Introduction

Esophageal cancer is the seventh most common malignancy in the world1. Esophageal squamous cell carcinoma confined to intraepithelial (Tis), mucosa (T1a) and submucosal (T1b), irrespective of lymph node metastasis (LNM), is considered to be superficial esophageal squamous cell carcinoma (SESCC) and have a good outcome2. Conventional view holds that the standard treatment for most esophageal cancers is radical esophagectomy, even for cancers confined to the mucosa3. However, this procedure has high rates of morbidity and mortality4,5. Furthermore, surgery may not be carried out in patients who are older or have multiple co-morbidities6. In order to reduce surgery-related complications and obtain a higher quality of post-operative life, currently a less invasive treatment method (such as endoscopic resection) has been considered as an alternative to esophageal surgery7,8.

Nevertheless, SESCC has LNM potential because the lymphatic-capillary plexus in the mucosa of lamina propria and the submucosa of esophagus are plentiful. As a minimally invasive treatment and do not dissect lymph node, endoscopic resection (ER) is applied to SESCC without LNM. For SESCC with LNM, the proper support is the radical esosophagectomy to remove all potentially involved nodes. Consequently, it is critical to explore the predictive factors of LNM in SESCC patients before ER. In several studies, some imaging methods (EUS, CT or PET) can detect LNM of SESCC, but these methods are not precise enough to completely rule out the presence of the LNM9,10,11. Additionally, the clinicopathological risk factors associated with LNM in SESCC are still understood incompletely2.

The purpose of this study was to determine the risk factors of LNM in SESCC patients. Then a nomogram was established using these risk factors, which can help predict LNM and determine whether or not a supplementary esophagus resection is necessary after ER.

Methods

Patients selection and data collection

Between March 2005 and April 2018, the data of patients with histopathologically-confirmed esophageal cancer (Tis or T1 stage) who underwent esophagus resection at Zhejiang Cancer Hospital were retrospectively analyzed. The exclusion criteria were: (1) patients who received chemotherapy or radiotherapy before surgery; (2) patients with basaloid squamous cell carcinoma, adenosquamous carcinoma, sarcomatoid carcinoma, neuroendocrine carcinoma, or spindle cell carcinoma. The final eligible patients with SESCC who were admitted between March 2005 and August 2012 were assigned to the training set and those admitted between September 2012 and February 2018 were assigned to the validation set. Endoscopic findings of the tumors macroscopic type were classified according to the Paris classification12. Nonprotruding or nonexcavated superficial tumors were classified as type II (flat type), protruding and excavated superficial tumors were classified as type I and type III (type I and III were considered as nonflat type). The flowchart of patient selection is summarized in Fig. 1.

Figure 1
figure 1

Flowchart of patients included in the analysis.

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Lymph node dissection

In this study, lymph node dissections were performed according to esophageal cancer location13. For upper thoracic esophageal cancer, the rate of cervical and upper mediastinal lymph node metastasis is high. Thus, lymph node dissection included the neck (two-field lymph node dissections). For middle thoracic esophageal cancer, lymph node metastasis mainly occurs in the neck and the upper, middle, and lower mediastinum as well as the abdominal cavity. The extent of lymph node dissection included the neck and supraclavicular area (three-field lymph node dissections). For the lower thoracic esophageal cancer, lymph node metastasis mainly occurs in the mediastinum and abdomen, and cervical metastasis is relatively low. So the two-field lymph node dissections were performed for these patients.

Histopathologic evaluation

Surgical specimens were fixed with formaldehyde and were then cut serially to make slices. The intervals between the tumor tissue and adjacent normal tissues in the slices were 2–5 mm. Tumors that exceed the muscularis mucosa were considered as submucosal invasion14. We then classified the location of esophageal cancer according to the guidelines of the American Joint Committee on Cancer15. The portion of the esophagus extending from the entrance of the thoracic cavity to the bifurcation of the trachea is considered the upper esophagus, the section from the trachea bifurcation to the distal esophagus (above the esophagogastric junction) is regarded as the middle esophagus, and the intra-abdominal portion of the esophagus and the junction of the esophagus and stomach constituted the lower esophagus.

Ethics statement

This retrospective study was performed in accordance with the Helsinki Declaration of 1975 and approved by the Ethics Committee of the Zhejiang Cancer Hospital, Hangzhou, China. Written informed consent was obtained from all patients before surgery.

Statistical analysis

Continuous variables are expressed as median (range) and compared using Mann–Whitney test. Categorical variables were compared using the χ2 test or Fisher exact test. All variables associated with LNM at a significant level were candidates for stepwise multivariate logistic analysis. The integrated discrimination improvement (IDI) is the difference in the discrimination slopes for a prediction model with and without one variable, which indicates whether the discrimination slope of a model will improve if one important parameter is added. The net reclassification improvement (NRI) is an index that attempts to quantify how well a new model correctly reclassifies subjects. Typically, this comparison is between an original model and a new model (the original model plus one additional component)16,17. The IDI and NRI were calculated using R, version 4.0.3 with the PredictABEL package.

According to the results of multivariate logistic regression analysis, we used R software (version 4.0.3) with the rms package to formulate a nomogram. The nomogram can proportionally convert each regression coefficient in the logistic regression to a scale of 0–100 points18. The points of each independent variable were summed and the predicted probabilities were derived from the total points. The area under the curve (AUC) and calibration curve were used to assess the predictive performance of this nomogram. The most important and final line of evidence for the use of this nomogram is based on the need to interpret individual requirements with regard to additional treatment or care. Decision curve analysis (DCA) offers insight into clinical outcomes on the basis of threshold probability, from which the net benefit could be derived. Net benefit is defined as the proportion of true positives minus the proportion of false positives, weighted by the relative harm of false-positive and false-negative results19. In order to evaluate the clinical utility of the nomogram, DCA was performed using R with the rmda package. In all analyses, P < 0.05 was considered to indicate statistical significance. All analyses were performed using SPSS version 22.0 (SPSS Inc, Chicago, Ill) and R, version 4.0.3.

Results

Clinicopathologic characteristics

The clinicopathologic characteristics of the 520 patients are listed in Table 1, and no significant difference was found between training (n = 280) and validation (n = 240) sets. Histopathologically-confirmed LNM was found in 69 (24.6%) and 59 (24.6%) patients in the two sets, respectively. The mean tumor size was 2.87 ± 1.26 cm in training set and 2.93 ± 1.48 cm in validation set. According to the depth of tumor invasion, 62 patients (22.1%) had mucosal cancer and 218 (77.9%) had submucosal cancer in training set. In validation set, 53 patients (22.1%) had mucosal cancer and 187 (77.9%) had submucosal cancer. Lymphovascular invasion (LVI) was found in 29 patients (10.4%) in training set and found in 26 patients (10.8%) in validation set. 33(11.8%) patients in training set underwent 3-field lymph node dissection, and 247(88.2%) underwent 2-field lymph node dissection. In validation set, 21(8.7%) patients underwent 3-field lymph node dissection, and 219 (91.3%) underwent 2-field lymph node dissection. For whole patients (n = 520), paratracheal lymph nodes were the most frequently involved (6.92%), followed by the lesser curvature (6.35%), paracardial nodes (3.27%) and middle paraesophageal (3.27%) (Fig. S1).

Table 1 Participant characteristics.
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Independent risk factors of LNM

Comparisons of clinicopathological characteristics between the LNM-positive and-negative groups are summarized in Table 2. In training and validation sets, variables such as tumor size, tumor invasion depth, tumor differentiation, LVI and macroscopic type, were significantly associated with the LNM according to the univariate analysis (Table 2). However, age, gender, circumferential extension, tumor location and the presence of multiple lesions did not show any statistical correlation with LNM. Furthermore, tumor size, tumor invasion depth, tumor differentiation and LVI were identified as independent risk factors of LNM in training and validation sets by multivariate analysis. Interestingly, in training set macroscopic type was not correlated with LNM (P = 0.064), while it was considered as a risk factor in validation set (Table 3).

Table 2 Clinicopathologic findings according to lymph node metastasis in training and validation sets.
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Table 3 Multivariate logistic analysis of risk factors for lymph node metastasis in training and validation sets.
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Predictive utility of macroscopic type for LNM prediction

Then model A (including tumor size, depth of tumor invasion depth, tumor differentiation and LVI) was built according to the multivariate logistic analysis results. By adding macroscopic type to the model A, we constructed a new model named model B. The AUC values for LNM prediction between model A and model B was not statistically different (Table 4, Fig. S2). However, the IDI and NRI values showed statistically significant improvement after adding macroscopic type to model A (Table 4), meaning that macroscopic type can also be considered as a risk factor of LNM. Reclassification results of patients who had LNM and those did not have were showed in Table S1 and Table S2.

Table 4 Accuracy of the prediction model based on multivariate logistic analysis for estimating the risk of LNM presence.
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Development and validation of a LNM-predicting nomogram

Subsequently, we used ROC analysis to determine the cutoff value of tumor size as 2 cm in training set and 2.5 cm in validation set (Fig. S3). The LNM rates according to the risk factors based on the results of multivariate logistic analysis are summarized in Table S3 and Table S4. Patients with tumors of > 2 cm (training set) or > 2.5 cm (validation set) in size, submucosal invasion, LVI, poor tumor differentiation and non-flat type (I or III) of tumor gross examination seemed to have high LNM rate.

Finally, a nomogram for LNM prediction was formed by incorporating five variables—tumor size, tumor invasion depth, tumor differentiation, LVI and macroscopic type (Fig. 2). The nomogram was validated by internal (bootstrap method) and external validation (validation set). The Hosmer–Lemeshow test yielded a P value of 0.995, indicating that the model was well fitted. This nomogram showed a good performance for predicting LNM risk, with an AUC (or C- statistics) of 0.777 (95% CI 0.724–0.825) (Table 4, Fig. S2A) and a bootstrap-corrected AUC of 0.778 (Table 4). Additionally, a calibration curve of the training set demonstrated good consistency between the predicted and observed results regarding the LNM status (Fig. 3A). In validation set, the nomogram achieved an AUC of 0.790 (95% CI 0.737–0.836) for the estimation of LNM risk (Table 4, Fig. S2B), and its calibration curve also fitted well (Fig. 3B).

Figure 2
figure 2

Nomogram for predicting the probability of lymph node metastasis in patients with superficial esophageal squamous cell carcinoma in training set (A) and validation set (B). Locate the patient’s characteristic on a variable row and draw a vertical line straight up to the points’ row (top) to assign a point value for the variable. Add up the total number of points and drop a vertical line from the total points’ row to obtain the probability of lymph node metastasis.

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Figure 3
figure 3

Calibration plot for the nomogram. Validity of the predictive performance of the nomogram in estimating the risk of LNM presence in the training cohort (A) and validation set (B).

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The nomogram score system for LNM risk prediction and clinical use

Each predictive variable displayed in the nomogram was assigned a risk score. The detailed scores of five variables (tumor size, tumor invasion depth, tumor differentiation, LVI and macroscopic type) in training and validation sets are presented in Fig. 2, Table S5 and Table S6. We predicted the presence of LNM by summing the scores of these five variables, and the final total scores ranged from 0 to 317 in training set and 0 to 281 in validation set. The optimal cutoff points of the total nomogram score for LNM in the training set and validation set were determined to be 150 and 148 respectively according to the ROC curve analysis (Table S7 and Table S8). As a result, patients with total scores ≤ 150 in the training set and ≤ 148 in the validation set were classified as low risk, and patients with total scores of > 150 (the training set) and > 148 (the validation set) were classified as high risk (Table S7 and Table S8). In addition, the DCA in the training and validation sets indicated that our nomogram had significant net benefits for almost all threshold probabilities at different points, suggesting a good clinical utility of this nomogram (Fig. 4).

Figure 4
figure 4

Decision curves of the nomogram predicting LNM in training set (A) and validation set (B).

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Discussion

For the histopathological type of esophageal cancer, adenocarcinoma account for the majority in western countries, while esophageal squamous carcinoma is the predominate type in China20. Superficial esophageal squamous cell carcinoma (SESCC) just invade the mucosa and submucosa and lack of any subjective symptoms. Hence, early diagnosis was difficult for these patients, and most esophageal cancers were at a locally advanced stage when the diagnosis was confirmed in the past. However, due to the progress in flexible endoscopic procedure and widespread use of endoscopic screening, the incidence of SESCC is increasing21.

In patients of SESCC, LNM contributes a lot to the unfavourable prognosis22, resulting in a significantly lower 5-year survival rate in LNM positive patients than in LNM negative patients23,24. Endoscopic resection (ER) is mainly suitable for the low-risk LNM patients whose tumors can be completely removed by endoscopic surgery in the light of the guidelines of SESCC diagnosis and treatment25. Because of the restriction of ER for lymph node biopsy, we aimed to identify predictors of LNM to prevent them from the presence of tumor cells after ER. Our findings indicated that positive LNM patients were statistically more likely to have larger tumors, poorer differentiation, deeper tumor invasion and LVI in the training and validation sets. Macroscopic type was also determined to be significantly associated with LNM in the multivariate analysis of the validation set, but lost significance in the multivariate analysis of the training set.

Some previous studies reported that LNM seemed to be correlated with tumor size and also these results had statistical significance in SESCC patients26,27,28,29. It can be concluded from our study that tumor size was significantly correlated with LNM in entire 520 patients and also identified as an important predictor of LNM. Although SESCC comprises both mucosal and submucosal cancers, the LNM status may differ between mucosal and submucosal cancers. Taking mucosal infiltration as reference, the odds ratio of the submucosal infiltration was 3.112 (95% CI 1.025–9.436) for prediction of LNM in our training set (Table 3), demonstrating that the presence of submucosal infiltration was identified as a significant risk factor of LNM. The LNM rate among SESCC patients with mucosal cancer was 8.1% (5/62), while the incidence of LNM increased dramatically to 29.36% (64/218) in patients with submucosal invasion (Table S3). Tumor invasion depth was also reported as a risk factor of LNM in previous studies6,30,31, which was similar to our results, suggesting that endoscopic resection might not be appropriate for submucosal cancers32.

As well as the tumor invasion depth, LVI was also considered as a remarkable risk factor for LNM in SESCC patients from several studies31,33,34. Similarly, it was shown from our data that LVI was significantly related to LNM in SESCC patients (Table 3). For that reason, supplementary surgical therapy with lymph node dissection should be pondered when LVI is detected in the tumor specimen resected by endoscopic surgery. Interestingly, we also found that the LNM rates were still high even in tumors without LVI. For tumors confining to muscularis mucosa with negative LVI, the LNM rates were 6.6% (4/61) and 5.8% (3/52) in training set and validation set respectively; while for the tumors invading to the submucosa without LVI, the LNM rates of training and validation sets increased to 24.2% (46/190) and 26.5% (43/162) respectively (Table S3). Eguchi et al.31 pointed out that the LNM rate in SESCC without LVI was 10.3% for tumors involving the muscularis mucosa and was 28.6% for tumors with SM invasion, which is similar to ours. The high rate LNM in SESCC with negative LVI may attribute to the existence of early and skip metastasis along the abundant lymphatic channels in the mucosa and submucosa cancers without LVI. In general, the absence of LVI is also a requirement for curative endoscopic resection.

It was previously reported that histological differentiation was a potential risk factor of LNM26,28,35. Consistently, we also found a significant association between tumor differentiation and LNM in the current study (Table 3). Macroscopic appearance of esophageal cancer was seemed to be related to the tumor invasion depth, which might be crucial to evaluate the LNM risk36. Interestingly, from our multivariate analysis, there was no correlation between the nonflat type morphology and LNM in training set; in contrast, tumor with nonflat type was identified as an independent risk factor for LNM in validation set (Table 3). Four variables (tumor size, tumor invasion depth, tumor differentiation and LVI) were incorporated to build a model A on the basis of multivariate analyses results. Then model B was further constructed by adding macroscopic type to the model A. Herein, addition of macroscopic type to the model A did not improve AUC values for predicting LNM, but the IDI and NRI values significantly improved (Table 4), indicating that macroscopic type could be considered as a risk factor for LNM.

Moreover, a nomogram was developed for LNM prediction by incorporating the five significant predictors (tumor size, tumor invasion depth, tumor differentiation, LVI and macroscopic type), with an AUC of 0.777 in the training set and 0.790 in the validation set (Table 4, Fig. S2).

The great accuracy and consistency of our nomogram for predicting LNM were confirmed by the calibration curves (Fig. 3). Then the cutoff values of total nomogram score were determined as 150 in training set (Table S7) and 148 in validation set (Table S8) according to the ROC analysis. Patients with a total score of > 150 in the training set and a total score of > 148 in the validation set were considered high-risk, which can guide us to make best treatment decision. Finally, the DCA was performed to confirm the clinical utility of our nomogram and its result showed that if the threshold probability of a patient was > 20%, more benefit was added than either the scheme of treating all patients or the scheme of treating zero patient by using our nomogram to predict LNM (Fig. 4).

In summary, tumor size, tumor invasion depth, tumor differentiation, and LVI were identified as significant predictive factors for LNM in patients with SESCC. Tumor macroscopic type was also identified as a predictor for LNM by calculating the IDI and NRI. Furthermore, a nomogram scoring system was established using these five variables, making individualized LNM prediction easier and facilitating optimal treatment strategy selection for patients with SESCC. Judging from the nomogram scoring system, careful follow-up observation can be recommended if the LNM of SESCC patients after ER is low risk, and supplementary surgery need to be taken if the LNM of SESCC patients after ER is high risk. DCA demonstrated that the nomogram was clinically useful. However, this was a retrospective study based on data from a single institution. Therefore, it is necessary to validate the results using data from multiple centers and a prospective study is required to further confirm the reliability of the nomogram. Last but not least, our nomogram may improve and facilitate treatment strategy selection, which may lead to early diagnosis and prompt treatment initiation for patients with SESCC.