PathAI, a leading provider of AI-powered pathology tools to advance precision medicine, announced that the organization’s recent research will be presented at the Society for Immunotherapy of Cancer’s 37th annual meeting (SITC), which will be held in Boston, MA from November 8-12, 2022. PathAI will share five new posters, all developed in collaboration with biopharmaceutical partners, that highlight their new AI-model development to support advances in oncology research across several disease areas such as non-small cell lung cancer (NSCLC), cholangiocarcinoma, and urothelial carcinoma.
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“Our latest results support and further extend our research in immuno-oncology for multiple disease areas,” said Dr. Mike Montalto, Chief Scientific Officer at PathAI. “This is another big step forward in innovating and improving pathology research”
Last year at SITC, PathAI presented data highlighting their machine learning (ML) model that was developed to identify and quantify CD8+ T cells in digitized whole slide images of melanoma patients. PathAI has since expanded upon that research and will share data demonstrating how their newly developed ML-based models quantify CD8+ lymphocytes and CD8 topology classifiers across seven cancer types: NSCLC, urothelial carcinoma, head and neck squamous cell carcinoma, gastric cancer, colorectal cancer, pancreatic cancer, and hepatocellular carcinoma.
“Concordance analysis of AI-powered CD8 quantification and automated CD8 topology with manual histopathological assessment across seven solid tumor types” shows that PathAI’s ML model-predicted CD8+ cell counts are highly correlated with pathologist-generated counts across these tumor types. This poster also highlights PathAI’s ability to characterize the topology of CD8 expression across the tumor to predict immune-inflamed, excluded and desert immunophenotypes in these seven cancer types. This work demonstrates the power of PathAI’s digital pathology models for automated quantitation of the CD8+ lymphocytes and immunophenotyping in clinical samples, confirming the potential for this approach in immuno-oncology.
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“Our latest results support and further extend our research in immuno-oncology for multiple disease areas,” said Dr. Mike Montalto, Chief Scientific Officer at PathAI. “This is another big step forward in innovating and improving pathology research, future drug development, and, ultimately, patient outcomes.”
Additionally, PathAI has developed a new deep-learning-based method for the analysis of whole slide image multiplex immunofluorescence (mIF) data in NSCLC. As the importance of spatial relationships between cells increases in immuno-oncology, “Identification of clinically relevant spatial phenotypes in large-scale multiplex immunofluorescence data via unsupervised graph learning in non-small cell lung cancer” aims to show how a deep-learning approach to mIF analysis can capture spatial phenotypes in NSCLC. The graph neural network (GNN) approach used in this study revealed distinct spatial phenotypes that can describe the organization of distinct cell types, such as cancer and immune cells. The relative amounts of these phenotypes in a cancer specimen are related to the immunogenicity and antigenicity of the cancer, as well as patient outcome. This new approach has potential to identify patterns in the spatial relationship between cells in NSCLC tissue.
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