In:
International Journal of Surgery, Ovid Technologies (Wolters Kluwer Health)
Kurzfassung:
Preoperative evaluation of the metastasis status of lateral lymph nodes (LNs) in papillary thyroid cancer (PTC) is challenging. Strategies for using deep learning (DL) to diagnosis of lateral LN metastasis require additional development and testing. This study aimed to build a DL-based model to distinguish benign lateral LNs from metastatic lateral LNs in PTC and test the model’s diagnostic performance in a real-world clinical setting. Methods: This was a prospective diagnostic study. An ensemble model integrating a three-dimensional residual network (ResNet) algorithm with clinical risk factors available before surgery was developed based on CT images of lateral LNs in an internal dataset and validated in two external datasets. The diagnostic performance of the ensemble model was tested and compared with the results of fine-needle aspiration (FNA) (used as the standard reference method) and the diagnoses made by two senior radiologists in 113 suspicious lateral LNs in patients enrolled prospectively. Results: The area under the receiver operating characteristic curve of the ensemble model for diagnosing suspicious lateral LNs was 0.824 (95% CI, 0.738-0.911). The sensitivity and specificity of the ensemble model were 0.839 (95% CI, 0.762-0.916) and 0.769 (95% CI, 0.607-0.931), respectively. The diagnostic accuracy of the ensemble model was 82.3%. With FNA results as the criterion standard, the ensemble model had excellent diagnostic performance ( P =0.115), similar to that of the two senior radiologists ( P =1.000 and P =0.392, respectively). Conclusion: A three-dimensional ResNet-based ensemble model was successfully developed for diagnostic assessment of suspicious lateral LNs and achieved diagnostic performance similar to that of FNA and senior radiologists. The model appears promising for clinical application.
Materialart:
Online-Ressource
ISSN:
1743-9159
DOI:
10.1097/JS9.0000000000000660
Sprache:
Englisch
Verlag:
Ovid Technologies (Wolters Kluwer Health)
Publikationsdatum:
2023
ZDB Id:
2201966-2
