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Abstract

Background

This study aimed to develop a novel predictive nomogram to identify specific stage IB non-small cell lung cancer (NSCLC) populations who could benefit from adjuvant chemotherapy (ACT).

Method

Stage IB NSCLC patients were included in the Surveillance, Epidemiology, and End Results (SEER) database and divided into the ACT and non-ACT groups. Then the methods of Kaplan-Meier analysis, propensity score matching (PSM), Least absolute shrink and selection operator (LASSO) regression, and multivariate logistic regression analyses were implemented. Finally, the predictive nomogram was constructed and validated.

Results

9055 stage IB NSCLC patients were enrolled from the SEER database while 47 patients from Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University were identified as the external validation cohort. Of these patients, 1334 cases underwent ACT while the other 7721 patients didn’t receive ACT. After PSM, the patients in the ACT group presented longer median overall survival (100 vs 82 months, P < .001). Among the ACT group, 482 (49.6%) patients achieving more prolonged overall survival than 82 months were regarded as the beneficiary population. Then the LASSO regression and multivariate logistic regression analyses were implemented. Finally, 8 predictors were selected for model construction, including age, gender, marital status, laterality, pathology, tumor size, regional nodes examined, and tumor size. The predictive nomogram demonstrated good discrimination in the training cohort (AUC = .781), internal validation cohort (AUC = .772), and external validation cohort (AUC = .851). And calibration curves indicated ideal consistency between the predicted and observed probabilities. Decision curve analysis presented a clinically useful model.

Conclusion

The practical nomogram could guide treatment decision-making and select optimal ACT candidates among stage IB NSCLC patients.

Keywords

non-small cell lung cancer SEER program nomogram adjuvant chemotherapy

Introduction

Lung cancer is the leading cause of cancer mortality among malignant tumors.¹ Non-small cell lung cancer (NSCLC) is the most frequent subtype,² with a 5-year relative survival rate to be 12-15%.³ The curative-intent anatomic operation is the best therapeutic method for early-stage NSCLC, providing the greatest long-term survival opportunity. However, 18-32% of stage IB NSCLC patients died from tumor recurrence within 5 years following operation.^4,5 Adjuvant chemotherapy (ACT), a comprehensive therapy, can decrease the onset of recurrence and distant metastases. It has been regarded as the standard therapy for stage II and III NSCLC patients after operation, with an approximately 5% improvement in 5-year survival.^6-8

However, whether postoperative ACT is required among stage IB NSCLC patients remains controversial. The National Comprehensive Cancer Network guideline recommends postoperative ACT for high-risk patients with characteristics of poor differentiation grade, unspecified N stage, and visceral pleural invasion.⁹ But according to the American Society of Clinical Oncology guidelines, there is no adequate evidence supporting ACT in stage IB NSCLC patients.¹⁰

There is no consensus on the role of ACT in stage IB NSCLC patients. Numerous research demonstrated that ACT could prolong overall survival (OS) in these patients.^11-13 Jang et al.’s study enrolled 328 stage IB patients, observing that ACT could significantly prolong the 5-year recurrence-free survival (RFS) and OS compared to the non-ACT patients.¹¹ Another study found that postoperative stage IB NSCLC patients could benefit from ACT regardless of tumor size.¹² Conversely, several studies indicated that survival did not differ significantly by ACT.^6,14,15 Despite the controversial role of ACT, it’s clear that a specific group of these patients can obtain more prolonged survival after ACT. Identifying candidates who could gain potential benefits from ACT is an urgent issue that needs to be addressed.

The retrospective research aimed to use the Surveillance, Epidemiology, and End Results (SEER) database to construct an effective model to identify specific stage IB NSCLC populations who could benefit from ACT.

Materials and Methods

Data Source and Patient Selection

The records of NSCLC patients between 2010 and 2015 were extracted from the SEER database using SEER*Sat software (Version 8.3.5). And the external validation data was extracted from Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University from January 2012 to December 2016. The inclusion criteria were as follows: (1) age over 18 years; (2) NSCLC as the only histologically confirmed primary tumor; (3) patients underwent lobectomy, sublobar resection, or pneumonectomy; (4) pathological diagnosis as stage IB according to the eighth edition of the AJCC Staging Manual; (5) no lymph nodes or distant metastasis. The exclusion criteria were as follows: (1) patients who survived less than 1 month; (2) patients who received radiotherapy; (3) patients without complete clinicopathologic features, demographic data, and survival information. The workflow of the research is presented in Figure 1. The project involving human participants was reviewed and approved by the Ethics Committee of Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University.

Figure 1.

The workflow of the patient selection process.

Clinicopathological Variables

Demographic data (age, gender, race, marital status), features of tumors (size, laterality, grade, primary site, and pathological subtype), treatment information (chemotherapy, surgical type, regional nodes examined), and follow-up details (survival status and survival time) were retrieved from the SEER database. The stage IB patients were reclassified according to the eighth edition TNM (tumor node metastasis) classification based on the sixth edition recorded in the SEER database. The ACT patients who presented longer OS than the median OS in the non-ACT cohort were regarded as the benefit population.

Statistical Analysis

The chi-square test was used for categorical data. Then the subgroup analysis was performed using a univariate Cox proportional hazard model. Hazard ratios (HRs) and 95% confidential intervals (CIs) in each subgroup were recorded in the forest plots. To adjust for the potential imbalance between groups, a propensity score matching (PSM) method was applied.¹⁶ Cases in both cohorts were one-to-one matched by the nearest-neighbor algorithm. Standardized difference (SD) was used to assess the alternation of variables before and after PSM. Then the ACT cohort was randomly separated into a training set (70%, n = 680) and a validation set (30%, n = 291) for further analysis.

Least absolute shrink and selection operator (LASSO) regression, a method used to identify independent risk factors, could remarkably prevent redundancy or overfitting of the multifactor models.¹⁷ After selecting significant variables by LASSO regression, a multivariable logistic regression model was constructed on the training set. Then a predictive nomogram was developed to identify potential ACT-beneficial cases. In the model, a vertical line was drawn to each parameter’s “points” line, and the sum of every point corresponds to the benefit probability.

The discrimination of the predictive model was assessed by receiver operating characteristics (ROC) curves and the area under the curves (AUC). AUC with a higher value indicated that the prediction power was greater. The calibration curve was used to measure if the predicted and observed survival probabilities were consistent. To further evaluate the benefits and advantages of the nomogram, the decision curve analysis (DCA) was performed. It could estimate the practicability of the nomogram by plotting net benefit (NB) at a range of clinically reasonable risk thresholds.¹⁸ The entire evaluation procedure was repeated 1000 times using bootstrapping.

R software (version 4.0.3) was employed for the statistical analyses and visualizations. It was determined that a P-value <.05 (2-sided) was statistically significant.

Results

Clinicopathological Characteristics

9055 patients were enrolled in the SEER database from 2004 to 2015. And 47 patients from Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University were identified as the external validation cohort. Of these patients, 1334 cases underwent ACT while the other 7721 patients didn’t receive ACT. There were significant dissimilarities between the 2 cohorts in the characteristics, including age, marital status, grade, pathology, and tumor size. The patients who underwent ACT tended to present a higher proportion of marital status (61.7% vs 57.4%), grade III (43.6% vs 37.0%), grade IV (3.4% vs 1.9%), and tumor size over 3 cm (84.0% vs 79.9%). The non-ACT group presented more percentage of squamous cell carcinoma (35.6% vs 27.7%) than the patients in the ACT group.

Then PSM was performed to decrease the confounding factors’ effects between the 2 cohorts. After PSM, SD in most variables was less than .1, which indicated good balancing performance (Figure S1). The patients were divided into cohort ACT (n = 1329) and cohort non-ACT (n = 1329). Table 1 displays the baseline characteristics before and after PSM.

Table 1.

The Characteristics of Stage IB NSCLC Patients Before and After PSM.

Characteristics	Before PSM			P Value	After PSM			P Value
	All	Chemo	Non-chemo		All	Chemo	Non-chemo
	N = 9055	N = 1334	N = 7721		N = 2658	N = 1329	N = 1329
Age	69.6 (9.4)	64.6 (9.2)	70.4 (9.2)	<.001	64.7 (9.2)	64.7 (9.1)	64.6 (9.4)	.809
Gender:				.085				.587
Female	4476 (49.4%)	689 (51.6%)	3787 (49.0%)		1387 (52.2%)	686 (51.6%)	701 (52.7%)
Male	4579 (50.6%)	645 (48.4%)	3934 (51.0%)		1271 (47.8%)	643 (48.4%)	628 (47.3%)
Race:				.933				.957
White	7667 (84.7%)	1128 (84.6%)	6539 (84.7%)		2250 (84.7%)	1124 (84.6%)	1126 (84.7%)
Non-White	1388 (15.3%)	206 (15.4%)	1182 (15.3%)		408 (15.3%)	205 (15.4%)	203 (15.3%)
Marital status:				.003				.426
Married	5253 (58.0%)	823 (61.7%)	4430 (57.4%)		1621 (61.0%)	821 (61.8%)	800 (60.2%)
Unmarried	3802 (42.0%)	511 (38.3%)	3291 (42.6%)		1037 (39.0%)	508 (38.2%)	529 (39.8%)
Grade:				<.001				.397
I	1038 (11.5%)	111 (8.3%)	927 (12.0%)		210 (7.9%)	111 (8.4%)	99 (7.4%)
II	4384 (48.4%)	597 (44.8%)	3787 (49.0%)		1232 (46.4%)	596 (44.8%)	636 (47.9%)
III	3441 (38.0%)	581 (43.6%)	2860 (37.0%)		1133 (42.6%)	577 (43.4%)	556 (41.8%)
IV	192 (2.1%)	45 (3.4%)	147 (1.9%)		83 (3.1%)	45 (3.4%)	38 (2.9%)
Pathology:				<.001				.58
Adenocarcinoma	5645 (62.3%)	884 (66.3%)	4761 (61.7%)		1787 (67.2%)	881 (66.3%)	906 (68.2%)
Squamous cell carcinoma	3116 (34.4%)	370 (27.7%)	2746 (35.6%)		721 (27.1%)	370 (27.8%)	351 (26.4%)
Others	294 (3.2%)	80 (6.0%)	214 (2.8%)		150 (5.6%)	78 (5.9%)	72 (5.4%)
Laterality:				.066				1
Left	3747 (41.4%)	521 (39.1%)	3226 (41.8%)		1037 (39.0%)	519 (39.1%)	518 (39.0%)
Right	5308 (58.6%)	813 (60.9%)	4495 (58.2%)		1621 (61.0%)	810 (60.9%)	811 (61.0%)
Primary site:				.306				.121
Lower lobe	2900 (32.0%)	420 (31.5%)	2480 (32.1%)		837 (31.5%)	420 (31.6%)	417 (31.4%)
Upper lobe	5480 (60.5%)	801 (60.0%)	4679 (60.6%)		1622 (61.0%)	796 (59.9%)	826 (62.2%)
Others	675 (7.5%)	113 (8.5%)	562 (7.3%)		199 (7.5%)	113 (8.5%)	86 (6.5%)
Regional nodes examined:				.398				.305
0	765 (8.4%)	125 (9.4%)	640 (8.3%)		230 (8.7%)	124 (9.3%)	106 (8.0%)
1-3	1580 (17.4%)	226 (16.9%)	1354 (17.5%)		434 (16.3%)	224 (16.9%)	210 (15.8%)
≥4	6710 (74.1%)	983 (73.7%)	5727 (74.2%)		1994 (75.0%)	981 (73.8%)	1013 (76.2%)
Tumor size (cm):				<.001				.486
≤3	1767 (19.5%)	213 (16.0%)	1554 (20.1%)		412 (15.5%)	213 (16.0%)	199 (15.0%)
>3	7288 (80.5%)	1121 (84.0%)	6167 (79.9%)		2246 (84.5%)	1116 (84.0%)	1130 (85.0%)
Surgery:				.06				.817
Lobectomy	7504 (82.9%)	1107 (83.0%)	6397 (82.9%)		2219 (83.5%)	1104 (83.1%)	1115 (83.9%)
Sublobar resection	1359 (15.0%)	188 (14.1%)	1171 (15.2%)		365 (13.7%)	186 (14.0%)	179 (13.5%)
Pneumonectomy	192 (2.1%)	39 (2.9%)	153 (2.0%)		74 (2.8%)	39 (2.9%)	35 (2.6%)

Survival Analysis

In the Kaplan-Meier analysis, significant differences in OS were found. Before PSM, the patients who underwent ACT presented longer median OS than the non-ACT cohort (100 vs 72 months, P < .001) (Figure 2(A)). After PSM, a similar result was observed (100 vs 82 months, P < .001) (Figure 2(B)). Then the subgroup analysis of pathology was performed, indicating that the ACT group present better OS in adenocarcinoma (P < .001), but not in squamous carcinoma (P > .05) (Figure 2(C)-(D)). In the forest plots, HRs in most subgroups were less than 1 before and after PSM, indicating that stage IB patients could benefit from ACT in most subgroups (Figure 3). Then 358 ACT patients diagnosed between 2013 and 2015 (longest follow-up time as 71 months) were excluded from further study because we couldn’t specify if these patients occurred the event of interest.

Figure 2.

Overall survival curves in the ACT group and non-ACT group. (A) Before PSM. (B) After PSM. (C) Patients with adenocarcinoma. (D) Patients with squamous cell carcinoma.

Figure 3.

Forest plot of hazard ratios (HRs) for overall survival in the 2 groups. Diamonds represent effect size, calculated separately in different subgroups, and error bars indicate 95% CIs. (A) Before PSM. (B) After PSM.

Independent Prognostic Factors Selection

The patients in the ACT cohort were used for further analysis and randomly divided into a training group (70%, n = 680) and an internal validation group (30%, n = 291). The essential characteristics of the 2 groups are shown in Table S1. A total of 11 variables were included in the LASSO logistic regression based on the training group to prevent overfitting. Finally, 8 predictors were selected for multivariate logistic regression and model construction, including age, gender, marital status, laterality, pathology, tumor size, regional nodes examined, and tumor size (Figure 4, Table 2).

Figure 4.

Feature selection using the LASSO logistic regression. (A) Profiles of LASSO coefficient for clinical and pathological features. (B) Selection of tuning parameter (lambda) in the LASSO regression using 5-fold cross-validation.

Table 2.

Logistic Regression Analysis of the Significant Factors for ACT Benefit.

Characteristics	OR	95%CI	P Value
Age	.97	.96-.99	.003
Gender
Female	Reference
Male	.87	.63-.92	.037
Marital status
Married	Reference
Unmarried	0.7	.51-.98	.038
Pathology
Adenocarcinoma	Reference
Squamous cell carcinoma	.88	.73-.95	.022
Others	.66	.34-1.28	.221
Laterality
left	Reference
Right	.73	.52-.86	.003
Regional_nodes_examined
0	Reference
1-3	1.8	.87-3.73	.114
≥4	2.42	1.23-4.75	.01
Tumor size
≤3	Reference
>3	1.21	1.05-2.06	.001
Surgery
Lobectomy	Reference
Sublobar resection	0.6	.34-.78	.016
Pneumonectomy	.75	.33-1.69	.482

Constructing and Verifying the Nomogram

A predictive nomogram was constructed to identify potential ACT-beneficial cases based on the variables selected by LASSO analysis (Figure 5). The total points were calculated by adding the score in each row of variables, which was used to assess the likelihood of ACT-beneficial probability in stage IB NSCLC patients.

Figure 5.

The nomogram to identify candidates among stage IB NSCLC patients who could obtain survival benefits from ACT. The calculated points correspond to a benefit probability.

The AUC value assessed the discrimination ability of the model in the training cohort (AUC = .781), internal validation cohort (AUC = .772), and external validation group (AUC = .851), which presented good discriminatory power (Figure 6(A), (D), and (G)). The calibration plots showed ideal consistency between the predicted and observed probabilities (Figure 6(B), (E), and (H)). Then the DCA curves demonstrated the good clinical practical utility of the predictive model in the 3 sets (Figure 6(C), (F), and (I)). Thus, the nomogram exhibited powerful predictive performance and accurate predictive efficacy.

Figure 6.

ROC curves, calibration curves, and DCA curves of the nomogram in the training, internal validation, and external validation cohorts. (A-C) Training cohort. (D-F) Internal validation cohort. (G-I) External validation cohort.

Discussion

Some studies had investigated the survival of postoperative stage IB NSCLC patients and constructed prognostic stratification models.^19,20 However, these prognostic models only presented the prognosis at a specific point in time. And the role of ACT wasn’t deeply expatiated. When clinicians made decisions, selecting appropriate patients for ACT was an important issue. This study discovered that stage IB NSCLC patients who underwent ACT presented a better prognosis than those who did not. A predictive nomogram was constructed to identify the patients who could benefit from postoperative ACT. The nomogram incorporating age, gender, pathology, regional nodes examined, surgery, and radiation presented potential clinical applicability. To the best of our knowledge, this was the first study to establish a novel nomogram to identify stage IB NSCLC patients who might benefit from ACT.

Previous research had revealed that stage IB NSCLC patients could obtain survival benefits from ACT.^19,21-25 Jang et al and a meta-analysis observed that stage IB NSCLC patients who received ACT significantly prolonged the 5-year RFS and OS compared to the non-ACT patients after radical operation.^11,26 Tu et al. also discovered that stage IB NSCLC patients who underwent ACT had a considerably longer OS than those who were not.¹⁹ Furthermore, 2 studies based on the National Cancer Data Base found that patients with node-negative and diameter less than 4 cm tumors could also benefit from ACT.^27,28 However, several studies proposed the opposite view. A meta-analysis suggested that patients with stage IB did not present a clear benefit from ACT.¹⁵ A study by Moon Y et al involving 211 stage IB NSCLC patients, also showed that ACT did not improve OS.²⁹ However, only 20 patients received ACT in the study, so the conclusions should be cautiously taken. CALBG 9633 trial, the only randomized controlled study (RCT) primarily focused on individuals with stage IB NSCLC, failed to reveal an OS benefit with ACT.¹⁴ The small sample size (only 344 participants) and low event occurrence rate might partly explain the disappointing results.

Despite these conflicting results, it was clear that a specific population of stage IB NSCLC could benefit from ACT. A recent study classified the entire cohort of stage IB NSCLC patients into low-risk, intermediate-risk, and high-risk groups according to the recurrence-free survival (RFS), revealing that the high-risk patients could most benefit from ACT.³⁰ In our study, among the total patients who underwent ACT, 571 patients obtained longer OS than the median OS of the non-ACT group, which was regarded as the beneficiary population. This better survival in the ACT group could be partially attributed to decreased local recurrence and distant metastasis.^31,32 It might be interpreted that the value of postoperative adjuvant chemotherapy could reduce the chances of recessive micrometastases.³³

In our visualized nomogram, age, gender, marital status, laterality, pathology, tumor size, regional nodes examined, and tumor size were primary predictive variables, providing individualized estimates of whether stage IB NSCLC patients could benefit from ACT. These factors were confirmed to be related to prognosis in previous research. Xu et al indicated that ACT improved survival in eighth edition stage IB NSCLC patients. Age, grade, lymph nodes examined, visceral pleural invasion, surgical method, and radiotherapy were significant factors.²⁵ Tu et al identified gender, age, grade, pathological subtype, visceral pleural invasion, tumor size, surgical procedure, and regional nodes examined as independent prognostic factors in stage IB NSCLC patients.¹⁹ Previous research found an association between the examined lymph node count with survival.^34-37 One reason could be that removing more lymph nodes would increase the likelihood of detecting metastatic lymph nodes and contribute to improved nodal staging accuracy. Some patients with the N0 stage might have a node-positive condition. This group of patients was less likely to receive ACT due to the underestimated tumor stage. The analysis also presented that the adenocarcinoma subtype and younger age could increase the benefit probability. Two recent studies observed that stage IB patients with adenocarcinoma subtype and more youthful age cohorts achieved better long-term prognoses compared with other pathological subtypes and older populations, which was in line with our research.^19,25 Moreover, our research indicated that patients undergoing lobectomy could gain more benefit probability of ACT than sublobar resection. Lobectomy was associated with more lymph node excision during surgery, and thus, survival benefits might result from the extensive lymph node resection and the more chance of ACT.^38,39

The model was assessed in the training and validation sets. The results of AUCs (.781, .772, and .851) and calibration curves presented reliable discrimination and calibration ability. Moreover, the DCA analysis indicated the good clinical value of the model. Certain individual conditions were crucial to selecting optimal candidates for ACT among stage IB patients. The combination of multiple predicting factors could provide a more reliable prediction than any simple single indicator. Thus, this exploratory study built an individualized prediction nomogram to identify ACT benefit candidates, which could assist clinicians in decision-making.

Limitation

Several limitations in the research should be concerned. First, detailed chemotherapy regimens weren’t recorded in the SEER database, so we couldn’t compare the effect among different chemotherapy regimens. Second, the lack of recurrence data prevented further comparison with other investigations. Also, the external validation cohort was relatively small and further external multicenter prospective validations were required.

Conclusion

Our research indicated that stage-IB NSCLC patients who underwent ACT presented longer median OS than the non-ACT cohort. The high-performing nomogram could guide decision-making and selecting optimal candidates for the ACT. But this conclusion came from American data, so external validation in other countries is required to verify the conclusion further.

Supplemental Material

Supplemental Material - A Novel Nomogram for Identifying Candidates for Adjuvant Chemotherapy in Patients With Stage IB Non-small Cell Lung Cancer

Supplemental Material for A Novel Nomogram for Identifying Candidates for Adjuvant Chemotherapy in Patients With Stage IB Non-small Cell Lung Cancer by Xue Song, Yangyang Xie, Haoran Deng, Fei Yu, Shiqiang Wang, and Yafang Lou in Cancer Control

Footnotes

Author Contributions

1. Study design: Xue Song, Yafang Lou.

2. Data collection: Xue Song, Yangyang Xie.

3. Statistical analysis: Xue Song, Haoran Deng.

4. Data interpretation: Xue Song, Fei Yu.

5. Literature search: Xue Song, Shiqiang Wang.

6. Manuscript writing: Xue Song, Yangyang Xie.

7. Final approval of manuscript: Yafang Lou.

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) received no financial support for the research, authorship, and/or publication of this article.

Ethical Approval

The patient’s data acquired from the SEER database can be accessed publicly, so no ethical approval was needed. The project involving human participants was reviewed and approved by the Ethics Committee of Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University. In compliance with national law and institutional standards, written informed permission was not needed for participation in this study.

Data Availability

The SEER database () contained the datasets that were produced and examined throughout this project. Supplemental information could be obtained from the corresponding author.

ORCID iD

Yafang Lou

Supplemental Material

Supplemental material for this article is available online.

References

Siegel

Miller

Jemal

. Cancer statistics, 2018. CA A Cancer J Clin. 2018;68(1):7-30. doi:10.3322/caac.21442

Jemal

Center

DeSantis

Ward

. Global patterns of cancer incidence and mortality rates and trends. Cancer Epidemiol Biomarkers Prev. 2010;19(8):1893-1907. doi:10.1158/1055-9965.epi-10-0437

Morgensztern

Gao

Govindan

. Trends in stage distribution for patients with non-small cell lung cancer: A national cancer database survey. J Thorac Oncol. 2010;5(1):29-33. doi:10.1097/JTO.0b013e3181c5920c

Goldstraw

Chansky

Crowley

, et al. The IASLC lung cancer staging project: Proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer. J Thorac Oncol. 2016;11(1):39-51.

Yang

Kim

Jheon

, et al. Recurrence risk-scoring model for stage I adenocarcinoma of the lung. Ann Surg Oncol. 2015;22(12):4089-4097.

Pignon

Tribodet

Scagliotti

LACE Collaborative Group , et al.. Lung adjuvant cisplatin evaluation: A pooled analysis by the LACE collaborative group. J Clin Oncol. 2008;26(21):3552-3559.

Douillard

Rosell

De Lena

, et al. Adjuvant vinorelbine plus cisplatin versus observation in patients with completely resected stage IB-IIIA non-small-cell lung cancer (Adjuvant Navelbine International Trialist Association [ANITA]): A randomised controlled trial. Lancet Oncol. 2006;7(9):719-727.

Winton

Livingston

Johnson

, et al. Vinorelbine plus cisplatin vs observation in resected non-small-cell lung cancer. N Engl J Med. 2005;352(25):2589-2597.

Network

NCC

. (NCCN) Clinical practice guidelines in oncology. Non-small cell lung cancer. Version 5 (2020). Available at: https://www.nccn.org/store/login/login.aspx?ReturnURL=https://www.nccn.org/professionals/physician_gls/pdf/nscl.pdf. Accessed May 27, 2020.

10.

Kris

Gaspar

Chaft

Kennedy

. Adjuvant systemic therapy and adjuvant radiation therapy for stages I to Iiia resectable non-small-cell lung cancers: American society of clinical oncology/cancer care ontario clinical practice guideline update summary guideline summary. J Oncol Pract 2017;13:449-451.

11.

Jang

Cho

Kim

Jheon

Yang

Kim

. Effect of adjuvant chemotherapy after complete resection for pathologic stage IB lung adenocarcinoma in high-risk patients as defined by a new recurrence risk scoring model. Cancer Res Treat. 2017;49(4):898-905. doi:10.4143/crt.2016.312

12.

Morgensztern

Waqar

, et al. Adjuvant chemotherapy for patients with T2N0M0 NSCLC. J Thorac Oncol. 2016;11:1729-1735.

13.

Park

Lee

Kim

, et al. Efficacy of platinum-based adjuvant chemotherapy in T2aN0 stage IB non-small cell lung cancer. J Cardiothorac Surg. 2013;8(1):151.

14.

Strauss

Herndon

Maddaus

, et al. Adjuvant paclitaxel plus carboplatin compared with observation in stage IB non-small-cell lung cancer: CALGB 9633 with the cancer and Leukemia group B, radiation therapy oncology group, and north central cancer treatment group study groups. J Clin Oncol. 2008;26(31):5043-5051.

15.

Yang

Tian

. Comparing the benefits of postoperative adjuvant chemotherapy vs observation for stage IB non-small cell lung cancer: A meta-analysis. J Thorac Dis. 2019;11(7):3047-3054. doi:10.21037/jtd.2019.07.47

16.

Austin

. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399-424. doi:10.1080/00273171.2011.568786

17.

Pavlou

Ambler

Seaman

De Iorio

Omar

. Review and evaluation of penalised regression methods for risk prediction in low‐dimensional data with few events. Stat Med. 2016;35(7):1159-1177.

18.

Van Calster

Wynants

Verbeek

, et al. Reporting and interpreting decision curve analysis: A guide for investigators. Eur Urol. 2018;74(6):796-804. doi:10.1016/j.eururo.2018.08.038

19.

Tian

Chen

. Overall survival analyses following adjuvant chemotherapy or nonadjuvant chemotherapy in patients with stage IB non-small-cell lung cancer. JAMA Oncol. 2021;2021:8052752. doi:10.1155/2021/8052752

20.

Tian

Chen

. A risk classification system predicting the cancer-specific survival for postoperative stage IB non-small-cell lung cancer patients without lymphovascular and visceral pleural invasion. Lung Cancer. 2021;161:114-121. doi:10.1016/j.lungcan.2021.09.014

21.

Moon

Zhao

Pluta

Ahn

. Subgroup analysis based on prognostic and predictive gene signatures for adjuvant chemotherapy in early-stage non-small-cell lung cancer patients. J Biopharm Stat. 2018;28(4):750-762. doi:10.1080/10543406.2017.1397006

22.

Williams

Gajra

Ganti

Kelley

. Use and impact of adjuvant chemotherapy in patients with resected non-small cell lung cancer. Cancer. 2014;120(13):1939-1947. doi:10.1002/cncr.28679

23.

Woodard

Wang

Kratz

, et al. Adjuvant Chemotherapy Guided by Molecular Profiling and Improved Outcomes in Early Stage, Non-Small-Cell Lung Cancer. Amsterdam, Netherlands: Clinical Lung Cancer; 2017:S152573041730150X.

24.

Liu

Jiang

, et al. Stratification of patients with stage IB NSCLC based on the 8th edition of the American Joint Committee on Cancer (AJCC) staging manual. Front Oncol. 2020;10:571. doi:10.3389/fonc.2020.00571

25.

Wan

Zhu

, et al. Effect of adjuvant chemotherapy on survival of patients with 8th edition stage IB non-small cell lung cancer. Front Oncol. 2021;11:784289. doi:10.3389/fonc.2021.784289

26.

Zhang

Guo

Zhang

Liu

Zhou

. Meta-analysis of adjuvant chemotherapy versus surgery alone in T2aN0 stage IB non-small cell lung cancer. J Cancer Res Therapeut. 2018;14(1):139-144.

27.

Morgensztern

Waqar

, et al. Adjuvant chemotherapy for patients with T2N0M0 NSCLC. J Thorac Oncol. 2016;11(10):1729-1735. doi:10.1016/j.jtho.2016.05.022.

28.

Speicher

Wang

Hartwig

D'Amico

Berry

. Adjuvant chemotherapy after lobectomy for T1-2N0 non-small cell lung cancer: Are the guidelines supported? J Natl Compr Cancer Netw. 2015;13(6):755-761. doi:10.6004/jnccn.2015.0090

29.

Moon

Choi

Park

Lee

. Prognostic factors in stage IB non-small cell lung cancer according to the 8(th) edition of the TNM staging system after curative resection. J Thorac Dis. 2019;11(12):5352-5361. doi:10.21037/jtd.2019.11.71

30.

Qian

Wang

, et al. Adjuvant chemotherapy candidates in stage I lung adenocarcinomas following complete lobectomy. Ann Surg Oncol. 2019;26(8):2392-2400. doi:10.1245/s10434-019-07366-z

31.

Roselli

Mariotti

Ferroni

, et al. Postsurgical chemotherapy in stage IB nonsmall cell lung cancer: Long-term survival in a randomized study. Int J Cancer. 2006;119(4):955-960.

32.

Mineo

Ambrogi

Corsaro

Roselli

. Postoperative adjuvant therapy for stage IB non-small-cell lung cancer. Eur J Cardio Thorac Surg. 2001;20(2):378-384.

33.

Tezel

Ersev

Kiral

, et al. The impact of immunohistochemical detection of positive lymph nodes in early stage lung cancer. Thorac Cardiovasc Surg. 2006;54(2):124-128.

34.

Smeltzer

Faris

Ray

Osarogiagbon

. Association of pathologic nodal staging quality with survival among patients with non–small cell lung cancer after resection with curative intent. JAMA Oncol. 2018;4:80-87.

35.

Samayoa

Pezzi

, et al. Rationale for a minimum number of lymph nodes removed with non-small cell lung cancer resection: Correlating the number of nodes removed with survival in 98,970 patients. Ann Surg Oncol. 2016;23:1005-1011.

36.

Osarogiagbon

Decker

Ballman

Wigle

Allen

Darling

. Survival implications of variation in the thoroughness of pathologic lymph node examination in American college of surgeons oncology group Z0030 (Alliance). Ann Thorac Surg. 2016;102(2):363-369.

37.

Liang

Shen

, et al. Impact of examined lymph node count on precise staging and long-term survival of resected non-small-cell lung cancer: A population study of the US SEER database and a Chinese multi-institutional registry. Journal of Clinical Oncology Official Journal of the American Society of Clinical Oncology. 2017;35(11):1162-1170.

38.

Zhang

Feng

Zhao

, et al. Sublobar resection is associated with better perioperative outcomes in elderly patients with clinical stage I non-small cell lung cancer: A multicenter retrospective cohort study. J Thorac Dis. 2019;11(5):1838-1848.

39.

Dai

Liu

Yang

, et al. Optimal lymph node examination and adjuvant chemotherapy for stage I lung cancer. J Thorac Oncol. 2019;14(7):1277-1285.

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