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Detection of Bone Metastases on Bone Scans through Image Classification with Contrastive Learning
Patients with bone metastases have poor prognoses. A bone scan is a commonly applied diagnostic tool for this condition. However, its accuracy is limited by the nonspecific character of radiopharmaceutical accumulation, which indicates all-cause bone remodeling. The current study evaluated deep lear...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708961/ https://www.ncbi.nlm.nih.gov/pubmed/34945720 http://dx.doi.org/10.3390/jpm11121248 |
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author | Hsieh, Te-Chun Liao, Chiung-Wei Lai, Yung-Chi Law, Kin-Man Chan, Pak-Ki Kao, Chia-Hung |
author_facet | Hsieh, Te-Chun Liao, Chiung-Wei Lai, Yung-Chi Law, Kin-Man Chan, Pak-Ki Kao, Chia-Hung |
author_sort | Hsieh, Te-Chun |
collection | PubMed |
description | Patients with bone metastases have poor prognoses. A bone scan is a commonly applied diagnostic tool for this condition. However, its accuracy is limited by the nonspecific character of radiopharmaceutical accumulation, which indicates all-cause bone remodeling. The current study evaluated deep learning techniques to improve the efficacy of bone metastasis detection on bone scans, retrospectively examining 19,041 patients aged 22 to 92 years who underwent bone scans between May 2011 and December 2019. We developed several functional imaging binary classification deep learning algorithms suitable for bone scans. The presence or absence of bone metastases as a reference standard was determined through a review of image reports by nuclear medicine physicians. Classification was conducted with convolutional neural network-based (CNN-based), residual neural network (ResNet), and densely connected convolutional networks (DenseNet) models, with and without contrastive learning. Each set of bone scans contained anterior and posterior images with resolutions of 1024 × 256 pixels. A total of 37,427 image sets were analyzed. The overall performance of all models improved with contrastive learning. The accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve, and negative predictive value (NPV) for the optimal model were 0.961, 0.878, 0.599, 0.712, 0.92 and 0.965, respectively. In particular, the high NPV may help physicians safely exclude bone metastases, decreasing physician workload, and improving patient care. |
format | Online Article Text |
id | pubmed-8708961 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87089612021-12-25 Detection of Bone Metastases on Bone Scans through Image Classification with Contrastive Learning Hsieh, Te-Chun Liao, Chiung-Wei Lai, Yung-Chi Law, Kin-Man Chan, Pak-Ki Kao, Chia-Hung J Pers Med Article Patients with bone metastases have poor prognoses. A bone scan is a commonly applied diagnostic tool for this condition. However, its accuracy is limited by the nonspecific character of radiopharmaceutical accumulation, which indicates all-cause bone remodeling. The current study evaluated deep learning techniques to improve the efficacy of bone metastasis detection on bone scans, retrospectively examining 19,041 patients aged 22 to 92 years who underwent bone scans between May 2011 and December 2019. We developed several functional imaging binary classification deep learning algorithms suitable for bone scans. The presence or absence of bone metastases as a reference standard was determined through a review of image reports by nuclear medicine physicians. Classification was conducted with convolutional neural network-based (CNN-based), residual neural network (ResNet), and densely connected convolutional networks (DenseNet) models, with and without contrastive learning. Each set of bone scans contained anterior and posterior images with resolutions of 1024 × 256 pixels. A total of 37,427 image sets were analyzed. The overall performance of all models improved with contrastive learning. The accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve, and negative predictive value (NPV) for the optimal model were 0.961, 0.878, 0.599, 0.712, 0.92 and 0.965, respectively. In particular, the high NPV may help physicians safely exclude bone metastases, decreasing physician workload, and improving patient care. MDPI 2021-11-24 /pmc/articles/PMC8708961/ /pubmed/34945720 http://dx.doi.org/10.3390/jpm11121248 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 Hsieh, Te-Chun Liao, Chiung-Wei Lai, Yung-Chi Law, Kin-Man Chan, Pak-Ki Kao, Chia-Hung Detection of Bone Metastases on Bone Scans through Image Classification with Contrastive Learning |
title | Detection of Bone Metastases on Bone Scans through Image Classification with Contrastive Learning |
title_full | Detection of Bone Metastases on Bone Scans through Image Classification with Contrastive Learning |
title_fullStr | Detection of Bone Metastases on Bone Scans through Image Classification with Contrastive Learning |
title_full_unstemmed | Detection of Bone Metastases on Bone Scans through Image Classification with Contrastive Learning |
title_short | Detection of Bone Metastases on Bone Scans through Image Classification with Contrastive Learning |
title_sort | detection of bone metastases on bone scans through image classification with contrastive learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708961/ https://www.ncbi.nlm.nih.gov/pubmed/34945720 http://dx.doi.org/10.3390/jpm11121248 |
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