A nomogram for predicting lymph node metastasis in surgically resected T1 esophageal squamous cell carcinoma

Comprehensive pathological evaluation of risk factors for metastasis after endoscopic resection of superficial esophageal squamous cell carcinoma

BACKGROUND

Esophageal cancer is a major cause of cancer mortality worldwide. Endoscopic resection (ER) is a curative treatment for esophageal squamous cell carcinoma (ESCC). Predicting risk of metastasis is crucial for post-ER management. In this study, we aimed to identify predictors of metastasis by examining endoscopically resected specimens.

METHODS

The cohort of this retrospective multicenter study comprised 422 patients who had undergone ER for ESCC from 1994 to 2017. Inclusion required a histological diagnosis of pT1a-muscularis mucosa or pT1b-submucosa (SM) cancer. Central pathological review comprehensively evaluated depth of invasion, lymphovascular invasion (LVI), droplet infiltration (DI), infiltrative growth pattern, histological differentiation, intraductal and intraglandular involvement, solitary nest, resected margin, and other factors. Logistic regression was used to identify predictors of metastasis.

RESULTS

Metastases were identified in 103 patients. Univariate analysis identified LVI, depth of invasion, and DI as significant predictive factors. Multivariate analysis identified LVI, depth of invasion pT1b-SM2 (odds ratio 2.72) and indeterminate (positive vertical margin) (odds ratio 3.63) compared with the reference category of pT1b-SM1 as independent predictors of metastasis. Conversely, there were no significant associations between metastasis and lesion size, differentiation, cytological atypia, or infiltration pattern. Subgroup analysis showed that both the number and layer of LVI were associated with metastasis risk. In addition, four or more foci of DI was an independent predictor of LVI.

CONCLUSIONS

LVI and depth of invasion were significant predictors of metastasis in ESCC. Detailed pathological evaluation and standardized criteria are essential for accurately assessing risk of metastasis and guiding post-ER treatment strategies.

Risk and pathologic factors of recurrence after endoscopic resection for superficial esophageal squamous cell carcinoma: a systematic review and meta-analysis

BACKGROUND AND AIMS

The risk and pathologic factors of recurrence after endoscopic resection (ER) for superficial esophageal squamous cell carcinoma (ESCC) are inconsistent across studies. We evaluated this in a systematic review and meta-analysis.

METHODS

The data of recurrence in such patients were extracted from all studies. Risk ratios (RRs) were combined using random-effects meta-analysis to assess pooled recurrence rate and pathologic risk factors. Relapse-free survival was combined using the Kaplan-Meier method to estimate the relationship between various pathologic factors and recurrence time.

RESULTS

We identified 26 studies, with a total of 5100 patients and 321 with recurrences (pooled rate, 6.2%). The risk of recurrence was significantly higher in positive vertical margin (RR, 4.51; 95% confidence interval [CI], 2.16-9.44), positive horizontal margin (RR, 2.54; 95% CI, 1.57-4.13), lymphovascular invasion (LVI) (RR, 2.33; 95% CI, 1.75-3.11; P < .001), lymphatic invasion (LI) (RR, 2.24; 95% CI, 1.24-4.06), and tumor invading submucosa of ≤200 μm (SM1) (RR, 1.71; 95% CI, 1.32-2.21, compared to muscularis mucosa). Patients with LI (hazard ratio, 2.47; 95% CI, 1.24-4.90; P = .02) and LVI (HR, 2.36; 95% CI, 2.22-4.59; P = .0006) tended to have earlier recurrence after ER.

CONCLUSIONS

The recurrence rate of superficial esophageal squamous cell carcinoma after ER is acceptable. Patients with positive margins, LVI, LI, and SM1 need to pay significant attention to the risk of recurrence. LI and VI should be evaluated separately. (PROSPERO CRD42023406309.).

Exploration of the pattern of lymph node metastasis in esophageal cancer based on membrane anatomy theory

OBJECTIVE

To analyze the pattern of lymph node metastasis in esophageal cancer based on the theory of membrane anatomy.

METHODS

A retrospective analysis was conducted on 143 patients who underwent esophageal surgery at the Cancer Hospital of the Chinese Academy of Medical Sciences from March 2021 to March 2022. Lymph node metastasis was observed and categorized according to postoperative T staging. The characteristics and patterns of lymph node metastasis in different regions were observed, and the lymph node metastasis patterns in patients with clinical T3 esophageal cancer were analyzed using membrane anatomy theory.

RESULTS

Among the 143 patients with esophageal squamous cell carcinoma, 21 were treated with surgery alone, while the rest received preoperative adjuvant therapy. A total of 5456 lymph nodes were cleared from the 143 patients, with 204 positive lymph nodes, resulting in a positive rate of 3.74%. In the thoracic lymph node dissection, the metastatic rates exceeded 5% for the following regions: 106recR (17.36%), 106recL (12.5%), 107 (10.42%), and 108 (5.56%) station. When analyzing the abdominal lymph node metastasis, the metastatic rates exceeded 5% for regions 7 (13.19%), 3a (7.64%), 2 (6.94%), and 1 (6.25%) station. Group analysis of patients with esophageal squamous cell carcinoma before postoperative pathological T3 stage revealed an increasing trend in tumor lymph node metastasis rate with later T staging. Lymph node metastasis in region 106recR can occur early, with a metastasis rate of 18.37% in T1 tumors. Analysis of lymph node metastasis characteristics in 103 patients clinically staged as T3 showed that 3966 lymph nodes were cleared, with 186 positive nodes, resulting in a positive rate of 4.69%. Lymph node metastasis rates were higher in regions 106recL, 106recR, 107, 108, 110, 1, 2, 3a, and 7, all exceeding 5%.

CONCLUSION

The theory of membrane anatomy can effectively explain the pattern of lymph node metastasis in esophageal cancer.

Machine learning to predict lymph node metastasis in T1 esophageal squamous cell carcinoma: a multicenter study

BACKGROUND

Existing models do poorly when it comes to quantifying the risk of lymph node metastases (LNM). This study aimed to develop a machine-learning model for LNM in patients with T1 esophageal squamous cell carcinoma (ESCC).

METHODS AND RESULTS

The study is multicenter and population based. Elastic net regression (ELR), random forest (RF), extreme gradient boosting (XGB), and a combined (ensemble) model of these were generated. The contribution to the model of each factor was calculated. The models all exhibited potent discriminating power. The elastic net regression performed best with an externally validated the area under the curve (AUC) of 0.803, whereas the NCCN guidelines identified patients with LNM with an AUC of 0.576 and the logistic model with an AUC of 0.670. The most important features were lymphatic and vascular invasion and depth of tumor invasion.

CONCLUSIONS

Models created utilizing machine learning approaches had excellent performance estimating the likelihood of LNM in T1 ESCC.

Prognostic prediction model for patients with pathological T1N0 stage esophageal squamous cell carcinoma undergone esophagectomy

Background

There is a shortage of reliable predictive models to provide valuable prognostic information for early esophageal squamous cell carcinoma (ESCC) without lymph node metastasis (LNM). We aimed to develop and validate a nomogram using the prognostic factors in T1N0 ESCC patients.

Methods

Patients with pathological T1N0 ESCC who underwent esophagectomy between 2014 and 2021 at three institutes were reviewed. The prognostic factors were evaluated by Cox proportional hazards model and a nomogram was developed. Patients were divided into high- and low-risk groups based on cut-off value of total points in the nomogram. Overall survival (OS) was estimated by the Kaplan-Meier method and compared using the log-rank test.

Results

A total of 275 patients were included and split into training (n=180) and external validation (n=95) cohorts. In the training cohort, multivariable analysis showed that the surgical approach, T1 substage, and carcinoembryonic antigen (CEA) level were independent prognostic factors. The developed nomogram had relatively high performance, with the area under the receiver operating characteristic (ROC) curve (AUC) of 0.783, 0.711 and 0.612 for 1-, 3-, and 5-year OS, respectively. The calibration curves showed that the predicted probability was in good agreement with the actual probability. Forty-seven was determined as cut-off value of total points. High-risk group (n=148) showed a significant poor OS than low-risk group (n=127) (P<0.001).

Conclusions

Left surgical approach, stage T1b, and higher CEA were associated with poorer prognosis in T1N0 ESCC patients. The nomogram demonstrated a good performance to predict the individual survival.

Nomograms and prognosis for superficial esophageal squamous cell carcinoma

In recent years, endoscopic resection, particularly endoscopic submucosal dissection, has become increasingly popular in treating non-metastatic superficial esophageal squamous cell carcinoma (ESCC). In this evolving paradigm, it is crucial to identify factors that predict higher rates of lymphatic invasion and poorer outcomes. Larger tumor size, deeper invasion, poorer differentiation, more infiltrative growth patterns (INF-c), higher-grade tumor budding, positive lymphovascular invasion, and certain biomarkers have been associated with lymph node metastasis and increased morbidity through retrospective reviews, leading to the construction of comprehensive nomograms for outcome prediction. If validated by future prospective studies, these nomograms would prove highly applicable in guiding the selection of treatment for superficial ESCC.

Long-term survival in esophagectomy for early-stage esophageal cancer versus endoscopic resection plus additional chemoradiotherapy: a systematic review and meta-analysis

Background

Esophagectomy is still advised as an additional treatment for patients with superficial esophageal cancer (EC, T1a-T1b) after endoscopic resection (ER). However, esophagectomy often deteriorates the general condition of EC patients. In recent years, adjuvant chemoradiotherapy (CRT) has been recognized as a reliable, non-surgical treatment that can improve the prognosis. How to combine ER with adjuvant therapy to bring maximal benefits to patients has become a hot clinical research hot topic. However, the current studies have mostly been conducted retrospectively, in single centers, and with small clinical samples; there have been few prospective and large sample size randomized controlled trials (RCTs). The aim of this systematic review and meta-analysis was to compare the outcomes of adjuvant CRT versus esophagectomy in the treatment of early EC, and to provide a reference for clinical research and practice.

Methods

A comprehensive and extensive literature search was performed via the databases of PubMed, Cochrane Library, Embase, and Web of Science online and all randomized cohort studies and retrospective cohort studies were collected. The quality of research was evaluated according to Cochrane's quality standards, and statistical analysis was conducted with Stata 13.0 and RevMan 5.3 software and followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA).

Results

A total of 9 cohort studies, including 790 patients, were included for meta-analysis. The long term effects of the esophagectomy group were better than those of the CRT after ER group [odds ratio (OR) =6.08, 95% confidence interval (CI): 1.96 to 18.84, P=0.002] in disease-free survival (DFS) [hazard ratio (HR) =0.24, 95% CI: 0.07 to 0.85, P=0.03] and overall survival (OS) (HR =1.02, 95% CI: 0.57 to 1.82, P=0.94). Other survival indicators showed no significant difference (P>0.05).

Conclusions

The 2 groups showed no significant results in OS. Although we found that CRT may be suitable for patients with high-risk of relapse or unable to tolerate surgery, it cannot totally replace surgical treatment; further randomized trials are required to verify this view.

Prognostic significance of lymphovascular invasion in patients with pT1b esophageal squamous cell carcinoma

BACKGROUND

Lymphovascular invasion (LVI) is a crucial predictor of lymph node metastasis (LNM). However, few studies have investigated the LVI positivity rate and its clinical significance in pT1b esophageal squamous cell carcinoma (ESCC) using immunohistochemistry and elastin staining.

METHODS

We collected data from158 patients with pT1b ESCC who had undergone radical esophagectomy. All paraffin blocks of invasive carcinoma from each patient were subjected to HE staining, elastin staining + CK (AE1/AE3) immunohistochemistry (E&IHC), and CD31/D2-40 + CK (AE1/AE3) double immunohistochemistry (D-IHC). The LVI was classified into types, i.e., vascular invasion (VI) and lymphatic vessel invasion (LI), and its location, quantity, and clinical significance were explored.

RESULTS

The positivity rates of VI by E&IHC (E-VI), VI by CD31D-IHC (CD31-VI), and LI by D2-40 D-IHC (D2-40-LI) were significantly higher than those obtained by HE staining (P < 0.001, respectively). CD31-VI and E-VI were independent adverse prognostic factors for recurrence-free survival (RFS), and they were significantly associated with poor distant metastasis-free survival and overall survival in pT1b ESCC. Intratumoral LVI was also crucial in pT1b ESCC, and L2 (the count of D2-40-LI was 5 or more) was the strongest predictor for LNM and RFS in pT1b ESCC.

CONCLUSION

E&IHC and D-IHC can dramatically improve the detection rate of LVI in pT1b ESCC, and the classification and grading of LVI can help to improve the prediction of LNM and prognosis.

Lymphatic Drainage System and Lymphatic Metastasis of Cancer Cells in the Mouse Esophagus

BACKGROUND

Lymphatic metastasis is commonly seen in patients with esophageal squamous cell carcinoma (ESCC). Both lymphatic metastasis and the number of involved nodes are prognostic for post-operative survival. To better understand lymphatic metastasis of ESCC, there is a need to develop proper animal models.

AIMS

This study is aimed to characterize the morphology and function of the lymphatic drainage system in the mouse esophagus.

METHODS

Immunostaining and fluorescence imaging were used to visualize the lymphatic drainage system in the mouse esophagus. Tracers and cancer cells were orthotopically inoculated into the submucosa of the mouse esophagus to mimic lymphatic metastasis of T1 ESCC.

RESULTS

Using immunostaining of a lymphatic vessel marker (LYVE1), we found that lymphatic vessels were located in the submucosa and muscularis propria of the mouse esophagus, similar to the human esophagus. In the esophagus of ProxTom mice expressing tdTomato in the lymphatic vessels, we discovered a microscopic meshwork of lymphatic vessels. Functionally, orthotopically inoculated tracers (Indian ink and FITC-dextran) were drained from the submucosa into peri-esophageal lymph nodes via lymphatic vessels. Orthotopically inoculated mouse cancer cells (LLC-eGFP, MOC2) metastasized from the submucosa to lymphatic vessels, peri-esophageal lymph nodes, and distant organs (liver and lung) in immunocompetent mice. Similarly, in immunodeficient mice, orthotopically inoculated human ESCC cells (KYSE450-eGFP-Luc) metastasized via the same route.

CONCLUSION

We have characterized the morphology and function of the lymphatic drainage system of the mouse esophagus. These observations lay a foundation for mechanistic and therapeutic studies on lymphatic metastasis of T1 ESCC.

Machine learning models predict lymph node metastasis in patients with stage T1-T2 esophageal squamous cell carcinoma

Background

For patients with stage T1-T2 esophageal squamous cell carcinoma (ESCC), accurately predicting lymph node metastasis (LNM) remains challenging. We aimed to investigate the performance of machine learning (ML) models for predicting LNM in patients with stage T1-T2 ESCC.

Methods

Patients with T1-T2 ESCC at three centers between January 2014 and December 2019 were included in this retrospective study and divided into training and external test sets. All patients underwent esophagectomy and were pathologically examined to determine the LNM status. Thirty-six ML models were developed using six modeling algorithms and six feature selection techniques. The optimal model was determined by the bootstrap method. An external test set was used to further assess the model’s generalizability and effectiveness. To evaluate prediction performance, the area under the receiver operating characteristic curve (AUC) was applied.

Results

Of the 1097 included patients, 294 (26.8%) had LNM. The ML models based on clinical features showed good predictive performance for LNM status, with a median bootstrapped AUC of 0.659 (range: 0.592, 0.715). The optimal model using the naive Bayes algorithm with feature selection by determination coefficient had the highest AUC of 0.715 (95% CI: 0.671, 0.763). In the external test set, the optimal ML model achieved an AUC of 0.752 (95% CI: 0.674, 0.829), which was superior to that of T stage (0.624, 95% CI: 0.547, 0.701).

Conclusions

ML models provide good LNM prediction value for stage T1-T2 ESCC patients, and the naive Bayes algorithm with feature selection by determination coefficient performed best.

Development and validation of a nomogram for preoperative prediction of lymph node metastasis in pathological T1 esophageal squamous cell carcinoma

Endoscopic resection is increasingly used to treat patients with pathological T1 (pT1) esophageal squamous cell carcinoma (ESCC) because of its small surgical trauma. However, reports of the risk factors for lymph node metastasis (LNM) have been controversial. Therefore, we aim to build a nomogram to individually predict the risk of LNM in pT1 ESCC patients, to make an optimal balance between surgical trauma and surgical income.One hundred seventy patients with pT1 esophageal cancer in our hospital were analyzed retrospectively. Logistic proportional hazards models were conducted to find out the risk factor associated with LNM independently, and those were imported into R library "RMS" for analysis. A nomogram is generated based on the contribution weights of variables. Finally, decision analysis and clinical impact curve were used to determine the optimal decision point.Twenty-five (14.7%) of the 170 patients with pT1 ESCC exhibited LNM. Multivariable logistic regression analysis showed that smoking, carcinoembryonic antigen, vascular tumor thromboembolus, and tumor differentiation degree were independent risk factors for LNM. The nomogram had relatively high accuracy (C index of 0.869, 95% confidence interval: 0.794-0.914, P < .0001). The decision curve analysis provided the most significant clinical benefit for the entire included population, with scores falling just above the total score of 85 in the nomogram.Smoking, carcinoembryonic antigen, vascular tumor thromboembolus, and tumor differentiation degree may predict the risk of LNM in tumor 1 ESCC. The risk of LNM can be predicted by the nomogram.

Clinical Risk Prediction Model for Neoadjuvant Therapy in Resectable Esophageal Adenocarcinoma

GOALS AND BACKGROUND

Clinical staging with endoscopic ultrasound (EUS) and positron emission tomography (PET) is used to identify esophageal adenocarcinoma (EAC) patients with locally advanced disease and therefore, benefit from neoadjuvant therapy. However, EUS is operator dependent and subject to interobserver variability. Therefore, we aimed to identify clinical predictors of locally advanced EAC and build a predictive model that can be used as an adjunct to current staging methods.

STUDY

This was a cross-sectional study of patients with EAC who underwent preoperative staging with EUS and PET scan followed by definitive therapy at our institution from January 2011 to December 2017. Demographic data, symptoms, endoscopic findings, EUS, and PET scan findings were obtained.

RESULTS

Four hundred and twenty-six patients met the study criteria, of which 86 (20.2%) patients had limited stage EAC and 340 (79.8%) had locally advanced disease. The mean age was 65.4±10.3 years of which 356 (83.6%) were men and 393 (92.3%) were White. On multivariable analysis, age (above 75 or below 65 y), dysphagia [odds ratio (OR): 2.84], weight loss (OR: 2.06), protruding tumor (OR: 2.99), and tumor size >2 cm (OR: 3.3) were predictive of locally advanced disease, while gastrointestinal bleeding (OR: 0.36) and presence of visible Barrett's esophagus (OR: 0.4) were more likely to be associated with limited stage. A nomogram for predicting the risk of locally advanced EAC was constructed and internally validated.

CONCLUSIONS

We constructed a nomogram to facilitate an individualized prediction of the risk of locally advanced EAC. This model can aid in decision making for neoadjuvant therapy in EAC.

Significance of the dissection of common hepatic arterial lymph nodes in patients with oesophageal carcinoma: a multicentre retrospective study

OBJECTIVES

To explore the significance of intraoperative common hepatic arterial lymph node dissection in patients with ooesophageal squamous carcinoma (ESCC) without coeliac trunk lymph node metastasis indicated by abdominal enhanced CT.

METHODS

Patients aged 18-75 years who underwent oesophagectomy in three medical centres from June 2012 to June 2015, for whom R0 resection was completed and lymph node metastasis in the abdominal trunk was not identified before the operation were retrospectively analysed. The effects of the application value of common hepatic arterial lymph node dissection on survival were evaluated in patients with ESCC without coeliac trunk lymph node metastasis indicated by preoperative CT. According to the eighth version ofAmerican Joint Committee on Cancer (AJCC)/Union for International Cancer Control (UICC) staging, we selected patients with a Pathological Tumor Node Metastasis (pTNM) stage ranging from IA to IVA for analysis.

RESULTS

Among the 816 qualified patients, 577 did not have coeliac trunk lymph node metastasis based on preoperative abdominal enhanced CT, and common hepatic arterial lymph node dissection was performed during the operation (observation group). Two hundred and thirty-nine preoperative CT examinations indicated no coeliac trunk lymph node metastasis, and common hepatic arterial lymph node dissection was not performed during the operation (control group). A multifactor Cox proportional hazards model showed no risk factors for overall survival (OS) (adjusted HR (HRadj)=0.91; p=0.404) or disease-free survival (DFS) (HRadj=0.86; p=0.179), regardless of whether common hepatic arterial lymph node dissection was performed. For patients with positive left gastric arterial lymph node metastasis, a multifactor Cox proportional hazards model indicated that common hepatic arterial lymph node dissection was a risk factor for OS (HRadj=0.63; p=0.035) and DFS (HRadj=0.58; p=0.026).

CONCLUSIONS

For patients with ESCC without celiac trunk metastasis indicated by abdominal enhanced CT, common hepatic arterial lymph node dissection conferred no survival benefits. However, for patients with left gastric arterial lymph node metastasis, common hepatic arterial lymph node dissection was beneficial.

Computed tomography-based radiomic analysis for prediction of treatment response to salvage chemoradiotherapy for locoregional lymph node recurrence after curative esophagectomy

Objective

To investigate the capability of computed tomography (CT) radiomic features to predict the therapeutic response and local control of the locoregional recurrence lymph node (LN) after curative esophagectomy by chemoradiotherapy.

Methods

This retrospective study included 129 LN from 77 patients (training cohort: 102 LN from 59 patients; validation cohort: 27 LN from 18 patients) with postoperative esophageal squamous cell carcinoma (ESCC). The region of the tumor was contoured in pretreatment contrast‐enhanced CT images. The least absolute shrinkage and selection operator with logistic regression was used to identify radiomic predictors in the training cohort. Model performance was evaluated using the area under the receiver operating characteristic curves (AUC). The Kaplan–Meier method was used to determine the local recurrence time of cancer.

Results

The radiomic model suggested seven features that could be used to predict treatment response. The AUCs in training and validated cohorts were 0.777 (95% CI: 0.667–0.878) and 0.765 (95% CI: 0.556–0.975), respectively. A significant difference in the radiomic scores (Rad‐scores) between response and nonresponse was observed in the two cohorts (p < 0.001, 0.034, respectively). Two features were identified for classifying whether there will be relapse in 2 years. AUC was 0.857 (95% CI: 0.780–0.935) in the training cohort. The local control time of the high Rad‐score group was higher than the low group in both cohorts (p < 0.001 and 0.025, respectively). As inferred from the Cox regression analysis, the low Rad‐score was a high‐risk factor for local recurrence within 2 years.

Conclusions

The radiomic approach can be used as a potential imaging biomarker to predict treatment response and local control of recurrence LN in ESCC patients.

A nomogram for predicting lymph node metastasis in superficial esophageal squamous cell carcinoma

Superficial esophageal squamous cell carcinoma (SESCC) is defined as carcinoma with mucosal or submucosal invasion, regardless of regional lymph node metastasis (LNM). The lymph node status is not only a key factor to determine the training strategy, but also the most important prognostic factor in esophageal cancer. In this study, we establish a clinical nomogram for predicting LNM in patients with SESCC. A predictive model was established based on the training cohort composed of 711 patients who underwent esophagectomy for SESCC from December 2009 to June 2018. A prospective cohort of 203 patients from June 2018 to January 2019 was used for validation. Favorable calibration and well-fitted decision curve analysis were conducted and good discrimination was observed (concordance index [C-index], 0.860; 95% confidence interval [CI], 0.825-0.894) through internal validation. The external validation cohort presented good discrimination (C-index, 0.916; 95% CI, 0.860-0.971). This model may facilitate the prediction of LNM in patients with SESCCs.

Risk factors for lymph node metastasis in T1 esophageal squamous cell carcinoma: A systematic review and meta-analysis

BACKGROUND

Lymph node metastasis (LNM) affects the application and outcomes of endoscopic resection in T1 esophageal squamous cell carcinoma (ESCC). However, reports of the risk factors for LNM have been controversial.

AIM

To evaluate risk factors for LNM in T1 ESCC.

METHODS

We searched Embase, PubMed and Cochrane Library to select studies related to LNM in patients with T1 ESCC. Included studies were divided into LNM and non-LNM groups. We performed a meta-analysis to examine the relationship between LNM and clinicopathologic features. Odds ratio (OR), mean differences and 95% confidence interval (CI) were assessed using a fixed-effects or random-effects model.

RESULTS

Seventeen studies involving a total of 3775 patients with T1 ESCC met the inclusion criteria. After excluding studies with heterogeneity based on influence analysis, tumor size (OR = 1.93, 95%CI = 1.49-2.50, P < 0.001), tumor location (OR = 1.46, 95%CI = 1.17-1.82, P < 0.001), macroscopic type (OR = 3.17, 95%CI = 2.33-4.31, P < 0.001), T1 substage (OR = 6.28, 95%CI = 4.93-8.00, P < 0.001), differentiation (OR = 2.11, 95%CI = 1.64-2.72, P < 0.001) and lymphovascular invasion (OR = 5.86, 95%CI = 4.60-7.48, P < 0.001) were found to be significantly associated with LNM. Conversely, sex, age and infiltrative growth pattern were not identified as risk factors for LNM.

CONCLUSION

A tumor size > 2 cm, lower location, nonflat macroscopic type, T1b stage, poor differentiation and lymphovascular invasion were associated with LNM in patients with T1 ESCC.

Distribution of lymph node metastases in esophageal adenocarcinoma after neoadjuvant chemoradiation therapy: a prospective study

BACKGROUND

The distribution of lymph node metastases in esophageal adenocarcinoma following neoadjuvant chemoradiation (nCRTx) is unclear, but may have consequences for radiotherapy and surgery. The aim of this study was to define the distribution of lymph node metastases and relation to the radiation field in patients following nCRTx and esophagectomy.

METHODS

Between April 2014 and August 2015 esophageal adenocarcinoma patients undergoing transthoracic esophagectomy with 2-field lymphadenectomy following nCRTx were included in this prospective observational study. Lymph node stations according to AJCC 7 were separately investigated. The location of lymph node metastases in relation to the radiation field was determined. The primary endpoint was the distribution of lymph node metastases and relation to the radiation field, the secondary endpoints were high-risk stations and risk factors for lymph node metastases and relation to survival.

RESULTS

Fifty consecutive patients were included. Lymph node metastases were found in 60% of patients and most frequently observed in paraesophageal (28%), left gastric artery (24%), and celiac trunk (18%) stations. Fifty-two percent had lymph node metastases within the radiation field. The incidence of lymph node metastases correlated significantly with ypT-stage (p = 0.002), cT-stage (p = 0.005), lymph angioinvasion (p = 0.004), and Mandard (p = 0.002). The number of lymph node metastases was associated with survival in univariable analysis (HR 1.12, 95% CI 1.068-1.173, p < 0.001).

CONCLUSIONS

Esophageal adenocarcinoma frequently metastasizes to both the mediastinal and abdominal lymph node stations. In this study, more than half of the patients had lymph node metastases within the radiation field. nCRTx is therefore not a reason to minimize lymphadenectomy in patients with esophageal adenocarcinoma.

Development of a predictive nomogram for cause-specific mortality in surgically resected early-stage oesophageal cancer: a Surveillance, Epidemiology, and End Results (SEER) analysis

Background

The aim of this population-based study was to perform competing risk analysis and estimate cancer- and other cause-specific mortality in patients who underwent oesophagectomy with pT1N0M0 oesophageal cancer (EC). A competing risks nomogram was also developed to predict the proportional of death from each specific cause.

Methods

A total of 1,144 patients who received oesophagectomy for pT1N0M0 EC between 2010 and 2015 from SEER database were included. The cumulative incidence function was used to evaluate each cause of death, and the significant difference was assessed by the Grey’s test. A nomogram was established using the proportional subdistribution hazard analysis to identify predictors for each cause-specific death.

Results

The 5-year cumulative incidence of cancer-specific death for surgically resected pT1N0M0 EC was 15.7%, and the incidence was 11.2% for other cause-specific death. Age, tumour length, pT1 substage, grade, history and primary site were identified as predictive factors for EC-specific death, but only age, tumor length and pT1 substage were associated with death from other cause. Our nomograms showed a relative good discriminative ability, with c-index of 0.663 for the EC-specific mortality model and 0.699 for the other cause-specific mortality model. The calibration curves showed a good match between the nomogram-predicted probabilities and the actual probabilities.

Conclusions

In patients who underwent curative-intent resection for pT1N0M0 EC, death from other causes was an important competing event. During clinical decision making and patient-clinician communication, our quantifiable nomograms could provide a rapid and precise judgement of the risk of death from each cause.

Nomogram for prediction of lymph node metastasis in patients with superficial esophageal squamous cell carcinoma

BACKGROUND AND AIM

Knowledge of lymph node metastasis (LNM) status is crucial to determine whether patients with superficial esophageal squamous cell carcinoma (ESCC) can be cured with endoscopic resection alone, without the need for additional esophagectomy. The present study aimed to identify predictive factors and develop a prediction model for LNM in patients with superficial ESCC.

METHODS

Clinicopathologic data from 501 patients with superficial ESCC treated with radical esophagectomy were reviewed. Stepwise logistic regression analysis determined the predictors of LNM. Using these predictors, a nomogram for predicting the risk of LNM was constructed and internally validated using a bootstrap resampling method.

RESULTS

LNM rates of tumors invading the lamina propria, muscularis mucosa, and SM1 layers were 3.7%, 15.5%, and 40.7%, respectively. Deep tumor invasion depth, moderately or poorly differentiated histology, and lymphovascular invasion were independent predictors of LNM. ESCC with muscularis mucosa and SM1 invasion had odds ratios of 3.635 and 11.834, respectively, compared with that for ESCC confined to the lamina propria. Large tumor size (>2.0 cm) and presence of tumor budding showed borderline significance for LNM prediction. These five variables were incorporated into a nomogram. A constructed nomogram showed good calibration and good discrimination with an area under the receiver-operating characteristic curve (area under the curve [AUC]) of 0.812. After bootstrapping, AUC was 0.811.

CONCLUSIONS

We developed a nomogram that can facilitate individualized prediction of risk of LNM in patients with superficial ESCC. This model can aid in decision-making for the need for additional esophagectomy after endoscopic resection for superficial ESCC.

Clinical nomogram for lymph node metastasis in pathological T1 esophageal squamous cell carcinoma: a multicenter retrospective study

BACKGROUND

Endoscopic resection is increasingly used to treat pathological T1 (pT1) esophageal cancer (EC) patients. However, the procedures are limited by lymph node metastasis (LNM) and remain controversial. We aimed to construct a nomogram to predict the risk of LNM in patients with pT1 esophageal squamous cell carcinoma (ESCC).

METHODS

A total of 243 patients with pT1 ESCC who underwent esophagectomy and lymph node dissection at two different institutes between February 2013 and June 2019 were analyzed retrospectively. Patients were categorized into the negative group and the positive group according to whether there was LNM. Risk factors for LNM were evaluated by univariate and multivariate analyses. The nomogram was used to estimate the individual risk of LNM.

RESULTS

Forty-six (18.9%) of the 243 patients with pT1 ESCC exhibited LNM. The LNM rate in patients with stage T1a disease was 5.7% (5/88), and the rate in patients with stage T1b disease was 26.5% (41/155). Multivariable logistic regression analysis showed that tumor differentiation [odds ratio (OR) =1.942, 95% confidence interval (CI): 1.067-3.536, P=0.030], the T1 sub-stage (OR =4.750, 95% CI: 1.658-13.611, P=0.004), the preoperative alanine aminotransferase/aspartate aminotransferase ratio (LSR) (OR =5.371, 95% CI: 1.676-17.210, P=0.005), and the high-density lipoprotein cholesterol (HDL-C) level (OR =5.894, 95% CI: 1.917-18.124, P=0.002) were independent risk factors for LNM. The nomogram had relatively high accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.803 (95% CI: 0.732-0.873). The calibration curve showed that the predicted probability of LNM was in good agreement with the actual probability.

CONCLUSIONS

Clinicopathological and hematological parameters of tumor differentiation, the T1 sub-stage, the preoperative LSR, and the HDL-C level may predict the risk of LNM in T1 ESCC. The risk of LNM can be predicted by the nomogram.

Development and validation of a novel nomogram for predicting distant metastasis-free survival among breast cancer patients

Background

Distant metastasis (DM) from breast cancer has a poor prognosis. Our objective was to develop and validate a nomogram to predict individual distant metastasis-free survival (DMFS) and risk stratification in non-metastatic breast cancer patients.

Methods

A nomogram was based on an analysis of 1,201 breast cancer patients treated at Sun Yat-sen Memorial Hospital from 2001 to 2014. Using univariate and multivariate analyses to identify the predictors, this model was externally validated in an independent cohort of 538 patients from the Guangdong General Hospital between 2004 and 2012. The predictive discrimination and calibration ability of this nomogram were assessed using concordance index (C-index), risk group stratification, and calibration curve.

Results

The 5-year DMFS in the training and validation cohorts were 95.74% and 91.02%, respectively. On multivariable analysis of training cohort, the prognostic factors in the nomogram comprised age, tumor size, lymph node status, molecular subtype, and lymphovascular invasion (LVI). The C-index of our model was 0.75 [95% confidence interval (CI): 0.67-0.83] for the training cohort and 0.71 (95% CI: 0.64-0.78) for the validation cohort. The calibration curves for 5-year DMFS showed good agreement between the model prediction and actual observation. Based on the risk stratification, Kaplan-Meier curves indicated that the low-risk group had significantly better prognosis than the high-risk group (P<0.001).

Conclusions

Our nomogram can provide an individual prediction of 5-year DMFS in non-metastatic breast cancer patients. This prognostic tool may help clinicians to make appropriate treatment regimens and optimal surveillance plans.