Construction and validation of a nomogram for predicting cervical lymph node metastasis in classic papillary thyroid carcinoma

Construction and validation of a nomogram for predicting lateral lymph node metastasis in pediatric and adolescent with differentiated thyroid carcinoma

BACKGROUND

Limited research has been conducted to specifically investigate the identification of risk factors and the development of prediction models for lateral lymph node metastasis (LNM) in pediatric and adolescent differentiated thyroid carcinoma (DTC) populations, despite its significant association with unfavorable prognosis.

METHODS

This study entails a retrospective analysis of the clinical characteristics exhibited by pediatric and adolescent patients who have been diagnosed with DTC. The data utilized for this analysis was sourced from the Surveillance, Epidemiology, and End Results (SEER) database, spanning the time frame from 2000 to 2020. Furthermore, the study incorporates patients who were treated at the Departments of Breast and Thyroid Surgery in the Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine, as well as The General Hospital of Western Theater Command, during the period from 2010 to 2020.

RESULTS

A cohort of 2631 patients from the SEER database, along with an additional 339 patients from our departments who met the specified inclusion criteria, were included in this study. Subsequently, four clinical variables, namely age, tumor size, multifocality, and extrathyroidal invasion, were identified as being significantly associated with lateral LNM in pediatric and adolescent DTC patients. These variables were then utilized to construct a nomogram, which demonstrated effective discrimination with a concordance index (C-index) of 0.731. Furthermore, the performance of this model was validated through both internal and external assessments, yielding C-index values of 0.721 and 0.712, respectively. Afterward, a decision curve analysis was conducted to assess the viability of this nomogram in predicting lymph node metastasis.

CONCLUSION

The current investigation has effectively constructed a nomogram model utilizing visualized multipopulationsal data. Our findings demonstrate a significant association between various clinical characteristics and lateral LNM in pediatric and adolescent DTC patients. These outcomes hold substantial significance for healthcare practitioners, as they can employ this model to inform individualized clinical judgments for the pediatric and adolescent cohorts.

Risk factors, prognostic factors, and nomograms for distant metastases in patients with gastroenteropancreatic neuroendocrine tumors: a population-based study

Background

Patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs) have a poor prognosis for distant metastasis. Currently, there are no studies on predictive models for the risk of distant metastasis in GEP-NETs.

Methods

In this study, risk factors associated with metastasis in patients with GEP-NETs in the Surveillance, Epidemiology, and End Results (SEER) database were analyzed by univariate and multivariate logistic regression, and a nomogram model for metastasis risk prediction was constructed. Prognostic factors associated with distant metastasis in patients with GEP-NETs were analyzed by univariate and multivariate Cox, and a nomogram model for prognostic prediction was constructed. Finally, the performance of the nomogram model predictions is validated by internal validation set and external validation set.

Results

A total of 9145 patients with GEP-NETs were enrolled in this study. Univariate and multivariate logistic analysis demonstrated that T stage, N stage, tumor size, primary site, and histologic types independent risk factors associated with distant metastasis in GEP-NETs patients (p value < 0.05). Univariate and multivariate Cox analyses demonstrated that age, histologic type, tumor size, N stage, and primary site surgery were independent factors associated with the prognosis of patients with GEP-NETs (p value < 0.05). The nomogram model constructed based on metastasis risk factors and prognostic factors can predict the occurrence of metastasis and patient prognosis of GEP-NETs very effectively in the internal training and validation sets as well as in the external validation set.

Conclusion

In conclusion, we constructed a new distant metastasis risk nomogram model and a new prognostic nomogram model for GEP-NETs patients, which provides a decision-making reference for individualized treatment of clinical patients.

A nomogram based on clinicopathological and ultrasound characteristics to predict central neck lymph node metastases in papillary thyroid cancer

Purpose

Papillary thyroid cancer (PTC) has grown rapidly in prevalence over the past few decades, and central neck lymph node metastasis (CNLNM) is associated with poor prognoses. However, whether to carry out preventive central neck lymph node dissection (CNLND) is still controversial. We aimed to construct a prediction model of CNLNM to facilitate making clinical surgical regimens.

Methods

A total of 691 patients with PTC between November 2018 and December 2021 were included in our study. Univariate and multivariate analyses were performed on basic information and clinicopathological characteristics, as well as ultrasound characteristics (American College of Radiology (ACR) scores). The prediction model was constructed and performed using a nomogram, and then discriminability, calibrations, and clinical applicability were evaluated.

Results

Five variables, namely, male, age >55 years, clinical lymph node positivity, tumor size ≥1 cm, and ACR scores ≥6, were independent predictors of CNLNM in the multivariate analysis, which were eventually included to construct a nomogram model. The area under the curve (AUC) of the model was 0.717, demonstrating great discriminability. A calibration curve was developed to validate the calibration of the present model by bootstrap resampling, which indicated that the predicted and actual values were in good agreement and had no differentiation from the ideal model. The decision curve analysis (DCA) indicated that the prediction model has good clinical applicability.

Conclusions

Our non-invasive prediction model combines ACR scores with clinicopathological features presented through nomogram and has shown good performance and application prospects for the prediction of CNLNM in PTCs.

Construction of a nomogram for central lymph node metastasis in papillary thyroid cancer based on gender grouping

BACKGROUND

The status of central lymph nodes is crucial for determining the surgical approach to papillary thyroid carcinoma (PTC). Because of the differences between genders in central lymph node metastasis (CLNM), we aimed to construct separate predictive models for CLNM according to gender.

METHODS

In our study, a total of 1258 PTC patients who underwent thyroid cancer surgery from September 2021 to March 2023 were analyzed retrospectively. The data were analysed univariately and multivariately using SPSS software grouped according to gender and nomograms of CLNM were plotted using R software. The variables included in this study were sex, Age, body mass index, Diabetes, chronic lymphocytic thyroiditis (CLT), Suspicious central lymph node (SCLN), A/T, Margin, Microcalcification (MC), BRAF, Number, Location, CLNM.

RESULTS

The preoperative nomogram in male patients included four clinical variables: CLT, Margin, Number, Size. The preoperative nomogram of female patients included six clinical variables: Age, SCLN, Margin, MC, Number, Size. The calibration curves showed great agreement in both the training group and the validation group. The decision curve analysis showed the feasibility of nomogram in predicting CLNM in both man and woman.

CONCLUSION

Based on the successful establishment of nomogram, we can analyze the variability of CLNM between male and female, which may provide clinicians with personalized clinical schemes in the treatment of PTC.

Machine learning based on SEER database to predict distant metastasis of thyroid cancer

OBJECTIVE

Distant metastasis of thyroid cancer often indicates poor prognosis, and it is important to identify patients who have developed distant metastasis or are at high risk as early as possible. This paper aimed to predict distant metastasis of thyroid cancer through the construction of machine learning models to provide a reference for clinical diagnosis and treatment.

MATERIALS & METHODS

Data on demographic and clinicopathological characteristics of thyroid cancer patients between 2010 and 2015 were extracted from the National Institutes of Health (NIH) Surveillance, Epidemiology, and End Results (SEER) database. Our research used univariate and multivariate logistic models to screen independent risk factors, respectively. Decision Trees (DT), ElasticNet (ENET), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multilayer Perceptron (MLP), Radial Basis Function Support Vector Machine (RBFSVM) and seven machine learning models were compared and evaluated by the following metrics: the area under receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), sensitivity(also called recall), specificity, precision, accuracy and F1 score. Interpretable machine learning was used to identify possible correlation between variables and distant metastasis.

RESULTS

Independent risk factors for distant metastasis, including age, gender, race, marital status, histological type, capsular invasion, and number of lymph nodes metastases were screened by multifactorial regression analysis. Among the seven machine learning algorithms, RF was the best algorithm, with an AUC of 0.948, sensitivity of 0.919, accuracy of 0.845, and F1 score of 0.886 in the training set, and an AUC of 0.960, sensitivity of 0.929, accuracy of 0.906, and F1 score of 0.908 in the test set.

CONCLUSIONS

The machine learning model constructed in this study helps in the early diagnosis of distant thyroid metastases and helps physicians to make better decisions and medical interventions.

Exploration and validation of key genes associated with early lymph node metastasis in thyroid carcinoma using weighted gene co-expression network analysis and machine learning

Background

Thyroid carcinoma (THCA), the most common endocrine neoplasm, typically exhibits an indolent behavior. However, in some instances, lymph node metastasis (LNM) may occur in the early stages, with the underlying mechanisms not yet fully understood.

Materials and methods

LNM potential was defined as the tumor's capability to metastasize to lymph nodes at an early stage, even when the tumor volume is small. We performed differential expression analysis using the 'Limma' R package and conducted enrichment analyses using the Metascape tool. Co-expression networks were established using the 'WGCNA' R package, with the soft threshold power determined by the 'pickSoftThreshold' algorithm. For unsupervised clustering, we utilized the 'ConsensusCluster Plus' R package. To determine the topological features and degree centralities of each node (protein) within the Protein-Protein Interaction (PPI) network, we used the CytoNCA plugin integrated with the Cytoscape tool. Immune cell infiltration was assessed using the Immune Cell Abundance Identifier (ImmuCellAI) database. We applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF) algorithms individually, with the 'glmnet,' 'e1071,' and 'randomForest' R packages, respectively. Ridge regression was performed using the 'oncoPredict' algorithm, and all the predictions were based on data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. To ascertain the protein expression levels and subcellular localization of genes, we consulted the Human Protein Atlas (HPA) database. Molecular docking was carried out using the mcule 1-click Docking server online. Experimental validation of gene and protein expression levels was conducted through Real-Time Quantitative PCR (RT-qPCR) and immunohistochemistry (IHC) assays.

Results

Through WGCNA and PPI network analysis, we identified twelve hub genes as the most relevant to LNM potential from these two modules. These 12 hub genes displayed differential expression in THCA and exhibited significant correlations with the downregulation of neutrophil infiltration, as well as the upregulation of dendritic cell and macrophage infiltration, along with activation of the EMT pathway in THCA. We propose a novel molecular classification approach and provide an online web-based nomogram for evaluating the LNM potential of THCA (http://www.empowerstats.net/pmodel/?m=17617_LNM). Machine learning algorithms have identified ERBB3 as the most critical gene associated with LNM potential in THCA. ERBB3 exhibits high expression in patients with THCA who have experienced LNM or have advanced-stage disease. The differential methylation levels partially explain this differential expression of ERBB3. ROC analysis has identified ERBB3 as a diagnostic marker for THCA (AUC=0.89), THCA with high LNM potential (AUC=0.75), and lymph nodes with tumor metastasis (AUC=0.86). We have presented a comprehensive review of endocrine disruptor chemical (EDC) exposures, environmental toxins, and pharmacological agents that may potentially impact LNM potential. Molecular docking revealed a docking score of -10.1 kcal/mol for Lapatinib and ERBB3, indicating a strong binding affinity.

Conclusion

In conclusion, our study, utilizing bioinformatics analysis techniques, identified gene modules and hub genes influencing LNM potential in THCA patients. ERBB3 was identified as a key gene with therapeutic implications. We have also developed a novel molecular classification approach and a user-friendly web-based nomogram tool for assessing LNM potential. These findings pave the way for investigations into the mechanisms underlying differences in LNM potential and provide guidance for personalized clinical treatment plans.

Risk factors for central lymph node metastasis in patients with papillary thyroid carcinoma: a retrospective study

Introduction

Thyroid cancer is the most prevalent endocrine malignancy, with its global incidence increasing annually in recent years. Papillary carcinoma is the most common subtype, frequently accompanied by cervical lymph node metastasis early on. Central lymph node metastasis (CLNM) is particularly the common metastasis form in this subtype, and the presence of lymph node metastasis correlates strongly with tumor recurrence. However, effective preoperative assessment methods for CLNM in patients with papillary thyroid carcinoma (PTC) remain lacking.

Methods

Data from 400 patients diagnosed with PTC between January 1, 2018, and January 1, 2022, at the Shandong Provincial Hospital were retrospectively analyzed. This data included clinicopathological information of the patients, such as thyroid function, BRAF V600E mutation, whether complicated with Hashimoto's thyroiditis, and the presence of capsular invasion. Univariate and multivariate logistic regression analyses were performed to assess the risk factors associated with cervical CLNM in patients with PTC. Subsequently, a clinical prediction model was constructed, and prognostic risk factors were identified based on univariate and multivariate Cox regression analyses.

Results

Univariate and multivariate analyses identified that age >45 years (P=0.014), body mass index ≥25 (P=0.008), tumor size ≥1 cm (P=0.001), capsular invasion (P=0.001), and the presence of BRAF V600E mutation (P<0.001) were significantly associated with an increased risk of CLNM. Integrating these factors into the nomogram revealed an area-under-the-curve of 0.791 (95% confidence interval 0.735-0.846) and 0.765 (95% confidence interval: 0.677-0.852) for the training and validation sets, respectively, indicating strong discriminative abilities. Subgroup analysis further confirmed that patients with papillary thyroid microcarcinoma and BRAF V600E mutations who underwent therapeutic central compartment neck dissection had significantly better 3-year disease-free survival than those who had prophylactic central compartment neck dissection (P<0.001).

Conclusion

The study revealed that age >45 years, body mass index ≥25, tumor size ≥1 cm, BRAF V600E mutation, and capsular invasion are the related risk factors for CLNM in patients with PTC. For patients with clinically nodal-negative (cN0) papillary thyroid microcarcinoma, accurately identifying the BRAF V600E mutation is essential for guiding the central lymph node dissection approach and subsequent treatments.

Ultrasonic Feature Prediction of Large-Number Central Lymph Node Metastasis in Clinically Node-Negative Solitary Papillary Thyroid Carcinoma

BACKGROUND

The purpose of this study was to investigate the preoperative prediction of large-number central lymph node metastasis (CLNM) in single thyroid papillary carcinoma (PTC) with negative clinical lymph nodes.

METHODS

A total of 634 patients with clinically lymph node-negative single PTC who underwent thyroidectomy and central lymph node dissection at the First Affiliated Hospital of Anhui Medical University and the Nanchong Central Hospital between September 2018 and September 2021 were analyzed retrospectively. According to the CLNM status, the patients were divided into two groups: small-number (≤5 metastatic lymph nodes) and large-number (>5 metastatic lymph nodes). Univariate and multivariate analyses were used to determine the independent predictors of large-number CLNM. Simultaneously, a nomogram based on risk factors was established to predict large-number CLNM.

RESULTS

The incidence of large-number CLNM was 7.7%. Univariate and multivariate analyses showed that age, tumor size, and calcification were independent risk factors for predicting large-number CLNM. The combination of the three independent predictors achieved an AUC of 0.806. Based on the identified risk factors that can predict large-number CLNM, a nomogram was developed. The analysis of the calibration map showed that the nomogram had good performance and clinical application.

CONCLUSION

In patients with single PTC with negative clinical lymph nodes large-number CLNM is related to age, size, and calcification in patients with a single PTC with negative clinical lymph nodes. Surgeons and radiologists should pay more attention to patients with these risk factors. A nomogram can help guide the surgical decision for PTC.

Insight of novel biomarkers for papillary thyroid carcinoma through multiomics

Introduction

The overdiagnosing of papillary thyroid carcinoma (PTC) in China necessitates the development of an evidence-based diagnosis and prognosis strategy in line with precision medicine. A landscape of PTC in Chinese cohorts is needed to provide comprehensiveness.

Methods

6 paired PTC samples were employed for whole-exome sequencing, RNA sequencing, and data-dependent acquisition mass spectrum analysis. Weighted gene co-expression network analysis and protein-protein interactions networks were used to screen for hub genes. Moreover, we verified the hub genes' diagnostic and prognostic potential using online databases. Logistic regression was employed to construct a diagnostic model, and we evaluated its efficacy and specificity based on TCGA-THCA and GEO datasets.

Results

The basic multiomics landscape of PTC among local patients were drawn. The similarities and differences were compared between the Chinese cohort and TCGA-THCA cohorts, including the identification of PNPLA5 as a driver gene in addition to BRAF mutation. Besides, we found 572 differentially expressed genes and 79 differentially expressed proteins. Through integrative analysis, we identified 17 hub genes for prognosis and diagnosis of PTC. Four of these genes, ABR, AHNAK2, GPX1, and TPO, were used to construct a diagnostic model with high accuracy, explicitly targeting PTC (AUC=0.969/0.959 in training/test sets).

Discussion

Multiomics analysis of the Chinese cohort demonstrated significant distinctions compared to TCGA-THCA cohorts, highlighting the unique genetic characteristics of Chinese individuals with PTC. The novel biomarkers, holding potential for diagnosis and prognosis of PTC, were identified. Furthermore, these biomarkers provide a valuable tool for precise medicine, especially for immunotherapeutic or nanomedicine based cancer therapy.

Prediction model of cervical lymph node metastasis based on clinicopathological characteristics of papillary thyroid carcinoma: a dual-center retrospective study

Background

The overall prevalence of papillary thyroid carcinoma (PTC) patients is expanding along with an ongoing increase in thyroid cancer incidence. Patients with PTC who have lymph node metastases have a poor prognosis and a high death rate. There is an urgent need for indicators that can predict lymph node metastasis (LNM) before surgery as current imaging techniques, such as ultrasonography, do not have sufficient sensitivity to detect LNM. To predict independent risk factors for Central lymph node metastasis (CLNM) or Lateral lymph node metastasis (LLNM), we therefore developed two nomograms based on CLNM and LLNM, separately.

Methods

In two centers, the Second Affiliated Hospital of Nanchang University and Yichun People's Hospital, we retrospectively analyzed clinicopathological characteristics of PTC patients. We utilized multivariate analysis to screen for variables that might be suspiciously related to CLNM or LLNM. Furthermore, we developed nomograms to graphically depict the independent risk valuables connected to lymph node metastasis in PTC patients.

Result

Ultimately, 6068 PTC patients in all were included in the research. Six factors, including age<45, male, mETE, TSH>1.418, tumor size>4cm, and location (multicentric and lobe), were observed to be related to CLNM. Age<45, male, mETE (minimal extrathyroidal extension), multifocality, TSH≥2.910, CLNM positive, and tumor size>4cm were regarded as related risk factors for LLNM. The two nomograms developed subsequently proved to have good predictive power with 0.706 and 0.818 and demonstrated good clinical guidance functionality with clinical decision curves and impact curves.

Conclusion

Based on the successful establishment of this dual-institution-based visual nomogram model, we found that some clinical features are highly correlated with cervical lymph node metastasis, including CLNM and LLNM, which will better help clinicians make individualized clinical decisions for more effectively rationalizing managing PTC patients.

Clinical-Radiomics Nomogram Based on Contrast-Enhanced Ultrasound for Preoperative Prediction of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

This study aimed to establish a new clinical-radiomics nomogram based on ultrasound (US) for cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC). We collected 211 patients with PTC between June 2018 and April 2020, then we randomly divided these patients into the training set (n = 148) and the validation set (n = 63). 837 radiomics features were extracted from B-mode ultrasound (BMUS) images and contrast-enhanced ultrasound (CEUS) images. The maximum relevance minimum redundancy (mRMR) algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and backward stepwise logistic regression (LR) were applied to select key features and establish a radiomics score (Radscore), including BMUS Radscore and CEUS Radscore. The clinical model and clinical-radiomics model were established using the univariate analysis and multivariate backward stepwise LR. The clinical-radiomics model was finally presented as a clinical-radiomics nomogram, the performance of which was evaluated by the receiver operating characteristic curves, Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA). The results show that the clinical-radiomics nomogram was constructed by four predictors, including gender, age, US-reported LNM, and CEUS Radscore. The clinical-radiomics nomogram performed well in both the training set (AUC = 0.820) and the validation set (AUC = 0.814). The Hosmer-Lemeshow test and the calibration curves demonstrated good calibration. The DCA showed that the clinical-radiomics nomogram had satisfactory clinical utility. The clinical-radiomics nomogram constructed by CEUS Radscore and key clinical features can be used as an effective tool for individualized prediction of cervical LNM in PTC.

Nomogram for preoperative prediction of high-volume lymph node metastasis in the classical variant of papillary thyroid carcinoma

Introduction

The objective of our study was to construct a preoperative prediction nomogram for the classical variant of papillary thyroid carcinoma (CVPTC) patients with a solitary lesion based on demographic and ultrasonographic parameters that can quantify the individual probability of high-volume (>5) lymph node metastasis (HVLNM).

Materials and methods

In this study, a total of 626 patients with CVPTC from December 2017 to November 2022 were reviewed. Their demographic and ultrasonographic features at baseline were collected and analyzed using univariate and multivariate analyses. Significant factors after the multivariate analysis were incorporated into a nomogram for predicting HVLNM. A validation set from the last 6 months of the study period was conducted to evaluate the model performance.

Results

Male sex, tumor size >10 mm, extrathyroidal extension (ETE), and capsular contact >50% were independent risk factors for HVLNM, whereas middle and old age were significant protective factors. The area under the curve (AUC) was 0.842 in the training and 0.875 in the validation set.

Conclusions

The preoperative nomogram can help tailor the management strategy to the individual patient. Additionally, more vigilant and aggressive measures may benefit patients at risk of HVLNM.

The Value of Ki-67 Labeling Index in Central Lymph Node Metastasis and Survival of Papillary Thyroid Carcinoma: Evidence From the Clinical and Molecular Analyses

BACKGROUND

Recent evidence suggests that the Ki-67 labeling index is associated with lymph node metastasis and the prognosis of papillary thyroid carcinoma (PTC).

METHODS

We retrospectively evaluated the clinicopathological features of consecutive PTC patients between Jan 2019 and Oct 2020 in our medical center. The molecular analysis was also conducted by using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) program. The Chi-square test was performed for the comparison of variables between patients with central lymph node metastasis (CLNM) and not. Besides, univariate and stepwise multivariate logistic regression analyses were further used to determine the risk factors for CLNM in PTC.

RESULTS

Our results showed that male gender (odd ratio (OR) = 3.02; 95% CI: 1.81-5.04), tumor size >1 cm (OR = 2.81; 95% CI: 1.84-4.29), multifocality (OR = 2.08; 95% CI: 1.31-3.30, and Ki-67 labeling index (>3% and ≤5%: OR = 1.20; 95% CI: .73-1.97; >5%: OR = 3.85; 95% CI: 1.62-9.14) were independent risk factors for CLNM. After excluding the patients with harvested central lymph nodes <3, increased Ki-67 labeling index was still associated with the number of CLNM and the lymph node ratio. Additionally, the expression level of Ki-67 was significantly correlated with a higher N stage and worse disease-free survival in TCGA and validated GSE60542 datasets.

CONCLUSIONS

Higher Ki-67 labeling index (>5%) is significantly associated with the CLNM in PTC patients, like other indicators of the male gender, larger tumor size, and multifocality. Besides, the Ki-67 was also determined to be associated with CLNM and DFS in PTC patients, which may act as an important molecular marker in PTC.

Cervical lymph node metastasis prediction of postoperative papillary thyroid carcinoma before 131I therapy based on clinical and ultrasound characteristics

BACKGROUND

The status of lymph nodes is crucial to determine the dose of radioiodine-131(¹³¹I) for postoperative papillary thyroid carcinoma (PTC). We aimed to develop a nomogram for predicting residual and recurrent cervical lymph node metastasis (CLNM) in postoperative PTC before ¹³¹I therapy.

METHOD

Data from 612 postoperative PTC patients who underwent ¹³¹I therapy from May 2019 to December 2020 were retrospectively analyzed. Clinical and ultrasound features were collected. Univariate and multivariate logistic regression analyses were performed to determine the risk factors of CLNM. Receiver operating characteristic (ROC) analysis was used to weigh the discrimination of prediction models. To generate nomograms, models with high area under the curves (AUC) were selected. Bootstrap internal validation, calibration curves and decision curves were used to assess the prediction model's discrimination, calibration, and clinical usefulness.

RESULTS

A total of 18.79% (115/612) of postoperative PTC patients had CLNM. Univariate logistic regression analysis found serum thyroglobulin (Tg), serum thyroglobulin antibodies (TgAb), overall ultrasound diagnosis and seven ultrasound features (aspect transverse ratio, cystic change, microcalcification, mass hyperecho, echogenicity, lymphatic hilum structure and vascularity) were significantly associated with CLNM. Multivariate analysis revealed higher Tg, higher TgAb, positive overall ultrasound and ultrasound features such as aspect transverse ratio ≥ 2, microcalcification, heterogeneous echogenicity, absence of lymphatic hilum structure and abundant vascularity were independent risk factors for CLNM. ROC analysis showed the use of Tg and TgAb combined with ultrasound (AUC = 0.903 for "Tg+TgAb+Overall ultrasound" model, AUC = 0.921 for "Tg+TgAb+Seven ultrasound features" model) was superior to any single variant. Nomograms constructed for the above two models were validated internally and the C-index were 0.899 and 0.914, respectively. Calibration curves showed satisfied discrimination and calibration of the two nomograms. DCA also proved that the two nomograms were clinically useful.

CONCLUSION

Through the two accurate and easy-to-use nomograms, the possibility of CLNM can be objectively quantified before ¹³¹I therapy. Clinicians can use the nomograms to evaluate the status of lymph nodes in postoperative PTC patients and consider a higher dose of ¹³¹I for those with high scores.

Nomogram for predicting central lymph node metastasis in T1-T2 papillary thyroid cancer with no lateral lymph node metastasis

OBJECTIVE

Whether routine central lymph node dissection (CLND) is necessary for T1-T2 papillary thyroid carcinoma (PTC) patients without certain lateral lymph node metastases (LLNM) remains controversial. This study aims to construct a nomogram that predicts central lymph node metastasis (CLNM) for T1-T2 PTC patients without LLNM.

METHODS

We retrospectively reviewed adult T1-T2 PTC patients with no LLNM retrieved from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. We also collected data from patients treated at the First Hospital of China Medical University between February and April 2021 for external validation. Logistic regression model was used to construct a risk prediction model nomogram. The receiver-operating characteristic (ROC) curve, calibration plot, and decision curve analyses (DCA) were used for assessing the nomogram.

RESULTS

5,094 patients from the SEER database and 300 patients from our department were finally included in this study. Variables such as age, gender, race, tumor size, multifocality, and minimal extrathyroidal extension (mETE) were found to be associated with CLNM and were subsequently incorporated into our nomogram. The C-index of our constructed model was 0.704, while the internal and external validation C-indexes were 0.693 and 0.745, respectively. The nomogram was then evaluated using calibration and decision curve analyses.

CONCLUSION

A visualized nomogram was successfully developed to predict CLNM in T1-T2 PTC patients without LLNM and assist clinicians in making personalized clinical decisions.

Clinical Value of Ultrasonography and Serum Markers in Preoperative N Staging of Thyroid Cancer

We aimed to determine factors influencing lymph node metastasis (LNM) and develop a more effective method to assess preoperative N staging. Overall, data of 2130 patients who underwent thyroidectomy for thyroid cancer between 2018 and 2021 were retrospectively analysed. Patients were divided into groups according to pN0, pN1a, and pN1b stages. Pathology was used to analyse the correlation between preoperative serum marker indicators and LNM. Receiver operating characteristic curves were used to compare the diagnostic value of ultrasound (US) examination alone, serum thyroglobulin, age, and combined method for LNM. A significant moderate agreement was observed between preoperative US and postoperative pathology for N staging. Between the pN0 and pN1 (pN1a + pN1b) groups, the differences in free triiodothyronine, anti-thyroid peroxidase antibody, and serum thyroglobulin levels were statistically significant. Among the indicators, serum thyroglobulin was an independent predictor of LNM. The area under the receiver operating characteristic curve was 0.610 for serum thyroglobulin level for predicting LNM, 0.689 for US alone, and 0.742 for the combined method. Both preoperative US and serum thyroglobulin level provide a specific value when evaluating the N staging of thyroid cancer, and the combined method is more valuable in the diagnosis of LNM than US alone.

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.

Development and validation of an individualized nomogram for predicting the high-volume (> 5) central lymph node metastasis in papillary thyroid microcarcinoma

PURPOSE

Papillary thyroid microcarcinoma (PTMC) frequently presents a favorable clinical outcome, while aggressive invasiveness can also be found in some of this population. Identifying the risk clinical factors of high-volume (> 5) central lymph node metastasis (CLNM) in PTMC patients could help oncologists make a better-individualized clinical decision.

METHODS

We retrospectively reviewed the clinical characteristics of adult patients with PTC in the Surveillance, Epidemiology, and End Results (SEER) database between Jan 2010 and Dec 2015 and in one medical center affiliated to Chongqing Medical University between Jan 2018 and Oct 2020. Univariate and multivariate logistic regression analyses were used to determine the risk factors for high volume of CLNM in PTMC patients.

RESULTS

The male gender (OR = 2.02, 95% CI 1.46-2.81), larger tumor size (> 5 mm, OR = 1.64, 95% CI 1.13-2.38), multifocality (OR = 1.87, 95% CI 1.40-2.51), and extrathyroidal invasion (OR = 3.67; 95% CI 2.64-5.10) were independent risk factors in promoting high-volume of CLNM in PTMC patients. By contrast, elderly age (≥ 55 years) at diagnosis (OR = 0.57, 95% CI 0.40-0.81) and PTMC-follicular variate (OR = 0.60, 95% CI 0.42-0.87) were determined as the protective factors. Based on these indicators, a nomogram was further constructed with a good concordance index (C-index) of 0.702, supported by an external validating cohort with a promising C-index of 0.811.

CONCLUSION

A nomogram was successfully established and validated with six clinical indicators. This model could help surgeons to make a better-individualized clinical decision on the management of PTMC patients, especially in terms of whether prophylactic central lymph node dissection and postoperative radiotherapy should be warranted.

Nomograms based on preoperative multimodal ultrasound of papillary thyroid carcinoma for predicting central lymph node metastasis

OBJECTIVES

To establish a nomogram for predicting central lymph node metastasis (CLNM) based on the preoperative clinical and multimodal ultrasound (US) features of papillary thyroid carcinoma (PTC) and cervical LNs.

METHODS

Overall, 822 patients with PTC were included in this retrospective study. A thyroid tumor ultrasound model (TTUM) and thyroid tumor and cervical LN ultrasound model (TTCLNUM) were constructed as nomograms to predict the CLNM risk. Areas under the curve (AUCs) evaluated model performance. Calibration and decision curves were applied to assess the accuracy and clinical utility.

RESULTS

For the TTUM training and test sets, the AUCs were 0.786 and 0.789 and bias-corrected AUCs were 0.786 and 0.831, respectively. For the TTCLNUM training and test sets, the AUCs were 0.806 and 0.804 and bias-corrected AUCs were 0.807 and 0.827, respectively. Calibration and decision curves for the TTCLNUM nomogram exhibited higher accuracy and clinical practicability. The AUCs were 0.746 and 0.719 and specificities were 0.942 and 0.905 for the training and test sets, respectively, when the US tumor size was ≤ 8.45 mm, while the AUCs were 0.737 and 0.824 and sensitivity were 0.905 and 0.880, respectively, when the US tumor size was > 8.45 mm.

CONCLUSION

The TTCLNUM nomogram exhibited better predictive performance, especially for the CLNM risk of different PTC tumor sizes. Thus, it serves as a useful clinical tool to supply valuable information for active surveillance and treatment decisions.

KEY POINTS

• Our preoperative noninvasive and intuitive prediction method can improve the accuracy of central lymph node metastasis (CLNM) risk assessment and guide clinical treatment in line with current trends toward personalized treatments. • Preoperative clinical and multimodal ultrasound features of primary papillary thyroid carcinoma (PTC) tumors and cervical LNs were directly used to build an accurate and easy-to-use nomogram for predicting CLNM. • The thyroid tumor and cervical lymph node ultrasound model exhibited better performance for predicting the CLNM of different PTC tumor sizes. It may serve as a useful clinical tool to provide valuable information for active surveillance and treatment decisions.

A Clinical Predictive Model of Central Lymph Node Metastases in Papillary Thyroid Carcinoma

BACKGROUND

Thyroid carcinoma is one of the most common endocrine tumors, and papillary thyroid carcinoma (PTC) is the most common pathological type. Current studies have reported that PTC has a strong propensity for central lymph node metastases (CLNMs). Whether to prophylactically dissect the central lymph nodes in PTC remains controversial. This study aimed to explore the risk factors and develop a predictive model of CLNM in PTC.

METHODS

A total of 2,554 patients were enrolled in this study. The basic information, laboratory examination, characteristics of cervical ultrasound, genetic test, and pathological diagnosis were collected. The collected data were analyzed by univariate logistic analysis and multivariate logistic analysis. The risk factors were evaluated, and the predictive model was constructed of CLNM.

RESULTS

The multivariate logistic analysis showed that Age (p < 0.001), Gender (p < 0.001), Multifocality (p < 0.001), BRAF (p = 0.027), and Tumor size (p < 0.001) were associated with CLNM. The receiver operating characteristic curve (ROC curve) showed high efficiency with an area under the ROC (AUC) of 0.781 in the training group. The calibration curve and the calibration of the model were evaluated. The decision curve analysis (DCA) for the nomogram showed that the nomogram can provide benefits in this study.

CONCLUSION

The predictive model of CLNM constructed and visualized based on the evaluated risk factors was confirmed to be a practical and convenient tool for clinicians to predict the CLNM in PTC.

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.

Cancer-Specific Survival Outcome in Early-Stage Young Breast Cancer: Evidence From the SEER Database Analysis

BACKGROUND

Young women with breast cancer are determined to present poorer survival compare with elderly patients. Therefore, identifying the clinical prognostic factors in young women with early-stage (T_1-2N_0-1M₀) breast cancer is pivotal for surgeons to make better postoperative management.

METHODS

The clinicopathological characteristics of female patients with early-stage breast cancer from the Surveillance, Epidemiology, and End Results program between Jan 2010 and Dec 2015 were retrospectively reviewed and analyzed. Univariate and multivariate Cox regression analyses were used to determine the potential risk factors of cancer-specific survival in young women with early-stage breast cancer. The nomogram was constructed and further evaluated by an internal validation cohort. The Kaplan-Meier survival curves were used to estimate cancer-specific survival probability and the cumulative incidence.

RESULTS

Six variables including race, tumor location, grade, regional lymph node status, tumor subtype, and size were identified to be significantly associated with the prognosis of young women with early-stage breast cancer during the postoperative follow-up. A nomogram for predicting the 3-, 5- year cancer-specific survival probability in this subpopulation group was established with a favorable concordance index of 0.783, supported by an internal validation cohort with the AUC of 0.722 and 0.696 in 3-, 5- year cancer-specific survival probability, respectively.

CONCLUSIONS

The first predictive nomogram containing favorable discrimination is successfully established and validated for predicting the 3-, 5- year cancer-specific survival probability in young women with early-stage breast cancer during the postoperative follow-up. This model would help clinicians to make accurate treatment decisions in different clinical risk population.

Preoperatively Predicting the Central Lymph Node Metastasis for Papillary Thyroid Cancer Patients With Hashimoto's Thyroiditis

BACKGROUND

The preoperative distinguishment of lymph nodes with reactive hyperplasia or tumor metastasis plays a pivotal role in guiding the surgical extension for papillary thyroid carcinoma (PTC) with Hashimoto's thyroiditis (HT), especially in terms of the central lymph node (CLN) dissection. We aim to identify the preparative risk factors for CLN metastasis in PTC patients concurrent with HT.

MATERIALS AND METHODS

We retrospectively reviewed and analyzed the data including the basic information, preoperative sonographic characteristics, and thyroid function of consecutive PTC patients with HT in our medical center between Jan 2019 and Apr 2021. The Chi-square and Fisher's exact tests were used for comparison of qualitative variables among patients with or without CLN metastasis. Univariate and multivariate logistic regression analyses were used to determine the risk factors for CLN metastasis. The nomogram was constructed and further evaluated by two cohorts produced by 1,000 resampling bootstrap analysis.

RESULTS

A total of 98 in 214 (45.8%) PTC patients were identified with CLN metastasis. In multivariate analysis, four variables including high serum thyroglobulin antibody (TgAb) level (>1,150 IU/ml), lower tumor location, irregular margin of CLN, and micro-calcification in the CLN were determined to be significantly associated with the CLN metastasis in PTC patients with HT. An individualized nomogram was consequently established with a favorable C-index of 0.815 and verified via two internal validation cohorts.

CONCLUSIONS

Our results indicated that preoperatively sonographic characteristics of the tumor and lymph node condition combined with serum TgAb level can significantly predict the CLN in PTC patients with HT and the novel nomogram may further help surgeons to manage the CLN in this subpopulation.