# A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
## 1 Introduction
### Literature Review in Bone Metastasis Classification Using CNNs in Nuclear Medicine  
As far as prostate cancer bone metastasis classification is concerned, most of the studies applying CNNs are devoted to PET/CT imaging and SPECT scans [67; 68; 69; 70; 71; 72; 73; 74; 75; 76]. These studies have been gathered in Table 2.  
In [70], a master thesis, was the first study devoted to CNN application by correctly classifying metastasis in bone scintigraphy images for prostate cancer. That thesis focused mainly on classification problems without considering any identification and segmentation tasks. The used dataset was provided by Exini Diagnostics AB, in the form of image patches of hotspots already found. Due to time frame restrictions, only the hotspots found in the spine had been used to train the CNN, as those were considered the easiest to classify. Bone scan index (BSI) was calculated for whole-body bone scans, by segmenting the entire skeleton from the background in both the anterior and posterior views. A shape model based on a mean shape of several normal whole-body scans was fitted to the skeleton, using an image analysis algorithm called Morphon registration. The outcomes of this master thesis [70] showed that the calculated accuracy of the validation set was 0.875, whereas the calculated accuracy of the testing set was 0.89. In PET/CT imaging, a deep convolutional neural network has been implemented to distinguish benign from malignant lesions in NaF PET/CT images of patients with metastatic prostate cancer [71]. Also, a CNN-based system was examined to detect malignant findings in FDG (fluorodeoxyglucose)PET-CT examinations in a retrospective study [72]. Various CNN models with equivalent configurations, such as ResNet, DenseNet and Visual Geometry Group (VGG), were used to classify whole-body FDG PET images in three categories of benign, malignant and equivocal patient cases. Location of malignant uptake was classified at the same time into, (A) head-and-neck, (B) chest, (C) abdomen, (D) pelvic region. Different predictions were derived for each location [73]. In another research work [74], a simple CNN model with an equivalent configuration to VGG, that predicts patient sex from FDG PET-CT images was proposed. [PERSON] and his colleagues in [75] proposed an alternative CNN-based diagnosis system for whole-body FDG PET-CT that predicts whether physician's further diagnosis is required or not. A thorough analysis of the results showed an accuracy of \(93.2\%\pm 3.9\%\) regarding images of patients with malignant uptake while the respective accuracy for images of equivocal were \(87.8\%\pm 5.3\%\). Furthermore, the task of segmentation in skeletal scintigraphy images with deep learning models has been discussed in the study of [76], in which the authors examined different approaches to convert convolutional neural networks, designed for classification tasks, into powerful pixel-wise predictors. However, this research domain of bone scintigraphy segmentation has not been established well yet with the inclusion of advanced deep neural networks. Reviewing the relevant literature for diagnosis of bone metastasis using whole body images, the authors notice that all previous works focus mainly on PET imaging and prostate metastatic bone cancer, while no research paper seems to have been published, dealing with bone scintigraphy analysis and classification, in patients with breast cancer, using CNNs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application Type** & **Modality** & **Reference** & **Data Available** \\ \hline Metastatic prostate cancer classification & SPECT & [70] & NO \\ Metastatic prostate cancer classification & NaF PET/CT images & [71] & NO \\ Malignancy detection & FDG PET-CT & [72] & NO \\ Metastatic prostate cancer classification & FDG PET & [73] & NO \\ Patient’s sex prediction & FDG PET-CT & [74] & NO \\ predicts whether physician’s further & FDG PET-CT & [75] & NO \\ diagnosis is required or not & & & \\ Segmentation & SPECT & [76] & NO \\ \hline \hline \end{tabular}
\end{table}