Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring

This paper presents novel feature descriptors and classification algorithms for the automated scoring of HER2 in Whole Slide Images (WSI) of breast cancer histology slides. Since a large amount of processing is involved in analyzing WSI images, the primary design goal has been to keep the computational complexity to the minimum possible level and to use simple, yet robust feature descriptors that can provide accurate classification of the slides. We propose two types of feature descriptors that encode important information about staining patterns and the percentage of staining present in ImmunoHistoChemistry (IHC)-stained slides. The first descriptor is called a characteristic curve, which is a smooth non-increasing curve that represents the variation of percentage of staining with saturation levels. The second new descriptor introduced in this paper is a local binary pattern (LBP) feature curve, which is also a non-increasing smooth curve that represents the local texture of the staining patterns. Both descriptors show excellent interclass variance and intraclass correlation and are suitable for the design of automatic HER2 classification algorithms. This paper gives the detailed theoretical aspects of the feature descriptors and also provides experimental results and a comparative analysis

Learning where to see : a novel attention model for automated immunohistochemical scoring

Estimatingover-amplification of human epidermal growth factor receptor2 (HER2) on invasive breast cancer (BC) is regarded as a significant predictive and prognostic marker. We propose a novel deep reinforcement learning (DRL) based model that treats immunohistochemical (IHC) scoring of HER2 as a sequential learning task. For a given image tile sampled from multi-resolution giga-pixel whole slide image (WSI), the model learns to sequentially identify some of the diagnostically relevant regions of interest (ROIs) by following a parameterized policy. The selected ROIs are processed by recurrent and residual convolution networks to learn the discriminative features for different HER2 scores and predict the next location, without requiring to process all the subimage patches of a given tile for predicting the HER2 score, mimicking the histopathologist who would not usually analyse every part of the slide at the highest magnification. The proposed model incorporates a task-specific regularization term and inhibition of return mechanism to prevent the model from revisiting the previously attended locations. We evaluated our model on two IHC datasets: a publicly available dataset from the HER2 scoring challenge contest and another dataset consisting of WSIs of gastroenteropancreatic neuroendocrine tumor sections stained with Glo1 marker. We demonstrate that the proposed model out performs other methods based on state-of-the-art deep convolutional networks. To the best of our knowledge, this is the first study using DRL for IHC scoring and could potentially lead to wider use of DRL in the domain of computational pathology reducing the computational burden of the analysis of large multi-gigapixel histology images

Combined quantitative measures of ER, PR, HER2, and KI67 provide more prognostic information than categorical combinations in luminal breast cancer.

Although most women with luminal breast cancer do well on endocrine therapy alone, some will develop fatal recurrence thereby necessitating the need to prospectively determine those for whom additional cytotoxic therapy will be beneficial. Categorical combinations of immunohistochemical measures of ER, PR, HER2, and KI67 are traditionally used to classify patients into luminal A-like and B-like subtypes for chemotherapeutic reasons, but this may lead to the loss of prognostically relevant information. Here, we compared the prognostic value of quantitative measures of these markers, combined in the IHC4-score, to categorical combinations in subtypes. Using image analysis-based scores for all four markers, we computed the IHC4-score for 2498 patients with luminal breast cancer from two European study populations. We defined subtypes (A-like (ER + and PR + : and HER2- and low KI67) and B-like (ER + and/or PR + : and HER2 + or high KI67)) by combining binary categories of these markers. Hazard ratios and 95% confidence intervals for associations with 10-year breast cancer-specific survival were estimated in Cox proportional-hazard models. We accounted for clinical prognostic factors, including grade, tumor size, lymph-nodal involvement, and age, by using the PREDICT-score. Overall, Subtypes [hazard ratio (95% confidence interval) B-like vs. A-like = 1.64 (1.25-2.14); P-value < 0.001] and IHC4-score [hazard ratio (95% confidence interval)/1 standard deviation = 1.32 (1.20-1.44); P-value < 0.001] were prognostic in univariable models. However, IHC4-score [hazard ratio (95% confidence interval)/1 standard deviation = 1.24 (1.11-1.37); P-value < 0.001; likelihood ratio chi-square (LRχ2) = 12.5] provided more prognostic information than Subtype [hazard ratio (95% confidence interval) B-like vs. A-like = 1.38 (1.02-1.88); P-value = 0.04; LRχ2 = 4.3] in multivariable models. Further, higher values of the IHC4-score were associated with worse prognosis, regardless of subtype (P-heterogeneity = 0.97). These findings enhance the value of the IHC4-score as an adjunct to clinical prognostication tools for aiding chemotherapy decision-making in luminal breast cancer patients, irrespective of subtype

Topology and attention in computational pathology

Histopathology serves as the gold standard in the process of cancer diagnosis and unravelling the disease heterogeneity. In routine practice, a trained histopathologist performs visual examination of tissue glass slides under the microscope. The objective of the visual examination is to observe the morphological appearance of tissue sections, analyse the density of tumour rich areas, spatial arrangement, and architecture of diferent types of cells. However, careful visual examination of tissue slides is a demanding task especially when workloads are high, and the subjective nature of the histological grading inevitably leads to inter- and even intra-observer variability. Attaining high accuracy and objective quantification of tissue specimens in cancer diagnosis are some of the ongoing challenges in modern histopathology. With the recent advent of digital pathology, tissue glass slides can now be scanned with digital slides scanners to produce whole slide images (WSIs). A WSI contains a high-resolution pixel representation of tissue slide, stored in a pyramidal structure and typically containing 1010 pixels. Automated algorithms are generally based on the concepts of digital image analysis which can analyse WSIs to improve the precision and reproducibility in cancer diagnostics. The reliability of the results of an algorithm can be objectively measured and improved against an objective standard. In this thesis, we focus on developing automated methods for quantitative assessment of histology WSIs with the aim of improving the precision and reproducibility of cancer diagnosis. More specifically, the designed automated computational pathology algorithms are based on deep learning models in conjunction with algebraic topology and visual attention mechanisms. To the best of our knowledge, the applicability of attention and topology based methods have not been explored in the domain of computational pathology. In this regard, we propose an algorithm for computing persistent homology profiles (topological features) and propose two variants for effective and reliable tumour segmentation of colorectal cancer WSIs. We show that incorporation of deep features along with topological features improves the overall performance for tumour segmentation. We then present the first-ever systematic study (contest) for scoring the human epidermal growth factor receptor 2 (HER2) biomarker on breast cancer histology WSIs. Further, we devise a reinforcement learning based attention mechanism for HER2 scoring that sequentially identifies and analyses the diagnostically relevant regions within a given image, mimicking the histopathologist who would not usually analyse every part of the slide at the highest magnification. We demonstrate the proposed model outperforms other methods participated in our systematic study, most of them were using state-of-the-art deep convolutional networks. Finally, we propose a multi-task learning framework for simultaneous cell detection and classifi- cation, which we named as Hydra-Net. We then compute an image based biomarker which we refer as digital proximity signature (DPS), to predict overall survival in diffuse large B-cell lymphoma (DLBCL) patients. Our results suggest that patients with high collagen-tumour proximity are likely to experience better overall survival

Development of biomarkers for the risk stratification and targeted therapy of Barrett's oesophagus and oesophageal adenocarcinoma

Barrett's oesophagus is the most important risk factor for the development of oesophageal adenocarcinoma (OA), but progression is unpredictable. Dysplasia predicts which Barrett’s patients are at greatest risk for OA but achieving the diagnosis can be challenging. Immunohistochemistry with p53 is recommended as an adjunct to assist with dysplasia diagnosis. This thesis will examine if replication licensing factors and DNA ploidy status are as good if not better than p53 to assist in the diagnosis of dysplasia. Overexpression of HER2 in foregut cancer is an indication for HER2 targeted treatments. Its influence on prognosis is less understood. The relationships between clinicopathological variables, HER2 overexpression and prognosis will next be evaluated. Current ablative techniques for Barrett’s neoplasia are limited to superficial disease. Photodynamic therapy was a treatment for Barrett’s that could penetrate more deeply into diseased tissue but was limited by the side effects of off-target photosensitivity. Combining targeting vehicles such as antibodies to newer and more deeply penetrating photosensitiser drugs, may overcome the previous limitations of this technology. A photosensitive ADC against HER2 will be created and its efficacy in vitro and in vivo evaluated. However, even the most effective ADC against HER2 will not treat the majority of cancers, as we will show HER2 is only expressed in the minority of foregut tumours. The final experiments will look to characterise the mucin MUC1 in Barrett’s and associated neoplasia. Studies have previously shown it to be present in up to 100% of cancers while others say far fewer. We will show proof of principle data for the development of a MUC1 targeting photosensitive ADC in vitro and postulate how it may in future enable treatment of locoregional invasive tumours endoscopically