## 4 Conclusions and Future Perspectives  
Chondrosarcoma and the classification of its various degrees of malignancy has recently become a case study for the application of Raman spectroscopy in oncology. In this investigation, we tested the ability of different ML and DL routines to accurately discriminate between CS and EC, as well as the identification of the most relevant Raman bands associated with any specific malignant degree. The classifiers used in this paper allowed us to achieve two main objectives. First of all, we observed how the MLPC(ADAM) classifier was able to distinguish between CS and EC with an accuracy reaching 99.7% and to distinguish between the specific grades of CS with an accuracy reaching 99.2%. Hence, the aforementioned DL algorithm was able to distinguish the three grades of CS and EC within a single-stage process, with an accuracy reaching 97.6%. This outcome highlighted the technique as a promising route for future applications in a clinical scenario. From an ex vivo point of view, engineered devices based on this technology could be useful for speeding up the conventional diagnostic protocol, based on the analysis of histological sections. Due to its label-free character, Raman spectroscopy could potentially allow the traditional staining procedure to be skipped, essential to bring out the histological properties of the tissues. Furthermore, its sensitivity to the molecular composition makes this approach helpful in case of ambiguous histological patterns. On the other hand, devices based on the coupling between RS and ML could find applications in the in vivo estimation of tumor margins, to increase the effectiveness of the surgical therapies and therefore the patient's life expectancy. In comparison to MLPC, SVM and RFC achieved worse classification performances. However, the analysis of RBI revealed that SVM and RFC rely on a smaller number of spectral components in comparison to MLPC to reach the aforementioned classification performances. This feature has two important implications: first of all, the spectral components maximizing the RBI of SVM and RFC potentially indicated the presence of biomarkers (proline, DNA, bioapatites) for the diseases under investigation. In particular, according to this analysis, Raman bands associated with bioapatites turned out to be the most relevant in the distinction between CS and EC. On the other hand, phenylalanine (Phe) turned out to be the most relevant amino acid in grading CS. Secondly, these findings suggest the possibility of restricting the spectral interval on the bands maximizing RBI, with negligible loss in classification performance. In view of future implementations of this technology in engineered devices, the perspective of restricting the spectral interval needed for accurate \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification Problem** & **Model** & \(A\) (\(\pm\)0.1\%) & \(S\) (\(\pm\)0.1\%) & \(SP\) (\(\pm\)0.1\%) \\ \hline EC-CS & SVM & 78.9 & 78.9 & 79.7 \\ \hline EC-CS & RFC & 98.5 & 98.5 & 97.0 \\ \hline EC-CS & MLPC(ADAM) & 99.7 & 99.7 & 99.0 \\ \hline EC-CS & MLPC(L-BFSG-B) & 99.1 & 99.1 & 97.1 \\ \hline G1-G2-G3 & SVM & 75.9 & 75.9 & 87.4 \\ \hline G1-G2-G3 & RFC & 99.2 & 99.2 & 99.6 \\ \hline G1-G2-G3 & MLPC(ADAM) & 99.2 & 99.2 & 96.6 \\ \hline G1-G2-G3 & MLPC(L-BFSG-B) & 99.2 & 99.2 & 99.6 \\ \hline EC-G1-G2-G3 & SVM & 76.6 & 76.6 & 92.4 \\ \hline EC-G1-G2-G3 & RFC & 97.3 & 97.3 & 99.1 \\ \hline EC-G1-G2-G3 & MLPC(ADAM) & 97.6 & 97.6 & 99.2 \\ \hline EC-G1-G2-G3 & MLPC(L-BFSG-B) & 97.3 & 97.3 & 99.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy \(A\), sensitivity \(S\), and specificity \(SP\), averaged over the 5 folds and over the values of the label and related to the four classifiers examined. and robust diagnosis undoubtedly represents an advantage, either in terms of acquisition speed or cost of the hardware. Among the limitations of this approach is the small intensity of the Raman signal. This issue could be challenging, especially in case of transparent ex vivo tissues.