# A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
## 6 Conclusions  
In this research study, convolutional neural network algorithms have been used to deal with the problem of bone metastasis diagnosis in breast cancer patients due to their significant applicability in image analysis. A total of 408 images were acquired with similar numbers of both benign and malignant cases (metastasis present). Two different types of processing images, RGB-mode and grayscale-mode, denoting their colored or monochromatic tone, were tested in order to evaluate their contribution to the increase of performance. Several CNN architectures were tested, and only one ended up with the best performance under all hyperparameter selection cases, with the results ranging from 85.35% to 93.75%. In conclusion, these preliminary image data suggest that bone scintigraphy combined with the CNN method can have a considerable effect in the detection of bone metastasis. In particular, this approach allows an easier and more precise interpretation of the images that can have a positive impact on diagnosis accuracy as well as on decision making regarding the treatment that will be further administered. Due to an insufficient amount of data in our study, a more detailed classification and diagnosis of breast cancer diseases needs to be carried out, and that is going to be our next priority in our future research. Since deep neural networks are based on complex, inter-connected hierarchical representations of the training data, which are used to make predictions, the task of interpreting these predictions becomes quite demanding. Moreover, due to the resemblance between deep neural networks and \"blackbox\" models, interpretability needs to be enhanced, while the research community should further investigate how to measure sensitivity and visualize features in deep learning. In future work, the authors intend to show how the proposed algorithm could be integrated into a CAD system of whole-body diagnosis in nuclear medicine and also provide evidence that the clinical decision support system could be generalized across continents. **Author Contributions:** Conceptualization, N.P. and E.P. ; methodology, N.P., E.P. and A.A.; software, A.F. and A.A.; validation, N.P., E.P. and A.A.; formal analysis, N.P. ; investigation, N.P. ; resources, N.P. ; data curation, N.P. ; writing--original draft preparation, N.P. ; writing--review and editing, N.P., and E.P. ; visualization, A.F. and A.A.; supervision, N.P. and E.P. ; project administration, E.P. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Acknowledgments:** In this section you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments). **Conflicts of Interest:** The authors declare no conflict of interest. **Appendix A**  
* [PERSON] et al. (2006) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] The detection of bone metastases in patients with high-risk prostate cancer: 99 mTc-MDP Planar bone scintigraphy, single- and multi-field-of-view SPECT, 18F-fluoride PET, and 18F-fluoride PET/CT. _J. Nucl. Med._**2006**, _47_, 287-297. * [PERSON] et al. (2011) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Comparison of FDG-PET/CT and bone scintigraphy for detection of bone metastases in breast cancer. _Acta Radiol._**2011**, _52_, 1009-1014. [CrossRef]
* [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON] Comparison of diagnostic certainty and accuracy of 18F-NaF PET/CT and planar 99 mTc-MDP bone scan in patients with prostate cancer. _J. Nucl. Med._**2014**, _55_, 1669. * [PERSON] et al.