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

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, w...

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Bibliographic Details
Published in:Annals of nuclear medicine 2020-11, Vol.34 (11), p.824-832
Main Authors: Papandrianos, Nikolaos, Papageorgiou, Elpiniki I., Anagnostis, Athanasios
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Bone cancer
Classification
Decision making
Deep learning
Diagnostic systems
Image classification
Imaging
Medical imaging
Medicine
Medicine & Public Health
Metastases
Metastasis
Neural networks
Nuclear Medicine
Original Article
Patients
Prostate cancer
Radiology
Scintigraphy
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Summary: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.
ISSN:0914-7187
1864-6433
DOI:10.1007/s12149-020-01510-6