Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques.

Yeong-Byn Hwang,Kouayep Sonia Carole,Nuwan Madusanka,Kobiljon Ikromjanov,Nam-Hoon Cho,Heung-Kook Choi,Subrata Bhattacharjee,Hee-Cheol Kim,Rashadul Islam Sumon

doi:10.3390/diagnostics12010015

Abstract

Biomarker identification is very important to differentiate the grade groups in the histopathological sections of prostate cancer (PCa). Assessing the cluster of cell nuclei is essential for pathological investigation. In this study, we present a computer-based method for cluster analyses of cell nuclei and performed traditional (i.e., unsupervised method) and modern (i.e., supervised method) artificial intelligence (AI) techniques for distinguishing the grade groups of PCa. Two datasets on PCa were collected to carry out this research. Histopathology samples were obtained from whole slides stained with hematoxylin and eosin (H&E). In this research, state-of-the-art approaches were proposed for color normalization, cell nuclei segmentation, feature selection, and classification. A traditional minimum spanning tree (MST) algorithm was employed to identify the clusters and better capture the proliferation and community structure of cell nuclei. K-medoids clustering and stacked ensemble machine learning (ML) approaches were used to perform traditional and modern AI-based classification. The binary and multiclass classification was derived to compare the model quality and results between the grades of PCa. Furthermore, a comparative analysis was carried out between traditional and modern AI techniques using different performance metrics (i.e., statistical parameters). Cluster features of the cell nuclei can be useful information for cancer grading. However, further validation of cluster analysis is required to accomplish astounding classification results.

Highlights

Introduction published maps and institutional affilMany techniques are used for analysis, color enhancement, segmentation, and classification of medical images, such as those yielded by magnetic resonance (MR), positron emission tomography (PET), and microscopic biopsy; many internal bodily structures can be imaged non-invasively
We proposed an ensemble model for supervised classification, and it was designed by stacking five different machine learning algorithms
Two-dimensional tissue images stained with hematoxylin and eosin (H&E) were subjected to cluster shape and size analyses

Summary

Introduction

Many techniques are used for analysis, color enhancement, segmentation, and classification of medical images, such as those yielded by magnetic resonance (MR), positron emission tomography (PET), and microscopic biopsy; many internal bodily structures can be imaged non-invasively. Computers can be used for image gain, storage, presentation, and communication. Biochemical, and pathological images are used to diagnose and stage PCa; computer scientists are very active in this field. The sensitivity and specificity of the techniques remain controversial [1]. PCa diagnosis requires prostate MR and microscopic biopsy images. A traditional cancer diagnosis is subjective; pathologists examine biopsy samples under a microscope. It is difficult to objectively describe tissue texture, tissue color, and cell morphology

Methods

Results

Conclusion