Pretracheal Lymph Node Subdivision in Predicting Contralateral Central Lymph Node Metastasis for Unilateral Papillary Thyroid Carcinoma: Preliminary Results

Effectiveness of prophylactic central compartment neck dissection following hemithyroidectomy in papillary thyroid cancer: a meta-analysis

INTRODUCTION

In this study, we aimed to assess the effect of prophylactic central compartment neck dissection (pCCND) in conjunction with hemithyroidectomy (HT) for clinically low-risk node-negative (cN0) papillary thyroid carcinoma (PTC).

METHODS

A thorough literature search was performed utilizing PubMed and EMBASE for articles published until October 2023. Subsequently, a meta-analysis was performed on studies involving patients with cN0 PTC, with postoperative locoregional recurrence (LRR) and survival data, treated with HT + pCCND or HT. The study was registered with PROSPERO (CRD42024560962).

RESULTS

We included seven studies in this meta-analysis, including 2132 patients who met the inclusion criteria: six retrospective cohort studies and one randomized controlled trial. The HT + pCCND group consisted of 1090 cases, and the HT group had 1042 cases. The LRR rates after HT with or without pCCND were similar (3.58% vs. 4.51%; odds ratio (OR) = 0.65; 95% confidence interval (CI) = 0.41-1.03). Five of the seven studies provided prognostic and survival data, particularly the log hazard ratio (log HR) of disease-free survival (DFS) between the two groups. There was also no significant difference in terms of DFS between the HT + pCCND and HT groups (OR = 0.67; 95% CI = 0.42-1.07).

CONCLUSIONS

There was no significant difference in LRR and DFS between the HT + pCCND and HT groups. pCCND did not demonstrate significant efficacy in improving oncological outcomes for low-risk patients with cN0 PTC. Therefore, for patients with low-risk cN0 PTC, thyroid surgeons should make reasonable and individualized decisions regarding the extent of surgical removal.

A machine learning model utilizing Delphian lymph node characteristics to predict contralateral central lymph node metastasis in papillary thyroid carcinoma: a prospective multicenter study

BACKGROUND

This study aimed to use artificial intelligence (AI) to integrate various radiological and clinical pathological data to identify effective predictors of contralateral central lymph node metastasis (CCLNM) in patients with papillary thyroid carcinoma (PTC) and to establish a clinically applicable model to guide the extent of surgery.

METHODS

This prospective cohort study included 603 patients with PTC from three centers. Clinical, pathological, and ultrasonographic data were collected and utilized to develop a machine learning (ML) model for predicting CCLNM. Model development at the internal center utilized logistic regression along with other ML algorithms. Diagnostic efficacy was compared among these methods, leading to the adoption of the final model (random forest). This model was subject to AI interpretation and externally validated at other centers.

RESULTS

CCLNM was associated with multiple pathological factors. The Delphian lymph node metastasis ratio, ipsilateral central lymph node metastasis number, and presence of ipsilateral central lymph node metastasis were independent risk factors for CCLNM. Following feature selection, a Delphian lymph node-CCLNM (D-CCLNM) model was established using the Random forest algorithm based on five attributes. The D-CCLNM model demonstrated the highest area under the curve (AUC; 0.9273) in the training cohort and exhibited high predictive accuracy, with AUCs of 0.8907 and 0.9247 in the external and validation cohorts, respectively.

CONCLUSIONS

The authors developed a new, effective method that uses ML to predict CCLNM in patients with PTC. This approach integrates data from Delphian lymph nodes and clinical characteristics, offering a foundation for guiding surgical decisions, and is conveniently applicable in clinical settings.

The Oncologic Outcomes of Bilateral Central Lymph Node Dissection in Unilobar Papillary Thyroid Cancer and Its Risks: A Prospective Cohort Study

Background Indications for performing a prophylactic central neck dissection (pCND) in papillary thyroid cancer (PTC) remain controversial. Thyroidectomy and central neck dissection (CND) are often recommended in all cases with proven differentiated thyroid cancer (DTC) and clinically positive lymph nodes (LNs), as well as in high risk for micro-metastasis patients with T3-T4 tumors or established metastatic nodes in the lateral compartments. Aims The aims of this study were to ascertain the role of performing bilateral central LN dissection in unilobar PTC in improving the oncological outcomes and outline the risks involved. Methods This was a department-based, prospective cohort study. We included all 20 patients who had unilobar PTC and underwent total thyroidectomy with bilateral CND. A postoperative histopathological analysis was used to identify metastatic central LNs. Results Twenty total thyroidectomies plus bilateral CNDs were performed, of which 10 were prophylactic bilaterally (those with N0), and all 20 were prophylactic on the contralateral side of PTC. Conventional risk factors (age, tumor size, and extrathyroidal extension) were not associated with performing a pCND. The presence of unilobar PTC by preoperative FNAC was the only factor associated with performing bilateral CND. Positive ipsilateral LNs were retrieved in 55% of CNDs, while positive contralateral LNs were retrieved in only 15% of the patients. Conclusions The incidence of contralateral cervical LN metastasis in patients with unilateral PTC is low, while there is clear evidence of postoperative morbidity from routine contralateral CND in unilobar PTC. Contralateral CND in patients with unilobar PTC may be reserved for high-risk patients: males, those aged ≤45 years, tumors larger than 1.0 cm, and cases with extrathyroidal extension and micro-calcification on ultrasound.

Nomogram model based on preoperative clinical characteristics of unilateral papillary thyroid carcinoma to predict contralateral medium-volume central lymph node metastasis

Objectives

To explore the preoperative high-risk clinical factors for contralateral medium-volume central lymph node metastasis (conMVCLNM) in unilateral papillary thyroid carcinoma (uPTC) and the indications for dissection of contralateral central lymph nodes (conCLN).

Methods

Clinical and pathological data of 204 uPTC patients who underwent thyroid surgery at the Hangzhou First People's Hospital from September 2010 to October 2022 were collected. Univariate and multivariate logistic regression analyses were conducted to determine the independent risk factors for contralateral central lymph node metastasis (conCLNM) and conMVCLNM in uPTC patients based on the preoperative clinical data. Predictive models for conCLNM and conMVCLNM were constructed using logistic regression analyses and validated using receiver operating characteristic (ROC) curves, concordance index (C-index), calibration curves, and decision curve analysis (DCA).

Results

Univariate and multivariate logistic regression analyses showed that gender (P < 0.001), age (P < 0.001), tumor diameter (P < 0.001), and multifocality (P = 0.008) were independent risk factors for conCLNM in uPTC patients. Gender(P= 0.026), age (P = 0.010), platelet-to-lymphocyte ratio (PLR) (P =0.003), and tumor diameter (P = 0.036) were independent risk factors for conMVCLNM in uPTC patients. A predictive model was established to assess the risk of conCLNM and conMVCLNM, with ROC curve areas of 0.836 and 0.845, respectively. The C-index, the calibration curve, and DCA demonstrated that the model had good diagnostic value.

Conclusion

Gender, age, tumor diameter, and multifocality are high-risk factors for conCLNM in uPTC patients. Gender, age, tumor diameter, and PLR are high-risk factors for conMVCLNM in uPTC patients, and preventive conCLN dissection should be performed.

LASSO-based machine learning models for the prediction of central lymph node metastasis in clinically negative patients with papillary thyroid carcinoma

BACKGROUND

The presence of central lymph node metastasis (CLNM) is crucial for surgical decision-making in clinical N0 (cN0) papillary thyroid carcinoma (PTC) patients. We aimed to develop and validate machine learning (ML) algorithms-based models for predicting the risk of CLNM in cN0 patients.

METHODS

A total of 1099 PTC patients with cN0 central neck from July 2019 to March 2022 at our institution were retrospectively analyzed. All patients were randomly split into the training dataset (70%) and the validation dataset (30%). Eight ML algorithms, including the Logistic Regression, Gradient Boosting Machine, Extreme Gradient Boosting (XGB), Random Forest (RF), Decision Tree, Neural Network, Support Vector Machine and Bayesian Network were used to evaluate the risk of CLNM. The performance of ML models was evaluated by the area under curve (AUC), sensitivity, specificity, and decision curve analysis (DCA).

RESULTS

We firstly used the LASSO Logistic regression method to select the most relevant factors for predicting CLNM. The AUC of XGB was slightly higher than RF (0.907 and 0.902, respectively). According to DCA, RF model significantly outperformed XGB model at most threshold points and was therefore used to develop the predictive model. The diagnostic performance of RF algorithm was dependent on the following nine top-rank variables: size, margin, extrathyroidal extension, sex, echogenic foci, shape, number, lateral lymph node metastasis and chronic lymphocytic thyroiditis.

CONCLUSION

By incorporating clinicopathological and sonographic characteristics, we developed ML-based models, suggesting that this non-invasive method can be applied to facilitate individualized prediction of occult CLNM in cN0 central neck PTC patients.