Determining early-stage prognostic markers and stratifying patients for effective treatment are two key challenges for improving outcomes for melanoma patients. Previous studies have used tumour transcriptome data to stratify patients into immune subgroups, which were associated with differential melanoma specific survival and potential predictive biomarkers. However, acquiring transcriptome data is a time-consuming and costly process. Moreover, it is not routinely used in the current clinical workflow. Here, we attempt to overcome this by developing deep learning models to classify gigapixel haematoxylin and eosin (H&E) stained pathology slides, which are well established in clinical workflows, into these immune subgroups. We systematically assess six different multiple instance learning (MIL) frameworks, using five different image resolutions and three different feature extraction methods. We show that pathology-specific self-supervised models using 10x resolution patches generate superior representations for the classification of immune subtypes. In addition, in a primary melanoma dataset, we achieve a mean area under the receiver operating characteristic curve (AUC) of 0.80 for classifying histopathology images into 'high' or 'low immune' subgroups and a mean AUC of 0.82 in an independent TCGA melanoma dataset. Furthermore, we show that these models are able to stratify patients into 'high' and 'low immune' subgroups with significantly different melanoma specific survival outcomes (log rank test, P< 0.005). We anticipate that MIL methods will allow us to find new biomarkers of high importance, act as a tool for clinicians to infer the immune landscape of tumours and stratify patients, without needing to carry out additional expensive genetic tests.

OBJECTIVE: There is increasing interest in using artificial intelligence (AI) in pathology to improve accuracy and efficiency. Studies of clinicians' perceptions of AI have found only moderate acceptability, suggesting further research is needed regarding integration into clinical practice. This study aimed to explore stakeholders' theories concerning how and in what contexts AI is likely to become integrated into pathology. MATERIALS AND METHODS: A literature review provided tentative theories that were revised through a realist interview study with 20 pathologists and 5 pathology trainees. Questions sought to elicit whether, and in what ways, the tentative theories fitted with interviewees' perceptions and experiences. Analysis focused on identifying the contextual factors that may support or constrain uptake of AI in pathology. RESULTS: Interviews highlighted the importance of trust in AI, with interviewees emphasizing evaluation and the opportunity for pathologists to become familiar with AI as means for establishing trust. Interviewees expressed a desire to be involved in design and implementation of AI tools, to ensure such tools address pressing needs, but needs vary by subspecialty. Workflow integration is desired but whether AI tools should work automatically will vary according to the task and the context. CONCLUSIONS: It must not be assumed that AI tools that provide benefit in one subspecialty will provide benefit in others. Pathologists should be involved in the decision to introduce AI, with opportunity to assess strengths and weaknesses. Further research is needed concerning the evidence required to satisfy pathologists regarding the benefits of AI.

Digital slide images produced from routine diagnostic histopathological preparations suffer from variation arising at every step of the processing pipeline. Typically, pathologists compensate for such variation using expert knowledge and experience, which is difficult to replicate in automated solutions. The extent to which inconsistencies affect image analysis is explored in this work, examining in detail, the results from a previously published algorithm automating the generation of tumor:stroma ratio (TSR) in colorectal clinical trial datasets. One dataset consisting of 2,211 cases and 106,268 expert-labelled images is used to identify quality issues, by visually inspecting cases where algorithm-pathologist agreement is lowest. Twelve categories are identified and used to analyze pathologist-algorithm agreement in relation to these categories. Of the 2,211 cases, 701 were found to be free from any image quality issues. Algorithm performance was then assessed, comparing pathologist agreement with image quality classification. It was found that agreement was lowest on poorly differentiated tissue, with a mean TSR difference of 0.25 (sd = 0.24). Removing images that contained quality issues increased accuracy from 80% to 83%, at the expense of reducing the dataset to 33,736 images (32%). Training the algorithm on the optimized dataset, prior to testing on all images saw a decrease in accuracy of 4%, indicating that the optimized dataset did not contain enough variation to generate a fully representative model. The results provide an in-depth perspective on image quality, highlighting the importance of the effects on downstream image analysis.

We present a visualization application that enables effective interactive visual analysis of large-scale 3D histopathology, that is, high-resolution 3D microscopy data of human tissue. Clinical work flows and research based on pathology have, until now, largely been dominated by 2D imaging. As we will show in the paper, studying volumetric histology data will open up novel and useful opportunities for both research and clinical practice. Our starting point is the current lack of appropriate visualization tools in histopathology, which has been a limiting factor in the uptake of digital pathology. Visualization of 3D histology data does pose difficult challenges in several aspects. The full-color datasets are dense and large in scale, on the order of 100,000 × 100,000× 100 voxels. This entails serious demands on both rendering performance and user experience design. Despite this, our developed application supports interactive study of 3D histology datasets at native resolution. Our application is based on tailoring and tuning of existing methods, system integration work, as well as a careful study of domain specific demands emanating from a close participatory design process with domain experts as team members. Results from a user evaluation employing the tool demonstrate a strong agreement among the 14 participating pathologists that 3D histopathology will be a valuable and enabling tool for their work.

In this paper, we tackle the challenge of effective Transfer Function (TF) design for Direct Volume Rendering (DVR) of full-color datasets. We propose a novel TF design toolbox based on color similarity which is used to adjust opacity as well as replacing colors. We show that both CIE L*u*v* chromaticity and the chroma component of YCbCr are equally suited as underlying color space for the TF widgets. In order to maximize the area utilized in the TF editor, we renormalize the color space based on the histogram of the dataset. Thereby, colors representing a higher share of the dataset are depicted more prominently, thus providing a higher sensitivity for fine-tuning TF widgets. The applicability of our TF design toolbox is demonstrated by volume ray casting challenging full-color volume data including the visible male cryosection dataset and examples from 3D histology.

This article describes the design and evaluation of two generations of an interface for navigating datasets of gigapixel images that pathologists use to diagnose cancer. The interface design is innovative because users panned with an overview:detail view scale difference that was up to 57 times larger than established guidelines, and 1 million pixel “thumbnail” overviews that leveraged the real estate of high-resolution workstation displays. The research involved experts performing real work (pathologists diagnosing cancer), using datasets that were up to 3,150 times larger than those used in previous studies that involved navigating images. The evaluation provides evidence about the effectiveness of the interfaces and characterizes how experts navigate gigapixel images when performing real work. Similar interfaces could be adopted in applications that use other types of high-resolution images (e.g., remote sensing or high-throughput microscopy).