## 4 Discussion
In addition, a special procedure should be developed to remove CytoRich(tm) fixatives prior to SPME extraction and LC-MS/MS analysis. Gas chromatography (GC-MS/MS), another MS-based method that may be less sensitive to contaminants from the CytoRich(tm) solution, and lipidomic analysis could be performed. With the ever-increasing prevalence of ITNs, the molecular tests already available are being used more and more frequently [31]. Despite the good diagnostic performance of these tests, their high cost remains a challenge for health-care systems worldwide [8]. Newmethods are constantly emerging to provide better patient management [20; 32; 33]. Our aim was to identify a metabolomic signature that would allow accurate, rapid and cost-effective discrimination between malignant and benign ITNs using LBC fluid, thus avoiding the burden of additional biopsies on the patient. As described above, we first processed the database by filtering and denoising the data to retain only the most intense, frequent, and statistically significant metabolites. In addition, we used robust supervised machine learning and deep learning algorithms to perform statistical analyses on multiple samples, which allowed us to accurately diagnose benign and malignant ITNs (Figures 3 and 4, Tables 2-4). The deep learning (SAE) algorithm provided the best performance for a model classifying benign and malignant nodules in terms of accuracy and AUC. In the context of unbalanced data, the obtained F1 score was also a metric to be emphasized. By combining two competing metrics (precision and recall scores), the SAE approach assesses the predictive ability of a model by determining its class-specific performance rather than its overall performance, as is the case when calculating accuracy. Regarding the F1 score, the SAE was still the best classification method compared to all the other methods tested. The consistent performance of the different algorithms tested strongly supported the relevance of the metabolomic signature identified. As mentioned above, the metabolomic analysis was performed with the CytoRichTM solution remaining after cytological examination of the FNAC sample. Therefore, the approach can be used for all cytological ITNs without requiring an additional biopsy. Previous studies have shown the usefulness of identifying metabolomic signatures to distinguish TCs from benign TNs or healthy tissue. However, to our knowledge, this is the first study to determine an LC/MS-based metabolomic signature using LBC samples to diagnose ITNs. Several groups describe alterations of metabolic pathways found in tissue samples from TCs that are also detectable in serum, plasma or whole-blood samples from these patients [34; 35; 36; 37]. [PERSON] et al. and [PERSON] et al. report metabolic differences in the urine and feces of patients with papillary TC and healthy volunteers [36; 38]. [PERSON] and collaborators describe a high-resolution magic angle spinning magnetic resonance spectroscopy (HRMAS-MRS) approach on ex-vivo FNAC samples collected from postsurgical thyroid nodules. The authors show that although NMR spectral features allow the differentiation between follicular adenomas and papillary thyroid carcinomas, diagnostic accuracy is only 84.8% when samples from ITNs are analyzed [39]. Although our approach enabled the determination of a specific signature for ITNs with high accuracy, many metabolites of the FNAC samples could not be identified. Storage at 4 \({}^{\circ}\)C in the CytoRichTM solution may have led to the chemical transformation of many metabolites, which then made the identification of ions produced during LC-MS/MS impossible. While this did not compromise the discriminatory performance of the metabolomic approach, it was more difficult to gain insights into the underlying biological processes. Further strategies need to be developed to reduce the impact of the CytoRichTM solution to improve the identification of relevant metabolites and consequently the quality of the analyses.