PathAI to Present on AI-based Models to Advance Tumor Analysis and Oncology Drug Development at American Association for Cancer Research Annual Meeting 2023

Latest research underscores advantages of digital tools at scale to enhance tumor microenvironment and biomarker understanding in non-small cell lung cancer and renal cell carcinoma

BOSTON, Mass., April 11, 2023 /PRNewswire/ -- PathAI, a leading provider of AI-powered pathology tools to advance precision medicine, today announced their recent research will be presented at the American Association for Cancer Research Annual Meeting 2023, which will be held in Orlando, FL from April 14-19, 2023. PathAI will share three posters that highlight uses and advantages of AI-based methods to identify and examine non-small cell lung cancer (NSCLC) and renal cell carcinoma (RCC) specimens. Additionally, PathAI collaborated with Genentech, a member of the Roche Group, on two submissions, an oral presentation on H&E-based digital pathology biomarkers in metastatic NSCLC, and a poster presentation on digital PD-L1 tumor cell scoring in NSCLC. PathAI will also be exhibiting in booth 315, where it will showcase the capabilities of its newly launched PathExplore™ product, its AI-powered panel of histopathology features that spatially characterize the tumor microenvironment (TME) with single-cell resolution from H&E slide images.

"Our research demonstrates forward momentum in utilizing machine learning, cell segmentation models, and image analysis at scale to better recognize and analyze tissue morphology at the cellular level, revealing new biomarkers and predictive links to targeted therapies," said Mike Montalto, Ph.D., chief scientific officer at PathAI. "With this body of research, we are another step closer to improving oncology drug development and outcomes for these difficult to treat cancers."

PathAI collaborator Genentech will give an oral presentation, "Digital pathology-based prognostic and predictive biomarkers in metastatic non-small cell lung cancer," highlighting the relationship between the tumor microenvironment (TME) and patient response to targeted cancer immunotherapy by applying machine learning algorithms to study the TME in metastatic NSCLC. By quantifying digital pathology cell and region features, and using feature variability as a discovery tool, the study identified a feature set associated with outcome to PD-L1 targeted therapy, illustrating how novel data modalities can be integrated to elucidate biomarkers of immunotherapy response.

In a poster presentation in partnership with Genentech, "Digital SP263 PD-L1 tumor cell scoring in NSCLC achieves comparable outcome prediction to manual pathology scoring," the companies will demonstrate the effectiveness of an AI-based model for PD-L1 quantification in predicting NSCLC outcomes compared to manual scoring.

In a poster on renal cell carcinoma, "Machine learning models identify key histological features of renal cell carcinoma subtypes," PathAI will explain how their machine learning model quantified the RCC environment, allowing identification of spatially specific differences that correlate with histological subtypes, mutations and vascularization.

The full list of PathAI's research submissions is listed below. More information on each research abstract can be found here.

Oral Presentation: Digital pathology based prognostic and predictive biomarkers in metastatic NSCLC

Session MS.CL01.02 - Immune-based Biomarkers for Prognostic and Predictive Benefit

Abstract presentation number: 5705

Session time: April 18, 2023, 2:30 PM - 4:30 PM

Presentation time: 3:37 PM - 3:52 PM

Collaborator: Genentech

Poster Presentation: Digital SP263 PD-L1 tumor cell scoring in non-small cell lung cancer achieves comparable outcome prediction to manual pathology scoring

Session PO.BCS01.02 - Artificial Intelligence and Machine/Deep Learning 1

Abstract presentation number: 5358 / 7

Poster hours: April 18, 2023, 1:30 PM - 5:00 PM

Collaborator: Genentech

Poster Presentation: Machine learning models identify key histological features of renal cell carcinoma subtypes

Session PO.BCS02.03 - Artificial Intelligence: From Pathomics to Radiomics

Abstract presentation number: 5422 / 5

Poster hours: April 18, 2023, 1:30 PM - 5:00 PM

Poster Presentation: Artificial intelligence (AI)-based classification of stromal subtypes reveals associations between stromal composition and prognosis in NSCLC

Session PO.BCS02.03 - Artificial Intelligence: From Pathomics to Radiomics

Abstract presentation number: 5447 / 30

Poster hours: April 18, 2023, 1:30 PM - 5:00 PM

Poster Presentation: Development of a high-throughput image processing pipeline for multiplex immunofluorescence whole slide images at scale

Session PO.BCS02.02 - Integrative Spatial and Temporal Multi-omics of Cancer

Abstract presentation number: 6616 / 21

Poster hours: April 19, 2023, 9:00 AM - 12:30 PM

About PathAI

PathAI is the only AI-focused technology company to provide comprehensive precision pathology solutions from wet lab services to algorithm deployment for clinical trials and diagnostic use. Rigorously trained and validated with data from more than 15 million annotations, its AI-powered models can be leveraged to optimize the analysis of patient samples to improve diagnostic efficiency and accuracy, as well as to better gauge therapeutic efficacy and accelerate drug development for complex diseases.

PathAI, which is headquartered in Boston, MA, and operates a CAP/CLIA-certified laboratory in Memphis, TN, is proud to be a rapidly expanding organization comprised of innovative thinkers from around the globe, For more information, please visit www.pathai.com.

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SOURCE PathAI