A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections

Spatial distribution of tumour immune infiltrate predicts outcomes of patients with high-risk soft tissue sarcomas after neoadjuvant chemotherapy

BACKGROUND

Anthracycline-based neoadjuvant chemotherapy (NAC) may modify tumour immune infiltrate. This study characterized immune infiltrate spatial distribution after NAC in primary high-risk soft tissue sarcomas (STS) and investigate association with prognosis.

METHODS

The ISG-STS 1001 trial randomized STS patients to anthracycline plus ifosfamide (AI) or a histology-tailored (HT) NAC. Four areas of tumour specimens were sampled: the area showing the highest lymphocyte infiltrate (HI) at H&E; the area with lack of post-treatment changes (highest grade, HG); the area with post-treatment changes (lowest grade, LG); and the tumour edge (TE). CD3, CD8, PD-1, CD20, FOXP3, and CD163 were analyzed at immunohistochemistry and digital pathology. A machine learning method was used to generate sarcoma immune index scores (SIS) that predict patient disease-free and overall survival (DFS and OS).

FINDINGS

Tumour infiltrating lymphocytes and PD-1+ cells together with CD163+ cells were more represented in STS histologies with complex compared to simple karyotype, while CD20+ B-cells were detected in both these histology groups. PD-1+ cells exerted a negative prognostic value irrespectively of their spatial distribution. Enrichment in CD20+ B-cells at HI and TE areas was associated with better patient outcomes. We generated a prognostic SIS for each tumour area, having the HI-SIS the best performance. Such prognostic value was driven by treatment with AI.

INTERPRETATION

The different spatial distribution of immune populations and their different association with prognosis support NAC as a modifier of tumour immune infiltrate in STS.

FUNDING

Pharmamar; Italian Ministry of Health [RF-2019-12370923; GR-2016-02362609]; 5 × 1000 Funds-2016, Italian Ministry of Health; AIRC Grant [ID#28546].

How the variability between computer-assisted analysis procedures evaluating immune markers can influence patients' outcome prediction

Differences between computer-assisted image analysis (CAI) algorithms may cause discrepancies in the identification of immunohistochemically stained immune biomarkers in biopsies of breast cancer patients. These discrepancies have implications for their association with disease outcome. This study aims to compare three CAI procedures (A, B and C) to measure positive marker areas in post-neoadjuvant chemotherapy biopsies of patients with triple-negative breast cancer (TNBC) and to explore the differences in their performance in determining the potential association with relapse in these patients. A total of 3304 digital images of biopsy tissue obtained from 118 TNBC patients were stained for seven immune markers using immunohistochemistry (CD4, CD8, FOXP3, CD21, CD1a, CD83, HLA-DR) and were analyzed with procedures A, B and C. The three methods measure the positive pixel markers in the total tissue areas. The extent of agreement between paired CAI procedures, a principal component analysis (PCA) and Cox multivariate analysis was assessed. Comparisons of paired procedures showed close agreement for most of the immune markers at low concentration. The probability of differences between the paired procedures B/C and B/A was generally higher than those observed in C/A. The principal component analysis, largely based on data from CD8, CD1a and HLA-DR, identified two groups of patients with a significantly lower probability of relapse than the others. The multivariate regression models showed similarities in the factors associated with relapse for procedures A and C, as opposed to those obtained with procedure B. General agreement among the results of CAI procedures would not guarantee that the same predictive breast cancer markers were consistently identified. These results highlight the importance of developing additional strategies to improve the sensitivity of CAI procedures.

Regucalcin expression profiles in veal calf testis: validation of histological and molecular tests to detect sex steroids illicit administration

Background

Sex steroids administration in meat producing animals is forbidden within the EU to preserve consumers’ safety, but continuous monitoring to identify resurgence of their misuse is needed. Among biomarkers related to sex steroids abuse in veal calves the regucalcin (RGN) mRNA perturbations in testis have been described in RNAlater samples. To setup novel diagnostic method, to update current tests available in National Residue Control Plans (NRCPs) and in legal dispute when illicit practices on farm animals are suspected, the reliability of RGN profiling was assessed by histological and molecular techniques.

Methods

Formalin fixed paraffin embedded (FFPE) testis samples, chosen being the most effective preservation strategy adopted by histological NRCPs and allowing easier retrospective analysis if required by legal disputes, were analyzed from veal calves treated with nandrolone, 17β-estradiol and a cocktail of the two hormones. RGN levels were determined by quantitative Real Time PCR and Immunohistochemistry assays. Test performances were assessed and compared by multiple ROC curves.

Results

Both tests resulted sensitive and specific, allowing to enrich, in future field investigation, novel integrated diagnostic protocols needed to unveil sex steroid abuse.

Discussion

Developed RT-qPCR and IHC methods confirmed RGN as a useful and robust biomarker to detect illegal administration of sex steroid hormones in veal calves. The developed methods, successfully applied to ten years old FFPE blocks, could allow both retrospective analysis, when supplementary investigations are requested by authorities, and future implementation of current NRCPs.

Marker controlled superpixel nuclei segmentation and automatic counting on immunohistochemistry staining images

MOTIVATION

For the diagnosis of cancer, manually counting nuclei on massive histopathological images is tedious and the counting results might vary due to the subjective nature of the operation.

RESULTS

This paper presents a new segmentation and counting method for nuclei, which can automatically provide nucleus counting results. This method segments nuclei with detected nuclei seed markers through a modified simple one-pass superpixel segmentation method. Rather than using a single pixel as a seed, we created a superseed for each nucleus to involve more information for improved segmentation results. Nucleus pixels are extracted by a newly proposed fusing method to reduce stain variations and preserve nucleus contour information. By evaluating segmentation results, the proposed method was compared to five existing methods on a dataset with 52 immunohistochemically (IHC) stained images. Our proposed method produced the highest mean F1-score of 0.668. By evaluating the counting results, another dataset with more than 30 000 IHC stained nuclei in 88 images were prepared. The correlation between automatically generated nucleus counting results and manual nucleus counting results was up to R2 = 0.901 (P < 0.001). By evaluating segmentation results of proposed method-based tool, we tested on a 2018 Data Science Bowl (DSB) competition dataset, three users obtained DSB score of 0.331 ± 0.006.

AVAILABILITY AND IMPLEMENTATION

The proposed method has been implemented as a plugin tool in ImageJ and the source code can be freely downloaded.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling

Missing data imputation has been a hot topic in the past decade, and many state-of-the-art works have been presented to propose novel, interesting solutions that have been applied in a variety of fields. In the past decade, the successful results achieved by deep learning techniques have opened the way to their application for solving difficult problems where human skill is not able to provide a reliable solution. Not surprisingly, some deep learners, mainly exploiting encoder-decoder architectures, have also been designed and applied to the task of missing data imputation. However, most of the proposed imputation techniques have not been designed to tackle “complex data”, that is high dimensional data belonging to datasets with huge cardinality and describing complex problems. Precisely, they often need critical parameters to be manually set or exploit complex architecture and/or training phases that make their computational load impracticable. In this paper, after clustering the state-of-the-art imputation techniques into three broad categories, we briefly review the most representative methods and then describe our data imputation proposals, which exploit deep learning techniques specifically designed to handle complex data. Comparative tests on genome sequences show that our deep learning imputers outperform the state-of-the-art KNN-imputation method when filling gaps in human genome sequences.

Myosoft: An automated muscle histology analysis tool using machine learning algorithm utilizing FIJI/ImageJ software

METHODS

Muscle sections were stained for cell boundary (laminin) and myofiber type (myosin heavy chain isoforms). Myosoft, running in the open access software platform FIJI (ImageJ), was used to analyze myofiber size and type in transverse sections of entire gastrocnemius/soleus muscles.

RESULTS

Myosoft provides an accurate analysis of hundreds to thousands of muscle fibers within 25 minutes, which is >10-times faster than manual analysis. We demonstrate that Myosoft is capable of handling high-content images even when image or staining quality is suboptimal, which is a marked improvement over currently available and comparable programs.

CONCLUSIONS

Myosoft is a reliable, accurate, high-throughput, and convenient tool to analyze high-content muscle histology. Myosoft is freely available to download from Github at https://github.com/Hyojung-Choo/Myosoft/tree/Myosoft-hub.

Engineering Tissue Fabrication With Machine Intelligence: Generating a Blueprint for Regeneration

Regenerating lost or damaged tissue is the primary goal of Tissue Engineering. 3D bioprinting technologies have been widely applied in many research areas of tissue regeneration and disease modeling with unprecedented spatial resolution and tissue-like complexity. However, the extraction of tissue architecture and the generation of high-resolution blueprints are challenging tasks for tissue regeneration. Traditionally, such spatial information is obtained from a collection of microscopic images and then combined together to visualize regions of interest. To fabricate such engineered tissues, rendered microscopic images are transformed to code to inform a 3D bioprinting process. If this process is augmented with data-driven approaches and streamlined with machine intelligence, identification of an optimal blueprint can become an achievable task for functional tissue regeneration. In this review, our perspective is guided by an emerging paradigm to generate a blueprint for regeneration with machine intelligence. First, we reviewed recent articles with respect to our perspective for machine intelligence-driven information retrieval and fabrication. After briefly introducing recent trends in information retrieval methods from publicly available data, our discussion is focused on recent works that use machine intelligence to discover tissue architectures from imaging and spectral data. Then, our focus is on utilizing optimization approaches to increase print fidelity and enhance biomimicry with machine learning (ML) strategies to acquire a blueprint ready for 3D bioprinting.

ki67 nuclei detection and ki67-index estimation: a novel automatic approach based on human vision modeling

BACKGROUND

The protein ki67 (pki67) is a marker of tumor aggressiveness, and its expression has been proven to be useful in the prognostic and predictive evaluation of several types of tumors. To numerically quantify the pki67 presence in cancerous tissue areas, pathologists generally analyze histochemical images to count the number of tumor nuclei marked for pki67. This allows estimating the ki67-index, that is the percentage of tumor nuclei positive for pki67 over all the tumor nuclei. Given the high image resolution and dimensions, its estimation by expert clinicians is particularly laborious and time consuming. Though automatic cell counting techniques have been presented so far, the problem is still open.

RESULTS

In this paper we present a novel automatic approach for the estimations of the ki67-index. The method starts by exploiting the STRESS algorithm to produce a color enhanced image where all pixels belonging to nuclei are easily identified by thresholding, and then separated into positive (i.e. pixels belonging to nuclei marked for pki67) and negative by a binary classification tree. Next, positive and negative nuclei pixels are processed separately by two multiscale procedures identifying isolated nuclei and separating adjoining nuclei. The multiscale procedures exploit two Bayesian classification trees to recognize positive and negative nuclei-shaped regions.

CONCLUSIONS

The evaluation of the computed results, both through experts' visual assessments and through the comparison of the computed indexes with those of experts, proved that the prototype is promising, so that experts believe in its potential as a tool to be exploited in the clinical practice as a valid aid for clinicians estimating the ki67-index. The MATLAB source code is open source for research purposes.

A method for semi-automated image analysis of HLA class I tumour epithelium expression in rectal cancer

Biomarkers may hold the key towards development and improvement of personalized cancer treatment. For instance, tumour expression of immune system-related proteins may reveal the tumour immune status and, accordingly, determine choice for type of immunotherapy. Therefore, objective evaluation of tumour biomarker expression is needed but often challenging. For instance, human leukocyte antigen (HLA) class I tumour epithelium expression is cumbersome to quantify by eye due to its presence on both tumour epithelial cells and tumour stromal cells, as well as tumourinfiltrating immune cells. In this study, we solved this problem by setting up an immunohistochemical (IHC) double staining using a tissue microarray (TMA) of rectal tumours wherein HLA class I expression was coloured with a blue chromogen, whereas non-epithelial tissue was visualized with a brown chromogen. We subsequently developed a semi-automated image analysis method that identified tumour epithelium as well as the percentage of HLA class I-positive tumour epithelium. Using this technique, we compared HCA2/HC10 and EMR8-5 antibodies for the assessment of HLA class I tumour expression and concluded that EMR8-5 is the superior antibody for this purpose. This IHC double staining can in principle be used for scoring of any biomarker expressed by tumour epithelium.

BITS 2017: the annual meeting of the Italian Society of Bioinformatics

This preface introduces the content of the BioMed Central journal Supplement related to the 14th annual meeting of the Bioinformatics Italian Society, held in Cagliari, Italy, from the 5th to the 7th of July, 2017.