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Hindawi Journal of Oncology Volume 2020, Article ID 7863984, 9 pages https://doi.org/10.1155/2020/7863984 Research Article A Novel Nomogram including AJCC Stages Could Better Predict Survival for NSCLC Patients Who Underwent Surgery: A Large Population-Based Study 1,2 3 4 5 6 Xiaoling Shang , Haining Yu, Jiamao Lin, Zhenxiang Li, Chenglong Zhao, 7 4 Jian Sun , and Haiyong Wang Department of Clinical Laboratory, Shandong University, Jinan 250012, China Department of Clinical Laboratory, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China Personnel Division, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China Department of Internal Medicine-Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China Department of -oracic Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China Correspondence should be addressed to Jian Sun; email@example.com and Haiyong Wang; firstname.lastname@example.org Received 4 March 2020; Accepted 16 April 2020; Published 20 May 2020 Academic Editor: Dali Zheng Copyright © 2020 Xiaoling Shang et al. /is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objective. In this study, we aimed to establish a novel nomogram model which was better than the current American Joint Committee on Cancer (AJCC) stage to predict survival for non-small-cell lung cancer (NSCLC) patients who underwent surgery. Patients and Methods. 19617 patients with initially diagnosed NSCLC were screened from Surveillance Epidemiology and End Results (SEER) database between 2010 and 2015. /ese patients were randomly divided into two groups including the training cohort and the validation cohort. /e Cox proportional hazard model was used to analyze the inﬂuence of diﬀerent variables on overall survival (OS). /en, using R software version 3.4.3, we constructed a nomogram and a risk classiﬁcation system combined with some clinical parameters. We visualized the regression equation by nomogram after obtaining the regression coeﬃcient in multivariate analysis. /e concordance index (C-index) and calibration curve were used to perform the validation of nomogram. Receiver operating characteristic (ROC) curves were used to evaluate the clinical utility of the nomogram. Results. Univariate and multivariate analyses demonstrated that seven factors including age, sex, stage, histology, surgery, and positive lymph nodes (all, P< 0.001) were independent predictors of OS. Among them, stage (C-index = 0.615), positive lymph nodes (C-index = 0.574), histology (C-index = 0.566), age (C-index = 0.563), and sex (C-index = 0.562) had a relatively strong ability to predict the OS. Based on these factors, we established and validated the predictive model by nomogram. /e calibration curves showed good consistency between the actual OS and predicted OS. And the decision curves showed great clinical usefulness of the nomogram. /en, we built a risk classiﬁcation system and divided NSCLC patients into two groups including high-risk group and low-risk group. /e Kaplan–Meier curves revealed that OS in the two groups was accurately diﬀerentiated in the training cohort (P< 0.001). And then, we validated this result in the validation cohort which also showed that patients in the high-risk group had worse survival than those in the low-risk group. Conclusion. /e results proved that the nomogram model had better performance to predict survival for NSCLC patients who underwent surgery than AJCC stage. /ese tools may be helpful for clinicians to evaluate prognostic indicators of patients undergoing operation. 2 Journal of Oncology approved by the ethics committee of the Shandong Cancer 1. Introduction Hospital. /is study did not involve any personal infor- NSCLC accounts for about 85% of all lung cancer, which mation, and therefore, informed patient consent was not remains the leading cause of cancer-related death in the required. world [1, 2]. In recent years, with the wide application of high-resolution spiral computed tomography (CT) screen- 2.3. Statistical Analysis. /ese eligible patients were ran- ing technology, the detection rate of early lung cancer has domly divided into the training cohort (70%, n � 13732) and increased signiﬁcantly . Surgery treatment is the ﬁrst the validation cohort (30%, n � 5885) to establish and val- choice for patients diagnosed with early NSCLC, including idate the nomogram. /e OS was deﬁned as the time from stage I, stage II, and partial stage III cases.  /e current diagnosis to death due to any reason. /e data in training treatment options for NSCLC mainly depend on the eighth cohort were used to develop the prediction model and edition of the American Joint Committee on Cancer TNM construct nomogram and risk classiﬁcation system. Fur- staging. However, patients’ survival rate varies greatly at the thermore, the data of the validation cohort were used to same stage [5–7]. /e 5-year survival rates range from 60% make a validation. of stage I to about 30% of stage IIIA [8,9]. And patients with Univariate and multivariate analyses were used to de- the same stage showed diﬀerent rates of survival. It is of great termine independent prognostic variables. And then, based signiﬁcance in guiding clinical treatment to ﬁnd indepen- on these variables contained in the ﬁnal model, we built the dent prognostic factors. Previous studies [5–7] have reported nomogram and the risk classiﬁcation system. /e C-index that some factors may signiﬁcantly promote the survival was used to determine discrimination ability of the no- prediction of patients, such as age, race, sex, stage, and mogram, and each parameter and ROC curves were used to histology. evaluate the clinical utility of the nomogram. /e calibration Nomogram is a convenient tool to predict and quantify for 1-, 3-, and 5-year OS was evaluated using a calibration risk for patients’ prognosis by incorporating and validating curve by comparing the predicted survival and the observed some relevant factors. In some other types of tumors, no- survival. Furthermore, based on the total score of each mograms that calculate numerical probability of clinical patient in the validation cohort, the risk classiﬁcation system events, such as cancer-speciﬁc survival (CSS) and OS, have was established and all patients were divided into low-risk shown more precise prediction than the traditional TNM and high-risk prognosis groups. /e OS was estimated using staging systems. At present, AJCC TNM staging is the main the Kaplan–Meier method and compared by the log-rank criterion to guide the treatment and prognosis of NSCLC test. patients. However, the staging could not be good to predict All statistical analyses were made using R software the survival for these patients. Other variables including age, version 3.4.3 (R Foundation) and Statistical Product Service sex, and histology may be signiﬁcant independent prog- Solutions (SPSS) 22.0 software package. All statistical P nostic factors for NSCLC patients. /erefore, the combi- values were 2-sided, and P< 0.05 was considered statistically nation of AJCC staging and these variables may be better to signiﬁcant. predict the outcomes and it would be better in clinical guidance. 3. Results /erefore, in the present study, we built and validated the nomogram combined with several clinical variables to predict 3.1. Patients Characteristics. A total of 19617 patients ini- prognosis for patients with NSCLC who underwent surgery. tially diagnosed with NSCLC from the SEER database were included for analysis. All enrolled patients were randomly 2. Materials and Methods divided into the training cohort (13732, 70%) and the val- idation cohort (5885, 30%). According to age, all patients 2.1. Data Source. /e SEER Program (http://www.seer. were divided into four groups including <60 years old cancer.gov) consists of 9 Regs Research Data in the (n � 4203, 21.4%), 60–69 years old (n � 7054, 36.0%), 70–79 United States . Information for patients with stages I–III years old (n � 6588, 33.6%), and >80 years old (n � 1772, NSCLC between 2010 and 2015 was extracted from the SEER 33.6%). In the total cohort, training cohort, and validation database. According to the AJCC criteria, we selected a total cohort, the proportion of patients aged 60–69 (36.0%, 36.1% of 19617 patients diagnosed with NSCLC using the ∗ and 35.6, respectively) was the largest. /e majority of cases SEER Stat 8.3.5 software. /e inclusion criteria for were white (n � 16312, 83.2%). Male and female patients recruiting patients were as follows: NSCLC patients, only accounted for the same proportion (50% vs. 50%). one malignant primary lesion, available clinical information, According to the AJCC stage, patients of stage T1 were and active follow-up. /e exclusion criteria were patients the largest in the total cohort, training cohort, and validation with benign tumor. In addition, patients containing any cohort (58.8%, 58.6%, and 59.4 respectively), followed by the missing information on extracted data were all excluded. T2 stage (23.3%, 23.5%, and 22.9%, respectively). And pa- tients with stage T3 was the least in the total cohort, training 2.2. Ethics Statement. Our study was constructed in ac- cohort, and validation cohort (17.9%, 17.9%, and 17.7%, cordance with the Helsinki Declaration. /is study was also respectively). 12278 (62.6%) patients had adenocarcinoma Journal of Oncology 3 3.4. Risk Classiﬁcation System. According to the total scores and 7336 (37.4%) had squamous. 5.6% of patients under- went complete surgical resection, and 94.4% of patients of every patient, we also developed a risk classiﬁcation system in the training cohort generated by nomogram. All underwent partial surgical resection. Of these patients, only 24.5% patients had positive lymph nodes. Baseline clini- patients in the training cohort and validation cohort were copathological characteristics of all patients in the training divided into the high-risk and low-risk groups. /e cohort and the validation cohort are shown in Table 1. Kaplan–Meier curve was used to draw the OS curves for the high-risk group and low-risk group in the training cohort and validation cohort. In the training cohort, the 3.2. Independent Prognostic Factors in Predicting OS. Kaplan–Meier curves revealed that patients’ survival in the Univariate and multivariate Cox proportional hazards re- low-risk group was better than that in the high-risk group gression models were used to assess each factor’s ability in (P< 0.001) (Figure 4(a)). /en, we validated it in the vali- predicting OS. In univariate analysis, we found that age dation cohort. Similarly, patients in the low-risk group had (P< 0.001), race (P< 0.001), sex (P � 0.03), stage better survival than those in the high-risk group (P< 0.001) (P< 0.001), histology (P< 0.001), surgery (P< 0.001), and (Figure 4(b)). positive lymph nodes (P< 0.001) were associated with OS in patients with stages I–III NSCLC. Among them, stage (C- 4. Discussion index � 0.615), positive lymph nodes (C-index � 0.574), histology (C-index � 0.566), age (C-index � 0.563), and sex In this study, we established and developed a nomogram and (C-index � 0.562) had superior discrimination power in a risk classiﬁcation to predict the OS of patients with stages predicting OS compared with other variables. Multivariate I–III NSCLC after surgery using the data originated from analysis further analyzed the factors of a P< 0.05 in uni- SEER database. A total of 19167 patients were included, and variate analysis. In the multivariate analysis, we found that seven signiﬁcant prognosis factors including age, race, sex, age (P< 0.001), other races (P< 0.001), sex (P< 0.001), stage stage, histology, surgery, and positive nodes were identiﬁed. (P< 0.001), histology (P< 0.001), surgery (P< 0.001), and And these predictive factors could be easily obtained from positive lymph nodes (P< 0.001) were independent prog- clinical practice. /en, we established the validation of nostic factors and were incorporated into the predictive model and used diﬀerent statistical methods to demonstrate model. Univariate and multivariate analyses of each factor’s its great performance. ability in predicting OS are shown in Table 2. Over time, the prospects for lung cancer patients and treatment have changed. Lung lobectomy is often considered the best treatment option for stages I, II, and partial III 3.3. Building and Validating the Predictive Nomogram. NSCLC patients [7,8,11]. Recurrence and metastasis have We built a novel nomogram that included the signiﬁcant and become important factors aﬀecting the 5-year survival rate independent prognostic factors (Figure 1). Each factor had a of patients with lung cancer after operation. So, it is very score on the point scale. We can draw a straight line to important to predict factors of survival after surgery in determine the estimated probability of prognosis at each NSCLC patients. Furthermore, NSCLC has signiﬁcant time point by adding up the total score and locating it on the heterogeneity in individual survival, and it is inaccurate to total point scale. And then, the validation cohort was used to use the TNM staging system to predict survival. Although verify the novel nomogram. In the validation cohort, we several prognostic models have been reported previously compared the OS rate predicted by the nomogram with [6,12], a relevant nomogram was rarely developed to predict observed 1-, 3-, and 5-year OS rates. prognostic variables for patients NSCLC after surgery. In a well-calibrated model, the prediction will fall on a Some research studies [13–18] reported that a nomo- 45-degree diagonal line. From Figure 2, the calibration gram could predict the prognosis of NSCLC patients. curves revealed good consistency between the actual ob- However, most studies focused on patients with early or servation and the nomogram prediction for 1-, 3-, and 5- advanced NSCLC. Nonetheless, both research studies had a year survival rates. Figure 2(a) shows good consistency small sample size which may inhibit their generalization. between the actual 1-year overall survival and predicted 1- Liang et al.  showed that the C-index for the year overall survival. And the ROC curve revealed that the established nomogram to predict OS was 0.71 in the primary area under the curve (AUC) is 0.701. Figure 2(b) shows good cohort and 0.67 in the IASLC cohort. Sun et al.  showed consistency between the actual 3-year overall survival and that the C-index of the nomogram was 0.638 which predicted 3-year overall survival. And the ROC curve exhibited a suﬃcient level of discrimination. However, in revealed that the AUC is 0.687. Figure 2(c) shows good our study, the C-index of the nomogram is higher than that consistency between the actual 5-year overall survival and of other previous models. In addition to a nomogram, we predicted 5-year overall survival. And the ROC curve also developed a risk classiﬁcation system and the risk revealed that the AUC is 0.669. classiﬁcation divided the whole NSCLC patients into two In addition, decision curves exhibited great positive net distinct prognostic groups which could supplement the beneﬁts in the predictive model among almost all of the nomogram in our study. threshold probabilities at diﬀerent time points, indicating In univariable and subsequent multivariable analysis, we the favorable potential clinical eﬀect of the predictive model identiﬁed age, race, sex, stage, histology, surgery types, and (Figures 3(a) and 3(b)). positive lymph nodes as independent prognostic factors. 4 Journal of Oncology Table 1: Baseline clinicopathological characteristics of all patients and those in the training and validation cohorts. Variables All cohort (n � 19617) Training cohort (n � 13732) Validation cohort (n � 5885) P Age 0.026 <60 4203(21.4) 4958 (36.1) 1302 (22.1) 60–69 7054(36.0) 2096 (35.6) 70–79 6588(33.6) 4619 (33.6) 1969 (33.5) >80 1772(9.0) 1254 (9.1) 518 (8.8) Race 0.019 White 16312(83.2) 11445 (83.3) 4867 (82.7) Black 1814(9.2) 1262 (9.2) 552 (9.4) Others 1491(7.6) 1025 (7.5) 466 (7.9) Sex 0.013 Male 9807(50.0) 6839 (49.8) 2968 (50.4) Female 9810(50.0) 6893 (50.2) 2917 (49.6) Stage 0.017 I 11543(58.8) 8047 (58.6) 3496 (59.4) II 4572(23.3) 3226 (23.5) 1346 (22.9) III 3502(17.9) 2459 (17.9) 1043 (17.7) Histology 0.009 Adenocarcinoma 12278(62.6) 8579 (62.5) 3702 (62.9) Squamous 7336(37.4) 5153 (37.5) 2183 (37.1) Surgery 0.014 Complete resection 1092(5.6) 778 (5.7) 314 (5.3) Partial resection 18525(94.4) 12954 (94.3) 5571 (94.7) Positive nodes 0.005 Yes 4812(24.5) 3360 (24.5) 1452 (24.7) No 14805(75.5) 10372 (75.5) 4433 (75.3) Table 2: Univariate and multivariate analyses of each factor’s ability in predicting OS. Univariate analyses Multivariate analyses Variable HR 95% CI P C-index HR 95% CI P Age 0.563 <60 Reference Reference 60–69 1.110 1.010–1.220 0.038 1.174 1.065–1.294 0.001 70–79 1.430 1.300–1.570 <0.001 1.604 1.455–1.768 <0.001 >80 2.00 1.780–2.260 <0.001 2.367 2.095–2.674 <0.001 Race 0.516 White Reference Reference Black 0.913 0.813–1.025 0.120 1.022 0.909–1.148 0.717 Others 0.748 0.649–0.863 <0.001 0.777 0.673–0.897 <0.001 Sex 0.562 Male Reference Reference Female 0.649 0.607–0.694 0.030 <0.001 0.714 0.667–0.764 <0.001 Stage 0.615 I Reference Reference II 2.100 1.940–2.270 <0.001 1.832 1.672–2.006 <0.001 III 2.610 2.410–2.830 <0.001 2.287 2.047–2.554 <0.001 Histology 0.566 Adenocarcinoma Reference Reference Squamous 1.570 1.470–1.6770 <0.001 1.325 1.237–1.420 <0.001 Surgery 0.528 Complete resection Reference Reference Partial resection 1.990 1.780–2.230 <0.001 1.297 1.150–1.462 <0.001 Positive nodes <0.001 0.574 Yes Reference Reference No 2.030 1.900–2.170 1.183 1.077–1.299 <0.001 Journal of Oncology 5 0 102030405060708090 100 Points 60−69 >80 Age <60 70−79 Male Sex Female White Race Other Black T2 Stage T1 T3 Histology Complete resection Surgery Partial resection Yes Positive_nodes No Total Points 0 50 100 150 200 250 300 350 400 1−year survival 0.95 0.9 0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 3−year survival 0.9 0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 5−year survival 0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 Figure 1: A nomogram for prediction of 1-, 3-, and 5-year OS rates of stages I–III NSCLC patients after surgery. 1.0 1.0 1.0 0.8 0.8 0.8 0.6 0.6 0.6 0.4 0.4 0.4 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Predicted 1-year overall survival Predicted 3-year overall survival Predicted 5-year overall survival 1.0 1.0 1.0 0.8 0.8 0.8 0.6 0.6 0.6 0.4 0.4 0.4 AUC = 0.701 AUC = 0.687 AUC = 0.669 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 1-Speciﬁcity 1-Speciﬁcity 1-Speciﬁcity (a) (b) (c) Figure 2: Calibration curves of the nomogram predicting 1-year, 3-year, and 5-year OS rates of stages I–III NSCLC patients after surgery. On the calibration plot, the x-axis is nomogram-predicted probability of over survival. /e y-axis is the actual over survival. Sensitivity Actual 1-year overall survival Sensitivity Actual 3-year overall survival Sensitivity Actual 5-year overall survival 6 Journal of Oncology 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 High risk threshold High risk threshold 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 1:100 1:4 2:3 3:2 4:1 100:1 1:100 1:4 2:3 3:2 4:1 100:1 Cost: benefit ratio Cost: benefit ratio Baseline model Baseline model All Full model None All None (a) (b) Figure 3: Decision curves of the nomogram predicting OS. /e x-axis represents the threshold probabilities, and the y-axis measures the net beneﬁt calculated by adding the true positives and subtracting the false positives. Training Cohort Validation Cohort 1.00 1.00 0.75 0.75 0.50 0.50 p < 0.001 p < 0.001 0.25 0.25 0.00 0.00 0 500 1000 1500 2000 0 500 1000 1500 2000 Day Day High risk High risk Low risk Low risk (a) (b) Figure 4: Kaplan–Meier curves of OS for patients in the low- and high-risk groups. (a) Kaplan–Meier curves of OS for patients in the low- and high-risk groups in the training cohort. (b) Kaplan–Meier curves of OS for patients in the low- and high-risk groups in the validation cohort. /ese ﬁndings are consistent with previous reports on risk assess the prognostic survival . However, in the present factors for non-small-cell lung cancer [7,8,20]. It is necessary study, we did not divide these stages into speciﬁc T and N to validate the nomogram and avoid excessive ﬁtting of the category, which were reported as the signiﬁcant and inde- model and determine the extensibility . Notably, pendent factors in other research studies. We need future according to our nomogram, stage is the most powerful studies to assess each factor of stage which may impact on predictor of OS, and C-index (C-index � 0.615) was the survival for patients with resected NSCLC. highest among all predictors. One of the possible reasons is In addition, positive lymph node was another impor- tant predictor for OS and the C-index was 0.574. Several that TNM staging is the current important tool to make decision about the stage-speciﬁc therapeutic strategy and research studies [22,23] reported the relationship between Proportion survival Standardized net benefit Proportion survival Standardized net benefit Journal of Oncology 7 positive lymph nodes and survival. /e reason may be that had better performance to predict survival for NSCLC pa- with more positive lymph nodes being cleared out, po- tients who underwent surgery than AJCC stage. Although tential metastatic lymph nodes will be removed. For pa- future validation is necessary, these tools may be helpful for tients with resected NSCLC, the number of positive lymph clinicians to evaluate prognostic indicators of patients un- nodes was also demonstrated as an important prognostic dergoing operation. factor [24,25]. And in many other cancers, positive lymph node is an important factor aﬀecting survival [26–28]. Abbreviations Moreover, complete sampling of lymph nodes results in NSCLC: Non-small-cell lung cancer precise staging and, therefore, appropriate adjuvant SEER: Surveillance epidemiology and end results treatments for patients. OS: Overall survival In this study, we deﬁned 1-, 3-, and 5-year survival rates CT: Computed tomography as our endpoints. Calibration curves showed good agree- HR: Hazard ratio ment between nomogram prediction and actual observation. CI: Conﬁdence interval /e nomogram performed well by AUC at every measured AJCC: American Joint Committee on Cancer time point, which revealed that the nomogram had good C-index: Concordance index performance to predict 1-, 3-, and 5-year OS rates for pa- ROC: Receiver operating characteristic tients with resected NSCLC. Kaplan–Meier curves showed AUC: Area under the curve. that OS in the diﬀerent groups was accurately diﬀerentiated by the risk classiﬁcation system in the training cohort and validation cohort, both of P< 0.05. Data Availability Although surgery is the ﬁrst choice treatment for pa- /e datasets used and analyzed during the current study are tients with stages I, II, and partial III NSCLC [29, 30], available from the corresponding author upon reasonable postoperative adjuvant treatment could decrease the risk of request. disease recurrence and improve outcome [30–32]. It should be noted that postoperative adjuvant therapies including Conflicts of Interest chemotherapy, radiotherapy, target treatment, and any other adjuvant therapies were not selected as candidate factors All the authors have no conﬂicts of interest to declare. because they were only recommended for a proportion of patients with potentially high risk of locoregional Authors’ Contributions recurrence. In addition, patients with N2 disease were a heteroge- X-L S was in charge of analysis and wrote the article.H-N Y, neous group . Operation may have some limitations for Z-X L, and J-M L helped with acquisition and analysis of the these patients, and the treatment should be individualized data. C-L Z helped in editing language. J S and H-Y W, the . Mao et al.  showed that the C-index of the no- corresponding authors, were in charge of guidance of the mogram was 0.673 in the training cohort and 0.664 in the design and analysis the whole research. H-Y W and X-L S validation cohort. In our study, we did not specify the were major contributors to the manuscript. All authors read proportion of these patients with N2 disease who were and approved the ﬁnal manuscript. treated with surgery from SEER database. /e future studies are necessary to validate this result. Acknowledgments However, there are several limitations in our study. First, this was a retrospective study from the SEER database which /is study was supported jointly by Special Funds for could not represent the global population. Second, some Taishan Scholars Project (Grant no. 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Journal of Oncology – Hindawi Publishing Corporation
Published: May 20, 2020
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