A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans

Abstract

(1) Background: Bone metastasis is one of the most frequent diseases in breast, lung and prostate cancer; bone scintigraphy is the primary imaging method of screening that offers the highest sensitivity (95%) regarding metastases. To address the considerable problem of bone metastasis diagnosis, focused on breast cancer patients, artificial intelligence methods devoted to deep-learning algorithms for medical image analysis are investigated in this research work; (2) Methods: Deep learning is a powerful algorithm for automatic classification and diagnosis of medical images whereas its implementation is achieved by the use of convolutional neural networks (CNNs). The purpose of this study is to build a robust CNN model that will be able to classify images of whole-body scans in patients suffering from breast cancer, depending on whether or not they are infected by metastasis of breast cancer; (3) Results: A robust CNN architecture is selected based on CNN exploration performance for bone metastasis diagnosis using whole-body scan images, achieving a high classification accuracy of 92.50%. The best-performing CNN method is compared with other popular and well-known CNN architectures for medical imaging like ResNet50, VGG16, MobileNet, and DenseNet, reported in the literature, providing superior classification accuracy; and (4) Conclusions: Prediction results show the efficacy of the proposed deep learning approach in bone metastasis diagnosis for breast cancer patients in nuclear medicine.

A Lightweight Convolutional Neural Network Architecture Applied for Bone Metastasis Classification in Nuclear Medicine: A Case Study on Prostate Cancer Patients

“…To evaluate the classification performance of LB-FCN light architecture, we adopted the methodology applied in [ 14 ] where state-of-the-art convolutional neural networks were employed for solving the three-class classification problem of BS detection on P-Ca patients’ images. These CNNs have already been applied in similar problems of bone metastasis classification in nuclear medicine [ 10 , 13 , 14 , 15 , 42 , 43 ]. To this end, LB-FCN light was compared with ResNet50, VGG16, MobileNet, InceptionV3, Xception, and the fast CNN proposed in [ 14 ], namely Papandrianos et al, following 10-fold stratified cross-validation.…”

“…The problem was formulated as a three-class classification problem aligned with [ 14 ]. The LB-FCN light architecture was chosen to address the limitations derived from previous works [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] such as: A large annotated dataset of medical images is necessary to achieve strong generalization ability. Abnormalities in images can also be presented due to non-neoplastic diseases.…”

“…The study modifies the holistically nested edge detection (HED) network to match the characteristics of BS. Diagnosis of breast cancer metastasis in bones via deep learning was presented in [ 13 ] taking advantage of WBS. The study compared various convolutional neural networks (CNN) models for solving the binary classification problem of bone metastasis from breast.…”

“…In our study, we employed the lightweight look-behind fully convolutional neural network (LB-FCN light ) architecture, which is a revised version of the original LB-FCN, focused on the computational performance reduction by decreasing the number of required free parameters. The lightweight version of LB-FCN has been already used for mobile applications such as staircase detection in natural images [ 13 ]. Variation of multiscale feature extraction and the low number of free parameters enables the network to generalize well, even when the number of training samples is limited.…”

Applications of artificial intelligence in nuclear medicine image generation

“…In recent years, DL technologies such as CNNs, artificial neural networks (ANNs), and generative adversarial networks (GANs) have developed very fast and shown better performance than traditional ML in some cases. The applications of DL in nuclear medicine include disease diagnosis [PET (24), SPECT (25,26)], imaging physics [PET (27), SPECT (28)], image reconstruction [PET (29), SPECT (30)], image denoising [PET (31,32), SPECT (33)], image segmentation [PET (34), SPECT (35)], image classification [PET (36), SPECT (37)], and internal dose prediction (38,39).…”