Abstract
OBJECTIVE: The assessment of lateral lymph node metastasis (LLNM) in patients with papillary thyroid carcinoma (PTC) holds great significance. This study aims to develop and evaluate a deep learning-based automatic pipeline system (DLAPS) for diagnosing LLNM in PTC using computed tomography (CT). METHODS: A total of 1,266 lateral lymph nodes (LLNs) from 519 PTC patients who underwent CT examinations from January 2019 to November 2022 were included and divided into training and validation set, internal test set, pooled external test set, and prospective test set. The DLAPS consists of an auto-segmentation network based on RefineNet model and a classification network based on ensemble model (ResNet, Xception, and DenseNet). The performance of the DLAPS was compared with that of manually segmented DL models, the clinical model, and Node Reporting and Data System (Node-RADS). The improvement of radiologists' diagnostic performance under the DLAPS-assisted strategy was explored. In addition, bulk RNA-sequencing was conducted based on 12 LLNs to reveal the underlying biological basis of the DLAPS. RESULTS: The DLAPS yielded good performance with area under the receiver operating characteristic curve (AUC) of 0.872, 0.910, and 0.822 in the internal, pooled external, and prospective test sets, respectively. The DLAPS significantly outperformed clinical models (AUC 0.731, P<0.001) and Node-RADS (AUC 0.602, P<0.001) in the internal test set. Moreover, the performance of the DLAPS was comparable to that of the manually segmented deep learning (DL) model with AUCs ranging 0.814-0.901 in three test sets. Furthermore, the DLAPS-assisted strategy improved the performance of radiologists and enhanced inter-observer consistency. In clinical situations, the rate of unnecessary LLN dissection decreased from 33.33% to 7.32%. Furthermore, the DLAPS was associated with the cell-cell conjunction in the microenvironment. CONCLUSIONS: Using CT images from PTC patients, the DLAPS could effectively segment and classify LLNs non-invasively, and this system had a good generalization ability and clinical applicability.