Interpretable machine learning models for predicting skip metastasis in cN0 papillary thyroid cancer based on clinicopathological and elastography radiomics features

Abstract

Background: Skip lymph node metastasis (SLNM) in papillary thyroid cancer (PTC) involves cancer cells bypassing central nodes to directly metastasize to lateral nodes, often undetected by standard preoperative ultrasonography. Although multiple models exist to identify SLNM, they are inadequate for clinically node-negative (cN0) patients, resulting in underestimated metastatic risks and compromised treatment effectiveness. Our study aims to develop and validate a machine learning (ML) model that combines elastography radiomics with clinicopathological data to predict pre-surgical SLNM risk in cN0 PTC patients with increased risk of lymph node metastasis (LNM), improving their treatment strategies.

Methods: Our study conducted a retrospective analysis of 485 newly diagnosed primary PTC patients, divided into training and external validation cohorts. Patients were categorized into SLNM and non-SLNM groups based on follow-up outcomes and postoperative pathology. We collected preoperative clinicopathological data and extracted, standardized radiomics features from elastography imaging to develop various ML models. These models were internally validated using radiomics and clinicopathological data, with the optimal model's feature importance analyzed through the Shapley Additive Explanations (SHAP) approach and subsequently externally validated.

Results: In our study of 485 patients, 67 (13.8%) exhibited SLNM. The extreme gradient boosting (XGBoost) model, integrating elastography radiomics with clinicopathological data, demonstrated superior performance in both internal and external validations. SHAP analysis identified five key determinants of SLNM: three radiomics features from elastography images, one clinical variable, and one pathological variable.

Conclusion: Our evaluation highlights the XGBoost model, which integrates elastography radiomics and clinicopathological data, as the most effective ML approach for the prediction of SLNM in cN0 PTC patients with increased risk of LNM. This innovative model significantly enhances the accuracy of risk assessments for SLNM, enabling personalized treatments that could reduce postoperative metastases in these patients.

Keywords: clinically node-negative (cN0); machine learning; papillary thyroid cancer; radiomics; skip lymph node metastasis.

Figure 1

Flowchart for PTC patient selection and cohort distribution for developing and validating predictive model. PTC, papillary thyroid cancer; CLND, central lymph node dissection; cN0, clinically node-negative; LNM, lymph node metastasis; US, ultrasonography; FNAB, fine-needle aspirations biopsy; LLNM, lateral lymph node metastases; CLNM, central lymph node metastasis; SHAP, Shapley Additive Explanations.

Figure 2

Elastography images were acquired and subsequently segmented.

Figure 3

Radiomics feature selection via LASSO logistic regression. (A) LASSO coefficient profiles were plotted against the lambda values. (B) Repeat the 10-fold cross-validation process 50 times to identify the optimal penalization coefficient, lambda, in the LASSO model, yielding 16 nonzero coefficients. The red dots indicate the mean value of the target parameters. LASSO, least absolute shrinkage and selection operator.

Figure 4

Comparative analysis of ML classifiers (Logit, SVM, RF, and XGBoost) using clinicopathological data: performance metrics including (A) ROC curves, (B) calibration plots, and (C) DCA. They achieved ROC-AUCs of 0.744, 0.769, 0.752, and 0.760, respectively. ML, machine learning; ROC, receiver operating characteristic; AUC, area under the curve; DCA, decision curve analysis; Logit, logistic regression; SVM, support vector machine; RF, random forest; XGBoost, extreme gradient boosting.

Figure 5

Comparative analysis of ML classifiers (Logit, SVM, RF, and XGBoost) using radiomics features: performance metrics including (A) ROC curves, (B) calibration plots, and (C) DCA. They achieved ROC-AUCs of 0.844, 0.809, 0.818, and 0.847, respectively. ML, machine learning; ROC, receiver operating characteristic; AUC, area under the curve; DCA, decision curve analysis; Logit, logistic regression; SVM, support vector machine; RF, random forest; XGBoost, extreme gradient boosting.

Figure 6

Comparative analysis of ML classifiers (Logit, SVM, RF, and XGBoost) on clinicopathological and radiomics data: performance metrics (A) ROC curves, (B) calibration plots, and (C) DCA. They achieved ROC-AUCs of 0.922, 0.861, 0.872, and 0.934, respectively. ML, machine learning; ROC, receiver operating characteristic; AUC, area under the curve; DCA, decision curve analysis; Logit, logistic regression; SVM, support vector machine; RF, random forest; XGBoost, extreme gradient boosting.

Figure 7

Evaluation of optimal ML model’s predictive performance with external verification cohort. (A) ROC curve (AUC = 0.907) indicating significant discriminative capacity, (B) calibration curve confirming strong agreement between predictions and observations, especially above 40%, and (C) DCA highlighting net clinical benefit across prediction probabilities. ML, machine learning; ROC, receiver operating characteristic; AUC, area under the curve; DCA, decision curve analysis.

Figure 8

SHAP analysis of XGBoost model predicting SLNM risk in cN0 PTC patients: (A) feature significance ranking based on absolute mean SHAP values, (B) summary plot visualizing cumulative influence. SHAP, Shapley Additive Explanation; XGBoost, extreme gradient boosting; SLNM, skip lymph node metastasis; cN0, clinically node-negative; PTC, papillary thyroid cancer.

Figure 9

SHAP force plots illustrating individual prediction results: (A) for a patient with SLNM; (B) for a patient without SLNM. SHAP, Shapley Additive Explanations; SLNM, skip lymph node metastasis.