Application of machine learning methods to guide patient management by predicting the risk of malignancy of Bethesda III-V thyroid nodules.

Grégoire D'Andréa,Jocelyn Gal,Loïc Mandine,Olivier Dassonville,Clair Vandersteen,Nicolas Guevara,Laurent Castillo,Gilles Poissonnet,Dorian Culié,Roxane Elaldi,Jérôme Sarini, Anne Decotte,Claire Renaud,Sébastien Vergez,Renaud Schiappa,Emmanuel Chamorey,Yann Château,Alexandre Bozec

European journal of endocrinology（2023）

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摘要

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.

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cytopathological features,machine learning,predictive model,risk of malignancy,thyroid cancer,thyroid nodules,ultrasonographic features

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