A prediction model incorporating the BRAFV600E protein status for determining the risk of cervical lateral lymph node metastasis in papillary thyroid cancer patients with central lymph node metastasis

A lasso-based model to predict lateral lymph node metastasis in unifocal papillary thyroid carcinoma with central lymph node metastasis

OBJECTIVE

To screen the risk factors for lateral lymph node metastasis (LLNM) in unifocal papillary thyroid carcinoma (PTC) with central lymph node metastasis (CLNM) and create a corresponding model.

METHODS

A retrospective analysis of 362 patients from our hospital was performed. All patients were randomized into training and validation groups in a ratio of 7:3. Risk factors were screened using the least absolute shrinkage and selection operator (LASSO) and logistic regression analysis.

RESULTS

The analysis indicated that upper location, number of CLNM ≥ 3, rate of CLNM ≥ 0.172, prelaryngeal LNM, pretracheal LNM, and tall cell variant of papillary thyroid carcinoma (TCV-PTC) are independent risk factors. Visualizing the model with a nomogram, receiver operating characteristic (ROC) curves revealed an area under the curve (AUC) of 0.773 for the training group and 0.779 for the validation group. This confirms the stability and outstanding accuracy of the model. Also, the calibration curves and clinical descision curves reflect strong calibration,offering potential clinical benefits.

CONCLUSIONS

The risk factors for LLNM include metastasis to the prelaryngeal lymph nodes, metastasis to the pretracheal lymph nodes, location in the upper level, number of metastases ≥3 in CLNM, TCV-PTC and metastasis rate ≥0.172. A nomogram incorporating these factors exhibits excellent predictive value and stability.

Risk factors of cervical central lymph node metastasis in stage T1a unifocal papillary thyroid carcinoma

To investigate the correlation of cervical central lymph node metastasis (CLNM) in stage T1a unifocal papillary thyroid carcinoma (PTC) with the clinicopathological characteristics, ultrasonography features and the number of lymph node dissection, and to analyze the risk factors of CLNM. Data from 493 unifocal PTC patients (T1a) who underwent partial or total thyroidectomy and pCLND at the Guizhou Provincial People's Hospital were collected and retrospectively analyzed. They were divided into two groups in accordance with cervical CLNM or not. Their information, including clinical characteristics, ultrasound (US) features, pathological results, and other characteristics of the groups, was analyzed and compared using univariate and multivariate logistic regression analyses. A total of 493 patients were eligible in this study. Among them, 33.7% (166/493) of PTC patients had cervical CLNM, and 66.3% (327/493) did not. The two groups were compared using a univariate analyses, and there were no significant differences between the two groups in age, maximum tumor size, tumor location, aspect ratio, boundary, morphology, echogenicity, BRAFV600E and HT (P > 0.05), and there were significant differences between gender, capsule contact, microcalcifications, rich vascularity, and number of lymph node dissection (P < 0.05). A multivariate logistic regression analyses was performed to further clarify the correlation of these indices. However, only male (OR = 1.770, P = 0.009), microcalcifications (OR = 1.791, P = 0.004), capsule contact (OR = 1.857, P = 0.01), and number of lymph node dissection (OR = 2.274, P < 0.001) were independent predictors of cervical CLNM. In conclusion, four independent predictors of cervical CLNM, including male, microcalcifications, capsule contact, and number of lymph node dissection, were screened out. Therefore, a comprehensive assessment of these risk factors should be conducted when designing individualized treatment regimens for PTC patients.

Prospective application of a prediction model for lateral lymph node metastasis in papillary thyroid cancer patients with central lymph node metastasis

Objective

This study aimed to develop and apply a prediction model to estimate the probability of lateral lymph node metastasis (LLNM) in patients with cN0 unilateral papillary thyroid carcinoma (PTC) with central lymph node metastasis (CLNM).

Setting

All study data were collected from a single tertiary hospital.

Methods

Univariable and multivariable logistic regression analyses were used to explore independent predictors of LLNM in the derivation and internal validation cohorts, which were used to construct and validate a nomogram. Another 96 patients were included prospectively to evaluate the efficacy of this nomogram.

Results

Maximum tumor diameter greater than 1.0 cm (OR, 2.712; 95% CI, 1.412-5.210), multifocality (OR, 2.758; 95% CI, 1.120-6.789), the number of CLNM ≥3 (OR, 2.579; 95% CI, 1.315-5.789), CLNM ratio ≥0.297 (OR, 2.905; 95% CI, 1.396-6.043), and tumors located in the upper portion (OR 2.846, 95% CI 1.151-7.039) were independent predictors associated with LLNM. The prediction model showed excellent discrimination with an AUC of 0.731 (95% CI, 0.635-0.827). Novel risk stratification for LLNM was constructed based on this nomogram. In the prospective cohort, we stratified these patients into three risk subgroups: low-, moderate-, and high-risk subgroups and we found that the probability of LLNM was positively correlated with the total points from the nomogram.

Conclusion

This nomogram was applied in prospective clinical practice and distinguished PTC patients with a genuinely high risk of LLNM. Surgeons can use our nomogram to tailor the surgical plan and to credibly determine further postoperative therapy.

The impact of multifocality on lateral lymph node metastasis in papillary thyroid carcinoma

PURPOSE

Multifocality is a common feature of papillary thyroid carcinoma (PTC) and is strongly associated with unfavorable outcome, but its association with lateral lymph node metastasis (lateral LNM) remains unclear.

METHODS

The association between tumor foci numbers and lateral LNM was assessed with unadjusted and adjusted logistic regression. Propensity score matching analysis was used to investigate the impact of tumor foci numbers on lateral LNM.

RESULTS

Increasing tumor foci numbers was strongly associated with a higher risk of lateral LNM (P < 0.05). After adjusting for several confounding factors, ≥4 tumor foci can be regarded as an independent predictor of lateral LNM (multivariable adjusted OR = 1.848, P = 0.011). Similarly, compared with solitary foci, multifocality was associated with a significantly higher risk of lateral LNM after matching propensity scores (11.9% vs. 14.4%, P = 0.018), especially for patients with ≥4 tumor foci (11.2% vs. 23.4%; P = 0.001). Furthermore, age-stratified analyses revealed multifocality was significantly positively correlated with lateral LNM in younger patients (P = 0.013) compared with older patient cohorts (P = 0.669).

CONCLUSIONS

Overall, tumor foci numbers significantly increased the risk of lateral LNM in PTCs, especially for patients with four or more tumor foci, and patient's age should be considered when interpreting the multifocality and risk of lateral LNM.

Nomogram for preoperative estimation risk of lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multicenter study

BACKGROUND

Lateral lymph node metastasis (LLNM) is frequent in papillary thyroid carcinoma (PTC) and is associated with a poor prognosis. This study aimed to developed a clinical-ultrasound (Clin-US) nomogram to predict LLNM in patients with PTC.

METHODS

In total, 2612 PTC patients from two hospitals (H1: 1732 patients in the training cohort and 578 patients in the internal testing cohort; H2: 302 patients in the external testing cohort) were retrospectively enrolled. The associations between LLNM and preoperative clinical and sonographic characteristics were evaluated by the univariable and multivariable logistic regression analysis. The Clin-US nomogram was built basing on multivariate logistic regression analysis. The predicting performance of Clin-US nomogram was evaluated by calibration, discrimination and clinical usefulness.

RESULTS

The age, gender, maximum diameter of tumor (tumor size), tumor position, internal echo, microcalcification, vascularization, mulifocality, and ratio of abutment/perimeter (A/P) > 0.25 were independently associated with LLNM metastatic status. In the multivariate analysis, gender, tumor size, mulifocality, position, microcacification, and A/P > 0.25 were independent correlative factors. Comparing the Clin-US nomogram and US features, Clin-US nomogram had the highest AUC both in the training cohort and testing cohorts. The Clin‑US model revealed good discrimination between PTC with LLNM and without LLNM in the training cohort (AUC = 0.813), internal testing cohort (AUC = 0.815) and external testing cohort (AUC = 0.870).

CONCLUSION

Our findings suggest that the ClinUS nomogram we newly developed can effectively predict LLNM in PTC patients and could help clinicians choose appropriate surgical procedures.

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.

Construction of prediction models for determining the risk of lateral lymph node metastasis in patients with thyroid papillary carcinoma based on gender stratification

BACKGROUND

Lateral lymph node metastasis (LLNM) is associated with poor prognosis in patients with papillary thyroid cancer (PTC). The purpose of this study was to determine the risk factors for LLNM and establish prediction models that could individually assessed the risk of LLNM.

METHODS

A total of 619 PTC patients were retrospectively analyzed in our study. Univariate and multivariate analysis were performed for male and female patients, respectively, to assess relationships between clinicopathological features and LLNM. By integrating independent predictors selected by binary logistic regression modeling, preoperative and postoperative nomograms were developed to estimate the risk of LLNM.

RESULTS

LLNM was detected in 80 of 216 male patients. Of 403 female patients, 114 had LLNM. The preoperative nomogram of male patients included three clinical variables: the number of foci, tuner size, and echogenic foci. In addition to the above three variables, the postoperative nomogram of male patients included extrathyroidal extension (ETE) detected in surgery, central lymph node metastasis (CLNM) and high-volume CLNM. The preoperative nomogram of female patients included the following variables: age, chronic lymphocytic thyroiditis (CLT), BRAF V600E, the number of foci, tumor size and echogenic foci. Variables such as CLT, BRAF V600E, the number of foci, tumor size, ETE detected in surgery, CLNM, high-volume CLNM and central lymph node ratio were included in the postoperative nomogram. Above Nomograms show good discrimination.

CONCLUSIONS

Considering the difference in the incidence rate of LLNM between men and women, a separate prediction system should be established for patients of different genders. These nomograms are helpful in promoting the risk stratification of PTC treatment decision-making and postoperative management.

A clinical and molecular pathology prediction model for central lymph node metastasis in cN0 papillary thyroid microcarcinoma

BACKGROUND

The frequency of thyroid cancer has rapidly increased in recent years globally. Thus, more papillary thyroid microcarcinoma (PTMC) patients are being diagnosed, including clinical lymph node-negative (cN0) patients. Our study attempted to develop a prediction model for assessing the probability of central lymph node metastasis (CLNM) in cN0 PTMC patients.

METHODS

A total of 595 patients from the Affiliated Hospital of Qingdao University (training cohort: 456 patients) and the Affiliated Hospital of Jining Medical University (verification cohort: 139 patients) who underwent thyroid surgery between January 2020 and May 2022 were enrolled in this study. Their clinical and molecular pathology data were analyzed with multivariate logistic regression to identify independent factors, and then we established a prediction model to assess the risk of CLNM in cN0 PTMC patients.

RESULTS

Multivariate logistic regression analysis revealed that sex, Hashimoto's thyroiditis (HT), tumor size, extrathyroidal extension, TERT promoter mutations and NRAS mutation were independent factors of CLNM. The prediction model demonstrated good discrimination ability (C-index: 0.757 and 0.753 in the derivation and validation cohorts, respectively). The calibration curve of the model was near the optimum diagonal line, and decision curve analysis (DCA) showed a noticeably better benefit.

CONCLUSION

CLNM in cN0 PTMC patients is associated with male sex, tumor size, extrathyroidal extension, HT, TERT promoter mutations and NRAS mutation. The prediction model exhibits good discrimination, calibration and clinical usefulness. This model will help to assess CLNM risk and make clinical decisions in cN0 PTMC patients.

Nomograms based on sonographic and clinicopathological characteristics to predict lateral lymph node metastasis in classic papillary thyroid carcinoma

BACKGROUND

Lateral lymph node metastasis (LLNM) has been considered a risk factor of recurrence in papillary thyroid cancer (PTC). Preoperative detecting LLNM accurately is difficult. Solitary lateral lymph node metastasis is a special type of LLNM. We aimed to develop nomograms for predicting LLNM and multiple lateral lymph node metastasis (MLLNM).

METHODS

We retrospectively retrieved 528 classic PTC patients that underwent surgery between March 2019 and May 2020. Sonographic and clinicopathological features were collected. Risk factors of LLNM and MLLNM were determined by univariate and multivariate analysis. Nomograms for predicting LLNM and MLLNM were developed.

RESULTS

LLNM was independently associated with tumor size, the number of foci, location, margin, central lymph node metastasis, and lymph node ratio. Independent predictors of MLLNM were age, margin, and the number of metastatic lymph nodes in central compartment. By using above variables, we constructed nomograms for predicting LLNM and MLLNM, with area under curves of 0.864 and 0.748, respectively.

CONCLUSION

Through these accurate and easy-to-use nomograms, we can detect the risk of residual LLNM postoperatively for classic PTC patients who did not receive lateral neck dissection and provide an individualized plan for postoperative management of classic PTC patients.

Nomogram model based on preoperative serum thyroglobulin and clinical characteristics of papillary thyroid carcinoma to predict cervical lymph node metastasis

OBJECTIVE

Preoperative evaluation of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) has been one of the serious clinical challenges. The present study aims at understanding the relationship between preoperative serum thyroglobulin (PS-Tg) and LNM and intends to establish nomogram models to predict cervical LNM.

METHODS

The data of 1,324 PTC patients were retrospectively collected and randomly divided into training cohort (n = 993) and validation cohort (n = 331). Univariate and multivariate logistic regression analyses were performed to determine the risk factors of central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM). The nomogram models were constructed and further evaluated by 1,000 resampling bootstrap analyses. The receiver operating characteristic curve (ROC curve), calibration curve, and decision curve analysis (DCA) of the nomogram models were carried out for the training, validation, and external validation cohorts.

RESULTS

Analyses revealed that age, male, maximum tumor size >1 cm, PS-Tg ≥31.650 ng/ml, extrathyroidal extension (ETE), and multifocality were the significant risk factors for CLNM in PTC patients. Similarly, such factors as maximum tumor size >1 cm, PS-Tg ≥30.175 ng/ml, CLNM positive, ETE, and multifocality were significantly related to LLNM. Two nomogram models predicting the risk of CLNM and LLNM were established with a favorable C-index of 0.801 and 0.911, respectively. Both nomogram models demonstrated good calibration and clinical benefits in the training and validation cohorts.

CONCLUSION

PS-Tg level is an independent risk factor for both CLNM and LLNM. The nomogram based on PS-Tg and other clinical characteristics are effective for predicting cervical LNM in PTC patients.