## 4 Conclusions and Future Perspectives
For in vivo applications, the weakness of the Raman signal could represent an obstacle, due to the presence of several interfering signals. Another crucial point is represented by the decision time, i.e., the time interval between the beginning of the data acquisition and the output of a diagnosis. First of all, this parameter is influenced by experimental limits in the acquisition speed. In this sense, the use of RBI could help in detecting specific and relevant Raman bands for the decision process. The restriction of the acquisition spectral interval to these specific bands could represent a valid and simple solution to decrease the acquisition time and the duration of the prediction phase. As regards the employment of ML, the main limitation associated with our analysis is represented by the employment of a relatively small statistical sample, both in terms of the number of patients and the number of collected Raman spectra. In general, the adoption of datasets where the number of features of each instance is similar or larger than the number of instances available is traditionally associated with the occurrence of overfitting. Despite observing a negligible level of overfitting in this work, further investigations have to be carried out in order to further monitor the robustness of the models. For this reason, the results generated by our analysis have to be considered an exploratory investigation, representing the basis for further studies relying on more statistically significant and larger RS-based datasets. In general, further efforts should be distributed on multiple routes. These include testing ML or DL routines on specific Raman bands, such as the Raman bands corresponding to the peaks of RBI, to assess the ratio between the computational time needed to obtain the response and the classification performance; to realize cost-effective and powerful hardware, able to speed up the calculation; and to operate on the software, by implementing the classification routines in high-performance computing codes. Conceptualization, G.L. and M.D. ; methodology, G.L. ; software, G.L. ; validation, G.L. and M.D. ; experimental investigation, M.D. ; resources, M.D. ; data curation, M.D. ; writing--original draft preparation, G.L. ; writing--review and editing, G.L. ; supervision, M.D. ; project administration, M.D. ; funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript. This research was funded by the Regione Toscana through the TELEMO Project under 146 Grant Ricerca Salute 2018. The study was approved by the local Ethical Committee Comitato Etico Regionale per la Sperimentazione Clinica della Regione Toscana, Sezione AREA VASTA NORD OVEST (protocol number 14249, 17 September 2018). All the experiments were carried out in accordance with Good Clinical Practice (GCP) and with the ethical principles of the Declaration of Helsinki. Ten patients affected by primary chondrogenic tumors of the skeleton were enrolled in this study. All patients were diagnosed and treated at our Institution, Azienda Ospedaliera Universitaria Pisana, Pisa, in 2018. An informed consent was collected from all patients. For study participation of patients under the age of 18 years, a specific informed consent from a parent has been acquired (mod. C2; protocol number 14249, 17 September 2018). The request for datasets, both raw and processed data, generated during the present study can be agreed and made directly to the corresponding author. The authors wish to thank [PERSON] and [PERSON], from Azienda Ospedaliera Universitaria Pisana, for useful support. The NanoBioTLab CNR-IBF is warmly acknowledged. The authors declare no conflicts of interest.