Ultrasound radiomics signature for predicting central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma

Abstract

Background: Whether prophylactic central lymph node dissection is necessary for patients with clinically node-negative (cN0) papillary thyroid microcarcinoma (PTMC) remains controversial. Herein, we aimed to establish an ultrasound (US) radiomics (Rad) score for assessing the probability of central lymph node metastasis (CLNM) in such patients.

Methods: 480 patients (327 in the training cohort, 153 in the validation cohort) who underwent thyroid surgery for cN0 PTMC at two institutions between January 2018 and December 2020 were included. Radiomics features were extracted from the US images. Least absolute shrinkage and selection operator logistic regression were utilized to generate a Rad score. A nomogram consisting of the Rad score and clinical factors was then constructed for the training cohort. Both cohorts assessed model performance using discrimination, calibration, and clinical usefulness.

Results: Based on the six most valuable radiomics features, the Rad score was calculated for each patient. A multivariate analysis revealed that a higher Rad score (P < 0.001), younger age (P = 0.006), and presence of capsule invasion (P = 0.030) were independently associated with CLNM. A nomogram integrating these three factors demonstrated good calibration and promising clinical utility in the training and validation cohorts. The nomogram yielded areas under the curve of 0.795 (95% confidence interval [CI], 0.745-0.846) and 0.774 (95% CI, 0.696-0.852) in the training and validation cohorts, respectively.

Conclusions: The radiomics nomogram may be a clinically useful tool for the individual prediction of CLNM in patients with cN0 PTMC.

Keywords: Central lymph node metastasis; Machine learning; Nomogram; Papillary thyroid microcarcinoma; Radiomics.

Fig. 1

Radiomics feature selection using the least absolute shrinkage and selection operator (LASSO) logistic regression model. (A) Ten-fold cross-validation for tuning parameter selection in the LASSO logistic model. Solid vertical lines represent binomial deviance ± standard error. The vertical lines are drawn at the optimal values by minimum criteria and 1 - S.E. criteria. (B) LASSO coefficient profiles of the 107 radiomics features. A coefficient profile plot was produced against the log (λ) sequence. A vertical line was drawn at the value selected using ten-fold cross-validation, where optimal λ resulted in six nonzero coefficients

Fig. 2

Univariate logistic analysis of central lymph node metastasis with restricted cubic splines (RCS) in the training (A) and validation (B) cohorts

Fig. 3

(A) A nomogram combining the Rad score, age, and capsule invasion for predicting probability of central lymph node metastasis (CLNM). (B) Plots depict the calibration of the nomogram in terms of agreement between predicted probability and actual probability in the training and validation cohorts. (C) Areas under the receiver operating characteristic curves for CLNM in the training and validation cohorts. (D) Decision curve analysis for the nomogram in the training and validation cohorts