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DERI Seminar with Arvydas Laurinavičius, MD, PhD, Pathology Professor at Vilnius University

When: Thursday, January 18, 2024, 11:00 AM - 12:00 PM
Where: zoom

Speaker: Arvydas Laurinavičius, MD, PhD, Pathology Professor at Vilnius University

Zoom link: https://qmul-ac-uk.zoom.us/j/81148100921

Please join us for our first seminar of 2024.

Title: Hexagonal Tiling-Based Computational Pathology Models for Assessing the Tumor Microenvironment

Bio: Arvydas Laurinavičius, MD, PhD, is Pathology Professor at Vilnius University and serves as the Director of the National Center of Pathology, affiliated with Vilnius University Hospital Santaros Klinikos. He earned his medical degree from Vilnius University in 1987, completed his PhD program at the Moscow Medical Academy from 1989 to 1992, and underwent a Fellowship in Renal Pathology at the Brigham and Women’s Hospital/Harvard Medical School from 1996 to 1997. Dr. Laurinavičius leads a research team with a primary focus on computational image-based biomarkers in tissue pathology.

Abstract: The field of precision oncology relies on the examination of tissue pathology, increasingly augmented by machine learning techniques facilitated by digital technologies. Targeted cancer therapies face challenges posed by the intratumoral heterogeneity of biomarker expression, while anti-cancer immunotherapies demand an evaluation of immune cells within the spatial context of the tissue microenvironment. Various computational methods have been proposed to analyze these mostly imperceptible features, aiming to enhance prognostic and predictive disease models.

This presentation will focus on the potential of hexagonal tiling analytics, utilizing data generated through digital image analysis to scrutinize spatial aspects of the tumor microenvironment. Firstly, a hexagonal grid-based systematic subsampling of digital immunohistochemistry data allows for the quantification of intratumoral heterogeneity of biomarker expression. This is achieved by computing Haralick’s entropy and other texture indicators, often surpassing the performance of biomarker expression rates alone in prognostic models. Secondly, hexagonal tiling facilitates the extraction of the tumor-host interface zone, preserving its spatial directionality to compute density distributions of immune cells across the zone, conceptualized as an immunogradient. Both computational models have been reported to yield independent prognostic indicators in various solid tumors.

 

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