A machine-learning approach for the assessment of the proliferative compartment of solid tumors on Hematoxylin-Eosin stained sections

We introduce a machine learning-based analysis to predict the immunohistochemical (IHC) labeling index for the cell proliferation marker Ki67/MIB1 on cancer tissues based on morphometrical features extracted from hematoxylin and eosin (H&E)-stained formalin-fixed, paraffin-embedded (FFPE) tumor tissue samples. We provided a proof-of-concept prediction of Ki67/MIB1 IHC positivity of cancer cells through the definition and quantitation of single nuclear features. In the first instance, we set our digital framework on Ki67/MIB1-stained OSCC tissue sample whole slide images, using QuPath as a working platform and its integrated algorithms, we built a classifier in order to distinguish tumor and stroma classes and, within them, Ki67-positive and Ki67-negative cells; then we sorted out morphometric features of tumor cells related to their Ki67 IHC status. Among the evaluated features, Nuclear Hematoxylin Mean Optical Density (NHMOD) resulted as the best one to distinguish Ki67/MIB1 positive from negative cells. We confirmed our findings in a single-cell level analysis of H&E staining on Ki67-immunostained/H&E-decolored tissue samples. We finally tested our digital framework on a case series of Oral Squamous cell carcinomas (OSCC), arranged in tissue microarrays; we selected two consecutive sections of each OSCC FFPE TMA block, respectively stained with H&E and immuno-stained for Ki67/MIB1. We automatically detected tumor cells in H&E slides, and we generated a "false color map" (FCM), based on NHMOD through the QuPath measurements map tool. FCM nearly coincided with the actual immunohistochemical result, allowing the prediction of Ki67/MIB1 positive cells in a direct visual fashion. Our proposed approach provides the pathologist with a fast method to identify the proliferating compartment of the tumor through a quantitative assessment of a nuclear feature on H&E slides, readily appreciable by visual inspection. Although this technique needs to be fine-tuned and tested on larger series of tumors, the digital analysis approach appears as a promising tool to quickly forecast the tumor's proliferation fraction directly on H&E routinely stained digital section.