Development of Convolutional Neural Networks to identify bone metastasis for prostate cancer patients in bone scintigraphy.

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.

journal_name

Ann Nucl Med

authors

Papandrianos N,Papageorgiou EI,Anagnostis A

doi

10.1007/s12149-020-01510-6

subject

Has Abstract

pub_date

2020-11-01 00:00:00

pages

824-832

issue

11

eissn

0914-7187

issn

1864-6433

pii

10.1007/s12149-020-01510-6

journal_volume

34

pub_type

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