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Although it is the gold standard for assessing the malignancy of thyroid nodules (TNs) preoperatively, the cytological analysis of fine-needle aspiration cytology (FNAC) samples results in 20–30% of cases in indeterminate lesions (ITNs). As two-thirds of these lesions will appear benign after diagnostic surgery, improved preoperative diagnostic methods need to be developed. In this pilot study, we evaluate if the metabolomic profiles of liquid-based (CytoRich®) FNAC samples of benign and malignant nodules can allow the molecular diagnosis of TNs. We performed untargeted metabolomic analyses with CytoRich® FNAC in a monocentric retrospective study. The cohort was composed of cytologically benign TNs, histologically benign or papillary thyroid carcinomas (PTCs) cytologically ITNs, and suspicious/malignant TNs histologically confirmed as PTCs. The diagnostic performance of the identified metabolomic signature was assessed using several supervised classification methods. Seventy-eight patients were enrolled in the study. We identified 7690 peaks, of which 2697 ions were included for further analysis. We selected a metabolomic signature composed of the top 15 metabolites. Among all the supervised classification methods, the supervised autoencoder deep neural network exhibited the best performance, with an accuracy of 0.957 (0.842–1), an AUC of 0.945 (0.833–1), and an F1 score of 0.947 (0.842–1). Here, we report a promising new ancillary molecular technique to differentiate PTCs from benign TNs (including among ITNs) based on the metabolomic signature of FNAC sample fluids. Further studies with larger cohorts are now needed to identify a larger number of biomarkers and obtain more robust signatures. [ABSTRACT FROM AUTHOR]

Hearing loss is a major public health problem with significant evidence correlating it with cognitive performance. Verbal fluency tests are commonly used to assess lexical access. They provide a great deal of information about a subject's cognitive function. The aim of our study was to evaluate phonemic and semantic lexical access abilities in adults with bilateral severe to profound hearing loss and then to re-evaluate a cohort after cochlear implantation. 103 adult subjects underwent phonemic and semantic fluency tests during a cochlear implant candidacy evaluation. Of the total 103 subjects, 43 subjects underwent the same tests at 3 months post-implantation. Our results showed superior performance in phonemic fluency compared to semantic fluency in subjects prior to implantation. Phonemic fluency was positively correlated with semantic fluency. Similarly, individuals with congenital deafness had better semantic lexical access than individuals with acquired deafness. Results at 3 months post-implantation showed an improvement in phonemic fluency. No correlation was found between the evolution of pre- and post-implant fluency and the auditory gain of the cochlear implant, and we found no significant difference between congenital and acquired deafness. Our study shows an improvement in global cognitive function after cochlear implantation without differentiation of the phonemic-semantic pathway. [ABSTRACT FROM AUTHOR]

Objective: Indeterminate thyroid nodules (ITN) are common and often lead to (sometimes unnecessary) diagnostic surgery. We aimed to evaluate the performance of two machine learning methods (ML), based on routinely available features to predict the risk of malignancy (RM) of ITN. Design: Multi-centric diagnostic retrospective cohort study conducted between 2010 and 2020. Methods: Adult patients who underwent surgery for at least one Bethesda III-V thyroid nodule (TN) with fully available medical records were included. Of the 7917 records reviewed, eligibility criteria were met in 1288 patients with 1335 TN. Patients were divided into training (940 TN) and validation cohort (395 TN). The diagnostic performance of a multivariate logistic regression model (LR) and its nomogram, and a random forest model (RF) in predicting the nature and RM of a TN were evaluated. All available clinical, biological, ultrasound, and cytological data of the patients were collected and used to construct the two algorithms. Results: There were 253 (19%), 693 (52%), and 389 (29%) TN classified as Bethesda III, IV, and V, respectively, with an overall RM of 35%. Both cohorts were well-balanced for baseline characteristics. Both models were validated on the validation cohort, with performances in terms of specificity, sensitivity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve of 90%, 57.3%, 73.4%, 81.4%, 84% (CI95%: 78.5%-89.5%) for the LR model, and 87.6%, 54.7%, 68.1%, 80%, 82.6% (CI95%: 77.4%-87.9%) for the RF model, respectively. Conclusions: Our ML models performed well in predicting the nature of Bethesda III-V TN. In addition, our freely available online nomogram helped to refine the RM, identifying low-risk TN that may benefit from surveillance in up to a third of ITN, and thus may reduce the number of unnecessary surgeries. [ABSTRACT FROM AUTHOR]