The role of multifocality in predicting central lymph node metastasis in initially treated 18-55 years old female patients with unilateral papillary thyroid microcarcinoma

A nomogram based on ultrasound radiomics for predicting the invasiveness of cN0 single papillary thyroid microcarcinoma

Background

Up to 15.3% of papillary thyroid microcarcinoma (PTMC) patients with negative clinical lymph node metastasis (cN0) were confirmed to have pathological lymph node metastasis in level VI. Conventional ultrasound (US) focuses on the characteristics of tumor capsule and the periphery to determine whether the tumor has invasive growth. However, due to its small size, the typical features of invasiveness shown by conventional 2-dimensional (2D) US are not well visualized. US-based radiomics makes use of artificial intelligence and big data to build a model that can help improving diagnostic accuracy and providing prognostic implication of the disease. We hope to establish and assess the value of a nomogram based on US radiomics combined with independent risk factors in predicting the invasiveness of a single PTMC without clinical lymph node metastasis (cN0).

Methods

A total of 317 patients with cN0 single PTMC who underwent US examination and operation were included in this retrospective cohort study. Patients were randomly divided into training and testing set in the ratio of 8:2. The US images of all patients were segmented, and the radiomics features were extracted. In the training dataset, the US with features of minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) were selected and radiomics signatures were then established according to their respective weighting coefficients. Univariate and multivariate logistic regression analyses were employed to generate the risk factors of possible invasive PTMC. The nomogram is then made by combining high risk factors and the radiomics signature. The efficiency of the nomogram was evaluated by the receiver operating characteristic (ROC) curve and calibration curve, and its clinical application value was assessed by decision curve analysis (DCA). The testing dataset was used to validate the model.

Results

In the model, seven radiomics features were selected to establish the radiomics signature. A nomogram was made by incorporating clinically independent risk factors and the radiomics signature. Both the ROC curve and calibration curve showed good prediction efficiency. The area under the curve (AUC), accuracy, sensitivity, and specificity of the nomogram in the training data were 0.76 [95% confidence interval (CI): 0.71-0.82], 0.811, 0.914, and 0.727, respectively whereas the results of the testing dataset were 0.71 (95% CI: 0.58-0.84), 0.841, 0.533, and 0.868. As such, the efficacy of the nomogram in predicting the invasiveness of PTMC was subsequently validated by the DCA.

Conclusions

Nomogram based on thyroid US radiomics has an excellent predictive value of the potential invasiveness of a single PTMC without clinical lymph node metastasis. With these promising results, it can potentially be the imaging marker used in daily clinical practice.