# A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
## 1 Introduction  
Bone metastasis is cancer that initially started in another part of the body and spread to the bone. Various names can be found in the literature, such as secondary bone cancer or metastatic bone disease [1, 2]. Bone metastasis commonly occurs in the spine, thigh and pelvis but far from that, it can also be seen in any bone. Sometimes, bone metastasis appears years after cancer treatment, or may be the first indication of cancer. Metastatic cancer in bones is a life-threatening disease. Proper diagnosis and treatment of metastatic cancer in bones can save people's lives [3]. Identification of normal, benign, and malignant tissues seems to be a step of high importance before treatment of bone metastasis tumors. The mostcommon tumors, which frequently metastasaze to bone, are those of the breast, lung and prostate, whereas the skeleton area especially in patients with advanced disease, accumulates most of the tumor burden [4]. In particular, breast cancer and prostate cancer are the most common types of cancer likely to spread to bone [3]. Previous studies clearly report that 60%-75% of metastatic breast cancers are initially diagnosed with bone metastasis [5; 6]. Bone metastasis in women who suffer from breast cancer is clinically important [7], and has a significant impact on the quality of their life [8]. Rapid diagnosis of bone metastases can be achieved using modern imaging techniques such as scintigraphy, positron emission tomography (PET) and whole-body magnetic resonance imaging (MRI) [9; 10; 11]. Among them, bone scintigraphy offers the highest sensitivity (95%) regarding metastases, making it the primary imaging method of screening, [12; 13; 14]. In particular, whole-body bone scintigraphy emerges as the most common diagnostic procedure in nuclear medicine for clinical routine investigation [14; 15; 16] and can reveal the sites that bone metastases are distributed in all types of cancer, possibly predicting the outcome [17; 18]. It actually presents high sensitivity whereas the changes of bone metabolism are identified by doctors and physicians earlier than the changes in bone structure detected by skeletal radiograms [2; 7]. Infections, degenerative changes, benign or malignant diseases and other clinical entities are among indications in bone scintigraphy [16]. This method, however, shows low specificity, as it cannot distinguish between the causes of bone turnovers and those of metastatic origin (leukaemia, healing fracture, etc. ), which is a significant drawback of this method. At present, bone scintigraphy which uses single-photon emission computed tomography (SPECT) imaging, remains the most used and well-known imaging method for clinical diagnosis of metastatic cancer, both men and women [12; 13; 18]. It turned out to be an effective tool for early diagnosis of bone metastasis in breast and prostate cancer patients [15; 17]. In particular, with the help of nuclear medical imaging of the targeted area of the body, which is used for the identification of benign and malignant conditions, the doctor and the physician are able to diagnose bone metastasis [10; 19]. To address the considerable problem of bone metastasis diagnosis, machine learning (ML) methods focused on deep-learning algorithms for medical image analysis, have been recently investigated by many researchers. The advancements in the field of machine learning have led to more intelligent and robust computer-aided diagnosis (CAD) systems, as the learning ability of ML methods have been constantly improving [20; 21]. Also, the groundbreaking performance of ML in CAD systems is based on deep learning (DL). DL has outstanding capabilities which make a very significant impact on improving the diagnostics potential of CAD systems in medical imaging [22; 23; 24].