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September 2022

Using Digital Pathology to Determine the Clinical Significance of a CD3/CD8-Based Immunoscore in Neuroblastoma Patients

2022-09-13T07:26:27+00:00

Background Infiltrating immune cells have been reported as prognostic markers in many cancer types. We aimed to evaluate the prognostic role of tumor-infiltrating lymphocytes, namely CD3+ T cells, CD8+ cytotoxic T cells and memory T cells (CD45RO+), in neuroblastoma.   Patients and Methods Immunohistochemistry was used to determine the expression of CD3, CD8 and CD45RO in the [...]

Using Digital Pathology to Determine the Clinical Significance of a CD3/CD8-Based Immunoscore in Neuroblastoma Patients2022-09-13T07:26:27+00:00

Virtual Histopathological Stain Transformation Improves Detection and Quantification of Liver Fibrosis

2022-09-08T08:01:38+00:00

The microbiota, especially gut bacteria, play a key role in shaping the immune system and modulating immunological responses against invading pathogens and cancer cells. Recently, the extensive interactions between microbiota and cancer cells and the role of the microbiota-tumor interplay in tumor progression have been discovered. Although the gut microbiota has emerged as an attractive biomarker for predicting survival and treatment response in patients with various cancer types, little is known about the prognostic role of intratumoral microbiota in patients with nasopharyngeal carcinoma.

Virtual Histopathological Stain Transformation Improves Detection and Quantification of Liver Fibrosis2022-09-08T08:01:38+00:00

Will 2023 be the Year of Digital Pathology for US Labs?

2022-09-07T13:43:54+00:00

by Imogen Fitt  – Medical Writer & Market Analyst In this digital age it may come as a surprise that in one of the most advanced healthcare markets in the world, primary pathological diagnosis remains conducted by pathologists peering down the lens of a microscope. Especially now, when artificial intelligence algorithms and advanced visualization techniques are increasingly [...]

Will 2023 be the Year of Digital Pathology for US Labs?2022-09-07T13:43:54+00:00

Digital Pathology for Microscope Lovers. How Augmentiqs Approaches Digital Pathology Differently w/ Gabe Siegel

2025-05-06T13:37:54+00:00

Due to the coronavirus pandemic, so many pathologists need to work from home today. The microscopes and the slides were packed and brought home. Everyone is now on their own. No more knocking at a colleague’s door to consult a diagnosis…

Digital Pathology for Microscope Lovers. How Augmentiqs Approaches Digital Pathology Differently w/ Gabe Siegel2025-05-06T13:37:54+00:00

Leica Biosystems Announces Partnership with Indica Labs to Deliver Integrated Digital Pathology Workflow Solutions for Mutual Customers

2022-09-05T08:00:39+00:00

(Vista, CA – Sep 5, 2022) Leica Biosystems, a cancer diagnostics company and a global leader in workflow solutions, and Indica Labs, the industry-leading provider of computational and image management software in digital pathology, today announced a partnership focused on delivering compatible digital pathology workflow solutions. The agreement establishes a cooperation between Leica Biosystems and Indica Labs [...]

Leica Biosystems Announces Partnership with Indica Labs to Deliver Integrated Digital Pathology Workflow Solutions for Mutual Customers2022-09-05T08:00:39+00:00

New Study Highlights The Use of AI in Digital Pathology: Providing Greater Insights into Treatment Induced Fibrosis Regression in NASH

2022-09-05T07:37:27+00:00

Background & Aims Liver fibrosis is a key prognostic determinant for clinical outcomes in non-alcoholic steatohepatitis (NASH). Current scoring systems have limitations, especially in assessing fibrosis regression. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence analyses provides standardized evaluation of NASH features, especially liver fibrosis and collagen fiber quantitation on a continuous scale. This approach [...]

New Study Highlights The Use of AI in Digital Pathology: Providing Greater Insights into Treatment Induced Fibrosis Regression in NASH2022-09-05T07:37:27+00:00

August 2022

HistoWiz: fast histology and an image database for mining from Brooklyn NY w/ Ke Cheng

2025-05-06T13:39:05+00:00

To learn more about the company and its offer visit Histowiz website. And to learn about how to automatically tag whole slide images with multiple tags read “Patch Transformer for Multi-tagging Whole Slide Histopathology Images”.

HistoWiz: fast histology and an image database for mining from Brooklyn NY w/ Ke Cheng2025-05-06T13:39:05+00:00

Mayo Clinic Study Shows AI May Improve Prediction of Colorectal Cancer Recurrence

2022-08-30T10:51:11+00:00

Mayo Clinic researchers apply AI algorithm to a digitized image of colorectal cancer highlighting different regions within the tumor.PHOENIX — In a multinational study led by a Mayo Clinic research team using artificial intelligence (AI), investigators developed an algorithm to improve the prediction of colorectal cancer recurrence. Study results are published in Gastroenterology.Excluding skin cancers, colorectal cancer is the third most common cancer diagnosed in the U.S., according to the American Cancer Society.Rish Pai, M.D., Ph.D., a pathologist at Mayo Clinic in Arizona and senior author, developed QuantCRC, a deep-learning segmentation algorithm, to identify different regions within tumors using nearly 6,500 digital slide images.Fifteen parameters were recorded from each image of colorectal cancer and compared to the findings in the pathology report and health records. Then a prognostic model using QuantCRC was developed to predict recurrence-free survival.The investigators used biospecimens of colorectal cancers from the Colon Cancer Family Registry participating locations in Australia, Canada and the U.S., including Mayo Clinic, to make up the internal training cohort. They validated the results with an external cohort of locations not participating in the Colon Cancer Family Registry in Canada and the U.S.“QuantCRC can identify different regions within the tumor and extract quantitative data from these regions,” says Dr. Pai. “The algorithm converts an image into a set of numbers that is unique to that tumor. The large number of tumors that we analyzed allowed us to learn which features were most predictive of tumor behavior. We can now apply what we have learned to new colon cancers to predict how the tumor will behave.”For example, the algorithm can identify a subset of patients who may not need to receive chemotherapy, given the low probability of recurrence. It also can help identify those patients at high risk of recurrence that may benefit from more intensive treatment or follow-up.Dr. Pai says he hopes this study will be of value to patients with colon cancer, pathologists who look at colon cancer specimens, and oncologists who treat colon cancer.“For patients with colon cancer, the algorithm gives oncologists another tool to help guide therapy and follow-up,” says Dr. Pai.The team of researchers from Australia, Canada and the U.S. concluded that QuantCRC provides a powerful addition to routine pathologic reporting of colorectal cancer. A prognostic model using QuantCRC could improve prediction of recurrence-free survival.As a next step in his research, Dr. Pai says he plans to use QuantCRC to better understand the mechanisms of tumor recurrence and see if it can predict the response to certain treatments, like immunotherapy.Funding for this study was provided in part by the Colon Cancer Family Registry, which is supported in part by funding from the National Cancer Institute and National Institutes of Health (U01 CA167551).In addition to Dr. Pai, other Mayo Clinic authors are Imon Banerjee, Ph.D.; Noralane Lindor M.D.; Bhavik Patel, M.D.; and Niloy Jewel Samadder, M.D.For the full author list, disclosure information and a complete list of those who funded the study, see the paper.About Mayo ClinicMayo Clinic is a nonprofit organization committed to innovation in clinical practice, education and research, and providing compassion, expertise and answers to everyone who needs healing. Visit the Mayo Clinic News Network for additional Mayo Clinic news.About Mayo Clinic Comprehensive Cancer CenterDesignated as a comprehensive cancer center by the National Cancer Institute, Mayo Clinic Comprehensive Cancer Center is defining new boundaries in possibility, focusing on patient-centered care, developing novel treatments, training future generations of cancer experts, and bringing cancer research to communities. At Mayo Clinic Comprehensive Cancer Center, a culture of innovation and collaboration is driving research breakthroughs that are changing approaches to cancer prevention, screening, and treatment, and improving the lives of cancer survivors.Media contact:SOURCE: May Clinic News Network

Mayo Clinic Study Shows AI May Improve Prediction of Colorectal Cancer Recurrence2022-08-30T10:51:11+00:00

Deep Bio Acquires MDSAP Certification, Paving the Way for Global Expansion

2022-08-24T08:00:37+00:00

Deep Bio: Demonstrated globally-recognized standards in safety and quality management as a medical software manufacturerSEOUL, SOUTH KOREA (PRWEB) AUGUST 16, 2022 Deep Bio, a pioneer in medical AI for pathologic cancer diagnostics, announced that it has been recently granted Medical Device Single Audit Program (MDSAP) certificate.MDSAP is a unified global regulatory audit of medical device manufacturing operated by the International Medical Device Regulators Forum (IMDRF). MDSAP-certified manufacturers are considered to have satisfied the relevant requirements of the regulatory authorities participating in the program including the United States (FDA), Canada (Health Canada), Japan (MHLW), Australia (TGA), and Brazil (ANVISA).Through the MDSAP certificate, Deep Bio demonstrated its commitment to high product quality standards and safety management. Also, it is expected that Deep Bio will be able to enter the US, Canada, Japan, Australia, and Brazil markets faster and more efficiently in the future, saving time and cost associated with Quality Management System (QMS) in each market.“Obtaining MDSAP certification is meaningful in that our products are recognized for outstanding quality management on top of innovative technology in the global market,” said Sun Woo Kim, CEO of Deep Bio. “In particular, as these five countries account for more than half of the global medical device market, I am confident that passing the MDSAP audit can be a great opportunity to expand our presence in overseas markets. At the same time, we will make every effort to strengthen our differentiated technology through continuous product research and establish ourselves as a leader in AI-based cancer diagnostics in global markets,” added he.Deep Bio continues to focus on not only AI diagnostics but also R&D and presenting the results in various international conferences and events. The company also continues to build its presence in the global market through overseas digital pathology solution providers in the US, Europe, and India, as well as conducting research cooperation with Stanford Medical School, Harvard Dana-Farber Cancer Center, and other top research institutions in the US.About Deep BioDeep Bio Inc. is an AI healthcare company with in-house expertise in deep learning and cancer pathology. Our vision is to radically improve efficiency and accuracy of pathologic cancer diagnosis and prognosis, by equipping pathologists with deep learning-based IVD SaMDs (In Vitro Diagnostics Software as a Medical Device), for optimal cancer treatment decisions. Deep Bio is actively engaged in the research space and participating in ongoing collaborations with top US medical centers. To learn more, visit http://www.deepbio.co.kr.DeepDx® Prostate is a clinically-validated AI for prostate core needle biopsy tissue image analysis. Whole-slide images (WSIs) of H&E-stained biopsy tissue specimens are analyzed for prostate cancer, Gleason scores and grade groups. Extensively tested at US CLIA labs (500k cores in 2021), DeepDx® Prostate can alleviate the shortage of pathologists and the resultant increase in workload, while reducing diagnostic subjectivity and variability.” To learn more, visit http://www.deepbio.co.kr.SOURCE: CISION PRWeb

Deep Bio Acquires MDSAP Certification, Paving the Way for Global Expansion2022-08-24T08:00:37+00:00

BU Researchers Develop a Novel AI Algorithm for Digital Pathology Analysis

2022-08-23T16:23:19+00:00

Digital pathology is an emerging field which deals with mainly microscopy images that are derived from patient biopsies. Because of the high resolution, most of these whole slide images (WSI) have a large size, typically exceeding a gigabyte (Gb). Therefore, typical image analysis methods cannot efficiently handle them. Seeing a need, researchers from Boston University School of Medicine (BUSM) have developed a novel artificial intelligence (AI) algorithm based on a framework called representation learning to classify lung cancer subtype based on lung tissue images from resected tumors. We are developing novel AI-based methods that can bring efficiency to assessing digital pathology data. Pathology practice is in the midst of a digital revolution. Computer-based methods are being developed to assist the expert pathologist. Also, in places where there is no expert, such methods and technologies can directly assist diagnosis." Vijaya B. Kolachalama, PhD, FAHA, corresponding author, assistant professor of medicine and computer science at BUSM Related StoriesThe researchers developed a graph-based vision transformer for digital pathology called Graph Transformer (GTP) that leverages a graph representation of pathology images and the computational efficiency of transformer architectures to perform analysis on the whole slide image. "Translating the latest advances in computer science to digital pathology is not straightforward and there is a need to build AI methods that can exclusively tackle the problems in digital pathology", explains co-corresponding author Jennifer Beane, PhD, associate professor of medicine at BUSM. Using whole slide images and clinical data from three publicly available national cohorts, they then developed a model that could distinguish between lung adenocarcinoma, lung squamous cell carcinoma, and adjacent non-cancerous tissue. Over a series of studies and sensitivity analyses, they showed that their GTP framework outperforms current state-of-the-art methods used for whole slide image classification. They believe their machine learning framework has implications beyond digital pathology. "Researchers who are interested in the development of computer vision approaches for other real-world applications can also find our approach to be useful," they added. These findings appear online in the journal IEEE Transactions on Medical Imaging. Boston University School of MedicineJournal reference:Zheng, Y., et al. (2022) A graph-transformer for whole slide image classification. IEEE Transactions on Medical Imaging. doi.org/10.1109/TMI.2022.3176598.

BU Researchers Develop a Novel AI Algorithm for Digital Pathology Analysis2022-08-23T16:23:19+00:00
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