Cargando…

A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens

Prostate cancer is a significant cause of morbidity and mortality in the USA. In this paper, we develop a computer-aided diagnostic (CAD) system for automated grade groups (GG) classification using digitized prostate biopsy specimens (PBSs). Our CAD system aims to firstly classify the Gleason patter...

Descripción completa

Detalles Bibliográficos
Autores principales: Hammouda, Kamal, Khalifa, Fahmi, El-Melegy, Moumen, Ghazal, Mohamed, Darwish, Hanan E., Abou El-Ghar, Mohamed, El-Baz, Ayman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8538079/
https://www.ncbi.nlm.nih.gov/pubmed/34695922
http://dx.doi.org/10.3390/s21206708
_version_ 1784588421678759936
author Hammouda, Kamal
Khalifa, Fahmi
El-Melegy, Moumen
Ghazal, Mohamed
Darwish, Hanan E.
Abou El-Ghar, Mohamed
El-Baz, Ayman
author_facet Hammouda, Kamal
Khalifa, Fahmi
El-Melegy, Moumen
Ghazal, Mohamed
Darwish, Hanan E.
Abou El-Ghar, Mohamed
El-Baz, Ayman
author_sort Hammouda, Kamal
collection PubMed
description Prostate cancer is a significant cause of morbidity and mortality in the USA. In this paper, we develop a computer-aided diagnostic (CAD) system for automated grade groups (GG) classification using digitized prostate biopsy specimens (PBSs). Our CAD system aims to firstly classify the Gleason pattern (GP), and then identifies the Gleason score (GS) and GG. The GP classification pipeline is based on a pyramidal deep learning system that utilizes three convolution neural networks (CNN) to produce both patch- and pixel-wise classifications. The analysis starts with sequential preprocessing steps that include a histogram equalization step to adjust intensity values, followed by a PBSs’ edge enhancement. The digitized PBSs are then divided into overlapping patches with the three sizes: 100 × 100 ([Formula: see text]), 150 × 150 ([Formula: see text]), and 200 × 200 ([Formula: see text]), pixels, and 75% overlap. Those three sizes of patches represent the three pyramidal levels. This pyramidal technique allows us to extract rich information, such as that the larger patches give more global information, while the small patches provide local details. After that, the patch-wise technique assigns each overlapped patch a label as GP categories (1 to 5). Then, the majority voting is the core approach for getting the pixel-wise classification that is used to get a single label for each overlapped pixel. The results after applying those techniques are three images of the same size as the original, and each pixel has a single label. We utilized the majority voting technique again on those three images to obtain only one. The proposed framework is trained, validated, and tested on 608 whole slide images (WSIs) of the digitized PBSs. The overall diagnostic accuracy is evaluated using several metrics: precision, recall, F1-score, accuracy, macro-averaged, and weighted-averaged. The ([Formula: see text]) has the best accuracy results for patch classification among the three CNNs, and its classification accuracy is 0.76. The macro-averaged and weighted-average metrics are found to be around 0.70–0.77. For GG, our CAD results are about 80% for precision, and between 60% to 80% for recall and F1-score, respectively. Also, it is around 94% for accuracy and NPV. To highlight our CAD systems’ results, we used the standard ResNet50 and VGG-16 to compare our CNN’s patch-wise classification results. As well, we compared the GG’s results with that of the previous work.
format Online
Article
Text
id pubmed-8538079
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-85380792021-10-24 A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens Hammouda, Kamal Khalifa, Fahmi El-Melegy, Moumen Ghazal, Mohamed Darwish, Hanan E. Abou El-Ghar, Mohamed El-Baz, Ayman Sensors (Basel) Article Prostate cancer is a significant cause of morbidity and mortality in the USA. In this paper, we develop a computer-aided diagnostic (CAD) system for automated grade groups (GG) classification using digitized prostate biopsy specimens (PBSs). Our CAD system aims to firstly classify the Gleason pattern (GP), and then identifies the Gleason score (GS) and GG. The GP classification pipeline is based on a pyramidal deep learning system that utilizes three convolution neural networks (CNN) to produce both patch- and pixel-wise classifications. The analysis starts with sequential preprocessing steps that include a histogram equalization step to adjust intensity values, followed by a PBSs’ edge enhancement. The digitized PBSs are then divided into overlapping patches with the three sizes: 100 × 100 ([Formula: see text]), 150 × 150 ([Formula: see text]), and 200 × 200 ([Formula: see text]), pixels, and 75% overlap. Those three sizes of patches represent the three pyramidal levels. This pyramidal technique allows us to extract rich information, such as that the larger patches give more global information, while the small patches provide local details. After that, the patch-wise technique assigns each overlapped patch a label as GP categories (1 to 5). Then, the majority voting is the core approach for getting the pixel-wise classification that is used to get a single label for each overlapped pixel. The results after applying those techniques are three images of the same size as the original, and each pixel has a single label. We utilized the majority voting technique again on those three images to obtain only one. The proposed framework is trained, validated, and tested on 608 whole slide images (WSIs) of the digitized PBSs. The overall diagnostic accuracy is evaluated using several metrics: precision, recall, F1-score, accuracy, macro-averaged, and weighted-averaged. The ([Formula: see text]) has the best accuracy results for patch classification among the three CNNs, and its classification accuracy is 0.76. The macro-averaged and weighted-average metrics are found to be around 0.70–0.77. For GG, our CAD results are about 80% for precision, and between 60% to 80% for recall and F1-score, respectively. Also, it is around 94% for accuracy and NPV. To highlight our CAD systems’ results, we used the standard ResNet50 and VGG-16 to compare our CNN’s patch-wise classification results. As well, we compared the GG’s results with that of the previous work. MDPI 2021-10-09 /pmc/articles/PMC8538079/ /pubmed/34695922 http://dx.doi.org/10.3390/s21206708 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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hammouda, Kamal
Khalifa, Fahmi
El-Melegy, Moumen
Ghazal, Mohamed
Darwish, Hanan E.
Abou El-Ghar, Mohamed
El-Baz, Ayman
A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens
title A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens
title_full A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens
title_fullStr A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens
title_full_unstemmed A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens
title_short A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens
title_sort deep learning pipeline for grade groups classification using digitized prostate biopsy specimens
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8538079/
https://www.ncbi.nlm.nih.gov/pubmed/34695922
http://dx.doi.org/10.3390/s21206708
work_keys_str_mv AT hammoudakamal adeeplearningpipelineforgradegroupsclassificationusingdigitizedprostatebiopsyspecimens
AT khalifafahmi adeeplearningpipelineforgradegroupsclassificationusingdigitizedprostatebiopsyspecimens
AT elmelegymoumen adeeplearningpipelineforgradegroupsclassificationusingdigitizedprostatebiopsyspecimens
AT ghazalmohamed adeeplearningpipelineforgradegroupsclassificationusingdigitizedprostatebiopsyspecimens
AT darwishhanane adeeplearningpipelineforgradegroupsclassificationusingdigitizedprostatebiopsyspecimens
AT abouelgharmohamed adeeplearningpipelineforgradegroupsclassificationusingdigitizedprostatebiopsyspecimens
AT elbazayman adeeplearningpipelineforgradegroupsclassificationusingdigitizedprostatebiopsyspecimens
AT hammoudakamal deeplearningpipelineforgradegroupsclassificationusingdigitizedprostatebiopsyspecimens
AT khalifafahmi deeplearningpipelineforgradegroupsclassificationusingdigitizedprostatebiopsyspecimens
AT elmelegymoumen deeplearningpipelineforgradegroupsclassificationusingdigitizedprostatebiopsyspecimens
AT ghazalmohamed deeplearningpipelineforgradegroupsclassificationusingdigitizedprostatebiopsyspecimens
AT darwishhanane deeplearningpipelineforgradegroupsclassificationusingdigitizedprostatebiopsyspecimens
AT abouelgharmohamed deeplearningpipelineforgradegroupsclassificationusingdigitizedprostatebiopsyspecimens
AT elbazayman deeplearningpipelineforgradegroupsclassificationusingdigitizedprostatebiopsyspecimens