Generating a multimodal artificial intelligence model to differentiate benign and malignant follicular neoplasms of the thyroid: A proof-of-concept study

• Published in: Surgery Vol. 175; no. 1; pp. 121 - 127
• Main Authors: Lin, Ann C, Liu, Zelong, Lee, Justine, Ranvier, Gustavo Fernandez, Taye, Aida, Owen, Randall, Matteson, David S, Lee, Denise
• Format: Journal Article
• Language: English
• Published: United States 01.01.2024
• Subjects: Adenocarcinoma, Follicular - diagnostic imaging; Adenoma - diagnostic imaging; Artificial Intelligence; Humans; Retrospective Studies; Thyroid Neoplasms - diagnostic imaging; Thyroid Neoplasms - pathology
• ISSN: 0039-6060; 1532-7361
• DOI: 10.1016/j.surg.2023.06.053

Machine learning has been increasingly used to develop algorithms that can improve medical diagnostics and prognostication and has shown promise in improving the classification of thyroid ultrasound images. This proof-of-concept study aims to develop a multimodal machine-learning model to classify follicular carcinoma from adenoma. This is a retrospective study of patients with follicular adenoma or carcinoma at a single institution between 2010 and 2022. Demographics, imaging, and perioperative variables were collected. The region of interest was annotated on ultrasound and used to perform radiomics analysis. Imaging features and clinical variables were then used to create a random forest classifier to predict malignancy. Leave-one-out cross-validation was conducted to evaluate classifier performance using the area under the receiver operating characteristic curve. Patients with follicular adenomas (n = 7) and carcinomas (n = 11) with complete imaging and perioperative data were included. A total of 910 features were extracted from each image. The t-distributed stochastic neighbor embedding method reduced the dimension to 2 primary represented components. The random forest classifier achieved an area under the receiver operating characteristic curve of 0.76 (clinical only), 0.29 (image only), and 0.79 (multimodal data). Our multimodal machine learning model demonstrates promising results in classifying follicular carcinoma from adenoma. This approach can potentially be applied in future studies to generate models for preoperative differentiation of follicular thyroid neoplasms.