Deep learning-based differentiation of benign and malignant thyroid follicular neoplasms on multiscale intraoperative frozen pathological images: A multicenter diagnostic study

Abstract

Objective: This study aims to develop a deep multiscale image learning system (DMILS) to differentiate malignant from benign thyroid follicular neoplasms on multiscale whole-slide images (WSIs) of intraoperative frozen pathological images.

Methods: A total of 1,213 patients were divided into training and validation sets, an internal test set, a pooled external test set, and a pooled prospective test set at three centers. DMILS was constructed using a deep learning-based weakly supervised method based on multiscale WSIs at 10×, 20×, and 40× magnifications. The performance of the DMILS was compared with that of a single magnification and validated in two pathologist-unidentified subsets.

Results: The DMILS yielded good performance, with areas under the receiver operating characteristic curves (AUCs) of 0.848, 0.857, 0.810, and 0.787 in the training and validation sets, internal test set, pooled external test set, and pooled prospective test set, respectively. The AUC of the DMILS was higher than that of a single magnification, with 0.788 of 10×, 0.824 of 20×, and 0.775 of 40× in the internal test set. Moreover, DMILS yielded satisfactory performance on the two pathologist-unidentified subsets. Furthermore, the most indicative region predicted by DMILS is the follicular epithelium.

Conclusions: DMILS has good performance in differentiating thyroid follicular neoplasms on multiscale WSIs of intraoperative frozen pathological images.

Keywords: Deep learning; intraoperative frozen pathological image; pathological diagnosis; thyroid follicular neoplasm.