Undoubtedly one of the most common endocrine cancers is thyroid carcinoma (TC) (1). Despite its steady disease-specific mortality (0.5/100,000) (2), the TC incidence rate over the past 20 years has increased by approximately 2.5 times (5.57/100,000-13.98/100,000) (3). By 2030, TC is anticipated to be the fourth most prevalent cancer in the USA (4). However, the South Korean experience indicates that they will need to discourage early thyroid cancer discovery if they wish to stop their own “epidemic”.Vital statistics and cancer registry data for South Korea illustrate the effect of thyroid-cancer screening since 1999. Thyroid-cancer incidence increased rapidly after the turn of the century In 2011, the rate of thyroid-cancer diagnoses was 15 times that observed in 1993. This entire increase can be attributed to the detection of papillary thyroid cancer. Furthermore, despite the dramatic increase in incidence, mortality from thyroid cancer remains stable — a combination that is pathognomonic for over diagnosis (5).

With the continuously improved cancer prevention and treatment, the population aged ≥65 years will rise from 15% to 21% by 2030 in USA (6). In 2000, Americans aged ≥80 years represented approximately 3.3% of the population, which is expected to show a 2-fold increase by 2050 (7). Given the advanced age of patients with TC, we must consider the challenges that may arise as a direct result. In the 2015 Korean Central Cancer Registry, thyroid cancer was reported to be the fourth most common cancer in women aged 65 years or older (8).Recent literature shows that particularly poor prognosis is associated with age greater than 60 years (9, 10).Additionally, older individuals typically have more advanced-stage, aggressive, and widespread TC (11). Elderly individuals with TC often have follicular histology, vascular invasion, and extrathyroidal extension (12).In fact, Anaplastic thyroid cancer (ATC) is relatively more common with advanced age (13–16).. In several studies, older patients have had large volume tumor, lymph node metastasis, and distant metastasis at diagnosis and recurrence (17, 18). The survival rate was independently associated with a poorer prognosis starting at 60 years; elderly patients >70 years had the worst prognosis (12)..

TC has a satisfying good prognosis, with an average survival rate of 10 years for 90% of patients (19, 20). The median age of death for patients with TC is 73 years, and >70% of the deaths occur when patients are aged ≥65 years (21). A risk stratification technique that can enhance outcomes is required to predict overall survival (OS) for older patients with TC due to the limits of available treatment choices. Within this context, TNM staging can be used to determine the clinical staging of patients with cancer. However, the TNM classification is still insufficient in covering tumor biology and predicting all TC outcomes and treatment decisions made by elderly patients.

Nomograms have proved superior to the TNM staging system in different cancer studies (22, 23). A nomogram is a simple, user-friendly statistical prediction tool used to predict and quantify individual patient outcomes (24, 25). Population-based statistics show that, nevertheless, no study has created a nomogram of elderly TC persons. With the support of the Surveillance, Epidemiology, and End Results database (SEER) (26), we aimed to construct and verify a web-based survival prediction model for geriatric patients with TC. This model may be useful for individualized therapy, prognostic prediction, and follow-up strategy.

In this study, 16475 individuals with geriatric TC were included. The histologic type, tumor grade, TNM stage, tumor size, and surgery were determined by the univariate and multivariate Cox analyses. The C-index and calibration charts were used to test the model and revealed good differentiation and calibration. According to DCA, our OS nomogram had a superior clinical net and a more excellent threshold probability range in predicting 1-, 3-, and 5-year OS in the training and validation groups than the usual TNM stage system. Meanwhile, the CSS nomogram was constructed using eight independent parameters: age, tumor size, grade, histologic type, surgery, T stage, N stage, and M stage. Although the prognosis of thyroid cancer varies considerably by histological type, inspired by previous studies (27), we included elderly patients with thyroid cancer of various tissue types and included tissue type as a variable in the regression analysis. Histologic type was included as an important variable in the prediction model’s construction with poorer survival for more aggressive histologic subtypes compared to papillary ( Table 2 , medullary: HR 1.361, 95%CI 1.167–1.589 and anaplastic: HR 1.671, 95% CI 1.243–2.245). In the online APP based on the model construction, clinicians can enter the patient’s tissue type. The survival prediction of patients with thyroid cancer including MTC is obtained.

Many scoring systems are used for predictive purposes. Despite a more simplified utilization in the clinic, only a stratified population risk assessment could be conducted for each patient (28). Nomograms are helpful tools for evaluating patient survival outcomes. Statistical modeling and risk quantification are used to handle the difficulty of balancing multiple factors. Their methodical methodology also eliminates the influence of individual physicians’ biases or aberrant clinical factors. Nomograms are more accurate than typical stage score methods (29–31). They may also be the most beneficial when the prospective advantages of additional therapy are unknown (32, 33). They are also great for personalized risk assessment and assisting clinicians with clinical care management when there are no definite guidelines.

To the best of our knowledge, this is the first study to describe the development and validation of a nomogram to forecast 5-year OS and CSS in TC-affected elderly individuals. Our nomograms exhibited good calibration and discrimination. The nomograms surpassed the accuracy of the TNM staging mechanism, as seen by the receiver operating characteristic curve. Our nomogram models are straightforward therapeutic aids that can support patient counseling and treatment individualization.

Our nomograms found several independent variables potentially impacting the outcome in elderly patients with TC. First, age is a significant factor for CSS in patients with TC (34). As a separate risk factor, patients with geriatric thyroid cancer have a reduced chance of surviving (35–37). Patients with geriatric TC have unique psychological features compared to younger patients including more comorbidities and shorter life expectancy. Less life expectancy, more comorbidities, and a shorter life expectancy are among these factors. The previous version of the AJCC staging system divided people by 45 years, but the 8th version utilizes 55 years for the same purpose. Age is recognized as a significant prognostic factor independent of the cut-off number.

The gender disparity in TC prevalence has also been thoroughly documented (38). Women are more likely to develop TC than men, although men have worse clinical outcomes (39). The findings of our patients with geriatric were similar to those in prior investigations. In addition to the above-mentioned characteristics, marital status, histologic type, tumor grade, T stage, N stage, M stage, tumor size, and surgery were significant prognostic indicators. However, we discovered that neither gender nor marital status was a risk factor for the 1-, 3-, or 5-year CSS.

The association of marital status and survival was explored in many tumors, including breast cancer, rectal cancer, and non-small cell lung cancer (40–42).We identified that marital status was an independent prognostic factor in the univariate analysis, with married patients having a decreased chance of mortality (43). After adjusting for demographic and clinical characteristics, married patients were shown to have a lower mortality risk than unmarried patients. In a prior study concentrating on differentiated patients with TC, Shi et al. have discovered that single individuals had a higher risk of tumor death (44). In a study of breast cancer patients aged ≥70 years, marriage has been found to provide higher protection from poorer prognosis (45). According to a study that analyzed more than a million patients diagnosed with various diseases, unmarried individuals have had a greater chance of metastatic cancer and death from cancer (46). Consistent with the findings of the above researches, our results found that marriage was a factor associated with superior survival. There are two possibilities that could explain why married patients live longer than unmarried patients. On the one hand, these married patients were overseen by their spouses for frequent physical checkups before being diagnosed, which helps detect TC early. Meanwhile spouses may also provide more economic support for subsequent treatments. On the other hand, cancer patients are more than four times more likely to suffer from psychological disorders (47). After being diagnosed with cancer, married persons had reduced despair and psychological suffering, which may be attributed to the encouragement and support from their spouses (43, 48).

Our prediction model may be used in clinical practice to estimate patient survival by alerting doctors about the predicted advantages of various therapies. In this study, we found that for elderly patients with TC, almost all patients in the low-risk group undergo thyroidectomy. Regardless of surgical method, patients undergoing surgery have better overall survival. Most high-risk patients did not undergo surgery.

Patients with TC have a variety of risk variables, including age, grade, TNM stage, and tumor size. Elderly patients are more likely to have comorbidities and therefore surgery could reduce the OS rate of patients. However, for the first time, our study found that surgery is advantageous to cancer-specific higher-risk senior individuals with TC (P < 0.0001) but not to low-risk groups (P = 0.069), providing doctors with suggestions for extending their patients’ lives.

This study had some limitations. First, the nomograms were created using historical data. As a result, there was a possibility of selection bias. Second, the SEER does not cover all factors; hence, only 14 variables were included in our analysis. Some critical factors were not included, such as the degree of surgery, radioiodine dose, thyrotropin suppression, etc. Third, as a retrospective cohort research, selection bias might have existed because only patients with comprehensive information on essential characteristics were included. Fourth, the majority of the participants in this study were Americans. As a result, prospective clinical pilot studies are needed to see if the findings can be generalized to different groups.

Based on this study, the first applicable nomograms were created, along with an online application that predicts the personalized long-term OS and CSS of geriatric TC patients. The nomogram performed effectively and had great accuracy and dependability. It is the first nomogram based on a large number of patients with external validation.

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.

T-TZ, YY, and JZ all helped with the idea and design. The data were analyzed by T-TZ, YY, and Y-JK. The manuscript was written by T-TZ. A critical revision of the manuscript was contributed by XW and YF. The final version of the work has been reviewed and approved by all authors.