Age increased the cancer-specific mortality risk of thyroid cancer with lung metastasis

Survival trends and conditional survival in patients with pulmonary metastases from differentiated thyroid carcinoma

INTRODUCTION

Patients with pulmonary metastases from differentiated thyroid carcinoma (DTC) have a significantly poorer prognosis compared to DTC patients without metastases. This study aimed to establish a nomogram combined with dynamic survival analysis to predict the survival probability of patients with pulmonary metastases from differentiated thyroid carcinoma and compensate for the underestimation of survival in patients with very poor prognosis.

PATIENTS AND METHODS

Patient data were collected from the Surveillance, Epidemiological, and End Result (SEER) data resource from 2010 to 2019. Multivariate analysis was carried out by the Cox proportional hazards regression to construct a nomogram. Receiver operating characteristic (ROC) curves along with calibration were employed to assess the effectiveness of the model.The life table was used to estimate the conditional cancer-specific survival (CSS).

RESULTS

In the training set, the AUCs for the CSS nomogram were 0.728, 0.741, and 0.779, with a c-index of 0.682, indicating good predictive performance at 3, 5, and 10 years. In the validation set, the AUCs for the CSS nomogram were 0.706, 0.726, and 0.769, with a c-index of 0.696, while the AUCs for the 8th TNM staging system were 0.521, 0.555, and 0.601, with a c-index of 0.579. The overall 5-year conditional survival rate for patients increased slightly from 63.44 to 70.52%. The survival gap was greatest between patients aged <55 years and those aged ≥55 years.

CONCLUSION

We established a nomogram combined with dynamic survival analysis, which serve as promising options for prognosis estimation, to enhance personalized evaluation of survival risks and provide the basis for the development of more clinical treatment approaches.

Development and validation of survival nomograms for patients with anaplastic thyroid carcinoma: a SEER program-based study

BACKGROUND

We aimed to study the prognostic risk factors affecting patients with anaplastic thyroid carcinoma (ATC), develop a clinical prognostic model, and assess patient survival outcomes.

METHODS

Patients with anaplastic thyroid carcinoma from 2000 to 2019 were selected from the Surveillance, Epidemiology, and End Results (SEER) Program to extract the clinical variables used for analysis. The dataset was divided into training (70%) and validation (30%) sets based on a 7:3 ratio. Univariate and LASSO regression analyses were performed on clinical variables from the training set to identify independent prognostic factors. Independent prognostic factors were determined by Univariate and lasso regression according to the clinical variables of the training set, and a nomogram model was established to construct a prognostic model based on the contribution degree of the predictors. The prognostic model was evaluated and internally verified by C-index, ROC curve and calibration curve.

RESULTS

A total of 713 ATC patients were included in the SEER database. LASSO regression results indicated that age, marital status, race, tumor size, whether the primary lesion was limited to the thyroid gland, surgery, radiotherapy and chemotherapy, were associated with overall survival(OS) prognosis of ATC, and were used to construct nomograms. In the training cohort, the OS nomogram's C-index was 0.708 (95% CI 0.672-0.745); in the internal validation cohort, the C-index was 0.677 (95% CI 0.620-0.735). ROC curves demonstrated that the OS nomogram exhibits excellent predictive accuracy and discriminative ability. Calibration curves indicated strong consistency between the OS nomogram's predicted survival rates and actual survival rates.

CONCLUSIONS

We established a survival prediction model for ATC, which can assist clinicians in assessing patient prognosis and making personalized treatment decisions.

Development and validation of a prognostic nomogram for predicting liver metastasis in thyroid cancer: a study based on the surveillance, epidemiology, and end results database

This study aimed to create a prognostic nomogram to predict the risk of liver metastasis (LM) in thyroid cancer (TC) patients and assess survival outcomes for those with LM. Data were collected from the SEER database, covering TC patients from 2010 to 2020, totaling 110,039 individuals, including 142 with LM. Logistic regression and stepwise regression based on the Akaike information criterion (AIC) identified significant factors influencing LM occurrence: age, histological type, tumor size, bone metastasis, lung metastasis, and T stage (p < 0.05). A nomogram was constructed using these factors, achieving a Cindex of 0.977, with ROC curve analysis showing an area under the curve (AUC) of 0.977. For patients with TCLM, follicular TC, medullary TC, papillary TC, and examined regional nodes were associated with better prognosis (p < 0.001, HR < 1), while concurrent brain metastasis indicated poorer outcomes (HR = 2.747, p = 0.037). In conclusion, this nomogram effectively predicts LM risk and evaluates prognosis for TCLM patients, aiding clinicians in personalized treatment decisions.

Automatic prediction of non-iodine-avid status in lung metastases for radioactive I131 treatment in differentiated thyroid cancer patients

Objectives

The growing incidence of differentiated thyroid cancer (DTC) have been linked to insulin resistance and metabolic syndrome. The imperative need for developing effective diagnostic imaging tools to predict the non-iodine-avid status of lung metastasis (LMs) in differentiated thyroid cancer (DTC) patients is underscored to prevent unnecessary radioactive iodine treatment (RAI).

Methods

Primary cohort consisted 1962 pretreated LMs of 496 consecutive DTC patients with pretreated initially diagnosed LMs who underwent chest CT and subsequent post-treatment radioiodine SPECT. After automatic lesion segmentation by SE V-Net, SE Net deep learning was trained to predict non-iodine-avid status of LMs. External validation cohort contained 123 pretreated LMs of 24 consecutive patients from other two hospitals. Stepwise validation was further performed according to the nodule's largest diameter.

Results

The SE-Net deep learning network yielded area under the receiver operating characteristic curve (AUC) values of 0.879 (95% confidence interval: 0.852-0.906) and 0.713 (95% confidence interval: 0.613-0.813) for internal and external validation. With the LM diameter decreasing from ≥10mm to ≤4mm, the AUCs remained relatively stable, for smallest nodules (≤4mm), the model yielded an AUC of 0.783. Decision curve analysis showed that most patients benefited using deep learning to decide radioactive I131 treatment.

Conclusion

This study presents a noninvasive, less radioactive and fully automatic approach that can facilitate suitable DTC patient selection for RAI therapy of LMs. Further prospective multicenter studies with larger study cohorts and related metabolic factors should address the possibility of comprehensive clinical transformation.

Risk-stratified Distant Metastatic Thyroid Cancer with Clinicopathological Factors and BRAF/TERT Promoter Mutations

OBJECTIVE

To assess the prognostic value of clinicopathological factors as well as BRAF and TERT promoter mutations in predicting distant metastasis in patients with papillary thyroid cancer.

DESIGN

The status of BRAF and TERTp mutations were available in 1,208 thyroid cancer patients who received thyroidectomy at Jiangyuan Hospital Affiliated to Jiangsu Institute of Nuclear Medicine from January 2008 to December 2021. Based on inclusion criteria, 99 distant metastasis thyroid cancers (DM-TCs) and 1055 patients without DM (Non-DM-TCs) were retrospectively reviewed.

RESULTS

After univariate and multivariate analyses, a risk model was established for DM prediction based on factors: T3/T4 stage, lymph node metastasis (LNM) number over 5, and BRAF/TERT mutations (TLBT). It was defined based on the number of TLBT factors: low risk (no risk factor, n=896), intermediate risk (1 risk factor, n=199), and high risk (≥2 risk factors, n=59). Notably, compared with patients with low and intermediate risks, patients assigned to high TLBT risk have a shorter time of DM disease-free survival. Except for gene mutation, other factors were also included in the 2015 American Thyroid Association (ATA) risk guideline. Comparing with the ATA risk category, this risk model showed a better performance in predicting DM-TCs.

CONCLUSIONS

This study proposes a TLBT risk classifier consisting of T3/T4 stages, LNM (n>5), and BRAF+TERTp mutations for predicting DM-TCs. TLBT risk stratification may help clinicians make personalized treatment management and follow-up strategies.

Development and validation of a nomogram for risk of pulmonary metastasis in non-papillary thyroid carcinoma: A SEER-based study

In this study, a nomogram was established and validated by assessing the risk factors for the development of pulmonary metastases in patients with non-papillary thyroid carcinoma (NPTC) and was used to predict the risk of developing pulmonary metastases. Demographic and clinicopathological variables of patients with NPTC from 2010 to 2015 in the Surveillance, Epidemiology, and End Results database were retrospectively analyzed, and independent risk factors were identified using χ2 tests and full subset regression analysis. Based on this, a nomogram was developed and validated for predicting the risk of pulmonary metastasis in patients with NPTC. The predictive performance of the nomogram was calculated using the consistency index, and the clinical application value of the nomogram was evaluated using calibration curve and decision curve analyses. In addition, risk stratification of patients with NPTC based on these results was performed to facilitate early diagnosis and treatment of patients with pulmonary metastases in the clinic. Data from 1435 patients with NPTC were used for the analysis based on the inclusion and exclusion criteria. Statistical analysis yielded a high risk of pulmonary metastasis in patients with older age, high T-stage, poorly differentiated, undifferentiated thyroid carcinoma, follicular thyroid carcinoma (NOS), and the presence of other distant metastases. We further developed a nomogram with a consistency index of 0.898 (95% confidence interval: 0.880-0.920) in the training cohort and 0.895 (95% confidence interval: 0.862-0.927) in the validation cohort. The calibration curve and decision curve analyses also demonstrated the strong reliability and accuracy of this clinical prediction model. In this study, a nomogram was constructed to accurately identify patients with NPTC at a high risk of pulmonary metastasis, which will help clinicians in personalized decision-making.

Hepatopulmonary metastases from papillary thyroid microcarcinoma: A case report

BACKGROUND

Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy. Papillary thyroid microcarcinoma (PTMC) accounts for the majority of PTC cases. However, concurrent pulmonary and hepatic metastases of PTMC are rarely seen. Here, we present a patient with coexisting liver and lung metastases from PTMC.

CASE SUMMARY

We describe a 26-year-old woman with PTMC with multiple concurrent metastases. After 3 d of unexplained fever, she was admitted to our hospital. Her thyroid functional tests were abnormal. Her positron emission tomography (PET)/magnetic resonance imaging (MRI) examination showed increased fluorodeoxyglucose (FDG) metabolism and space-occupying lesions in the left lobe of the thyroid. Additionally, PET/MRI images revealed multiple nodules in the lung and liver with increased FDG metabolism. Chest computer tomography (CT) showed multiple pulmonary metastases. Abdominal ultrasound and liver MRI showed multiple space-occupying lesions in the liver. The patient underwent total thyroidectomy and central lymph node dissection. Postoperative pathological analysis showed a papillary microcarcinoma multiplex in the left lobe of the thyroid. A diagnosis of hepatopulmonary metastases from papillary thyroid microcarcinoma was made. The patient was given iodine-131 treatment one year after the surgery. She recovered well after the operation, and the incision healed well. After discharge, she was treated with oral levothyroxine sodium tablets, and symptomatic and supportive treatments were also given to promote radioactive excretion and prevent bone marrow suppression by iodine-131 treatment.

CONCLUSION

Since patients with thyroid cancer concurrent with hepatopulmonary metastases have rarely been reported, our case will highlight the clinical and pathological profiles of these patients.