Patient's age with papillary thyroid cancer: Is it a key factor for cervical lymph node metastasis?

EMAS position statement: Thyroid disease and menopause

INTRODUCTION

Thyroid diseases are common in women in their late reproductive years; therefore, thyroid disease and menopause may co-exist. Both conditions may present with a wide range of symptoms, leading to diagnostic challenges and delayed diagnosis. Aim To construct the first European Menopause and Andropause Society (EMAS) statement on thyroid diseases and menopause.

MATERIALS AND METHODS

Literature review and consensus of expert opinion (EMAS executive board members/experts on menopause and thyroid disease).

SUMMARY RECOMMENDATIONS

This position paper highlights the diagnostic and therapeutic dilemmas in managing women with thyroid disease during the menopausal transition, aiming to increase healthcare professionals' awareness of thyroid disorders and menopause-related symptoms. Clinical decisions regarding the treatment of both conditions should be made with caution and attention to the specific characteristics of this age group while adopting a personalized patient approach. The latter must include the family history, involvement of the woman in the decision-making, and respect for her preferences, to achieve overall well-being.

Risk factors for cervical lymph node metastasis of papillary thyroid cancer in elderly patients aged 65 and older

Objective

To assess the risk factors of cervical lymph node metastasis in elderly patients aged 65 years and older diagnosed with papillary thyroid cancer (PTC).

Design and method

In this retrospective analysis, we included a total of 328 elderly patients aged 65 years and older diagnosed with PTC. We thoroughly examined clinical features from these patients. Utilizing univariate and multivariate logistic regression analyses, we aimed to identify factors contributing to the risk of central and lateral lymph node metastasis (CLNM/LLNM) in this specific population of PTC patients aged 65 years and older.

Results

In the univariate analysis, CLNM was significantly associated with tumor size, multifocality, bilaterality, and microcalcification, while only tumor size ≥ 1cm (OR = 0.530, P = 0.019, 95% CI = 0.311 - 0.900) and multifocality (OR = 0.291, P < 0.001, 95% CI = 0.148 - 0.574) remained as risk factors in the multivariate analysis. LLNM was confirmed to be associated with male (OR = 0.454, P < 0.020, 95% CI = 0.233 - 0.884), tumor size ≥ 1cm (OR = 0.471, P = 0.030, 95% CI = 0.239 - 0.928), age ≥ 70 (OR = 0.489, P = 0.032, 95% CI = 0.254 - 0.941), and microcalcification (OR = 0.384, P = 0.008, 95% CI = 0.189 - 0.781) in the multivariate analysis. In elderly PTC patients with CLNM, male gender (OR = 0.350, P = 0.021, 95% CI = 0.143 - 0.855), age ≥ 70 (OR = 0.339, P = 0.015, 95% CI = 0.142 - 0.810), and bilaterality (OR = 0.320, P = 0.012, 95% CI = 0.131 - 0.779) were closely associated with concomitant LLNM in both univariate and multivariate analyses.

Conclusion

For elderly PTC patients aged 65 and older, tumor size ≥ 1cm and multifocality are significant risk factors for CLNM. Meanwhile, male, tumor size ≥ 1cm, age ≥ 70, and microcalcification are crucial predictors for LLNM. In patients already diagnosed with CLNM, male, age ≥ 70, and bilaterality increase the risk of LLNM.

Prediction of cervical lymph node metastasis in solitary papillary thyroid carcinoma based on ultrasound radiomics analysis

Objective

To assess the utility of predictive models using ultrasound radiomic features to predict cervical lymph node metastasis (CLNM) in solitary papillary thyroid carcinoma (PTC) patients.

Methods

A total of 570 PTC patients were included (456 patients in the training set and 114 in the testing set). Pyradiomics was employed to extract radiomic features from preoperative ultrasound images. After dimensionality reduction and meticulous selection, we developed radiomics models using various machine learning algorithms. Univariate and multivariate logistic regressions were conducted to identify independent risk factors for CLNM. We established clinical models using these risk factors. Finally, we integrated radiomic and clinical models to create a combined nomogram. We plotted ROC curves to assess diagnostic performance and used calibration curves to evaluate alignment between predicted and observed probabilities.

Results

A total of 1561 radiomics features were extracted from preoperative ultrasound images. After dimensionality reduction and feature selection, 16 radiomics features were identified. Among radiomics models, the logistic regression (LR) model exhibited higher predictive efficiency. Univariate and multivariate logistic regression results revealed that patient age, tumor size, gender, suspicious cervical lymph node metastasis, and capsule contact were independent predictors of CLNM (all P < 0.05). By constructing a clinical model, the LR model demonstrated favorable diagnostic performance. The combined model showed superior diagnostic efficacy, with an AUC of 0.758 (95% CI: 0.712-0.803) in the training set and 0.759 (95% CI: 0.669-0.849) in the testing set. In the training dataset, the AUC value of the nomogram was higher than that of the clinical and radiomics models (P = 0.027 and 0.002, respectively). In the testing dataset, the AUC value of the nomogram model was also greater than that of the radiomics models (P = 0.012). However, there was no significant statistical difference between the nomogram and the clinical model (P = 0.928). The calibration curve indicated a good fit of the combined model.

Conclusion

Ultrasound radiomics technology offers a quantitative and objective method for predicting CLNM in PTC patients. Nonetheless, the clinical indicators persists as irreplaceable.

Landscape of gene mutation in Chinese thyroid cancer patients: Construction and validation of lymph node metastasis prediction model based on clinical features and gene mutation marker

OBJECTIVES

Reporting the clinicopathological information of thyroid cancer (TC) patients from a central medical center of east China, and constructing the nomogram predicting lymph node metastasis (LNM).

METHODS

We collected the patients who underwent thyroid cancer surgery in our institute from July 1, 2019 to July 31, 2021, a total of 253 subjects were enrolled. We used HiPure FFPE DNA Kit to extract DNA and RNApure FFPE Kit to extract RNA from the paraffin sections of tumor tissue, the extracted DNA samples and RNA samples were used for NGS sequencing. The clinical and pathological information of TCGA-THCA cohort was obtained as the validation cohort. Multivariate logistic regression analysis was performed to identify the independent prognostic factor, and the nomogram was subsequently constructed by "rms" R package.

RESULTS

Secondary cases contained more mutation of BRAF (90.48% vs. 62.07%) and TERT (33.0% vs. 3.0%), as compared with primary cases. Primary patients with positive lymph node were younger (40.9 ± 10.8 vs. 45.3 ± 11.8, p = 0.0031) and contained advanced TI-RADS levels (4c: 22.8% vs. 8.3%, 5: 6.5% vs. 0/0%, p = 1.878e-03), as well as more RET genetic alteration (16.3% vs. 2.7%, p = 2.566e-03). We chose age, tumor diameter, RET fusion, and gender to construct the LNM predicting nomogram. Calibration plot, DCA curve, and the clinical impact plot verified the preferable prognostic value of the nomogram, with an AUC value of 0.724 (0.656-0.792). We successfully validated the prognostic value of the nomogram in TCGA-THCA cohort. RET fusion might impact the process of protein digestion and absorption, cytokine-cytokine receptor interaction, ECM-receptor interaction, focal adhesion.

CONCLUSION

We provide a novel nomogram to predict the LNM for TC patients, including the features of patient's age, gender, tumor diameter, and RET alteration. Further studies from multiple medical centers are essential to validate the nomogram.