A Novel Nomogram Integrated with Preablation Stimulated Thyroglobulin and Thyroglobulin/Thyroid-Stimulating Hormone Ratio to Predict the Therapeutic Response of Intermediate‑ and High‑Risk Differentiated Thyroid Cancer Patients: a Bi-center Retrospective Study

doi:10.21203/rs.3.rs-3320204/v1

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Research Article

A Novel Nomogram Integrated with Preablation Stimulated Thyroglobulin and Thyroglobulin/Thyroid-Stimulating Hormone Ratio to Predict the Therapeutic Response of Intermediate‑ and High‑Risk Differentiated Thyroid Cancer Patients: a Bi-center Retrospective Study

https://doi.org/10.21203/rs.3.rs-3320204/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 12 Dec, 2023

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Purpose

To investigate the factors influencing the outcome of radioactive iodine (RAI) treatment in intermediate- to high-risk patients with differentiated thyroid carcinoma (DTC).

Methods

We enrolled 553 DTC patients who underwent total thyroidectomy and categorized them into two groups according to their response to RAI therapy: excellent response (ER) and non-ER groups. Clinical and pathological characteristics of the patients were collected and retrospectively analyzed using univariate and multivariate binary logistic regression. Receiver operating characteristic (ROC) curves and diagnostic cutoff values were analyzed to assess the predictive value of important quantitative influences on ¹³¹I treatment outcomes. A new nomogram model was developed based on the above independent risk factors. R software was used to develop nomograms with all the independent prognostic factors included.

Results

The multivariate analysis showed that lymph node metastasis (LNM), stimulated thyroglobulin (sTg), thyroglobulin antibodies (TgAb), and sTg/thyroid-stimulating hormone (TSH) were significantly associated with non-ER of DTC patients. In the training set, the consistency index (C-index) of the new column line graph was 0.868 (95% CI 0.865–0.871), which was significantly higher than the C-index of the conventional 8th edition AJCC TNM staging.

Conclusion

We proposed a new nomogram to predict non-ER for DTC with excellent discrimination and calibration.

nomogram

prognosis

thyroid cancer

thyroglobulin

Thyroid cancer is the most common endocrine malignancy in the world, with rapidly increasing rates of incidence [1]. Thyroid carcinoma is classified as differentiated or undifferentiated by its histological type. Differentiated thyroid carcinoma (DTC), which represents nearly 90% of thyroid cancer, is characterized by indolent treatment with low mortality and a favorable long-term prognosis. Nevertheless, long-term follow-up of DTC patients is still crucial because there is a risk of recurrence that may exceed the boundaries of the 10-years follow-up period [2, 3]. The mainstay of treatment for DTC patients used to include total or near-total thyroidectomy, radioactive iodine (RAI) treatment, and thyroid-stimulating hormone (TSH) suppressive therapy [4]. RAI therapy is an essential complementary treatment for patients after surgery, and this treatment plays an important role in reducing disease-specific mortality in patients with DTC.

The 2015 American Thyroid Association has recommended a dynamic risk assessment system to tailor the intensity of therapy and follow-up, and the system classified clinical therapeutic results into four categories: Excellent Response (ER), Indeterminate Response (IDR), Biochemical Incomplete Response (BIR), and Structural Incomplete Response (SIR) [4]. Previous studies pointed out that the recurrence rate of patients with ER was only 1–4% [5]. In addition, a series of studies demonstrated that the stimulated thyroglobulin (sTg), which is obtained as early as a few weeks after surgery is correlated to the recurrence and development of cancer [6–9]. However, the relationship between sTg and the therapeutic response to RAI is still unknown.

Currently, there are relatively well-established risk assessment criteria according to age, pathological subtypes, primary tumor sizes, primary tumor surgery, extrathyroidal extension (ETE), and other indicators, which can guide the prognosis of clinical work. However, there is a lack of a simple and easy-to-use predictive tool for the disease prognostic assessment system to determine the therapeutic response of patients. In DTC, staging based on the tumor-node-metastasis (TNM) classification has been used to predict prognosis and develop treatment strategies. However, different patients with the same stage may have different treatment outcomes after receiving similar treatment regimens, suggesting that the current staging system is insufficient for predicting the therapeutic results.

Predictive models that take into account the sTg and TSH, which are simple and valid predictors, have rarely been established. Therefore, we conducted this retrospective study in which we expanded the sample size, provided a framework for individualized treatment, and developed a predictive model through a nomogram to complement existing staging systems and assist in clinical work. We also tested whether the nomogram model could provide a more accurate prediction of the therapeutic response compared with the traditional TNM staging system.

2.1 Patients

A cohort of patients with DTC from the First Affiliated Hospital of Soochow University and Suzhou Science and Technology Town Hospital between January 2017 and May 2023 were enrolled, retrospectively. The study was approved by the Institutional Ethics Committee of the First Affiliated Hospital of Soochow University. Informed consent was obtained from all participants.

The inclusion criteria were listed below: (1) patients pathologically diagnosed with papillary carcinoma or follicular carcinoma; (2) patients with complete follow-up information; (3) patients aged 18 years or older; (4) patients underwent total thyroidectomy and/or lymph node (LN) dissection; (5) patients who were intermediate to high risk according to the 2015 ATA guidelines. Patients with the following criteria were excluded: (1) patients with incomplete survival information; (2) patients with positive stimulated thyroglobulin antibodies (TgAb >115 IU/mL) or low TSH (< 30 mU/L); (3) patients with second primary malignancies.

According to the 2015 ATA guidelines, all patients were divided into two groups (ER and non-ER).

2.2 Radioiodine Therapy and Follow-up

Levothyroxine (LT4) replacement was withdrawn for at least 3 weeks before RAI treatment to enable an adequate TSH rise, and patients followed a low-iodine diet for 2 weeks before RAI treatment. Doses of RAI, varying from 1.11 GBq to 7.4 GBq, were administered to patients according to their recurrence risk stratification based on a patient risk profile including surgical pathology findings, stimulated Tg levels, clinical parameters, pre-ablation neck ultrasound, and other imaging studies if available. Posttreatment whole-body RAI scan (RxWBS) was routinely performed 5–8 days after RAI treatment, and LT4 replacement was initiated or resumed 3 days after RAI therapy.

After RAI therapy, routine follow-up was carried out for DTC patients, which consisted of the measurement of free triiodothyronine (FT3), free thyroxine (FT4), Tg, TgAb, and TSH levels, as well as neck ultrasonography (USG) every 3–6 months. And the appropriate TSH suppression therapy was routinely given to all patients (TSH < 0.1 IU/mL). To assess the status of distant metastases, imaging modalities, including CT, MRI, bone scintigraphy, ¹⁸F-FDG PET/CT, or ¹³¹I-SPECT/CT, were conducted at least once per year. Repeated RAI treatment was usually performed 6 to 12 months after the previous treatment.

2.3 Evaluation of the therapeutic response to RAI therapy

According to the results of the relevant studies [5, 10], we categorized the treatment response to RAI into excellent response (ER), biochemical incomplete response (BIR), indeterminate response (IDR), and structural incomplete response (SIR) based on the changes in serology and imaging after 6 to 12 months of RAI treatment. Patients with IDR, BIR, and SIR were then categorized into the non-ER group [11].

2.4 Statistical analyses

Statistical analyses were conducted using the SPSS version 26.0 (IBM, Armonk, NY, USA), and the nomogram was constructed using R software (version 4.3.1; http://www.r-project.org).

The population was randomly divided into a training cohort and a validation cohort with a ratio of 3:1. Quantitative data are expressed as means ± SD. Categorical data are presented as percentages. We determined the optimal cutoff points by using receiver operating characteristic (ROC) curves for converting continuous variables into categorical variables. The construction of the nomogram was based on the independent prognostic variables determined by multivariate Cox proportional hazards regression analyses. Discriminant and calibration tests were performed on the training and validation cohorts to examine the accuracy of the nomogram. Harrell's concordance index (C-index) tested the discrimination of nomograms. The value ranged from 0.5 (agreement by chance) to 1 (perfect prediction). Then, the calibration curve was created to evaluate the performance of the nomogram prediction model, and decision curve analysis (DCA) was performed to assess the clinical usefulness of the models. The discriminative abilities of the nomogram and the traditional TNM staging system were compared using ROC analysis. All statistical tests were two-sided, and P values < 0.05 were considered statistically significant.

3.1 Patients’ characteristics

A total of 553 patients with DTC were enrolled in this study. The flow diagram of data selection is presented in Fig. 1. The clinicopathological features of patients in the primary cohort and validation cohort are shown in Table 1. Patients in the two cohorts had similar clinicopathological features.

Table 1

Clinicopathologic characteristics of patients in training cohort and validation cohort.
Characteristics	Training Cohort (n = 415)	Validation Cohort (n = 138)	P
Therapeutic responses			0.980
ER	207 (49.9%)	68 (49.3%)
Non-ER	208 (50.1%)	70 (50.7%)
Age, years	41.45 ± 11.63	41.01 ± 11.42	0.723
Sex			0.062
Male	139 (33.5%)	59 (42.8%)
Female	276 (66.5%)	79 (57.2%)
Tumor lesion			0.979
Unilateral	192 (46.3%)	63 (45.7%)
Bilateral	223 (53.7%)	75 (54.3%)
Multifocality			0.612
One lesion	138 (33.3%)	42 (30.4%)
More than one lesion	277 (66.7%)	96 (69.6%)
Tumor size, cm	1.73 ± 1.02	1.63 ± 0.97	0.510
LNM	9.63 ± 7.27	10.56 ± 7.14	0.075
sTg, ng/mL	36.18 ± 106.79	48.57 ± 164.16	0.406
TgAb, IU/mL	21.47 ± 21.25	21.04 ± 21.79	0.745
sTg/TSH	0.42 ± 1.28	0.57 ± 1.8	0.524
Cumulative RAI dose, mCi	151.22 ± 76.16	151.23 ± 101	0.529
Recurrence risk stratification			0.999
Intermediate-risk	220 (53%)	73 (52.9%)
High-risk	195 (47%)	65 (47.1%)
T stage			0.745
T1	144 (34.7%)	55 (39.9%)
T2	37 (8.9%)	10 (7.2%)
T3	60 (14.5%)	21 (15.2%)
T4a	166 (40%)	51 (37%)
T4b	8 (1.9%)	1 (0.7%)
N stage			0.505
N0	23 (5.5%)	5 (3.6%)
N1	392 (94.5%)	133 (96.4%)
M stage			0.906
M0	397 (95.7%)	133 (96.4%)
M1	18 (4.3%)	5 (3.6%)
ETE			0.409
Absent	156 (37.6%)	58 (42%)
Present	259 (62.4%)	80 (58%)
^aER, Excellent Response. ^bLNM, lymph node metastasis. ^cRAI, radioactive iodine. ^dETE, extrathyroidal extension.

3.2 ROC analysis

ROC curve analysis was performed and the Youden Index was calculated to determine the best cut-off value with the highest sensitivity and specificity. Cumulative RAI dose was 120 mCi, sTg was 4.7 ng/mL, TgAb was 16.5 IU/mL, sTg/TSH was 0.0583, tumor size was 2.2 cm, and lymph node metastasis (LNM) was 12 (Table 2). According to the sensitivity and specificity, for each index, patients were divided into two groups for further analysis.

Table 2

The ROC analysis in patients with DTC.
Variable	AUC	95% CI	Sensitivity	Specificity	Cutoff value	P
Cumulative RAI dose	0.699	0.652–0.743	70.19	59.90	120	0.000
sTg	0.922	0.892–0.946	90.38	81.16	4.7	0.000
TgAb	0.613	0.564–0.660	81.25	42.72	16.5	0.000
sTg/TSH	0.927	0.897–0.950	89.42	82.13	0.0583	0.000
Tumor size	0.588	0.539–0.636	31.25	85.99	2.2	0.002
LNM	0.655	0.607–0.701	38.94	85.51	12	0.000
^aAUC, area under curve. ^bCI, confidence interval. ^cLNM, lymph node metastasis. ^dRAI, radioactive iodine. ^eDTC, differentiated thyroid carcinoma

3.3 Construction and validation of the nomogram

A significant correlation among gender (P = 0.032), tumor size (P = 0.000), tumor stage (P = 0.021), LNM (P = 0.000), sTg (P = 0.000), TgAb (P = 0.000), sTg/TSH (P = 0.000), cumulative RAI dose (P = 0.000) was determined by univariate analysis. After performing a multivariate analysis, 4 out of the 8 variables including LNM, sTg, TgAb and sTg/TSH were finally remained as independent predictors for non-ER (Table 2).

Therefore, for prediction of the risk of non-ER in DTC patients, these independent variables in the training cohort were extracted to build the nomogram model (Fig. 2). In the prediction model, for the variable “LNM”, “1” meant the number of total LNMs was greater than 12; for the variable “sTg”, “1” meant the sTg level was greater than 4.7 ng/mL; for the variable “TgAb”, “1” meant the TgAb level was greater than 16.5 IU/mL; for the variable “ratio”, “1” meant the sTg/TSH ratio was greater than 0.0583. Points were assigned to each variable based on its contribution to the outcome event. Based on the total number of points assigned to each factor in the nomogram, a higher total score indicated a worse treatment efficacy.

3.4 Internal and External Validation of the Nomogram Model

We next validated the nomogram. Following an internal validation in the training set, the C-index for the nomogram to predict the non-ER was 0.868 (95% CI 0.865–0.871). Following an external validation using the validation cohort, C-index was found to be 0.781 (95% CI 0.775–0.786). The calibration curve indicated good consistency between the prediction by the novel nomogram and the observation in the probability of non-ER in both the training and validation cohorts (Fig. 3).

Furthermore, the C-index was also used to compare the accuracy of the nomogram model and the conventional TNM staging system in predicting treatment outcomes in patients with intermediate- and high-risk DTC patients (Table 3). In the training cohort, the C-index of the nomogram was 0.868 (95% CI 0.865–0.871); in the validation cohort, the C-index of the nomogram model was 0.781 (95% CI 0.775–0.786), which was higher than that of the C-index of TNM staging system (Table 4).

Table 3

Univariate and multivariate Cox hazard analysis of the training cohort.
Variable	Univariate		Multivariate
Variable	HR (95% CI)	P	HR (95% CI)	P
Age, year		0.879
<55	1.000
≥55	0.961 (0.576–1.602)
Sex		0.032		0.283
Male	1.000		1.000
Female	0.638 (0.423–0.962)		0.698 (0.362–1.346)
Recurrence risk stratification		0.301
Intermediate-risk	1.000
High-risk	1.226 (0.833–1.804)
Tumor size, cm		0.000		0.376
≤2.2	1.000		1.000
>2.2	2.790 (1.709–4.554)		1.533 (0.595–3.953)
T stage
T1	1.000		1.000
T2	2.462 (1.149–5.277)	0.021	0.889 (0.187–4.215)	0.882
T3	0.967 (0.528–1.771)	0.913	0.562 (0.215–1.467)	0.239
T4a	1.240 (0.793–1.940)	0.346	1.011 (0.478–2.139)	0.977
T4b	1.970 (0.454–8.553)	0.366	0.556 (0.066–4.667)	0.589
N stage		0.136
N0	1.000
N1	1.953 (0.810–4.712)
LNM		0.000		0.002
≤12	1.000		1.000
>12	3.763 (2.336–6.062)		2.970 (1.469–6.003)
Tumor lesion		0.155
Unilateral	1.000
Bilateral	1.324 (0.899–1.950)
Multifocality		0.509
One lesion	1.000
More than one lesion	1.147 (0.762–1.727)
ETE		0.713
Absent	1.000
Present	0.928 (0.624–1.381)
sTg, ng/mL		0.000		0.000
≤4.7	1.000		1.000
>4.7	40.492 (22.721–72.163)		6.939 (2.668–18.045)
TgAb, IU/mL		0.000		0.034
≤16.5	1.000		1.000
>16.5	0.306 (0.196–0.477)		0.479 (0.243–0.946)
sTg/TSH, ng/ηIU		0.000		0.000
≤0.0583	1.000		1.000
>0.0583	38.845 (22.030-68.495)		6.093 (2.369–15.670)
Cumulative RAI dose, mCi		0.000		0.076
≤120	1.000		1.000
>120	3.518 (2.342–5.284)		1.772 (0.942–3.331)
^aCI, confidence interval. ^bLNM, lymph node metastasis. ^cRAI, radioactive iodine. ^dETE, extrathyroidal extension

Table 4

C-indexes for the nomogram and TNM stage system in patients with DTC.
Factors	Training Cohort		Validation Cohort
Factors	C-index	95%CI	C-index	95%CI
Nomogram	0.868	0.865–0.871	0.781	0.775–0.786
TNM stage	0.471	0.467–0.476	0.467	0.460–0.474
^aCI, confidence interval. ^bDTC, differentiated thyroid carcinoma

According to the ROC curve analysis (Table 5), in the training cohort, the AUC values for the nomogram and the TNM staging system predicting the non-ER were 0.905 and 0.573, respectively, which showed that the nomogram model had a high accuracy prediction. Taken together, the established nomogram showed superior discriminative capacity compared to the TNM staging system (Fig. 4).

Table 5

The AUCs for the nomogram and TNM staging system predicting the non-ER.
Factors	Training Cohort			Validation Cohort
Factors	AUC	95%CI	P	AUC	95%CI	P
Nomogram	0.905	0.873–0.932	0.000	0.832	0.759–0.890	0.000
TNM stage	0.573	0.524–0.621	0.000	0.572	0.485–0.655	0.000
^aAUC, area under curve. ^bCI, confidence interval

3.5 Decision curve analysis for clinical utility

As can be seen in the DCA (Fig. 5), the net benefit (NB) of the decision curve of the model is higher than that of the two invalid lines within the range of threshold probability between 1%-95%.

Generally, DTC patients with a low risk of recurrence have a good prognosis after total thyroidectomy and RAI treatment. However, if the response to initial RAI treatment in intermediate- to high-risk DTC patients is not ideal, the ¹³¹I dose may be increased. Therefore, accurate prediction of treatment response is important when determining the final treatment or management plan. The traditional TNM staging system can be used to assess the response to treatment in patients with DTC, but it has a number of drawbacks. First, the system seems to pay more attention to the anatomical extent of the disease and does not take into account considering the individual differences in DTC. Second, in patients accompanied by different numbers of LNM, the response to treatment is not well assessed by the TNM system. Therefore, although these systems are easier to use in the clinic, they provide a stratified population risk assessment rather than an individualized patient risk. Nomograms are useful tools that have been widely used for addressing the complexity of balancing different variables through statistical modelling and risk quantification.

In a variety of tumors, nomograms have been shown to be superior to traditional staging systems, and they can help physicians in the management of clinical care when no firm guidelines are available[12–15].

At present, it is of great clinical significance to identify the independent prognostic factors of non-ER, provide a strategy for adjuvant therapy for DTC. In this study, LNM, sTg, sTg/TSH, and TgAb were regarded as the key prognostic factors for non-ER. To the best of our knowledge, this is the first study that describes the development and validation of the nomogram to predict therapeutic outcome following RAI therapy for intermediate‑ to high-risk patients. A novel nomogram was constructed based on 415 patients and validated by 138 patients.

Tg is a tumor marker for therapy monitoring and a significant parameter used in the follow-up of subjects with DTC [16]. Two factors determine Tg concentration in most clinical situations: thyroid cell mass and activation of TSH receptors. According to recent studies, a Tg dynamic parameter, such as Tg/TSH and TSH/Tg might lessen the impact of TSH levels on Tg [17]. Several articles have assessed the role of preoperative serum hormones, antibodies, and several proteins in predicting malignancy in thyroid nodules [8]. However, the diagnostic role of these markers (TgAb, TSH, etc.) and Tg remains debatable, especially in intermediate‑ to high‑risk DTC patients. Therefore, this retrospective study was specifically aimed at further evaluating the clinical value of sTg, sTg/TSH and TgAb.

In recent years, some scholars believe that serum Tg levels not only can be used to monitor the disease progression in thyroid cancer patients during follow-up, but also can be used as an indicator of risk grading, and thus have great potential research value [18–20]. This study provided evidence for this option. In addition, Wang et al. [21] proposed that Tg levels were correlated with LNM. On the one hand, LNM is common in DTC [22]. According to previous reports, LNM has been demonstrated to be a risk factor for poor prognosis in thyroid cancer [23, 24]. It has been shown that DTC patients with LNM have a reduced survival rate. 10%-15% of these patients will have concomitant or developing distant metastases (DM) [25]. On the other hand, elevated serum Tg usually occurs when thyroid tissue is stimulated or when the tumor load increases [26]. Thus, both LNM and Tg indicate tumor progression.

Since serum Tg is mainly produced by the thyroid gland or metastatic sites, sTg levels can be an important tumor marker for detecting residual or recurrent disease in patients after surgery. In this study, we found that patients with higher sTg levels were more likely to have non-ER. sTg critical value for predicting non-ER was 4.7 µg/L, with an area under the curve of 0.922, a sensitivity of 90.38%, and a specificity of 81.16%, which was close to the previously reported data [27–30]. However, sTg may be more accurate when corrected by TSH, because Tg levels are significantly affected by thyrotropic stimulation of TSH on the thyroid remnant [5]. The sTg/TSH ratio at lower TSH indicates a greater tumor load than the same Tg/TSH ratio corrected for higher TSH. Simple sTg level do not accurately reflect the degree of tumor stimulation by coexisting TSH. Therefore, some scholars have suggested TSH correction and demonstrated that the sTg/TSH ratio is more sensitive than sTg [31], and other authors have suggested that the ratio may be able to predict metastasis [32, 33].

A retrospective study revealed that patients with sTg > 18 ng/ml and sTg/TSH > 0.35 before RAI therapy were significantly associated with unsuccessful ablation [31]. Another study indicated that the sTg < 9.51 ng/ml and sTg/TSH < 0.11 predicted a better therapeutic outcome [30]. In this study, we found that, like sTg, the sTg/TSH ratio was strongly correlated with the therapeutic response. Barres et al. [29] showed that preablation stimulated thyroglobulin (presTg) < 10 ng/mL was a good predictor of long-term remission and sustained or recurrence-free survival. Although our results suggest that a lower sTg (4.7 ng/mL) is needed to predict ER, a possible reason for our results is that our study focused on intermediate- to high-risk patients rather than on all DTC patients, and therefore more stringent parameters should be considered. In addition, to increase the reliability of the results, the above results were validated in a separate cohort. Comparison of our results with those of Wang et al. [28] and Trevizam et al. [27] revealed that the cutoff values, sensitivity, and AUC of sTg and sTg/TSH ratios obtained in the present study were similar to those of the previous studies, with the highest specificity. Differences in results may be due to different inclusion criteria or grouping methods in different studies.

As a useful tool, the nomogram utilizes convenient clinical information to provide physicians with simple prognostic information from complex statistical analyses. Compared to the TNM staging system, the nomogram is more accurate for DTC patients and have a higher concordance rate. Our approach combines thyroid tumor markers with pathologic features to more accurately predict a patient's response to treatment by accounting for anatomical and individual patient differences.

This study has some limitations. First, our results may suffer from sample selection bias because this was a retrospective study. Second, our cohort was recruited from only one region; two cohorts would have been larger. Therefore, in order to better build and validate our model, it may be necessary to obtain more data from other organizations in other regions. The third reason is that the two laboratories in which the two cohorts are located use different testing reagents and instruments, so there may be some differences in the data. Moreover, there is some heterogeneity in the treatment of patients after surgery, which may lead to different clinical outcomes.

In conclusion, a novel nomogram was developed to predict the RAI therapeutic response in patients with intermediate- to high-risk DTC. Our nomogram is an improvement in prediction compared to traditional TNM staging systems. Thus it can be considered as a simple and convenient predictive tool that can be used in clinical work.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Authors’ Contributions

Renze Wen designed the study, analyzed the data, and wrote the manuscript. Chang Chen and Min Zhao collected the data. Yi Yang and Bin Zhang designed the study and revised the manuscript. All authors read and approved the final manuscript.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent to publish

Not applicable.

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published 12 Dec, 2023

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A Novel Nomogram Integrated with Preablation Stimulated Thyroglobulin and Thyroglobulin/Thyroid-Stimulating Hormone Ratio to Predict the Therapeutic Response of Intermediate‑ and High‑Risk Differentiated Thyroid Cancer Patients: a Bi-center Retrospective Study

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Journal Publication

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Abstract

Purpose

Methods

Results

Conclusion

Figures

1. Introduction

2. Methods

2.1 Patients

2.2 Radioiodine Therapy and Follow-up

2.3 Evaluation of the therapeutic response to RAI therapy

2.4 Statistical analyses

3. Results

3.1 Patients’ characteristics

3.2 ROC analysis

3.3 Construction and validation of the nomogram

3.4 Internal and External Validation of the Nomogram Model

3.5 Decision curve analysis for clinical utility

4. Discussion

5. Conclusion

Declarations

References

Status:

Journal Publication

Version 1