Development and validation of a prediction model for lymph node metastasis in thyroid cancer: integrating deep learning and radiomics features from intra- and peri-tumoral regions

Abstract

Background: Current preoperative imaging methods, such as ultrasound, are limited by operator dependency and suboptimal sensitivity for detecting central lymph node metastasis (CLNM). This study aimed to propose a method that integrates deep learning and radiomics to accurately predict lymph node metastasis in thyroid cancer by analyzing intra- and peri-tumoral imaging features, thereby improving the preoperative prediction accuracy.

Methods: From July 2020 to June 2022, 405 patients diagnosed with PTC were enrolled from two centers: Center 1 (Shanghai Sixth People's Hospital) with 294 patients divided into a training set (n=294) and an internal validation set, and Center 2 (Tongji Hospital Affiliated to Tongji University) with 111 patients as the external test set. Postoperative pathological confirmation served as the reference standard for CLNM diagnosis. A total of 1,561 radiomics features and 2,048 deep learning features were extracted from intra- and peri-tumoral regions of each ultrasound image. Feature selection was performed using analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO), resulting in the selection of relevant features for constructing support vector machine (SVM) models. Additionally, radiomics-deep learning fusion models were developed by combining selected radiomics and deep learning features.

Results: Among 405 patients (mean age: 46.59±12.74 years; 68.6% female), 171 exhibited CLNM, highlighting the clinical urgency for accurate prediction. Among the 405 patients, 171 exhibited CLNM. The radiomics models demonstrated area under the curve (AUC) values of 0.760 in internal validation and 0.748 in the external test cohort. The deep learning models demonstrated improved performance with AUCs of 0.794 and 0.756 in the internal and external test sets. Notably, the highest AUC values of 0.897 (internal validation) and 0.881 (external test set) were obtained by the radiomics-deep learning fusion SVM model incorporating both intra- and peri-tumoral regions. DeLong's test confirmed statistically significant improvements (P<0.05) of the fusion model over the intra-tumoral radiomics model (P=0.008), intra-tumoral deep learning model (P=0.005), and combined intra-tumoral radiomics-deep learning model (P=0.01). However, no significant differences were observed compared to the combined intra- and peri-tumoral deep learning model (P=0.17). Decision curve analysis indicated that the fusion model offers greater clinical utility in predicting CLNM.

Conclusions: The integration of radiomics and deep learning features significantly enhances the diagnostic performance for predicting CLNM in papillary thyroid carcinoma (PTC). The radiomics-deep learning fusion SVM model outperforms individual radiomics and deep learning models, demonstrating substantial potential for clinical application in improving surgical decision-making and patient management. The fusion model could reduce unnecessary central lymph node dissections (CLNDs) and improve surgical planning by providing personalized risk stratification.

Keywords: Papillary thyroid carcinoma (PTC); central lymph node metastasis (CLNM); deep learning; intra-tumoral; peri-tumoral.

Figure 1

Flow chart of participants recruitment. Center 1: Shanghai Sixth People’s Hospital; Center 2: Tongji Hospital Affiliated to Tongji University. CLND, central lymph node dissection; PTC, papillary thyroid carcinoma.

Figure 2

Research workflow diagram. LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic.

Figure 3

Comparison among radiomics model, deep learning model and radiomics-deep learning model in the internal validation set and external test set. (A) AUC in the internal validation set; (B) AUC in the external test set; (C) DeLong’s test for the external test set; and (D) DCA curves for the external test set. AUC, area under the curve; CI, confidence interval; DCA, decision curve analysis; DL, deep learning; intra, intra-tumoral; peri, peri-tumoral; RAD, radiomics; SVM, support vector machine.