Author: Michael Grunkin

  • Augmenting Pathologists—Image Analysis with AI

    ​CIO Applications recently sat down with our founder and CEO Michael Grunkin. The technology print magazine focuses on usage of various tech start ups. In the latest issue the spotlight was placed on the marketplace of machine learning providers and the difficulties in zeroing in on the best. To address this, CIO Applications took up the challenge and located the “Top 25 Machine Learning Solution Providers – 2019”.

    Here is the full interview from CIO Applications where CEO Michael Grunkin, shared his insights into how AI and machine learning-enabled image analysis software has demonstrated the potential to make a real difference in tissue-based research and diagnostics.

    Augmenting Pathologists—Image Analysis with AI


    Clinicians rely heavily on tissue samples that are interpreted by pathologists under a microscope. This practice has been in place for hundreds of years and is now ready to be automated, as leading quantitative digital pathology solution provider, Visiopharm, applies AI and Machine Learning in their image analysis software. Digitized pathology images, known as Whole Slide Images (WSI), represent several challenges to obtain meaningful, reproducible, and standardized analysis.

    Interesting advances in technology usually always happens at the intersection between different fields of science and technology. Diagnostic quantitative digital pathology is no exception: it lies firmly at the intersection between computer science, image analysis, and applied mathematics. I was a technical founder of two earlier diagnostic companies based on image analysis. This was a valuable learning experience that showed the enormous potential diagnostic image analysis has toward providing both data quality and automation. It later became clear that there is a huge unmet need in the field of pathology.

    Pathology relies heavily on manual reading and interpretation of biological tissue structures. With subjective and error-prone manual processes, diagnostic digital pathology has an enormous potential to automate and improve tissue-based diagnostics. This is becoming increasingly important in an era of new targeted cancer therapies. These treatments have the potential to significantly improve the prognosis for cancer patients. The driving vision behind Visiopharm, which my co-founder Johan Dore and I founded back in 2001, is to support pathologists and scientists with the tools to identify the right treatment, for the right patient, at the right time.

    What are some trends that you see emerging in pathology, and how is Visiopharm leveraging those?


    The advent of WSI has enabled the market for quantitative digital pathology both in diagnostics and research. WSI allows you to digitize an entire microscope slide at very high resolution and view it from a monitor. Digital Pathology helps scientists and pathologists to replace the traditional microscope, and assess important cellular and morphometric structures more efficiently directly on screen.

    The ability to digitize slides is just the first step in the journey toward automated precision pathology. The manual assessment of complex biological tissue structures and biomarker response often represents a formidable cognitive challenge, especially with a whole new generation of biomarkers that are used to characterize the tumor micro-environment. Over the years, we have been able to understand, identify, and quantify the error sources from biopsy to data. We have taken a radical approach to simplify and improve the entire process for pathologists. We use machine learning and AI technology to mitigate or eliminate these error sources and achieve higher interpretive accuracy of tissue data.

    ​Visiopharm’s main focus is cancer research and diagnostics. As we speak, there are more than 600 new immunotherapies in late stage development.This new generation of cancer treatments presents new challenges for pathology. Immunotherapies are expensive targeted therapies, meaning that only a sub- population of patients will respond to the treatment.This is where Visiopharm makes a difference. Our image analysis workflows and algorithms improve the interpretive accuracy of some of the most challenging and time-consuming manual pathology reads.Our technology can provide decision support for pathologists and scientists that are tasked with making important diagnostic, research or business decisions based on tissue data.

    How has Visiopharm’s Oncotopix® and AI been able to imprint its exclusivity in the pathology market? 

    The simplicity in workflow, combined with the flexibility and power of the software, enables our customers to easily configure new diagnostic or research APPs without the need for programming skills. In particular, our commitment to data quality, has made us one of the most widely-adopted solutions in the market.

    Without consistent good stain quality, it is often not possible to make an accurate diagnosis. Visiopharm has been able to use our image analysis platform to find new and effective ways to measure the variability in stain quality. We are working closely with EQA organizations to provide Qualitopix™, our platform that will effect change and significantly impact the standardization of tissue diagnostics.

    Lastly, we have invested significantly in integrating to existing digital infrastructure, including Image Management, Lab Information, and PACS systems. Our solutions now plug directly into existing workflows and augment the pathologist with APPs for precision pathology.Could you cite a case study describing how you enable clients to overcome hurdles and obtain desired outcomes with your innovative digital pathology solutions? 

    We recently supported Karolinska Institute in Stockholm on a research project to understand the magnitude of manual reading errors throughout the diagnostic process. The researchers used Ki-67, a biomarker for distinguishing between two sub-types of breast cancers, where the treatment selection depends on the classification of the patient into one of these two sub-types. The study demonstrated a 30 percent manual misclassification rate between these cancer types. Visiopharm’s software significantly reduced this error.

    We also recently finished installation of our Oncotopix® Dx for breast cancer in hospitals throughout Denmark.


    Some of the most notable results, was considerable time and money savings on reflex molecular testing for inconclusive breast cancer cases. For a large fraction of patients, we could completely eliminate the need for the molecular test. This saves money for laboratories, but more importantly, it greatly reduced the waiting time for these patients.

    What does the future hold for your organization? 

    Visiopharm continues to demonstrate double-digit year over year growth. This validates both the magnitude of the unmet need in the market, and our ability to address that need effectively. We are selling our solutions to diagnostic pathology labs, biopharmaceutical companies, academic medical centers, and contract research companies that all have similar needs. We see a clear window of opportunity to continue this growth, expand geographically and lead market innovation. With the recent strong financial support from Danish investors, our next phase of growth involves launching our diagnostic technology into the U.S., a market that is already strong for Visiopharm in drug development and research.

  • What Is a Hot-Spot and Does It Matter?

    The importance of hot-spots

    As discussed in the recent post on managing tumor heterogeneity, it is widely considered best practice to determine the Ki-67 proliferative index in a hot-spot of biomarker expression. A growing body of scientific evidence support that correct identification of hot-spots is essential to achieve sufficient interpretive accuracy for predictive and/or prognostic use [1,2]. Hot-spot scoring has recently been adopted in both Swedish and Danish clinical guidelines for breast cancer [3,4].

    Heterogeneity is a general challenge

    Heterogeneity with respect to expression levels is evident for many tissue biomarkers. Although it may be less obvious how to technically determine heterogeneity and hot-spots for membrane markers, or markers that express in several sub-cellular compartments, it is likely that the ability to visualize and quantify heterogeneity and identify hot-spots will be generally important to cancer research, drug development, biomarker validation, and in diagnostics. To further explore this, we developed a general research tool for quantification of tissue biomarkers, including generation of heatmaps of expression and identification of hot-spots..

    The definition of a hot-spot

    As an example, this general research tool was used to create an APP for visualizing heatmaps for Ki67 expression across entire tissue sections, and determine the location of hot-spots. Although this seems an intuitively appealing approach for managing heterogeneity, it generates new questions relating to the definition of a hot-spot. Clinical guidelines are usually defining a hot-spot as a square of fixed dimensions, or recommend counting a certain number of cells around the hot-spot. For their presentation at the 29th European Congress of Pathology, Omanovic and Schönauwere comparing five different approaches to defining a hot-spot as outlined in the figure below. Note how the hot spot location and area changes between methods, and that heatmap area and count can be defined along the iso-curve of the biomarker response.

    Need for a standardized approach

    In the study above, they found that different definitions as well as area of hot-spots has a statistically significant impact on the proliferation index. Therefore, the fact that different guidelines and hot-spot definitions are adopted in clinical practice and for research purposes, also means that results cannot be compared. Thus, it will difficult to define generally applicable clinical standards (e.g. cut-offs) until there is a consensus on a unified / optimal definition of a hot-spot.

    Clinical relevance

    Apart from the need for standardization, there is also a need to understand to the impact of the observed differences on the predictive or prognostic power of tissue-based assays. At Karolinska University Hospital, Johan Hartman et. al. are currently exploring this in a study design with a longitudinal cohort, clinical outcomes and access to sequencing data. The outcome of this study will hopefully provide insights allowing for a recommendation of an optimal definition of hot-spots, for understanding the impact on diagnostic/prognostic accuracy, and to what extent a modern tissue-diagnostic approach provide statistically independent information compared to molecular methods. 

    References:

    ​1.Stålhammer et. al.; Digital image analysis outperforms manual biomarker assessment in breast cancer; Modern Pathology 29, 318-329 (2016)

    2.Gudlaugsson et. al.; Comparison of the effect of different techniques for measurement of Ki67 proliferation on reproducibility and prognosis prediction accuracy in breast cancer

    3.KVAST, Swedish breast cancer guidelines, 2018 (in Swedish)

    4.DBCG – Danish Guidelines, May 2017 (in Danish).

  • Managing Tumor Heterogeneity for Quantification of Biomarker Response

    Tumor heterogeneity in research and diagnostics


    Diagnostic pathologists and scientists often rely on tissue data for important diagnostic-, research-, or even business- decisions. One of the major concerns relate to tumor heterogeneity in the context of quantifying tissue biomarker response. Questions revolve around how to reliably visualize, quantify, and in practical terms deal with heterogeneity.

    The impact of heterogeneity on diagnostic accuracy​


    Concerns are apparently well founded. In a recent publication, Stålhammer et. al. [1] demonstrated that manual stratification of breast cancer patients into Luminal A vs. Luminal B based on Ki67 was associated with an error rate of 31%. Using image analysis with computer identified hot-spots reduced the error rate to 19%, when using PAM50 as a pseudo gold-standard.

    Heterogeneity and hot-spots


    What really made the difference in reducing error rates, was automated identification of hot-spots, using image analysis. Gudlagsson et. al [2] showed that as much as 50% of pathologists were unable to correctly identify the hottest hot-spot which, in some cases, can represent a major cognitive challenge. This challenge was effectively mitigated using image analysis.

    Diagnostic applications


    As a first practical diagnostic application, we considered automated hot-spot identification for Ki67 expression in breast cancer. By creating heat maps of biomarker expression, the intended use of the APP is to support pathologists in both visualizing heterogeneity and locate the hottest hot-spot (s). This can be used as input to APPs that quantify biomarker expression in the hot-spot(s). CE-marking for In-Vitro Diagnostic purposes, required special attention to the study designs for validating clinical performance.

    Research applications


    The challenges related to heterogeneity may well be further amplified when interrogating far more complex, and sometimes multiplexed, biomarkers across the entire tumor micro-environment. With Oncotopix® Author, the ability to visualize and quantify heterogeneity wrt. biomarker response has been generalized for tissue-based cancer research.

    Click here to learn more about the CE-IVD Hot Spot APP.

    Download the Hot Spot brochure

    References​

    1.Stålhammer et. al.; Digital image analysis outperforms manual biomarker assessment in breast cancer; Modern Pathology 29, 318-329 (2016)

    2.Gudlaugsson et. al.; Comparison of the effect of different techniques for measurement of Ki67 proliferation on reproducibility and prognosis prediction accuracy in breast cancer