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Nomogram based on radiomics analysis of ultrasound images can improve preoperative BRAF mutation diagnosis for papillary thyroid microcarcinoma
Ist Teil von
Frontiers in endocrinology (Lausanne), 2022-08, Vol.13, p.915135-915135
Ort / Verlag
Frontiers Media S.A
Erscheinungsjahr
2022
Quelle
EZB-FREE-00999 freely available EZB journals
Beschreibungen/Notizen
Background
The preoperative identification of
BRAF
mutation could assist to make appropriate treatment strategies for patients with papillary thyroid microcarcinoma (PTMC). This study aimed to establish an ultrasound (US) radiomics nomogram for the assessment of
BRAF
status.
Methods
A total of 328 PTMC patients at the China-Japan Friendship Hospital between February 2019 and November 2021 were enrolled in this study. They were randomly divided into training (
n
= 232) and validation (
n
= 96) cohorts. Radiomics features were extracted from the US images. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the
BRAF
status-related features and calculate the radiomics score (Rad-score). Univariate and multivariate logistic regression analyses were subsequently performed to identify the independent factors among Rad-score and conventional US features. The US radiomics nomogram was established and its predictive performance was evaluated
via
discrimination, calibration, and clinical usefulness in the training and validation sets.
Results
Multivariate analysis indicated that the Rad-score, composition, and aspect ratio were independent predictive factors of
BRAF
status. The US radiomics nomogram which incorporated the three variables showed good calibration. The discrimination of the US radiomics nomogram showed better discriminative ability than the conventional US model both in the training set (AUC 0.685 vs. 0.592) and validation set (AUC 0.651 vs. 0.622). Decision curve analysis indicated the superior clinical applicability of the nomogram compared to the conventional US model.
Conclusions
The US radiomics nomogram displayed better performance than the conventional US model in predicting
BRAF
mutation in patients with PTMC.