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Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering

SIMPLE SUMMARY: Artificial intelligence techniques were used for the detection of prostate cancer through tissue feature engineering. A radiomic method was used to extract the important features or information from histopathology tissue images to perform binary classification (i.e., benign vs. malig...

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Autores principales: Kim, Cho-Hee, Bhattacharjee, Subrata, Prakash, Deekshitha, Kang, Suki, Cho, Nam-Hoon, Kim, Hee-Cheol, Choi, Heung-Kook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8036750/
https://www.ncbi.nlm.nih.gov/pubmed/33810251
http://dx.doi.org/10.3390/cancers13071524
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author Kim, Cho-Hee
Bhattacharjee, Subrata
Prakash, Deekshitha
Kang, Suki
Cho, Nam-Hoon
Kim, Hee-Cheol
Choi, Heung-Kook
author_facet Kim, Cho-Hee
Bhattacharjee, Subrata
Prakash, Deekshitha
Kang, Suki
Cho, Nam-Hoon
Kim, Hee-Cheol
Choi, Heung-Kook
author_sort Kim, Cho-Hee
collection PubMed
description SIMPLE SUMMARY: Artificial intelligence techniques were used for the detection of prostate cancer through tissue feature engineering. A radiomic method was used to extract the important features or information from histopathology tissue images to perform binary classification (i.e., benign vs. malignant). This method can identify a histological pattern that is invisible to the human eye, which helps researchers to predict and detect prostate cancer. We used different performance metrics to evaluate the results of the classification. In the future, it is expected that a method like radiomic will provide a consistent contribution to analyze histopathology tissue images and differentiate between cancerous and noncancerous tumors. ABSTRACT: The optimal diagnostic and treatment strategies for prostate cancer (PCa) are constantly changing. Given the importance of accurate diagnosis, texture analysis of stained prostate tissues is important for automatic PCa detection. We used artificial intelligence (AI) techniques to classify dual-channel tissue features extracted from Hematoxylin and Eosin (H&E) tissue images, respectively. Tissue feature engineering was performed to extract first-order statistic (FOS)-based textural features from each stained channel, and cancer classification between benign and malignant was carried out based on important features. Recursive feature elimination (RFE) and one-way analysis of variance (ANOVA) methods were used to identify significant features, which provided the best five features out of the extracted six features. The AI techniques used in this study for binary classification (benign vs. malignant and low-grade vs. high-grade) were support vector machine (SVM), logistic regression (LR), bagging tree, boosting tree, and dual-channel bidirectional long short-term memory (DC-BiLSTM) network. Further, a comparative analysis was carried out between the AI algorithms. Two different datasets were used for PCa classification. Out of these, the first dataset (private) was used for training and testing the AI models and the second dataset (public) was used only for testing to evaluate model performance. The automatic AI classification system performed well and showed satisfactory results according to the hypothesis of this study.
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spelling pubmed-80367502021-04-12 Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering Kim, Cho-Hee Bhattacharjee, Subrata Prakash, Deekshitha Kang, Suki Cho, Nam-Hoon Kim, Hee-Cheol Choi, Heung-Kook Cancers (Basel) Article SIMPLE SUMMARY: Artificial intelligence techniques were used for the detection of prostate cancer through tissue feature engineering. A radiomic method was used to extract the important features or information from histopathology tissue images to perform binary classification (i.e., benign vs. malignant). This method can identify a histological pattern that is invisible to the human eye, which helps researchers to predict and detect prostate cancer. We used different performance metrics to evaluate the results of the classification. In the future, it is expected that a method like radiomic will provide a consistent contribution to analyze histopathology tissue images and differentiate between cancerous and noncancerous tumors. ABSTRACT: The optimal diagnostic and treatment strategies for prostate cancer (PCa) are constantly changing. Given the importance of accurate diagnosis, texture analysis of stained prostate tissues is important for automatic PCa detection. We used artificial intelligence (AI) techniques to classify dual-channel tissue features extracted from Hematoxylin and Eosin (H&E) tissue images, respectively. Tissue feature engineering was performed to extract first-order statistic (FOS)-based textural features from each stained channel, and cancer classification between benign and malignant was carried out based on important features. Recursive feature elimination (RFE) and one-way analysis of variance (ANOVA) methods were used to identify significant features, which provided the best five features out of the extracted six features. The AI techniques used in this study for binary classification (benign vs. malignant and low-grade vs. high-grade) were support vector machine (SVM), logistic regression (LR), bagging tree, boosting tree, and dual-channel bidirectional long short-term memory (DC-BiLSTM) network. Further, a comparative analysis was carried out between the AI algorithms. Two different datasets were used for PCa classification. Out of these, the first dataset (private) was used for training and testing the AI models and the second dataset (public) was used only for testing to evaluate model performance. The automatic AI classification system performed well and showed satisfactory results according to the hypothesis of this study. MDPI 2021-03-26 /pmc/articles/PMC8036750/ /pubmed/33810251 http://dx.doi.org/10.3390/cancers13071524 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Kim, Cho-Hee
Bhattacharjee, Subrata
Prakash, Deekshitha
Kang, Suki
Cho, Nam-Hoon
Kim, Hee-Cheol
Choi, Heung-Kook
Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering
title Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering
title_full Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering
title_fullStr Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering
title_full_unstemmed Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering
title_short Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering
title_sort artificial intelligence techniques for prostate cancer detection through dual-channel tissue feature engineering
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8036750/
https://www.ncbi.nlm.nih.gov/pubmed/33810251
http://dx.doi.org/10.3390/cancers13071524
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