Development and Evaluation of Prognostic Nomogram for Patients with Differentiated Thyroid Carcinoma


 Background: Papillary thyroid carcinoma and follicular thyroid carcinoma are both well-differentiated thyroid carcinomas. Here, we aimed to establish and evaluate a nomogram for patients with differentiated thyroid cancer.﻿Methods: Patient records were available from SEER database. We enrolled 17,659 patients in total and randomly separated them into a modeling cohort (n = 12,363, 70%) and a validation cohort (n = 5,296, 30%). Predictive models were established via univariate and multivariate Cox regression analysis of potential risk factors and used to produce a nomogram. ﻿Performance of the nomograms in terms of discrimination ability and calibration was evaluated by determining the concordance index (C-index) and by generating calibration plots, respectively, using the internal (modeling cohort) and external (validation cohort) validity.﻿Results: Seven independent prognostic factors (age, race, sex, grade, AJCC T stage, AJCC N stage, and AJCC M stage) were identified and used to develop the nomogram for OS prediction of patients with DTC. The C-index for the modeling cohort was 0.829 (95% CI: 0.807-0.851), and the C-index for the validation cohort was 0.833 (95% CI: 0.803-0.862). Calibration plots of the nomogram indicated acceptable agreement between the predicted 3-, 5-year survival rates and the actual observations in the modeling and validation groups.﻿Conclusions: We have constructed and verified a nomogram containing clinical factors, which showed better prognostic judgment and predictive accuracy for DTC. This will enable clinicians and patients to easily personalize and quantify the probability of DTC during the postoperative period.


Background
Thyroid cancer is the most prevalent endocrine malignancy, and its incidence has increased considerably over the past 20 years [1,2]. It can be divided into differentiated thyroid carcinoma (DTC) and anaplastic carcinoma (ATC) [3]. Differentiated thyroid cancer is the most common type of thyroid cancer. Among them, papillary thyroid carcinoma (PTC) and follicular thyroid carcinoma (FTC) have an excellent prognosis with a long-term survival rate of more than 90% [4]. Although it is generally considered to have a good prognosis, its prognosis is related to the presence of certain clinicopathological features.
PTC constitutes 90% of thyroid cancers, and it will become the top ve most common malignant tumors in the United States by 2030 [5]. The incidence of thyroid cancer is increasing, but its mortality rate is still very low, which may be related to the development of medical diagnosis technology. The recurrence rate of PTC within 5 years after operation was about 77%, and patients with recurrent PTC will receive additional surgical treatment, and it is likely to be accompanied by surgical laryngeal nerve injury [4]. The mechanism of this high recurrence rate is still unclear [6]. It is speculated that BRAFV600E, RAS, and TERT mutations are related to this phenomenon and need to be further determined.

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Among all well-differentiated thyroid cancers, follicular thyroid cancer accounts for approximately 10%-15% [7]. Follicular adenoma is more common than FTC, and the incidence rate reported by postoperative analysis has increased by approximately 5-fold [8]. Compared with the very little spread of lymph nodes in PTC, lymphatic spread in FTC is also possible, so follicular thyroid cancer is generally considered to be more aggressive than PTC because FTC patients usually present with a more serious disease. However, when age and stage are matched, the prognosis of these two cancer types is similar, although FTC-related causes have a slightly higher mortality rate compared to PTC [4,7].
The nomogram is used as a reliable scale tool to quantify risk. It integrates and illustrates important factors for accurate and discriminatory prediction of prognosis. Numerous staging systems have been available for estimating prognosis in oncology and medicine and the AJCC TNM staging system remains the gold standard for prognostication in oncology [9]. However, the AJCC TNM system has several disadvantages. First, it is developed for a patient population, but has little effect on the prognosis of individual patients. Second, The AJCC TNM system does not include variables that control prognosis, such as genetic differences, tumor mitosis rate, or histology, all of which have a huge impact on tumor prognosis. Overcoming the limitations of TNM staging, the nomogram has become a simpler and more complex tool with advantages of accurate predictability, accessibility, and strong robustness [10,11].
However, so far, the nomogram has not yet been applied to patients with DTC. Thus, we constructed a comprehensive prognostic nomogram combing the AJCC staging system and examined its performance in patients diagnosed with DTC.

Patient selection from SEER
Patients with a diagnosis of papillary or follicular thyroid carcinoma from 1975 to 2016 were retrieved from the SEER using permission number 14641-Nov2019 without informed patient consent. This study did not require the ethics approval for all data present here were selected from the SEER. We used ICD to

Clinical Variables of Study Population
Clinicopathological features included age, race, sex, grade, AJCC TNM stage (7th), survival related information. X-Tile software was utilized to group the age category by the optimal cut-off value [12] Statistical Analysis Survival analysis was performed by the Kaplan-Meier analysis and log-rank test and comparisons of categorical variables was conducted by the χ2 test. Univariate and multivariate Cox regression were conducted to evaluate and further con rm the signi cant variables for the generation of the nomogram. For model validation, C-index was developed to assess the nomogram between performance and predicted results. The Calibration plots were generated to evaluate the consistency between the predicted 3-, 5-year survival rates and the actual observations in the modeling and validation groups. All statistical analyses were performed by Excel software (Microsoft Corporation, California) and R software, version 4.0.2 (R Foundation, Vienna, Austria). P < 0.05 was considered statistically signi cant.

Patient selection from SEER
This study included surgical patients with DTC derived from the SEER from 2010 to 2015. A total of 17,659 were involved in our study, of which 12,363 were randomly divided to the modeling cohort while 5,296 cases were placed into the validation cohort in a ratio of 7: 3 ( Fig. 1). Of the enrolled patients, 13,317 were female (75.4%), and 14,223 (80.5%) were White. Based on the optimal cutoff value in age (age ≤ 54, 55-68, and age ≥ 69), 4767 (27.0%) were between 55-68 years old. Besides, the majority of the grade is I (81.4%) while 57.5% were in the T1 stage, 75.4% were in the N0 stage and 98.5% were in the M0 stage. For the modeling and validation cohort, the median follow-up period was 43.3 (range, 0-83 months) months and 43.0 (range, 0-83 months) months, respectively. The clinicopathological features of patients enrolled in the study are summarized in Table 1.

Construction of a prognostic nomogram
Based on the 7 Clinicopathological features, we developed a prognostic nomogram to predict the 3-and 5-year OS (Fig. 2). The nomogram shows that age is most closely related to prognosis, followed by the grade, AJCC M stage, AJCC T stage, race, AJCC N stage, and sex. We can simply and intuitively calculate the survival probability of a single patient through the cumulative score of each selected variable (Table 3). Validation of a prognostic nomogram The OS nomogram was validated both internally (model cohort) and externally (validation cohort). For the model validation, the C-index of the OS nomogram was 0.829 (95% CI: 0.807-0.851), and the C-index of the external validation was 0.833 (95% CI: 0.803-0.862). Calibration plots of the nomogram close to the dotted line showed the good consistency between the predicted 3-, 5-year survival rates and the actual observations in the modeling and validation groups (Fig. 3).

Discussion
The incidence rate of thyroid cancer has been rising rapidly all over the world in recent decades [3].
Overall, the prognosis of DTC patients is excellent. However, there are also a small number of patients who experience a more aggressive disease, which is often associated with certain poor prognostic factors. Regardless of the treatment provided, only 5% of DTC patients may die. Approximately 15% of patients may bene t from more aggressive tumor resection, supplemented with radioactive iodine therapy, and external beam radiation therapy [13]. Therefore, early identi cation of these patients is essential to guide treatment decisions.
A nomogram is a graphical tool that is designed to approximate complicated calculations quickly and without a computer or calculator. And it has been applied to most types of cancer [14][15][16][17][18]. We performed the data analysis from 17,659 patients with DTC from the SEER to establish a comprehensive prognostic nomogram and examined its prognostic value with the identi ed clinical factors in patients with DTC.
Our study has several limitations. We conducted patient selection from the SEER, which makes this a retrospective research with unavoidable inherent biases. There is a selection bias in selecting and excluding patients. Besides, many factors were not included, such as BRAF600E, RAS, and TRER mutation. Because we enrolled only DTC patients, our results cannot be applied to other thyroid cancer types, such as medullary, or anaplastic thyroid carcinoma. Furthermore, we excluded the patients who did not undergo the operation in the present study.
We recommend that clinicians use the nomogram presented in our study when making treatment decisions. This study has several strengths. First, our nomogram was developed based on a large group of DTC patients, which was a population-based cancer database. The population-based design, adequate sample size, and long-term follow-up enhanced the strength of our study. Furthermore, to the best of our knowledge, this is the rst study to present a multi-factor survival model of DTC with a nomogram.
Second, our nomogram displayed a good discrimination ability veri ed by both internal and external validations, showing good performance. Third, our nomogram model contains risk factors easily found from medical records with the advantages of clinical applicability and ease of use.

Conclusions
In conclusion, we generated and validated a high nomogram for the prediction of survival for individual patients with DTC using 7 clinicopathological features based on a large, population-based cohort. This is conducive to individualized treatment and medical decision-making.

Declarations
Ethics approval and consent to participate Not applicable.

Consent for publication
Not applicable.

Availability of data and materials
The datasets analyzed for this study can be found in the SEER (https://seer.cancer.gov/).

Funding
Not applicable.

Author' Contributions
ZZ conceived and designed the study. GM performed the data collection, bioinformatics analysis, and wrote the initial manuscript. SLanalyzed the data and generated the gures and tables. ZZ conceptualized and develop an outline for the manuscript and revised the manuscript. All authors read and approved the nal manuscript. Flowchart of the DTC patients with modeling and validation cohorts.