C-path: a Watson-like visit to the pathology lab

Reproducible phenotype alteration due to prolonged cooling of the pupae of Polyommatus icarus butterflies

The phenotypic changes induced by prolonged cooling (2-12 weeks at 5 °C in the dark) of freshly formed Polyommatus icarus pupae were investigated. Cooling halted the imaginal development of pupae collected shortly after transformation from the larval stage. After cooling, the pupae were allowed to continue their developmental cycle. The wings of the eclosed specimens were investigated by optical microscopy, scanning and cross-sectional transmission electron microscopy, UV-VIS spectroscopy and microspectroscopy. The eclosed adults presented phenotypic alterations that reproduced results that we published previously for smaller groups of individuals remarkably well; these changes included i) a linear increase in the magnitude of quantified deviation from normal ventral wing patterns with increasing cooling time; ii) slight alteration of the blue coloration of males; and iii) an increasing number of blue scales on the dorsal wing surface of females with increasing cooling time. Several independent factors, including disordering of regular scale rows in males, the number of blue scales in females, eclosion probability and the probability of defect-free eclosion, showed that the cooling time can be divided into three periods: 0-4 weeks, 4-8 weeks, and 8-12 weeks, each of which is characterized by specific changes. The shift from brown female scales to first blue scales with a female-specific shape and then to blue scales with a male-specific shape with longer cooling times suggests slow decomposition of a substance governing scale formation.

Quantification of sterol-specific response in human macrophages using automated imaged-based analysis

BACKGROUND

The transformation of normal macrophage cells into lipid-laden foam cells is an important step in the progression of atherosclerosis. One major contributor to foam cell formation in vivo is the intracellular accumulation of cholesterol.

METHODS

Here, we report the effects of various combinations of low-density lipoprotein, sterols, lipids and other factors on human macrophages, using an automated image analysis program to quantitatively compare single cell properties, such as cell size and lipid content, in different conditions.

RESULTS

We observed that the addition of cholesterol caused an increase in average cell lipid content across a range of conditions. All of the sterol-lipid mixtures examined were capable of inducing increases in average cell lipid content, with variations in the distribution of the response, in cytotoxicity and in how the sterol-lipid combination interacted with other activating factors. For example, cholesterol and lipopolysaccharide acted synergistically to increase cell lipid content while also increasing cell survival compared with the addition of lipopolysaccharide alone. Additionally, ergosterol and cholesteryl hemisuccinate caused similar increases in lipid content but also exhibited considerably greater cytotoxicity than cholesterol.

CONCLUSIONS

The use of automated image analysis enables us to assess not only changes in average cell size and content, but also to rapidly and automatically compare population distributions based on simple fluorescence images. Our observations add to increasing understanding of the complex and multifactorial nature of foam-cell formation and provide a novel approach to assessing the heterogeneity of macrophage response to a variety of factors.

计算机深度学习与智能图像诊断对胃高分化腺癌病理诊断的价值

随着计算机技术的发展, 机器学习被深入研究并应用到各个领域, 机器学习在医学中的应用将转换现在的医学模式, 利用机器学习处理医学中庞大数据可提高医生诊断准确率, 指导治疗, 评估预后. 机器学习中的深度学习已广泛应用在病理智能图像诊断方面, 目前在有丝分裂检测, 细胞核的分割和检测, 组织分类中已取得较好成效. 在病理组织学上, 胃高分化腺癌因其组织结构和细胞形态异型性小, 取材标本表浅等原因容易漏诊. 现有的早期胃癌的病理智能图像诊断系统中没有关于腺腔圆度的研究, 圆度测量可以将腺腔结构的不规则, 腺腔扩张等特征转换为具体数值的定量指标, 通过数值大小来进行诊断分析, 为病理诊断提供参考价值.

Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review

With the advance of digital pathology, image analysis has begun to show its advantages in information analysis of hematoxylin and eosin histopathology images. Generally, histological features in hematoxylin and eosin images are measured to evaluate tumor grade and prognosis for breast cancer. This review summarized recent works in image analysis of hematoxylin and eosin histopathology images for breast cancer prognosis. First, prognostic factors for breast cancer based on hematoxylin and eosin histopathology images were summarized. Then, usual procedures of image analysis for breast cancer prognosis were systematically reviewed, including image acquisition, image preprocessing, image detection and segmentation, and feature extraction. Finally, the prognostic value of image features and image feature–based prognostic models was evaluated. Moreover, we discussed the issues of current analysis, and some directions for future research.

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Immunohistochemistry is a commonly used clinical and research lab detection technique for investigating protein expression and localization within tissues. Many semi-quantitative systems have been developed for scoring expression using immunohistochemistry, but inherent subjectivity limits reproducibility and accuracy of results. Furthermore, the investigation of spatially overlapping biomarkers such as nuclear transcription factors is difficult with current immunohistochemistry techniques. We have developed and optimized a system for simultaneous investigation of multiple proteins using high throughput methods of multiplexed immunohistochemistry and multispectral imaging. Multiplexed immunohistochemistry is performed by sequential application of primary antibodies with secondary antibodies conjugated to horseradish peroxidase or alkaline phosphatase. Different chromogens are used to detect each protein of interest. Stained slides are loaded into an automated slide scanner and a protocol is created for automated image acquisition. A spectral library is created by staining a set of slides with a single chromogen on each. A subset of representative stained images are imported into multispectral imaging software and an algorithm for distinguishing tissue type is created by defining tissue compartments on images. Subcellular compartments are segmented by using hematoxylin counterstain and adjusting the intrinsic algorithm. Thresholding is applied to determine positivity and protein co-localization. The final algorithm is then applied to the entire set of tissues. Resulting data allows the user to evaluate protein expression based on tissue type (ex. epithelia vs. stroma) and subcellular compartment (nucleus vs. cytoplasm vs. plasma membrane). Co-localization analysis allows for investigation of double-positive, double-negative, and single-positive cell types. Combining multispectral imaging with multiplexed immunohistochemistry and automated image acquisition is an objective, high-throughput method for investigation of biomarkers within tissues.

Automated prognostic pattern detection shows favourable diffuse pattern of FOXP3(+) Tregs in follicular lymphoma

BACKGROUND

Histopathological prognostication relies on morphological pattern recognition, but as numbers of biomarkers increase, human prognostic pattern-recognition ability decreases. Follicular lymphoma (FL) has a variable outcome, partly determined by FOXP3 Tregs. We have developed an automated method, hypothesised interaction distribution (HID) analysis, to analyse spatial patterns of multiple biomarkers which we have applied to tumour-infiltrating lymphocytes in FL.

METHODS

A tissue microarray of 40 patient samples was used in triplex immunohistochemistry for FOXP3, CD3 and CD69, and multispectral imaging used to determine the numbers and locations of CD3(+), FOXP3/CD3(+) and CD69/CD3(+) T cells. HID analysis was used to identify associations between cellular pattern and outcome.

RESULTS

Higher numbers of CD3(+) (P=0.0001), FOXP3/CD3(+) (P=0.0031) and CD69/CD3(+) (P=0.0006) cells were favourable. Cross-validated HID analysis of cell pattern identified patient subgroups with statistically significantly different survival (35.5 vs 142 months, P=0.00255), a more diffuse pattern associated with favourable outcome and an aggregated pattern with unfavourable outcome.

CONCLUSIONS

A diffuse pattern of FOXP3 and CD69 positivity was favourable, demonstrating ability of HID analysis to automatically identify prognostic cellular patterns. It is applicable to large numbers of biomarkers, representing an unsupervised, automated method for identification of undiscovered prognostic cellular patterns in cancer tissue samples.

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.

Fast and accurate automated cell boundary determination for fluorescence microscopy

Detailed measurement of cell phenotype information from digital fluorescence images has the potential to greatly advance biomedicine in various disciplines such as patient diagnostics or drug screening. Yet, the complexity of cell conformations presents a major barrier preventing effective determination of cell boundaries, and introduces measurement error that propagates throughout subsequent assessment of cellular parameters and statistical analysis. State-of-the-art image segmentation techniques that require user-interaction, prolonged computation time and specialized training cannot adequately provide the support for high content platforms, which often sacrifice resolution to foster the speedy collection of massive amounts of cellular data. This work introduces a strategy that allows us to rapidly obtain accurate cell boundaries from digital fluorescent images in an automated format. Hence, this new method has broad applicability to promote biotechnology.

Analysis and validation of tissue biomarkers for renal cell carcinoma using automated high-throughput evaluation of protein expression

The objective of this study was to compare the predictive ability of potential tissue biomarkers to known prognostic factors that predict renal cell carcinoma (RCC) recurrence using an automated system of immunohistochemical analysis. After institutional review board approval, a tissue microarray was constructed using tissue from patients who had partial or radical nephrectomy for RCC. Patients with metastatic disease were excluded. Immunohistochemical staining of the tissue microarray for Ki-67, C-reactive protein, carbonic anhydrase 9, and hypoxia-inducible factors 1α and 2α was analyzed using automated image analysis. Univariable and multivariable analyses were performed to evaluate the association of putative biomarkers and known prognostic factors. Of 216 patients who met the entrance criteria, 34 (16%) patients developed metastatic recurrence within a median follow-up interval of 60.9 (interquartile range, 13.9-87.1) months. RCC morphotypes analyzed in this study include clear cell (n = 156), papillary (n = 38), chromophobe (n = 16), and collecting duct/unclassified (n = 6). Univariate analysis identified that only increased Ki-67 was predictive of RCC recurrence among the proteins evaluated, in addition to other known clinicopathological prognostic factors. After multivariate analysis, Ki-67 was identified as an independently predictive risk factor for RCC recurrence (hazard ratio [HR], 3.73 [confidence interval {CI}, 1.60-8.68]). Other independent predictors of RCC recurrence included tumor diameter (HR, 1.20 [CI, 1.02-1.41]) and perinephric fat invasion (HR, 4.49 [CI, 1.11-18.20]). We conclude that Ki-67 positivity is independently predictive of RCC recurrence after surgery in nonmetastatic patients. Automated analysis of tissue protein expression can facilitate a more objective and expedient investigation of tissue biomarkers for RCC.

Digital imaging in pathology--current applications and challenges

Conventional histopathology is rapidly shifting towards digital integration. Will microscopes (and pathologists) soon be obsolete? Or are we dealing with just another image modality that leaves the core of tissue diagnosis intact? This article provides an overview of current digital pathology applications and research with emphasis on whole slide imaging (WSI). Static or interactive digital pathology work stations already can be used for many purposes, e.g. telepathology expert consultations, frozen section diagnosis in remote areas, cytology screening, quality assurance, diagnostic validations for clinical trials, quantitation of hormone receptor or HER2 studies in breast cancer, or three-dimensional visualization of anatomical structures, among others. Changes of workflow in histology laboratories are beginning to enable digital image acquisition and WSI in a routine setting. WSI plays an increasing role in pathology education, glass slide boxes in medical schools are being replaced by digital slide collections; digital slide seminars and virtual microscopy are used for postgraduate and continuing medical education in pathology. Research and efforts to validate WSI systems for diagnostic settings are ongoing.