Cargando…
A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning
The histopathological diagnosis of prostate adenocarcinoma in needle biopsy specimens is of pivotal importance for determining optimum prostate cancer treatment. Since diagnosing a large number of cases containing 12 core biopsy specimens by pathologists using a microscope is time-consuming manual s...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947489/ https://www.ncbi.nlm.nih.gov/pubmed/35328321 http://dx.doi.org/10.3390/diagnostics12030768 |
_version_ | 1784674451653132288 |
---|---|
author | Tsuneki, Masayuki Abe, Makoto Kanavati, Fahdi |
author_facet | Tsuneki, Masayuki Abe, Makoto Kanavati, Fahdi |
author_sort | Tsuneki, Masayuki |
collection | PubMed |
description | The histopathological diagnosis of prostate adenocarcinoma in needle biopsy specimens is of pivotal importance for determining optimum prostate cancer treatment. Since diagnosing a large number of cases containing 12 core biopsy specimens by pathologists using a microscope is time-consuming manual system and limited in terms of human resources, it is necessary to develop new techniques that can rapidly and accurately screen large numbers of histopathological prostate needle biopsy specimens. Computational pathology applications that can assist pathologists in detecting and classifying prostate adenocarcinoma from whole-slide images (WSIs) would be of great benefit for routine pathological practice. In this paper, we trained deep learning models capable of classifying needle biopsy WSIs into adenocarcinoma and benign (non-neoplastic) lesions. We evaluated the models on needle biopsy, transurethral resection of the prostate (TUR-P), and The Cancer Genome Atlas (TCGA) public dataset test sets, achieving an ROC-AUC up to 0.978 in needle biopsy test sets and up to 0.9873 in TCGA test sets for adenocarcinoma. |
format | Online Article Text |
id | pubmed-8947489 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89474892022-03-25 A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning Tsuneki, Masayuki Abe, Makoto Kanavati, Fahdi Diagnostics (Basel) Article The histopathological diagnosis of prostate adenocarcinoma in needle biopsy specimens is of pivotal importance for determining optimum prostate cancer treatment. Since diagnosing a large number of cases containing 12 core biopsy specimens by pathologists using a microscope is time-consuming manual system and limited in terms of human resources, it is necessary to develop new techniques that can rapidly and accurately screen large numbers of histopathological prostate needle biopsy specimens. Computational pathology applications that can assist pathologists in detecting and classifying prostate adenocarcinoma from whole-slide images (WSIs) would be of great benefit for routine pathological practice. In this paper, we trained deep learning models capable of classifying needle biopsy WSIs into adenocarcinoma and benign (non-neoplastic) lesions. We evaluated the models on needle biopsy, transurethral resection of the prostate (TUR-P), and The Cancer Genome Atlas (TCGA) public dataset test sets, achieving an ROC-AUC up to 0.978 in needle biopsy test sets and up to 0.9873 in TCGA test sets for adenocarcinoma. MDPI 2022-03-21 /pmc/articles/PMC8947489/ /pubmed/35328321 http://dx.doi.org/10.3390/diagnostics12030768 Text en © 2022 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 Tsuneki, Masayuki Abe, Makoto Kanavati, Fahdi A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning |
title | A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning |
title_full | A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning |
title_fullStr | A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning |
title_full_unstemmed | A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning |
title_short | A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning |
title_sort | deep learning model for prostate adenocarcinoma classification in needle biopsy whole-slide images using transfer learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947489/ https://www.ncbi.nlm.nih.gov/pubmed/35328321 http://dx.doi.org/10.3390/diagnostics12030768 |
work_keys_str_mv | AT tsunekimasayuki adeeplearningmodelforprostateadenocarcinomaclassificationinneedlebiopsywholeslideimagesusingtransferlearning AT abemakoto adeeplearningmodelforprostateadenocarcinomaclassificationinneedlebiopsywholeslideimagesusingtransferlearning AT kanavatifahdi adeeplearningmodelforprostateadenocarcinomaclassificationinneedlebiopsywholeslideimagesusingtransferlearning AT tsunekimasayuki deeplearningmodelforprostateadenocarcinomaclassificationinneedlebiopsywholeslideimagesusingtransferlearning AT abemakoto deeplearningmodelforprostateadenocarcinomaclassificationinneedlebiopsywholeslideimagesusingtransferlearning AT kanavatifahdi deeplearningmodelforprostateadenocarcinomaclassificationinneedlebiopsywholeslideimagesusingtransferlearning |