Establishment and Validation of a Prognostic Model in Patients with Stage T1-3N0M0 of Esophageal Squamous Cell Carcinoma (ESCC): An Observational Study from Two Centers

doi:10.21203/rs.3.rs-22744/v1

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Establishment and Validation of a Prognostic Model in Patients with Stage T1-3N0M0 of Esophageal Squamous Cell Carcinoma (ESCC): An Observational Study from Two Centers

https://doi.org/10.21203/rs.3.rs-22744/v1

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PURPOSE: To explore the postoperative prognosis of patients diagnosed with esophageal squamous cell carcinoma (ESCC) of stage T1-3N0M0 using a survival prognostic model (SPM).

PATIENTS AND METHODS: Patients diagnosed with stage T1-3N0M0 ESCC from two cancer centers—Sun Yat-sen University Cancer Center (SYSUCC-A/ training cohort: N = 555), SYSUCC-B/ internal validation cohort: N = 241) and Henan Cancer Hospital (HNCH/ external validation cohort: N = 170)—that had undergone esophagectomy between 1995 and 2015 were enrolled in this study. The primary clinical endpoint was overall survival (OS). We have identified and integrated significant prognostic factors for OS by univariate and multivariate Cox regression methods applied in the training cohort to build a SPM that could be validated in the validation cohorts.

RESULTS: The OS evaluation by the SPM was comparable in three cohorts (SYSUCC-A: C-index 0.654, SYSUCC-B: C-index 0.630, HNCH: C-index 0.688). Discretization of patients was done using a fixed survival score cutoff of 136.434 based on our SPM determined from the training cohort divided into low- and high-risk subgroups with stratified OS in the validation cohort (SYSUCC-A: hazard ratio [HR] 1.009, 95% confidence intervals [CI], 1.006–1.011, P < 0.001; SYSUCC-B: HR 1.009, 95% CI, 1.004–1.013, P ˂ 0.001; HNCH: HR 1.010, 95% CI 1.005-1.015, P = 0.0017). The 48-month OS in the low-risk subgroup vs. that in the high-risk subgroup was 80.8% vs. 60.9% for SYSUCC-A, 86.4% vs. 60.7% for SYSUCC-B, and 89.7% vs. 64.1% for HNCH.

CONCLUSION: We have established and validated a novel SPM that can predict the OS for T1-3N0M0 ESCC patients and could help clinicians to detect subgroups of patients with poor prognosis.

Cancer Biology

esophageal squamous cell carcinoma

prognostic model

survival

stage T1-3N0M0

Esophageal carcinoma (EC) is one of the worldwide malignancies, which is caused by multiple factors. The incidence and mortality of esophageal malignancies rank 7th and 6th, respectively, in the global cancer spectrum[1]. The major histological subtypes of EC include squamous cell carcinoma and adenocarcinoma, with ESCC being the most common[2]. About 509,000 cases of mortality caused by these carcinomas were reported in 2018 only[1]. EC is prevalent in the following countries: China, Japan, South Africa, Uruguay, France, and Italy[3]. However, more than half of the newly diagnosed EC cases occur in China, resulting in the highest mortality rate in this country. Importantly, squamous cell carcinoma is the major histological subtype, which accounts for more than 90% of EC cases[4–6]. Although research had shown that neoadjuvant therapy could improve the prognosis of ESCC patients who underwent esophagectomy, the postoperative prognosis for these patients unfortunately remains poor. Specifically, in China, a postoperative five-year survival rate is only 20–40%[7, 8]. In order to improve the OS in patients with EC, it is necessary to identify the patients with high risk of cancer recurrence and metastasis as early as possible. Accurate staging system and individualized prognosis prediction could help clinicians to correctly identify patients with poor prognosis [9]. Such patients had received a multi-disciplinary treatment to improve curative effect of treatment on EC. The existing research had identified specific factors that have a direct influence on the occurrence and progress of EC. These factors include the following: the level of preoperative C-reactive protein/albumin ratio[10], neutrophil-to-lymphocyte ratio[11], thoracic lymph nodes metastasis[12], and other factors[13–18]. However, there are no methods that would accurately predict postoperative prognosis in patients with stage T1-3N0M0 ESCC.

To address this issue, we have developed a survival prognostic model (SPM) that was based on five different parameters, including the age and the T stage, which were independently validated for survival. We used patients’ data obtained from Sun Yat-sen University Cancer Center (SYSUCC) and that from the affiliated Cancer Hospital of Zhengzhou university/ Henan Cancer Hospital (HNCH). The patients’ data originating from SYSUCC were randomly divided into two groups, the training group (SYSUCC-A) and the internal validation group (SYSUCC-B). The data obtained from HNCH were regarded as an external validation group. Then we used a survival score (SC) based on the SPM described above to dichotomize the cohort into low-risk and high-risk subgroups that were correlated with survival outcomes, which provided us with a clinically applicable tool to assist with treatment recommendations in this heterogeneous patient subgroup.

Patients

A total number of 966 patients (796 patients from SYSUCC; 170 patients from HNCH) who underwent esophagectomy at the Department of Thoracic Surgery of Sun Yat-sen University Cancer Center (Guangzhou, China) and Henan Cancer Hospital (Zhengzhou, China) between April 1995 and December 2015, were enrolled retrospectively in the present study. Patients eligible for this cohort study had pathologically confirmed stage T1-3N0M0 according to the 8th edition of American Joint Committee on Cancer (AJCC) Staging Manual. During the operation, the median number of lymph nodes (LNs) dissected were 18.4 ± 13.12 and 17.8 ± 9.3 in the SYSUCC and HNCH, respectively. There was no pathological diagnosis of lymph node metastases. Patients were excluded from the study if one of these occurred: (1) they had received adjuvant and neoadjuvant cytotoxic chemotherapy, or radiotherapy, or immune checkpoint inhibitors; (2) they had a past or current history of another malignancy; (3) the resection or margin residual tumor cells was not complete; (4) they had died because of postoperative complications; (5) patients’ tumors were located at the esophagogastric junction or cervical esophagus; (6) they had other histological subtypes of esophageal cancer besides ESCC. Patients did not exhibit a clinical evidence of infection or other inflammatory conditions. According to the patients’ records, we have accurately identified the pathological staging according to the 8th edition of AJCC. The data obtained from SYSUCC were randomly divided into two groups, the training group (SYSUCC-A, N = 555), and the internal validation group (SYSUCC-B, N = 241). The data of HNCH (N = 170) were regarded as an external validation group. The Ethics Committee of Sun Yat-sen University Cancer Center approved the study’s protocol (IRB: YB2016-070). The data of study was recorded at the Research Data Deposit (RDD) of SYSUCC for future reference, with approval number: RDDB2019000777. The flow chart of the study is shown in Fig. 1.

Surgery

The following standard surgical approaches were used: the Sweet (left thoracotomy and diaphragm incision); the McKeown (right thoracotomy, laparotomy, and neck incision); and the Ivor Lewis (laparotomy and right thoracotomy) procedures. In all cohorts of patients, thoracoabdominal lymphadenectomy was performed.

Follow-up

The median follow-up time was 75.0 and 59.0 months for the SYSUCC and HNCH cohorts, respectively. We recommended that the patients came to the outpatient department for a follow-up examination every 3 months for the first 2 years, then every 6 months for the next 3 years, and then every year after that. Follow-up examinations consisted of history assessment, barium esophagography, physical examination, chest radiography, cervical ultrasonography, abdominal ultrasonography, and neck-abdomen CT scans. If necessary, patients underwent positron emission tomography-CT and/or endoscopy.

Statistical analysis

Statistical analysis was performed using SPSS Statistics 25.0 software (IBM SPSS, Inc., Chicago, IL, USA), and R version 3.6.2 (https://www.r-project.org/). Hazard ratios (HR) with 95% confidence intervals (95% CIs) were calculated by univariate and multivariate Cox regression analysis. Multivariate analysis was performed to evaluate the effect of sex, age, the number of lymph lodes removed (LNs), smoking history, alcohol consumption, T stage, tumor grade, albumin level (Alb), Forced Expiratory Volume in the first second (FEV1), globulin (Glb), and hemoglobin (Hb) on OS. Pearson’s ꭓ² statistical test was used to determine the association between clinical information and groups. Standard deviations (std) were used to access the stability of data. A two-sided p values of P < 0.1 was considered statistically significant. Variables with univariate analysis had p-values of P < 0.1 were selected to enter in the multivariate analysis. The most valuable prognostic factors were further confirmed by multivariate analysis. In addition, Kaplan–Meier analysis and the log-rank tests were used to compare survival curves between different groups. Cases were censored either at patients’ death or at the end of the follow-up. The selection of OS as a primary clinical end point was considered most clinically relevant. We adopted a model development and validation approach using a randomized method to extract trained and validated data sets to maintain a transparent report of the multivariate predictive model on individual prognostic guidelines.

Patient demographics and clinical characteristics were reported for training cohort. The SPM system for OS was constructed using the linear predictor of the finalized model, which was derived from the training data set. The selected variables with multivariate analysis had p-values of P < 0.05. The cohort was dichotomized into low-risk and high-risk subgroups based on the mean SC among the non-surviving patients in the training cohort, and a risk score cutoff was defined for classifying patients in the validation cohorts. Indices of concordance (C-index) were generated to evaluate the prediction effect of SPM.

The SC was applied to calculate the risk scores in the validation cohort, and patient discretization into low-risk and high-risk subgroups was done using the same cutoffs defined in the training data set. Individual and population-averaged survival curves on the basis of risk groups were predicted using the estimated baseline survival function from the training data set. Discrimination and calibration were evaluated using the same methods.

Patient Characteristics

The clinical characteristics of the patients from SYSUCC database are listed in Table 1. Among the 796 patients, 583 (73.2%) patients were men and 213 (26.8%) were women. The patients’ age ranged between 28 and 88 years (median, 58 years). In this cohort, the 1-, 3- and 4-year OS rates were 91.0%, 72.0%, and 66.0%, respectively, and the mean time from surgery to the last censoring date were 77.26 months. In the training cohort, the 1-, 3- and 4-year OS rates were 91.0% vs. 72.0% vs. 64.0%, respectively (SYSUCC-A). Furthermore, in the validation cohorts, the 1-, 3- and 4-year OS rates were 92.0% vs. 74.0% vs. 70.0% (SYSUCC-B) and 95.0% vs. 76.0% vs. 69.0% (HNCH), respectively. The clinical characteristics of the patients in the HNCH are listed in Table S1.

Table 1

The associations of clinicopathological characteristics between training cohort (SYSUCC-A) and internal validation cohort (SYUCC-B).
	All patients (N = 796)	Training Cohort (SYSUCC-A, N = 555)	Validation Cohort (SYSUCC-B, N = 241)
Variables	No. of patients/ mean ± std			P value
Sex				0.191
Male	583 (73.2%)	414 (71.0%)	169 (29.0%)
Female	213 (26.8%)	141 (66.2%)	72 (33.8%)
Smoking history				0.185
No	306 (38.4%)	205 (67.0%)	101 (33.0%)
Yes	490 (61.6%)	350 (71.4%)	140 (28.6%)
Drinking history				0.325
No	577 (72.5%)	408 (70.7%)	169 (29.3%)
Yes	219 (27.5%)	147 (67.1%)	72 (32.9%)
Tumor grade				0.280
Well	228 (28.6%)	168 (73.7%)	60 (26.3%)
Moderate	373 (46.9%)	252 (67.6%)	121 (32.4%)
Poor	195 (24.5%)	135 (69.2%)	60 (30.8%)
T stage				0.686^a
T1a	11 (1.4%)	7 (63.6%)	4 (36.4%)
T1b	112 (14.1%)	77 (68.8%)	35 (31.2%)
T2	237 (29.8%)	160 (67.5%)	77 (32.5%)
T3	436 (54.8%)	311 (71.3%)	125 (28.7%)
Tumor Location				0.079^a
Cervical	3 (0.4%)	2 (66.7%)	1 (33.3%)
Upper	53 (7.5%)	38 (71.7%)	15 (28.3%)
Middle	225 (31.7%)	143 (63.6%)	82 (36.4%)
Lower	429 (60.5%)	313 (73.0%)	116 (27.0%)
Age (year)	58.24 ± 9.203	58.15 ± 9.223	58.47 ± 9.172	0.651^b
Hb (g/L)	136.6 ± 14.52	136.9 ± 14.13	135.8 ± 15.39	0.318^b
Alb (g/L)	43.17 ± 3.98	43.05 ± 4.09	43.44 ± 3.70	0.198^b
FEV1 (L)	2.55 ± 0.765	2.57 ± 0.79	2.51 ± 0.70	0.259^b
LNs	18.4 ± 13.12	18.6 ± 12.97	17.96 ± 13.47	0.531^b
Glb (g/L)	27.85 ± 4.879	27.62 ± 4.792	28.37 ± 5.044	0.047^b
* a: Fisher’s exact test; b: Student’s t test.

Selection of prognostic factors and construction of SPM

As shown in Table 2, univariate and multivariate analyses identified the following five clinical characteristics as dependent significant OS prognostic factors in patients with ESCC: age, Alb, T stage, classifying of LNs, and tumor differentiation. Our study revealed that five factors were significantly associated with prognosis in stage T1-3N0M0 ESCC patients. Based on the results of the training group, we constructed the SPM and tested the covariates listed in Table 3 for their association with OS. The SPM was based on weighting (derived from the β-coefficient of the respective log [HRs]) of the five significant variables in the training cohort (Table 3). Then we used a SC based above model to dichotomize the cohort into low- and high-risk subgroups depending on OS. The cutoff value of SC was determined in order to distinguish between the high-risk and the low-risk groups, using the mean score of 136.434. Our five-factor SPM predicted that the 12-month, 36-month and 48-month OS in the low-risk subgroup vs. that in the high-risk subgroup was 98.8% vs. 96.2%, 86.1% vs. 72.4% and 80.8% vs. 60.9%, respectively in the training cohort (SYSUCC-A). We found that there was a significant difference between the low- and high-risk groups (Fig. 2A, HR 1.009, 95% CI, 1.006–1.011, P < 0.001). The SPM had yielded a C index of 0.654 ± 0.022 for OS.

Table 2

Univariable and multivariable Cox regression of patients with stage T1-3N0M0 in ESCC.
	Univariable Cox Regression		Multivariable Cox Regression
Covariate	P value	HR (95% CI)	P value	HR (95% CI)
Sex	0.353	1.195 (0.8203–1.741)
Age	0.0086	1.023 (1.006–1.041)	0.00622	1.0255 (1.0072–1.0442)
Weight loss	0.469	1.024 (0.9612–1.09)
FEV1	0.98	1.003 (0.8042–1.251)
ALB	0.0128	0.9562 (0.923–0.9905)	0.04992	0.9616 (0.9247–1.0000)
HB	0.527	0.9966 (0.9859–1.007)
Smoking history	0.0365	1.44 (1.023–2.028)	0.09068	1.3778 (0.9505–1.9973)
Drinking history	0.0987	1.3 (0.9215–1.834)	0.28201	1.2263 (0.8457–1.7782)
T stage (T1a vs. T1b vs. T2 vs. T3)	< 0.001	1.507 (1.191–1.907)	< 0.001	1.6499 (1.2929–2.1056)
LN dissection volume
≥ 16	0.0031	0.5508 (0.3712–0.8173)	< 0.001	0.4687 (0.3145–0.6985)
9–15	0.4135	0.8466 (0.5680–1.2619)	0.19879	0.7676 (0.5129–1.1490)
Tumor differentiation
Well	0.0698	0.6731 (0.4388–1.033)	0.01243	0.5753 (0.3730–0.8875)
Middle	0.3723	0.8420 (0.5773–1.229)	0.34721	0.8325 (0.5681–1.2200)

Table 3

Constructed prognostic score to predict overall survival of patients with stage T1-3N0M0 in ESCC.
Covariate	β [HR = exp(β)]	Score
Age (continuous)	0.025172	0.025172 * age
ALB (continuous)	-0.039126	-0.039126 * ALB
Tumor invasion	0.500736	0.500736 * (grade; 2/3/4/5)
LN dissection volume
≥ 16	-0.757697	-0.757697 * 1
9–15	-0.264431	-0.264431 * 1
8	/	0
Tumor differentiation
Well	-0.552839	-0.552839 * 1
Middle	-0.183297	-0.183297 * 1
Poor	/	0
Total Risk		*Total computed score 100**
Low Risk		≤ 136.434
High Risk		༞136.434

Validation of the SPM

In order to validate the predictive accuracy of the SPM for OS in stage T1-3N0M0 ESCC patients, we tested the SPM independently in the following validation groups: an internal cohort of 241 patients from SYSUC and an external cohort of 170 patients from HNCH. The same SC cutoff of 136.434 allowed us to stratify the patients within these validation groups into the high-risk subgroup with a significantly lower OS or the low-risk subgroup (SYSUCC-B: HR 1.009, 95% CI, 1.004–1.013, P ˂ 0.001; HNCH: HR 1.010, 95% CI 1.005–1.015, P = 0.0017, Fig. 2BC). The SPM of validation predicted that the 1-, 3- and 4-year OS in the low-risk subgroup were better than that in the high-risk subgroup (SYSUCC-B: 97.5% vs. 97.5%, 89.9% vs. 73.9% and 86.4% vs. 60.7%, respectively; HNCH: 98.7% vs. 95.7%, 92.3% vs. 81.5%, and 89.7% vs. 64.1%, respectively). The SPM in the validation cohorts yielded C index of 0.630 ± 0.034 (SYSUCC-B) and 0.688 ± 0.04 (HNCH) for OS.

There was no median survival time in three cohorts in the SYSUCC and HNCH, for the proportion of patients that died did not exceed 50.0%.

Stratified effect of SPM in the T stage

To further explore the stratified effect of SPM in different T stages, we used the K-M analysis to draw a survival curve. As we expected, the results had shown that SPM could identify the cohort with poor prognosis from patients with ESCC in every T stage (all P < 0.1, Fig. 3). There was no doubt that SPM had a better distinction ability in stage T2-3 (all P < 0.05) than that in stage T1 (P = 0.061).

It is well known that ESCC is associated with poor prognosis and the five-year overall survival rate is about 20%-40%. More and more research is being directed at developing better treatment and improving prognosis for patients diagnosed with ESCC. Previous studies suggested that certain factors had an effect on the prognosis of ESCC patients however, the systematic analysis of these factors as a prognostic took has not been done[19–24]. In the present study, we have analyzed the ESCC patients’ data from two academic institutes, SYSUCC and HNCH. Then, we divided randomly data of SYSUCC into two groups, training group (SYUCC-A, N = 555) and internal validation group (SYUCC-B, N = 241). Next, we analyzed the information of training group by applying univariate and multivariable Cox regression. We have identified the five following factors: age, Alb levels, T stage, LN classification, and tumor differentiation as independent prognostic factors. To construct the SPM, we processed the data based on assessing these five significant variables mentioned above in the training cohort (Table 3). We regarded SC as prognostic score of patients, which was derived from SPM. Mean SC was presented as a cutoff value for classifying the prognosis. In the present study, we found that our SPM could provide a stratified effect on the patients’ prognosis, and we could detect the group with the poor after esophagectomy (Fig. 2A, HR 1.009, 95% CI, 1.006–1.011, P < 0.001). To validate the SPM, we used the same method and cutoff values in the internal validation and external validation cohorts. As we have expected, there was a strong stratified effect in the validation cohorts (Fig. 2BC). The SPM yielded a C index of 0.654 ± 0.022 (SYSUCC-A), 0.630 ± 0.034 (SYSUCC-B) and 0.688 ± 0.04 (HNCH) for OS, respectively. In the present study, we have established and validated a survival prognostic model (SPM) that could predict the OS for patients with stage T1-3N0M0 ESCC patients, and might help clinicians to screen subgroups in order to identify patients with poor prognosis. To further explore the stratified effect of SPM in the different T stages, we used the K-M analysis to draw a survival curve. The results, as expected, had shown that SPM had a better distinction power in stage T2-3 (all P < 0.05) than in stage T1 (P = 0.061, Fig. 3).

The selected factors from clinical characteristics could be easily obtained from patient’ medical record. We could get the information on the age at the time of diagnosis from patients’ medical record, and obtain additional data on tumor differentiation, T stage, and the number of lymph nodes removed from patients’ pathological reports. Preoperative level of Alb could be accessed using the results of labor test. Five factors listed above were very common in patients’ records and could be easily obtained, which was very beneficial for the testing of our model. In addition, the areas with high incidence of esophageal cancer in China were mainly located in Henan Province, Hebei Province, Chaoshan region (Guangdong Province), and other regions[25–27]. SYSUCC is located far from the HNCH, and both cancer centers accept patients with ESCC across China. Our model was constructed using the cases from SYSUCC-A and further validated using the cases from both: SYSUCC-B and HNCH. Model validation using two patients’ cohorts from SYSUCC-B and HNCH also fully illustrated the applicability of the five-factor model presented in this study. We excluded the ESCC patients with stage T4 and those with metastases in lymph nodes and other organs because previous study showed that neoadjuvant therapy could improve the prognosis in patients who were diagnosed with metastases in lymph nodes before surgery[28]. Accordingly, in the present study, we classified the LNs according to the approach used in our previous study[29].

However, there are some limitations in the study presented here. First of all, the sample size of ESCC patients was not large enough; the T stage was restricted to only the T1-3, and the data distribution of the T stage was not balanced. For further work, to improve this aspect, the sample size would need to be expended. Second, the patients’ data used in this study originated from two academic institutes only, which might also impact the accuracy of our model, so a larger scale using patients’ information from multiple centers was necessary to confirm our findings. While the results presented here proved that our five-factor prognosis model could be useful when predicting OS for postoperative T1-3N0M0 ESCC patients, data from more centers would still be required to verify the applicability of this model. Third, this model could only provide a certain reference information to the clinicians but not the treatment recommendations. The doctors would need to make decisions on the patients’ treatment according to the relevant guidelines and clinical experience. Fourth, the follow-up time period for the HNCH cohort was not long enough. The main observation end-point was 4-year OS for the patients who underwent surgery during the period between January 2014 and December 2015. More follow-up research should be done for evaluating prognosis for these patients. Fifth, the patients’ data was lacking the results of molecular diagnosis, such as the information on the expression of Programmed death-ligand 1 (PD-L1) which is an effective prognostic factor that could contribute to the more accurate prediction[30, 31]. Therefore, a substantial research at the molecular level is still needed for further improvement of the proposed model.

In conclusion, we have established and validated a SPM that can predict the OS for patients with stage T1-3N0M0 ESCC and could help clinicians screen for subgroups with poor prognosis. In addition, we suggested that this model from two academic institutes had a value of utilization for other researchers apply on their own data.

Acknowledgements

We thank our patients who were willing to provide personal information for medical research, they were the best teachers for doctors.

Authors’ contributions

MGW, LY and XWQ designed and supervised this work. WLL, JYX, LX and ZY analyzed the data and wrote the manuscript. HYY acquired data. LP, LH and ZLJ revised the manuscript. The authors read and approved the final manuscript.

Funding

This work was supported by the Guangdong Esophageal Cancer Institute Science and Technology Program under Grant M201916.

Availability of data and materials

Not applicable.

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors agree to submit the article for publication.

Competing interests

The authors declare that they have no competing interests.

Bray F, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.
Arnold M, et al. Global incidence of oesophageal cancer by histological subtype in 2012. Gut. 2015;64(3):381–7.
Lin Y, et al. Epidemiology of esophageal cancer in Japan and China. J Epidemiol. 2013;23(4):233–42.
Abnet CC, Arnold M, Wei WQ. Epidemiology of Esophageal Squamous Cell Carcinoma Gastroenterology. 2018;154(2):360–73.
Liang H, Fan JH, Qiao YL. Epidemiology, etiology, and prevention of esophageal squamous cell carcinoma in China. Cancer Biol Med. 2017;14(1):33–41.
Chen W, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–32.
Feng Y, et al. Comparison of Ivor Lewis esophagectomy and Sweet esophagectomy for the treatment of middle-lower esophageal squamous cell carcinoma. J Thorac Dis. 2019;11(8):3584–92.
Yu S, et al. A propensity-score matching analysis comparing long-term survival of surgery alone and postoperative treatment for patients in node positive or stage III esophageal squamous cell carcinoma after R0 esophagectomy. Radiother Oncol. 2019;140:159–66.
Donohoe CL, Phillips AW. Cancer of the esophagus and esophagogastric junction: an 8(th) edition staging primer. J Thorac Dis. 2017;9(3):E282–4.
Liu Z, Shi H, Chen L. Prognostic role of pre-treatment C-reactive protein/albumin ratio in esophageal cancer: a meta-analysis. BMC Cancer. 2019;19(1):1161.
Pirozzolo G, et al. Neutrophil-to-lymphocyte ratio as prognostic marker in esophageal cancer: a systematic review and meta-analysis. J Thorac Dis. 2019;11(7):3136–45.
Shang QX, et al. Prognostic significance and role of thoracic lymph node metastasis based on Chinese expert consensus in esophageal cancer. Ann Transl Med. 2019;7(16):381.
Zheng Y, et al. Predicting prognosis in resected esophageal squamous cell carcinoma using a clinical nomogram and recursive partitioning analysis. Eur J Surg Oncol. 2018;44(8):1199–204.
Zhang J, et al. SERPINE2 promotes esophageal squamous cell carcinoma metastasis by activating BMP4. Cancer Lett. 2020;469:390–8.
Du X, et al. HIC-5 in cancer-associated fibroblasts contributes to esophageal squamous cell carcinoma progression. Cell Death Dis. 2019;10(12):873.
Gruber ES, et al., The Oncogene AF1Q is Associated with WNT and STAT Signaling and Offers a Novel Independent Prognostic Marker in Patients with Resectable Esophageal Cancer. Cells, 2019. 8(11).
Kudou M, et al. The expression and role of TRPV2 in esophageal squamous cell carcinoma. Sci Rep. 2019;9(1):16055.
Zhou H, et al. The prognostic value of B7-H6 in esophageal squamous cell carcinoma. Sci Rep. 2019;9(1):18122.
Mariette C, et al. Self-expanding covered metallic stent as a bridge to surgery in esophageal cancer: impact on oncologic outcomes. J Am Coll Surg. 2015;220(3):287–96.
O'Grady G, et al. Patient Selection for Oesophagectomy: Impact of Age and Comorbidities on Outcome. World J Surg. 2015;39(8):1994–9.
Xie SH, Lagergren J. Social group disparities in the incidence and prognosis of oesophageal cancer. United European Gastroenterol J. 2018;6(3):343–8.
Shen C, et al. Building CT Radiomics Based Nomogram for Preoperative Esophageal Cancer Patients Lymph Node Metastasis Prediction. Transl Oncol. 2018;11(3):815–24.
Vendrely V, et al. Prognostic factors in esophageal cancer treated with curative intent. Dig Liver Dis. 2018;50(10):991–6.
Huang W, et al. Preoperative serum C-reactive protein levels and postoperative survival in patients with esophageal squamous cell carcinoma: a propensity score matching analysis. J Cardiothorac Surg. 2019;14(1):167.
Lin Y, et al. Esophageal cancer in high-risk areas of China: research progress and challenges. Ann Epidemiol. 2017;27(3):215–21.
Tang WR, et al. Development of esophageal cancer in Chaoshan region, China: association with environmental, genetic and cultural factors. Int J Hyg Environ Health. 2015;218(1):12–8.
Liu S, et al. Patrilineal background of esophageal cancer and gastric cardia cancer patients in a Chaoshan high-risk area in China. PLoS One. 2013;8(12):e81670.
Hong Yang HL, Chen Y. Neoadjuvant Chemoradiotherapy Followed by SurgeryVersus Surgery Alone for Locally Advanced Squamous Cell Carcinoma of the Esophagus (NEOCRTEC5010): A Phase III Multicenter, Randomized, Open-Label Clinical Trial. J Clin Oncol, 2018. (36): p. 2796–2803.
Liu Q, et al. Impact of the number of resected lymph nodes on postoperative survival of patients with node-negative oesophageal squamous cell carcinoma. Eur J Cardiothorac Surg. 2013;44(4):631–6.
Jiang C, et al. High PD-L1 expression is associated with a favorable prognosis in patients with esophageal squamous cell carcinoma undergoing postoperative adjuvant radiotherapy. Oncol Lett. 2019;17(2):1626–34.
Noske A, et al. Relevance of tumour-infiltrating lymphocytes, PD-1 and PD-L1 in patients with high-risk, nodal-metastasised breast cancer of the German Adjuvant Intergroup Node-positive study. Eur J Cancer. 2019;114:76–88.

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Establishment and Validation of a Prognostic Model in Patients with Stage T1-3N0M0 of Esophageal Squamous Cell Carcinoma (ESCC): An Observational Study from Two Centers

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Introduction

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Patients

Surgery

Follow-up

Statistical analysis

Results

Patient Characteristics

Selection of prognostic factors and construction of SPM

Validation of the SPM

Stratified effect of SPM in the T stage

Discussion

Declarations

References

Supplementary Files

Status:

Version 1