Nomogram Predict Overall Survival of Patients with Thymic Epithelial Tumors After Surgery

doi:10.21203/rs.3.rs-414351/v1

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Nomogram Predict Overall Survival of Patients with Thymic Epithelial Tumors After Surgery

https://doi.org/10.21203/rs.3.rs-414351/v1

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published 22 Jul, 2021

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Background: Hematological indicators and clinical characteristics play an important role in the evaluation of the progression and prognosis of thymic epithelial tumors. Therefore, we aimed to combine these potential indicators to establish a prognostic nomogram to determine the overall survival (OS) of patients with thymic epithelial tumors undergoing thymectomy.

Methods: This retrospective study was conducted on 167 patients who underwent thymectomy between May 2004 and August 2015. Cox regression analysis were performed to determine the potential indicators related to prognosis and combine these indicators to create a nomogram for visual prediction. The prognostic predictive ability of the nomogram was evaluated using the consistency index (C-index), receiver operating characteristic (ROC) curve, and risk stratification. Decision curve analysis was used to evaluate the net benefits of the model.

Results: Preoperative albumin levels, neutrophil-to-lymphocyte ratio (NLR), T stage, and underlying diseases (with hypertension and/or diabetes) were included in the nomogram. In the training cohort, the nomogram showed a stronger prognostic predictive ability than the T staging (C index: 0.886 vs 0.725). Calibration curves for the overall survival (OS) were in good agreement with the standard lines in cohorts. The net benefit of the nomogram was higher than that of the T staging model.

Conclusions: The nomogram showed better performance in predicting the prognosis and survival of this patient population than the T staging prediction model. And it has potential to identify high-risk patients at an early stage. This is a relatively novel approach for the prediction of OS in this patient population.

Cancer Biology

Oncology

thymic epithelial tumor

overall survival

nomogram

albumin

ratio of neutrophils to lymphocytes

Thymic epithelial tumors common occur in the anterior mediastinum and can be divided into thymoma and thymic carcinoma according to histology. [1, 2] The Masaoka-Koga staging system, which is based on the progression of the primary tumor and the degree of involvement of the surrounding organs, has been widely accepted for thymoma and thymic carcinoma. [3–5] However, Yanagiya et al. found that age and histological type were significant prognostic factors in their cohort, which were not observed or reported in the Masaoka-Koga staging system. [6] Similarly, the results of the study published by Fukui et al. revealed that the new classification showed a better prognostic effect for thymic tumors than the Masaoka-Koga classification. [7, 8] Moreover, compared with the staging systems for most other malignant tumors, the Masaoka-Koga system does not include the effect of lymph node or distant organ metastasis on prognosis as finely as the TNM staging.

At the same time, an increasing number of studies have used clinical factors such as history of hypertension, diabetes, [9] smoking, [10] and body mass index (BMI) [11]and hematological indicators including hemoglobin (Hb) [12], neutrophil-to-lymphocyte ratio (NLR) [13, 14], albumin (ALB) [15], and other such indicators to analyze the prognosis of various tumors. However, only few studies have comprehensively analyzed a combination of the two types of indicators to establish a prognostic model for patients with thymic epithelial tumors after thymectomy. Currently, nomograms have been developed for most cancer types. [16–18] Compared with the traditional staging system for many cancers, the use of a nomogram has advantages in terms of prognostic prediction. Therefore, it has been proposed as an alternative method for cancer staging. [19–21]

Therefore, in this study, we aimed to use both preoperative hematological indicators and clinical factors to construct a prognostic predictive nomogram for patients with thymic epithelial tumors after thymectomy for a comprehensive evaluation. The nomogram results were also compared with those of the currently available T staging system to understand whether the nomogram can provide more accurate information for patient prognosis prediction, and use the nomogram score for risk stratification to identify high-risk patients.

Study population

This study was approved by the Medical Ethics Committee of Sun Yat-sen University Cancer Center (SYSUCC; Approval No. B2020-353-01) and complies with the Declaration of Helsinki.

This study retrospectively analyzed 167 patients with thymic epithelial tumors who underwent thymectomy at SYSUCC between May 2004 and August 2015. Most patients were included in the training group (n = 134), and the remaining patients were included in the verification group (n = 33). The inclusion criteria were as follows: (1) patients older than 18 years; (2) patients who underwent thymectomy at our center; (3) presence of histopathologically confirmed thymic epithelial tumors; (4) related laboratory examinations (blood routine, biochemical routine and so on) were completed within seven days before the operation. The exclusion criteria and the screening process are shown in Fig. 1.

Clinical data collection

Data were collected for the following clinical variables: hematological indicators (obtained within one week before the operation), neutrophil count(NE), lymphocyte count(LY), platelet count, hemoglobin levels, albumin levels, globulin levels as well as patients’ sex, age, smoking history, drinking history(Drinking alcohol every day, and the specific amount of drinking is not limited and described), family history of tumor, underlying disease (hypertension and/or diabetes), tumor size, histological subtype, myasthenia gravis symptoms, body mass index, tumor capsule status(complete or incomplete), great vessel infiltration, Masaoka-Koga staging, and T staging. In addition, T staging and Masaoka staging were obtained by combining imaging data with intraoperative records and postoperative pathological information, we staged all patients according to the eighth edition of the TNM staging system and the modified Masaoka-Koga staging system.

Follow-Up

We followed up patients regularly. In the first two years, all patients were followed up every 6–12 months, every 12 months from the third to the fifth year, and then an annual follow-up was continued. The last follow-up date was August 22, 2020. The primary observational endpoint was OS. OS was defined as the time from surgery to death.

Statistical Analysis

Statistical analysis was performed using SPSS 25.0 (IBM, Chicago, Illinois, USA) and R software (version 4.0.3; https://www.r-project.org/). In addition to age and tumor size, each component was converted to binary according to the best cutoff value (using X-tile software; http://www.tissuearray.org/rimmlab) according to the best cutoff value defined by the minimum P value method variables. This method has also been applied in other studies. [22] In the training cohort, the Cox regression model was used to analyze risk factors through univariate and multivariate analyses. Univariate analysis was performed to determine important risk factors for OS. Variables with P values of less than 0.05 were further included in the multivariate Cox proportional hazard regression model. In the final multivariate analysis, P values of less than 0.05 were considered independent prognostic factors. Then, based on the results of the multivariate Cox analysis, we constructed a nomogram showing three-year and five-year survival rates.

The R statistical software packages "rms," "survival," "foreign," "survivalROC," and "rmda" were used to calculate the C index; to generate the calibration curve, receiver operating characteristic (ROC) curves, decision curve analysis (DCA) curve, and Kaplan-Meier (KM) curve; and to construct a nomogram. The nomogram was used to calculate the prognostic risk score for each patient; X-tile was used to divide the patient's score into different risk levels (low risk, medium risk, and high risk) and to show their stratification effect through the KM curve. [23] The C-index, DCA curve, and ROC curve were used to evaluate the predictive ability of the nomogram and T stage for prognosis. All statistical tests were two-sided, and P values of less than 0.05 were considered statistically significant.

Basic Characteristics

A total of 167 patients participated in the study. Among them, 134 patients (Approximate 70%) were randomly assigned to the training group to build a nomogram, and the remaining 33 patients (Approximate 30%) were assigned to the verification group. Table 1 shows the data of the clinicopathological characteristics of the 167 patients. The three-year and five-year survival rates were 0.934 and 0.844, respectively. We found that 141 patients (84.4%) achieved a five-year survival time in all patients. These clinicopathological factors did not differ significantly between the training and validation cohorts.

Table 1

Patient, tumor, and treatment-related characteristics of thymic tumor (n = 167)
Characteristic	Training Cohort(n = 134)		Validation Cohort(n = 33)
	N	%	N	%
Gender Male Female	71 63	53.0 47.0	20 13	60.6 39.4
Age(years) ≤ 60 >60	104 30	77.6 22.4	28 5	84.8 15.2
Smoking history Never Ever	102 32	76.1 23.9	24 9	72.7 27.3
Drinking history No Yes	116 18	86.6 13.4	30 3	90.0 9.1
Family history of tumor No Yes	112 22	83.6 16.4	29 4	87.9 12.1
Underlying diseases No Yes	103 31	76.9 23.1	23 10	69.7 30.3
Tumor size ≤ 6 >6	77 57	57.5 42.5	15 18	45.5 54.5
pT stage T1 T2-3 T4	100 25 9	74.6 18.7 6.7	22 9 2	66.7 27.3 6.1
M stage M0 M1	127 7	94.8 5.2	30 3	90.9 9.1
Masaoka stage I II-III IV	63 64 7	47.0 47.8 5.2	13 17 3	39.4 51.5 9.1
WHO stage A-AB B1-B3 C	52 71 11	38.8 53.0 8.2	11 14 8	33.3 42.4 24.2
Myasthenia gravis, No Yes	123 11	91.8 8.2	31 2	93.9 6.1
BMI ≤ 18.8 >18.8	13 121	9.7 90.3	4 29	12.1 87.9
tumor capsule status Incomplete Complete	47 87	64.9 35.1	11 22	66.7 33.3
Invasion of great vessels No Yes	102 32	76.1 23.9	25 8	75.8 24.2
ALB ≤ 42.6 >42.6	49 85	36.6 63.4	14 19	42.4 57.6
GLB ≤ 23.2 >23.2	15 119	11.2 88.8	7 26	21.2 78.8
A/G ≤ 1.3 >1.3	28 106	20.9 79.1	4 29	12.1 87.9
Hb ≤ 124 >124	32 102	23.9 76.1	6 27	18.2 81.8
NE ≤ 5.6 >5.6	116 18	86.6 13.4	28 5	84.8 15.2
LY ≤ 1.5 >1.5	26 108	19.4 80.6	7 26	21.2 78.8
NE/LY(NLR) ≤ 3.1 >3.1	119 15	88.8 11.2	29 4	87.9 12.1
PLT ≤ 314 >314	121 13	90.3 9.7	29 4	87.9 12.1
PLT/LY(PLR) ≤ 145.7 >145.7	105 29	78.4 21.6	28 5	84.8 15.2
PLT/NE*LY(SII) ≤ 688.5 >688.5	116 18	86.6 13.4	24 9	72.7 27.3
NLR: neutrophil-to-lymphocyte ratio; Hb: hemoglobin; ALB: albumin; BMI: body mass index; NE: neutrophil count; LY: lymphocyte count; GLB: Globulin; SII: systemic immune-inflammation Index; PLT: platelet; PLR: platelet-lymphocyte ratio; pT stage: Pathological T stage.

Univariable and Multivariable Analyses in the Training Cohort

According to the results of univariate Cox regression analysis, there were nine variables related to OS: underlying disease, BMI, T stage, histology, ALB, Neutrophils(NE), NLR, systemic immune-inflammation Index (SII), and Globulin (GLB) (Table 2). In the multivariate Cox regression analysis, four parameters were defined as independent prognostic factors of OS: T stage (T1 vs. T2-3, hazard ratio, HR = 6.138, 95% confidence interval, CI [1.557–24.189], T1 vs. T4, HR = 6.892, 95% CI [1.752–27.109]), ALB (HR = 0.172, 95% CI [0.044–0.676]), underlying disease (HR = 12.584, 95% CI [3.067–51.634]), and NLR (HR = 13.215, 95% CI [3.074–56.817]) (Table 2).

Table 2

Univariate and multivariate analysis results in Training cohort(n = 134)
Variable	Univariate analysis	Multivariate analysis
	P	HR	95%CI	P
Gender Male vs Female	.275
Age(years) ≤ 60 vs ༞60	.694
Smoking history Never vs Ever	.394
Drinking history No vs Yes	.133
Family history of tumor No vs Yes	.272
Underlying diseases No vs Yes	.018	Reference 12.584	3.067–51.634	.000
Tumor size ≤ 6 vs ༞6	.380
pT stage T1 vs T2-3 T1 vs T4	.001	Reference 6.138 6.892	1.557–24.189 1.752–27.109	.010 .006
M stage M0 vs M1	.795
Masaoka stage I vs II-III I vs IV	.112
WHO stage A-AB vs B1-B3 A-AB vs C	.021
Myasthenia gravis, No vs Yes	.476
BMI ≤ 18.8 vs ༞18.8	.015
tumor capsule status Incomplete vs Complete	.118
Invasion of great vessels No vs Yes	.406
ALB ≤ 42.6 vs ༞42.6	.004	Reference .172	.044-.676	.012
GLB ≤ 23.2 vs ༞23.2	.009
A/G ≤ 1.3 vs ༞1.3	.193
Hb ≤ 124 vs ༞124	.344
NE ≤ 5.6 vs ༞5.6	.000
LY ≤ 1.5 vs ༞1.5	.154
NE/LY(NLR) ≤ 3.1 vs ༞3.1	.000	Reference 13.215	3.074–56.817	.001
PLT ≤ 314 vs ༞314	.733
PLT/LY(PLR) ≤ 145.7 vs ༞145.7	.065
PLT/NE*LY(SII) ≤ 688.5 vs ༞688.5	.002
NLR: neutrophil-to-lymphocyte ratio; Hb: hemoglobin; ALB: albumin; BMI: body mass index; NE: neutrophil count; LY: lymphocyte count; GLB: Globulin; SII: systemic immune-inflammation Index; PLT: platelet; PLR: platelet-lymphocyte ratio; pT stage: Pathological T stage.

Establishment of the Nomogram

According to the results of the multivariate Cox regression analysis, T stage, ALB, underlying disease, and NLR were defined as independent prognostic factors, and these factors were integrated to form a nomogram (Fig. 2). In the training cohort, the C index was 0.886 (95% CI: 0.804–0.968), which was higher than that of the T staging prediction model (C index: 0.725, 95% CI: 0.602–0.848). Internal calibration curves for the three- and five-year OS closely matched those of the baseline in the training cohort (Fig. 3Aand B).

Verification of the Nomogram

To better verify the actual predictive power of the nomogram, the above results were verified using the verification group data, showing that the C index was 0.741 (95% CI: 0.592–0.890), and the five-year and three-year external calibration curves met those of the standard baseline (Fig. 3C and D). We also used the ROC curve to verify the nomogram performance (Fig. 4). The AUC values of the training and validation groups at three and five years were both greater than 0.7 and by comparing the AUC values of the two groups, the nomogram model were significantly better than those of the T staging, showing better accuracy of the nomogram in predicting OS.

Decision Curve Analysis

Decision Curve Analysis (DCA) is a novel method for evaluating prognostic strategies that can evaluate the predictive power of prognostic models. Figure 5 shows the nomogram and DCA curve of T staging in the training and validation cohorts. Compared with the T staging, the DCA of the nomogram has a higher net benefit, which indicates that the nomogram has a better net benefit. The clinical utility of the nomogram was better than that of the T staging.

Risk Stratification of OS

Based on the nomogram scores, patients were divided into low-risk (0–73 points), medium-risk (74–169 points), and high-risk (170 points or higher) subgroups. In the training cohort, there were 82 patients in the low-risk group, 43 patients in the intermediate-risk group, and 9 patients in the high-risk group. In the validation cohort, 16 patients were included in the low-risk group, 8 patients were included in the medium-risk group, and 9 patients were included in the high-risk group. There were significant differences in the incidence of OS among the subgroups, and the survival rate of the high-risk subgroup was lower than that of the other groups (P < 0.05) (Fig. 6).

In this study, we obtained results from 167 patients at SYSUCC, and ALB, NLR, T stage, and underlying diseases were found to be independent prognostic factors of OS. We developed a nomogram that can effectively predict the OS rate of patients with thymic epithelial tumors at three and five years.

Slow disease progression and good prognosis have largely limited research on thymic epithelial tumors. At the same time, in the era of precision medicine, it is very important to analyze patient information as comprehensively as possible to screen factors affecting the prognosis for treatment decision-making. At present, the Masaoka-Koga staging system is still the gold standard for predicting the prognosis of thymic epithelial tumors. However, similar to the results of Fukui et al., [7] our results show that the T staging is better than the Masaoka-Koga staging in predicting prognosis. Therefore, the nomogram developed in this study was mainly compared with T staging model. The T staging prediction model does not contain other information related to prognosis, such as the patient’s preoperative baseline disease status and hematological indicators. Based on this, the nomogram along with the patient’s various clinical and hematological indicators was used to construct a prognostic model. The nomogram has the advantage of a multi-dimensional comprehensive prediction of prognosis.[24] Some studies have reported nomograms related to the prognosis of thymic epithelial tumors. However, they considered only the clinicopathological characteristics or treatment methods of patients and do not consider relevant hematological indicators. At the same time, many studies on hematological indicators have shown that Hb, NE, LY, platelet (PLT), NLR, ALB, GLB, ALB/GLB, platelet-lymphocyte ratio (PLR), NLR, and SII [12, 13, 15, 25] have potential to become prognostic hematological indicators for various tumors including thymic epithelial tumors.

Our nomogram is composed of several factors that affect prognosis, which are commonly used in clinical practice. The nomogram showed that a history of diabetes and hypertension was related to poor OS in patients with thymic epithelial tumors. This finding is consistent with the findings of other studies. [33] [9] In terms of T staging, lower T staging can result in a more satisfactory OS. In the studies of other researchers, the patient's preoperative clinical staging was a factor affecting the prognosis of thymic epithelial tumors. [26] At the same time, we found that elevated NLR was associated with poor prognosis in patients with thymic epithelial tumors. This is the same as other people’s research results. [6, 27] In addition, a higher preoperative serum ALB level can often result in a more satisfactory prognosis, which is consistent with the findings of other studies reporting better prognosis in patients with thymic epithelial tumors with higher ALB levels. [15]

In predicting the prognosis of certain cancers, nomograms have been developed and proven to be more accurate than traditional staging systems. [28, 29] Therefore because thymectomy is an effective treatment for thymic epithelial tumors, [30–33] we constructed a prognostic nomogram for patients with thymic epithelial tumors after surgery. The nomogram performs well in predicting the survival rate. Its prediction is supported by the C index (0.886 and 0.741 for the training and verification cohorts, respectively), and the calibration curve was consistent with that of the baseline. Compared with other staging systems, the nomogram has higher accuracy in predicting survival, and the DCA curve also showed that the nomogram had better predictive ability than the T staging.

To the best of our knowledge, this is a relatively new attempt to develop a prognostic OS nomogram for patients with thymic epithelial tumors by combining hematological and clinical indicators. Although this nomogram did not include many hematological indicators in the end, it combines preoperative clinical indicators, hematological indicators, and other important clinical information to achieve the ultimate goal of integrating multi-dimensional data to jointly predict the prognosis of thymic epithelial tumors.

This study has several limitations. First, this was a retrospective study. Moreover, this was only a single-center study that included a small number of patients. Further research including more number of cases is still needed to verify our results. Second, tumor markers (CEA, SCC, AFP, etc.) and other potentially valuable hematological indicators were not included in this study. Third, the dynamic changes in hematological indicators considered in this study were not followed up after the operation.

In summary, by combining hematological and clinical indicators, we established and validated a nomogram for predicting the personalized survival rate of patients with thymic epithelial tumors. This convenient nomogram had better performance than the T staging to distinguish the prognosis and risk of patients. Our findings suggest that it may be a potentially easy-to-use tool for physicians and can aid in postoperative personalized prognosis assessment and early identification of high-risk patients. Although the nomogram appears useful for prognostication and identifying high-risk patients, further prospective studies are needed to validate the nomogram and confirm the contribution of each prognostic factor.

NLR

neutrophil-to-lymphocyte ratio; Hb:hemoglobin; ALB:albumin; BMI:body mass index; NE:neutrophil count; LY:lymphocyte count; GLB:Globulin; SII:systemic immune-inflammation Index; PLT:platelet; PLR:platelet-lymphocyte ratio; pT stage:Pathological T stage; OS:overall survival; C-index:consistency index; ROC:receiver operating characteristic; DCA:decision curve analysis; KM:Kaplan-Meier.

1 Acknowledgments

We would like to thank the Sun Yat-sen University Cancer Center for providing valuable clinical data, Thanks to our respected pioneer, Professor Tie-Hua Rong, who oriented us to surgical oncology. We thank our patients who silently provided data, Finally, we are grateful to Editage (https://app.editage.com/) for its excellent polishing work.

2 Author Contributions

Conception and design of the work: MGW, HYY and WLL. Provision of study materials or patients: MGW. Acquisition of data: HYY, WLL and LX. Analysis of data: MGW, HYY and WLL. Interpretation of data: MGW, HYY. HYY and LSH drafted the manuscript; MGW and HYY substantially revised the manuscript. All authors read and approved the final manuscript.

3 Funding

This work was supported by grants from the Wu Jie-ping Medical Foundation. (Nos. 320.6750.2020-15-7).

4 Availability of data and materials

Data from this study are available to any interested researchers upon reasonable request to the corresponding author.

Declarations

5 Ethics approval and consent to participate

This study was approved by the Medical Ethics Committee of Sun Yat-sen University Cancer Center (SYSUCC; Approval, No. B2020-353-01) and complies with the Declaration of Helsinki. At the same time, this study has obtained the exemption of informed consent application from the Ethics Committee of Sun Yat-sen University Cancer Center.

6 Consent for publication

All authors agreed to the publication.

7 Competing interests

The authors disclose no conflicts of interest.

8 Author details

¹State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China. ²Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People’s Republic of China.

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No competing interests reported.

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15 Apr, 2021
First submitted to journal
12 Apr, 2021

You are reading this latest preprint version

Nomogram Predict Overall Survival of Patients with Thymic Epithelial Tumors After Surgery

Status:

Journal Publication

Version 1

Abstract

Figures

1 Background

2 Materials And Methods

3 Results

4 Discussion

5 Conclusions

Abbreviations

Declarations

1 Acknowledgments

2 Author Contributions

3 Funding

4 Availability of data and materials

Declarations

5 Ethics approval and consent to participate

6 Consent for publication

7 Competing interests

8 Author details

References

Additional Declarations

Status:

Journal Publication

Version 1