The lymph node yield in the initial lateral neck dissection predicts recurrence in the lateral neck of papillary thyroid carcinoma: a revision surgery cohort study

Machine learning algorithms for identifying contralateral central lymph node metastasis in unilateral cN0 papillary thyroid cancer

Purpose

The incidence of thyroid cancer is growing fast and surgery is the most significant treatment of it. For patients with unilateral cN0 papillary thyroid cancer whether to dissect contralateral central lymph node is still under debating. Here, we aim to provide a machine learning based prediction model of contralateral central lymph node metastasis using demographic and clinical data.

Methods

2225 patients with unilateral cN0 papillary thyroid cancer from Wuhan Union Hospital were retrospectively studied. Clinical and pathological features were compared between patients with contralateral central lymph node metastasis and without. Six machine learning models were constructed based on these patients and compared using accuracy, sensitivity, specificity, area under the receiver operating characteristic and decision curve analysis. The selected models were then verified using data from Differentiated Thyroid Cancer in China study. All statistical analysis and model construction were performed by R software.

Results

Male, maximum diameter larger than 1cm, multifocality, ipsilateral central lymph node metastasis and younger than 50 years were independent risk factors of contralateral central lymph node metastasis. Random forest model performed better than others, and were verified in external validation cohort. A web calculator was constructed.

Conclusions

Gender, maximum diameter, multifocality, ipsilateral central lymph node metastasis and age should be considered for contralateral central lymph node dissection. The web calculator based on random forest model may be helpful in clinical decision.

A modified, single-incision, gasless, endoscopic thyroidectomy and bilateral central neck dissection via axillary approach technique for bilateral papillary thyroid microcarcinoma: A preliminary report

Background

Our objective was to assess the viability and oncological security of a gasless, transaxillary single-incision endoscopic procedure for performing total thyroidectomy and bilateral central neck dissection (TT + BCND). This study focused on patients diagnosed with bilateral papillary thyroid microcarcinoma (PTMC).

Method

Between April 2020 and November 2021, 22 patients with bilateral PTMC underwent single-incision, gasless, transaxillary endoscopic TT + BCND. The patients' clinicopathologic characteristics, surgical completeness and complications were analyzed.

Result

Single-incision, gasless, transaxillary endoscopic TT + BCND was successful performed in all patients. The median (IQR) total surgical time was 143 (85-160) min. Only two patients experienced transient unilateral RLN palsy or transient hypocalcemia. All these complications resolved within 1 month after surgery. The median duration of hospital stay after surgery was 4 (3-4.5) days. The median hospitalization expense for these patients was 3848 (3781-4145) USD. The median number of lymph node yielded was 10.5 (8-15). The cosmetic outcomes were well-received by all individuals.

Conclusion

In certain cases, gasless, transaxillary endoscopic TT + BCND procedure performed through a single incision proved to be a secure alternative for managing bilateral PTMC.

A propensity score matching analysis of gasless endoscopic transaxillary thyroidectomy with five-settlement technique versus conventional open thyroidectomy in patients with papillary thyroid microcarcinoma

BACKGROUND

In a previous study, we proposed a novel anatomy-based five-settlement method for transaxillary endoscopic thyroidectomy (fs-TAT) for patients with papillary thyroid carcinoma. The safety of this new method has been reported in a retrospective study of a single cohort. The safety and short-term oncological outcome of this method was confirmed by comparing it with conventional open surgery (COT) in patients with papillary thyroid microcarcinoma.

METHODS

The medical records of patients who underwent fs-TAT or COT by a single surgeon from February 2019 to December 2021 were reviewed retrospectively. All patients were diagnosed with papillary thyroid microcarcinoma and underwent lobectomy and ipsilateral central compartment neck dissection. Propensity score matching was used to compare the technical safety and short-term oncologic outcomes of fs-TAT and COT for the purpose of reducing potential selection bias. Reporting was consistent with the STROCSS 2021 guidelines.

RESULT

After propensity score matching, 460 (fs-TAT: 230; COT: 230) patients remained in the study population. There were no significant differences in sex, age, tumor size, Hashimoto's thyroiditis, or tumor multifocality between the groups. The operative time was longer [104.5 (90.3, 120.0) vs. 62.0 (52.0, 76.0), P < 0.001] and the total postoperative drainage volume [135(90, 210) vs. 75 (55, 115), P < 0.001] was greater in the fs-TAT group than in the COT group. However, intraoperative bleeding [3.0 (2.0, 5.0) vs. 5.0 (5.0, 7.5), P < 0.001] was greater, and the median number of lymph nodes yielded [5.0 (2.3, 8.0) vs. 7.0 (5.0, 11.0), P < 0.001] was greater in the COT group than in the fs-TAT group. The groups exhibited no significant difference in the rate of complications (fs-TAT: 2.2% vs. COT: 2.6%, P = 0.856), rate of positive lymph nodes (fs-TAT: 32.2% vs. COT: 36.5%, P = 0.377), length of postoperative hospital stay (3 days vs. 3 days, P = 0.305) or total medical costs (26,936 vs. 26,549, P = 0.144).

CONCLUSION

Compared to conventional open surgery, fs-TAT offered excellent safety and acceptable short-term oncological outcomes in a selected cohort of patients with papillary thyroid microcarcinoma.

Interpretable machine learning model based on the systemic inflammation response index and ultrasound features can predict central lymph node metastasis in cN0T1-T2 papillary thyroid carcinoma

Background

It is arguable whether individuals with T1-T2 papillary thyroid cancer (PTC) who have a clinically negative (cN0) diagnosis should undergo prophylactic central lymph node dissection (pCLND) on a routine basis. Many inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and systemic immune-inflammatory index (SII), have been reported in PTC. However, the associations between the systemic inflammation response index (SIRI) and the risk of central lymph node metastasis (CLNM) remain unclear.

Methods

Retrospective research involving 1,394 individuals with cN0T1-T2 PTC was carried out, and the included patients were randomly allocated into training (70%) and testing (30%) subgroups. The preoperative inflammatory indices and ultrasound (US) features were used to train the models. To assess the forecasting factors as well as drawing nomograms, the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were utilized. Then eight interpretable models based on machine learning (ML) algorithms were constructed, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). The performance of the models was evaluated by incorporating the area under the precision-recall curve (auPR) and the area under the receiver operating characteristic curve (auROC), as well as other conventional metrics. The interpretability of the optimum model was illustrated via the shapley additive explanations (SHAP) approach.

Results

Younger age, larger tumor size, capsular invasion, location (lower and isthmus), unclear margin, microcalcifications, color Doppler flow imaging (CDFI) blood flow, and higher SIRI (≥0.77) were independent positive predictors of CLNM, whereas female sex and Hashimoto thyroiditis were independent negative predictors, and nomograms were subsequently constructed. Taking into account both the auROC and auPR, the RF algorithm showed the best performance, and superiority to XGBoost, CatBoost and ANN. In addition, the role of key variables was visualized in the SHAP plot.

Conclusions

An interpretable ML model based on the SIRI and US features can be used to predict CLNM in individuals with cN0T1-T2 PTC.

Performance of CT-based deep learning in diagnostic assessment of suspicious lateral lymph nodes in papillary thyroid cancer: a prospective diagnostic study

BACKGROUND

Preoperative evaluation of the metastasis status of lateral lymph nodes (LNs) in papillary thyroid cancer is challenging. Strategies for using deep learning to diagnosis of lateral LN metastasis require additional development and testing. This study aimed to build a deep learning-based model to distinguish benign lateral LNs from metastatic lateral LNs in papillary thyroid cancer and test the model's diagnostic performance in a real-world clinical setting.

METHODS

This was a prospective diagnostic study. An ensemble model integrating a three-dimensional residual network algorithm with clinical risk factors available before surgery was developed based on computed tomography images of lateral LNs in an internal dataset and validated in two external datasets. The diagnostic performance of the ensemble model was tested and compared with the results of fine-needle aspiration (FNA) (used as the standard reference method) and the diagnoses made by two senior radiologists in 113 suspicious lateral LNs in patients enrolled prospectively.

RESULTS

The area under the receiver operating characteristic curve of the ensemble model for diagnosing suspicious lateral LNs was 0.829 (95% CI: 0.732-0.927). The sensitivity and specificity of the ensemble model were 0.839 (95% CI: 0.762-0.916) and 0.769 (95% CI: 0.607-0.931), respectively. The diagnostic accuracy of the ensemble model was 82.3%. With FNA results as the criterion standard, the ensemble model had excellent diagnostic performance ( P =0.115), similar to that of the two senior radiologists ( P =1.000 and P =0.392, respectively).

CONCLUSION

A three-dimensional residual network-based ensemble model was successfully developed for the diagnostic assessment of suspicious lateral LNs and achieved diagnostic performance similar to that of FNA and senior radiologists. The model appears promising for clinical application.