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January 2023

Pathology’s Next Frontier: Spatial Biology | Proscia

2023-01-10T08:11:20+00:00

Imaging has been the tool of choice for analyzing spatial organization and better understanding disease progression and treatment options at the tissue level. More specifically, fluorescent imaging is one of the most widely used techniques in life sciences research, from cancer research and immunology to neuroscience and more. Both fluorescent imaging – and more specifically, highly multiplexed imaging, an emerging technique in spatial biology – has provided unprecedented insights into disease states. Most recently, Nature Methods highlighted the value of multiplexed tissue imaging.

Pathology’s Next Frontier: Spatial Biology | Proscia2023-01-10T08:11:20+00:00

AI Application In Pathology Reveals Novel Insights In Endometrial Cancer Diagnostics

2023-01-09T09:44:57+00:00

Research at the Leiden University Medical Center (LUMC) Department of Pathology shows the power of artificial intelligence (AI) applied to endometrial carcinoma microscopy images. The group of Dr. Tjalling Bosse offers novel insights that could improve diagnosis and treatment of uterine cancer. Their findings have been published in The Lancet Digital Health.

AI Application In Pathology Reveals Novel Insights In Endometrial Cancer Diagnostics2023-01-09T09:44:57+00:00

An Exclusive Virtual Tour Of A Precision Medicine Laboratory | Source LDPath

2023-01-06T13:03:19+00:00

The application of Molecular Diagnostic services is at the heart of current advancements towards precision therapeutics. Take a tour of Source LDPath's new precision medicine laboratory.

An Exclusive Virtual Tour Of A Precision Medicine Laboratory | Source LDPath2023-01-06T13:03:19+00:00

New Method Precisely Locates Gene Activity And Proteins Across Tissues

2023-01-06T08:45:41+00:00

A new method can illuminate the identities and activities of cells throughout an organ or a tumor at unprecedented resolution, according to a study co-led by researchers at Weill Cornell Medicine, NewYork-Presbyterian and the New York Genome Center. The method, described Jan. 2 in a paper in Nature Biotechnology, records gene activity patterns and the presence of key proteins in cells across tissue samples, while retaining information about the cells' precise locations. This enables the creation of complex, data-rich "maps" of organs, including diseased organs and tumors, which could be widely useful in basic and clinical research. "This technology is exciting because it allows us to map the spatial organization of tissues, including cell types, cell activities and cell-to-cell interactions, as never before," said study co-senior author Dr. Dan Landau, an associate professor of medicine in the Division of Hematology and Medical Oncology and a member of the Sandra and Edward Meyer Cancer Center at Weill Cornell Medicine and a core faculty member at the New York Genome Center. The other co-senior author was Dr. Marlon Stoeckius of 10x Genomics, a California-based biotechnology company that makes laboratory equipment for the profiling of cells within tissue samples. The three co-first authors were Dr. Nir Ben-Chetrit, Xiang Niu, and Ariel Swett, respectively a postdoctoral researcher, graduate student, and research technician in the Landau laboratory during the study. The new method is part of a broad effort by scientists and engineers to develop better ways of "seeing" at micro scale how organs and tissues work. Researchers in recent years have made big advances particularly in techniques for profiling gene activity and other layers of information in individual cells or small groups of cells. However, these techniques typically require the dissolution of tissues and the separation of cells from their neighbors, so that information about profiled cells' original locations within the tissues is lost. The new method captures that spatial information as well, and at high resolution. The method, called Spatial PrOtein and Transcriptome Sequencing (SPOTS), is based in part on existing 10x Genomics technology. It uses glass slides that are suitable for imaging tissue samples with ordinary microscope-based pathology methods, but are also coated with thousands of special probe molecules. Each of the probe molecules contains a molecular "barcode" denoting its two-dimensional position on the slide. When a thinly sliced tissue sample is placed on the slide and its cells are made permeable, the probe molecules on the slide grab adjacent cells' messenger RNAs (mRNAs), which are essentially the transcripts of active genes. The method includes the use of designer antibodies that bind to proteins of interest in the tissue -- and also bind to the special probe molecules. With swift, automated techniques, researchers can identify the captured mRNAs and selected proteins, and map them precisely to their original locations across the tissue sample. The resulting maps can be considered alone, or compared to standard pathology imaging of the sample. The team demonstrated SPOTS on tissue from a normal mouse spleen, revealing the complex functional architecture of this organ including clusters of different cell types, their functional states, and how those states varied with the cells' locations. Highlighting SPOTS' potential in cancer research, the investigators also used it to map the cellular organization of a mouse breast tumor. The resulting map depicted immune cells called macrophages in two distinct states as denoted by protein markers -- one state active and tumor-fighting, the other immune-suppressive and forming a barrier to protect the tumor. "We could see that these two macrophage subsets are found in different areas of the tumor and interact with different cells -- and that difference in microenvironment is likely driving their distinct activity states," said Dr. Landau, who is also an oncologist at NewYork-Presbyterian/Weill Cornell Medical Center. Such details of the tumor immune environment -- details that often can't be resolved due to immune cells' sparseness within tumors -- might help explain why some patients respond to immune-boosting therapy and some don't, and thus could inform the design of future immunotherapies, he added. This initial version of SPOTS has a spatial resolution such that each "pixel" of the resulting dataset sums gene activity information for at least several cells. However, the researchers hope soon to narrow this resolution to single cells, while adding other layers of key cellular information, Dr. Landau said. Many Weill Cornell Medicine physicians and scientists maintain relationships and collaborate with external organizations to foster scientific innovation and provide expert guidance. The institution makes these disclosures public to ensure transparency. For this information, see profile for Dr. Landau.

New Method Precisely Locates Gene Activity And Proteins Across Tissues2023-01-06T08:45:41+00:00

How, for whom, and in what contexts will artificial intelligence be adopted in pathology? A realist interview study

2023-01-03T21:06:39+00:00

Interviews on the direction of adoption of AI in digital pathology with input from over 20 pathology medical professionals

How, for whom, and in what contexts will artificial intelligence be adopted in pathology? A realist interview study2023-01-03T21:06:39+00:00

Drs. Wen Ng and Juan Retamero Discuss Overcoming Challenges in Breast Cancer Diagnosis with AI at DP&AI Europe

2023-01-03T16:44:44+00:00

Breast cancer diagnosis is uniquely complex and poses many challenges for pathologists. In recent years, clinical-grade artificial intelligence (AI) such as the Paige Breast Suite has been introduced to support pathologists in overcoming these challenges, as well as offer enhanced efficiency and confidence. At this year’s Digital Pathology & AI Congress: Europe, Dr. Juan Retamero, Paige’s Medical Director, Digital Pathology Transformation, and Dr. Wen Ng, Consultant Pathologist in Breast, Urology and Endocrine at St Thomas Hospital, London, hosted a session examining the value breast AI can offer labs, as well as sharing a first look at new research to continue to transform the breast cancer diagnostic experience.

Drs. Wen Ng and Juan Retamero Discuss Overcoming Challenges in Breast Cancer Diagnosis with AI at DP&AI Europe2023-01-03T16:44:44+00:00

Proscia: Dissecting Diagnostic Discordance With AI: Recapping Our Digital Pathology & AI Congress Poster Presentation

2023-01-02T13:29:20+00:00

Pathology’s transition from glass slides to digital images has opened a huge frontier, enabling the application of artificial intelligence (AI) to improve everything from workflow efficiency to patient outcomes. However, developing AI systems that achieve these goals requires more than just high-quality digital images. Accurate diagnostic AI also requires accurate ‘ground truth’ diagnoses from which to learn. Unfortunately, in many diagnostic domains, two pathologists can provide differing diagnoses on the same case, and sometimes the same pathologist may provide a different diagnosis when reviewing the same case months later.Disagreement among medical professionals is called diagnostic discordance. It is the reason why it can be important for a patient to obtain a second opinion for some medical diagnoses. Diagnostic discordance is a common challenge in the field of dermatopathology, particularly when it comes to identifying the deadliest form of skin cancer: melanoma. On the surface, melanoma tumors can appear quite similar to perfectly benign moles. The impact of these borderline cases is felt when you consider that accurately diagnosing which melanocytic lesions contain cancer is crucial for ensuring that patients receive proper and timely treatment.What’s more, during their many years of training, every pathologist learns their own individual criteria for determining which borderline cases are benign and require observation, and which cases need immediate treatment for cancer. This means that while some cases are likely to be borderline for many pathologists, others are likely to be borderline for just a specific pathologist.At The 9th Digital Pathology & AI Congress: Europe, Proscia’s AI R&D team presented work that predicts the reactions of individual pathologists to borderline cases, demonstrating the potential of AI to identify cases with the highest diagnostic uncertainty. We conducted a multi-reader study and used an AI model of benign vs. suspected malignant melanocytic lesions to disentangle cases that are borderline for all pathologists from those that are likely to be closest to the border for a specific individual diagnosing pathologist. This enables the model to flag cases that are likely to be the most difficult to diagnose – even after accounting for pathologist-level differences in their implicit diagnostic criteria.When run on a hold out test set, our AI system correctly predicted 82% of the agreement between pathologists, indicating that we are able to accurately model the majority of diagnostic uncertainty in the test cases. This finding highlights the promise of AI to help high-volume pathology practices route the most diagnostically challenging cases to the relevant specialist, automatically flag a case for additional review, and know when to order special staining and other testing to provide a more complete look prior to pathologist review.See the full poster that we presented below.SOURCE: Proscia

Proscia: Dissecting Diagnostic Discordance With AI: Recapping Our Digital Pathology & AI Congress Poster Presentation2023-01-02T13:29:20+00:00

Virtual Immunohistochemistry. How Owkin Uses Artificial Intelligence To Generate IHC Stains Without Antibodies With Victor Dillard

2025-05-06T13:23:13+00:00

If you are working with immunohistochemistry (IHC) you know how challenging it can sometimes be to optimize all the steps in the process to obtain a high-quality stain. It often takes testing different antibodies, antibody concentrations, antigen retrieval methods, and incubation times.

Virtual Immunohistochemistry. How Owkin Uses Artificial Intelligence To Generate IHC Stains Without Antibodies With Victor Dillard2025-05-06T13:23:13+00:00

Whole Slide Image Analysis Using Deep Learning Helps Map Colon Mucosal Immune Cells in Inflammatory Bowel Disease

2023-01-02T09:21:18+00:00

Inflammatory bowel diseases (IBDs) are a group of diseases of the gastrointestinal system caused by immunological dysfunction. Ulcerative colitis and Crohn’s disease are associated with significant histopathological alterations in the intestinal mucosal immune cells and are among the most common IBDs. However, determining the differences in the number and distribution of immune cells between the two conditions is challenging because of the lack of high-resolution quantitative methods for analyzing colon mucosal immune cells.

Whole Slide Image Analysis Using Deep Learning Helps Map Colon Mucosal Immune Cells in Inflammatory Bowel Disease2023-01-02T09:21:18+00:00

December 2022

Artificial Intelligence Tool Developed to Help Make Real-Time Diagnoses During Surgery

2022-12-26T08:01:09+00:00

When a patient undergoes a surgical operation to remove a tumor or treat a disease, the course of surgery is often not predetermined. To decide how much tissue needs to be removed, surgeons must know more about the condition they are treating, including a tumor’s margins, its stage and whether a lesion is malignant or benign - determinations that often hinge upon collecting, analyzing, and diagnosing a disease while the patient is on the operating table. When surgeons send samples to a pathologist for examination, both speed and accuracy are of the essence. The current gold-standard approach for examining tissues often takes too long and a faster approach, which involves freezing tissue, can introduce artifacts that can complicate diagnostics. Now, researchers have developed a new method that leverages artificial intelligence to translate between frozen sections and the gold-standard approach, thereby improving the quality of images to increase the accuracy of rapid diagnostics. For making final diagnoses, pathologists use formalin-fixed and paraffin-embedded (FFPE) tissue samples - this method preserves tissue in a way that produces high-quality images but the process is laborious and typically takes 12 to 48 hours. For a rapid diagnosis, pathologists use an approach known as cryosectioning that involves fast freezing tissue, cutting sections, and observing these thin slices under a microscope. Cryosectioning takes minutes rather than hours but can distort cellular details and compromise or tear delicate tissue. Researchers at the Brigham and Women’s Hospital (Boston, MA, USA) have developed a deep-learning model that can be used to translate between frozen sections and more commonly used FFPE tissue. The team demonstrated that the method could be used to subtype different kinds of cancer, including glioma and non-small-cell lung cancer. The researchers validated their findings by recruiting pathologists to a reader study in which they were asked to make a diagnosis from images that had gone through the AI method and traditional cryosectioning images. The AI method not only improved image quality but also improved diagnostic accuracy among experts. The algorithm was also tested on independently collected data from Turkey. The researchers note that in the future, prospective clinical studies should be conducted to validate the AI method and determine if it can contribute to diagnostic accuracy and surgical decision-making in real hospital settings. “We are using the power of artificial intelligence to address an age-old problem at the intersection of surgery and pathology,” said corresponding author Faisal Mahmood, PhD, of the Division of Computational Pathology at BWH. “Making a rapid diagnosis from frozen tissue samples is challenging and requires specialized training, but this kind of diagnosis is a critical step in caring for patients during surgery.” “Our work shows that AI has the potential to make a time-sensitive, critical diagnosis easier and more accessible to pathologists,” said Mahmood. “And it could potentially be applied to any type of cancer surgery. It opens up many possibilities for improving diagnosis and patient care.” Related Links:Brigham and Women’s Hospital

Artificial Intelligence Tool Developed to Help Make Real-Time Diagnoses During Surgery2022-12-26T08:01:09+00:00
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