Cargando…
An Iterative Algorithm for Semisupervised Classification of Hotspots on Bone Scintigraphies of Patients with Prostate Cancer
Prostate cancer (PCa) is the second most diagnosed cancer in men. Patients with PCa often develop metastases, with more than 80% of this metastases occurring in bone. The most common imaging technique used for screening, diagnosis and follow-up of disease evolution is bone scintigraphy, due to its h...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8404946/ https://www.ncbi.nlm.nih.gov/pubmed/34460784 http://dx.doi.org/10.3390/jimaging7080148 |
_version_ | 1783746240070025216 |
---|---|
author | Providência, Laura Domingues, Inês Santos, João |
author_facet | Providência, Laura Domingues, Inês Santos, João |
author_sort | Providência, Laura |
collection | PubMed |
description | Prostate cancer (PCa) is the second most diagnosed cancer in men. Patients with PCa often develop metastases, with more than 80% of this metastases occurring in bone. The most common imaging technique used for screening, diagnosis and follow-up of disease evolution is bone scintigraphy, due to its high sensitivity and widespread availability at nuclear medicine facilities. To date, the assessment of bone scans relies solely on the interpretation of an expert physician who visually assesses the scan. Besides this being a time consuming task, it is also subjective, as there is no absolute criteria neither to identify bone metastases neither to quantify them by a straightforward and universally accepted procedure. In this paper, a new algorithm for the false positives reduction of automatically detected hotspots in bone scintigraphy images is proposed. The motivation relies in the difficulty of building a fully annotated database. In this way, our algorithm is a semisupervised method that works in an iterative way. The ultimate goal is to provide the physician with a fast, precise and reliable tool to quantify bone scans and evaluate disease progression and response to treatment. The algorithm is tested in a set of bone scans manually labeled according to the patient’s medical record. The achieved classification sensitivity, specificity and false negative rate were 63%, 58% and 37%, respectively. Comparison with other state-of-the-art classification algorithms shows superiority of the proposed method. |
format | Online Article Text |
id | pubmed-8404946 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84049462021-10-28 An Iterative Algorithm for Semisupervised Classification of Hotspots on Bone Scintigraphies of Patients with Prostate Cancer Providência, Laura Domingues, Inês Santos, João J Imaging Article Prostate cancer (PCa) is the second most diagnosed cancer in men. Patients with PCa often develop metastases, with more than 80% of this metastases occurring in bone. The most common imaging technique used for screening, diagnosis and follow-up of disease evolution is bone scintigraphy, due to its high sensitivity and widespread availability at nuclear medicine facilities. To date, the assessment of bone scans relies solely on the interpretation of an expert physician who visually assesses the scan. Besides this being a time consuming task, it is also subjective, as there is no absolute criteria neither to identify bone metastases neither to quantify them by a straightforward and universally accepted procedure. In this paper, a new algorithm for the false positives reduction of automatically detected hotspots in bone scintigraphy images is proposed. The motivation relies in the difficulty of building a fully annotated database. In this way, our algorithm is a semisupervised method that works in an iterative way. The ultimate goal is to provide the physician with a fast, precise and reliable tool to quantify bone scans and evaluate disease progression and response to treatment. The algorithm is tested in a set of bone scans manually labeled according to the patient’s medical record. The achieved classification sensitivity, specificity and false negative rate were 63%, 58% and 37%, respectively. Comparison with other state-of-the-art classification algorithms shows superiority of the proposed method. MDPI 2021-08-17 /pmc/articles/PMC8404946/ /pubmed/34460784 http://dx.doi.org/10.3390/jimaging7080148 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 Providência, Laura Domingues, Inês Santos, João An Iterative Algorithm for Semisupervised Classification of Hotspots on Bone Scintigraphies of Patients with Prostate Cancer |
title | An Iterative Algorithm for Semisupervised Classification of Hotspots on Bone Scintigraphies of Patients with Prostate Cancer |
title_full | An Iterative Algorithm for Semisupervised Classification of Hotspots on Bone Scintigraphies of Patients with Prostate Cancer |
title_fullStr | An Iterative Algorithm for Semisupervised Classification of Hotspots on Bone Scintigraphies of Patients with Prostate Cancer |
title_full_unstemmed | An Iterative Algorithm for Semisupervised Classification of Hotspots on Bone Scintigraphies of Patients with Prostate Cancer |
title_short | An Iterative Algorithm for Semisupervised Classification of Hotspots on Bone Scintigraphies of Patients with Prostate Cancer |
title_sort | iterative algorithm for semisupervised classification of hotspots on bone scintigraphies of patients with prostate cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8404946/ https://www.ncbi.nlm.nih.gov/pubmed/34460784 http://dx.doi.org/10.3390/jimaging7080148 |
work_keys_str_mv | AT providencialaura aniterativealgorithmforsemisupervisedclassificationofhotspotsonbonescintigraphiesofpatientswithprostatecancer AT dominguesines aniterativealgorithmforsemisupervisedclassificationofhotspotsonbonescintigraphiesofpatientswithprostatecancer AT santosjoao aniterativealgorithmforsemisupervisedclassificationofhotspotsonbonescintigraphiesofpatientswithprostatecancer AT providencialaura iterativealgorithmforsemisupervisedclassificationofhotspotsonbonescintigraphiesofpatientswithprostatecancer AT dominguesines iterativealgorithmforsemisupervisedclassificationofhotspotsonbonescintigraphiesofpatientswithprostatecancer AT santosjoao iterativealgorithmforsemisupervisedclassificationofhotspotsonbonescintigraphiesofpatientswithprostatecancer |