A nomogram to predict lymph node metastasis risk for early esophageal squamous cell carcinoma

Latest guideline of endoscopic submucosal dissection of early gastric cancer may not be suitable for Chinese patients: retrospective study findings from two centers

BACKGROUND

To analyze the diagnostic efficiency of the four absolute endoscopic submucosal dissection (ESD) indications for lymph node metastasis (LNM) of Chinese patients with early gastric cancer (EGC).

METHODS

We retrospectively analyzed EGC patients who underwent radical D2 gastrectomy from January 2019 to December 2022. We evaluated the rate of LNM, false-negative rate, and negative predictive value of the four ESD indications.

RESULTS

Of enrolled 2722 EGC patients, 388 (14.3%) patients presented LNM. Tumor size > 2 cm, ulceration, submucosal invasion, undifferentiated type, and lymphovascular invasion were independent risk factors of LNM in patients with EGC. 1062 (39%) cases of EGC conformed to the four EDS indications; however, 4% of them had LNM. 451 cases were fully in accord with the fourth ESD indication (undifferentiated intramucosal carcinoma without ulceration and a maximum lesion diameter of ≤ 2 cm), and 35 of them had LNM, with a false-negative rate (FNR) of 9.02% and a negative predictive value (NPV) of 92.24%. There was significant difference among the four indications in terms of the rate of LNM (1.0% vs 1.5% vs 1.3% vs 7.8%, P < 0.001), FNR (1.03% vs 0.52% vs 0.26% vs 9.02%, P < 0.001), and NPV (98.99% vs 98.53% vs 98.75% vs 92.24%, P < 0.001).

CONCLUSION

Overall, the fourth ESD indication was associated with a high rate of LNM compared to the other three indications. Thus, it might not be safe to classify it as an absolute indication in Chinese patients with EGC.

A practical nomogram included hyperlipidemia for predicting lymph node metastasis in patients with superficial esophageal squamous cell carcinoma

To select an optimal treatment, it is crucial to evaluate the risk of lymph node metastasis (LNM) in patients with superficial esophageal squamous cell carcinoma (SESCC). The research aimed to explore more risk factors than before and construct a practical nomogram to predict LNM in patients with SESCC. We retrospectively reviewed 1080 patients diagnosed with esophageal cancer who underwent esophagectomy with lymphadenectomy between January 2013 and October 2021 at the Affiliated Hospital of Qingdao University. The clinical parameters, endoscopic features, and pathological characteristics of the 123 patients that were finally enrolled in this study were collected. The independent risk factors for LNM were determined using univariate and multivariate analyses. Using these factors, a nomogram was constructed to predict LNM. LNM was observed in 21 patients. Univariate analysis showed that the absence or presence of hypertriglyceridemia, tumor location, lesion size, macroscopic type, invasion depth, differentiation, absence or presence of lymphovascular invasion (LVI), and perineural invasion were significantly associated with LNM. According to the multivariate analysis, hypertriglyceridemia, tumors located in the lower thoracic esophagus, lesion size > 20 mm, submucosal invasion, and LVI were independent risk factors for LNM. A nomogram was established using these 5 factors. It showed good calibration and discrimination. Hypertriglyceridemia, tumors located in the lower thoracic esophagus, lesion size > 20 mm, submucosal invasion, and LVI were independent risk factors for LNM. A nomogram was constructed using these 5 factors. This model can help clinicians assess the risk of LNM in patients with SESCC for optimal treatment selection.

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.

Clinical models to predict lymph nodes metastasis and distant metastasis in newly diagnosed early esophageal cancer patients: A population-based study

BACKGROUND

Patients with early esophageal cancer (EC) receive individualized therapy based on their lymph node metastasis (LNM) and distant metastasis (DM) status; however, deficiencies in current clinical staging techniques and the issue of cost-effectiveness mean LNM and DM often go undetected preoperatively. We aimed to develop three clinical models to predict the likelihood of LNM, DM, and prognosis in patients with early EC.

METHOD

The Surveillance, Epidemiology, and End Results database was queried for T1 EC patients from 2004 to 2015. Multivariable logistic regression and Cox proportional hazards models were used to recognize the risk factors of LNM and DM, predict overall survival (OS), and develop relevant nomograms. Receiver operating characteristic (ROC)/concordance index and calibration curves were used to evaluate the discrimination and accuracy of the three nomograms. Decision curve analyses (DCAs), clinical impact curves, and subgroups based on model scores were used to determine clinical practicability.

RESULTS

The area under the curve of the LNM and DM nomograms were 0.668 and 0.807, respectively. The corresponding C-index of OS nomogram was 0.752. Calibration curves and DCA showed an effective predictive accuracy and clinical applicability. In patients with T1N0M0 EC, surgery alone (p < 0.01) proved a survival advantage. Chemotherapy and radiotherapy indicated a better prognosis in the subgroup analysis for T1 EC patients with LNM or DM.

CONCLUSIONS

We created three nomograms to predict the likelihood of LNM, DM, and OS probability in patients with early EC using a generalizable dataset. These useful visual tools could help clinical physicians deliver appropriate perioperative care.

Squamous Cell Carcinoma of the Esophagus

Esophageal squamous cell carcinoma (ESCC) is common in the developing world with decreasing incidence in developed countries and carries significant morbidity and mortality. Major risk factors for ESCC development include significant use of alcohol and tobacco. Screening for ESCC can be recommended in high-risk populations living in highly endemic regions. The treatment of ESCC ranges from endoscopic resection therapy or surgery in localized disease to chemoradiotherapy in metastatic disease, and prognosis is directly related to the stage at diagnosis. New immunotherapies and molecular targeted therapies may improve the dismal survival outcomes in patients with metastatic ESCC.

Prediction model for selection of adjuvant therapy population after endoscopic submucosal dissection for early esophageal lesions

BACKGROUND

Endoscopic treatment is the first choice for early esophageal lesions without obvious metastasis or infiltration, especially endoscopic submucosal dissection (ESD). However, the occurrence of non-curative resection, recurrence, or metastasis after ESD is inevitable, which may require adjuvant therapy such as additional surgery or chemora-diotherapy.

AIM

To analyze the risk factors for additional surgery or chemora-diotherapy after ESD for early esophageal lesions, and explore the effect of curative resection on additional adjuvant therapy.

METHODS

Patients who underwent ESD for stage T1 esophageal cancer (EC) and precancerous lesions were analyzed retrospectively. Postoperative recurrence, metastasis, and adjuvant treatment were followed, and a prediction model for adjuvant treatment population selection was established.

RESULTS

Non-curative resection was correlated with patient age, proportion of lesions, and microscopic size(P < 0.05), among which the proportion of lesions > 2/3 (odds ratio [OR] = 7.958, P = 0.007) was an independent risk factor. Two of curative and ten of non-curative resection cases received additional treatment, indicating that further treatment after ESD might be required regardless of whether curative resection was performed. Patient age, proportion of lesions, lesion lifting, differentiation, vascular invasion, and tumor invasion depth were correlated with the need for further treatment (P < 0.05). Age ≥ 70 years (OR = 4.210, P = 0.049), poor lifting (OR = 18.171, P = 0.047), and depth of tumor up to SM2 (OR = 38.781, P = 0.012) were independent risk factors. The AUC of the nomogram model was 0.864 (95%CI: 0.766-0.962); the specificity and sensitivity were 95.8% and 75.0%, respectively. Calibration curve analysis, decision curve analysis, and Hosmer and Lemeshow test (χ² = 1.5954, P = 0.991) showed that the predictive value of the model was acceptable.

CONCLUSION

For patients with stage T1 EC and precancerous lesions, whether further surgery or chemoradiotherapy is needed after ESD should be comprehensively considered.

A Machine Learning-Based Predictive Model for Predicting Lymph Node Metastasis in Patients With Ewing's Sarcoma

Objective

In order to provide reference for clinicians and bring convenience to clinical work, we seeked to develop and validate a risk prediction model for lymph node metastasis (LNM) of Ewing’s sarcoma (ES) based on machine learning (ML) algorithms.

Methods

Clinicopathological data of 923 ES patients from the Surveillance, Epidemiology, and End Results (SEER) database and 51 ES patients from multi-center external validation set were retrospectively collected. We applied ML algorithms to establish a risk prediction model. Model performance was checked using 10-fold cross-validation in the training set and receiver operating characteristic (ROC) curve analysis in external validation set. After determining the best model, a web-based calculator was made to promote the clinical application.

Results

LNM was confirmed or unable to evaluate in 13.86% (135 out of 974) ES patients. In multivariate logistic regression, race, T stage, M stage and lung metastases were independent predictors for LNM in ES. Six prediction models were established using random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), logistic regression (LR). In 10-fold cross-validation, the average area under curve (AUC) ranked from 0.705 to 0.764. In ROC curve analysis, AUC ranged from 0.612 to 0.727. The performance of the RF model ranked best. Accordingly, a web-based calculator was developed (https://share.streamlit.io/liuwencai2/es_lnm/main/es_lnm.py).

Conclusion

With the help of clinicopathological data, clinicians can better identify LNM in ES patients. Risk prediction models established in this study performed well, especially the RF model.