Quantitative estimation of tumor cellularity based on histology data

Tumor cellularity, the number of cells in the tumor, is an important tissue microstructural feature, which is useful for cancer diagnosis and cell number related treatment. Histopathological examination of tissues reveals the tissue microstructure hence permits to investigate cellularity, but is usually available only as a small sample or after resection. Diffusion-weighted MRI (DWI) is a non-invasive imaging technique in cancer diagnosis and treatment, which is supposed to provide an indirect measure of tumor cellularity. How to quantitatively estimate the tumor cell density is still challenging. In this work, we introduce an automated cell nuclei segmentation algorithm for 2D cell density estimation and a model-based method for 3D cell density estimation from tumor histology data, which is an important component in concluding 3D cellularity from 2D histological sections as those usually analyzed in routine histology. The algorithm is part of a novel approach for the estimation of tumor cellularity from histology, as demonstrated by quantitatively analyzing representative tumor samples to study the tumor cellularity. These results might be of importance for the personalized adjustment of therapies and can be applied to many different solid tumors.