In:
Frontiers in Oncology, Frontiers Media SA, Vol. 13 ( 2023-8-23)
Abstract:
To develop and validate a three-dimensional ultrasound (3D US) radiomics nomogram for the preoperative prediction of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC). Methods This retrospective study included 168 patients with surgically proven PTC (non-ETE, n = 90; ETE, n = 78) who were divided into training (n = 117) and validation (n = 51) cohorts by a random stratified sampling strategy. The regions of interest (ROIs) were obtained manually from 3D US images. A larger number of radiomic features were automatically extracted. Finally, a nomogram was built, incorporating the radiomics scores and selected clinical predictors. Receiver operating characteristic (ROC) curves were performed to validate the capability of the nomogram on both the training and validation sets. The nomogram models were compared with conventional US models. The DeLong test was adopted to compare different ROC curves. Results The area under the receiver operating characteristic curve (AUC) of the radiologist was 0.67 [95% confidence interval (CI), 0.580–0.757] in the training cohort and 0.62 (95% CI, 0.467–0.746) in the validation cohort. Sixteen features from 3D US images were used to build the radiomics signature. The radiomics nomogram, which incorporated the radiomics signature, tumor location, and tumor size showed good calibration and discrimination in the training cohort (AUC, 0.810; 95% CI, 0.727–0.876) and the validation cohort (AUC, 0.798; 95% CI, 0.662–0.897). The result suggested that the diagnostic efficiency of the 3D US-based radiomics nomogram was better than that of the radiologist and it had a favorable discriminate performance with a higher AUC (DeLong test: p & lt; 0.05). Conclusions The 3D US-based radiomics signature nomogram, a noninvasive preoperative prediction method that incorporates tumor location and tumor size, presented more advantages over radiologist-reported ETE statuses for PTC.
Type of Medium:
Online Resource
ISSN:
2234-943X
DOI:
10.3389/fonc.2023.1046951
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2023
detail.hit.zdb_id:
2649216-7
