Nomogram to assess risk of central cervical lymph node metastasis in patients with cN0 papillary thyroid carcinoma

Ultrasound-Based Nomogram for Predicting the Aggressiveness of Papillary Thyroid Carcinoma in Adolescents and Young Adults

RATIONALE AND OBJECTIVES

Assessing the aggressiveness of papillary thyroid carcinoma (PTC) preoperatively might play an important role in guiding therapeutic strategy. This study aimed to develop and validate a nomogram that integrated ultrasound (US) features with clinical characteristics to preoperatively predict aggressiveness in adolescents and young adults with PTC.

MATERIALS AND METHODS

In this retrospective study, a total of 2373 patients were enrolled and randomly divided into two groups with 1000 bootstrap sampling. The multivariable logistic regression (LR) analysis or least absolute shrinkage and selection operator LASSO regression was applied to select predictive US and clinical characteristics in the training cohort. By incorporating most powerful predictors, two predictive models presented as nomograms were developed, and their performance was assessed with respect to discrimination, calibration, and clinical usefulness.

RESULTS

The LR_model that incorporated gender, tumor size, multifocality, US-reported cervical lymph nodes (CLN) status, and calcification demonstrated good discrimination and calibration with an area under curve (AUC), sensitivity and specificity of 0.802 (0.781-0.821), 65.58% (62.61%-68.55%), and 82.31% (79.33%-85.46%), respectively, in the training cohort; and 0.768 (0.736-0.797), 60.04% (55.62%-64.46%), and 83.62% (78.84%-87.71%), respectively, in the validation cohort. Gender, tumor size, orientation, calcification, and US-reported CLN status were combined to build LASSO_model. Compared with LR_model, the LASSO_model yielded a comparable diagnostic performance in both cohorts, the AUC, sensitivity, and specificity were 0.800 (0.780-0.820), 65.29% (62.26%-68.21%), and 81.93% (78.77%-84.91%), respectively, in the training cohort; and 0.763 (0.731-0.792), 59.43% (55.12%-63.93%), and 84.98% (80.89%-89.08%), respectively, in the validation cohort. The decision curve analysis indicated that using the two nomograms to predict the aggressiveness of PTC provided a greater benefit than either the treat-all or treat-none strategy.

CONCLUSION

Through these two easy-to-use nomograms, the possibility of the aggressiveness of PTC in adolescents and young adults can be objectively quantified preoperatively. The two nomograms may serve as a useful clinical tool to provide valuable information for clinical decision-making.

Ultrasound-based Nomogram for Predicting the Pathological Nodal Negativity of Unilateral Clinical N1a Papillary Thyroid Carcinoma in Adolescents and Young Adults

RATIONALE AND OBJECTIVES

To develop and validate a nomogram incorporating clinical and ultrasound (US) characteristics for predicting the pathological nodal negativity of unilateral clinically N1a (cN1a) papillary thyroid carcinoma (PTC) among adolescents and young adults.

MATERIALS AND METHODS

From December 2016 to August 2021, 278 patients aged ≤ 30 years from two medical centers were enrolled and randomly assigned to the training and validation cohorts at a ratio of 2:1. After performing univariate and multivariate analyses, a nomogram combining all independent predictive factors was constructed and applied to the validation cohort. The performance of the nomogram was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis .

RESULTS

Multivariate logistic regression analysis showed that unilateral cN1a PTC in young patients with Hashimoto's thyroiditis, T1 stage, no intra-tumoral microcalcification, and tumors located in the upper third of the thyroid gland was more likely to be free of central lymph node metastases. The nomogram revealed good calibration and discrimination in both cohorts, with areas under the receiver operating characteristic curve of 0.764 (95% confidence interval [CI]: 0.684-0.843) and 0.728 (95% CI: 0.602-0.853) in the training and validation cohorts, respectively. The clinical application of the nomogram was further confirmed using decision curve analysis.

CONCLUSION

This US-based nomogram may assist the assessment of central cervical lymph nodes in young patients with unilateral cN1a PTC, enabling improved risk stratification and optimal treatment management in clinical practice.

Accuracy of papillary thyroid cancer prognostic nomograms: a systematic review

Objective

Current staging and risk-stratification systems for predicting survival or recurrence of patients with differentiated thyroid carcinoma may be ineffective at predicting outcomes in individual patients. In recent years, nomograms have been proposed as an alternative to conventional systems for predicting personalized clinical outcomes. We conducted a systematic review to evaluate the predictive performance of available nomograms for thyroid cancer patients.

Design and methods

PROSPERO registration (CRD42022327028). A systematic search was conducted without time and language restrictions. PICOT questions: population, patients with papillary thyroid cancer; comparator prognostic factor, single-arm studies; outcomes, overall survival, disease-free survival, cancer-specific survival, recurrence, central lymph node metastases, or lateral lymph node metastases; timing, all periods; setting, hospital setting. Risk of bias was assessed through PROBAST tool.

Results

Eighteen studies with a total of 20 prognostic models were included in the systematic review (90,969 papillary thyroid carcinoma patients). Fourteen models were at high risk of bias and four were at unclear risk of bias. The greatest concerns arose in the analysis domain. The accuracy of nomograms for overall survival was assessed in only one study and appeared limited (0.77, 95% CI: 0.75-0.79). The accuracy of nomograms for disease-free survival ranged from 0.65 (95% CI: 0.55-0.75) to 0.92 (95% CI: 0.91-0.95). The C-index for predicting lateral lymph node metastasis ranged from 0.72 to 0.92 (95% CI: 0.86-0.97). For central lymph node metastasis, the C-index of externally validated studies ranged from 0.706 (95% CI: 0.685-0.727) to 0.923 (95% CI: 0.893-0.946).

Conclusions

Our work highlights the extremely high heterogeneity among nomograms and the critical lack of external validation studies that limit the applicability of nomograms in clinical practice. Further studies ideally using commonly adopted risk factors as the backbone to develop nomograms are required.

Significance statement

Nomograms may be appropriate tools to plan treatments and predict personalized clinical outcomes in patients with papillary thyroid cancer. However, the nomograms developed to date are very heterogeneous, and their results seem to be closely related to the specific samples studied to generate the same nomograms. The lack of rigorous external validation procedures and the use of risk factors that sometimes appear to be far from those commonly used in clinical practice, as well as the great heterogeneity of the risk factors considered, limit the ability of nomograms to predict patient outcomes and thus their current introduction in clinical practice.

Risk factors and prediction models of lymph node metastasis in papillary thyroid carcinoma based on clinical and imaging characteristics

BACKGROUND

Papillary thyroid carcinoma (PTC) commonly presents with lymph node metastasis, which may be associated with worsened prognosis. This study aimed to comprehensively evaluate the risk factors of lymph node metastasis in PTC based on preoperative clinical and imaging data and to construct a nomogram model to predict the risk of lymph node metastasis.

METHODS

A total of 989 patients with PTC were enrolled and randomly divided into training and validation cohorts in an 8:2 ratio. Independent risk factors for lymph node metastasis in PTC were analyzed using univariate and stepwise multivariate logistic regression. An importance analysis of independent risk factors affecting lymph node metastasis was performed according to the random forest method. Subsequently, a nomogram to predict lymph node metastasis was constructed, and the predictive effect of the nomogram was evaluated using receiver operating characteristic analysis and calibration curves.

RESULTS

Univariate regression analysis revealed that age, sex, body weight, systolic blood pressure, free triiodothyronine, nodule location, nodule number, Thyroid Imaging Reporting and Data System (TI-RADS) grade on color Doppler ultrasound, enlarged lymph node present on imaging, and nodule diameter could affect lymph node metastasis in PTC. Stepwise multivariate regression analysis showed that sex, age, enlarged lymph node present on imaging, nodule diameter, and color Doppler TI-RADS grade were independent risk factors for lymph node metastasis in PTC. Combining these five independent risk factors, a nomogram prediction model was constructed. The area under the curve (AUC) of the nomogram in the training and validation cohorts was 0.742 and 0.765, respectively, with a well-fitted calibration curve.

CONCLUSION

Our study showed that independent risk factors for lymph node metastasis in PTC were sex, age, enlarged lymph node present on imaging, nodule diameter, and color Doppler TI-RADS grade. The nomogram constructed based on these independent risk factors can better predict the risk of lymph node metastasis.

A radiomics nomogram for the ultrasound-based evaluation of central cervical lymph node metastasis in papillary thyroid carcinoma

PURPOSE

To develop and validate a radiomics nomogram based on ultrasound (US) to predict central cervical lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC).

METHODS

PTC patients with pathologically confirmed presence or absence of central cervical LN metastasis in our hospital between March 2021 and November 2021 were enrolled as the training cohort. Radiomics features were extracted from the preoperative US images, and a radiomics signature was constructed. Univariate and multivariate logistic regression analyses were used to screen out the independent risk factors, and a radiomics nomogram was established. The performance of the model was verified in the independent test cohort of PTC patients who underwent thyroidectomy and cervical LN dissection in our hospital from December 2021 to March 2022.

RESULTS

In the independent test cohort, the radiomics model based on long-axis cross-section and short-axis cross-section images outperformed the radiomics models based on either one of these sections (the area under the curve (AUC), 0.69 vs. 0.62 and 0.66). The radiomics signature consisted of 4 selected features. The US radiomics nomogram included the radiomics signature, age, gender, BRAF V600E mutation status, and extrathyroidal extension (ETE) status. In the independent test cohort, the AUC of the receiver operating curve(ROC) of this nomogram was 0.76, outperformingthe clinical model and the radiomics model (0.63 and 0.69, respectively), and also much better than preoperative US examination (AUC, 0.60). Decision curve analysis indicated that the radiomics nomogram was clinically useful.

CONCLUSIONS

This study presents an efficient and useful US radiomics nomogram that can provide comprehensive information to assist clinicians in the individualized preoperative prediction of central cervical LN metastasis in PTC patients.

Ultrasound and Contrast-Enhanced Ultrasound Characteristics Associated With cN1 and Microscopic pN1 in Papillary Thyroid Carcinoma

OBJECTIVES

Lymph node metastases (LNMs) could be stratified into clinical N1 (cN1) and microscopic pN1 (pathological N1), which bear different biological behavior and prognosis. Our study aimed to investigate the associations between LNMs and primary tumor's US (ultrasound) and CEUS (contrast-enhanced ultrasound) characteristics based on the stratification of LNMs into cN1 and microscopic pN1 in papillary thyroid carcinoma (PTC).

METHODS

From August 2019 to May 2020, 444 consecutive PTC patients who underwent preoperative neck US and CEUS evaluation were included. According to regional lymph node status, the patients were classified into cN1 group versus cN0 (clinical N0) group and microscopic pN1 group versus pN0 (pathological N0) group. For multiple PTCs, the largest one was selected for the evaluation of US, CEUS and clinical features. Univariate and multivariate analyses were performed to determine independent predictors of cN1 and microscopic pN1.

RESULTS

85 cN1 versus 359 cN0 patients and 117 microscopic pN1 versus 242 pN0 patients were analyzed. Multivariate logistic regression analysis showed that <55-years-old (OR: 2.56 (1.08-6.04), male [OR: 2.18 (1.22-3.91)], large size [OR: 2.59 (1.71-3.92)], calcification [OR: 3.88 (1.58-9.51)], and hyper-enhancement [OR: 2.78 (1.22-6.30)] were independent risk factors of cN1, while <55-years-old [OR: 1.91 (1.04-3.51)], large size [OR: 1.56 (1.003-2.42)], multifocality [OR: 1.67 (1.04-2.66)] were independent risk factors of microscopic pN1.

CONCLUSIONS

For patients with PTC, young age, male, large size, calcification, and hyper-enhancement were independent predictors of cN1, while young age, large size and multifocality were independent predictors of microscopic pN1.