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This paper is in the following e-collection/theme issue:

Cancer Prognosis Models and Machine Learning (92) Clinical Information and Decision Making (2171) Imaging Informatics (358) Machine Learning (2330) Innovations in Cancer Diagnostic and Decision Support (125) Artificial Intelligence in Surgery and Perioperative Medicine (54)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/73069, first published .
Doctor examines patient's neck for thyroid issues

Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis

Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis

Authors of this article:

Xin Li1 Author Orcid Image ;   Wen-yu Yang2 Author Orcid Image ;   Fan Zhang3 Author Orcid Image ;   Rui Shan4 Author Orcid Image ;   Fang Mei5 Author Orcid Image ;   Shi-Bing Song1 Author Orcid Image ;   Bang-Kai Sun6 Author Orcid Image ;   Jing Chen4 Author Orcid Image ;   Run-ze Hu4 Author Orcid Image ;   Yang Yang4 Author Orcid Image ;   Yi-hang Yang4 Author Orcid Image ;   Jing-yao Liu4 Author Orcid Image ;   Chun-Hui Yuan1 Author Orcid Image ;   Zheng Liu4 Author Orcid Image
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Journals

  1. Guo X, Zhou L, Bao X, Liu S, Feng J, Zhu Y, Jiang Y, Zhang S. Deep Learning-Based Multimodal Fusion of Ultrasound, Cytology, and Clinical Features to Distinguish Follicular Thyroid Carcinoma from Adenoma: A Multicenter Study. Academic Radiology 2026;33(7):2921 View