Evaluation of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Clinical-Ultrasound Radiomic Machine Learning-Based Model

SIMPLE SUMMARY: Accurate preoperative cervical lymph node metastasis (CLNM) prediction in papillary thyroid cancer (PTC) patients is essential for clinical treatment effectiveness, particularly for surgeons assessing the degree of surgical resection and the requirement for cervical lymph node dissection.

As a result, a definite diagnosis of CLNM before surgery can assist the surgeon in selecting the best surgical technique and reducing the likelihood of reoperation.

This research used several machine learning models based on clinical risk factors in conjunction with radiomics features to preoperatively evaluate CLNM in PTC patients, which can assist clinicians to choose a suitable treatment strategy for patients.

ABSTRACT: We aim to develop a clinical-ultrasound radiomic (USR) model based on USR features and clinical factors for the evaluation of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC).

This retrospective study used routine clinical and US data from 205 PTC patients.

According to the pathology results, the enrolled patients were divided into a non-CLNM group and a CLNM group.

All patients were randomly divided into a training cohort (n = 143) and a validation cohort (n = 62).

A total of 1046 USR features of lesion areas were extracted.

The features were reduced using Pearson’s Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE) with stratified 15-fold cross-validation.

Several machine learning classifiers were employed to build a Clinical model based on clinical variables, a USR model based solely on extracted USR features, and a Clinical-USR model based on the combination of clinical variables and USR features.

The Clinical-USR model could discriminate between PTC patients with CLNM and PTC patients without CLNM in the training (AUC, 0.78) and validation cohorts (AUC, 0.71).

When compared to the Clinical model, the USR model had higher AUCs in the validation (0.74 vs. 0.63) cohorts.

The Clinical-USR model demonstrated higher AUC values in the validation cohort (0.71 vs. 0.63) compared to the Clinical model.

The newly developed Clinical-USR model is feasible for predicting CLNM in patients with PTC.