A Nomogram Based on Clinical and Ultrasound Characteristics to Predict Central Lymph Node Metastasis of Papillary Thyroid Carcinoma

Feng, Jia-Wei; Hong, Li-Zhao; Wang, Fei; Wu, Wan-Xiao; Hu, Jun; Liu, Shengyong; Jiang, Yong

doi:10.3389/fendo.2021.666315

Cited by 31 publications

(33 citation statements)

References 33 publications

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“…In previous studies (32,33), serum thyroglobulin could generally be assessed by lymph node puncture before surgery to determine the status of lymph node metastasis or tumor recurrence after surgery. Besides, previous studies (27,28) have shown that male patients are more likely to have CLNM in PTC, which was reflected in patients older than 45 years, but this trend was not obvious in younger patients. It is well known that differences in sex hormones are more common in younger patients, so the above finding is intriguing.…”

Section: Discussionmentioning

confidence: 84%

“…There are other studies on proteomics and fibronectin expression in CLNM (24,25). In almost all studies (26)(27)(28), age, tumor size, gender, multifocality and extrathyroidal extension were considered risk factors for CLNM, but all these reports were based on the overall population without age stratification. It is well-known that age is an important factor affecting the biological behavior of the thyroid, and an important prognostic indicator in thyroid cancer (13).…”

Section: Discussionmentioning

confidence: 99%

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Central Compartment Lymph Nodes Have Distinct Metastatic Patterns in Different Age Groups

Yan

Chen

et al. 2022

Front. Endocrinol.

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Background and PurposeCentral compartment lymph node metastasis (CLNM) is a manifestation of tumor aggressiveness and an indicator of tumor prognosis. The purpose of this study was to construct a nomogram for evaluating CLNM patterns in papillary thyroid carcinoma (PTC) in different age groups.MethodA total of 907 patients diagnosed with PTC from August 2014 to December 2018 were enrolled. A nomogram illustrating CLNM was generated using the results of multivariate logistic regression analysis.ResultsAccording to the best Youden index, we set the cut-off age at 45 years. Multivariate logistic regression analysis showed that in patients aged <45 years, large tumor size (P<0.05), extra-thyroid extension (P<0.05) and thyroglobulin level >40 ng/ml (OR=2.985, 95% CI 1.379-6.462; P<0.05) were independent risk factors; meanwhile, Hashimoto’s thyroiditis (OR=0.532, 95% CI 0.324-0.874; P<0.05) was a protective factor of CLNM. In the subgroup with age ≥45 years, large tumor size (P<0.05), extra-thyroid extension (P<0.05), unclear margin (OR=1.604, 95% CI 1.065-2.416; P<0.05), male gender (OR=2.009, 95% CI 1.257-3.212; P<0.05) were independent risk factors for CLNM. In the subgroup with age <45 years, an area under the curve (AUC) of 0.729 (95% CI 0.680-0.777); P<0.05) was obtained. In the ≥45 years subgroup, the AUC was 0.668 (95% CI 0.619-0.716; P<0.05).ConclusionCLNM of PTC in different age groups may have distinct patterns. Based on the potential risk factors for CLNM in patients with different age stratification, a user-friendly predictive model was established.

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Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Central Compartment Lymph Nodes Have Distinct Metastatic Patterns in Different Age Groups

Yan

Chen

et al. 2022

Front. Endocrinol.

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“…After that, a nomogram was constructed to screen out the relatively high-risk patients in clinical practice; and this population might need extended resection and lymphadenectomy. Nomograms are widely used to predict prognosis, skip metastasis, and central lymph node metastasis in thyroid carcinoma [ 59 – 61 ]. To give clinicians a practical tool for individualized prognosis prediction, we constructed a nomogram based on the independent factors in the multivariate Cox regression analysis.…”

Section: Discussionmentioning

confidence: 99%

A seven-autophagy-related gene signature for predicting the prognosis of differentiated thyroid carcinoma

Yuan

et al. 2022

World J Surg Onc

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Background Numerous studies have implicated autophagy in the pathogenesis of thyroid carcinoma. This investigation aimed to establish an autophagy-related gene model and nomogram that can help predict the overall survival (OS) of patients with differentiated thyroid carcinoma (DTHCA). Methods Clinical characteristics and RNA-seq expression data from TCGA (The Cancer Genome Atlas) were used in the study. We also downloaded autophagy-related genes (ARGs) from the Gene Set Enrichment Analysis website and the Human Autophagy Database. First, we assigned patients into training and testing groups. R software was applied to identify differentially expressed ARGs for further construction of a protein-protein interaction (PPI) network for gene functional analyses. A risk score-based prognostic risk model was subsequently developed using univariate Cox regression and LASSO-penalized Cox regression analyses. The model’s performance was verified using Kaplan-Meier (KM) survival analysis and ROC curve. Finally, a nomogram was constructed for clinical application in evaluating the patients with DTHCA. Finally, a 7-gene prognostic risk model was developed based on gene set enrichment analysis. Results Overall, we identified 54 differentially expressed ARGs in patients with DTHCA. A new gene risk model based on 7-ARGs (CDKN2A, FGF7, CTSB, HAP1, DAPK2, DNAJB1, and ITPR1) was developed in the training group and validated in the testing group. The predictive accuracy of the model was reflected by the area under the ROC curve (AUC) values. Univariate and multivariate Cox regression analysis indicated that the model could independently predict the prognosis of patients with THCA. The constrained nomogram derived from the risk score and age also showed high prediction accuracy. Conclusions Here, we developed a 7-ARG prognostic risk model and nomogram for differentiated thyroid carcinoma patients that can guide clinical decisions and individualized therapy.

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“…Therefore, researchers made an effort to study the association between US characteristics of PTCs and CLNM, nding that it may be a promising strategy to predict CLNM before surgery to avoid unnecessary pCND [20,21]. Previous studies have shown that the most important contributing factors to CLNM including patient age, male, multifocality, tumor size and location, margin, microcalci cation, ETE and concomitant HT [14,22,23]. In this study, some conventional US characteristics of PTCs can be predictors of CLNM, including larger maximum size, tall than wide, contact capsule and US reported ACLN.…”

Section: Discussionmentioning

confidence: 99%

“…It was recognized that there remains an existing need for improved CLNM prediction performance. Available studies have established the prediction model by regression algorithm and nomogram, making it possible to forecast CLNM risk in clinical application [14][15][16]. Machine learning (ML) algorithm has been widely used in image diagnosis over the last few years.…”

Section: Introductionmentioning

confidence: 99%

Application of decision tree algorithms to predict central lymph node metastasis in well-differentiated papillary thyroid carcinoma based on multimodal ultrasound parameters: a retrospective study

Wan

Zhang

et al. 2022

Preprint

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Background: Prophylactic central neck dissection (pCND) in well-differentiated primary papillary thyroid carcinoma (PTC) patients have become controversial. Several attempts have been made to predict central compartment lymph node metastasis (CLNM) based on clinical and conventional ultrasonic parameters. This study seeks to develop a decision tree (DT) model for predicting the risk of CLNM in these patients based on clinical and preoperative multimodal ultrasound (US) characteristics.Methods: A total of 148 PTC nodules confirmed by surgical pathology at Beijing Tiantan Hospital were retrospectively analyzed. All nodules underwent multimodal US examinations from January 2020 to September 2021 before surgery. Correlation analysis of CLNM with clinical characteristics, multimodal US parameters of PTC lesions based on grayscale US, color Doppler flow imaging, superb microvascular imaging, contrast-enhanced US and shear wave elastography technology was carried out. Finally, the Chi-squared automatic interaction detector with a ten-fold cross test was used to establish DT models based on multimodal US parameters to predict CLNM. The area under the curve (AUC) was calculated for comparing prediction performance.Results: Univariate analysis indicated that CLNM was positively correlated with Tg level, maximum size, tall than wide, the number of microcalcifications greater than or equal to 5, contact capsule, US reported abnormal cervical lymph nodes, non-centripetal perfusion, delayed clearance, SWV mean and SWV ratio (p<0.05). The multimodal US algorithm based on tall than wide, contact the capsule, US reported abnormal cervical lymph nodes, centripetal enhancement as independent variables showed good discrimination: the sensitivity, specificity, accuracy and AUC were 80.0%, 76.7%, 78.4%, and 0.837 (95% CI: 0.771-0.902). There was a significant difference between multimodal US DT with conventional ultrasound DT (p<0.001). Conclusions: This study implies that the DT model based on preoperative multimodal US characteristics of PTCs has reasonable prediction ability for CLNM, which can be conveniently used for clinical decision-making of individualized treatment in well-differentiated PTC patients.

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A Nomogram Based on Clinical and Ultrasound Characteristics to Predict Central Lymph Node Metastasis of Papillary Thyroid Carcinoma

Cited by 31 publications

References 33 publications

Central Compartment Lymph Nodes Have Distinct Metastatic Patterns in Different Age Groups

Central Compartment Lymph Nodes Have Distinct Metastatic Patterns in Different Age Groups

A seven-autophagy-related gene signature for predicting the prognosis of differentiated thyroid carcinoma

Application of decision tree algorithms to predict central lymph node metastasis in well-differentiated papillary thyroid carcinoma based on multimodal ultrasound parameters: a retrospective study

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