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Simple Summary: We present a new molecular signature, based on altered miRNA expressions and specific mutations, allowing for improving the screening of malignant thyroid nodules. This is a prospective non-interventional study, including all Bethesda categories, carried out on an FNAB sampled in suspicious nodule(s) during thyroidectomy. The reference diagnosis was the pathological assessment of the surgical specimen. miRNA quantification and mutations detection were performed. Different classification algorithms were trained with molecular data to correctly classify the samples. The random forest was the best algorithm. This classifier used mostly miRNAs to classify the nodules. This classifier is able to identify malignant nodules with a high PPV and NPV (both 90%), a high specificity (96%) and a competitive sensitivity (76%). Our data suggest that miRNA expressions emerge as more reliable first-line diagnostic markers. This signature could be efficient to improve the screening of thyroid cancer as a complementary test in clinical practice, to reduce the rate of unnecessary surgery. Background: The diagnosis of malignant thyroid nodules is mainly based on the fine-needle aspiration biopsy (FNAB). To improve the detection of malignant nodules, different molecular tests have been developed. We present a new molecular signature based on altered miRNA expressions and specific mutations. Methods: This is a prospective non-interventional study, including all Bethesda categories, carried out on an FNAB sampled in suspicious nodule(s) during thyroidectomy. miRNA quantification and mutations detection were performed. The reference diagnosis was the pathological assessment of the surgical specimen. Different classification algorithms were trained with molecular data to correctly classify the samples. Results: A total of 294 samples were recorded and randomly divided in two equal groups. The random forest algorithm showed the highest accuracy and used mostly miRNAs to classify the nodules. The sensitivity and the specificity of our signature were, respectively, 76% and 96%, and the positive and negative predictive values were both 90% (disease prevalence of 30%). Conclusions: We have identified a molecular classifier that combines miRNA expressions with mutations detection. This signature could potentially help clinicians, as complementary to the Bethesda classification, to discriminate indeterminate FNABs. [ABSTRACT FROM AUTHOR]

Objectives First reports suggest that artificial intelligence (AI) such as ChatGPT-4 (Open AI, ChatGPT-4, San Francisco, USA) might represent reliable tools for therapeutic decisions in some medical conditions. This study aims to assess the decisional capacity of ChatGPT-4 in patients with head and neck carcinomas, using the multidisciplinary oncology meeting (MOM) and the National Comprehensive Cancer Network (NCCN) decision as references. Methods This retrospective study included 263 patients with squamous cell carcinoma of the oral cavity, oropharynx, hypopharynx, and larynx who were followed at our institution between January 1, 2016, and December 31, 2021. The recommendation of GPT4 for the first- and second-line treatments was compared to the MOM decision and NCCN guidelines. The degrees of agreement were calculated using the Kappa method, which measures the degree of agreement between two evaluators. Results ChatGPT-4 demonstrated a moderate agreement in first-line treatment recommendations (Kappa = 0.48) and a substantial agreement (Kappa = 0.78) in second-line treatment recommendations compared to the decisions from MOM. A substantial agreement with the NCCN guidelines for both first- and second-line treatments was observed (Kappa = 0.72 and 0.66, respectively). The degree of agreement decreased when the decision included gastrostomy, patients over 70, and those with comorbidities. Conclusions The study illustrates that while ChatGPT-4 can significantly support clinical decision-making in oncology by aligning closely with expert recommendations and established guidelines, ongoing enhancements and training are crucial. The findings advocate for the continued evolution of AI tools to better handle the nuanced aspects of patient health profiles, thus broadening their applicability and reliability in clinical practice., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Jules Bordet Institute issued approval CE3827. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Alami et al.)