Diagnostic value of an interpretable machine learning model based on clinical ultrasound features for follicular thyroid carcinoma.

ABSTRACT:

Background

Follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) present diagnostic challenges due to overlapping clinical and ultrasound features. Improving the diagnosis of FTC can enhance patient prognosis and effectiveness in clinical management. This study seeks to develop a predictive model for FTC based on ultrasound features using machine learning (ML) algorithms and assess its diagnostic effectiveness.

Methods

Patients diagnosed with FTA or FTC based on surgical pathology between January 2009 and February 2023 at Zhejiang Provincial Cancer Hospital and Zhejiang Provincial People's Hospital were retrospectively included. A total of 562 patients from Zhejiang Provincial Cancer Hospital comprised the training set, and 218 patients from Zhejiang Provin

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC11400673 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature