Continuous representation of tumor microvessel density and detection of angiogenic hotspots in histological whole-slide images

ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts

Introduction

The development of next-generation tissue-engineered medical devices such as tissue-engineered vascular grafts (TEVGs) is a leading trend in translational medicine. Microscopic examination is an indispensable part of animal experimentation, and histopathological analysis of regenerated tissue is crucial for assessing the outcomes of implanted medical devices. However, the objective quantification of regenerated tissues can be challenging due to their unusual and complex architecture. To address these challenges, research and development of advanced ML-driven tools for performing adequate histological analysis appears to be an extremely promising direction.

Methods

We compiled a dataset of 104 representative whole slide images (WSIs) of TEVGs which were collected after a 6-month implantation into the sheep carotid artery. The histological examination aimed to analyze the patterns of vascular tissue regeneration in TEVGs in situ. Having performed an automated slicing of these WSIs by the Entropy Masker algorithm, we filtered and then manually annotated 1,401 patches to identify 9 histological features: arteriole lumen, arteriole media, arteriole adventitia, venule lumen, venule wall, capillary lumen, capillary wall, immune cells, and nerve trunks. To segment and quantify these features, we rigorously tuned and evaluated the performance of six deep learning models (U-Net, LinkNet, FPN, PSPNet, DeepLabV3, and MA-Net).

Results

After rigorous hyperparameter optimization, all six deep learning models achieved mean Dice Similarity Coefficients (DSC) exceeding 0.823. Notably, FPN and PSPNet exhibited the fastest convergence rates. MA-Net stood out with the highest mean DSC of 0.875, demonstrating superior performance in arteriole segmentation. DeepLabV3 performed well in segmenting venous and capillary structures, while FPN exhibited proficiency in identifying immune cells and nerve trunks. An ensemble of these three models attained an average DSC of 0.889, surpassing their individual performances.

Conclusion

This study showcases the potential of ML-driven segmentation in the analysis of histological images of tissue-engineered vascular grafts. Through the creation of a unique dataset and the optimization of deep neural network hyperparameters, we developed and validated an ensemble model, establishing an effective tool for detecting key histological features essential for understanding vascular tissue regeneration. These advances herald a significant improvement in ML-assisted workflows for tissue engineering research and development.

Deep learning-based automated pipeline for blood vessel detection and distribution analysis in multiplexed prostate cancer images

Introduction: Prostate cancer is a highly heterogeneous disease, presenting varying levels of aggressiveness and response to treatment. Angiogenesis is one of the hallmarks of cancer, providing oxygen and nutrient supply to tumors. Micro vessel density has previously been correlated with higher Gleason score and poor prognosis. Manual segmentation of blood vessels (BVs) In microscopy images is challenging, time consuming and may be prone to inter-rater variabilities. In this study, an automated pipeline is presented for BV detection and distribution analysis in multiplexed prostate cancer images. Methods: A deep learning model was trained to segment BVs by combining CD31, CD34 and collagen IV images. In addition, the trained model was used to analyze the size and distribution patterns of BVs in relation to disease progression in a cohort of prostate cancer patients (N = 215). Results: The model was capable of accurately detecting and segmenting BVs, as compared to ground truth annotations provided by two reviewers. The precision (P), recall (R) and dice similarity coefficient (DSC) were equal to 0.93 (SD 0.04), 0.97 (SD 0.02) and 0.71 (SD 0.07) with respect to reviewer 1, and 0.95 (SD 0.05), 0.94 (SD 0.07) and 0.70 (SD 0.08) with respect to reviewer 2, respectively. BV count was significantly associated with 5-year recurrence (adjusted p = 0.0042), while both count and area of blood vessel were significantly associated with Gleason grade (adjusted p = 0.032 and 0.003 respectively). Discussion: The proposed methodology is anticipated to streamline and standardize BV analysis, offering additional insights into the biology of prostate cancer, with broad applicability to other cancers.

Immunohistochemical Assessment of Microvessel Density in OSCC: Spatial Heterogeneity of Angiogenesis and Its Impact on Survival

(1) Background Oral squamous cell carcinomas (OSCC) are a common malignancy of the oral cavity and are often diagnosed when they have already spread to the regional lymph nodes. Advanced stages of cancer are characterized by the development of distant metastases. Angiogenesis, a hallmark of cancer, is known to contribute to cancer progression and metastasis. High microvessel density (MVD) has been linked to poor clinical outcomes in various types of cancer. (2) Methods: In this study, we aimed to investigate the spatial heterogeneity of blood vessels by comparing the tumor center and invasion front and to evaluate its prognostic value in OSCC. A total of 71 OSCC patient specimens were collected. The tissue was immunohistochemically stained using CD31 antibody to assess the MVD in the tumor center and the invasion front. Furthermore, the associations between the histopathological parameters, including MVD, disease-free survival (DFS), and overall survival (OS) were computed. (3) Results: In our study, we found a significantly higher presence of blood vessels at the invasion front of OSCCs compared to the tumor center. However, we did not observe any significant differences in MVD between different tumor stages. High intratumoral MVD was shown to be a positive prognostic factor for DFS (p = 0.047). (4) Conclusions: To the best of our knowledge, we were the first to analyze MVD as a prognostic factor by considering its spatial heterogeneity in OSCC. However, further studies are warranted to further elucidate the complexity of microvascular spatial heterogeneity and its influence on prognosis.

Oral squamous cell carcinomas: state of the field and emerging directions

Oral squamous cell carcinoma (OSCC) develops on the mucosal epithelium of the oral cavity. It accounts for approximately 90% of oral malignancies and impairs appearance, pronunciation, swallowing, and flavor perception. In 2020, 377,713 OSCC cases were reported globally. According to the Global Cancer Observatory (GCO), the incidence of OSCC will rise by approximately 40% by 2040, accompanied by a growth in mortality. Persistent exposure to various risk factors, including tobacco, alcohol, betel quid (BQ), and human papillomavirus (HPV), will lead to the development of oral potentially malignant disorders (OPMDs), which are oral mucosal lesions with an increased risk of developing into OSCC. Complex and multifactorial, the oncogenesis process involves genetic alteration, epigenetic modification, and a dysregulated tumor microenvironment. Although various therapeutic interventions, such as chemotherapy, radiation, immunotherapy, and nanomedicine, have been proposed to prevent or treat OSCC and OPMDs, understanding the mechanism of malignancies will facilitate the identification of therapeutic and prognostic factors, thereby improving the efficacy of treatment for OSCC patients. This review summarizes the mechanisms involved in OSCC. Moreover, the current therapeutic interventions and prognostic methods for OSCC and OPMDs are discussed to facilitate comprehension and provide several prospective outlooks for the fields.

Artificial Intelligence Assists in the Detection of Blood Vessels in Whole Slide Images: Practical Benefits for Oncological Pathology

The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are important aspects of oncological diagnosis. These features determine the prognosis and aggressiveness of the tumor. Traditional manual evaluation methods are time consuming and subject to inter-observer variability. Blood vessel detection is a perfect task for artificial intelligence, which is capable of rapid analyzing thousands of tissue structures in whole slide images. The development of computer vision solutions requires the segmentation of tissue regions, the extraction of features and the training of machine learning models. In this review, we focus on the methodologies employed by researchers to identify blood vessels and vascular invasion across a range of tumor localizations, including breast, lung, colon, brain, renal, pancreatic, gastric and oral cavity cancers. Contemporary models herald a new era of computational pathology in morphological diagnostics.

PAK1 overexpression promotes myxofibrosarcoma angiogenesis through STAT5B-mediated CSF2 transactivation: clinical and therapeutic relevance of amplification and nuclear entry

Myxofibrosarcoma is genetically complex without established nonsurgical therapies. In public datasets, PAK1 was recurrently gained with mRNA upregulation. Using myxofibrosarcoma cells, we explored the oncogenic underpinning of PAK1 with genetic manipulation and a pan-PAK inhibitor (PF3758309). Myxofibrosarcoma specimens were analyzed for the levels of PAK1, phospho-PAKT423, CSF2 and microvascular density (MVD) and those of PAK1 gene and mRNA. PAK1-expressing xenografts were assessed for the effects of PF3758309 and CSF2 silencing. Besides pro-proliferative and pro-migrator/pro-invasive attributes, PAK1 strongly enhanced angiogenesis in vitro, which, not phenocopied by PAK2-4, was identified as CSF2-mediated using antibody arrays. PAK1 underwent phosphorylation at tyrosines153,201,285 and threonine423 to facilitate nuclear entry, whereby nuclear PAK1 bound STAT5B to co-transactivate the CSF2 promoter, increasing CSF2 secretion needed for angiogenesis. Angiogenesis driven by PAK1-upregulated CSF2 was negated by CSF2 silencing, anti-CSF2, and PF3758309. Clinically, overexpressed whole-cell phospho-PAKT423, related to PAK1 amplification, was associated with increased grades, stages, and PAK1 mRNA, higher MVD, and CSF2 overexpression. Overexpressed whole-cell phospho-PAKT423 and CSF2 independently portended shorter metastasis-free survival and disease-specific survival, respectively. In vivo, both CSF2 silencing and PF3758309 suppressed PAK1-driven tumor proliferation and angiogenesis. Conclusively, the nuclear entry of overexpressed/activated PAK1 endows myxofibrosarcomas with pro-angiogenic function, highlighting the vulnerable PAK1/STAT5B/CSF2 regulatory axis.

Spatial probabilistic mapping of metabolite ensembles in mass spectrometry imaging

Mass spectrometry imaging vows to enable simultaneous spatially resolved investigation of hundreds of metabolites in tissues, but it primarily relies on traditional ion images for non-data-driven metabolite visualization and analysis. The rendering and interpretation of ion images neither considers nonlinearities in the resolving power of mass spectrometers nor does it yet evaluate the statistical significance of differential spatial metabolite abundance. Here, we outline the computational framework moleculaR ( https://github.com/CeMOS-Mannheim/moleculaR ) that is expected to improve signal reliability by data-dependent Gaussian-weighting of ion intensities and that introduces probabilistic molecular mapping of statistically significant nonrandom patterns of relative spatial abundance of metabolites-of-interest in tissue. moleculaR also enables cross-tissue statistical comparisons and collective molecular projections of entire biomolecular ensembles followed by their spatial statistical significance evaluation on a single tissue plane. It thereby fosters the spatially resolved investigation of ion milieus, lipid remodeling pathways, or complex scores like the adenylate energy charge within the same image.

Butorphanol inhibits angiogenesis and migration of hepatocellular carcinoma and regulates MAPK pathway

Butorphanol, a synthetic opioid, exerts analgesic and anti-inflammatory effects against pathogenic diseases. Butorphanol repressed malignant behaviors of tumor cells. In this study, the role of butorphanol in hepatocellular carcinoma was evaluated. Firstly, hepatocellular carcinoma cells were treated with butorphanol. The results showed that butorphanol decreased cell viability of hepatocellular carcinoma cells. Cell proliferation and metastasis of hepatocellular carcinoma cells were inhibited by butorphanol. Secondly, butorphanol suppressed angiogenesis, and reduced phosphorylation levels of p38 and JNK in hepatocellular carcinoma cells. Thirdly, butorphanol reduced in vivo tumor growth of hepatocellular carcinoma in nude mice. Butorphanol reduced tumor micro-vascular density (MVD) and repressed lung metastasis. In conclusion, butorphanol exerted anti-angiogenic and anti-metastatic effects on hepatocellular carcinoma and induced inactivation of MAPKs signaling.

Clinical Applications of Classical and Novel Biological Markers of Pancreatic Cancer

The incidence and prevalence of pancreatic adenocarcinoma have increased in recent years. Pancreatic cancer is the seventh leading cause of cancer death, but it is projected to become the second leading cause of cancer-related mortality by 2040. Most patients are diagnosed in an advanced stage of the disease, with very limited 5-year survival. The discovery of different tissue markers has elucidated the underlying pathophysiology of pancreatic adenocarcinoma and allowed stratification of patient risk at different stages and assessment of tumour recurrence. Due to the invasive capacity of this tumour and the absence of screening markers, new immunohistochemical and serological markers may be used as prognostic markers for recurrence and in the study of possible new therapeutic targets because the survival of these patients is low in most cases. The present article reviews the currently used main histopathological and serological markers and discusses the main characteristics of markers under development.

CD34 and CD105 Microvessels in Resected Bone Specimen May Implicate Wound Healing in MRONJ

Clinical treatment outcome of MRONJ (medication-related osteonecrosis of the jaw) surgery despite radical osseous removal and primary closure healing still shows differences in terms of outcome and disease recurrence. The study aims to assess the rate of angiogenesis of MRONJ lesions in order to understand the impact of angiogenesis and neoangiogenesis status on MRONJ surgical treatment outcome. This is the first study correlating microvessel density with prognosis in MRONJ surgically-treated patients. The immunohistochemical expression of CD34 and CD105 in MRONJ specimens obtained from surgically-treated patients was evaluated. The most vascularized areas detected by CD34 and CD105 were selected and the microvessel density value of the samples was registered. Samples were retrospectively divided according to the clinical outcome of MRONJ surgical treatment, dividing patients into two groups, “healed” and “not healed”. Statistical analysis was performed to assess if neovessels could influence treatment outcome in patients undergoing radical surgery. In the examined cohort, this value was highly predictive of better treatment outcome after radical surgery of MRONJ. Understanding of angiogenesis-dependent factors deserves further attention as a future target for MRONJ prevention and therapies.

Characterization of in vitro 3D cultures

Over the past decade, 3D culture models of human and animal cells have found their way into tissue differentiation, drug development, personalized medicine and tumour behaviour studies. Embryoid bodies (EBs) are in vitro 3D cultures established from murine pluripotential stem cells, whereas tumoroids are patient-derived in vitro 3D cultures. This thesis aims to describe a new implication of an embryoid body model and to characterize the patient-specific microenvironment of the parental tumour in relation to tumoroid growth rate. In this thesis, we described a high-throughput monitoring method, where EBs are used as a dynamic angiogenesis model. In this model, digital image analysis (DIA) is implemented on immunohistochemistry (IHC) stained sections of the cultures over time. Furthermore, we have investigated the correlation between the genetic profile and inflammatory microenvironment of parental tumours on the in vitro growth rate of tumoroids. The EBs were cultured in spinner flasks. The samples were collected at days 4, 6, 9, 14, 18 and 21, dehydrated and embedded in paraffin. The histological sections were IHC stained for the endothelial marker CD31 and digitally scanned. The virtual whole-image slides were digitally analysed by Visiopharm® software. Histological evaluation showed vascular-like structures over time. The quantitative DIA was plausible to monitor significant increase in the total area of the EBs and an increase in endothelial differentiation. The tumoroids were established from 32 colorectal adenocarcinomas. The in vitro growth rate of the tumoroids was followed by automated microscopy over an 11-day period. The parental tumours were analysed by next-generation sequencing for KRAS, TP53, PIK3CA, SMAD4, MAP2K1, BRAF, FGFR3 and FBXW7 status. The tumoroids established from KRAS-mutated parental tumours showed a significantly higher growth rate compared to their wild-type counterparts. The density of CD3+ T lymphocytes and CD68+ macrophages was calculated in the centre of the tumours and at the invasive margin of the tumours. The high density of CD3+ cells and the low density of CD68+ cells showed a significant correlation with a higher growth rate of the tumoroids. In conclusion, a novel approach for histological monitoring of endothelial differentiation is presented in the stem cell-derived EBs. Furthermore, the KRAS status and density of CD3+ T cells and macrophages in the parental tumour influence the growth rate of the tumoroids. Our results indicate that these parameters should be included when tumoroids are to be implemented in personalized medicine.

Methods to Determine and Analyze the Cellular Spatial Distribution Extracted From Multiplex Immunofluorescence Data to Understand the Tumor Microenvironment

Image analysis using multiplex immunofluorescence (mIF) to detect different proteins in a single tissue section has revolutionized immunohistochemical methods in recent years. With mIF, individual cell phenotypes, as well as different cell subpopulations and even rare cell populations, can be identified with extraordinary fidelity according to the expression of antibodies in an mIF panel. This technology therefore has an important role in translational oncology studies and probably will be incorporated in the clinic. The expression of different biomarkers of interest can be examined at the tissue or individual cell level using mIF, providing information about cell phenotypes, distribution of cells, and cell biological processes in tumor samples. At present, the main challenge in spatial analysis is choosing the most appropriate method for extracting meaningful information about cell distribution from mIF images for analysis. Thus, knowing how the spatial interaction between cells in the tumor encodes clinical information is important. Exploratory analysis of the location of the cell phenotypes using point patterns of distribution is used to calculate metrics summarizing the distances at which cells are processed and the interpretation of those distances. Various methods can be used to analyze cellular distribution in an mIF image, and several mathematical functions can be applied to identify the most elemental relationships between the spatial analysis of cells in the image and established patterns of cellular distribution in tumor samples. The aim of this review is to describe the characteristics of mIF image analysis at different levels, including spatial distribution of cell populations and cellular distribution patterns, that can increase understanding of the tumor microenvironment.

Inter-Metastatic Heterogeneity of Tumor Marker Expression and Microenvironment Architecture in a Preclinical Cancer Model

BACKGROUND

Preclinical drug development studies rarely consider the impact of a candidate drug on established metastatic disease. This may explain why agents that are successful in subcutaneous and even orthotopic preclinical models often fail to demonstrate efficacy in clinical trials. It is reasonable to anticipate that sites of metastasis will be phenotypically unique, as each tumor will have evolved heterogeneously with respect to gene expression as well as the associated phenotypic outcome of that expression. The objective for the studies described here was to gain an understanding of the tumor heterogeneity that exists in established metastatic disease and use this information to define a preclinical model that is more predictive of treatment outcome when testing novel drug candidates clinically.

METHODS

Female NCr nude mice were inoculated with fluorescent (mKate), Her2/neu-positive human breast cancer cells (JIMT-mKate), either in the mammary fat pad (orthotopic; OT) to replicate a primary tumor, or directly into the left ventricle (intracardiac; IC), where cells eventually localize in multiple sites to create a model of established metastasis. Tumor development was monitored by in vivo fluorescence imaging (IVFI). Subsequently, animals were sacrificed, and tumor tissues were isolated and imaged ex vivo. Tumors within organ tissues were further analyzed via multiplex immunohistochemistry (mIHC) for Her2/neu expression, blood vessels (CD31), as well as a nuclear marker (Hoechst) and fluorescence (mKate) expressed by the tumor cells.

RESULTS

Following IC injection, JIMT-1mKate cells consistently formed tumors in the lung, liver, brain, kidney, ovaries, and adrenal glands. Disseminated tumors were highly variable when assessing vessel density (CD31) and tumor marker expression (mkate, Her2/neu). Interestingly, tumors which developed within an organ did not adopt a vessel microarchitecture that mimicked the organ where growth occurred, nor did the vessel microarchitecture appear comparable to the primary tumor. Rather, metastatic lesions showed considerable variability, suggesting that each secondary tumor is a distinct disease entity from a microenvironmental perspective.

CONCLUSIONS

The data indicate that more phenotypic heterogeneity in the tumor microenvironment exists in models of metastatic disease than has been previously appreciated, and this heterogeneity may better reflect the metastatic cancer in patients typically enrolled in early-stage Phase I/II clinical trials. Similar to the suggestion of others in the past, the use of models of established metastasis preclinically should be required as part of the anticancer drug candidate development process, and this may be particularly important for targeted therapeutics and/or nanotherapeutics.

TISSUE CLASSIFICATION FOR COLORECTAL CANCER UTILIZING TECHNIQUES OF DEEP LEARNING AND MACHINE LEARNING

Since the introduction of image pattern recognition and computer vision processing, the classification of cancer tissues has been a challenge at pixel-level, slide-level, and patient-level. Conventional machine learning techniques have given way to Deep Learning (DL), a contemporary, state-of-the-art approach to texture classification and localization of cancer tissues. Colorectal Cancer (CRC) is the third ranked cause of death from cancer worldwide. This paper proposes image-level texture classification of a CRC dataset by deep convolutional neural networks (CNN). Simple DL techniques consisting of transfer learning and fine-tuning were exploited. VGG-16, a Keras pre-trained model with initial weights by ImageNet, was applied. The transfer learning architecture and methods responding to VGG-16 are proposed. The training, validation, and testing sets included 5000 images of 150 × 150 pixels. The application set for detection and localization contained 10 large original images of 5000 × 5000 pixels. The model achieved F1-score and accuracy of 0.96 and 0.99, respectively, and produced a false positive rate of 0.01. AUC-based evaluation was also measured. The model classified ten large previously unseen images from the application set represented in false color maps. The reported results show the satisfactory performance of the model. The simplicity of the architecture, configuration, and implementation also contributes to the outcome this work.

Next-Generation Digital Histopathology of the Tumor Microenvironment

Progress in cancer research is substantially dependent on innovative technologies that permit a concerted analysis of the tumor microenvironment and the cellular phenotypes resulting from somatic mutations and post-translational modifications. In view of a large number of genes, multiplied by differential splicing as well as post-translational protein modifications, the ability to identify and quantify the actual phenotypes of individual cell populations in situ, i.e., in their tissue environment, has become a prerequisite for understanding tumorigenesis and cancer progression. The need for quantitative analyses has led to a renaissance of optical instruments and imaging techniques. With the emergence of precision medicine, automated analysis of a constantly increasing number of cellular markers and their measurement in spatial context have become increasingly necessary to understand the molecular mechanisms that lead to different pathways of disease progression in individual patients. In this review, we summarize the joint effort that academia and industry have undertaken to establish methods and protocols for molecular profiling and immunophenotyping of cancer tissues for next-generation digital histopathology-which is characterized by the use of whole-slide imaging (brightfield, widefield fluorescence, confocal, multispectral, and/or multiplexing technologies) combined with state-of-the-art image cytometry and advanced methods for machine and deep learning.

RSF1 requires CEBP/β and hSNF2H to promote IL-1β-mediated angiogenesis: the clinical and therapeutic relevance of RSF1 overexpression and amplification in myxofibrosarcomas

Myxofibrosarcoma is genetically complex and lacks effective nonsurgical treatment strategies; thus, elucidation of novel molecular drivers is urgently needed. Reanalyzing public myxofibrosarcoma datasets, we identified mRNA upregulation and recurrent gain of RSF1 and characterized this chromatin remodeling gene. Myxofibrosarcoma cell lines were employed to elucidate the oncogenic mechanisms of RSF1 by genetic manipulation and two IL-1β-neutralizing antibodies (RD24, P2D7KK), highlighting the regulatory basis and targetability of downstream IL-1β-mediated angiogenesis. Tumor samples were assessed for RSF1, IL-1β, and microvascular density (MVD) by immunohistochemistry and for RSF1 gene status by FISH. In vivo, RSF1-silenced and P2D7KK-treated xenografts were analyzed for tumor-promoting effects and the IL-1β-linked therapeutic relevance of RSF1, respectively. In vitro, RSF1 overexpression promoted invasive and angiogenic phenotypes with a stronger proangiogenic effect. RT-PCR profiling identified IL1B as a top-ranking candidate upregulated by RSF1. RSF1 required hSNF2H and CEBP/β to cotransactivate the IL1B promoter, which increased the IL1B mRNA level, IL-1β secretion and angiogenic capacity. Angiogenesis induced by RSF1-upregulated IL-1β was counteracted by IL1B knockdown and both IL-1β-neutralizing antibodies. Clinically, RSF1 overexpression was highly associated with RSF1 amplification, IL-1β overexpression, increased MVD and higher grades (all P ≤ 0.01) and independently predicted shorter disease-specific survival (P = 0.019, hazard ratio: 4.556). In vivo, both RSF1 knockdown and anti-IL-1β P2D7KK (200 μg twice weekly) enabled significant growth inhibition and devascularization in xenografts. In conclusion, RSF1 overexpression, partly attributable to RSF1 amplification, contributes a novel proangiogenic function by partnering with CEBP/β to cotransactivate IL1B, highlighting its prognostic, pathogenetic, and therapeutic relevance in myxofibrosarcomas.

Cyclic Multiplexed-Immunofluorescence (cmIF), a Highly Multiplexed Method for Single-Cell Analysis

Immunotherapy harnesses the power of the adaptive immune system and has revolutionized the field of oncotherapy, as novel therapeutic strategies have been introduced into clinical use. The development of immune checkpoint inhibitors has led to durable control of disease in a subset of advanced cancer patients, such as those with melanoma and non-small cell lung cancer. However, predicting patient responses to therapy remains a major challenge, due to the remarkable genomic, epigenetic, and microenvironmental heterogeneity present in each tumor. Breast cancer (BC) is the most common cancer in women, where hormone receptor-positive (HR+; estrogen receptor and/or progesterone receptor) BC comprises the majority (>50%) and has better prognosis, while a minority (<20%) are triple negative BC (TNBC), which has an aggressive phenotype. There is a clinical need to identify predictors of late recurrence in HR+ BC and predictors of immunotherapy outcomes in advanced TNBC. Tumor-infiltrating lymphocytes (TILs) have recently been shown to predict late recurrence in HR+, counter to the findings that TILs confer good prognosis in TNBC and human epidermal growth factor receptor 2 positive (HER2+) subtypes. Furthermore, the spatial arrangement of TILs also appears to have prognostic value, with dense clusters of immune cells predicting poor prognosis in HR+ and good prognosis in TNBC. Whether TIL clusters in different breast cancer subtypes represent the same or different landscapes of TILs is unknown and may have treatment implications for a significant portion of breast cancer patients. Current histopathological staining technology is not sufficient for characterizing the ensembles of TILs and their spatial patterns, in addition to tumor and microenvironmental heterogeneity. However, recent advances in cyclic immunofluorescence enable differentiation of the subsets based on TILs, tumor heterogeneity, and microenvironment composition between good and poor responders. A computational framework for understanding the importance of the spatial relationships between TILs and tumor cells in cancer tissues, which will allow for quantitative interpretation of cyclic immunostaining, is also under development. This chapter will explore the workflow for a newly developed cyclic multiplexed-immunofluorescence (cmIF) assay, which has been optimized for formalin-fixed. paraffin-embedded tissues and developed to process digital images for quantitative single-cell based spatial analysis of tumor heterogeneity and microenvironment, including immune cell composition.

Current Concepts in Multi-Modality Imaging of Solid Tumor Angiogenesis

Simple Summary

The recent increase in the use of targeted molecular therapy including anti-angiogenetic agents in cancer treatment necessitate the use of robust tools to assess and guide treatment. Angiogenesis, the formation of new disorganized blood vessels, is used by tumor cells to grow and spread using different mechanisms that could be targeted by anti-angiogenetic agents. In this review, we discuss the biological principles of tumor angiogenesis and the imaging modalities that could provide information beyond gross tumor size and morphology to capture the efficacy of anti-angiogenetic therapeutic response.

Abstract

There have been rapid advancements in cancer treatment in recent years, including targeted molecular therapy and the emergence of anti-angiogenic agents, which necessitate the need to quickly and accurately assess treatment response. The ideal tool is robust and non-invasive so that the treatment can be rapidly adjusted or discontinued based on efficacy. Since targeted therapies primarily affect tumor angiogenesis, morphological assessment based on tumor size alone may be insufficient, and other imaging modalities and features may be more helpful in assessing response. This review aims to discuss the biological principles of tumor angiogenesis and the multi-modality imaging evaluation of anti-angiogenic therapeutic responses.

Angiogenesis and vessel co-option in a mathematical model of diffusive tumor growth: The role of chemotaxis

This work considers the propagation of a tumor from the stage of a small avascular sphere in a host tissue and the progressive onset of a tumor neovasculature stimulated by a pro-angiogenic factor secreted by hypoxic cells. The way new vessels are formed involves cell sprouting from pre-existing vessels and following a trail via a chemotactic mechanism (CM). Namely, it is first proposed a detailed general family of models of the CM, based on a statistical mechanics approach. The key hypothesis is that the CM is composed by two components: i) the well-known bias induced by the angiogenic factor gradient; ii) the presence of stochastic changes of the velocity direction, thus giving rise to a diffusive component. Then, some further assumptions and simplifications are applied in order to derive a specific model to be used in the simulations. The tumor progression is favored by its acidic aggression towards the healthy cells. The model includes the evolution of many biological and chemical species. Numerical simulations show the onset of a traveling wave eventually replacing the host tissue with a fully vascularized tumor. The results of simulations agree with experimental measures of the vasculature density in tumors, even in the case of particularly hypoxic tumors.

Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends

Histopathology refers to the examination by a pathologist of biopsy samples. Histopathology images are captured by a microscope to locate, examine, and classify many diseases, such as different cancer types. They provide a detailed view of different types of diseases and their tissue status. These images are an essential resource with which to define biological compositions or analyze cell and tissue structures. This imaging modality is very important for diagnostic applications. The analysis of histopathology images is a prolific and relevant research area supporting disease diagnosis. In this paper, the challenges of histopathology image analysis are evaluated. An extensive review of conventional and deep learning techniques which have been applied in histological image analyses is presented. This review summarizes many current datasets and highlights important challenges and constraints with recent deep learning techniques, alongside possible future research avenues. Despite the progress made in this research area so far, it is still a significant area of open research because of the variety of imaging techniques and disease-specific characteristics.

Application of digital image analysis on histological images of a murine embryoid body model for monitoring endothelial differentiation

The in vitro 3D model established from murine pluripotential stem cells (i.e., embryoid bodies (EBs)) is a dynamic model for endothelial differentiation. The aim of the present study was to investigate whether digital image analysis (DIA) can be applied on histological sections of EBs in order to quantify endothelial differentiation over time. The EBs were established in suspension cultures for 21 days in three independent replicate experiments. At day 4, 6, 9, 14, 18, and 21, the EBs were fixed in formaldehyde, embedded in paraffin and immunohistochemically (IHC) stained for CD31. The IHC-stained slides were digitally scanned and analysed using the Visiopharm® Quantitative Digital Pathology software Oncotopix™. The EBs developed CD31+ vascular-like structures during their differentiation. The quantitative DIA of the EBs showed that the log10 values of the relative CD31+ areas increased from -0.574 ± 0.470 (mean ± SD) at day 4 to 0.093 ± 0.688 (mean ± SD) at day 21 (p < 0.001). The approach presented in this study is a fast, quantitative and reproducible alternative method for an otherwise time-consuming and observer-dependent histological investigation. The future perspectives for such a system would be implementation of a modified version of the method on different 3D cultures and IHC markers.

Glioblastoma multiforme restructures the topological connectivity of cerebrovascular networks

Glioblastoma multiforme alters healthy tissue vasculature by inducing angiogenesis and vascular remodeling. To fully comprehend the structural and functional properties of the resulting vascular network, it needs to be studied collectively by considering both geometric and topological properties. Utilizing Single Plane Illumination Microscopy (SPIM), the detailed capillary structure in entire healthy and tumor-bearing mouse brains could be resolved in three dimensions. At the scale of the smallest capillaries, the entire vascular systems of bulk U87- and GL261-glioblastoma xenografts, their respective cores, and healthy brain hemispheres were modeled as complex networks and quantified with fundamental topological measures. All individual vessel segments were further quantified geometrically and modular clusters were uncovered and characterized as meta-networks, facilitating an analysis of large-scale connectivity. An inclusive comparison of large tissue sections revealed that geometric properties of individual vessels were altered in glioblastoma in a relatively subtle way, with high intra- and inter-tumor heterogeneity, compared to the impact on the vessel connectivity. A network topology analysis revealed a clear decomposition of large modular structures and hierarchical network organization, while preserving most fundamental topological classifications, in both tumor models with distinct growth patterns. These results augment our understanding of cerebrovascular networks and offer a topological assessment of glioma-induced vascular remodeling. The findings may help understand the emergence of hypoxia and necrosis, and prove valuable for therapeutic interventions such as radiation or antiangiogenic therapy.

CD163+ immune cell infiltrates and presence of CD54+ microvessels are prognostic markers for patients with embryonal rhabdomyosarcoma

Rhabdomyosarcomas (RMS) are rare and often lethal diseases. It is assumed that the tumor microenvironment (TME) of RMS exerts an immunosuppressive function, but there is currently no systematic analysis of the immune cells infiltrating sarcoma tissue. Focusing on two common types of RMS (alveolar [RMA] and embryonal [RME]), we performed a comprehensive immunohistochemical analysis of tumor-infiltrating immune cells in the TME. We performed a qualitative estimation of infiltrating immune cells in the tumor microenvironment by an experienced pathologist as well as a quantitative digital pathology analysis. We found that (1) manual and automatic quantification of tumor-infiltrating immune cells were consistent; (2) RME tumors showed a higher degree of immune cell infiltration than RMA tumors but (3) the number of tumor infiltrating lymphocytes was low compared to other solid tumor types; (4) microvascular density correlated with immune cell infiltration and (5) CD163 positive macrophages as well as CD54 positive microvessels were more often detected in RME than in RMA and correlated with patient overall and event free survival. Our systematic analysis provides a comprehensive view of the immune landscape of RMS which needs to be taken into account for developing immunotherapies for this rare type of cancer.

Inhibition of Triple-Negative Breast Cancer Tumor Growth by Electroacupuncture with Encircled Needling and Its Mechanisms in a Mice Xenograft Model

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer without effective targeted drugs. While breast cancer patients often use acupuncture for the relief of cancer-induced pain or the side effects of chemo- or radiation therapy, little information is known regarding the direct effects of electroacupuncture on TNBC tumor and its potential mechanisms. Here, we created a mice model of TNBC and electroacupuncture with encircled needling around the tumors was given to the animals daily for 3 weeks at 15-20 Hz (3 min, each time). For sham electroacupuncture control, the skin was punctured to a depth of 5 mm and then the needle was quickly withdrawn without electrical stimulation or manual needle manipulation. We found that electroacupuncture significantly inhibited TNBC tumor growth and the inhibitory rate increased gradually overtime. Mechanistic analysis showed that electroacupuncture inhibited tumor angiogenesis by reducing the expression of vascular endothelial growth factor A (VEGF-A), its receptor VEGF-R and neuropilin 1 (NRP-1). Electroacupuncture also led to a significant decrease of matrix metalloproteinase-2 (MMP-2) expression and an increase of tissue inhibitor of MMP (TIMP-2) expression. Additionally, the expression of semaphorin 3A (Sema3A) and nerve growth factor receptor (NGFR) p75 in TNBC tissue was significantly upregulated in response to electroacupuncture. Furthermore, tumor necrosis factor (TNF)-alpha level in the serum was dramatically reduced after electroacupuncture. These results showed that electroacupuncture could directly inhibit TNBC tumor growth through the inhibition of proteins related to tumor angiogenesis and extracellular matrix, the suppression of TNBC-induced inflammation and the upregulation of nerve growth factor receptors.

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

BACKGROUND

For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images.

METHODS AND FINDINGS

We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows.

CONCLUSIONS

In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.

Automatic evaluation of tumor budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome

Background

Tumor budding, meaning a detachment of tumor cells at the invasion front of colorectal carcinoma (CRC) into single cells or clusters (<=5 tumor cells), has been shown to correlate to an inferior clinical outcome by several independent studies. Therefore, it has been discussed as a complementary prognostic factor to the TNM staging system, and it is already included in national guidelines as an additional prognostic parameter. However, its application by manual evaluation in routine pathology is hampered due to the use of several slightly different assessment systems, a time-consuming manual counting process and a high inter-observer variability. Hence, we established and validated an automatic image processing approach to reliably quantify tumor budding in immunohistochemically (IHC) stained sections of CRC samples.

Methods

This approach combines classical segmentation methods (like morphological operations) and machine learning techniques (k-means and hierarchical clustering, convolutional neural networks) to reliably detect tumor buds in colorectal carcinoma samples immunohistochemically stained for pan-cytokeratin. As a possible application, we tested it on whole-slide images as well as on tissue microarrays (TMA) from a clinically well-annotated CRC cohort.

Results

Our automatic tumor budding evaluation tool detected the absolute number of tumor buds per image with a very good correlation to the manually segmented ground truth (R2 value of 0.86).

Furthermore the automatic evaluation of whole-slide images from 20 CRC-patients, we found that neither the detected number of tumor buds at the invasion front nor the number in hotspots was associated with the nodal status. However, the number of spatial clusters of tumor buds (budding hotspots) significantly correlated to the nodal status (p-value = 0.003 for N0 vs. N1/N2). TMAs were not feasible for tumor budding evaluation, as the spatial relationship of tumor buds (especially hotspots) was not preserved.

Conclusions

Automatic image processing is a feasible and valid assessment tool for tumor budding in CRC on whole-slide images. Interestingly, only the spatial clustering of the tumor buds in hotspots (and especially the number of hotspots) and not the absolute number of tumor buds showed a clinically relevant correlation with patient outcome in our data.

Electronic supplementary material

The online version of this article (10.1186/s13000-018-0739-3) contains supplementary material, which is available to authorized users.

Histopathological Whole Slide Image Analysis Using Context-Based CBIR

Histopathological image classification (HIC) and content-based histopathological image retrieval (CBHIR) are two promising applications for the histopathological whole slide image (WSI) analysis. HIC can efficiently predict the type of lesion involved in a histopathological image. In general, HIC can aid pathologists in locating high-risk cancer regions from a WSI by providing a cancerous probability map for the WSI. In contrast, CBHIR was developed to allow searches for regions with similar content for a region of interest (ROI) from a database consisting of historical cases. Sets of cases with similar content are accessible to pathologists, which can provide more valuable references for diagnosis. A drawback of the recent CBHIR framework is that a query ROI needs to be manually selected from a WSI. An automatic CBHIR approach for a WSI-wise analysis needs to be developed. In this paper, we propose a novel aided-diagnosis framework of breast cancer using whole slide images, which shares the advantages of both HIC and CBHIR. In our framework, CBHIR is automatically processed throughout the WSI, based on which a probability map regarding the malignancy of breast tumors is calculated. Through the probability map, the malignant regions in WSIs can be easily recognized. Furthermore, the retrieval results corresponding to each sub-region of the WSIs are recorded during the automatic analysis and are available to pathologists during their diagnosis. Our method was validated on fully annotated WSI data sets of breast tumors. The experimental results certify the effectiveness of the proposed method.

Genomics and emerging biomarkers for immunotherapy of colorectal cancer

Colorectal cancer (CRC) is a common and lethal disease with a high therapeutic need. For most patients with metastatic CRC, chemotherapy is the only viable option. Currently, immunotherapy is restricted to the particular genetic subgroup of mismatch-repair deficient (MMRd)/microsatellite instable (MSI) CRC. Anti-PD1 therapy was recently FDA-approved as a second-line treatment in this subgroup. However, in a metastatic setting, these MMRd/MSI tumors are vastly outnumbered by mismatch-repair proficient (MMRp)/microsatellite stable (MSS) tumors. These MMRp/MSS tumors do not meaningfully respond to any traditional immunotherapy approach including checkpoint blockade, adoptive cell transfer and vaccination. This resistance to immunotherapy is due to a complex tumor microenvironment that counteracts antitumor immunity through a combination of poorly antigenic tumor cells and an immunosuppressive tumor microenvironment. To find ways of overcoming immunotherapy resistance in the majority of CRC patients, it is necessary to analyze the immunological makeup in an in-depth and personalized way and in the context of their tumor genetic makeup. Flexible, biomarker-guided early-phase immunotherapy trials are needed to optimize this workflow. In this review, we detail key mechanisms for immune evasion and emerging immune biomarkers for personalized immunotherapy in CRC. Also, we present a template for biomarker-guided clinical trials that are needed to move new immunotherapy approaches closer to clinical application.

Machine Learning Methods for Histopathological Image Analysis

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.

Prognostic value of endoglin-assessed microvessel density in cancer patients: a systematic review and meta-analysis

Background

Endoglin (ENG, CD105), an auxiliary receptor for several TGF-β superfamily ligands, is constitutively expressed in tumor microvessels. The prognostic value of ENG-assessed microvessel density (MVD) has not been systemically analyzed. This meta-analysis reviews and evaluates the association between ENG expression and prognosis in cancer patients.

Materials and Methods

Thirty published studies involving in 3613 patients were included after searching of PubMed, Web of Science, and EMBASE. The pooled hazard ratios (HRs) and 95% confidence intervals (CIs) for overall survival (OS), disease-free survival (DFS), and cancer-specific survival (CSS) were calculated using random-effects models. The publication bias was detected by a Begg's test and Egger's test. The outcome stability was verified by sensitivity analysis.

Results

The high ENG-assessed MVD was significantly associated with poor OS (HR = 2.14, 95% CI 1.62-2.81; P < 0.001), DFS (HR = 3.23, 95% CI 2.10-4.95; P < 0.001), CSS (HR = 3.33, 95% CI 1.32-8.37; P < 0.001). Furthermore, subgroup analysis revealed that the association between the overexpression of ENG in tumor microvessels and the outcome endpoints (OS or DFS) were also significant in the Asians and Caucasians patients with different cancer types.

Conclusions

ENG of tumor microvessels is a predictor of poor OS, DFS and CSS and may be a prognostic marker of patients with cancer.

In Silico Modeling of Immunotherapy and Stroma-Targeting Therapies in Human Colorectal Cancer

Despite the fact that the local immunological microenvironment shapes the prognosis of colorectal cancer, immunotherapy has shown no benefit for the vast majority of colorectal cancer patients. A better understanding of the complex immunological interplay within the microenvironment is required. In this study, we utilized wet lab migration experiments and quantitative histological data of human colorectal cancer tissue samples (n = 20) including tumor cells, lymphocytes, stroma, and necrosis to generate a multiagent spatial model. The resulting data accurately reflected a wide range of situations of successful and failed immune surveillance. Validation of simulated tissue outcomes on an independent set of human colorectal cancer specimens (n = 37) revealed the model recapitulated the spatial layout typically found in human tumors. Stroma slowed down tumor growth in a lymphocyte-deprived environment but promoted immune escape in a lymphocyte-enriched environment. A subgroup of tumors with less stroma and high numbers of immune cells showed high rates of tumor control. These findings were validated using data from colorectal cancer patients (n = 261). Low-density stroma and high lymphocyte levels showed increased overall survival (hazard ratio 0.322, P = 0.0219) as compared with high stroma and high lymphocyte levels. To guide immunotherapy in colorectal cancer, simulation of immunotherapy in preestablished tumors showed that a complex landscape with optimal stroma permeabilization and immune cell activation is able to markedly increase therapy response in silico These results can help guide the rational design of complex therapeutic interventions, which target the colorectal cancer microenvironment. Cancer Res; 77(22); 6442-52. ©2017 AACR.

Use of Ultrasmall Superparamagnetic Iron Oxide Enhanced Susceptibility Weighted Imaging and Mean Vessel Density Imaging to Monitor Antiangiogenic Effects of Sorafenib on Experimental Hepatocellular Carcinoma

We investigated effectiveness of ultrasmall superparamagnetic iron oxide enhanced susceptibility weighted imaging (USPIO-enhanced SWI) and mean vessel density imaging (Q) in monitoring antiangiogenic effects of Sorafenib on orthotopic hepatocellular carcinoma (HCC). Thirty-five HCC xenografts were established. USPIO-enhanced SWI and Q were performed on a 1.5 T MR scanner at baseline, 7, 14, and 21 days after Sorafenib treatment. Intratumoral susceptibility signal intensity (ITSS) and Q were serially measured and compared between the treated (n = 15) and control groups (n = 15). Both ITSS and Q were significantly lower in the treated group at each time point (P < 0.05). Measurements in the treated group showed that ITSS persisted at 7 days (P = 0.669) and increased at 14 and 21 days (P < 0.05), while Q significantly declined at 7 days (P = 0.028) and gradually increased at 14 and 21 days. In the treated group, significant correlation was found between Q and histologic microvessel density (MVD) (r = 0.753, P < 0.001), and ITSS correlated well with MVD (r = 0.742, P = 0.002) after excluding the data from baseline. This study demonstrated that USPIO-enhanced SWI and Q could provide novel biomarkers for evaluating antiangiogenic effects of Sorafenib on HCC.

Technical Note: Quantitative dynamic contrast-enhanced MRI of a 3-dimensional artificial capillary network

PURPOSE

Variability across devices, patients, and time still hinders widespread recognition of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as quantitative biomarker. The purpose of this work was to introduce and characterize a dedicated microchannel phantom as a model for quantitative DCE-MRI measurements.

METHODS

A perfusable, MR-compatible microchannel network was constructed on the basis of sacrificial melt-spun sugar fibers embedded in a block of epoxy resin. Structural analysis was performed on the basis of light microscopy images before DCE-MRI experiments. During dynamic acquisition the capillary network was perfused with a standard contrast agent injection system. Flow-dependency, as well as inter- and intrascanner reproducibility of the computed DCE parameters were evaluated using a 3.0 T whole-body MRI.

RESULTS

Semi-quantitative and quantitative flow-related parameters exhibited the expected proportionality to the set flow rate (mean Pearson correlation coefficient: 0.991, P < 2.5e-5). The volume fraction was approximately independent from changes of the applied flow rate through the phantom. Repeatability and reproducibility experiments yielded maximum intrascanner coefficients of variation (CV) of 4.6% for quantitative parameters. All evaluated parameters were well in the range of known in vivo results for the applied flow rates.

CONCLUSION

The constructed phantom enables reproducible, flow-dependent, contrast-enhanced MR measurements with the potential to facilitate standardization and comparability of DCE-MRI examinations.

Identification of a characteristic vascular belt zone in human colorectal cancer

BLOOD VESSELS IN CANCER

Intra-tumoral blood vessels are of supreme importance for tumor growth, metastasis and therapy. Yet, little is known about spatial distribution patterns of these vessels. Most experimental or theoretical tumor models implicitly assume that blood vessels are equally abundant in different parts of the tumor, which has far-reaching implications for chemotherapy and tumor metabolism. In contrast, based on histological observations, we hypothesized that blood vessels follow specific spatial distribution patterns in colorectal cancer tissue. We developed and applied a novel computational approach to identify spatial patterns of angiogenesis in histological whole-slide images of human colorectal cancer.

A CHARACTERISTIC SPATIAL PATTERN OF BLOOD VESSELS IN COLORECTAL CANCER

In 33 of 34 (97%) colorectal cancer primary tumors blood vessels were significantly aggregated in a sharply limited belt-like zone at the interface of tumor tissue to the intestinal lumen. In contrast, in 11 of 11 (100%) colorectal cancer liver metastases, a similar hypervascularized zone could be found at the boundary to surrounding liver tissue. Also, in an independent validation cohort, we found this vascular belt zone: 22 of 23 (96%) samples of primary tumors and 15 of 16 (94%) samples of liver metastases exhibited the above-mentioned spatial distribution.

SUMMARY AND IMPLICATIONS

We report consistent spatial patterns of tumor vascularization that may have far-reaching implications for models of drug distribution, tumor metabolism and tumor growth: luminal hypervascularization in colorectal cancer primary tumors is a previously overlooked feature of cancer tissue. In colorectal cancer liver metastases, we describe a corresponding pattern at the invasive margin. These findings add another puzzle piece to the complex concept of tumor heterogeneity.

Segmentation of digitized histological sections for quantification of the muscularized vasculature in the mouse hind limb

Immunohistochemical tissue staining enhances microvasculature characteristics, including the smooth muscle in the medial layer of the vessel walls that is responsible for regulation of blood flow. The vasculature can be imaged in a comprehensive fashion using whole-slide scanning. However, since each such image potentially contains hundreds of small vessels, manual vessel delineation and quantification is not practically feasible. In this work, we present a fully automatic segmentation and vasculature quantification algorithm for whole-slide images. We evaluated its performance on tissue samples drawn from the hind limbs of wild-type mice, stained for smooth muscle using 3,3'-Diaminobenzidine (DAB) immunostain. The algorithm was designed to be robust to vessel fragmentation due to staining irregularity, and artefactual staining of nonvessel objects. Colour deconvolution was used to isolate the DAB stain for detection of vessel wall fragments. Complete vessels were reconstructed from the fragments by joining endpoints of topological skeletons. Automatic measures of vessel density, perimeter, wall area and local wall thickness were taken. The segmentation algorithm was validated against manual measures, resulting in a Dice similarity coefficient of 89%. The relationships observed between these measures were as expected from a biological standpoint, providing further reinforcement of the accuracy of this system. This system provides a fully automated and accurate means of measuring the arteriolar and venular morphology of vascular smooth muscle.

Multi-class texture analysis in colorectal cancer histology

Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies.

New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images

BACKGROUND

Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions.

METHODS AND RESULTS

In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin-3,3'-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images.

VALIDATION

To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images.

CONTEXT

Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics.

Clinicopathological significance of NGX6 expression in breast cancer and its relationship to angiogenesis

OBJECTIVE

The aim was to explore clinicopathological significance of Nasopharyngeal carcinoma-associated gene 6 (NGX6) expression in breast cancer and its relationship to angiogenesis.

METHODS

Clinicopathological feature of 168 patients with breast cancer were analyzed. NGX6 expression and microvessel density (MVD) in tumor tissue were measured using immunohistochemistry methods. The association of NGX6 expression with MVD and clinicopathological features was assessed.

RESULTS

Among 168 cases of breast cancer, NGX6 positive expression were found in 92 (54.8%) cases and NGX6 negative expression were found in 76 (45.2%) cases. Incidence rate of large size tumor, high tumor node metastasis (TNM) stage and lymph node metastasis in NGX6 negative expression group were higher than NGX6 positive expression group in breast cancer (P=0.003, 0.007, and 0.003, respectively). MVD in NGX6 negative expression group is 22.5±4.8, MVD in NGX6 positive expression group is 15.2±4.2. MVD in the NGX6 negative expression group was significantly higher than the NGX6 positive expression group (P<0.05).

CONCLUSION

The expression of NGX6 was closely associated with tumor size, TNM stage, lymph node metastasis and MVD. NGX6 is involved in metastasis and angiogenesis activity of breast cancer. The study may provide a theoretical basis for anti-angiogenic therapy of breast cancer.

Computational pathology: Exploring the spatial dimension of tumor ecology

Tumors are evolving ecosystems where cancer subclones and the microenvironment interact. This is analogous to interaction dynamics between species in their natural habitats, which is a prime area of study in ecology. Spatial statistics are frequently used in ecological studies to infer complex relations including predator-prey, resource dependency and co-evolution. Recently, the emerging field of computational pathology has enabled high-throughput spatial analysis by using image processing to identify different cell types and their locations within histological tumor samples. We discuss how these data may be analyzed with spatial statistics used in ecology to reveal patterns and advance our understanding of ecological interactions occurring among cancer cells and their microenvironment.