HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin–Eosin Whole-Slide Imaging

Breast cancer is the most common malignancy in women worldwide, and is responsible for more than half a million deaths each year. The appropriate therapy depends on the evaluation of the expression of various biomarkers, such as the human epidermal growth factor receptor 2 (HER2) transmembrane protein, through specialized techniques, such as immunohistochemistry or in situ hybridization. In this work, we present the HER2 on hematoxylin and eosin (HEROHE) challenge, a parallel event of the 16th European Congress on Digital Pathology, which aimed to predict the HER2 status in breast cancer based only on hematoxylin–eosin-stained tissue samples, thus avoiding specialized techniques. The challenge consisted of a large, annotated, whole-slide images dataset (509), specifically collected for the challenge. Models for predicting HER2 status were presented by 21 teams worldwide. The best-performing models are presented by detailing the network architectures and key parameters. Methods are compared and approaches, core methodologies, and software choices contrasted. Different evaluation metrics are discussed, as well as the performance of the presented models for each of these metrics. Potential differences in ranking that would result from different choices of evaluation metrics highlight the need for careful consideration at the time of their selection, as the results show that some metrics may misrepresent the true potential of a model to solve the problem for which it was developed. The HEROHE dataset remains publicly available to promote advances in the field of computational pathology

Preliminary results from a crowdsourcing experiment in immunohistochemistry

Background: Crowdsourcing, i.e., the outsourcing of tasks typically performed by a few experts to a large crowd as an open call, has been shown to be reasonably effective in many cases, like Wikipedia, the Chess match of Kasparov against the world in 1999, and several others. The aim of the present paper is to describe the setup of an experimentation of crowdsourcing techniques applied to the quantification of immunohistochemistry. Methods: Fourteen Images from MIB1-stained breast specimens were first manually counted by a pathologist, then submitted to a crowdsourcing platform through a specifically developed application. 10 positivity evaluations for each image have been collected and summarized using their median. The positivity values have been then compared to the gold standard provided by the pathologist by means of Spearman correlation. Results: Contributors were in total 28, and evaluated 4.64 images each on average. Spearman correlation between gold and crowdsourced positivity percentages is 0.946 (p < 0.001). Conclusions: Aim of the experiment was to understand how to use crowdsourcing for an image analysis task that is currently time-consuming when done by human experts. Crowdsourced work can be used in various ways, in particular statistically agregating data to reduce identification errors. However, in this preliminary experimentation we just considered the most basic indicator, that is the median positivity percentage, which provided overall good results. This method might be more aimed to research than routine: when a large number of images are in need of ad-hoc evaluation, crowdsourcing may represent a quick answer to the need. \ua9 Della Mea et al

A Graph-Based Digital Pathology Approach To Describe Lymphocyte Clustering Patterns After Renal Transplantation

Introduction/ Background Renal transplantation (rTx) induces an adaptive immune response against foreign donor antigens mediated by lymphocytes of the recipient. Local accumulation of B- and T-cells is an important component of this response enabling and controlling immune cell interactions [1]. Combining digital microscopic images with network analysis [2][3] opens new perspectives to study the spa- tial dimension of lymphocyte clustering and to model their potential interactions.   Aims The aim of this study is to characterize the range of B- and T-lymphocytic infiltrates below the threshold of rejection defined by theBanffclassification [4][5] and to propose a mathematical description of immune cell clustering for use in systems medicine approaches.   Methods We established a workflow to comprehensively characterize lymphocyte clusters and compare their morphological features with organized structures such as secondary or tertiary lymphoid organs (TLO/SLO) [6]. 51 renal protocol and indication biopsies from 13 patients without evidence for severe rejection over 10 years were stained by CD3/CD20 duplex immunohisto- chemistry. Whole slide images (WSIs) were acquired to automatically detect biologically relevant regions of in- terest (ROIs) by means of density maps for lymphocytes (image analysis workflow illustrated in Fig. 1a). They are generated from single nuclei identification using an au- to-adaptive random forest pixelwise classifier (“nucleus container” module [7],Definiens,Germany). We imple- mented a graph-based tool in Java using individual cell coordinates to identify cell compartments (Fig. 1b) and applied it to each selected ROI. For this, a neighborhood graph is built by Delaunay triangulation and Euclidean distances. This analysis allows describing their specific clustering behavior based on features as described in [8]. The convex hull of the neighborhood graph allows a visualization of B- and T-cell compartments.   Results We identified B-cell rich compartments in about 55% of 150 ROIs in kidney tissue after successful transplantation (examples in Fig. 2). The B-cell compartments in rTx tended towards smaller overall size with on average about 90 cells in a B-cell cluster compared to more than 600 B-cells observed in mature TLOs and SLOs and they showed less prominent spatial organization (average degree on average 3.92 instead of 4.97; degree shows generally Poisson distribution as illustrated in Fig. 3A). Further, the graph analysis confirmed lower B-cell density (Fig. 3B displays the exponential character of the spatial B-cell distribution in a selected ROI), a different ratio between T- and B-cell compartments, and more frequent overlap between both regions than in mature lymphoid structures.   We conclude that the graph-based approach is feasible to distinguish relevant immune cell patterns in rTx and provides a useful mathematical description of neighborhood relationships between immune cells and their spatial organization. The workflow has the potential to improve throughput and robustness of immune cell evaluation for use in translational science

Block-Centric Visualization Of Histological Whole Slide Images With Application To Revealing Growth-Patterns Of Early Colorectal Adenomas And Aberrant Crypt Foci

Introduction/ Background Comfortable navigation through diagnostic images is a prospective challenge for the acceptance of virtual microscopy applications in routine pathology [1],[2]. Tracing different regions of interest through multiple sections on one or several slides is a typical task in diagnostic slide examination. This laborious and time-consuming co-localization is currently executed by pathologists. Retaining the relative positions of tissue structures while alternating between multiple slides is still not feasible in a satisfactory manner in conventional nor virtual microscopy. Aims To address this issue we present a more comfortable and intuitive method to read slides using computer-assisted navigation. Furthermore, we demonstrate the strengths of our method by applying it to large series of serial colorectal tissue sections, creating new kinds of visualizations of different adenomatous mucosal architectures in human tissue, while looking for human correlates of lesions recently described in mice [3]. Methods Histological images contain multiple distortions from different sources in the laboratory and digitalization process. An interconnection model was created to describe distortions by several layers, providing a normalized tissue representation. Layers were associated with specific distortions with each layer serving as a new level of abstraction. The first layers enabled a coarse alignment of tissue sections. Further alignment is achieved by piecewise, multi-resolution, SIFT-based [4] correspondence extraction and refinement. Inside the convex hull of all fiducial points local affine transformations were applied whereas a global affine transformation was used on the outside. Animated stacks were generated for regions of interest using local rigid transformations to preserve exact morphological coherences. For subsequent creation of 3D models, the relevant histological objects within these images were annotated by pathologists, partly using computer assisted segmentation based on active contours [5]. These annotations were used subsequently to create simplified 3D models by applying VTK [6].  Results The presented methods provide an efficient means to retrieve correspondences and additional spatial information from serial sections of histological slides. They also show good applicability for specimen from different origin. Alignment methods can be applied to generate block-centric visualizations such as parallel and transparent viewing of multiple stains. Moreover, the generated stack videos and 3D models demonstrate the very good accuracy of section alignment even in large series. The visualizations enable pathologists and researchers to grasp the 3D structural relationships in the tissue at a glance, providing an excellent tool to communicate more complex histomorphological findings. Interestingly, we see two kinds of tubular adenomas, which could imply multiple ways to tubular adenoma formation in FAP-patients, possibly akin to the recent observations in mice [3]

Micrometastasis Detection Guidance by Whole-Slide Image Texture Analysis in Colorectal Lymph Nodes

Introduction/ Background Cancer is a disease that affects millions worldwide and accurate determination of whether lymph nodes (LNs) near the primary tumor contain metastatic foci is of critical importance for proper patient management. Histopathological evaluation is the only accepted method to make that determination. However, the current standard of care only examines a single central histological section per LN and yields an unacceptable false-negative rate. Aims To help pathologists in their examination we propose a method that extracts textural features from histopathological LN whole slide images (WSI) and then applies support vector machines (SVMs) to automatically identify regions suspicious for metastatic foci. Methods The database consisted of WSI from 44 LNs. Sections were stained with hematoxylin-eosin and examined at 20x (0.45μm resolution). Twenty-eight of the LNs were identified by an expert pathologist as positive for cancer (P), and the remaining sixteen were negative (N). This database was divided into two groups. Group 1 (15P and 5N) was used for training and Group 2 (13P and 11N) was used for testing the classification technique. For all analysis each WSI was divided into non-overlapping 1000 x 1000 pixel sub-images that will be referred to as high-power fields (HPFs). For each LN in Group 1, at least one WSI was annotated by a pathologist to identify rectangular, HPF-scale regions as locally cancerous or locally non-cancerous. From these annotated slides, 924 HPFs (462 P and 462 N) were obtained. For each of these HPFs, statistical features based on gray-level co-occurrence matrices [1] and Law’s texture energy measures [2, 3] were extracted from 9 derived images [4]. The extracted features were submitted to a sequential forward selection (SFS) method [5] to select few non-redundant features providing best class separation (cancerous vs. non-cancerous region). Combinations of the selected features were tested on the 924 HPFs using k-fold cross-validation to find those that produced the best results and consequently to train our SVM-based classifier. In Group 2, WSI were not annotated for cancerous and non-cancerous zones on a HPF scale. Each LN, however, had been labeled by a pathologist as positive or negative for cancer. For each WSI, each section was divided into contiguous HPFs, and those which mainly contain fatty tissue, background, and tears were automatically excluded. Each selected HPFs was classified as cancerous or non-cancerous using the previously trained classifier to obtain the total number of cancer-classified per LN. A receiver operating characteristics (ROC) curve was traced by changing the discriminator threshold (T) used to label the LN as P for cancer as a function of the total number of cancer-classified HPFs. Results During training, 5 Laws features were selected by SFS. Highly satisfactory k-fold cross-validation with a F-score of 0.996 ± 0.005 was obtained using only 2 statistical features computed at different scales. The ROC curve obtained by applying the SVM-classifier to the test set is shown in the next figure. Two valuable operating points can be identified which both guaranteed no false-negative. At T=11 we got 2 false-positives and an optimal F-score of 0.917, and with a more conservative approach, T=1, we got 7 false-positives and a F-score of 0.759. The top-left part of the slide displayed in next figure would have been proposed to the pathologist as the most suspicious region of the cancerous LN