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

High galectin expression in Helicobacter pylori-infected gastric mucosa in childhood

BACKGROUND

Mild Th1 and Th17 immune responses in childhood against Helicobacter pylori are presumed to be responsible for H. pylori colonization and mucosal atrophy reduction. However, the mechanism remains unclear. In this study, we aimed to investigate the childhood-specific immune responses observed after H. pylori infection by analyzing galectin expression in the gastric mucosa. We focused on galectin-1 (Gal-1) and galectin-9 (Gal-9), which function to suppress Th1 and Th17 immune responses.

METHODS

We analyzed changes in the expression of Gal-1 and Gal-9 in the gastric mucosa of pediatric patients with H. pylori infection. Ten pediatric patients with and ten patients without H. pylori infection who underwent biopsy to assess the cause of chronic abdominal symptoms using esophagogastroduodenoscopy were evaluated. Gal-1 and Gal-9 expression in the biopsy tissues of the gastric antrum and corpus was analyzed by immunohistochemical staining.

RESULTS

Gal-1 expression was significantly increased in the stromal cells of the corpus owing to H. pylori infection. No alterations in Gal-1 expression due to H. pylori infection were observed in the antral tissue. Helicobacter pylori infection considerably increased Gal-9 expression in all tissues. According to previous reports, the increased expression of Gal-9 associated with H. pylori infection is not observed in adults. Therefore, the increased expression of Gal-9 associated with H. pylori infection is specific to pediatric patients.

CONCLUSION

The increased expression of Gal-1 and Gal-9 may suppress Th1 and Th17 immune responses against H. pylori infection during childhood, promote H. pylori colonization, and reduce inflammation in the gastric mucosa of pediatric patients.

A Vaginitis Classification Method Based on Multi-Spectral Image Feature Fusion

Vaginitis is one of the commonly encountered diseases of female reproductive tract infections. The clinical diagnosis mainly relies on manual observation under a microscope. There has been some investigation on the classification of vaginitis diseases based on computer-aided diagnosis to reduce the workload of clinical laboratory staff. However, the studies only using RGB images limit the development of vaginitis diagnosis. Through multi-spectral technology, we propose a vaginitis classification algorithm based on multi-spectral image feature layer fusion. Compared with the traditional RGB image, our approach improves the classification accuracy by 11.39%, precision by 15.82%, and recall by 27.25%. Meanwhile, we prove that the level of influence of each spectrum on the disease is distinctive, and the subdivided spectral image is more conducive to the image analysis of vaginitis disease.

Multispectral Staining and Analysis of Extracellular Matrix

Multiplexed immunofluorescent (IF) techniques enable the detection of multiple antigens within the same sample and are therefore useful in situations where samples are rare or small in size. Similar to standard IF, multiplexed IF yields information on both the location and relative amount of detected antigens. While this method has been used primarily to detail cell phenotypes, we have recently adapted it to profile the extracellular matrix (ECM), which provides technical challenges due to autofluorescence and spatial overlap. This chapter details the planning, execution, optimization, and troubleshooting to use multiplexed IF to profile the ECM of human fallopian tube tissue.

Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages

Cerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5-40 μm) stained with Prussian blue. Currently, users manually and subjectively identify and quantify Prussian blue-stained regions of interest, which is prone to inter-individual variability and can lead to significant delays in data analysis. To improve this labor-intensive process, we developed and compared three digital pathology approaches to identify and quantify CMHs from Prussian blue-stained brain sections: (1) ratiometric analysis of RGB pixel values, (2) phasor analysis of RGB images, and (3) deep learning using a mask region-based convolutional neural network. We applied these approaches to a preclinical mouse model of inflammation-induced CMHs. One-hundred CMHs were imaged using a 20 × objective and RGB color camera. To determine the ground truth, four users independently annotated Prussian blue-labeled CMHs. The deep learning and ratiometric approaches performed better than the phasor analysis approach compared to the ground truth. The deep learning approach had the most precision of the three methods. The ratiometric approach has the most versatility and maintained accuracy, albeit with less precision. Our data suggest that implementing these methods to analyze CMH images can drastically increase the processing speed while maintaining precision and accuracy.

The peritumoral hypointense rim around hepatocellular carcinoma on T2*-weighted magnetic resonance imaging: radiologic-pathologic correlation

BACKGROUND

A peritumoral hypointense rim (PTHR) is sometimes observed around hepatocellular carcinoma (HCC) on T2*-weighted images (T2*WIs). We aimed to investigate the association between the PTHR and histopathologic findings on T2*WIs.

METHODS

We assessed the presence of a PTHR on T2*WIs in 39 pathologically proven HCCs from April 2012 to December 2013. Prussian blue staining was performed, and iron deposition was evaluated by semiquantitative and quantitative methods. Optical density was used in the quantitative methods. The associations between a PTHR on T2*WI and histopathologic peritumoral or background liver iron deposition were analyzed.

RESULTS

A PTHR on T2*WI was observed in 23 of 39 (59%) HCCs. There was no significant difference in the histopathologic fibrous capsule findings (P = 0.394). In the semiquantitative methods, both peritumoral and background liver iron deposition grade were significantly higher in HCCs with a PTHR compared with HCCs without a PTHR (P < 0.001). The mean optical density in HCCs with a PTHR was significantly higher compared with HCCs without a PTHR, in the quantitative peritumoral (42,244.1 ± 20,854.9 vs. 18,739.1 ± 12,258.7, respectively; P < 0.001) and background liver iron deposition analyses (35,554.7 ± 19,854.8 vs. 17,292.4 ± 11,605.8, respectively; P < 0.001). Tumor size (P = 0.005), etiology (P = 0.001), and degree of fibrosis (P = 0.042) were significantly associated with the presence of a PTHR.

CONCLUSIONS

A PTHR in HCCs on T2*WIs was strongly associated with peritumoral iron deposition in the iron-deposited background liver but not with the fibrous capsule.

Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review [Invited]

Hyperspectral imaging (HSI) and multispectral imaging (MSI) technologies have the potential to transform the fields of digital and computational pathology. Traditional digitized histopathological slides are imaged with RGB imaging. Utilizing HSI/MSI, spectral information across wavelengths within and beyond the visual range can complement spatial information for the creation of computer-aided diagnostic tools for both stained and unstained histological specimens. In this systematic review, we summarize the methods and uses of HSI/MSI for staining and color correction, immunohistochemistry, autofluorescence, and histopathological diagnostic research. Studies include hematology, breast cancer, head and neck cancer, skin cancer, and diseases of central nervous, gastrointestinal, and genitourinary systems. The use of HSI/MSI suggest an improvement in the detection of diseases and clinical practice compared with traditional RGB analysis, and brings new opportunities in histological analysis of samples, such as digital staining or alleviating the inter-laboratory variability of digitized samples. Nevertheless, the number of studies in this field is currently limited, and more research is needed to confirm the advantages of this technology compared to conventional imagery.

Estrogen and Progesterone Receptor Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Guideline Update

PURPOSE.—

To update key recommendations of the American Society of Clinical Oncology/College of American Pathologists estrogen receptor (ER) and progesterone receptor (PgR) testing in breast cancer guideline.

METHODS.—

A multidisciplinary international Expert Panel was convened to update the clinical practice guideline recommendations informed by a systematic review of the medical literature.

RECOMMENDATIONS.—

The Expert Panel continues to recommend ER testing of invasive breast cancers by validated immunohistochemistry as the standard for predicting which patients may benefit from endocrine therapy, and no other assays are recommended for this purpose. Breast cancer samples with 1% to 100% of tumor nuclei positive should be interpreted as ER positive. However, the Expert Panel acknowledges that there are limited data on endocrine therapy benefit for cancers with 1% to 10% of cells staining ER positive. Samples with these results should be reported using a new reporting category, ER Low Positive, with a recommended comment. A sample is considered ER negative if < 1% or 0% of tumor cell nuclei are immunoreactive. Additional strategies recommended to promote optimal performance, interpretation, and reporting of cases with an initial low to no ER staining result include establishing a laboratory-specific standard operating procedure describing additional steps used by the laboratory to confirm/adjudicate results. The status of controls should be reported for cases with 0% to 10% staining. Similar principles apply to PgR testing, which is used primarily for prognostic purposes in the setting of an ER-positive cancer. Testing of ductal carcinoma in situ (DCIS) for ER is recommended to determine potential benefit of endocrine therapies to reduce risk of future breast cancer, while testing DCIS for PgR is considered optional. Additional information can be found at www.asco.org/breast-cancer-guidelines .

Estrogen and Progesterone Receptor Testing in Breast Cancer: ASCO/CAP Guideline Update

PURPOSE

To update key recommendations of the American Society of Clinical Oncology/College of American Pathologists estrogen (ER) and progesterone receptor (PgR) testing in breast cancer guideline.

METHODS

A multidisciplinary international Expert Panel was convened to update the clinical practice guideline recommendations informed by a systematic review of the medical literature.

RECOMMENDATIONS

The Expert Panel continues to recommend ER testing of invasive breast cancers by validated immunohistochemistry as the standard for predicting which patients may benefit from endocrine therapy, and no other assays are recommended for this purpose. Breast cancer samples with 1% to 100% of tumor nuclei positive should be interpreted as ER positive. However, the Expert Panel acknowledges that there are limited data on endocrine therapy benefit for cancers with 1% to 10% of cells staining ER positive. Samples with these results should be reported using a new reporting category, ER Low Positive, with a recommended comment. A sample is considered ER negative if < 1% or 0% of tumor cell nuclei are immunoreactive. Additional strategies recommended to promote optimal performance, interpretation, and reporting of cases with an initial low to no ER staining result include establishing a laboratory-specific standard operating procedure describing additional steps used by the laboratory to confirm/adjudicate results. The status of controls should be reported for cases with 0% to 10% staining. Similar principles apply to PgR testing, which is used primarily for prognostic purposes in the setting of an ER-positive cancer. Testing of ductal carcinoma in situ (DCIS) for ER is recommended to determine potential benefit of endocrine therapies to reduce risk of future breast cancer, while testing DCIS for PgR is considered optional. Additional information can be found at www.asco.org/breast-cancer-guidelines.

Immune landscapes associated with different glioblastoma molecular subtypes

Recent work has highlighted the tumor microenvironment as a central player in cancer. In particular, interactions between tumor and immune cells may help drive the development of brain tumors such as glioblastoma multiforme (GBM). Despite significant research into the molecular classification of glioblastoma, few studies have characterized in a comprehensive manner the immune infiltrate in situ and within different GBM subtypes.

In this study, we use an unbiased, automated immunohistochemistry-based approach to determine the immune phenotype of the four GBM subtypes (classical, mesenchymal, neural and proneural) in a cohort of 98 patients. Tissue Micro Arrays (TMA) were stained for CD20 (B lymphocytes), CD5, CD3, CD4, CD8 (T lymphocytes), CD68 (microglia), and CD163 (bone marrow derived macrophages) antibodies. Using automated image analysis, the percentage of each immune population was calculated with respect to the total tumor cells. Mesenchymal GBMs displayed the highest percentage of microglia, macrophage, and lymphocyte infiltration. CD68⁺ and CD163⁺ cells were the most abundant cell populations in all four GBM subtypes, and a higher percentage of CD163⁺ cells was associated with a worse prognosis. We also compared our results to the relative composition of immune cell type infiltration (using RNA-seq data) across TCGA GBM tumors and validated our results obtained with immunohistochemistry with an external cohort and a different method. The results of this study offer a comprehensive analysis of the distribution and the infiltration of the immune components across the four commonly described GBM subgroups, setting the basis for a more detailed patient classification and new insights that may be used to better apply or design immunotherapies for GBM.

Quantitative Image Analysis of Human Epidermal Growth Factor Receptor 2 Immunohistochemistry for Breast Cancer: Guideline From the College of American Pathologists

CONTEXT.—

Advancements in genomic, computing, and imaging technology have spurred new opportunities to use quantitative image analysis (QIA) for diagnostic testing.

OBJECTIVE.—

To develop evidence-based recommendations to improve accuracy, precision, and reproducibility in the interpretation of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) for breast cancer where QIA is used.

DESIGN.—

The College of American Pathologists (CAP) convened a panel of pathologists, histotechnologists, and computer scientists with expertise in image analysis, immunohistochemistry, quality management, and breast pathology to develop recommendations for QIA of HER2 IHC in breast cancer. A systematic review of the literature was conducted to address 5 key questions. Final recommendations were derived from strength of evidence, open comment feedback, expert panel consensus, and advisory panel review.

RESULTS.—

Eleven recommendations were drafted: 7 based on CAP laboratory accreditation requirements and 4 based on expert consensus opinions. A 3-week open comment period received 180 comments from more than 150 participants.

CONCLUSIONS.—

To improve accurate, precise, and reproducible interpretation of HER2 IHC results for breast cancer, QIA and procedures must be validated before implementation, followed by regular maintenance and ongoing evaluation of quality control and quality assurance. HER2 QIA performance, interpretation, and reporting should be supervised by pathologists with expertise in QIA.

Principles and approaches for reproducible scoring of tissue stains in research

Evaluation of tissues is a common and important aspect of translational research studies. Labeling techniques such as immunohistochemistry can stain cells/tissues to enhance identification of specific cell types, cellular activation states, and protein expression. While qualitative evaluation of labeled tissues has merit, use of semiquantitative and quantitative scoring approaches can greatly enhance the rigor of the tissue data. Adhering to key principles for reproducible scoring can enhance the quality and reproducibility of the tissue data so as to maximize its biological relevance and scientific impact.

Spectral Unmixing Methods and Tools for the Detection and Quantitation of Collagen and Other Macromolecules in Tissue Specimens

Collagen and other components in the extracellular matrix are proving of increasing importance for the understanding of complex cell and tissue interactions in a variety of settings. Detection and quantitation of these components can still prove challenging, and a number of techniques have been developed. We focus here on methods in fluorescence-based assessments, including multiplexed immunodetection and the use of simpler histochemical stains, both complemented by linear unmixing techniques. Typically, differentiating these components requires the use of a set of optical filters to isolate each fluorescent compound from each other and from often bright background autofluorescence signals. However, standard fluorescent microscopes are usually only able to separate a limited number of components. If the emission spectra of the fluorophores are spectrally distinct, but overlapping, sophisticated spectral imaging or computational methods can be used to optimize separation and quantitation. This chapter describes spectral unmixing methodology and associated open-source software tools available to analyze multispectral as well as simple color (RGB) images.

A Comparative Performance Analysis of Multispectral and RGB Imaging on HER2 Status Evaluation for the Prediction of Breast Cancer Prognosis

Despite the extensive application of multispectral imaging (MSI) in biomedical multidisciplinary researches, there is a paucity of data available regarding the implication of MSI in tumor prognosis prediction. We compared the behaviors of multispectral (MS) and conventional red-green-blue (RGB) images on assessment of human epidermal growth factor receptor 2 (HER2) immunohistochemistry to explore their impact on outcome in patients with invasive breast cancer (BC). Tissue microarrays containing 240 BC patients were introduced to compare the performance of MS and RGB imaging methods on the quantitative assessment of HER2 status and the prognostic value of 5-year disease-free survival (5-DFS). Both the total and average signal optical density values of HER2 MS and RGB images were analyzed, and all patients were divided into two groups based on the different 5-DFS. The quantification of HER2 MS images was negatively correlated with 5-DFS in lymph node–negative and –positive patients (P < .05), but RGB images were not in lymph node–positive patients (P = .101). Multivariate analysis indicated that the hazard ratio (HR) of HER2 MS was higher than that of HER2 RGB (HR = 2.454; 95% confidence interval [CI], 1.636-3.681 vs HR = 2.060; 95% CI, 1.361-3.119). Additionally, area under curve (AUC) by receiver operating characteristic analysis for HER2 MS was greater than that for HER2 RGB (AUC = 0.649; 95% CI, 0.577-0.722 vs AUC = 0.596; 95% CI, 0.522-0.670) in predicting the risk for recurrence. More importantly, the quantification of HER2 MS images has higher prediction accuracy than that of HER2 RGB images (69.6% vs 65.0%) on 5-DFS. Our study suggested that better information on BC prognosis could be obtained from the quantification of HER2 MS images and MS images might perform better in predicting BC prognosis than conventional RGB images.