Dynamic Nomogram for Predicting Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

Pretreatment level of circulating tumor cells is associated with lymph node metastasis in papillary thyroid carcinoma patients with ≤ 55 years old

OBJECTIVE

To investigate the relationship of pretreatment of circulating tumor cells (CTCs) and cervical lymph node metastasis (LNM) (central LNM (CLNM) and lateral LNM (LLNM)) in papillary thyroid carcinoma (PTC) patients with ≤ 55 years old.

METHODS

Clinicopathological data (CTCs level, Hashimoto's thyroiditis, thyroid function, multifocal, tumor size, invaded capsule, clinical stage, and LNM) of 588 PTC patients with ≤ 55 years old were retrospectively collected. The relationship of CLNM, LLNM and the clinical features of patients was analyzed. Univariate and multivariate logistic regression analyses were used to evaluate the relationship between the CTCs and CLNM, LLNM.

RESULTS

There were 273(46.4%) and 89(15.1%) patients with CLNM and LLNM, respectively. Patients with CLNM had higher proportions of multifocality, tumor size > 1 cm, invaded capsule, and positive CTCs level than those without (all p < 0.05). Patients with LLNM had higher proportions of multifocality, tumor size > 1 cm, and invaded capsule than those without (all p < 0.05). Logistic regression analysis showed that multifocality (odds ratio (OR): 1.821, 95% confidence interval (CI): 1.230-2.698, p = 0.003), tumor size > 1 cm (OR: 3.444, 95% CI: 2.296-5.167, p < 0.001), invaded capsule (OR: 1.699, 95% CI: 1.167-2.473, p = 0.006), and positive CTCs level (OR: 1.469, 95% CI: 1.019-2.118, p = 0.040) were independently associated with CLNM; and multifocality (OR: 2.373, 95% CI: 1.389-4.052, p = 0.002), tumor size > 1 cm (OR: 5.344, 95% CI: 3.037-9.402, p < 0.001), and invaded capsule (OR: 2.591, 95% CI: 1.436-4.674, p = 0.002) were independently associated with LLNM.

CONCLUSIONS

Preoperative CTCs positive was associated with CLNM in PTC patients with ≤ 55 years old, but not LLNM.

Construction of machine learning-based models for screening the high-risk patients with gastric precancerous lesions

BACKGROUND

The individualized prediction and discrimination of precancerous lesions of gastric cancer (PLGC) is critical for the early prevention of gastric cancer (GC). However, accurate non-invasive methods for distinguishing between PLGC and GC are currently lacking. This study therefore aimed to develop a risk prediction model by machine learning and deep learning techniques to aid the early diagnosis of GC.

METHODS

In this study, a total of 2229 subjects were recruited from nine tertiary hospitals between October 2022 and November 2023. We designed a comprehensive questionnaire, identified statistically significant factors, and created a web-based column chart. Then, a risk prediction model was subsequently developed by machine learning techniques. In addition, a tongue image-based risk prediction model was established by deep learning algorithms.

RESULTS

Based on logistic regression analysis, a dynamic web-based nomogram was developed and it is freely accessible at: https://yz6677.shinyapps.io/GC67/ . Then, the prediction model was established using ten different machine learning algorithms and the Random Forest (RF) model achieved the highest accuracy at 85.65%. According with the predictive results, the top 10 key risk factors were age, traditional Chinese medicine (TCM) constitution type, tongue coating color, tongue color, irregular meals, pickled food, greasy fur, over-hot eating habit, anxiety and sleep onset latency. These factors are all significant risk indicators for the progression of PLGC patients to GC patients. Subsequently, the Swin Transformer architecture was used to develop a tongue image-based model for predicting the risk for progression of PLGC. The verification set showed an accuracy of 73.33% and an area under curve (AUC) greater than 0.8 across all models.

CONCLUSIONS

Our study developed machine learning and deep learning-based models for predicting the risk for progression of PLGC to GC, which will offer the assistance to determine the high-risk patients from PLGC and improve the early diagnosis of GC.

Factors associated with occult lateral lymph node metastases in patients with clinically lymph node negative papillary thyroid carcinoma: a systematic review and meta-analysis

Background

It remains unclear which category of patients with clinically lymph node negative (cN0) papillary thyroid carcinoma (PTC) might have higher risk of occult lateral lymph node metastasis (OLLNM) due to the conflicting results in previous studies. This systematic review and meta-analysis aimed to investigate factors associated with OLLNM in patients with cN0 PTC.

Methods

PubMed, EMBASE, Cochrane Library and Web of Science were comprehensively searched by two independent investigators to 15 August 2022. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the pooled analysis. This systematic review and meta-analysis was registered in PROSPERO (CRD42022353567).

Results

Fifteen eligible studies involving 8369 patients with cN0 PTC were included in this meta-analysis. We found 7 factors significantly associated with OLLNM, including male (OR, 1.47; 95% CI, 1.30 to 1.66; P < 0.001), age<45y (OR, 1.65; 95% CI, 1.31 to 2.06; P < 0.001), tumor size > 10mm (OR, 3.17; 95% CI, 2.04 to 4.93; P <0.001), tumor located in upper pole (OR, 1.81; 95% CI, 1.44 to 2.27; P <0.001), bilaterality (OR, 1.66; 95% CI, 1.37 to 2.02; P <0.001), extrathyroidal extension (ETE) (OR, 2.52; 95% CI, 1.72 to 3.68; P <0.001) and increased number of central lymph node metastasis (CLNM) (OR, 6.84; 95% CI, 5.66 to 8.27; P <0.001). The results of sensitivity analysis and subgroup analysis were similar to the pooled results. No significant publication bias was observed.

Conclusions

The systematic review and meta-analysis identified 7 factors associated with OLLNM in patients with cN0 PTC. Future studies are needed to validate our results.

Systematic review registration

https://www.crd.york.ac.uk/prospero, identifier CRD42022353567.

Prediction model for lateral lymph node metastasis of papillary thyroid carcinoma in children and adolescents based on ultrasound imaging and clinical features: a retrospective study

BACKGROUND

The presence of lateral lymph node metastases (LNM) in paediatric patients with papillary thyroid cancer (PTC) is an independent risk factor for recurrence. We aimed to identify risk factors and establish a prediction model for lateral LNM before surgery in children and adolescents with PTC.

METHODS

We developed a prediction model based on data obtained from 63 minors with PTC between January 2014 and June 2023. We collected and analysed clinical factors, ultrasound (US) features of the primary tumour, and pathology records of the patients. Multivariate logistic regression analysis was used to determine independent predictors and build a prediction model. We evaluated the predictive performance of risk factors and the prediction model using the area under the receiver operating characteristic (ROC) curve. We assessed the clinical usefulness of the predicting model using decision curve analysis.

RESULTS

Among the minors with PTC, 21 had lateral LNM (33.3%). Logistic regression revealed that independent risk factors for lateral LNM were multifocality, tumour size, sex, and age. The area under the ROC curve for multifocality, tumour size, sex, and age was 0.62 (p = 0.049), 0.61 (p = 0.023), 0.66 (p = 0.003), and 0.58 (p = 0.013), respectively. Compared to a single risk factor, the combined predictors had a significantly higher area under the ROC curve (0.842), with a sensitivity and specificity of 71.4% and 81.0%, respectively (cutoff value = 0.524). Decision curve analysis showed that the prediction model was clinically useful, with threshold probabilities between 2% and 99%.

CONCLUSIONS

The independent risk factors for lateral LNM in paediatric PTC patients were multifocality and tumour size on US imaging, as well as sex and age. Our model outperformed US imaging and clinical features alone in predicting the status of lateral LNM.

Lateral lymph node metastasis in papillary thyroid cancer: Is there a difference between PTC and PTMC?

Papillary thyroid carcinoma (PTC) and papillary thyroid microcarcinoma (PTMC) are generally characterized as less invasive forms of thyroid cancer with favorable prognosis. However, once lateral cervical lymph node metastasis takes place, the prognosis may be significantly impacted. The purpose of this study was to evaluate whether there is a difference in the pattern of lateral lymph node metastasis between PTC and PTMC. A retrospective analysis was performed for PTC and PTMC patients that underwent central area dissection and unilateral lateral neck lymph node dissection (II-V area) between January 2020 and December 2021. Compared with PTMC group, the PTC group exhibited higher incidence of capsule invasion, extrathyroid invasion and lymphatic vessel invasion. Both the number and rate of central lymph nodes metastasis were elevated in the PTC group. While the number of lateral cervical lymph node metastasis was higher, the metastasis rate did not demonstrate significant difference. No significant differences were identified in the lymph node metastasis patterns between the 2 groups. The determination of the extent of lateral neck lymph node dissection solely based on the tumor size may be unreliable, as PTC and PTMC showed no difference in the number and pattern of lateral neck metastasis. Additional clinical data are warranted to reinforce this conclusion. For patients categorized as unilateral, bilateral, or contralateral cervical lymph node metastasis (including level I, II, III, IV, or V) or retropharyngeal lymph node metastasis who require unilateral lateral neck dissection, the size of the primary tumor may not need to be a central consideration when assessing and deciding the extent of lateral neck dissection.

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.

Machine learning-based dynamic prediction of lateral lymph node metastasis in patients with papillary thyroid cancer

OBJECTIVE

To develop a web-based machine learning server to predict lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC) patients.

METHODS

Clinical data for PTC patients who underwent primary thyroidectomy at our hospital between January 2015 and December 2020, with pathologically confirmed presence or absence of any LLNM finding, were retrospectively reviewed. We built all models from a training set (80%) and assessed them in a test set (20%), using algorithms including decision tree, XGBoost, random forest, support vector machine, neural network, and K-nearest neighbor algorithm. Their performance was measured against a previously established nomogram using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), precision, recall, accuracy, F1 score, specificity, and sensitivity. Interpretable machine learning was used for identifying potential relationships between variables and LLNM, and a web-based tool was created for use by clinicians.

RESULTS

A total of 1135 (62.53%) out of 1815 PTC patients enrolled in this study experienced LLNM episodes. In predicting LLNM, the best algorithm was random forest. In determining feature importance, the AUC reached 0.80, with an accuracy of 0.74, sensitivity of 0.89, and F1 score of 0.81. In addition, DCA showed that random forest held a higher clinical net benefit. Random forest identified tumor size, lymph node microcalcification, age, lymph node size, and tumor location as the most influentials in predicting LLNM. And the website tool is freely accessible at http://43.138.62.202/.

CONCLUSION

The results showed that machine learning can be used to enable accurate prediction for LLNM in PTC patients, and that the web tool allowed for LLNM risk assessment at the individual level.