Abstract
Due to extensive tumor spread, systemic chemotherapy is the main treatment for distant metastatic small-cell lung cancer (DM-SCLC). It is still unclear whether adding local radiotherapy (RT) on the basis of chemotherapy can improve the long-term survival of patients with DM-SCLC. This study aims to explore the population with DM-SCLC who can benefit from RT. Patients with metastatic SCLC with complete data were collected from the Surveillance, Epidemiology, and End Results database and divided into 2 groups according to whether RT was given or not. The propensity score matching method was used to balance the covariate differences between the RT group and the non-RT group. Lasso Cox regression model and Cox proportional hazards regression analyses were used to identifying independent risk factors affecting survival. Kaplan–Meier method was used to calculate the survival rate. P < .05 was considered statistically significant. After matching, there were 3150 patients in both groups. Sex, tumor size, N stage, RT, chemotherapy, brain metastasis, liver metastasis, age, and site metastasis were independent factors of survival in DM-SCLC. The 1- and 2-year survival rates were 24.5% and 5.8% in the RT group and 14.8% and 2.3% in the non-RT group (P < .001). The median survival time of the RT group was 9 months, and that of the non-RT group was 7 months, and the difference was statistically significant (P < .001). RT improved survival in all sex subgroups, any N stage subgroup, any tumor size subgroup, no brain metastases subgroup, no liver metastases subgroup, any age subgroup, and 1-2 organ metastases subgroup. RT improves 1- and 2-year survival in DM-SCLC.
Keywords: distant metastases, nomogram, overall survival, prognosis, small-cell lung cancer
1. Introduction
Lung cancer is one of the most important malignant tumors in the world with a poor prognosis.[1] According to pathological type, lung cancer is divided into small-cell lung cancer (SCLC) and non-SCLC. Among all the pathological types of lung cancer, SCLC is the most invasive, characterized by rapid proliferation and low differentiation, and is most prone to early distant metastasis clinically, with the shortest survival time and the worst prognosis.[2]
It has been reported that about 40% of lung cancer patients are accompanied by distant metastasis at the initial stage of diagnosis.[3] The 5-year survival rate of stage IVA patients is 10%, and that of stage IV B patients is 0. Currently, first-line treatment for DM-SCLC includes chemotherapy, combined with platinum drugs and etoposide or irinotecan as possible alternatives. The median survival with standard care is still only 9 to 10 months.[4]
The introduction of thoracic radiotherapy (RT) was a milestone in the history of SCLC treatment, and its place in limited-stage treatment was established in the early 1990s. However, it is still controversial whether RT should be used in the treatment of SCLC with lesions of the whole lung, even with extra-thoracic metastasis of the brain and bone, which has entered the extensive stage. Thoracic RT is recommended in the National Comprehensive Cancer Network for patients with extensive-stage SCLC that responds to chemotherapy. However, metastatic SCLC is a heterogeneous population, and there are no reports on the benefits population of RT.
In this study, we used the Surveillance, Epidemiology, and End Results (SEER) database to extract DM-SCLC with complete data from 2010 to 2015. Our aim was to test whether RT could produce a significant survival benefit in DM-SCLC and explore the specific population of DM-SCLC who can benefit from RT. In addition, we identified prognostic factors for these patients, highlighting subgroups that may be more suitable for RT. Accurate assessment of the impact of RT on survival in these patients could lead to more precise treatment and improved prognosis.
2. Methods
2.1. Data retrieved from SEER
All data in this study were extracted from the SEER database. The SEER database covers approximately 28% of the U.S. population and is made up of cancer registries in 18 geographic regions. The database has been de-identified by patient information in compliance with the institutional Review Board The council and the ethics committee requested that the information be publicly available.
2.2. Patient screening
Patients with primary SCLC from 2010 to 2015 were searched from SEER*Stat software (version 8.3.5). SCLC was diagnosed based on the International Classification of Diseases for Oncology (ICD-O-3) (ICD-O-3 codes: 8041/3, 8002/3, 8042/3, 8043/3, 8044/3, 8045/3). Inclusion criteria included age over 18 years; histologically diagnosed as small-cell carcinoma; patients diagnosed between 2010 and 2015; all enrolled patients had complete information on race, sex, age, T stage, N stage, RT, chemotherapy, the status of brain metastasis, bone metastasis, liver metastasis, bone metastasis, and complete survival data. Ultimately, patients with incomplete information and confirmed from clinical manifestations, imaging, and/or death certificates or autopsy reports were excluded; 13,404 patients were identified as eligible for inclusion and the filtering process is shown in Figure 1.
Figure 1.
The flow chart for the selection of the study population.
2.3. Prognostic variables
Definition of variables in this study: race/ethnicity (Black, White, or others), laterality (right/left/other), age at diagnosis (<66, 66–79, and >79 years), T stage (T1, T2 and T3-4), N stage (N0, N1 and N2-3), RT (yes/none/unknown), chemotherapy recode (yes/none/unknown), brain metastasis (yes/no), bone metastasis (yes/no), lung metastasis (yes/no), and liver metastasis (yes/no).
2.4. Statistical analysis
Overall survival (OS) curve was depicted by the Kaplan–Meier method. SPSS 21.0 was used for the above statistical analysis. Propensity score matching (PSM) was used to control for bias factors before survival analysis in the 3 risk groups. PSM is carried out by a greedy matching algorithm according to a 1:1 matching ratio, with a caliper width equal to 0.001. The χ2 test was used to determine whether the RT and non-RT covariables were balanced before and after matching. P > .05 was considered as an equilibrium between the RT group and the non-RT group. The optimal cut-off point of continuous variables (age and tumor size) is obtained by X-tile software (version3.6.1). Lasso Cox regression model was used to identify independent risk factors of OS, along with the “glmnet” package. The independent risk factors determined by the Lasso Cox regression model were then used to construct multivariate cox proportional risk regression.
3. Results
3.1. Characteristics of patients
A total of 11,897 DM-SCLC patients were enrolled in this study. In general, the majority of patient was male (6105; 51.7%), age < 66–79 (6135; 44.7%), White (10,259; 86.2%). In addition, most patients received chemotherapy (9473; 79.6%), and 5671 (47.6%) patients received RT. Overall, 35.6% (4233), 26.4% (3144), 41.2% (4896), and 20.5% of 2442 DM-SCLC had bone, brain, liver, and lung metastases, respectively. In addition, 54 (0.5%), 1387 (11.7%), 25.8% (3457%), 2919 (24.5%), and 4738 (39.8%) patients of DM-SCLC had stage T0, T1, T2, T3, and T4, respectively. 1513 (12.7%), 60 (6.4%), 6751 (56.7%) and 2873 (24.1%) patients had stage N0, N1, N2, and N3 stage, respectively. The nearest neighbor matching method was used to match patients in the postoperative RT group and the non-RT group according to 1:1, and the distribution of covariables between the 2 groups before and after matching was compared. Clinical Characteristics and demographics of DM-SCLC patients before and after PSM are shown in Table 1. After matching, the P values of all variables were >0.05, indicating a small difference between the 2 groups (Fig. 2).
Table 1.
Patients’ demographics and clinicopathological characteristics before and after PSM.
| Variables | Level | Total | Before PSM | After PSM | ||||
|---|---|---|---|---|---|---|---|---|
| Non-RT | RT | P value | Non-RT | RT | P value | |||
| Race (%) | 11,897 | 6226 | 5671 | .499 | 3150 | 3150 | .113 | |
| Black | 1166 (9.8) | 592 (9.5) | 574 (10.1) | 274 (8.7) | 265 (8.4) | |||
| White | 10,259 (86.2) | 5390 (86.6) | 4869 (85.9) | 2782 (88.3) | 2761 (87.7) | |||
| Other | 472 (4.0) | 244 (3.9) | 228 (4.0) | 94 (3.0) | 124 (3.9) | |||
| Sex (%) | Female | 5792 (48.7) | 3027 (48.6) | 2765 (48.8) | .895 | 1541 (48.9) | 1486 (47.2) | .173 |
| Male | 6105 (51.3) | 3199 (51.4) | 2906 (51.2) | 1609 (51.1) | 1664 (52.8) | |||
| Laterality (%) | Right | 6742 (56.7) | 3510 (56.4) | 3232 (57.0) | .511 | 1834 (58.2) | 1804 (57.3) | .46 |
| Left | 5155 (43.3) | 2716 (43.6) | 2439 (43.0) | 1316 (41.8) | 1346 (42.7) | |||
| T stage (%) | T0 | 54 (0.5) | 29 (0.5) | 25 (0.4) | .037 | 11 (0.3) | 16 (0.5) | .901 |
| T1 | 1387 (11.7) | 734 (11.8) | 653 (11.5) | 330 (10.5) | 332 (10.5) | |||
| T2 | 2919 (24.5) | 1584 (25.4) | 1335 (23.5) | 725 (23.0) | 717 (22.8) | |||
| T3 | 2799 (23.5) | 1479 (23.8) | 1320 (23.3) | 716 (22.7) | 725 (23.0) | |||
| T4 | 4738 (39.8) | 2400 (38.5) | 2338 (41.2) | 1368 (43.4) | 1360 (43.2) | |||
| N stage (%) | N0 | 1513 (12.7) | 770 (12.4) | 743 (13.1) | .167 | 309 (9.8) | 351 (11.1) | .098 |
| N1 | 760 (6.4) | 378 (6.1) | 382 (6.7) | 155 (4.9) | 185 (5.9) | |||
| N2 | 6751 (56.7) | 3585 (57.6) | 3166 (55.8) | 1869 (59.3) | 1817 (57.7) | |||
| N3 | 2873 (24.1) | 1493 (24.0) | 1380 (24.3) | 817 (25.9) | 797 (25.3) | |||
| M stage (%) | M1a | 1929 (16.2) | 1059 (17.0) | 870 (15.3) | .03 | 557 (17.7) | 543 (17.2) | .336 |
| M1b | 9838 (82.7) | 5105 (82.0) | 4733 (83.5) | 2573 (81.7) | 2577 (81.8) | |||
| M1NOS | 130 (1.1) | 62 (1.0) | 68 (1.2) | 20 (0.6) | 30 (1.0) | |||
| Chemotherapy (%) | No | 2424 (20.4) | 1648 (26.5) | 776 (13.7) | <.001 | 444 (14.1) | 480 (15.2) | .213 |
| Yes | 9473 (79.6) | 4578 (73.5) | 4895 (86.3) | 2706 (85.9) | 2670 (84.8) | |||
| Metastases sites (%) | None | 2303 (19.4) | 1224 (19.7) | 1079 (19.0) | <.001 | 722 (22.9) | 723 (23.0) | .004 |
| 1 | 5655 (47.5) | 3003 (48.2) | 2652 (46.8) | 1445 (45.9) | 1324 (42.0) | |||
| 2 | 2889 (24.3) | 1536 (24.7) | 1353 (23.9) | 743 (23.6) | 796 (25.3) | |||
| 3 | 918 (7.7) | 426 (6.8) | 492 (8.7) | 212 (6.7) | 271 (8.6) | |||
| 4 | 132 (1.1) | 37 (0.6) | 95 (1.7) | 28 (0.9) | 36 (1.1) | |||
| Bone metastases (%) | No | 7664 (64.4) | 3930 (63.1) | 3734 (65.8) | .002 | 1872 (59.4) | 1791 (56.9) | .041 |
| Yes | 4233 (35.6) | 2296 (36.9) | 1937 (34.2) | 1278 (40.6) | 1359 (43.1) | |||
| Brain metastases (%) | No | 8753 (73.6) | 5563 (89.4) | 3190 (56.3) | <.001 | 2596 (82.4) | 2554 (81.1) | .181 |
| Yes | 3144 (26.4) | 663 (10.6) | 2481 (43.7) | 554 (17.6) | 596 (18.9) | |||
| Liver metastases (%) | No | 7001 (58.8) | 3088 (49.6) | 3913 (69.0) | <.001 | 1905 (60.5) | 1873 (59.5) | .425 |
| Yes | 4896 (41.2) | 3138 (50.4) | 1758 (31.0) | 1245 (39.5) | 1277 (40.5) | |||
| Lung metastases (%) | No | 9455 (79.5) | 4822 (77.4) | 4633 (81.7) | <.001 | 2548 (80.9) | 2509 (79.7) | .229 |
| Yes | 2442 (20.5) | 1404 (22.6) | 1038 (18.3) | 602 (19.1) | 641 (20.3) | |||
| Tumor size (%) | <27 mm | 2364 (19.9) | 1293 (20.8) | 1071 (18.9) | .001 | 626 (19.9) | 577 (18.3) | .072 |
| 27–44 mm | 2786 (23.4) | 1503 (24.1) | 1283 (22.6) | 740 (23.5) | 700 (22.2) | |||
| >44 mm | 6747 (56.7) | 3430 (55.1) | 3317 (58.5) | 1784 (56.6) | 1873 (59.5) | |||
| Age (%) | <66 | 5280 (44.4) | 2379 (38.2) | 2901 (51.2) | <.001 | 1463 (46.4) | 1433 (45.5) | .221 |
| 66-79 | 5320 (44.7) | 2951 (47.4) | 2369 (41.8) | 1437 (45.6) | 1429 (45.4) | |||
| >79 | 1297 (10.9) | 896 (14.4) | 401 (7.1) | 250 (7.9) | 288 (9.1) | |||
PSM = propensity score matching, RT = radiotherapy.
Figure 2.
Distribution of the propensity score (A and B) for patients with RT and non-RT before the matching procedure, respectively, and (C and D) for patients after full propensity score matching.
3.2. Identify prognostic factors
Lasso Cox regression was used to analyze the independent risk factors for OS. Lasso method principle is based on least squares adds a penalty term to estimate parameters of compression, when the parameters are reduced to less than a threshold, make it become a 0, thus selected for a greater influence on the dependent variable of the independent variables and calculate the corresponding regression coefficient, often used to deal with the existence of multicollinearity sample data. The degree of Lasso regression complexity adjustment is controlled by the parameter λ, and the greater λ is, the greater the penalty of model complexity is. Through Lasso regression, all the variables included in the model were significantly correlated with the dependent variable (P < .05) and considered the set of independent variables of the collinearity problem of variables. Model screening methods mainly include lambdas: Min, Lambdas.1se, and Min.Lambda.1se. Min refers to the λ value of the mean value of the minimum target parameter; Lambdas.1se refers to the simplest model λ within this range of lambdas. Min.Lambda.1se can provide a model with excellent performance but a minimum number of independent variables because the model performance cannot be significantly improved by increasing the number of independent variables after the λ value reaches a certain value. In this study, according to lambda.1se variable screening criteria, a model with excellent performance and a minimum number of independent variables can be obtained when λ is 0.04342865 [log (λ) = −3.136636]. In this study, a total of 10 independent factors (sex, tumor size, N stage, RT, chemotherapy, brain metastasis, liver metastasis, age, race, and liver metastasis) were obtained by Lasso regression (Fig. 3), and then these factors were analyzed by Cox multivariate regression (Fig. 4). Significant differences were observed in 9 variables: sex (male: hazard ratio [HR] 1.132, 95% confidence interval [CI] 1.091–1.175; female as a reference), tumor size (27–44 mm: HR 1.069, 95% CI 1.01–1.131; >44mm HR 1.171, 95% CI 1.116–1.23; <27 mm as a reference), N stage (N1: HR 1.1, 95% CI 1.005–1.205; N2: HR 1.337, 95% CI 1.26–1.419; N3: HR 1.337, 95% CI 1.251–1.429; N0 as a reference), RT (yes: HR 0.748, 95% CI 0.717–0.78; no as a reference), chemotherapy (yes: HR 0.371, 95% CI 0.353–0.39; no as a reference), brain metastasis (yes: HR 1.37, 95% CI 1.299–1.445; no as a reference), liver metastasis (yes: HR 1.281, 95% CI 1.215–1.349; no as a reference), age (66–79: HR 1.196, 95% CI 1.149–1.244; >79: HR 1.514, 95% CI 1.42–1.615; <66 as a reference), site metastasis (1: HR 1.116, 95% CI 1.053–1.182; 2 HR 1.335, 95% CI 1.24–1.438; 3 HR 1.31, 95% CI 1.182–1.452; 4 HR1.414, 95% CI 1.159–1.725; none as a reference).
Figure 3.
Selection of informative factors associated with overall survival using the Lasso Cox regression model. (A) Lasso coefficient profiles of 11 clinical features. (B) Selection of the tuning parameter (λ).
Figure 4.
Forest plot of multivariate survival analysis.
3.3. Survival analysis
3.3.1. Comparison of survival between the RT and non-RT groups.
For DM-SCLC, compared with the non-RT group, RT can significantly prolong the survival of DM-SCLC patients, and the difference is statistically significant (HR 0.748, 95% CI 0.717–0.78, P < .001) (Fig. 5). The 1- and 2-year survival rates were 24.5% and 5.8% in the RT group and 14.8% and 2.3% in the non-RT group (P < .001).
Figure 5.
Kaplan–Meier estimate of overall survival of patients stratified by radiotherapy.
3.4. Subgroup analysis
Subgroup survival analyses were further investigated based on the impact of RT on OS in different independent factors (sex, tumor size, N stage, RT, chemotherapy, brain metastasis, liver metastasis, age, and site metastasis). The KM survival curve showed that RT did not significantly prolong OS only in brain metastasis (P = .092), no chemotherapy (P = .86), and 3 (P = .28) and 4 (P = .75) sites metastasis. The results of subgroup survival analysis were shown in Figures 6–12. Median survival of each subgroup was shown in Table 2.
Figure 6.
Kaplan–Meier estimate of overall survival of patients stratified by sex.
Figure 12.
Kaplan–Meier estimate of overall survival of patients stratified by age.
Table 2.
Median survival time of each subgroup.
| Variables | Level | RT | Media OS (mo) | 95% CI | P value | |
|---|---|---|---|---|---|---|
| Sex | ||||||
| Sex | Female | No | 8 | 7.623 | 8.377 | <.001 |
| Yes | 10 | 9.534 | 10.466 | |||
| Male | No | 7 | 6.709 | 7.291 | <.001 | |
| Yes | 9 | 8.613 | 9.387 | |||
| N stage | N0 | No | 8 | 7.014 | 8.986 | .001 |
| Yes | 10 | 9.037 | 10.963 | |||
| N1 | No | 8 | 6.597 | 9.403 | .015 | |
| Yes | 9 | 7.631 | 10.369 | |||
| N2 | No | 7 | 6.72 | 7.28 | <.001 | |
| Yes | 9 | 6.574 | 7.426 | |||
| N3 | No | 7 | 9.363 | 10.637 | <.001 | |
| Yes | 10 | 7.219 | 8.781 | |||
| Tumor size | <27 mm | No | 7 | 6.204 | 7.796 | .03 |
| Yes | 8 | 6.855 | 9.145 | |||
| 27–44 mm | No | 6 | 5.386 | 6.614 | <.001 | |
| Yes | 9 | 8.113 | 9.887 | |||
| >44 mm | No | 7 | 6.568 | 7.432 | <.001 | |
| Yes | 8 | 7.566 | 8.434 | |||
| Chemotherapy | No | No | 2 | .658 | ||
| Yes | 2 | |||||
| Yes | No | 7 | 6.681 | 7.319 | <.001 | |
| Yes | 9 | 8.625 | 9.375 | |||
| Brain metastasis | No | No | 7 | 6.654 | 7.346 | <.001 |
| Yes | 9 | 8.604 | 9.396 | |||
| Yes | No | 5 | 3.951 | 6.049 | .195 | |
| Yes | 4 | 2.949 | 5.051 | |||
| Liver metastasis | No | No | 7 | 6.645 | 7.355 | <.001 |
| Yes | 10 | 9.561 | 10.439 | |||
| Yes | No | 7 | 6.682 | 7.318 | <.001 | |
| Yes | 8 | 7.615 | 8.385 | |||
| Age | <66 | No | 8 | 7.647 | 8.353 | <.001 |
| Yes | 10 | 9.577 | 10.423 | |||
| 66–79 | No | 7 | 6.662 | 7.338 | <.001 | |
| Yes | 9 | 8.572 | 9.428 | |||
| >79 | No | 4 | 3.172 | 4.828 | .039 | |
| Yes | 4 | 3.28 | 4.72 | |||
| Metastases sites | 0 | No | 8 | 7.392 | 8.608 | <.001 |
| Yes | 13 | 12.176 | 13.824 | |||
| 1 | No | 7 | 6.636 | 7.364 | <.001 | |
| Yes | 10 | 9.5 | 10.5 | |||
| 2 | No | 6 | 5.555 | 6.445 | <.001 | |
| Yes | 8 | 7.541 | 8.459 | |||
| 3 | No | 6 | 5.268 | 6.732 | .279 | |
| Yes | 7 | 6.138 | 7.862 | |||
| 4 | No | 4 | 1.695 | 6.305 | .748 | |
| Yes | 5 | 2.652 | 7.348 | |||
CI = confidence interval, OS = overall survival, RT = radiotherapy.
Figure 7.
Kaplan–Meier estimate of overall survival of patients stratified by tumor size.
Figure 8.
Kaplan–Meier estimate of overall survival of patients stratified by N stage.
Figure 9.
Kaplan–Meier estimate of overall survival of patients stratified by chemotherapy.
Figure 10.
Kaplan–Meier estimate of overall survival of patients stratified by brain metastasis.
Figure 11.
Kaplan–Meier estimate of overall survival of patients stratified by liver metastasis.
4. Discussion
In the past 30 years, the treatment pattern of DM-SCLC has remained unchanged, and platinum-based chemotherapy is still the dominant regimen, with median survival of 9 to 12 months and 5-year survival rate of only 1%–2%.[5] In recent years, this theory has changed dramatically as a large number of studies have shown that RT is a safe and effective treatment for DM-SCLC.[6–9]
The purpose of this study was to identify the independent risk factors for DM-SCLC, analyze the survival differences between the RT group and the non-RT group, and further explore the survival effects of RT in the subgroups of different risk factors. 11,897 patients were included in this study, we use PSM method balanced equilibrium covariate differences between groups, covariate differences between the 2 groups have significant difference between factors as covariate, will the RT and non-RT group of patients according to 1:1 matching, matching successfully in 6300 cases, including RT group and non-RT therapy group, 3150 cases of each. There were no significant differences in general clinical data and pathological features between the 2 groups after matching.
Sex, tumor size, N stage, chemotherapy, brain metastasis, liver metastasis, age, race, liver metastasis and site metastasis were significant factors of survival in DM-SCLC. Studies[10] on the effect of gender on prognosis suggest that women have a better prognosis than men, and patients with good general condition have a better prognosis. The results of this study are similar. Published studies have shown that later tumor stage (tumor size and N stage) can significantly affect the prognosis of patients with DM-SCLC. The results of this study also reached the same conclusion.
Overall, RT significantly improves survival in DM-SCLC, which is consistent with a series of previously published studies.[8,11–13] Furthermore, in the current study, our results showed that the population that could benefit from RT included men, women, any tumor size group, and any N stage group. The results of this study in the subgroup analysis can also be found in the published studies mentioned above. Systemic treatment for DM-SCLC is the main treatment principle. In the non-chemotherapy group, no effect of RT on improving survival was observed in this study, but the addition of RT on the basis of chemotherapy can significantly improve the survival rate.
It is generally believed that SCLC develops rapidly when liver metastasis was diagnosed, the prognosis is worse than that of brain metastasis, and the survival period is significantly shorter than that of brain metastasis. Due to widespread liver metastases, liver function is often impaired and subsequent drug therapy is affected.[14–16] However, the results of this study showed that RT significantly improved survival in patients with liver metastases from SCLC. The distribution bias was further analyzed, and no distribution bias was found in the subgroups of sex, age, N stage, and whether or not brain metastases were present, and the distribution was balanced in these subgroups. Therefore, more rigorous randomized controlled trials are needed to investigate whether RT can prolong survival in patients with liver metastases of SCLC.
Another unusual finding of this study was that patients with brain metastases who received RT did not significantly improve OS. Exploring the reasons for this result, we found that males, later N stage, older age, and larger tumor size were more likely to be present in the composition of patients with brain metastases. We hypothesize that the benefit of RT in this group of patients counterbalances the disadvantage, so the benefit of RT is not present in patients with brain metastasis. We also found that patients with DM-SCLC with 1-2 organ metastases could benefit from RT, and no significant difference in survival was found in patients with <2 organ metastases, which is consistent with published studies.[11]
However, this study has several limitations. First of all, there is a lag in data updating, and the follow-up time is not sufficient. Second, there are defects in the integrity of evidence for radiation-related studies because the dose and range of RT cannot be obtained in SEER database. Finally, although the suspected confounding factors were corrected based on big data in this study, the shortcomings of retrospective study could not be completely avoided. We hope that there will be large-scale prospective studies to verify the conclusions of this paper in the future.
5. Conclusions
Gender, tumor size, N stage, chemotherapy, brain metastases, liver metastases, age, and number of organ metastases are independent prognostic factors affecting OS in DM-SCLC. Women with any tumor size, any N stage, any age subgroup, chemotherapy, no brain metastasis, with or without liver metastases, and 1-2 organ metastases may benefit from RT.
Author contributions
Data curation: Xiaoyue Sun.
Methodology: Fang Wang.
Software: Xin Zhang, Hongyun Shi.
Validation: Fangyu Liu.
Writing – original draft: Shuai Qie.
Abbreviations:
- CI =
- confidence interval
- DM-SCLC =
- small-cell lung cancer
- HR =
- hazard ratio
- OS =
- overall survival
- PSM =
- propensity score matching
- RT =
- radiotherapy
- SEER =
- Surveillance, Epidemiology, and End Results
All data generated or analyzed during this study are included in this published article [and its supplementary information files].
SQ and XZ contributed equally to this work.
How to cite this article: Qie S, Shi H, Wang F, Liu F, Zhang X, Sun X. The benefit of radiotherapy in distant metastatic small-cell lung cancer: A retrospective study based on propensity score matching (PSM). Medicine 2022;101:36(e30510).
The authors have no funding and conflicts of interest to disclose.
Contributor Information
Hongyun Shi, Email: 174812503@qq.com.
Fang Wang, Email: 1072978014@qq.com.
Fangyu Liu, Email: 496228560@qq.com.
Xin Zhang, Email: 375488467@qq.com.
Xiaoyue Sun, Email: 598625447@qq.com.
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