Primary thyroid lymphoma (PTL) is a rare malignancy constituting only 1% to 2% of all extranodal lymphomas1 2–7 8 6 1 , 9 10 10 , 11 12
Due to rarity of PTL, it is difficult to conduct randomized or prospective trials to guide management. As a result, the optimal management of PTL remains controversial.13 14 15 16 17 18
The overall prognosis of PTL has been described as generally excellent.16 19
In addition, on average, most PTL patients are diagnosed in their late 60s, and patients in this age group tend to have a higher prevalence of comorbidities than younger patients.20 , 21
Given the lack of clarity surrounding prognosis, we sought to engage data from Surveillance, Epidemiology, and End Results (SEER) to conduct a large-scale survival analysis of PTL: (1) to determine the survival outcomes of PTL and specify associated factors by building a prognostic nomogram and (2) to analyze competing risks of death in PTL, in order to clarify the hazards and benefits of therapeutic approaches, and the likelihood that patients will die from lymphoma versus other etiologies and associated factors.
MATERIALS AND METHODS
Data Sources
The study cohort initially diagnosed with PTL were extracted from the SEER program, which recodes cancer incidence and mortality data from 18 population-based cancer registries covering approximately 27.8% of the US population.22
Study Population
Using the selected database, we identified a consecutive cohort of 1638 patients diagnosed between 2000 and 2018 with lymphoma originating from the thyroid (lymphoblastic, Burkitt, DLBCL. NK/T-cell, MALT, Hodgkin, and other lymphomas). The clinical data were extracted regarding demographics, tumor staging, and therapy: age, gender, race, primary site of tumor, pathology, stage of lymphoma, primary surgery, lymph node dissection, radiotherapy, chemotherapy, systemic therapy, survival months, cause-specific death classification, and survival status. The Ann Arbor criteria is used for the staging of PTL.23 , 24 25
Outcome Definition
Cause of death was used to determine whether each patient died as a result of their PTL or another cause, or whether they were alive at the end of the follow-up period. In our study, the primary outcome was disease-specific survival (DSS), which was defined as the time between initial diagnosis and PTL-specific death. As the secondary outcome, overall survival (OS) was measured as the time between initial diagnosis and death from any cause, and included an analysis of competing risks between PTL-specific death and other causes of death.
Disease-specific Survival Analysis and Nomogram Development
Prognostic factors were determined by univariate Cox proportional hazards regression and Kaplan-Meier curves. Subsequently, significant factors screened by the univariate analysis (P < 0.05) were further included in multivariate Cox proportional hazards models. The best Cox model was selected by a backward selection process (entry criterion: P < 0.05, elimination criterion: P > 0.10). Multivariate Cox proportional hazard regression analysis was performed to identify variables that significantly affected the DSS of patients with PTL. Furthermore, the nomogram was built upon variables selected from the multivariate Cox regression (P < 0.05). The whole population was included as the training cohort. Finally, we incorporated the chosen prognostic factors to develop the nomograms for predicting 5- and 10-year DSS rates in PTL patients using the R software package “cmprsk.”
Competing Risk Analysis of All-cause Deaths
In the competing risk model, deaths from any other causes rather than PTL were regarded as a competing event for PTL. First, we computed the cumulative incidence function (CIF) for PTL and other causes. Subgroup analysis was then carried out according to age, sex, pathology, Ann arbor grading of lymphoma, primary surgery, lymph node dissection, radiation therapy, chemotherapy, and systemic therapy. Corresponding CIF curves were plotted for these variables. The significant differences in CIF values among subgroups were evaluated by Gray’s test.26 P < 0.1) were introduced into a stepwise competing risk regression model. Subsequently, the optimal regression model was fitted when incorporating the predictive variables selected by the stepwise regression procedure. Finally, we calculated the subdistribution hazard ratio (SHR) of the included variables for PTL patients based on the multivariate competing risk model with the aid of the R package “riskRegression.”
Statistical Analysis
We presented descriptive statistics in Table 1 for the entire study cohort and compared the results between the top 2 leading pathological types (DLBCL and MALT lymphoma). Continuous and categorical variables were assessed with the Kruskal-Wallis test and Pearson chi-square test, respectively. Continuous variables were expressed as the mean ± standard deviation/median. Categorical variables were displayed as number (percentage). All statistical analyses were carried out employing the R studio version 4.0.4. A two-tailed P < 0.05 was considered statistically significant.
Table 1. -
Demographic and Clinical Characteristics of Primary Thyroid MALT Lymphoma and DLBCL
Total
MALT
DLBCL
P
N = 1495
n = 380
n = 1115
Age (years), mean ± SD/median
65.4 ± 14.0/67.0
62.42 ± 13.44/63.0
66.41 ± 13.99/68.00
<0.001
Sex, n (%)
0.895
Female
1007 (67.4%)
257 (67.6%)
750 (67.3%)
Male
488 (32.6%)
123 (32.4%)
365 (32.7%)
Race, n (%)
<0.001
White
1315 (88.0%)
308 (81.0%)
1007 (90.3%)
Black
32 (2.1%)
12 (3.2%)
20 (1.8%)
Other
148 (9.9%)
60 (15.8%)
88 (7.9%)
Stage, n (%)
<0.001
Unknown
562 (37.6%)
128 (33.7%)
434 (38.9%)
IE
533 (35.7%)
186 (49.0%)
347 (31.1%)
IIE
375 (25.1%)
66 (17.3%)
309 (27.7%)
IIIE/IIIS/IIIES
25 (1.7%)
0 (0.00%)
25 (2.3%)
Primary Surgery, n (%)
<0.001
No surgery
684 (45.8%)
82 (21.6%)
602 (54.0%)
Lobectomy
438 (29.3%)
133 (35.00%)
305 (27.4%)
Total thyroidectomy
373 (24.9%)
165 (43.4%)
208 (18.6%)
Lymph node dissection, n (%)
<0.001
None
786 (62.4%)
187 (55.6%)
599 (64.9%)
Yes
136 (10.8%)
58 (17.3%)
78 (8.5%)
Unknown
337 (26.8%)
91 (27.1%)
246 (26.6%)
Radiation, n (%)
0.014
None/Unknown
836 (55.9%)
233 (61.3%)
603 (54.1%)
EBRT
659 (44.1%)
147 (38.7%)
512 (45.9%)
Chemotherapy, n (%)
<0.001
None/unknown
511 (34.2%)
301 (79.2%)
210 (18.8%)
Yes
984 (65.8%)
79 (20.8%)
905 (81.2%)
Systemic therapy, n (%)
<0.001
No
663 (68.3%)
216 (76.3%)
447 (65.0%)
Yes
308 (31.7%)
67 (23.7%)
241 (35.0%)
Disease-specific death, n (%)
<0.001
Censored
910 (60.9%)
281 (73.9%)
629 (56.4%)
Other causes
355 (23.7%)
84 (22.1%)
271 (24.3%)
Primary thyroid lymphoma
230 (15.4%)
15 (4.0%)
215 (19.3%)
Overall death, n (%)
<0.001
Alive
910 (60.9%)
281 (73.9%)
629 (56.4%)
Dead
585 (39.1%)
99 (26.1%)
486 (43.6%)
Survival months, mean ± SD/median
78.1 ± 63.7/ 68.0
86.14 ± 59.21/ 80.00
75.30 ± 64.96/ 63.00
<0.001
MALT, mucosa-associated lymphoid tissue; EBRT, external beam radiation therapy.
RESULTS
A total of 1638 PTL patients from 2000 to 2018 were identified from the SEER database. Rare pathology types (2 lymphoblastic lymphomas, 49 Burkitt lymphomas, 1 NK/T-cell lymphomas, 18 Hodgkin lymphomas, and 66 other lymphomas) in a total of 143 patients were excluded from the current study. Finally, 1495 patients eligible for our analysis were included. The histological composition was illustrated with a tree map in Figure 1 . The demographic portrait of the PTL population was a group of elderly (median age at diagnosis is 67 years, range from 13 to 85), White (1315, 88.0%), and female (1007, 67.4%) patients. The dominating histology type of PTL was DLBCL (1115, 74.6%). MALT lymphoma comprised approximately 1/4 (380, 25.4%) of the cohort. Most patients were diagnosed with Ann Arbor stage IE (533, 35.7%), followed by stage IIE (375, 25.1%), and stage IIIE/IIIS/IIIES (25, 1.7%). The stages of the remaining 37.6% of the patients were not indicated in the database. Therapy wise, more than half of the population was given surgery (438 lobectomy and 373 thyroidectomy, 811 in total, 54.2%), radiation (659, 44.1%), and chemotherapy (984, 65.8%). Systemic therapy was not favored (663, 68.3%), and most records of lymph node dissection (786, 62.4%) cannot be identified from the database. Table 1 shows the characteristics of the study population and a comparison between DLBCL and MALT lymphoma, including demographics, tumor characteristics, and therapeutic approaches.
FIGURE 1.: Tree map of histological distribution in the primary thyroid lymphoma population. MALT, mucosa-associated lymphoid tissue.
The median follow-up of the study cohort was 68 months (range from 1 to 226 months). In total, 585 (39.1%) patients died during the whole follow-up period, of whom 230 (15.4%) died due to PTL and 355 (23.7%) died due to non-PTL causes. Overall, the 5- and 10-year DSS was 85.5% (CI: 83.6%–87.4%) and 82.6% (CI: 80.4%–84.8%), respectively, while the 5- and 10-year OS was 73.6% (CI: 71.3%–76.0%) and 59.0% (CI: 56.1%–62.0%).
For DSS analysis, all variables were applied using an unadjusted Kaplan-Meier method. The significant survival curves were drawn in Figure 2 for initial detection of significant factors. Then, univariate and multivariate Cox proportional hazard models were adopted in sequence to determine significant DSS prognostic factors for the study population. In the univariate Cox analysis, age in years (HR = 1.08, 95% CI: 1.06–1.10, P < 0.001), DLBCL pathology (vs MALT, HR = 11.85, 95% CI: 5.27–26.65, P < 0.001), IE stage (vs unknown stage, HR = 0.57, 95% CI: 0.34–0.99, P = 0.045), lymph node dissection (vs no dissection, HR = 0.37, 95% CI: 0.14–0.97, P = 0.043), radiation (vs none/unknown, HR = 0.55, 95% CI: 0.37–0.82, P = 0.004), and chemotherapy (vs none/unknown, HR = 0.31, 95% CI: 0.19–0.51, P < 0.001) were prognostic factors for DSS. However, gender, race, surgical treatment, and systemic therapy were not statistically relevant to DSS. Furthermore, we incorporated these significant prognostic factors into a multivariate Cox proportional hazard regression for adjustment and found that age in years (HR = 1.07, 95% CI: 1.05–1.09, P < 0.001), DLBCL pathology (vs MALT, HR = 12.00, 95% CI: 5.38–26.76, P < 0.001), lymph node dissection (vs no dissection, HR = 0.37, 95% CI: 0.15–0.93, P = 0.034), radiation (vs none/unknown, HR = 0.54, 95% CI: 0.36–0.81, P = 0.003), and chemotherapy (vs none/unknown, HR = 0.29, 95% CI: 0.19–0.43, P < 0.001) were independent prognostic indicators, which are shown in Table 2 and visualized by forest plot in Figure 3A . Thus, the prognostic nomogram was developed to predict the 5- and 10-year DSS rates based on independent Cox proportional prognostic risk factors (Figure 3B ).
Table 2. -
Univariate and Multivariate Cox Proportional Hazard Regression for Analyses of Primary Thyroid Lymphoma Patients for Disease-specific Survival
Univariate
Multivariate
HR
95% CI
P
HR
95% CI
P
Age
Each increase in one year
1.08
1.06–1.10
<0.001
1.07
1.05–1.09
<0.001
Sex
Female
1
Reference
Male vs female
1.24
0.84–1.83
0.282
Race
White
1
Reference
1
Reference
Others
1.18
0.65–2.14
0.582
1.21
0.67–2.19
0.526
Pathology
MALT
1
Reference
1
Reference
DLBCL
11.85
5.27–26.65
<0.001
12.00
5.38–26.76
<0.001
Stage
Unknown
1
Reference
1
Reference
IE
0.57
0.34–0.99
0.045
0.62
0.37–1.04
0.070
IIE
1.19
0.72–1.97
0.507
1.25
0.76–2.05
0.373
IIIE/IIIS/IIIES
1.00
0.38–2.69
0.993
1.08
0.41–2.88
0.872
Primary surgery
No surgery
1
Reference
Lobectomy
1.43
0.79–2.57
0.235
Total thyroidectomy
0.98
0.48–2.01
0.961
Lymph node dissection
No
1
Reference
1
Reference
Yes
0.37
0.14–0.97
0.043
0.37
0.15–0.93
0.034
Unknown
1.46
0.98–2.19
0.066
1.47
0.99–2.20
0.058
Radiation
None/unknown
1
Reference
1
Reference
Yes
0.55
0.37–0.82
0.004
0.54
0.36–0.81
0.003
Chemotherapy
None/unknown
1
Reference
1
Reference
Yes
0.31
0.19–0.51
<0.001
0.29
0.19–0.43
<0.001
Systemic therapy
None/unknown
1
Reference
Yes
0.87
0.45–1.69
0.673
DLBCL, diffuse large B-cell lymphoma; EBRT, external beam radiation therapy; MALT, mucosa-associated lymphoid tissue.
FIGURE 2.: Kaplan-Meier survival curves of disease-specific survival based on A, pathology; B, stage; C, primary surgery; D, lymph node dissection; E, radiation; F, chemotherapy; and G, systemic therapy. MALT, mucosa-associated lymphoid tissue.
FIGURE 3.: Cox proportional hazard analysis of disease-specific survival in primary thyroid lymphoma patients: A, visualized forest plot of multivariate analysis in predicting disease-specific survival; B, nomogram predicting the 5- and 10-year disease-specific survival.
Subsequently, we analyzed the OS by calculating the cumulative incidence of death. The overall cumulative incidences of the 5- and 10-year PTL-specific cumulative death probability were 14.0% (95% CI: 12.3%–15.9%) and 16.3% (95% CI: 14.4%–18.4%), respectively, while the 5- and 10-year cumulative probabilities of death from other causes were 12.4% (95% CI: 10.6%–12.3%) and 24.7% (95% CI: 22.1%–27.4%). To separately evaluate the risks affecting OS, the 5- and 10-year cumulative incidences of mortality rates of PTL and other causes by pathological category and therapeutic approaches are displayed in Supplemental Table 1 (https://links.lww.com/AOSO/A188 Figure 4 .
FIGURE 4.: Cumulative incidence curves of death for patients with primary thyroid lymphoma by different variables. DLBCL, diffuse large B-cell lymphoma; MALT, mucosa-associated lymphoid tissue; EBRT, external beam radiation therapy.
Supplemental Figure 1 (https://links.lww.com/AOSO/A189 https://links.lww.com/AOSO/A189 https://links.lww.com/AOSO/A189
Results from the competing risk hazards regression analysis are shown in Table 3 . Older age and Ann Arbor grading were associated with both a greater probability of death from other causes (e.g., 70–80 years of age vs younger than 50 years, SHR = 8.41, 95% CI: 2.88–24.55, P < 0.001; IIE vs stage unspecified, SHR = 3.49, 95% CI: 1.73–7.05, P < 0.001) and death from PTL (e.g., 70–80 years of age vs younger than 50 years, SHR = 9.52, 95% CI: 2.66–34.13, P < 0.001; IIE vs stage unspecified, SHR = 2.03, 95% CI: 1.08–3.80, P = 0.027). Therapy by external beam radiation (vs none/unknown, SHR = 0.56, 95% CI: 0.38–0.84, P = 0.005) was associated with death from other causes. DLBCL histology (vs MALT lymphoma, SHR = 13.04, 95% CI: 4.41–38.84, P < 0.001), lymph node dissection (vs no dissection, SHR = 0.12, 95% CI: 0.02–0.95, P = 0.044), and chemotherapy (vs none/unknown, SHR = 0.30, 95% CI: 0.17–0.54, P < 0.001) were correlated with death from PTL.
Table 3. -
Competing Risk Hazard Regression for Death in Primary Thyroid Lymphoma Patients
Death from other causes
Death from primary thyroid lymphoma
SHR
95% CI
P
SHR
95% CI
P
Age at diagnosis (years)
<50
1
reference
1
reference
50–60
5.01
(1.66–15.17)
0.004
1.23
(0.25–6.08)
0.802
60–70
5.36
(1.82–15.74)
0.002
2.05
(0.51–8.16)
0.310
70–80
8.41
(2.88–24.55)
<0.001
9.52
(2.66–34.13)
<0.001
≥80
17.07
(5.72–50.95)
<0.001
11.38
(3.15–41.06)
<0.001
Pathology
MALT
1
reference
1
reference
DLBCL vs MALT
0.90
(0.54–1.49)
0.674
13.08
(4.41–38.84)
<0.001
Grading
Unknown
1
reference
1
reference
IE
3.99
(2.03–7.87)
<0.001
0.85
(0.42–1.72)
0.654
IIE
3.49
(1.73–7.05)
<0.001
2.03
(1.08–3.80)
0.027
IIIE/IIIS/IIIES
4.31
(1.49–12.48)
0.007
1.10
(0.27–4.52)
0.891
Primary surgery
No surgery
1
reference
1
reference
Lobectomy
0.93
(0.49–1.78)
0.833
1.44
(0.69–3.03)
0.331
Total thyroidectomy
1.63
(0.88–3.04)
0.121
1.29
(0.55–3.01)
0.562
Lymph node dissection
No
1
reference
1
reference
Yes
0.79
(0.39–1.60)
0.515
0.12
(0.02–0.95)
0.044
Unknown
0.845
(0.54–1.31)
0.450
1.26
(0.76–2.06)
0.3701
Radiation
None/unknown
1
reference
1
reference
Yes
0.56
(0.38–0.84)
0.005
0.72
(0.45–1.16)
0.176
Chemotherapy
None/unknown
1
reference
1
reference
Yes
1.10
(0.61–1.98)
0.757
0.30
(0.17–0.54)
<0.001
Systemic therapy
None/unknown
1
reference
1
reference
Yes
1.12
(0.58–2.15)
0.738
1.05
(0.46–2.39)
0.912
DLBCL, diffuse large B-cell lymphoma; EBRT, external beam radiation therapy; MALT, mucosa-associated lymphoid tissue; SHR, subdistribution hazard ratio.
DISCUSSION
Due to the rarity of PTL, very little literature addresses the optimal management of the disease. An initial objective of our study was to answer the question of how to optimally manage PTL by clarifying prognostic factors among the clinicopathologic characteristics and therapeutic approaches. In the present study, both Cox proportional hazard regression and competing risk analysis were performed to investigate the predictive factors for patients with PTL from the SEER database.
In the study cohort, PTL is more common in females and has a female-male ratio of 2 to 1 with a median age of 67 years at diagnosis, which is similar to previous reports.10 , 20 , 21 16 , 26
Cox regression revealed that age in years, DLBCL pathology, lymph node dissection, radiation, and chemotherapy were independent DSS predictive factors after adjustments, consistent with previous reports.6 , 17 , 27 28 , 29 30 -35 When assessing the prognosis for patients and making clinical decisions, it is imperative to take into account individual risk profiles. A prognostic nomogram enables clinicians to plan treatment strategies and evaluate outcomes more conveniently.
The results of competing risk analysis of PTL patients demonstrate that the risk of death is almost 13 times higher in those with DLBCL histology compared with those with MALT histology (SHR = 13.04, 95% CI: 4.41–38.84, P < 0.001). Lymph node dissection (vs no dissection, SHR = 0.12, 95% CI: 0.02–0.95, P = 0.044) and chemotherapy (vs none/unknown, SHR = 0.30, 95% CI: 0.17–0.54, P < 0.001) can effectively reduce PTL-specific death risk by 88% and 70%, respectively. As for survival hazards induced by other causes, older age (e.g., 70–80 years of age vs younger than 50 years, SHR = 8.41, 95% CI: 2.88–24.55, P < 0.001) and advanced Ann Arbor stage (e.g., IIE vs stage unspecified, SHR = 3.49, 95% CI: 1.73–7.05, P < 0.001) lead to the greatest increases in non-PTL death risk. External beam radioactive therapy has a major impact, as it significantly lowers non-PTL death risk by 44% (vs none/unknown, SHR = 0.56, 95% CI: 0.38–0.84, P = 0.005).
After reviewing and comparing the results, the most intriguing finding is that each therapy regimen for PTL improves survival outcomes by reducing a different type of mortality risk. For example, chemotherapy and lymph node dissection contribute to better survival outcomes by reducing PTL-specific mortality risks, or in other words, “treating the lymphoma itself.” In contrast, radioactive therapy benefits PTL patients by lowering non-PTL death risks, or “boosting the overall condition against risk factors of other causes.” Advanced age (age over 70) enhances the risk of both PTL-specific and non-PTL mortality. And since radiation can significantly decrease non-PTL death risk, this evidence supports a stronger recommendation for elderly PTL patients to receive this therapy.
Another important finding was that the likelihood of death from other causes versus death from PTL is determined by the pathological type of lymphoma (see Supplemental Figure 1, https://links.lww.com/AOSO/A189 28–30
Strengths of our study include a large sample size and the use of competing risk model to perform survival analysis. In general, the SEER database offers a sufficient sample to investigate predictive factors and further develop a model-based prognostic nomogram. Moreover, findings derived from the analysis of a population-based database are more generalizable and representative than those from single-center studies. In addition, the competing risk model fully takes into consideration other competing events, which renders a unique view of the correlation between mortality and the choice of treatments.
Admittedly, there are also several limitations to our study. For instance, some known prognostic variables, such as comorbidities, targeting therapy, and more specified treatment modality were not recorded in the SEER database. Further study is warranted to incorporate these variables, and another external validation cohort should be included to ensure the accuracy of the nomogram. In the meantime, the nomogram should be used with caution.
In spite of these limitations, we have conducted the first study using a competing risk model to evaluate the cumulative incidence of PTL patients and constructed a nomogram based on Cox hazard proportional regression. These findings can guide personalized medical decision making and highlight the importance of considering patient context when balancing the benefits and harms of PTL management.
ACKNOWLEDGMENTS
We acknowledge Surveillance Research Program, National Cancer Institute SEER*Stat software (www.seer.cancer.gov/seerstat www.seer.cancer.gov
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