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Development of Convolutional Neural Networks to identify bone metastasis for prostate cancer patients in bone scintigraphy
dc.creator | Papandrianos N., Papageorgiou E.I., Anagnostis A. | en |
dc.date.accessioned | 2023-01-31T09:43:59Z | |
dc.date.available | 2023-01-31T09:43:59Z | |
dc.date.issued | 2020 | |
dc.identifier | 10.1007/s12149-020-01510-6 | |
dc.identifier.issn | 09147187 | |
dc.identifier.uri | http://hdl.handle.net/11615/77775 | |
dc.description.abstract | Objective: The main aim of this work is to build a robust Convolutional Neural Network (CNN) algorithm that efficiently and quickly classifies bone scintigraphy images, by determining the presence or absence of prostate cancer metastasis. Methods: CNN, widely applied in medical image classification, was used for bone scintigraphy image classification. The retrospective study included 778 sequential male patients who underwent whole-body bone scans. A nuclear medicine physician classified all the cases into 3 categories: (1) normal, (2) malignant, and (3) degenerative, which were used as the gold standard. Results: An efficient CNN architecture was built, based on CNN exploration performance, achieving high prediction accuracy. The results showed that the method is sufficiently precise when it comes to differentiating a bone metastasis from other either degenerative changes or normal tissue (overall classification accuracy = 91.42% ± 1.64%). To strengthen the outcomes of this study the authors further compared the best performing CNN method to other popular CNN architectures for medical imaging, like ResNet50, VGG16 and GoogleNet, as reported in the literature. Conclusions: The prediction results reveal the efficacy of the proposed CNN-based approach and its ability for an easier and more precise interpretation of whole-body images in bone metastasis diagnosis for prostate cancer patients in nuclear medicine. This leads to marked effects on the diagnostic accuracy and decision-making regarding the treatment to be applied. © 2020, The Japanese Society of Nuclear Medicine. | en |
dc.language.iso | en | en |
dc.source | Annals of Nuclear Medicine | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089779447&doi=10.1007%2fs12149-020-01510-6&partnerID=40&md5=67efc936044b21c4f8453881a8cb4544 | |
dc.subject | medronate technetium tc 99m | en |
dc.subject | oxidronate technetium tc 99m | en |
dc.subject | poltechmdp | en |
dc.subject | Article | en |
dc.subject | back propagation | en |
dc.subject | bone metastasis | en |
dc.subject | bone scintiscanning | en |
dc.subject | cancer classification | en |
dc.subject | cancer patient | en |
dc.subject | clinical outcome | en |
dc.subject | comparative study | en |
dc.subject | computer assisted diagnosis | en |
dc.subject | controlled study | en |
dc.subject | convolutional neural network | en |
dc.subject | diagnostic accuracy | en |
dc.subject | diagnostic test accuracy study | en |
dc.subject | gold standard | en |
dc.subject | human | en |
dc.subject | image processing | en |
dc.subject | major clinical study | en |
dc.subject | male | en |
dc.subject | physician | en |
dc.subject | positron emission tomography-computed tomography | en |
dc.subject | priority journal | en |
dc.subject | prostate cancer | en |
dc.subject | radiodiagnosis | en |
dc.subject | retrospective study | en |
dc.subject | sensitivity and specificity | en |
dc.subject | whole body scintiscanning | en |
dc.subject | bone | en |
dc.subject | bone tumor | en |
dc.subject | diagnostic imaging | en |
dc.subject | pathology | en |
dc.subject | prostate tumor | en |
dc.subject | scintiscanning | en |
dc.subject | Bone and Bones | en |
dc.subject | Bone Neoplasms | en |
dc.subject | Humans | en |
dc.subject | Image Interpretation, Computer-Assisted | en |
dc.subject | Male | en |
dc.subject | Neural Networks, Computer | en |
dc.subject | Prostatic Neoplasms | en |
dc.subject | Radionuclide Imaging | en |
dc.subject | Retrospective Studies | en |
dc.subject | Springer Japan | en |
dc.title | Development of Convolutional Neural Networks to identify bone metastasis for prostate cancer patients in bone scintigraphy | en |
dc.type | journalArticle | en |
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