs)-based double imaging of nuclear Ki67 as red signal by QDs-655, cytoplasmic cytokeratin (CK) as yellow signal by QDs-585, and organic dye imaging of cell nucleus as blue signal by 4',6-diamidino-2-phenylindole (DAPI), and to develop a computer-aided automatic method for Ki67 index measurement. The newly developed automatic computerized Ki67 measurement could efficiently recognize and count Ki67-positive cancer cell nuclei with red signals and cancer cell nuclei with blue signals within cancer cell cytoplasmic with yellow signals. Comparisons of computerized Ki67 index, visual Ki67 index, and marked Ki67 index for 30 patients of 90 images with Ki67 ≤ 10% (low grade), 10% < Ki67 < 50% (moderate grade), and Ki67 ≥ 50% (high grade) showed computerized Ki67 counting is better than visual Ki67 counting, especially for Ki67 low and moderate grades. Based on QDs-based double imaging and organic dye imaging on BC tissues, this study successfully developed an automatic computerized Ki67 counting method to measure Ki67 index.

Morphological study and comprehensive cellular constituents of milky spots in the human omentum.

OBJECTIVE: To analyze morphological features of omental milky spots (MS). METHOD: Hematoxylin-eosin staining and immunohistochemistry technique were used to study the omental MS of gastric cancer (GC) patients and rectal cancer (RC) patients. We focused on morphological features of MS and conducted quantitative analysis on the cells number and cellular constituents. Differences in MS parameters between GC and RC were also analyzed. RESULTS: Various shapes of MS were mainly round, oval, irregular form in the adipose and perivascular annular. The median MS perimeter was 2752 (range 817~7753) computer-based pixels. The median value of immune cells in one MS was 141 (43~650), comprising T lymphocytes (46.1%), B lymphocytes (28.4%), macrophages (12.4%) and other immune cells (13.1%). Relatively high density of vessels in MS could be calculated by micro-vessel density (MVD) as 4 (0~13). The median value of mesothelial cells loosely arranged in the surface layer was 5 (0~51). There were no significant differences in MS perimeter, MVD, the number of mesothelial cells, total immune cells, T lymphocytes and macrophages between GC and RC (P>0.05), while the number of MS B lymphocytes in RC was significantly higher than that in GC (P<0.001). CONCLUSION: MS are primary immune tissues in the omentum and structural bases for development and progression of peritoneal dissemination of GC and RC. Analyzing the morphology and cellular constituents could help understanding the mechanism of peritoneal metastasis.

New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images.

Computer-aided image analysis (CAI) can help objectively quantify morphologic features of hematoxylin-eosin (HE) histopathology images and provide potentially useful prognostic information on breast cancer. We performed a CAI workflow on 1,150 HE images from 230 patients with invasive ductal carcinoma (IDC) of the breast. We used a pixel-wise support vector machine classifier for tumor nests (TNs)-stroma segmentation, and a marker-controlled watershed algorithm for nuclei segmentation. 730 morphologic parameters were extracted after segmentation, and 12 parameters identified by Kaplan-Meier analysis were significantly associated with 8-year disease free survival (P < 0.05 for all). Moreover, four image features including TNs feature (HR 1.327, 95%CI [1.001-1.759], P = 0.049), TNs cell nuclei feature (HR 0.729, 95%CI [0.537-0.989], P = 0.042), TNs cell density (HR 1.625, 95%CI [1.177-2.244], P = 0.003), and stromal cell structure feature (HR 1.596, 95%CI [1.142-2.229], P = 0.006) were identified by multivariate Cox proportional hazards model to be new independent prognostic factors. The results indicated that CAI can assist the pathologist in extracting prognostic information from HE histopathology images for IDC. The TNs feature, TNs cell nuclei feature, TNs cell density, and stromal cell structure feature could be new prognostic factors.

Quantum dots-based quantitative and in situ multiple imaging on ki67 and cytokeratin to improve ki67 assessment in breast cancer.

BACKGROUND: As a marker for tumor cell proliferation, Ki67 has important impacts on breast cancer (BC) prognosis. Although immunohistochemical staining is the current standard method, variations in analytical practice make it difficult for pathologists to manually measure Ki67 index. This study was to develop a fluorescent spectrum-based quantitative analysis of Ki67 expression by quantum-dots (QDs) multiple imaging technique. METHODS: A QDs-based in situ multiple fluorescent imaging method was developed, which stained nuclear Ki67 as red signal and cytoplasmic cytokeratin (CK) as green signal. Both Ki67 and CK signals were automatically separated and quantified by professional spectrum analysis software. This technique was applied to tissue microarrays from 240 BC patients. Both Ki67 and CK values, and Ki67/CK ratio were obtained for each patient, and their prognostic value on 5-year disease free survival was assessed. RESULTS: This method simultaneously stains nuclear Ki67 and cytoplasmic CK with clear signal contrast, making it easy for signal separation and quantification. The total fluorescent signal intensities of both Ki67 sum and CK sum were obtained, and Ki67/CK ratio calculated. Ki67 sum and Ki67/CK ratio were each attributed into two grades by X-tile software based on the best P value principle. Multivariate analysis showed Ki67 grade (P = 0.047) and Ki67/CK grade (P = 0.004) were independent prognostic factors. Furthermore, area under curve (AUC) of ROC analysis for Ki67/CK grade (AUC: 0.683, 95%CI: 0.613-0.752) was higher than Ki67 grade (AUC: 0.665, 95%CI: 0.596-0.734) and HER-2 gene (AUC: 0.586, 95%CI: 0.510-0.661), but lower than N stage (AUC: 0.760, 95%CI: 0.696-0.823) and histological grade (AUC: 0.756, 95%CI: 0.692-0.820) on predicting the risk for recurrence. CONCLUSIONS: A QDs-based quantitative and in situ multiple imaging on Ki67 and CK was developed to improve Ki67 assessment in BC, and Ki67/CK grade had better performance than Ki67 grade in predicting prognosis.

Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value.

BACKGROUND: The expending and invasive features of tumor nests could reflect the malignant biological behaviors of breast invasive ductal carcinoma. Useful information on cancer invasiveness hidden within tumor nests could be extracted and analyzed by computer image processing and big data analysis. METHODS: Tissue microarrays from invasive ductal carcinoma (n = 202) were first stained with cytokeratin by immunohistochemical method to clearly demarcate the tumor nests. Then an expert-aided computer analysis system was developed to study the mathematical and geometrical features of the tumor nests. Computer recognition system and imaging analysis software extracted tumor nests information, and mathematical features of tumor nests were calculated. The relationship between tumor nests mathematical parameters and patients' 5-year disease free survival was studied. RESULTS: There were 8 mathematical parameters extracted by expert-aided computer analysis system. Three mathematical parameters (number, circularity and total perimeter) with area under curve >0.5 and 4 mathematical parameters (average area, average perimeter, total area/total perimeter, average (area/perimeter)) with area under curve <0.5 in ROC analysis were combined into integrated parameter 1 and integrated parameter 2, respectively. Multivariate analysis showed that integrated parameter 1 (P = 0.040) was independent prognostic factor of patients' 5-year disease free survival. The hazard risk ratio of integrated parameter 1 was 1.454 (HR 95% CI [1.017-2.078]), higher than that of N stage (HR 1.396, 95% CI [1.125-1.733]) and hormone receptor status (HR 0.575, 95% CI [0.353-0.936]), but lower than that of histological grading (HR 3.370, 95% CI [1.125-5.364]) and T stage (HR 1.610, 95% CI [1.026 -2.527]). CONCLUSIONS: This study indicated integrated parameter 1 of mathematical features (number, circularity and total perimeter) of tumor nests could be a useful parameter to predict the prognosis of early stage breast invasive ductal carcinoma.