Visualization for Diagnostic Review of Copy Number Variants in Complex DNA Sequencing Data.

Genomics is at the core of precision medicine, and there are high expectations on genomics-enabled improvement of patient outcomes in the years to come. Around the world, initiatives to increase the use of DNA sequencing in clinical routine are being deployed, such as the use of broad panels in the standard care for oncology patients. Such a development comes at the cost of increased demands on throughput in genomic data analysis. In this paper, we use the task of copy number variant (CNV) analysis as a context for exploring visualization concepts for clinical genomics. CNV calls are generated algorithmically, but time-consuming manual intervention is needed to separate relevant findings from irrelevant ones in the resulting large call candidate lists. We present a visualization environment, named Copycat, to support this review task in a clinical scenario. Key components are a scatter-glyph plot replacing the traditional list visualization, and a glyph representation designed for at-a-glance relevance assessments. Moreover, we present results from a formative evaluation of the prototype by domain specialists, from which we elicit insights to guide both prototype improvements and visualization for clinical genomics in general.

The thorny complexities of visualization research for clinical settings: A case study from genomics.

In this perspective article we discuss a certain type of research on visualization for bioinformatics data, namely, methods targeting clinical use. We argue that in this subarea additional complex challenges come into play, particularly so in genomics. We here describe four such challenge areas, elicited from a domain characterization effort in clinical genomics. We also list opportunities for visualization research to address clinical challenges in genomics that were uncovered in the case study. The findings are shown to have parallels with experiences from the diagnostic imaging domain.

The human-in-the-loop: an evaluation of pathologists' interaction with artificial intelligence in clinical practice.

AIMS: One of the major drivers of the adoption of digital pathology in clinical practice is the possibility of introducing digital image analysis (DIA) to assist with diagnostic tasks. This offers potential increases in accuracy, reproducibility, and efficiency. Whereas stand-alone DIA has great potential benefit for research, little is known about the effect of DIA assistance in clinical use. The aim of this study was to investigate the clinical use characteristics of a DIA application for Ki67 proliferation assessment. Specifically, the human-in-the-loop interplay between DIA and pathologists was studied. METHODS AND RESULTS: We retrospectively investigated breast cancer Ki67 areas assessed with human-in-the-loop DIA and compared them with visual and automatic approaches. The results, expressed as standard deviation of the error in the Ki67 index, showed that visual estimation ('eyeballing') (14.9 percentage points) performed significantly worse (P < 0.05) than DIA alone (7.2 percentage points) and DIA with human-in-the-loop corrections (6.9 percentage points). At the overall level, no improvement resulting from the addition of human-in-the-loop corrections to the automatic DIA results could be seen. For individual cases, however, human-in-the-loop corrections could address major DIA errors in terms of poor thresholding of faint staining and incorrect tumour-stroma separation. CONCLUSION: The findings indicate that the primary value of human-in-the-loop corrections is to address major weaknesses of a DIA application, rather than fine-tuning the DIA quantifications.

Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.

Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions. However, in a real-world scenario, automatic mitosis detection should be performed in whole-slide images (WSIs) and an automatic method should be able to produce a tumor proliferation score given a WSI as input. To address this, we organized the TUmor Proliferation Assessment Challenge 2016 (TUPAC16) on prediction of tumor proliferation scores from WSIs. The challenge dataset consisted of 500 training and 321 testing breast cancer histopathology WSIs. In order to ensure fair and independent evaluation, only the ground truth for the training dataset was provided to the challenge participants. The first task of the challenge was to predict mitotic scores, i.e., to reproduce the manual method of assessing tumor proliferation by a pathologist. The second task was to predict the gene expression based PAM50 proliferation scores from the WSI. The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of κ = 0.567, 95% CI [0.464, 0.671] between the predicted scores and the ground truth. For the second task, the predictions of the top method had a Spearman's correlation coefficient of r = 0.617, 95% CI [0.581 0.651] with the ground truth. This was the first comparison study that investigated tumor proliferation assessment from WSIs. The achieved results are promising given the difficulty of the tasks and weakly-labeled nature of the ground truth. However, further research is needed to improve the practical utility of image analysis methods for this task.

Deep learning nuclei detection: A simple approach can deliver state-of-the-art results.

BACKGROUND: Deep convolutional neural networks have become a widespread tool for the detection of nuclei in histopathology images. Many implementations share a basic approach that includes generation of an intermediate map indicating the presence of a nucleus center, which we refer to as PMap. Nevertheless, these implementations often still differ in several parameters, resulting in different detection qualities. METHODS: We identified several essential parameters and configured the basic PMap approach using combinations of them. We thoroughly evaluated and compared various configurations on multiple datasets with respect to detection quality, efficiency and training effort. RESULTS: Post-processing of the PMap was found to have the largest impact on detection quality. Also, two different network architectures were identified that improve either detection quality or runtime performance. The best-performing configuration yields f1-measures of 0.816 on H&E stained images of colorectal adenocarcinomas and 0.819 on Ki-67 stained images of breast tumor tissue. On average, it was fully trained in less than 15,000 iterations and processed 4.15 megapixels per second at prediction time. CONCLUSIONS: The basic PMap approach is greatly affected by certain parameters. Our evaluation provides guidance on their impact and best settings. When configured properly, this simple and efficient approach can yield equal detection quality as more complex and time-consuming state-of-the-art approaches.

Training Nuclei Detection Algorithms with Simple Annotations.

BACKGROUND: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. METHODS: We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images. RESULTS: A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality. CONCLUSIONS: With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets.

Improving the creation and reporting of structured findings during digital pathology review.

BACKGROUND: Today, pathology reporting consists of many separate tasks, carried out by multiple people. Common tasks include dictation during case review, transcription, verification of the transcription, report distribution, and report the key findings to follow-up registries. Introduction of digital workstations makes it possible to remove some of these tasks and simplify others. This study describes the work presented at the Nordic Symposium on Digital Pathology 2015, in Linköping, Sweden. METHODS: We explored the possibility to have a digital tool that simplifies image review by assisting note-taking, and with minimal extra effort, populates a structured report. Thus, our prototype sees reporting as an activity interleaved with image review rather than a separate final step. We created an interface to collect, sort, and display findings for the most common reporting needs, such as tumor size, grading, and scoring. RESULTS: The interface was designed to reduce the need to retain partial findings in the head or on paper, while at the same time be structured enough to support automatic extraction of key findings for follow-up registry reporting. The final prototype was evaluated with two pathologists, diagnosing complicated partial mastectomy cases. The pathologists experienced that the prototype aided them during the review and that it created a better overall workflow. CONCLUSIONS: These results show that it is feasible to simplify the reporting tasks in a way that is not distracting, while at the same time being able to automatically extract the key findings. This simplification is possible due to the realization that the structured format needed for automatic extraction of data can be used to offload the pathologists' working memory during the diagnostic review.

A comparative study of input devices for digital slide navigation.

This paper describes work presented at the Nordic Symposium on Digital Pathology 2014, Linköping, Sweden. Quick and seamless integration between input devices and the navigation of digital slides remains a key barrier for many pathologists to "go digital." To better understand this integration, three different input device implementations were compared in terms of time to diagnose, perceived workload and users' preferences. Six pathologists reviewed in total nine cases with a computer mouse, a 6 degrees-of-freedom (6DOF) navigator and a touchpad. The participants perceived significantly less workload (P < 0.05) with the computer mouse and the 6DOF navigator, than with the touchpad, while no effect of the input device used on the time to diagnose was observed. Five out of six pathologists preferred the 6DOF navigator, while the touchpad was the least preferred device. While digital slide navigation is often designed to mimic microscope interaction, the results of this study demonstrate that in order to minimize workload there is reason to let the digital interaction move beyond the familiar microscope tradition.

Slide navigation patterns among pathologists with long experience of digital review.

AIMS: In order to develop efficient digital pathology workstations, we studied the navigation patterns of pathologists diagnosing whole-slide images. To gain a better understanding of these patterns, we built a conceptual model based on observations. We also determined whether or not new navigation patterns have emerged among pathologists with extensive digital experience. METHODS AND RESULTS: Five pathologists were asked to diagnose a set of four cases while thinking out loud. The navigation within the digital slides was recorded and divided into re-occurring navigation actions. The pathologists reused the same type of actions, but their occurrence differed. The most common action was a slow panning that followed an edge structure or covered an area systematically, which accounted for 30.2% of all actions and had a median duration of 7.2 s. Of all the actions, 49% were carried out within the navigation overview and 38% of the actions could not have been performed with a conventional microscope. CONCLUSIONS: The new navigation possibilities in the digital workstation were used to a large extent. The division of actions into different concepts can be used to find and prioritize between existing user interface designs as well as to understand the different navigation styles used by different pathologists.

Implementation of large-scale routine diagnostics using whole slide imaging in Sweden: Digital pathology experiences 2006-2013.

Recent technological advances have improved the whole slide imaging (WSI) scanner quality and reduced the cost of storage, thereby enabling the deployment of digital pathology for routine diagnostics. In this paper we present the experiences from two Swedish sites having deployed routine large-scale WSI for primary review. At Kalmar County Hospital, the digitization process started in 2006 to reduce the time spent at the microscope in order to improve the ergonomics. Since 2008, more than 500,000 glass slides have been scanned in the routine operations of Kalmar and the neighboring Linköping University Hospital. All glass slides are digitally scanned yet they are also physically delivered to the consulting pathologist who can choose to review the slides on screen, in the microscope, or both. The digital operations include regular remote case reporting by a few hospital pathologists, as well as around 150 cases per week where primary review is outsourced to a private clinic. To investigate how the pathologists choose to use the digital slides, a web-based questionnaire was designed and sent out to the pathologists in Kalmar and Linköping. The responses showed that almost all pathologists think that ergonomics have improved and that image quality was sufficient for most histopathologic diagnostic work. 38 ± 28% of the cases were diagnosed digitally, but the survey also revealed that the pathologists commonly switch back and forth between digital and conventional microscopy within the same case. The fact that two full-scale digital systems have been implemented and that a large portion of the primary reporting is voluntarily performed digitally shows that large-scale digitization is possible today.