Application of machine learning methods to guide patient management by predicting the risk of malignancy of Bethesda III-V thyroid nodules.
Grégoire D'AndréaJocelyn GalLoïc MandineOlivier DassonvilleClair VandersteenNicolas GuevaraLaurent CastilloGilles PoissonnetDorian CuliéRoxane ElaldiJérôme SariniAnne DecotteClaire RenaudSébastien VergezRenaud SchiappaEmmanuel ChamoreyYann ChâteauAlexandre BozecPublished in: European journal of endocrinology (2023)
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.