Speedy DNA sequencing and on-the-spot digital imaging may change the future of anatomic pathology procedures during surgery
Researchers at the Center for Molecular Medicine (CMM) at UMC Utrecht, a leading international university medical center in the Netherlands, have paired artificial intelligence (AI) and machine learning with DNA sequencing to develop a diagnostic tool cancer surgeons can use during surgeries to determine in minutes—while the patient is still on the operating table—whether they have fully removed all the cancerous tissue.
The method, “involves a computer scanning segments of a tumor’s DNA and alighting on certain chemical modifications that can yield a detailed diagnosis of the type and even subtype of the brain tumor,” according to The New York Times, which added, “That diagnosis, generated during the early stages of an hours-long surgery, can help surgeons decide how aggressively to operate, … In the future, the method may also help steer doctors toward treatments tailored for a specific subtype of tumor.”
This technology has the potential to reduce the need for frozen sections, should additional development and studies confirm that it accurately and reliably shows surgeons that all cancerous cells were fully removed. Many anatomic pathologists would welcome such a development because of the time pressure and stress associated with this procedure. Pathologists know that the patient is still in surgery and the surgeons are waiting for the results of the frozen section. Most pathologists would consider fewer frozen sections—with better patient outcomes—to be an improvement in patient care.
“It’s imperative that the tumor subtype is known at the time of surgery,” Jeroen de Ridder, PhD (above), associate professor in the Center for Molecular Medicine at UMC Utrecht and one of the study leaders, told The New York Times. “What we have now uniquely enabled is to allow this very fine-grained, robust, detailed diagnosis to be performed already during the surgery. It can figure out itself what it’s looking at and make a robust classification,” he added. How this discovery affects the role of anatomic pathologists and pathology laboratories during cancer surgeries remains to be seen. (Photo copyright: UMC Utrecht.)
Rapid DNA Sequencing Impacts Brain Tumor Surgeries
The UMC Utrecht scientists employed Oxford Nanopore’s “real-time DNA sequencing technology to address the challenges posed by central nervous system (CNS) tumors, one of the most lethal type of tumor, especially among children,” according to an Oxford Nanopore news release.
The researchers called their new machine learning AI application the “Sturgeon.”
According to The New York Times, “The new method uses a faster genetic sequencing technique and applies it only to a small slice of the cellular genome, allowing it to return results before a surgeon has started operating on the edges of a tumor.”
Jeroen de Ridder, PhD, an associate professor in the Center for Molecular Medicine at UMC Utrecht, told The New York Times that Sturgeon is “powerful enough to deliver a diagnosis with sparse genetic data, akin to someone recognizing an image based on only 1% of its pixels, and from an unknown portion of the image.” Ridder is also a principal investigator at the Oncode Institute, an independent research center in the Netherlands.
The researchers tested Sturgeon during 25 live brain surgeries and compared the results to an anatomic pathologist’s standard method of microscope tissue examination. “The new approach delivered 18 correct diagnoses and failed to reach the needed confidence threshold in the other seven cases. It turned around its diagnoses in less than 90 minutes, the study reported—short enough for it to inform decisions during an operation,” The New York Times reported.
But there were issues. Where the minute samples contain healthy brain tissue, identifying an adequate number of tumor markers could become problematic. Under those conditions, surgeons can ask an anatomic pathologist to “flag the [tissue samples] with the most tumor for sequencing, said PhD candidate Marc Pagès-Gallego, a bioinformatician at UMC Utrecht and a co-author of the study,” The New York Times noted.
“Implementation itself is less straightforward than often suggested,” Sebastian Brandner, MD, a professor of neuropathology at University College London, told The Times. “Sequencing and classifying tumor cells often still required significant expertise in bioinformatics as well as workers who are able to run, troubleshoot, and repair the technology,” he added.
“Brain tumors are also the most well-suited to being classified by the chemical modifications that the new method analyzes; not all cancers can be diagnosed that way,” The Times pointed out.
Thus, the research continues. The new method is being applied to other surgical samples as well. The study authors said other facilities are utilizing the method on their own surgical tissue samples, “suggesting that it can work in other people’s hands.” But more work is needed, The Times reported.
UMC Utrecht Researchers Receive Hanarth Grant
To expand their research into the Sturgeon’s capabilities, the UMC Utrecht research team recently received funds from the Hanarth Fonds, which was founded in 2018 to “promote and enhance the use of artificial intelligence and machine learning to improve the diagnosis, treatment, and outcome of patients with cancer,” according to the organization’s website.
The researchers will investigate ways the Sturgeon AI algorithm can be used to identify tumors of the central nervous system during surgery, a UMC Utrecht news release states. These type of tumors, according to the researchers, are difficult to examine without surgery.
“This poses a challenge for neurosurgeons. They have to operate on a tumor without knowing what type of tumor it is. As a result, there is a chance that the patient will need another operation,” said de Ridder in the news release.
The Sturgeon application solves this problem. It identifies the “exact type of tumor during surgery. This allows the appropriate surgical strategy to be applied immediately,” the news release notes.
The Hanarth funds will enable Jeroen and his team to develop a variant of the Sturgeon that uses “cerebrospinal fluid instead of (part of) the tumor. This will allow the type of tumor to be determined already before surgery. The main challenge is that cerebrospinal fluid contains a mixture of tumor and normal DNA. AI models will be trained to take this into account.”
The UMC Utrecht scientists’ breakthrough is another example of how organizations and research groups are working to shorten time to answer, compared to standard anatomic pathology methods. They are combining developing technologies in ways that achieve these goals.
Fujifilm acquired Inspirata’s Dynamyx digital pathology technology and business while GE Healthcare announced a partnership with Tribun Health in Europe
Clinical pathology laboratories, especially in the US, have been slow to adopt digital imaging systems. But recent industry deals suggest that the market may soon heat up, at least in the eyes of vendors. These collaborators may hope that, by integrating diagnostic data, the accuracy and productivity of anatomic pathologists will improve while also shortening the time to diagnosis.
In the press release, Fujifilm stated that 85% of US healthcare organizations use analog systems for pathology. That compares with 86% in Europe and 90% in Asia, the company stated.
“Acquiring Inspirata’s digital pathology business allows Fujifilm to be an even stronger healthcare partner—bridging a technological gap between pathology, radiology, and oncology to facilitate a more collaborative approach to care delivery across the enterprise,” said Fujifilm CEO and president Teiichi Goto in the press release.
The press release cited data from Signify Research, a healthcare technology marketing data firm that is predicting the global market for digital pathology systems would double from $320 million in 2021 to $640 million by 2025.
Fujifilm previously had a deal with Inspirata to sell the Dynamyx system exclusively in the UK, Italy, Spain, Portugal, Belgium, the Netherlands, and Luxembourg, an August press release noted.
“A $320 million global industry in 2021 projected to reach $640 million by 2025, the rising number of cancer cases and the demonstrated benefits of digital pathology are fueling significant demand and market growth in the hospital and pharmaceutical industries,” said Henry Izawa (above), president and CEO, Fujifilm Healthcare Americas Corporation, in a press release. “These evolving clinical needs fuel Fujifilm’s investment and innovation in the digital revolution, and we look forward to introducing Dynamyx and its host of unique features and benefits to our Synapse customers and prospects as we strive to enable more efficient medical diagnosis and high-quality care.” (Photo copyright: LinkedIn.)
In announcing their new collaboration, GE Healthcare and Tribun Health said the integration of their systems—Edison Datalogue and the Tribun Health suite—would foster collaboration between pathologists and clinicians by providing a consolidated location for imaging records. This capability is especially important in oncology, they said.
“The oncology care pathway is one of the most complex with multiple steps involving a variety of specialists, complex tools, frequent decisions, and large data sets,” said GE Healthcare CEO of Enterprise Digital Solutions Nalinikanth Gollagunta in a GE press release. “With this digital pathology collaboration, we continue our journey towards simplifying the oncology care pathway with improved data management, the digitization of pathology, and streamlined data access.”
Tribun Health, based in Paris, France, offers a digital pathology platform that incorporates a camera system, artificial intelligence (AI)-based analysis, remote collaboration, and storage management, plus integration with third-party automation apps.
GE Healthcare claims that Edison Datalogue has the largest share of the Vendor Neutral Archive (VNA) market. That term refers to image archiving systems that use standard formats and interfaces instead of proprietary formats. They are an alternative to the more widely used Picture Archiving and Communications Systems (PACS) used in medical imaging.
The collaboration between the companies “is probably a strategic move to position GE as an integrator of imaging data and digital pathology data in oncology,” said Robert Michel Editor-in-Chief of Dark Daily and its sister publication The Dark Report.
GE’s History with Dynamyx
This is not GE Healthcare’s first foray into digital pathology. In fact, the company had a major hand in launching the very Dynamyx system that Fujifilm recently acquired.
In “GE Healthcare Sells Omnyx to Inspirata,” The Dark Report interviewed Inspirata CEO Satish Sanan who at that time said the acquisition would allow his company to offer “a fully integrated, end-to-end digital pathology solution” in Canada and Europe. But GE Healthcare chose to end the partnership in 2016, citing regulatory uncertainty and variable global demand. Two years later, GE sold Omnyx to Inspirata.
GE Healthcare’s new collaboration with Tribun Health shows that the company “still recognizes the value of the pathology data in cancer diagnosis and wants to be in a position to manage that digital pathology data,” Michel said.
Fujifilm’s Plans
Fujifilm said it will incorporate Dynamyx into its Synapse Enterprise Imaging suite, which includes VNA, Radiology PACS, and Cardiology PACS. “Future releases of Dynamyx will also create opportunities for Fujifilm to support pharmaceutical and contract research organizations with toxicity testing data management for drug development,” the company stated in the press release.
With its recent moves into digital pathology, Fujifilm will be taking on major competitors including Philips, Danaher, and Roche, MedTech Dive reported.
Google designed the suite to ease radiologists’ workload and enable easy and secure sharing of critical medical imaging; technology may eventually be adapted to pathologists’ workflow
Clinical laboratory and pathology group leaders know that Google is doing extensive research and development in the field of cancer diagnostics. For several years, the Silicon Valley giant has been focused on digital imaging and the use of artificial intelligence (AI) algorithms and machine learning to detect cancer.
Now, Google Cloud has announced it is launching a new medical imaging suite for radiologists that is aimed at making healthcare data for the diagnosis and care of cancer patients more accessible. The new suite “promises to make medical imaging data more interoperable and useful by leveraging artificial intelligence,” according to MedCity News.
In a press release, medical technology company Hologic, and healthcare provider Hackensack Meridian Health in New Jersey, announced they were the first customers to use Google Cloud’s new suite of medical imaging products.
“Hackensack Meridian Health has begun using it to detect metastasis in prostate cancer patients earlier, and Hologic is using it to strengthen its diagnostic platform that screens women for cervical cancer,” MedCity News reported.
“Google pioneered the use of AI and computer vision in Google Photos, Google Image Search, and Google Lens, and now we’re making our imaging expertise, tools, and technologies available for healthcare and life sciences enterprises,” said Alissa Hsu Lynch (above), Global Lead of Google Cloud’s MedTech Strategy and Solutions, in a press release. “Our Medical Imaging Suite shows what’s possible when tech and healthcare companies come together.” Clinical laboratory companies may find Google’s Medical Imaging Suite worth investigating. (Photo copyright: Influencive.)
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Easing the Burden on Radiologists
Clinical laboratory leaders and pathologists know that laboratory data drives most healthcare decision-making. And medical images make up 90% of all healthcare data, noted an article in Proceedings of the IEEE (Institute of Electrical and Electronics Engineers).
More importantly, medical images are growing in size and complexity. So, radiologists and medical researchers need a way to quickly interpret them and keep up with the increased workload, Google Cloud noted.
“The size and complexity of these images is huge, and, often, images stay sitting in data siloes across an organization,” said Alissa Hsu Lynch, Global Lead, MedTech Strategy and Solutions at Google, told MedCity News. “In order to make imaging data useful for AI, we have to address interoperability and standardization. This suite is designed to help healthcare organizations accelerate the development of AI so that they can enable faster, more accurate diagnosis and ease the burden for radiologists,” she added.
According to the press release, Google Cloud’s Medical Imaging Suite features include:
Imaging Storage: Easy and secure data exchange using the international DICOM (digital imaging and communications in medicine) standard for imaging. A fully managed, highly scalable, enterprise-grade development environment that includes automated DICOM de-identification. Seamless cloud data management via a cloud-native enterprise imaging PACS (picture archiving and communication system) in clinical use by radiologists.
Imaging Lab: AI-assisted annotation tools that help automate the highly manual and repetitive task of labeling medical images, and Google Cloud native integration with any DICOMweb viewer.
Imaging Datasets and Dashboards: Ability to view and search petabytes of imaging data to perform advanced analytics and create training datasets with zero operational overhead.
Imaging AI Pipelines: Accelerated development of AI pipelines to build scalable machine learning models, with 80% fewer lines of code required for custom modeling.
Imaging Deployment: Flexible options for cloud, on-prem (on-premises software), or edge deployment to allow organizations to meet diverse sovereignty, data security, and privacy requirements—while providing centralized management and policy enforcement with Google Distributed Cloud.
First Customers Deploy Suite
Hackensack Meridian Health hopes Google’s imaging suite will, eventually, enable the healthcare provider to predict factors affecting variance in prostate cancer outcomes.
“We are working toward building AI capabilities that will support image-based clinical diagnosis across a range of imaging and be an integral part of our clinical workflow,” said Sameer Sethi, Senior Vice President and Chief Data and Analytics Officer at Hackensack, in a news release.
The New Jersey healthcare network said in a statement that its work with Google Cloud includes use of AI and machine learning to enable notification of newborn congenital disorders and to predict sepsis risk in real-time.
Hologic, a medical technology company focused on women’s health, said its collaboration integrates Google Cloud AI with the company’s Genius Digital Diagnostics System.
“By complementing our expertise in diagnostics and AI with Google Cloud’s expertise in AI, we’re evolving our market-leading technologies to improve laboratory performance, healthcare provider decision making, and patient care,” said Michael Quick, Vice President of Research and Development and Innovation at Hologic, in the press release.
Hologic says its Genius Digital Diagnostics System combines AI with volumetric medical imaging to find pre-cancerous lesions and cancer cells. From a Pap test digital image, the system narrows “tens of thousands of cells down to an AI-generated gallery of the most diagnostically relevant,” according to the company website.
Hologic plans to work with Google Cloud on storage and “to improve diagnostic accuracy for those cancer images,” Hsu Lynch told MedCity News.
Medical image storage and sharing technologies like Google Cloud’s Medical Imaging Suite provide an opportunity for radiologists, researchers, and others to share critical image studies with anatomic pathologists and physicians providing care to cancer patients.
One key observation is that the primary function of this service that Google has begun to deploy is to aid in radiology workflow and productivity, and to improve the accuracy of cancer diagnoses by radiologists. Meanwhile, Google continues to employ pathologists within its medical imaging research and development teams.
Assuming that the first radiologists find the Google suite of tools effective in support of patient care, it may not be too long before Google moves to introduce an imaging suite of tools designed to aid the workflow of surgical pathologists as well.
Experts in pathology and clinical laboratory informatics to gather in October in Pittsburgh
One respected expert in pathology informatics says that a “major sea change” is underway in pathology informatics. The pace of this transformation is steady and pathology groups should be responsive to these developments.
“Right now all of the action in the field revolves around digital pathology, stated Friedman. “Many new companies are entering the field, including system integrators. In earlier times, the emphasis was placed on slide scan times and the quality of images. Now there is much greater emphasis on end-to-end integration and workflow. (more…)
Second-opinion consults will be handled by UPMC’s sub-specialist pathologists
Here’s a unique anatomic pathology collaboration that crosses international borders and will utilize state-of-the-art digital pathology technology to support subspecialty pathologist consultations between the United States and China.
In recent weeks, the University of Pittsburgh Medical Center (UPMC) disclosed that it will provide a range of healthcare services to what is only described as “the largest pathology laboratory in Shanghai.” Of particular interest to pathologists and clinical laboratory managers in the United States, is the fact that pathologists at UPMC will provide second opinion anatomic pathology services to this as-yet-unnamed Chinese pathology laboratory.
