A nomogram for predicting postoperative overall survival of patients with lung squamous cell carcinoma: a SEER-based study

doi:10.21203/rs.3.rs-1844818/v1

Background

Lung squamous cell carcinoma (LSCC) is the most common histological subtype of all lung cancer. Although the overall prognosis of lung cancer has improved, the predictable nomogram model of LSCC has not improved significantly. Our study aimed to construct and validate a nomogram model and to predict overall survival (OS) for patients with LSCC undergoing surgical treatment.

Methods

A total of 8,078 patients identified to have LSCC between 2010 and 2015 were selected from the Surveillance, Epidemiology and End Results (SEER) database. They were further randomly divided into training and validation cohorts. Univariable and multivariable Cox regression analyses were used to identify the risk factors affecting the prognosis for constructing the nomogram. The discrimination and calibration of the established nomogram were evaluated using receiver operating characteristic (ROC) curve, area under ROC curve (AUC), and calibration plots. In addition, decision curve analysis (DCA) was performed to evaluate the clinical utility. Finally, we plotted Kaplan-Meier survival curves and developed risk stratification systems.

Results

Seven variables were selected to establish the nomogram for LSCC. The AUC value in the ROC curve analysis revealed 0.658, 0.651, and 0.647 in the training cohort and 0.673, 0.667, and 0.658 in the validation cohort at 1-, 2-, and 3-year survival, respectively. The calibration curves showed satisfactory consistencies between nomogram-predicted and observed survival probability at 1-, 2-, and 3- year OS in training and validation cohorts. The DCA curve showed the established nomogram had great clinical net benefit, indicating high clinical application value. In risk stratification systems, patients were divided into three risk groups: low risk (total points༜95), middle risk (95 ≤ total points༜143), high risk (total points ≥ 143). The Kaplan-Meier survival curves indicated that significant difference in OS were observed among the three groups (P༜0.001).

Conclusions

A prognostic nomogram for OS of LSCC patients after surgical treatment was developed and validated, which may assist clinicians in evaluating prognosis of and facilitate doctors to provide highly individualized therapy.

Cardiothoracic Surgery

nomogram

lung squamous cell carcinoma (LSCC)

surgery

prognosis

Surveillance

Epidemiology

and End Results (SEER)

Lung cancer serves as a predominant cause of cancer-related deaths worldwide with an estimated 2 million new cases and 1.76 million deaths per year and a significant obstacle to increasing life expectancy in every country^[1–3]. Lung squamous cell carcinoma (LSCC) is one of the major well-studied histological subtypes of non-small cell lung cancer (NSCLC), which accounts for over 30% of cases all lung cancers^[4–6]. Compared to lung adenocarcinoma (LUAD), LSCC is associated with distinct epidemiological features, lack of effective targeted treatment options, and poor clinical prognosis^[7]. Considering the high incidence and poor prognosis of the LSCC^[8], the significance of a rational predictive model that can precisely predict the prognosis of LSCC and contribute to the application of medication and operation treatment deserves much attention. However, according to current researches, the prognostic studies are predominantly about NSCLC^{[9, 10]} and the clinical prognostic models on LSCC are scanty. In addition, 8th tumor, node, metastasis (TNM) staging system issued by American Joint Committee on Cancer (AJCC) is currently used for predicting the prognosis for patients with NSCLC, of which only three parameters are involved^[11].

As simple statistical and reliable predictive tools, nomograms have been extensively used to evaluate the prognosis of numerous cancers in clinical practice^[12]. The nomogram incorporates a range of significant clinical factors and can process a simplification from the statistical prediction model into a single numerical estimate of the probability of an event, such as the survival rate of a certain disease or the probability of death tailored to the profile of an individual patient^[13]. Therefore, nomogram, a reliable tool, has been devoted to guiding decision-making and predicting long-term survival outcomes of many cancers. To date, no Surveillance, Epidemiology, and End Results (SEER)-based nomogram has been reported to analyze the prognosis of postoperative LSCC.

Within this study, we leveraged representative postoperative LSCC patients from the SEER database to construct a nomogram to predict overall survival (OS) of LSCC patients receiving surgical treatment.

Study design, data source and ethics statement

We conducted a retrospective data analysis of SEER database. The SEER database collects information on patient demographics, primary tumor site, tumor morphology and stage at diagnosis, first course of treatment, and mortality outcomes from 18 cancer registries of the National Cancer Institute since 1973. Data of approximately 35% of US population are included on SEER database^[14]. We obtained permission to access the dataset (authorization code: 11874-Nov2018). The data were extracted from the SEER database via SEER*Stat software (version 8.3.8; http://seer.cancer.gov/seerstat/). Primary cancer classification was confirmed according to the International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3), which identifies cancer categories according to primary site, histology, behavioral code, and classification/differentiation. This study was complied with the Declaration of Helsinki (as revised in 2013), and approved by the committee of Institutional Review Board of Changzheng Hospital (Naval Medical University, Shanghai, China). The content is solely the responsibility of the authors and does not necessarily represent the official views of the SEER database and NCI.

Cohort selection

From SEER database, patients were selected into our study cohort with eligibility criteria as followed: (I) age ＞18 years old; (II) patients diagnosed merely with primary cancer between 2010 and 2015 with tumor site codes C34.0-C34.9; (III) positive pathologic confirmation of histology codes 8051, 8052, 8070-8076, 8078, 8083, 8084, 8090, 8094, 8120, and 8123; (IV) patients who received surgical procedures. Exclusion criteria were as follows: (I) lack of essential details such as race, grade, TNM, laterality, marital status, insurance status, tumor size, and survival outcomes; (II) patients who died within one month after their operation for excluding the possibility that patients died of the operation or surgical complications; (III) patients with nodal (N2 and N3) or distant (M1) metastases for most of those patients not meeting the indication for an operation. The TNM stages of the patients in the SEER database were updated in line with the 8th edition of the AJCC criteria^[15].

Study covariates and outcomes

Information on the baseline demographics data of the patients included age at diagnosis, gender, race, marital status, and insurance status. Moreover, histologic features of tumor including primary site, laterality, grade, T classification, and N classification were extracted from the SEER. We also extracted therapeutic strategies including type of surgery, radiotherapy, and chemotherapy. Considering the optimum cutoff value according to X-tile software, the patients were categorized by age at diagnosis into three groups: ＜67 years, 67-77 years, and ＞77 years. Cases with known race were classified as black, white, and others (American Indian/AK Native, and Asian/Pacific Islander). T classification contained the tumor size parameters, which was grouped into T1, T2, T3, and T4. In addition, The N stage was grouped into N0, and N1 because of exclusion of N2 and N3 patients.

In the current study, overall OS was chosen as the endpoint of our study. Causes of death were coded by the SEER database according to information extracted from the death certificate data. OS was defined as the time from diagnosis to all-cause death. The SEER program is updated annually, including patients’ information on follow-up and prognosis. In this study, the latest patient information was released in December 2016. Therefore, the survival time was measured as the number of months from diagnosis until death or the last follow-up (December 31, 2016) for censored observations.

Construction and validation of the nomograms

For the construction of the nomogram, firstly the multivariable Cox regression models for 1-, 2- and 3-year OS with the optimal predictive performance were transformed into nomograms. The “plot” and “nom” function in the “rms” package were applied. In addition, time-dependent receiver operating characteristic (ROC) curves as well as the corresponding the area under the curve (AUC) at 1, 2, and 3 years was generated to assess the accuracy of the prediction. For the purpose of comparing the consistency between the nomogram-predicted and actual survival, calibration curves were plotted to evaluate calibration for 1-, 2-, and 3-year OS. Decision curve analysis (DCA) was performed to compare clinical benefit and application value.

Statistical analysis

All measurement data were converted into categorical data which were expressed as frequencies with percentage. Comparisons of baseline characteristics between the training and external validation cohorts were performed using Mann-Whitney U test or the chi-square test for ranked data or categorical data as appropriated.

Survival curves were plotted using the Kaplan-Meier method, stratified according to the clinical variables, and compared with the log-rank test. Univariable and multivariable Cox regression analyses were utilized for all variables to identify all risk factors independently associated with OS. Variables with a p-value ≤ 0.10 in univariable logistic regression analysis were then analyzed in multivariable logistic regression analysis. Spontaneously, hazard ratios (HR) were calculated with two-sided 95% confidence intervals (CI).

For all statistical analyses, a two-sided P<0.050 was considered to be statistically significant. All statistical analyses were performed using SPSS software (version 22.0; IBM Corporation, St. Louis, Missouri, USA) and R software (version.3.6.1; The R Project for Statistical Computing, TX, USA; http://www.r-project.org).

Patients characteristics

Data on 56,376 patients diagnosed with LSCC from the SEER database between January, 2010 and December, 2015, were collected. After considering these inclusion and exclusion criteria, our final study cohort consisted of 8,078 patients with LSCC. The total enrolled patients were randomized into a training cohort (n = 5,656) and a validation cohort (n = 2,422). Details on the inclusion and exclusion process are given in Table S1. Baseline demographic and clinicopathological characteristics of patients stratified by training and validation cohorts were summarized in Table 1. Among the 8,078 patients, 4,840 (59.9%) were male and 3,238 (40.1%) were female, and the number of patients aged less than 67, between 67 and 77, and over 77 were 2,894 (35.8%), 3,820 (47.3%), and 1,364 (16.9%), respectively. More patients were diagnosed at upper lobe of lung (58.8%), were diagnosed at T1 stage (50.3%) and N0 stage (83.4%), and received lobectomy (70.9%). In addition, the age composition was significantly different between the two groups (P = 0.010). Overall, baseline characteristics were comparable between both cohorts.

Table 1

Characteristics of LSCC in the training and validation cohorts
Characteristic	Training cohort (n = 5,656)		Validation cohort (n = 2,422)		P value
Characteristic	N	%	N	%	P value
Age at diagnosis (year)					0.010
༜67	2074	36.7	820	33.9
66–77	2666	47.1	1154	47.6
༞77	916	16.2	448	18.5
Gender					0.184
Male	3362	59.4	1478	61.0
Female	2294	40.6	944	39.0
Race					0.339
White	4953	87.6	2149	88.7
Black	444	7.9	171	7.1
Other	259	4.6	102	4.2
Marital status at diagnosis					0.563
Married	3209	56.7	1391	57.4
Unmarried	2447	43.3	1031	42.6
Insurance status at diagnosis					0.570
Insured	5572	98.5	2390	98.7
Uninsured	84	1.5	32	1.3
Laterality					0.848
Right	3140	55.5	1339	55.3
Left	2516	44.5	1083	44.7
Primary site					0.443
Main bronchus	67	1.2	38	1.6
Upper lobe	3347	59.2	1406	58.1
Middle lobe	1858	32.9	823	34.0
Lower lobe	232	4.1	89	3.7
Other	152	2.7	66	2.7
Differentiation grade					0.370
I (Well differentiated)	153	2.7	81	3.3
II (Medium differentiated)	2784	49.2	1162	48.0
III (Poorly differentiated)	2674	47.3	1161	47.9
IV (Undifferentiated)	45	0.8	18	0.7
T stage					0.135
T1	2885	51.0	1182	48.8
T2	1637	28.9	710	29.3
T3	761	13.5	369	15.2
T4	373	6.6	161	6.6
N stage					0.249
N0	4698	83.1	2037	84.1
N1	958	16.9	385	15.9
Surgical type					0.550
Lobectomy	4002	70.8	1727	71.3
Pneumonectomy	724	12.8	289	11.9
Sublobectomy	930	16.4	406	16.8
Radiotherapy					0.625
No/Unknown	5074	89.7	2164	89.3
Yes	582	10.3	258	10.7
Chemotherapy					0.501
No/Unknown	4342	76.8	1876	77.5
Yes	1314	23.2	546	22.5
LSCC, lung squamous cell carcinoma.

Nomogram variable screening

Univariable Cox regression analysis of OS showed that age, gender, marital status, T stage, N stage, surgical type, and radiotherapy were significant influencing factors. After multivariable Cox regression analysis, age (＞77: HR 1.90; 95% CI: 1.68-2.15; P＜0.001), gender (male: HR 1.30; 95% CI: 1.19-1.43; P＜0.001), marital status (unmarried: HR 1.20; 95% CI: 1.10-1.31; P＜0.001), T stage (T2: HR 1.25; 95% CI: 1.13-1.39; P＜0.001), N stage (N1: HR 1.27; 95% CI: 1.14-1.42; P＜0.001), radiotherapy (HR 1.37; 95% CI: 1.21-1.55; P＜0.001), surgical type (pneumonectomy: HR 1.24; 95% CI: 1.09-1.40; P=0.001 ) were significant predictors of OS (Table 2).

Table 2. Univariable and multivariable Cox regression analyses of predictors for OS in patients with LSCC

Characteristics	Univariable analysis		Multivariable analysis
Characteristics	HR (95%CI)	P value	HR (95%CI)	P value
Age at diagnosis (year)
＜67	1.00	-	1.00	-
66-77	1.27 (1.15,1.4)	＜0.001	1.41 (1.28,1.56)	＜0.001
＞77	1.74 (1.54,1.95)	＜0.001	1.90 (1.68,2.15)	＜0.001
Gender
Female	1.00	-	1.00	-
Male	1.27 (1.16,1.39)	＜0.001	1.30 (1.19,1.43)	＜0.001
Race
Black	1.00	-	-	-
White	1.07 (0.91,1.25)	0.436	-	-
Other	0.95 (0.74,1.23)	0.704	-	-
Marital status at diagnosis
Married	1.00	-	1.00	-
Unmarried	1.12 (1.03,1.22)	0.008	1.20 (1.10,1.31)	＜0.001
Insurance status at diagnosis
Insured	1.00	-	-	-
Uninsured	0.97 (0.68,1.39)	0.879	-	-
Laterality
Left	1.00	-	-	-
Right	1.02 (0.93,1.11)	0.709	-	-
Primary site
Main bronchus	1.00	-	-	-
Upper lobe	0.79 (0.54,1.14)	0.206	-	-
Middle lobe	0.89 (0.61,1.30)	0.550	-	-
Lower lobe	0.69 (0.45,1.06)	0.092	-	-
Other	1.14 (0.74,1.76)	0.559	-	-
Differentiation grade
II (Medium differentiated)	1.00	-	-	-
I (Well differentiated)	0.99 (0.76,1.30)	0.958	-	-
III (Poorly differentiated)	1.08 (0.99,1.17)	0.093	-	-
IV (Undifferentiated)	1.35 (0.88,2.09)	0.168	-	-
T stage
T1	1.00	-	1.00	-
T2	1.27 (1.15,1.40)	＜0.001	1.25 (1.13,1.39)	＜0.001
T3	1.54 (1.36,1.74)	＜0.001	1.49 (1.31,1.70)	＜0.001
T4	2.42 (2.09,2.80)	＜0.001	2.22 (1.90,2.61)	＜0.001
N stage
N0	1.00	-	1.00	-
N1	1.42 (1.28,1.57)	＜0.001	1.27 (1.14,1.42)	＜0.001
Surgical type
Lobectomy	1.00	-	1.00	-
Pneumonectomy	1.52 (1.35,1.70)	＜0.001	1.24 (1.09,1.40)	0.001
Sublobectomy	1.29 (1.16,1.44)	＜0.001	1.38 (1.23,1.55)	＜0.001
Radiotherapy
No	1.00	-	1.00	-
Yes	1.61 (1.43,1.81)	＜0.001	1.37 (1.21,1.55)	＜0.001
Chemotherapy
No	1.00	-	-	-
Yes	1.03 (0.93,1.14)	0.555	-	-

Note: data were presented as hazard ratio (HR) and a 95% confidence interval (CI). OS, overall survival; LSCC, lung squamous cell carcinoma; HR, Hazard ratio; CI, confidence interval.

Nomogram construction and validation

Seven independent risk factors including age, gender, marital status, T stage, N stage, surgical type, and radiotherapy were utilized as prognostic predictors to construct the nomogram. T stage has the effect with the highest beta, followed by age, surgical type, radiation, and so on, and marital status has the least influence. Figure 1 showed a given LSCC patient who meets the following conditions: male, aged＜67, unmarried, T3N1M0, lobectomy, and no radiotherapy. In this example, the patient scores 136 points, which predicts the probability of 1-, 2-, and 3- year OS are 0.832, 0.707, and 0.597, respectively.

The AUC value in the ROC curve analysis revealed 0.658, 0.651, and 0.647 in the training cohort and 0.673, 0.667, and 0.658 at 1-, 2-, and 3-year survival in the external validation cohort, respectively (Figure 2). The ROC and AUC results indicated that the nomogram performed with a favorable discrimination. Moreover, the calibration curves showed satisfactory consistencies between nomogram-predicted and observed survival probability at 1-, 2-, and 3- year OS in both cohorts (Figure 3). The DCA curve showed the established nomogram had great clinical net benefit, indicating high clinical application value (Figure 4).

Risk stratification and survival analysis

Finally, risk stratification systems were developed according to the total points on the established nomogram to distinguish the prognosis (Figure 5). Patients were divided into three risk groups: low risk (total points＜95), middle risk (95≤total points＜143), high risk (total points≥143). The Kaplan-Meier survival curves indicated that significant difference in OS were observed among the three groups (P＜0.001). In addition, patients classified into low-risk group had a higher survival probability than those in other groups.

In this study, a specific nomogram was constructed and validated to predict 1-, 2-, and 3-year OS of postoperative patients with LSCC. We performed nomogram variables screening and found seven clinical factors to establish a predictive nomogram for the prognosis of LSCC patients receiving a surgical procedure, as well as risk stratifications. Seven clinical risk factors were age, gender, marital status, T stage, N stage, surgical type, and radiotherapy. In terms of the predictive performance, we conducted ROC curve analysis, calibration curves, and DCA curve, which indicated clinical application value in guiding individualized treatment of patients.

Survival prediction tools, such as the medical nomogram, for postoperative patients with LSCC can play an important role in generating a probability of a clinical event, such as cancer recurrence or death, for a given individual^[16]. Recently, a growing number of studies have focused on survival for patients with NSCLC including LSCC and LUAD^[17–19]. However, much of the current effort in lung cancer nomogram studies is focused on NSCLC and LUAD^{[20, 21]} because NSCLC accounts for about 85% of lung cancers, with LUAD being the most abundant among the NSCLC subtypes^[22]. By contrast, fewer studies have examined the prognosis of LSCC patients. Zhang ^[23] constructed a nomogram to accurately predict the incidence of brain metastasis in patients with LSCC, which may contribute to the early identification of high-risk LSSC patients and the establishment of individualized treatment. This study focused on the brain metastasis while our study excluded M1, N2, and N3 stage LSCC patients who were not suitable for surgical treatment and focused on the patients’ survival. Zheng ^[24] performed a nomogram study that had several similarities to ours, which satisfactorily predicted 3-, 5-, and 7-year cancer-specific survival and OS rates for patients with LSCC. This study applied limited methods to evaluate the quality of predicted models and paid attention to all-stage lung cancer patients regardless of whether there were any indications for surgery or not. But it is noteworthy that LSCC and LUAD differ in the composition of genes, molecular characteristics, such as epidermal growth factor receptor gene mutations, and prognosis^[25]. Meanwhile, as the low-dose computed tomography screening in lung cancer diagnosis extensively applied, the identification of early-stage lung cancer has increased remarkably, making it possible for lung cancer patients to share more opportunities of a more conservative surgery and a better long-term survival^{[26, 27]}. It may be more reasonable to separately discussed the operable and inoperable LSCC patients as the prognosis of lung cancer patients with different stage varies greatly^[28]. Thus, we carried out the first study to predict 1-, 2-, and 3-, OS of postoperative LSCC patients selected from SEER database, which tends to stay close to the clinical problem.

In addition, Li ^[17] identified eight RNA binding proteins as prognosis-related hub genes, which were used to construct a prognostic nomogram model for overall survival of LUAD patients. Liu ^[18] found that thirty-three autophagy-associated genes could dichotomize patients with significantly different OS and independently predict the OS in LUAD and LSCC patients, respectively. Most of those nomogram-related prognosis studies applied ROC curve analysis, calibration curves, and DCA curve to assess the predictive performance. Those survival-related nomograms were all based on the information at the transcriptional or translational level. However, sequencing data is relatively difficult to achieve due to the fact that sequencing platforms may be not accessible or economical to all medical institutions and patients. Our predictive nomogram was based on patients’ clinicopathological and demographic characteristics which are more readily accessible to individual health professionals with the wide application of electronic medical record systems.

Multivariable Cox regression analysis showed that seven clinical factors including age, gender, marital status, T stage, N stage, surgical type, and radiation were significantly associated with OS. Among them, patients aged > 77 years presented a significantly higher risk of death than those aged < 67, implying that ageing is a poor prognostic factor for lung squamous cell carcinoma, which is consistent with previous studies^{[29, 30]}. Many studies revealed sex differences existing in survival and female lung cancer patients have higher survival^{[31, 32]}. Our nomogram showed the same result that male was a parameter with a higher score in the nomogram, which was negatively correlated with survival probability. As for marital status, several reports suggested it was significantly associated with survival in patients with lung cancer^{[33, 34]}. TNM stage, well-known prognostic factors, had significant contribution to prognosis for NSCLC patients^[35]. The mainstay of treatment of potentially resectable stage I, II, and part III lung cancer patients is surgical removal of the tumor, which includes pneumonectomy, lobectomy, sublobectomy, and so on^[36]. Our findings indicated that the nomogram variable lobectomy scored highest of the surgical types, as followed by pneumonectomy and sublobectomy and the nomogram scores was negatively correlated with the prognosis. Lung resection had a significant impact on pulmonary circulation and ventilation, as well as posing a hurdle in terms of elevating survival and different surgical types had differences in long-term survival^[37]. For instance, lobectomy has been associated with lower surgical complications and operative mortality rates than pneumonectomy, which may be associated with smaller surgical resection and more normal lung tissue preserved^[37]. Some researchers also compared the effect of surgical types on long-term survival of LSCC patients. Gezer ^[38] performed a retrospective study which indicated that LSCC patients receiving sleeve lobectomy proved to a lower mortality than standard pneumonectomy and resulted in better lung function and quality of life.

Our study has several strengths. Firstly, this study was the first to establish a long-term predictive nomogram for postoperative LSCC patients based on SEER database. It is worth noting that earlier and more accurate prediction of long-term survival in LSCC patients undergoing lung surgery could provide more time for physicians to offer customized treatment strategies. Secondly, our study had a large sample size including 8,078 LSCC patients, allowing us to clarify an available prognostic model. Thirdly, because our study was a population-based study, our results are representative of real-world clinical practice. Fourthly, compared to nomograms based on genomic data, our nomogram can be more readily adapted to different application scenarios varying from hospitals in smaller and rural communities to high-volume hospitals as a result of more accessible clinical characteristics.

There are several limitations of our study. Firstly, even though the SEER registries include patients from eighteen population-based regions, the data may not be generalizable to other regions not covered by SEER. Therefore, future multicenter and prospective studies are required to prove or disprove these findings. Secondly, nomogram variables derived from SEER database doesn’t cover clinical data such as symptoms and signs, co-morbidities, past cigarette use, recent weight loss, family history of cancer, type of radiotherapy, and occupational exposures, which could bias study findings. In addition, clinical data was not universally reasonable in SEER database. For example, tumor staging in SEER database was based on AJCC 6th and 7th edition TNM classifications which are not in accordance with the 8th edition. Although we updated the T, N, and M stages according to the 8th criteria, it still couldn’t reflect the real clinical situation. It could also lead to selection bias that patients who received and were lack of chemotherapy were classified into the same category. Thirdly, with the assumption that the predictors interact in an additive and linear way, our predictive model was developed using the traditional Cox regression analysis. The predictive power of the nomogram could be limited as a result of ignoring the fact that the interactions between predictors are inherently non-linear and multifactorial^[39].

In the current study, seven independent risk factors including age, gender, marital status, T stage, N stage, surgical type, and radiation were identified as independent predictive indicator of prognosis for postoperative LSCC patients in SEER database. We constructed and validated a prognostic nomogram for OS of LSCC patients after surgical treatment, which may be useful to assist practitioners in evaluating prognosis and offering highly individualized therapy during their clinical practice.

LSCC: lung squamous cell carcinoma; NSCLC: non-small cell lung cancer; LUAD: lung adenocarcinoma; TNM: tumor, node, metastasis staging system; AJCC: American Joint Committee on Cancer; SEER: Surveillance, Epidemiology and End Results; OS: overall survival; ICD-O-3: International Classification of Diseases for Oncology, 3rd Edition; ROC: receiver operating characteristic curve, AUC: area under ROC curve, DCA: decision curve analysis; HR: hazard ratios; CI: confidence intervals.

Ethics approval and consent to participate

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study utilized the anonymous data available in the SEER database with pre-existing institutional review board approval.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

DATA AVAILABILITY STATEMENT

Publicly available datasets were analyzed in this study.

Competing interests

The authors declare that they have no competing interests.

Funding

Shanghai Leading Talent Project (No. 2015044 to ZW)

Authors' contributions

ZW, JR, and YY conceived the article. JR extracted and analyzed all data. JR, YY, and LZ co-wrote the paper. YY and PW undertook and refined the inclusion process. JR and YY undertook the statistical analyses. YY directed the writing of discussion section. YY, PW, and ZW were consulted for clinical issues. All authors contributed to and revised the final manuscript.

Acknowledgements

None.

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Table S1 is not available with this version.

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Figure S1. Flowchart of included patients. LSCC, lung squamous cell carcinoma.

A nomogram for predicting postoperative overall survival of patients with lung squamous cell carcinoma: a SEER-based study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

Study design, data source and ethics statement

Results

Patients characteristics

Discussion

Conclusion

Abbreviations

Declarations

References

Table S1

Supplementary Files

Status:

Version 1