A Machine-learning Approach for the Assessment of the Proliferative Compartment of Solid Tumors on Hematoxylin-Eosin-Stained Sections

Abstract

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 the 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 (oral squamous cell carcinoma) tissue sample whole slide images, using QuPath as a working platform and its integrated algorithms, and we built a classifier in order to distinguish tumor and stroma classes and, within them, Ki67-positive and Ki67-negative cells; then, we sorted the morphometric features of tumor cells related to their Ki67 IHC status. Among the evaluated features, nuclear hematoxylin mean optical density (NHMOD) presented 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. Finally, we 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 (tissue microarray) block, respectively stained with H&E and immuno-stained for Ki67/MIB1. We automatically detected tumor cells in H&E slides and 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 of identifying the proliferating compartment of the tumor through a quantitative assessment of the nuclear features 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 to be a promising tool to quickly forecast the tumor’s proliferation fraction directly on routinely H&E-stained digital sections.

Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin

“…This consists of a range of activities such as the acquisition, storage, sharing, analysis and interpretation of histological images [ 10 ]. In this domain, computer-assisted classification of tissue samples has attracted considerable research interest in recent years as a means for assisting pathologists in several tasks, for instance, the classification of specimens into normal or abnormal [ 11 , 12 , 13 , 14 ], the grading of neoplastic tissue [ 15 , 16 , 17 , 18 ], the estimation of tumor proliferation [ 19 ] and the identification of tissue substructures such as epithelium, stroma, lymphocytes, necrosis, etc. [ 20 , 21 ].…”

Digital Pathology and PD-L1 Testing in Non Small Cell Lung Cancer: A Workshop Record

“…We are approaching the third revolution in pathology with the implementation of the artificial intelligence (AI) tool even in the everyday practice with the possibility to combine the use of digital pathology and AI tool to support pathologists in the diagnostic setting [ 30 , 31 ]. The possibility of predicting IHC findings directly from H&E slides has also been reported [ 32 ], including the PD-L1 status by the developing of a deep learning model [ 33 ]. The obtained results were robust to interpathologist variability, opening new possibilities for PD-L1 assessment, especially when there is insufficient tissue for all the needed IHC and molecular tests.…”

QuPath: The global impact of an open source digital pathology system

“…It is clear that the majority of publications have used QuPath in brightfield histopathological assessment of biomarkers in FFPE sections, be these in resections [39] , [41] , biopsies [42] , [43] , cytology specimens [9] , [44] , TMAs [10] , [45] , [46] , [47] , [48] , or embedded cell culture models [49] , [50] . This is in addition to the multitude of immunofluorescence applications of QuPath [14] , [16] , [36] , [51] , [52] .…”