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Original Research Article
Cancer Control
Volume 28: 1–12
© The Author(s) 2021
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DOI: 10.1177/10732748211051228
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Construction and Validation of a Novel
Nomogram to Predict the Overall Survival of
Patients With Combined Small Cell Lung
Cancer: A Surveillance, Epidemiology, and
End Results Population-Based Study
Aimin Jiang
1,†
, Na Liu
1,†
, Rui Zhao
2,†
, Shihan Liu
1
, Huan Gao
1
, Jingjing Wang
1
,
Xiaoqiang Zheng
1
, Mengdi Ren
1
, Xiao Fu
1
, Xuan Liang
1
, Tao Tian
1
, Zhiping Ruan
1
, and
Yu Yao
1
Abstract
Introduction: Combined small cell lung cancer (C-SCLC) represents a rare subtype of all small cell lung cancer cases, with
limited studies investigated its prognostic factors. The aim of this study was to construct a novel nomogram to predict the
overall survival (OS) of patients with C-SCLC.
Methods: In this retrospective study, a total of 588 C-SCLC patients were selected from the Surveillance, Epidemiology, and
End Results database. The univariate and multivariate Cox analyses were performed to identify optimal prognostic variables and
construct the nomogram, with concordance index (C-index), receiver operating characteristic curves, and calibration curves
being used to evaluate its discrimination and calibration abilities. Furthermore, decision curve analysis (DCA), integrated
discrimination improvement (IDI), and net reclassification index (NRI) were also adopted to assess its clinical utility and
predictive ability compared with the classic TNM staging system.
Results: Seven independent predictive factors were identified to construct the nomogram, including T stage, N stage, M stage,
brain metastasis, liver metastasis, surgery, and chemotherapy. We observed a higher C-index in both the training (.751) and
validation cohorts (.736). The nomogram has higher area under the curve in predicting 6-, 12-, 18-, 24-, and 36-month survival
probability of patients with C-SCLC. Meanwhile, the calibration curves also revealed high consistencies between the actual and
predicted OS. DCA revealed that the nomogram could provide greater clinical net benefits to these patients. We found that the
NRI for 6- and 12-month OS were .196 and .225, and the IDI for 6- and 12-month OS were .217 and .156 in the training group,
suggesting that the nomogram can predict a more accurate survival probability. Similar results were also observed in the
validation cohort.
Conclusion: We developed and verified a novel nomogram that can help clinicians recognize high-risk patients with C-SCLC
and predict their OS.
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1
Department of Medical Oncology, The First Affiliated Hospital of Xi’an
Jiaotong University, Xi’an, Shaanxi, People’s Republic of China
2
Department of Nutrition and Food Hygiene, School of Public Health, Tongji
Medical College, Huazhong University of Science and Technology, Wuhan,
Hubei, People’s Republic of China
†
These authors have contributed equally to this work and share the first
authorship.
Corresponding Authors:
Yu Yao, Department of Medical Oncology, The First Affiliated Hospital of
Xi’an Jiaotong University, No. 277 Yanta West Road, Xi’an, Shaanxi 710061,
People’s Republic of China.
Email: 13572101611@163.com
Zhiping Ruan, Department of Medical Oncology, The First Affiliated Hospital
of Xi’an Jiaotong University, No. 277 Yanta West Road, Xi’an, Shaanxi 710061,
People’s Republic of China.
Email: zopor@163.com
Keywords
combined small cell lung cancer, prognostic factor, nomogram, overall survival
Received July 13, 2021. Received revised August 24, 2021. Accepted for publication September 17, 2021.
Introduction
Small cell lung cancer (SCLC) consists of 15% of lung cancer
cases and is characterized by an exceptional aggressive, early
occurrence and metastasis, and poor prognosis.
1
According to
the 1999 World Health Organization classification of lung
cancer, SCLC can be divided into pure small cell lung cancer
(P-SCLC) and combined small cell lung cancer (C-SCLC).
2,3
C-SCLC refers to a mixture of SCLC and non-small cell lung
cancer (NSCLC) components, in which NSCLC components
could be squamous cell carcinoma (SCC), adenocarcinoma
(ADC), large-cell neuroendocrine carcinoma (LCNEC),
spindle-cell carcinoma, and giant cell carcinoma.
2,4
As a rare
subtype of SCLC, it is reported that C-SCLC makes up about
10% of all SCLC cases.
5
In fact, the actual incidence of C-
SCLC may be higher than this level because most C-SCLC
patients were diagnosed through postoperative pathology.
5
Because of increased crush artifact and fewer cells in small
sample biopsy, specimens from bronchoscopy and needle
biopsy are challenging to make a precise diagnosis for
C-SCLC.
1,6,7
Although an increasing number of studies focused on the
therapeutic progress and survival outcome of patients with
SCLC, only limited studies investigated the clinical charac-
teristics, prognosis, and relevant prognostic indicators of
C-SCLC. According to some previously published studies, C-
SCLC shared some common epidemiological and clinical
characteristics with SCLC: they are prevalent in men and
smokers, and most patients were diagnosed at the time of
advanced disease stage.
1,5
In a retrospective study conducted
in China, Lei et al. revealed that surgery is still the optimal and
effective treatment option for early-stage C-SCLC.
2
They also
indicated that the subsequent adjuvant chemotherapy could
improve the OS of these patients.
2
Recently, He and his
colleagues reported that IA-IB stage C-SCLC could benefit
from surgery.
5
However, adjuvant chemotherapy seemed to
have few effects on improving the survival outcome of these
patients.
5
Despite the fact that few studies have explored the
prognostic factors of C-SCLC, their conclusions are incon-
clusive and based on the small sample size single cohort
study.
8-11
Hence, it is urgently needed to identify the prog-
nostic factors of C-SCLC and develop a risk stratification
system to recognize high-risk patients and initiate an early
intervention for these patients.
To the best of our knowledge, there was no available
nomogram constructed to predict the survival probability of C-
SCLC so far. In this premier, we aimed to investigate the
prognostic factors of patients with C-SCLC using a large C-
SCLC cohort from the Surveillance, Epidemiology, and End
Results (SEER) database and develop a novel risk stratifi-
cation system to predict their overall survival (OS). Besides,
we also verified the nomogram in a validation cohort and
performed a series of tests to evaluate its performance and
clinical utility.
Methods
Data Acquisition
All patients were obtained from the SEER database in this
large population-based retrospective study, with SEER*Stat
8.35 used for data extraction. The SEER database collects data
from 18 cancer registries of the National Cancer Institute and it
includes data of nearly 30% of US population.
12
The latest
information on follow-up and prognosis of the SEER database
was released in December 31, 2016. We conducted this study
under the requirement of the Declaration of Helsinki.
Patients Selection
Patients diagnosed with C-SCLC between 1975 and 2016
were initially identified from the SEER database. The detailed
criteria for inclusion and exclusion are as follows: (1) ma-
lignancies that originated in main bronchus and lung (SEER
primary site code: C340-C349); (2) the International Classi-
fication of Diseases code O-3 morphology was 8045 (for all
SCLC patients: 8041-8045); and (3) patients without complete
records for American Joint Committee on Cancer (AJCC)-
TNM staging, treatment, OS, and other crucial clinical in-
formation were excluded. In the present study, we did not
perform power calculation for estimation of sample size.
Cohort Establishment and Variable Selection
All eligible patients enrolled in the whole dataset were ran-
domly divided into training and validation cohorts according
to a ratio of 7:3 by exploiting the “createDataPartition”
function in R software. In the current study, the training cohort
was used to develop a predictive signature, with the validation
cohort being adopted to verify its predictive ability and clinical
utility. Seventeen variables were obtained from the SEER
database, including age at diagnosis, gender, race, marital
status at diagnosis, tumor location, tumor grade, AJCC-T
stage, AJCC-N stage, AJCC-M stage, clinical stage, sur-
gery type, radiation status, chemotherapy status, and location
of distant metastasis. Then, we adopted univariate and mul-
tivariate Cox regression analyses to select optimal variables
for predictive model construction.
2Cancer Control
Statistical Analysis
All categorical variables were summarized as count and
percentage, with a Chi-square test being adopted to compare
the difference between the training cohort and the validation
cohort. The univariate and multivariate Cox regression
analyses were used to identify independent prognostic
factors for patients with C-SCLC. All variables with P-value
<.05 in the univariate analysis were selected into multi-
variate Cox regression analysis. The optimal variables in
the multivariate analysis were used to construct a nomo-
gram. Besides, we calculated the concordance index (C-
index) and generated receiver operating characteristic
(ROC) curves and calibration curves to evaluate the dis-
crimination ability and calibration ability of the nomogram
in the 2 cohorts. Furthermore, we also performed decision
curve analysis (DCA)
13-15
and calculated net reclassification
index(NRI)andintegrateddiscrimination improvement
(IDI)
16
to assess the clinical utility and net clinical benefits
when the nomogram was adopted to guide clinical practice.
In this study, R software version 3.6.3 and SPSS software
version 23.0 for Windows were adopted for all statistical
analyses.
Results
Clinical Characteristics of the Participants
Overall, 2329 cases were confirmed as C-SCLC in the SEER
database according to the previously defined criteria. After
excluding patients in accordance with the previously defined
inclusion and exclusion criteria, 588 C-SCLC patients were
included in this study, as presented in Figure 1. The mean age
of patients in the whole cohort was 67.6±9.0 years old. There
were 314 male patients and 274 female patients. White people
were the most predominant ethnicity, accounting for 84.0% of
cases. The vast majority of patients (69.9%) were diagnosed at
the advanced disease stage. It showed that 66.7% of patients
received chemotherapy and 46.3% received radiotherapy,
while only 30.1% of patients underwent surgery. Regarding
the detailed surgery type, 117 patients underwent lobectomy,
33 patients underwent wedge resection, and 16 patients re-
ceived pneumonectomy, respectively. Besides, we observed
that the liver was the most common distant metastasis organ,
accounting for 14.3% of patients, followed by bone (13.9%)
and lung (12.9%). The detailed demographical and clinico-
pathological characteristics were summarized in Table 1.
Figure 1. Flow chart of the study.
Jiang et al. 3
Table 1. Demographic and Clinical Characteristics of Patients with C-SCLC.
Characteristics Whole population (n = 588) Training cohort (n = 412) Validation cohort (n = 176) Pvalue
Gender (n, %) .716
Male 314 (53.4) 218 (37.1) 96 (16.3)
Female 274 (46.6) 194 (33.0) 80 (13.6)
Age (years) 67.6 ± 9.0 68.0 ± 9.0 66.9 ± 9.0 .343
<65 197 (33.5) 143 (24.3) 54 (9.2)
≥65 391 (66.5) 269 (45.7) 122 (20.7)
Ethnicity (n, %) .539
White 494 (84.0) 342 (58.2) 152 (25.9)
Black 70 (11.9) 53 (9.0) 17 (2.9)
Others 24 (4.1) 17 (2.9) 7 (1.2)
Marital status (n, %) .428
Yes 296 (50.3) 203 (34.5) 93 (15.8)
Others 391 (49.7) 269 (35.5) 122 (14.1)
Laterality (n, %) .932
Left 251 (42.7) 174 (29.6) 77 (13.1)
Right 326 (55.4) 230 (39.1) 96 (16.3)
Bilateral 11 (1.9) 8 (1.4) 3 (.5)
Grade (n, %) .032*
I-II 36 (6.1) 19 (3.2) 17 (2.9)
III-IV 272 (46.3) 200 (34.0) 72 (12.2)
Unknown 280 (47.6) 193 (32.8) 87 (14.8)
AJCC-T stage (n, %) .319
T1-2 319 (54.3) 218 (37.1) 101 (17.2)
T3-4 269 (45.7) 194 (33.0) 75 (12.8)
AJCC-N stage (n, %) .028*
N0 213 (36.2) 161 (27.4) 52 (8.8)
N1-3 375 (63.8) 251 (42.7) 124 (21.1)
AJCC-M stage (n, %) .390
M0 335 (57.0) 230 (39.1) 105 (17.9)
M1 253 (43.0) 182 (31.0) 71 (12.1)
TNM staging (n, %) .240
Stage I-II 177 (30.1) 130 (22.1) 47 (8.0)
Stage III-IV 411 (69.9) 282 (48.0) 129 (21.0)
Surgery (n, %) .623
Yes 177 (30.1) 120 (20.4) 57 (9.7)
None 411 (69.9) 292 (49.7) 119 (20.2)
Surgery type (n, %) 1.535
Lobectomy 117 (19.9) 80 (13.6) 37 (6.3)
Wedge resection 33 (5.6) 23 (3.9) 10 (1.7)
Pneumonectomy 16 (2.7) 11 (1.9) 5 (.9)
Others 11 (1.9) 6 (1.0) 5 (.9)
Radiation status (n, %) .121
Yes 272 (46.3) 182 (31.0) 90 (15.3)
None 316 (53.7) 230 (39.1) 86 (14.6)
Chemotherapy (n, %) .484
Yes 392 (66.7) 271 (46.1) 121 (20.6)
None 196 (33.3) 141 (24.0) 55 (9.4)
Bone metastasis (n, %) .688
Yes 82 (13.9) 59 (10.0) 23 (3.9)
None 506 (86.1) 353 (60.0) 153 (26.0)
Brain metastasis (n, %) .594
Yes 64 (10.9) 43 (7.3) 21 (3.6)
None 524 (89.1) 369 (62.8) 155 (26.4)
(continued)
4Cancer Control
Then, all patients were randomly divided into training
cohort (412 patients) and validation cohort (176 patients)
according to a ratio of 7:3, with a Chi-square test being
adopted to examine whether there was a statistical difference
between the two cohorts. It showed that except for tumor
grade and AJCC-N stage, there was no significant statistical
difference among other clinicopathological characteristics
(Tab le 1).
Univariate and Multivariate Cox Regression Analysis
The median OS for the whole cohort, training cohort, and
validation cohort was 11.0 months. In order to explore
potential influencing factors that were associated with the OS
of C-SCLC, we further conducted univariate and multi-
variate Cox regression analyses. The results of the univariate
analysis revealed that AJCC-T stage, AJCC-N stage, AJCC-
M stage, TNM staging, surgery, chemotherapy, brain me-
tastasis, lung metastasis, liver metastasis, and bone metas-
tasis were correlated with the OS of these individuals (Table 2).
Next, we selected variables with a Pvalue<.05inthe
univariate Cox regression analysis for the multivariate
analysis to identify the independent prognostic factors of OS
for patients with C-SCLC. We observed that patients with
advanced AJCC-T stage [Hazard Ratio (HR): 1.40; 95%
Confidence Interval (CI): 1.07-1.83, P= .013], N stage (HR:
1.44; 95%CI: 1.06-1.96, P= .019), M stage (HR: 1.48; 95%
CI: 1.06-2.07, P= .021), brain metastasis (HR: 1.54; 95%CI:
1.08-2.21, P= .017), and liver metastasis (HR: 1.67; 95%CI:
1.17-2.38, P= .005) were correlated with unfavorable OS.
However, we identified that receiving surgery (HR: .55; 95%
CI: .39-.78, P< .001) and chemotherapy (HR: .47; 95%CI:
.37-.60, P< .001) were associated with better OS in these
patients.
Nomogram Development and Validation
We constructed a nomogram to predict the survival probability
of patients with C-SCLC via R software, “rms”and “regplot”
packages. Figure 2 demonstrates an example of using the
nomogram to predict the survival probability of a given pa-
tient. In this nomogram, the independent predictive factors
identified through the multivariate analysis were employed to
predict the total point of each patient, thus predicting the 6-,
12-, 18-, 24-, and 36-month survival probability of these
patients (Figure 2). Besides, we calculated the C-index of this
nomogram in the two cohorts to estimate its predictive power,
suggesting the constructed predictive model had excellent
performance in predicting the OS of C-SCLC (training cohort:
.751; validation cohort: .736, respectively). Furthermore, we
also generated ROC curves and calibration curves to assess the
discrimination and calibration abilities of the nomogram in the
two cohorts. It showed that no matter in the training cohort
(Figure 3A) or validation cohort (Figure 3B), the constructed
nomogram has higher area under the curve (AUC) in pre-
dicting 6- (.874 vs. .803), 12- (.824 vs. .783), 18- (.795 vs.
.800), 24- (.800 vs. .808), and 36- (.795 vs. .807) month
survival probability of patients with C-SCLC. Meanwhile, the
calibration curves revealed high consistencies between the
actual and predicted OS in the two cohorts (Figures 3C and D).
To sum up, the above results elucidated that this nomogram
has an excellent predictive ability for the survival probability
of patients with C-SCLC.
Clinical Utility Evaluation of the Nomogram
Because the ROC curve and calibration curve are based on the
sensitivity and specificity of the predictive model, they cannot
recognize false positive and false negative cases. Therefore,
DCA was widely adopted to assess the clinical utility and net
clinical benefits when the predictive model guides clinical
practice. Therefore, we performed DCA to evaluate the net
clinical benefits that the nomogram would bring to patients
compared with the classic TNM staging system. We observed
that the nomogram could predict better 6-month OS and add
more clinical net benefits than the classic TNM staging system
for a specific range of threshold probabilities in both the
training cohort (range: .08-.83) and validation cohort (range:
.12-.80) (Figures 4A and B). A similar result was also ob-
served for the 12-month OS prediction (Figures 4C and D).
Subsequently, NRI and IDI were also calculated to evaluate
the accuracy of the nomogram for predicting OS compared
with the classic TNM staging system. In the training cohort,
we found that the NRI for 6- and 12-month OS were .196
Table 1. (continued)
Characteristics Whole population (n = 588) Training cohort (n = 412) Validation cohort (n = 176) Pvalue
Liver metastasis (n, %) .211
Yes 84 (14.3) 54 (9.2) 30 (5.1)
None 504 (85.7) 358 (60.9) 146 (24.8)
Lung metastasis (n, %) .461
Yes 76 (12.9) 56 (9.5) 20 (3.4)
None 512 (87.1) 356 (60.5) 156 (26.5)
Abbreviations: C-SCLC, combined small cell lung cancer; AJCC, American Joint Committee on Cancer. * represents Pvalue< .05.
Jiang et al. 5
(95%CI: .077-.309) and .225 (95% CI: .138-.319), and the IDI
for 6- and 12-month OS were .217 (95%CI: .153-.281) and
.156 (95% CI: .101-.215), suggesting that the constructed
nomogram can predict more accuracy survival probability for
patients with C-SCLC compared with the classic TNM staging
system (Tab l e 3). Of course, the performance of the nomogram
in the validation cohort also supported this result (Table 3).
Risk Stratification Ability Assessment of the Nomogram
Ultimately, all patients were divided into low- and high-risk
groups according to the median of total points in the training
cohort (195) and the validation cohort (138) to evaluate the
risk stratification ability of the constructed nomogram.
Meanwhile, we also generated Kaplan–Meier survival curves
to show the survival difference between different risk groups.
We observed that the survival probability of patients in the
high-risk groups was significantly lower than patients in the
low-risk groups (Figures 5A and B), suggesting the con-
structed nomogram could accurately recognize high-risk
patients.
Discussion
C-SCLC represents a rare subtype in SCLC, with limited
studies reported its clinical outcome and prognostic factors. In
the present study, we explored the clinical characteristics,
prognosis, and prognostic factors of these patients via a large
C-SCLC dataset from the SEER database. Most importantly,
we developed a nomogram based on 7 optimal prognostic
variables to predict the survival probability of C-SCLC. We
also performed a series of validations to evaluate its predictive
ability and clinical utility. Ultimately, we found that the
constructed nomogram has an excellent performance in pre-
dicting the OS of these individuals compared with the classic
TNM staging system. Besides, by calculating NRI and IDI, we
observed that if the nomogram were used to guide clinical
practice, it would bring more incredible clinical net benefits to
C-SCLC patients.
We identified that advanced AJCC-T stage, N stage, M
stage, brain metastasis, and liver metastasis were correlated
with unfavorable OS in C-SCLC in multivariate Cox re-
gression analysis. Nevertheless, we found that patients can
Table 2. Univariate and Multivariate Cox Analyses on Variables for the Prediction of Overall Survival of Patients With C-SCLC.
Characteristics
Univariate analysis Multivariate analysis
HR 95%CI Pvalue HR 95%CI Pvalue
Gender (Male vs. Female) .84 .67-1.04 .109
Age (years, <65 vs. ≥65) 1.13 .90-1.42 .276
Ethnicity
White 1.00 1.000
Black 1.15 .63-1.22 .421
Others .87 .66-2.01 .619
Marital status (Yes vs. Others) 1.21 .98-1.51 .080
Laterality
Left 1.00 1.000
Right 1.22 .66-1.02 .074
Bilateral .84 .52-2.69 .679
Grade
I-II 1.00 1.000
III-IV 1.12 .53-1.52 .681
Unknown .72 .82-2.35 .227
AJCC-T stage (T1-2 vs. T3-4) 2.24 1.80-2.80 <.001* 1.40 1.07-1.83 .013*
AJCC-N stage (N0 vs. N1-3) 2.10 1.66-2.65 <.001* 1.44 1.06-1.96 .019*
AJCC-M stage (M0 vs. M1) 2.90 2.32-3.62 <.001* 1.48 1.06-2.07 .021*
TNM staging (I-II vs. III-IV) 2.98 2.30-3.87 <.001* 1.17 .73-1.87 .527
Surgery (None vs. Yes) .33 .25-.43 <.001* .55 .39-.78 <.001*
Radiation status (None vs. Yes) .93 .75-1.16 .523
Chemotherapy (None vs. Yes) .70 .56-.88 .002* .47 .37-.60 <.001*
Bone metastasis (None vs. Yes) 2.91 2.16-3.91 <.001* 1.34 .94-1.90 .106
Brain metastasis (None vs. Yes) 2.56 1.84-3.56 <.001* 1.54 1.08-2.21 .017*
Liver metastasis (None vs. Yes) 3.05 2.25-4.13 <.001* 1.67 1.17-2.38 .005*
Lung metastasis (None vs. Yes) 2.24 1.66-3.02 <.001* .94 .66-1.34 .749
Abbreviations: C-SCLC, combined small cell lung cancer; AJCC, American Joint Committee on Cancer; HR, hazard ratio; CI, confidence interval. * represents P
value< .05.
6Cancer Control
benefit from surgery and chemotherapy. Previous studies had
proposed some factors that were potentially correlated with
OS of C-SCLC, including smoking history,
10
extensive-stage
disease,
11
lymph node metastasis,
2,8
adjuvant treatment,
2,10,11
and pathologically combined LCNEC
10
and SCC.
17
Lei et al.
reported that lymph node metastasis was significantly cor-
related with decreased disease-free survival (DFS) and OS in
surgically resected C-SCLC, consistent with our finding.
9
In
addition, in a previously published study, Men et al. observed
that positive lymph nodes ratio >10% was an independent risk
factor of OS for these patients.
8
As far as we can see, no study
reported the effect of distant organ metastasis on the OS of
C-SCLC. In this study, we observed that liver, bone, and lung
were the most predominantly distant metastatic organs. Only
10.9% of cases developed brain metastasis, which is similar to
the biological behavior of P-SCLC.
18,19
Furthermore, multi-
variate analysis revealed that brain metastasis and liver me-
tastasis were correlated with unfavorable OS in C-SCLC.
Therefore, consistent with P-SCLC, liver metastasis
20-22
and
brain metastasis
23
are also crucial negative prognostic factors
of OS for patients with C-SCLC. The above results suggest
that distant organ metastasis is not rare in C-SCLC, and de-
tailed examination should be considered when we make a
diagnosis the first time. Besides, precise and individualized
management should also be given for them since this subtype
of patients had limited survival time.
Adjuvant therapy is another important prognostic factor for
patients with C-SCLC. Although the vast majority of studies
elucidated that adjuvant therapy can provide survival benefits
for these patients, they included patients with different
characteristics from different research centers. Therefore, the
prognostic role of some adjuvant treatments is still contro-
versial in C-SCLC. This study found that patients who un-
derwent surgery and chemotherapy were significantly
associated with prolonged OS. Interestingly, we observed that
radiotherapy did not improve the prognosis of these patients.
In most retrospective studies, researchers revealed that surgery
was not significantly correlated with the prognosis of C-
SCLC, no matter what type of resection was
adopted.
2,8,10,17
On the contrary, Guo et al. indicated that
receiving sublobectomy was correlated with decreased OS for
patients with C-SCLC.
11
Besides, in a similar population-
based study, He et al. investigated the treatment options for
C-SCLC.
5
They reported that surgical treatment could im-
prove the OS of IA-IB C-SCLC patients.
5
The possible reason
for the above difference is that our study included both early
and advanced-stage patients, while most of the published
studies only enrolled surgically resected patients. Regarding
the treatments for advanced-stage C-SCLC patients, chemo-
therapy with or without radiotherapy was the paramount
consideration for these patients, similar to the treatment
strategy for P-SCLC patients. Recently, He et al. indicated that
chemotherapy-based treatment should be considered prior for
advanced-stage patients, while adjuvant chemotherapy
seemed to have few effects on early-stage patients.
5
On the
contrary, Lei et al. revealed that postoperative adjuvant
Figure 2. The constructed nomogram for predicting 6-,12-,18-,24-, and 36-month OS of patients with C-SCLC. The patient was a 67 years
old married male diagnosed as C-SCLC with T2bN0M1b stage. He underwent chemotherapy and radiotherapy and did not receive surgery.
This patient also combined brain metastasis. From the nomogram, we can easily calculate that his total point was 398, which belongs to the
high-risk group. Besides, we also can calculate that the 6-,12-,18-,24-, and 36-month death probability for this patient were 31.7%, 56.5%,
71.2%, 81.3%, and 88.7%, respectively.
Jiang et al. 7
Figure 3. Assessment of the discrimination and calibration abilities of the constructed nomogram using ROC curves and calibration curves.
(A), (B) The ROC curves for predicting 6-,12-,18-,24-, and 36-month OS of C-SCLC patients in the training cohort and validation cohort
based on the nomogram, (C), (D) The calibration curves for predicting 6-,12-,18-,24-, and 36-month OS of C-SCLC patients in the training
cohort and validation cohort based on the nomogram. ROC, receiver operating characteristic curve; C-SCLC, combined small cell lung
cancer.
8Cancer Control
chemotherapy significantly prolonged the OS of patients with
C-SCLC.
2
It can be attributed to the fact that the latter study
only analyzed surgically resected patients. Therefore, large-
scale and prospective studies are warranted to investigate the
effect of chemotherapy on the prognosis of patients with
different disease stages.
As we all know, P-SCLC is initially exceptionally responsive
to cytotoxic therapy. Early-stage P-SCLC patients can achieve
Figure 4. Decision curve analysis of the nomogram and classic TNM staging system for predicting survival benefits of patients with C-SCLC.
(A), (B) 6- and 12-month survival benefits in the training cohort, (C), (D) 6- and 12-month survival benefits in the validation cohort. C-
SCLC, combined small cell lung cancer.
Table 3. NRI and IDI of the Nomogram vs the TNM Staging System for Predicting OS of Patients with C-SCLC.
Index
Training cohort Validation cohort
Estimate 95%CI Pvalue Estimate 95%CI Pvalue
NRI (vs. the TNM staging system)
For 6-month survival .196 .077-.309 .493 .309-.672
For 12-month survival .225 .138-.319 .203 .049-.359
IDI (vs. the TNM staging system)
For 6-month survival .217 .153-.281 <.001 .248 .166-.350 <.001
For 12-month survival .156 .101-.215 <.001 .185 .105-.278 <.001
For 18-month survival .114 .064-.170 <.001 .189 .114-.275 <.001
For 24-month survival .088 .045-.145 <.001 .159 .084-.254 <.001
For 36-month survival .062 .012-.120 .014 .151 .058-.260 <.001
Abbreviations: NRI, net reclassification index; IDI, discrimination improvement; CI, confidence interval; C-SCLC, combined small cell lung cancer.
Jiang et al. 9
long-term disease control through concurrent chemo-
radiotherapy (CRT).
1
Numerous studies also explored the effect
of postoperative radiotherapy on the OS of C-SCLC.
2,8,11,17,24
In a study conducted by Men et al., they indicated that post-
operative chemotherapy was not significantly correlated with
improved OS of C-SCLC.
24
However, subgroup analysis re-
vealed that postoperative chemotherapy significantly improved
the survivals of patients with stage III or N2 disease.
24
No
similar results were reported in other studies. Hence, it proves
that C-SCLC is not very sensitive to chemotherapy and radi-
ation compared with P-SCLC. A personalized treatment
strategy should be considered for these patients. Although
SCLC initially responds well to CRT, it is easy to develop brain
metastasis.
25
Therefore, prophylactic cranial irradiation (PCI) is
recommended as part of the standard management in most non-
metastatic SCLC who respond well to initial cytotoxic treat-
ment.
1
Wang et al. suggested that the risk of brain metastasis is
relatively high in C-SCLC.
10
Besides, they also revealed that
PCI could improve progression-free survival and OS of these
patients and decrease the occurrence of brain metastasis in
surgically resected C-SCLC patients.
10
On the contrary, in a
study performed in China, Guo et al. aimed to compare the
clinical characteristics and prognosis between P-SCLC and
C-SCLC.
11
They indicated that PCI could only prolong OS of
P-SCLC.
11
However, no statistical difference was observed
when they analyzed the effect of PCI on OS of C-SCLC in
multivariate analysis.
11
Finally, we identified 7 optimal variables via multivariate
analysis and developed a nomogram to predict the survival
probability of patients with C-SCLC. No matter in the training
cohort or validation cohort, the nomogram showed excellent
predictive ability for the clinical outcome of these patients.
Due to the TNM staging system provides more precise lymph
nodal staging and better anatomic discrimination for the
measurement of outcome, it is more suitable for clinicians to
acquire a piece of more accurate staging information instead of
the previous Veterans Administration Lung Study Group
staging system.
1
In the present study, we also compared the
predictive ability of the constructed nomogram and classic
TNM staging system for OS of patients with C-SCLC by
conducting DCA and calculating NRI and IDI. DCA sug-
gested that the constructed nomogram could provide more
excellent clinical net benefits to patients with C-SCLC when it
was adopted to clinical practice. Furthermore, the positive
value of NRI and IDI also indicated that the constructed
nomogram had a good predictive ability of the prognosis for
these individuals compared with the classic TNM staging
system. Subsequently, all patients were divided into low- and
high-risk groups according to the median of total points.
Besides, it also suggested that high-risk patients had shorter
OS than low-risk patients through Kaplan–Meier survival
analysis. Taken together, the constructed nomogram had
excellent performance in predicting the survival probability of
patients with C-SCLC. Besides, it will bring more significant
net benefits to patients if we adopt the nomogram to support
clinical practice.
To the best of our knowledge, this is the first study that
constructed a novel nomogram to predict the OS of patients
with C-SCLC. Although the constructed nomogram has a
good performance and clinical utility, some inevitable dis-
advantages need to be discussed. First, although the SEER
database provides a large dataset of C-SCLC, we did not
perform sample size estimation in this study. Therefore, se-
lection bias cannot be eliminated completely. Second, some
crucial variables cannot be obtained from the SEER database,
such as smoking history, comorbidity, detailed mixed
Figure 5. Kaplan–Meier survival analysis for evaluating the risk stratification ability of the nomogram in patients with C-SCLC. (A) Kaplan–
Meier survival curve in the training cohort, (B) Kaplan–Meier survival curve in the validation cohort. C-SCLC, combined small cell lung
cancer.
10 Cancer Control
pathological components, chemotherapy regimens, and in-
formation of PCI. According to previously published studies,
SCLC combined with LCNEC, SCC, and ADC are common
pathological types in these patients. Due to the lack of a large
sample size study, the relationship between different types of
combined components and the prognosis of patients with C-
SCLC need to be further evaluated. Third, we all know that the
application of immune checkpoint inhibitors (ICIs) in SCLC
significantly improved the survival outcome of these patients.
To our regret, there were no available records when we tried to
evaluate the effect of immunotherapy on the prognosis of
C-SCLC. Could this rare subtype of patients also benefit from
immunotherapy? Maybe we need more relevant studies to
answer this question. Last but not least, despite that we verified
our results in the validation cohort and observed good per-
formance of the nomogram, validating the predictive model in
an independent external dataset is necessary in the future.
Conclusions
In summary, C-SCLC is a rare subtype in all SCLC cases. In
this study, we investigated the potential predictive factors of
prognosis for patients with C-SCLC. Ultimately, we con-
structed a novel nomogram that can accurately predict the OS
of patients with C-SCLC. Given its potential clinical utility
and good performance, our nomogram will provide potential
survival benefits for these individuals if it is adopted to guide
clinical practice. Furthermore, large-scale and prospective
studies are also warranted in the future to verify our findings.
Abbreviations
SCLC, Small cell lung cancer; WHO, World Health Organization; P-
SCLC, pure small cell lung cancer; C-SCLC, combined small cell
lung cancer; NSCLC, non-small cell lung cancer; SCC, squamous
cell carcinoma; ADC, adenocarcinoma; LCNEC, large-cell neuro-
endocrine carcinoma; SEER, Surveillance, Epidemiology and End
Results; OS, overall survival; AJCC, American Joint Committee on
Cancer; AIC, Akaike information criterion; C-index, Concordance
index; ROC, receiver operating characteristic; DCA, decision curve
analysis; NRI, net reclassification index; IDI, integrated discrimi-
nation improvement; AUC, area under the curve; CRT, concurrent
chemoradiotherapy; PCI, prophylactic cranial irradiation; PFS,
progression-free survival; VALSG, Veterans Administration Lung
Study Group; ICIs, immune checkpoint inhibitors.
Author Contributions
Conception/design: Y. Y., Z. R., X. F., T. T., and X. L.; Provision of
study material: A. J., N. L., R. Z., and S. L.; Collection and/or as-
sembly of data: A. J., H. G., J. W., X. Z., and M. R.; Data analysis and
interpretation: A. J., N. L., R. Z., and S. L.; Manuscript writing: A. J.;
Final approval of manuscript: Y. Y. and Z. R. All authors read and
approved the final manuscript and agree to be accountable for all
aspects of the research in ensuring that the accuracy or integrity of
any part of the work is appropriately investigated and resolved.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: This work
was supported by grant from the Shaanxi Provincial Science and
Technology Innovation Team [grant number 2021TD-44].
Ethics approval and consent to participate
Because the data extracted from the SEER database in this study did
not contain personally identifiable information, informed consent and
ethical proof were not required.
Data Availability
The data of this study are available in the SEER database (https://seer.
cancer.gov/).
ORCID iD
Aimin Jiang https://orcid.org/0000-0002-4092-342X.
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12 Cancer Control
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