Abstract
Objective
Recent research suggests that the addition of age improves the 2015 American Thyroid Association (ATA) Risk Stratification System for differentiated thyroid cancer (DTC). The aim of our study was to investigate the influence of age on disease outcome in ATA-high risk patients with a focus on differences between patients with papillary (PTC) and follicular thyroid cancer (FTC).
Methods
We retrospectively studied adult patients with high-risk DTC from a Dutch University hospital. Logistic regression and Cox proportional hazards models were used to estimate the effects of age (at diagnosis) and several age cutoffs (per 5 years increment between 20 and 80 years) on (i) response to therapy, (ii) developing no evidence of disease (NED), (iii) recurrence, and (iv) disease-specific mortality (DSM).
Results
We included 236 ATA high-risk patients (32% FTC) with a median follow-up of 6 years. Age, either continuously or dichotomously, had a significant influence on having an excellent response after initial therapy, developing NED, recurrence, and DSM for PTC and FTC. For FTC, an age cutoff of 65 or 70 years showed the best statistical model performance, while this was 50 or 60 years for PTC.
Conclusions
In a population of patients with high-risk DTC, older age has a significant negative influence on disease outcomes. Slightly different optimal age cutoffs were identified for the different outcomes, and these cutoffs differed between PTC and FTC. Therefore, the ATA Risk Stratification System may further improve should age be incorporated as an additional risk factor.
Introduction
The American Thyroid Association (ATA) Risk Stratification system is designed to predict response to therapy and recurring disease in patients with differentiated thyroid cancer (DTC) (1). Nowadays, it is widely used and several studies have shown its usefulness in predicting response to therapy and recurrence (2, 3, 4, 5, 6, 7, 8) and even disease-specific survival (DSS) (7, 8, 9, 10). In contrast to the joint Union International Contre le Cancer and American Joint Committee on Cancer (UICC/AJCC) Tumor, Node, Metastasis (TNM) staging system (11, 12, 13), age was not incorporated into the classification of patients into different prognostic groups. Recently, three studies investigated the influence of age on recurrence and disease outcome in patients with DTC (9, 14, 15), including those with ATA high-risk DTC. Subsequently, it was shown that the 2015 ATA Risk Stratification System can be improved through the addition of age as a factor in the risk classification, especially for ATA high-risk patients (14). Unfortunately, these three studies either comprised relatively small proportions of ATA high-risk patients, only investigated 45 and 55 years as age cutoff, or had low numbers of patients with follicular thyroid cancer (FTC). It is well-established that FTC has a different clinical manifestation than papillary thyroid cancer (PTC) as lymph node metastasis are uncommon, patients are generally older and more often have distant metastasis at initial presentation (16).
The aim of the present study was to investigate the influence of age on recurrence and disease outcome in ATA high-risk patients, and whether the 2015 ATA Risk Stratification System could be improved by adding an age cutoff. The secondary aim of our study was to examine whether differences regarding age and the optimal age cutoff exist between patients with PTC and FTC.
Subjects and methods
Study population and clinical outcomes
We retrospectively included all patients, aged 18 years or above, who were diagnosed and/or treated for either PTC or FTC (including Hürthle cell carcinoma (HCC)) between January 2002 and December 2015 at the Erasmus Medical Center, Rotterdam, The Netherlands. Thereafter, using the 2015 ATA Risk Stratification System, we retrospectively identified those patients fulfilling the ATA high-risk criteria (1), that is, macroscopic invasion of the tumor into the perithyroidal soft tissues (gross extrathyroidal extension (ETE)), incomplete tumor resection, distant metastases or postoperative serum thyroglobulin level (Tg) suggestive for distant metastatic disease (Tg >30 µg/L), any metastatic lymph node larger than 3 cm in size, or FTC with extensive vascular invasion. The same database was earlier used to evaluate the Risk Stratification System of the 2015 ATA Guidelines for ATA high-risk patients (8). From patient records, we obtained demographic, disease, treatment, response to therapy, recurrence, and mortality characteristics.
Response to therapy was defined according to the four categories defined in the 2015 ATA Guidelines and was continually assessed during follow-up (i.e. dynamic risk stratification (DRS)) (1). These four responses to therapy categories were excellent response (also called no evidence of disease (NED)), biochemical incomplete response, structural incomplete response, and indeterminate response. Persistent disease was defined as either structural or biochemical incomplete response. Response to therapy was recorded for the first time at 6 to 18 months after the first therapy based upon patients’ records; thereafter during and at end of follow-up. A recurrence was defined as a new biochemical, structural, or functional disease (e.g. radioiodine scan) after longer than 12 months of NED. Time to last follow-up, survival status, and date and cause of death were recorded. Survival was defined as the time of initial diagnosis to either last date of follow-up, death, or end of study (December 2017), whichever occurred first. Cause of death was obtained from hospital or general practitioner records. The study protocol was approved by the Institutional Review Board of the Erasmus Medical Center.
Age was defined as age at initial diagnosis throughout the whole manuscript. Patients were stratified into either the younger or older group based on different age cutoffs. For this purpose, we investigated age cutoffs at 5-year increments from 20 up to and including 80 years. Besides, age was also investigated as a continuous entity. Analyses were performed separately for both PTC and FTC, as well as combined for the overall DTC patient population.
Statistical analysis
For continuous variables, means and s.d., or medians with interquartile ranges (IQR) were calculated. For categorical variables, absolute numbers with percentages were recorded. Differences in characteristics between PTC and FTC were assessed using the Student’s t-test or χ2-test.
DSS was analyzed using the Kaplan–Meier method and compared across different age cutoffs using the log-rank test. For DTC, and PTC and FTC separately, univariate and multivariate logistic regression (outcome: odds ratio (OR)) or Cox proportional hazards models (outcome: hazard ratio (HR)) were used to examine the effect of age as a continuous entity, and the effect of the previously described age cutoffs on either initial response to therapy (excellent response), developing NED, recurrence, or disease-specific mortality (DSM). In the multivariate analyses, the effect of age and the age cutoffs was adjusted for the ATA high-risk criteria (gross ETE, incomplete tumor resection, distant metastases or postoperative serum Tg suggestive for distant metastatic disease, any metastatic lymph node larger than 3 cm in size, and in case of FTC also for FTC with extensive vascular invasion) to assess whether age is an independent predictor of disease outcome.
To assess the statistical model performance of the 2015 ATA Risk Stratification System with different age cutoffs, we used the concordance index (Harrell’s C-index) (17, 18) for the Cox proportional hazards models, the area under the curve (AUC) for the logistic regression models, and for both models also the Akaike information criterion (AIC) (19), and the Bayesian information criterion (BIC) (20). Both the C-index and AUC measure the discriminative power of a model and are a measure of goodness-of-fit. It ranges from 0.5 to 1.0, with 0.5 meaning the model predicts no better than random chance, and 1.0 being the perfect prediction model. Furthermore, the AIC and BIC measure the relative quality of a statistical model, and they provide the relative information lost when a statistical model is used to represent the true model. The model with the highest C-index/AUC and lowest AIC and BIC is considered to be the best model for predicting outcomes. Therefore, using these three criteria, we aimed to find the age cutoff that optimizes the statistical performance. The same analyses as for DTC were performed for both PTC and FTC separately.
P-values below 0.05 were considered significant. All analyses were performed using either SPSS Statistics for Windows (version 25.0) or the open-source statistical software R (version 3.4.1) with package survC1 for estimating the C-index (21).
Results
Population characteristics
A total of 236 patients fulfilled the inclusion criteria and had sufficient follow-up information. Table 1 lists the characteristics of the study population. Mean age was 56.3 years, and 148 (63%) were women. PTC was present in 160 (68%) patients, and the remaining 76 patients (32%) had FTC, including 29 patients (38%) with Hürthle cell carcinoma. Median follow-up time was 72 months, and during follow-up, 70 patients (30%) died, of which 49 (70%) due to thyroid cancer. Patients with FTC were significantly older (64.1 years vs 52.6 years; P < 0.001). Consequently, there were fewer patients with FTC than with PTC in the younger group for each age cutoff (Supplementary Table 1, see section on supplementary materials given at the end of this article).
Characteristics of the study population. Values are presented as mean ± s.d., median (25–75 IQR) or as n (%).
| DTC (n = 236) | PTC (n = 160) | FTC (n = 76) | P-valuea | |
|---|---|---|---|---|
| Age at diagnosis (years) | 56.3 ± 17.6 | 52.6 ± 17.8 | 64.1 ± 14.7 | <0.001 |
| Women | 148 (63%) | 102 (64%) | 46 (61%) | 0.632 |
| AJCC/TNM Staging system (8th edition) | 0.001 | |||
| I | 84 (36%) | 63 (39%) | 21 (28%) | |
| II | 79 (34%) | 56 (35%) | 23 (30%) | |
| III | 24 (10%) | 19 (12%) | 5 (7%) | |
| IV | 49 (21%) | 22 (14%) | 27 (36%) | |
| Hürthle cell | 29 (12%) | – | 29 (38%) | – |
| Tumor size (cm) | 3.4 (2.0–5.0) | 3.0 (1.6–4.2) | 5.0 (3.1–7.9) | <0.001 |
| Metastatic disease | 78 (33%) | 47 (29%) | 31 (41%) | 0.082 |
| Pulmonary | 58 (25%) | 39 (24%) | 19 (25%) | |
| Bone | 26 (11%) | 10 (6%) | 16 (21%) | |
| Surgery (TT or HT) | 0.996 | |||
| HT | 3 (1%) | 2 (1%) | 1 (1%) | |
| TT | 233 (99%) | 158 (99%) | 75 (99%) | |
| Total | 236 (100%) | 160 (100%) | 76 (100%) | – |
| Neck dissection | 105 (45%) | 88 (55%) | 17 (22%) | <0.001 |
| RAI treatment | 0.017 | |||
| Once | 68 (29%) | 38 (24%) | 30 (40%) | |
| Twice | 76 (32%) | 57 (36%) | 19 (25%) | |
| ≥3 | 82 (35%) | 61 (38%) | 21 (28%) | |
| Total | 227 (96%) | 156 (98%) | 71 (93%) | 0.126 |
| Cumulative dose (mCi) | 295 (150–450) | 298 (150–450) | 195 (142–400) | 0.019 |
| Other treatments | ||||
| Radiotherapy | 41 (17%) | 23 (14%) | 18 (24%) | 0.078 |
| TKI | 19 (8%) | 11 (7%) | 8 (11%) | 0.335 |
| Follow-up (months) | 72 (44–120) | 75 (44–128) | 66 (42–103) | 0.329 |
| Dead | 70 (30%) | 39 (24%) | 31 (41%) | 0.010 |
| Thyroid cancer | 49 (21%) | 28 (18%) | 21 (28%) | 0.073 |
aP-value comparing PTC and FTC.
cm, centimeter; DTC, differentiated thyroid cancer; FTC, follicular thyroid cancer; HT, hemi-thyroidectomy; mCi, milliCurie; PTC, papillary thyroid cancer; RAI, radioactive iodine; TKI, tyrosine kinase inhibitor; TT, total thyroidectomy.
Response to therapy and survival
Seven patients (3%) died within 6 months after initial therapy, precluding assessment of initial response to therapy in these patients. Therefore, these patients were excluded from the response to therapy analyses, leaving 229 patients for the remaining analyses. The youngest patient who died from PTC was 47.5 years at diagnosis, while this was 41.1 years for FTC. After initial therapy, the majority of the remaining 229 patients continued to have structural disease (51%), while an excellent response was seen in only 38 patients (17%). These percentages were similar for PTC and FTC separately (Table 2). During follow-up, 79 patients (35%) achieved NED after a median of 22 months. In 11 out of 79 patients (14%) who achieved NED, a recurrence occurred during follow-up after a median of 47 months. Both these percentages were similar for PTC and FTC, and also no significant differences were seen taking time into account. The numbers for the different age cutoffs are shown in Supplementary Tables 2, 3, 4 and 5.
Response to therapy. Values are presented as n (%).
| DTC (n = 229)a | PTC (n = 157) | FTC (n = 72) | P-valueb | |
|---|---|---|---|---|
| After initial therapy | ||||
| Excellent | 38 (17%) | 27 (17%) | 11 (15%) | 0.717 |
| Indeterminate | 59 (26%) | 44 (28%) | 15 (21%) | 0.250 |
| Biochemical incomplete | 15 (7%) | 11 (7%) | 4 (6%) | 0.681 |
| Structural incomplete | 117 (51%) | 75 (48%) | 42 (58%) | 0.139 |
| Persistent disease | 132 (58%) | 86 (55%) | 46 (64%) | 0.196 |
| Developing NED | 79 (35%) | 58 (37%) | 21 (29%) | 0.252 |
| Recurrence | 11 (14%) | 10 (17%) | 1 (5%) | 0.137 |
| At end of follow-up | ||||
| Excellent | 69 (29%) | 49 (31%) | 20 (26%) | 0.497 |
| Indeterminate | 38 (16%) | 30 (19%) | 8 (11%) | 0.113 |
| Biochemical incomplete | 9 (4%) | 9 (6%) | – | 0.997 |
| Structural incomplete | 120 (51%) | 72 (45%) | 48 (63%) | 0.010 |
| Local | 69 (57%) | 48 (67%) | 21 (44%) | 0.014 |
| Distant | 89 (74%) | 49 (68%) | 40 (83%) | 0.065 |
| Both | 38 (32%) | 25 (35%) | 13 (27%) | 0.379 |
| Persistent disease | 129 (55%) | 81 (51%) | 48 (63%) | 0.072 |
aSeven patients were excluded due to death precluding initial response to therapy assessment; bP-value comparing PTC and FTC.
DTC, differentiated thyroid cancer; FTC, follicular thyroid cancer; NED, no evidence of disease; PTC, papillary thyroid cancer.
Influence of age on disease outcome for the age cutoffs with the best statistical performance.
| DTC | PTC | FTC | |||||||
|---|---|---|---|---|---|---|---|---|---|
| na | OR or HR (95% CI)b | P-valuec | na | OR or HR (95% CI)b | P-valuec | na | OR or HR (95% CI)b | P-valuec | |
| Initial excellent responsed | |||||||||
| 50 years cutoff | 20/18 | 17/10 | 0.45 (0.19–1.05) | 0.063 | 3/8 | ||||
| 65 years cutoff | 33/5 | 0.24 (0.09–0.64) | 0.004 | 23/4 | 10/1 | 0.09 (0.01–0.70) | 0.022 | ||
| Developing NEDe | |||||||||
| 60 years cutoff | 60/19 | 0.33 (0.19–0.55) | <0.001 | 47/11 | 0.35 (0.18–0.67) | 0.002 | 13/8 | ||
| 65 years cutoff | 69/10 | 51/7 | 18/3 | 0.17 (0.05–0.56) | 0.004 | ||||
| Recurrencee | |||||||||
| 50 years cutoff | 3/8 | 3.68 (0.97–13.91) | 0.055 | 3/7 | 5.46 (1.39–21.52) | 0.015 | 0/1 | ||
| Disease-specific mortalitye | |||||||||
| 50 years cutoff | 4/45 | 3/25 | 2.74 (2.74–30.39) | <0.001 | 1/20 | ||||
| 55 years cutoff | 8/41 | 5.34 (2.49–11.47) | <0.001 | 5/23 | 3/18 | ||||
| 70 years cutoff | 26/23 | 17/11 | 9/12 | 2.88 (1.19–6.99) | 0.019 | ||||
aNumbers below/above cutoff; bUnivariate analysis; c P-value for influence of age (cutoff); dOdds ratio; eHazard ratio.
DTC, differentiated thyroid cancer; FTC, follicular thyroid cancer; HR, hazard ratio; NED, no evidence of disease; OR, odds ratio; PTC, papillary thyroid cancer.
Influence of age
For DTC, we observed that age has a significant influence on having an excellent response after initial therapy (OR: 0.98, 95% CI: 0.96–0.99 per year increase; P = 0.039), developing NED (HR: 0.98, 95% CI: 0.97–0.99 per year increase; P < 0.001), and DSM (HR: 1.06, 95% CI: 1.04–1.08 per year increase; P < 0.001) and a non-significant trend was seen for recurrence (HR: 1.03, 95% CI: 0.99–1.07 per year increase; P = 0.132). Therefore, older patients have a lower chance of having an excellent response and a higher risk of dying due to thyroid cancer. After adjustment for the ATA high-risk criteria, age remained significant for having NED during follow-up (HR: 0.95, 95% CI: 0.91–0.99 per year increase; P = 0.042). The different age cutoffs more or less also follow this pattern (Supplementary Tables 6, 7, 8 and 9). For PTC, we also observed that older age results in a significantly lower chance of developing NED (OR: 0.98, 95% CI: 0.97–0.99 per year increase; P = 0.009) and having a higher risk of dying due to thyroid cancer (HR: 1.08, 95% CI: 1.04–1.11 per year increase; P < 0.001), but no significant influence of age on having an excellent response after the initial therapy and on recurrence was found. After adjustment for the ATA high-risk criteria, age had a significant influence on having an excellent response after initial therapy (OR: 0.96, 95% CI: 0.92–0.99 per year increase; P = 0.044), developing NED (HR: 0.97, 95% CI: 0.95–0.99 per year increase; P = 0.018), and DSM (HR: 1.07, 95% CI: 1.02–1.11 per year increase; P = 0.002). The different age cutoffs more or less also follow this pattern (Supplementary Tables 6, 7, 8 and 9), and it is important to mention that, in univariate analysis, several age cutoffs (50, 55 and 60 years) showed a significant influence on recurrence; older age resulted into a higher recurrence risk. For FTC, we observed that age has a significant influence on developing NED (HR: 0.96, 95% CI: 0.94–0.99 per year increase; P = 0.005), but not on having an excellent response after initial therapy, recurrence and DSM. Therefore, older patients had a significantly lower chance of developing NED. After adjustment for the ATA high-risk criteria, age remained to have a significant influence on developing NED (HR: 0.95, 95% CI: 0.91–0.99 per year increase; P = 0.042). The different age cutoffs more or less also follow this pattern (Supplementary Tables 6, 7, 8 and 9).
Statistical model performance
Regarding having an excellent response after initial therapy (Fig. 1 and Supplementary Fig. 1), the optimal statistical performance (highest AUC, and lowest AIC and BIC) was identified for an age cutoff of 65 years for DTC. This also holds for FTC, but for PTC, an age cutoff of 50 years seemed to be optimal. For developing NED, the optimal statistical performance for DTC and PTC was identified for an age cutoff of 60 years, while this was 65 years for FTC (Fig. 1 and Supplementary Fig. 2). For recurrence (Fig. 1 and Supplementary Fig. 3), the optimal statistical performance for both DTC and PTC was observed with an age cutoff of 50 years. As there was only one patient with FTC that had a recurrence, no separate statistics were performed. Finally, regarding DSM (Fig. 1 and Supplementary Fig. 4) for DTC, the highest C-index was found with an age cutoff of 55 years, while the lowest AIC and BIC were found for 45 years. For PTC, these were respectively 50 years and 45 years, while for FTC the optimal statistical performance was identified for 70 years of age. The odds and hazard ratios for the optimal statistical performing age cutoffs are shown in Table 3, while the corresponding Kaplan–Meier curves are shown in Figs 2, 3 and 4.
Statistical model performance of (A) AUC for initial excellent response to therapy, and C-index for (B) developing no evidence of disease, (C) recurrence, and (D) disease-specific mortality.
Citation: European Journal of Endocrinology 185, 3; 10.1530/EJE-21-0365
Statistical model performance of (A) AUC for initial excellent response to therapy, and C-index for (B) developing no evidence of disease, (C) recurrence, and (D) disease-specific mortality.
Citation: European Journal of Endocrinology 185, 3; 10.1530/EJE-21-0365
Statistical model performance of (A) AUC for initial excellent response to therapy, and C-index for (B) developing no evidence of disease, (C) recurrence, and (D) disease-specific mortality.
Citation: European Journal of Endocrinology 185, 3; 10.1530/EJE-21-0365
Kaplan–Meier curves for developing no evidence of disease (NED) in (A and B) DTC, (C and D) PTC, and (E and F) FTC for either without an age cutoff or for the best statistical performing age cutoffs.
Citation: European Journal of Endocrinology 185, 3; 10.1530/EJE-21-0365
Kaplan–Meier curves for developing no evidence of disease (NED) in (A and B) DTC, (C and D) PTC, and (E and F) FTC for either without an age cutoff or for the best statistical performing age cutoffs.
Citation: European Journal of Endocrinology 185, 3; 10.1530/EJE-21-0365
Kaplan–Meier curves for developing no evidence of disease (NED) in (A and B) DTC, (C and D) PTC, and (E and F) FTC for either without an age cutoff or for the best statistical performing age cutoffs.
Citation: European Journal of Endocrinology 185, 3; 10.1530/EJE-21-0365
Kaplan–Meier curves for recurrence in (A and B) DTC or (C and D) PTC for either without an age cutoff or for the best statistical performing age cutoffs.
Citation: European Journal of Endocrinology 185, 3; 10.1530/EJE-21-0365
Kaplan–Meier curves for recurrence in (A and B) DTC or (C and D) PTC for either without an age cutoff or for the best statistical performing age cutoffs.
Citation: European Journal of Endocrinology 185, 3; 10.1530/EJE-21-0365
Kaplan–Meier curves for recurrence in (A and B) DTC or (C and D) PTC for either without an age cutoff or for the best statistical performing age cutoffs.
Citation: European Journal of Endocrinology 185, 3; 10.1530/EJE-21-0365
Kaplan–Meier curves for disease-specific survival in (A and B) DTC, (C and D) PTC, and (E and F) FTC for either without an age cutoff or for the best statistical performing age cutoffs.
Citation: European Journal of Endocrinology 185, 3; 10.1530/EJE-21-0365
Kaplan–Meier curves for disease-specific survival in (A and B) DTC, (C and D) PTC, and (E and F) FTC for either without an age cutoff or for the best statistical performing age cutoffs.
Citation: European Journal of Endocrinology 185, 3; 10.1530/EJE-21-0365
Kaplan–Meier curves for disease-specific survival in (A and B) DTC, (C and D) PTC, and (E and F) FTC for either without an age cutoff or for the best statistical performing age cutoffs.
Citation: European Journal of Endocrinology 185, 3; 10.1530/EJE-21-0365
Discussion
This study shows that in a population of patients with high-risk DTC, older age, either continuously or dichotomously, has a significant negative influence on disease outcome. Slightly different optimal age cutoffs were identified for the different outcomes, and these cutoffs differed between PTC and FTC.
We observed a significant influence of age on having an excellent response after initial therapy, developing NED, recurrence, and DSM for either PTC or FTC. Kim et al. showed no influence of age on recurrence using 55 years age cutoff in patients with PTC (15); the recurrence rate in their patients was with 16.5% in line with ours (13.9%). On the other hand, Trimboli et al. showed a significant influence of age on relapse using an age cutoff of 55 years of age in patients with high-risk DTC (14). Difference between the latter study and ours is that, although they had fewer high-risk patients (n = 87), relapse rates were higher which is probably caused by the fact that they used disease-free survival instead of recurrence. On the other hand, in our study, 11 (13.9%) out of 79 patients that achieved NED during follow-up experienced a recurrence, and therefore, numbers might be too low to observe a significant influence of age as a continuous variable on recurrence. Shah et al. showed that age is a major determinant of response to therapy as there were significantly more patients with an age below 55 years that achieved an excellent response at end of follow-up when comparing them to older patients (9). Besides, they also showed that age is a key predictor of DSS/DSM which corresponds with our results. In the current study, containing patients with high-risk DTC, including those with distant metastases, no patients younger than 40 years died from DTC. This implies that in the UICC/AJCC TNM Staging System, patients younger than 40 years of age at diagnosis having distant metastases might be better classified as stage I rather than stage II. Further research is needed to confirm this proposal.
For DTC, the best statistical performance was observed for an age cutoff of 65 years (excellent response after initial therapy), 60 years (developing NED), 55 years (DSM) or 50 years (recurrence). In patients with FTC, results were more consistent, as we showed that an age cutoff of 65 years (excellent response after initial therapy, developing NED) or 70 years (DSM) statistically outperformed the other age cutoffs. For PTC, an age cutoff of 50 years (excellent response after initial therapy, recurrence and DSM) or 60 years (developing NED) had the best statistical performance. These observations are partly in line with our earlier study regarding the 8th edition of the UICC/AJCC TNM Staging System also showing different age cutoffs for PTC and FTC (22). The observed optimal age cutoff for the 8th edition UICC/AJCC TNM Staging System for patients with FTC regarding DSM in that study (40 years) differs from the optimal age cutoff in FTC patients in the current study (70 years). Differences between these studies are (i) the study population, which comprise only ATA high-risk patients in the current study, (ii) the way age is incorporated in the 8th UICC/AJCC TNM Stage System as this includes different tiers, and (iii) the relatively low number of younger patients with FTC, therewith reducing the statistical power in the lower age cutoffs. To the best of our knowledge, the present study is the first one to investigate the optimal age cutoff specifically for patients with FTC. Compared to those with PTC, patients with FTC in our population were older and had a more advanced disease in terms of both local disease and distant metastases, which is in accordance with the literature (16).
We showed that age remained significant when adjusted for the original ATA high-risk factors, for having an excellent response after initial therapy for PTC, developing NED for PTC and FTC, and DSM for PTC. We recently showed, using the same population, that the presence of distant metastases and an elevated postoperative Tg are also independent predictors of either having an excellent response after initial therapy or developing NED in high-risk DTC patients (8). Combining this implies that age, either continuously or dichotomously, is an independent predictor of excellent response to therapy, developing NED and DSM in high-risk PTC or FTC patients, and therefore, should be considered to be included as a risk factor in the ATA Risk Stratification System. Based on our results, different age cutoffs for PTC and FTC are probably needed. One might suggest to use 65 years for FTC, and not 70 years, which was the optimal age cutoff for DSM, as the Risk Stratification System is not designed to predict DSS/DSM. For PTC, the optimal age cutoff was either 50 years or 60 years, and therefore, one might argue to use the average of the two which is 55 years of age. Recently, Trimboli et al. showed that ATA high-risk patients could be reclassified in two subgroups based on an age cutoff of 55 years with older patients having the highest relapse risk, while such an age cutoff could not be identified for low and intermediate risk patients (14); their population predominantly contained patients with PTC (91%). Therefore, further research is still needed, which besides ATA high-risk also includes patients with ATA low and intermediate-risk to determine in which way age can be incorporated into the ATA Risk Stratification System to further improve its predictive function regarding response to therapy and recurrence in both PTC and FTC patients. For example, single or multiple age cutoffs, or, like Trimboli et al. (14), define new risk categories for high-risk patients younger or older than a certain age cutoff. Therewith, clinical management can be better optimized for these older high-risk patients.
One of the main strengths of the current study is the relatively high number of FTC patients which enabled us to be, to our knowledge, the first to investigate the influence of age in patients with FTC, and consequently, observe differences between PTC and FTC patients. There is substantial evidence suggesting that Hürthle cell carcinoma (HCC) is not a subtype of ‘regular’ FTC (23, 24). However, in our previous study using the same dataset, we did not find any differences regarding disease outcome between HCC and‘regular’ FTC (8), and therefore, in the current study, we did not analyze HCC and ‘regular’ FTC separately. A possible limitation of the study is that patients were recruited from a single tertiary university hospital, which might attract patients with more aggressive diseases, especially FTC, because of the availability of advanced treatments. Another limitation might be the inability to perform multivariate analysis for recurrence because of the low number of events. Elaborating on this, the number of events in young patients, especially in those with FTC, was relatively low, which lead to less robust results (no estimates or large CIs) for these groups. Further, 16 patients had insufficient information to determine their ATA risk category, and 19 patients had insufficient follow-up information. It is, therefore, highly unlikely that such a small proportion would have altered the overall results. Further, it is possible that an (unknown) proportion of patients would at present be classified otherwise, for example, non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Also, different pathologists were involved during this 15-year study period. Unfortunately, the retrospective nature of our study precludes any ascertainment in this respect. Finally, we used three statistical measures (C-index, AIC, and BIC) which able to define the age cutoff that optimizes statistical performance. These three measures in the various analyses occasionally showed only minor discrepancies, allowing us to weigh the purely statistical analyses with pragmatic clinical considerations to balance the various results.
Conclusion
The present study shows that in a population of patients with high-risk DTC, harboring a large set of FTC patients, older age, either continuously or dichotomously, has a significant negative influence on disease outcome and, therefore, should be considered to be included as a risk factor in the ATA Risk Stratification System. Slightly different optimal age cutoffs were identified for the different outcomes, and these cutoffs differed between PTC and FTC. Therefore, our study implies that for an optimal estimate of disease outcome, PTC and FTC should be treated as separate entities. Next to this, further research is needed to determine in which way age can be incorporated as a risk factor in the ATA Risk Stratification System to further improve its predictive function.
Supplementary materials
This is linked to the online version of the paper at https://doi.org/10.1530/EJE-21-0365.
Declaration of interest
F A V has received consultancy fees from Sanofi, EISAI and Jubilant Draximage as well as speaker honoraria from Sanofi and research support from EISAI. R P P received teaching fees from Sanofi and Bayer. E V V, W E V, M T S, F J K and T V G declare no conflicts of interest and no competing financial interests exist. W Edward Visser is on the editorial board of EJE. W Edward Visser was not involved in the review or editorial process for this paper, on which he is listed as an author.
Funding
This research did not receive any specific grant from any from any funding agency in the public, commercial or not-for-profit sector.
References
- 1↑
Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, Pacini F, Randolph GW, Sawka AM & Schlumberger M et al. 2015 American Thyroid Association Management Guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the American Thyroid Association Guidelines task force on thyroid nodules and differentiated thyroid cancer. Thyroid 2016 26 1–133. (https://doi.org/10.1089/thy.2015.0020)
- 2↑
Shen FC, Hsieh CJ, Huang IC, Chang YH, Wang PW. Dynamic risk estimates of outcome in Chinese patients with well-differentiated thyroid cancer after total thyroidectomy and radioactive iodine remnant ablation. Thyroid 2017 27 531–53 6. (https://doi.org/10.1089/thy.2016.0479)
- 3↑
Pitoia F, Bueno F, Urciuoli C, Abelleira E, Cross G, Tuttle RM. Outcomes of patients with differentiated thyroid cancer risk-stratified according to the American Thyroid Association and Latin American Thyroid Society risk of recurrence classification systems. Thyroid 2013 23 1401–140 7. (https://doi.org/10.1089/thy.2013.0011)
- 4↑
Lee SG, Lee WK, Lee HS, Moon J, Lee CR, Kang SW, Jeong JJ, Nam KH, Chung WY & Jo YS et al. Practical performance of the 2015 American Thyroid Association guidelines for predicting tumor recurrence in patients with papillary thyroid cancer in South Korea. Thyroid 2017 27 174–1 81. (https://doi.org/10.1089/thy.2016.0252)
- 5↑
Suh S, Kim YH, Goh TS, Lee J, Jeong DC, Oh SO, Hong JC, Kim SJ, Kim IJ, Pak K. Outcome prediction with the revised American Joint Committee on cancer staging system and American Thyroid Association guidelines for thyroid cancer. Endocrine 2017 58 495–502. (https://doi.org/10.1007/s12020-017-1449-4)
- 6↑
Tuttle RM, Tala H, Shah J, Leboeuf R, Ghossein R, Gonen M, Brokhin M, Omry G, Fagin JA, Shaha A. Estimating risk of recurrence in differentiated thyroid cancer after total thyroidectomy and radioactive iodine remnant ablation: using response to therapy variables to modify the initial risk estimates predicted by the new American Thyroid Association staging system. Thyroid 2010 20 1341–134 9. (https://doi.org/10.1089/thy.2010.0178)
- 7↑
Vaisman F, Momesso D, Bulzico DA, Pessoa CH, Dias F, Corbo R, Vaisman M, Tuttle RM. Spontaneous remission in thyroid cancer patients after biochemical incomplete response to initial therapy. Clinical Endocrinology 2012 77 132–13 8. (https://doi.org/10.1111/j.1365-2265.2012.04342.x)
- 8↑
van Velsen EFS, Stegenga MT, van Kemenade FJ, Kam BLR, van Ginhoven TM, Visser WE, Peeters RP. Evaluating the 2015 American Thyroid Association risk stratification system in high-risk papillary and follicular thyroid cancer patients. Thyroid 2019 29 1073–107 9. (https://doi.org/10.1089/thy.2019.0053)
- 9↑
Shah S, Boucai L. Effect of age on response to therapy and mortality in patients with thyroid cancer at high risk of recurrence. Journal of Clinical Endocrinology and Metabolism 2018 103 689–6 97. (https://doi.org/10.1210/jc.2017-02255)
- 10↑
van Velsen EFS, Stegenga MT, van Kemenade FJ, Kam BLR, van Ginhoven TM, Visser WE, Peeters RP. Evaluation of the 2015 ATA guidelines in patients with distant metastatic differentiated thyroid cancer. Journal of Clinical Endocrinology and Metabolism 2020 105 e457–e465. (https://doi.org/10.1210/clinem/dgz137)
- 11↑
Tuttle RM, Haugen B, Perrier ND. Updated American Joint Committee on cancer/tumor-node-metastasis staging system for differentiated and anaplastic thyroid cancer (eighth edition): what changed and why? Thyroid 2017 27 751–75 6. (https://doi.org/10.1089/thy.2017.0102)
- 12↑
Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Annals of Surgical Oncology 2010 17 1471–147 4. (https://doi.org/10.1245/s10434-010-0985-4)
- 13↑
van Velsen EFS, Stegenga MT, van Kemenade FJ, Kam BLR, van Ginhoven TM, Visser WE, Peeters RP. Comparing the prognostic value of the eighth edition of the American Joint Committee on cancer/tumor node metastasis staging system between papillary and follicular thyroid cancer. Thyroid 2018 28 976–9 81. (https://doi.org/10.1089/thy.2018.0066)
- 14↑
Trimboli P, Piccardo A, Signore A, Valabrega S, Barnabei A, Santolamazza G, Di Paolo A, Stati V, Chiefari A & Vottari S et al. Patient age is an independent risk factor of relapse of differentiated thyroid carcinoma and improves the performance of the American Thyroid Association stratification system. Thyroid 2020 30 713–71 9. (https://doi.org/10.1089/thy.2019.0688)
- 15↑
Kim Y, Roh JL, Song D, Cho KJ, Choi SH, Nam SY, Kim SY. Predictors of recurrence after total thyroidectomy plus neck dissection and radioactive iodine ablation for high-risk papillary thyroid carcinoma. Journal of Surgical Oncology 2020 122 906–9 13. (https://doi.org/10.1002/jso.26090)
- 16↑
D’Avanzo A, Treseler P, Ituarte PH, Wong M, Streja L, Greenspan FS, Siperstein AE, Duh QY, Clark OH. Follicular thyroid carcinoma: histology and prognosis. Cancer 2004 100 1123–112 9. (https://doi.org/10.1002/cncr.20081)
- 17↑
Harrell Jr FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine 1996 15 361–3 87. (https://doi.org/10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4)
- 18↑
Harrell Jr FE, Lee KL, Califf RM, Pryor DB, Rosati RA. Regression modelling strategies for improved prognostic prediction. Statistics in Medicine 1984 3 143–1 52. (https://doi.org/10.1002/sim.4780030207)
- 19↑
Akaike H A new look at the statistical model identification. IEEE Transactions on Automatic Control 1974 19 716–7 23. (https://doi.org/10.1109/TAC.1974.1100705)
- 20↑
Schwarz G Estimating the dimension of a model. Annals of Statistics 1978 6 461–4 64. (https://doi.org/10.1214/aos/1176344136)
- 21↑
R Core team. R: A Language and Environmental of Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. 2013.
- 22↑
van Velsen EFS, Visser WE, Stegenga MT, Mader U, Reiners C, van Kemenade FJ, van Ginhoven TM, Verburg FA, Peeters RP. Finding the optimal age cutoff for the UICC/AJCC TNM staging system in patients with papillary or follicular thyroid cancer. Thyroid 2021 31 1041–1049. (https://doi.org/10.1089/thy.2020.0615)
- 23↑
Ganly I, Makarov V, Deraje S, Dong Y, Reznik E, Seshan V, Nanjangud G, Eng S, Bose P & Kuo F et al. Integrated genomic analysis of Hurthle cell cancer reveals oncogenic drivers, recurrent mitochondrial mutations, and unique chromosomal landscapes. Cancer Cell 2018 34 256.e5–270.e5. (https://doi.org/10.1016/j.ccell.2018.07.002)
- 24↑
Gopal RK, Kubler K, Calvo SE, Polak P, Livitz D, Rosebrock D, Sadow PM, Campbell B, Donovan SE & Amin S et al. Widespread chromosomal losses and mitochondrial DNA alterations as genetic drivers in Hurthle cell carcinoma. Cancer Cell 2018 34 242–255.e5. (https://doi.org/10.1016/j.ccell.2018.06.013)
