Multi-modal Convolutional Neural Network-based Thyroid Cytology Classification and Diagnosis

Dandan Yang; Tianlun Li; Lu Li; Shuai Chen; Xiangli Li

doi:10.1016/j.humpath.2025.105868

Multi-modal Convolutional Neural Network-based Thyroid Cytology Classification and Diagnosis

Hum Pathol. 2025 Jul 4:105868. doi: 10.1016/j.humpath.2025.105868. Online ahead of print.

Authors

Dandan Yang¹, Tianlun Li¹, Lu Li¹, Shuai Chen¹, Xiangli Li²

Affiliations

¹ The Department of Pathology, Affiliated Hospital of Jining Medical University, Jining, CHINA.
² The Institute of Technological Sciences, Wuhan University, Wuhan, CHINA.

PMID: 40617519
DOI: 10.1016/j.humpath.2025.105868

Abstract

Background: The cytologic diagnosis of thyroid nodules' benign and malignant nature based on cytological smears obtained through ultrasound-guided fine-needle aspiration is crucial for determining subsequent treatment plans. The development of artificial intelligence (AI) can assist pathologists in improving the efficiency and accuracy of cytological diagnoses. We propose a novel diagnostic model based on a network architecture that integrates cytologic images and digital ultrasound image features (CI-DUF) to solve the multi-class classification task of thyroid fine-needle aspiration cytology. We compare this model with a model relying solely on cytologic images (CI) and evaluate its performance and clinical application potential in thyroid cytology diagnosis.

Methods: A retrospective analysis was conducted on 384 patients with 825 thyroid cytologic images. These images were used as a dataset for training the models, which were divided into training and testing sets in an 8:2 ratio to assess the performance of both the CI and CI-DUF diagnostic models.

Results: The AUROC of the CI model for thyroid cytology diagnosis was 0.9119, while the AUROC of the CI-DUF diagnostic model was 0.9326. Compared with the CI model, the CI-DUF model showed significantly increased accuracy, sensitivity, and specificity in the cytologic classification of papillary carcinoma, follicular neoplasm, medullary carcinoma, and benign lesions.

Conclusions: The proposed CI-DUF diagnostic model, which intergrates multi-modal information, shows better diagnostic performance than the CI model that relies only on cytologic images, particularly excelling in thyroid cytology classification.

Keywords: CNN; Thyroid cytology; artificial intelligence; multi-modal; pathology; ultrasound.