Abstract
Background
Though follicular thyroid carcinoma can be confirmed postoperatively by the histological findings of capsular or vascular invasion, preoperative diagnosis of follicular-patterned lesions has long been a diagnostic challenge. This study seeks to establish a machine-learning (ML) model based on clinical, US, and CEUS features for the differential diagnosis of thyroid follicular-patterned lesions (TFPLs).Methods
Patients with surgical pathologically confirmed TFPLs who underwent preoperative US and CEUS between January 2013 to April 2023 were enrolled in this retrospective study. We utilized five ML algorithms (logistic regression, random forest [RF], k-nearest neighbor [KNN], support vector machine [SVM], and elastic net [EN]) to construct an optimized model via US, CEUS and clinical data for the differential diagnosis of TFPLs. Model performance was evaluated with sensitivity, area under the precision-recall curve (AUPRC), and area under the receiver operating characteristic curve (AUROC).Results
114 patients were finally included. The sensitivities of the five ML algorithms (Logistic, RF, KNN, SVM, EN) were 0.93, 0.97, 0.93, 0.97, 0.93 for the training set, and 0.40, 1.00, 0.80, 1.00, 0.60 for the test set. The AUPRCs were 0.91, 0.97, 0.99, 0.91, 0.91 for the training set, and 0.65, 0.71, 0.38, 0.57, 0.65 for the test set. The AUROCs were 0.95, 0.98, 0.99, 0.89, 0.93 for the training set. When applied to the test set, the RF model had a significantly higher AUROC value (0.92; 95% CI: 0.88, 0.96) than other ML algorithms (0.91, 0.89, 0.91, 0.91, P Conclusions
Our ML model integrating clinical, US, and CEUS features achieved high sensitivity for preoperative differentiation of TFPLs, potentially guiding surgical planning-a step toward clinical use that requires CEUS standardization and external validation.Trial registration
This study was registered at www.chictr.org.cn (no. ChiCTR2200066254, date: November 29, 2022).