Efficient Workflows for Quantifying Tumor Growth on H&E-Stained Digital Sections in Mouse Models of Lung Cancer.

Microscopic examination of tissue morphology using hematoxylin and eosin (H&E) staining is a standard practice in histopathology for analyzing tissue specimens. In preclinical models of cancer, H&E-stained tissue sections are routinely used to assess tumor development and progression, including parameters such as tumor number, size, and histopathological features. Recent advances in whole-slide scanning and digital pathology now enable quantitative analysis of high-resolution digital images on a computer, replacing the need to examine physical tissue slides under a microscope. However, quantifying tumors on H&E-stained digital sections remains challenging, highlighting the need for accessible and reproducible methods that can be adapted to diverse tumor models. Here, we present two efficient workflows using QuPath, an open-source software for digital pathology and whole-slide image analysis, to quantify tumor multiplicity (i.e., the number of tumors) and tumor burden (e.g., tumor surface area) based on digitally captured H&E-stained tissue sections. As a proof of concept, we apply these workflows to two distinct lung cancer models: (1) an orthotopic model in which Lewis lung carcinoma (LLC) cells are delivered intranasally or intravenously into syngeneic C57BL/6 mice, resulting in well-separated, easily distinguishable tumors amenable to manual quantification; and (2) a genetically engineered mouse model (GEMM) with lung-specific expression of oncogenic Kras