Generating a Multimodal Artificial Intelligence Model to Differentiate Benign and Malignant Follicular Neoplasms of the Thyroid: A Proof-of-Concept Study

Machine learning (ML) has been increasingly used to develop algorithms that can improve medical diagnostics and prognostication, and has shown promise in improving classification of thyroid ultrasound images. This proof-of-concept study aims to develop a multimodal ML 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 (RFC) to predict malignancy. Leave-one-out cross-validation was conducted to evaluate classifier performance using Area Under the ROC Curve (AUC). 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-SNE method reduced the dimension to two primary represented components. The RFC achieved AUCs of 0.76 (clinical only), 0.29 (image only), and 0.79 (multimodal data). Our multimodal ML model demonstrates promising results in classifying follicular carcinoma from adenoma. This approach can potentially be applied in future studies to generate models for pre-operative differentiation of follicular thyroid neoplasms.