Metastasis pattern and prognosis in men with esophageal cancer patients: A SEER-based study

Nomogram for predicting survival after lymphatic metastasis in esophageal cancer: A SEER analysis

Lymphatic metastasis (LM) is a significant mechanism for the spread of esophageal cancer (EC) and predicts the poor prognosis of EC patients. This research aimed to assess the survival of patients with LM from EC by developing a nomogram. In this retrospective study, EC patients with LM from 2004 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were divided by year of diagnosis into a training cohort and a validation cohort. Univariate and multivariate Cox regression analyses were employed to determine the prognostic factors of LM, and a nomogram was constructed. The discrimination and calibration of the nomogram were compared by the C-index, area under the curve value, and calibration plots. The survival time difference was compared using Kaplan-Meier curves. A total of 11,695 patients with EC were included in this analysis. LM occurred in 56.5% (n = 6614) of EC patients. In the post-propensity score matching (PSM) cohort, patients with LM had significantly lower median overall survival (OS) than those without LM. Multivariate Cox regression was used to identify the eleven independent prognostic factors. The C-index was 0.709 in both the training and test sets, revealing the good predictive performance of the nomogram. Based on the results of calibration plots and the receiver operating characteristic (ROC) curve, we demonstrate the great performance of the prognostic model. The survival time of EC patients with LM was remarkably lower than that of EC patients without LM. The nomogram model established in this study can precisely predict the survival of EC patients with LM.

Prediction of distant organ metastasis and overall survival of lung cancer patients: a SEER population-based cohort study

Background

Distant organ metastasis is a common event in lung cancer (LC). However, the preferential metastatic pattern of different pathological types of LC and its effect on prognosis have not been comprehensively elucidated. This study aimed to explore the distant metastasis pattern and construct nomograms predicting the metastasis and survival of LC patients using the Surveillance, Epidemiology, and End Results (SEER) database.

Methods

LC data were downloaded from the SEER database to conduct logistic regression and investigate risk factors for developing organ metastasis. A Cox regression analysis was conducted to investigate prognostic factors of LC. A Kaplan-Meier analysis was used to estimate overall survival outcomes. Nomograms were constructed to predict the probability of organ metastasis and the 1-, 3- and 5-year survival probability of LC patients. Receiver operating characteristic curves were used to evaluate the diagnostic accuracy of the nomograms. All statistical analyses were conducted within R software.

Results

The liver is the most common metastatic organ of small cell carcinoma. The brain is the most likely metastasis site of large cell carcinoma, and bone is the most likely metastasis site for squamous cell carcinoma and adenocarcinoma. Patients with triple metastases (brain-bone-liver) have the worst prognosis, and for nonsquamous carcinoma with single organ metastasis, liver metastases conferred the worst prognosis. Our nomograms based on clinical factors could predict the metastasis and prognosis of LC patients.

Conclusion

Different pathological types of LC have different preferential metastatic sites. Our nomograms showed good performance in predicting distant metastasis and overall survival. These results will provide a reference for clinicians and contribute to clinical evaluations and individualized therapeutic strategies.

Determinants of survival outcomes among esophageal cancer patients at a national referral hospital in Kenya

Introduction

The overall 5-year survival rate for esophageal cancer patients in low- and middle-income countries was reported to be low, despite the availability of advanced treatments. Thus, this study aimed to assess determinants of survival outcomes among esophageal cancer patients in Kenya.

Methods

A retrospective cohort study was employed among 299 adult esophageal cancer patients. The data were collected using a data abstraction tool consisting of patients' clinical characteristics and survival outcome measuring parameters. Statistical Package for the Social Sciences (SPSS) statistical software (version 20.0, IBM. USA) was used to analyze the data. The Kaplan-Meier and Cox regression analyses were used to determine the survival outcome and determinants of mortality, respectively.

Results

The mortality rate was 43.1%, and 11.1% of patients demonstrated distant metastases in the follow-up period. Despite treatment, 20.1% had progressed disease, and 13.0% did not respond to treatment. Radiotherapy (AHR: 3.3, 95% CI: 1.4-7.8, p = 0.007), chemotherapy (AHR: 3.9, 95% CI: 1.2-6.1, p = 0.020), and chemoradiation (AHR: 5.6, 95%CI: 1.6-10.2, p = 0.006) were the significant determinants of survival in advanced stage (III and and IV) patients.

Conclusions

There was a high mortality rate, disease progression, and nonresponse of esophageal cancer patients. Hence, it is essential to improve the survival of patients through early detection and timely initiation of the available treatment options.

Case report: A case of rectal metastatic squamous cell carcinoma from the esophagus

Esophageal cancer is prone to distant metastasis, and the prognosis is very poor; the occurrence of intestinal metastasis is extremely rare, with atypical clinical manifestations. We report a case of rectal metastasis after surgery for esophageal squamous cell carcinoma. The patient was a 63-year-old male who was admitted to the hospital due to progressive dysphagia. He was diagnosed with moderately differentiated esophageal squamous cell carcinoma after the surgery. He was not treated with chemoradiotherapy after surgery and had recurrent blood in his stool at 9 months post-surgery; rectal metastasis of esophageal squamous carcinoma was diagnosed based on postoperative pathology. Because the patient had a positive rectal margin, we used adjuvant chemoradiotherapy and carrelizumab immunotherapy, achieving very good short-term efficacy. The patient is currently in a state of tumor-free survival and is still being closely followed up and treated. Through this case report, we hope to deepen understanding of rare metastasis of esophageal squamous cell carcinoma and actively promote local radiotherapy plus chemotherapy and immunotherapy to improve survival.

Prediction of distant metastasis in esophageal cancer using a radiomics-clinical model

BACKGROUND

Distant metastasis, which occurs at a rate of 25% in patients with esophageal cancer (EC), has a poor prognosis, with previous studies reporting an overall survival of only 3-10 months. However, few studies have been conducted to predict distant metastasis in EC, owing to a dearth of reliable biomarkers. The purpose of this study was to develop and validate an accurate model for predicting distant metastasis in patients with EC.

METHODS

A total of 299 EC patients were enrolled and randomly assigned to a training cohort (n = 207) and a validation cohort (n = 92). Logistic univariate and multivariate regression analyses were used to identify clinical independent predictors and create a clinical nomogram. Radiomic features were extracted from contrast-enhanced computed tomography (CT) images taken prior to treatment, and least absolute shrinkage and selection operator (Lasso) regression was used to screen the associated features, which were then used to develop a radiomic signature. Based on the screened features, four machine learning algorithms were used to build radiomics models. The joint nomogram with radiomic signature and clinically independent risk factors was developed using the logical regression algorithm. All models were validated and compared by discrimination, calibration, reclassification, and clinical benefit.

RESULTS

Multivariable analyses revealed that age, N stage, and degree of pathological differentiation were independent predictors of distant metastasis, and a clinical nomogram incorporating these factors was established. A radiomic signature was developed by a set of sixteen features chosen from 851 radiomic features. The joint nomogram incorporating clinical factors and radiomic signature performed better [AUC(95% CI) 0.827(0.742-0.912)] than the clinical nomogram [AUC(95% CI) 0.731(0.626-0.836)] and radiomics predictive models [AUC(95% CI) 0.754(0.652-0.855), LR algorithms]. Calibration and decision curve analyses revealed that the radiomics-clinical nomogram outperformed the other models. In comparison with the clinical nomogram, the joint nomogram's NRI was 0.114 (95% CI 0.075-0.345), and its IDI was 0.071 (95% CI 0.030-0.112), P = 0.001.

CONCLUSIONS

We developed and validated the first radiomics-clinical nomogram for distant metastasis in EC which may aid clinicians in identifying patients at high risk of distant metastasis.

A Nomogram Model to Predict Post-Progression Survival in Esophageal Squamous Cell Carcinoma Patients With Recurrence After Radical Resection

Background

Esophageal squamous cell carcinoma (ESCC) is characterized clinically by frequent recurrence, leading to a poor prognosis after radical surgery. The aim of this study was to identify a prognostic nomogram to predict the post-progression survival (PPS) of ESCC patients based on the features of primary tumor and recurrence.

Methods

A total of 234 ESCC patients who underwent recurrence after radical surgery were enrolled in this study. The independent prognostic factors screened by the univariate and multivariate Cox regression analysis were subsequently used to construct a nomogram. The predictive performance of the nomogram was evaluated with the concordance index (C-index), decision curve, and the area under the receiver operating characteristic curve (AUC) and validated in two validation cohorts. The Kaplan-Meier curves of different recurrence patterns were analyzed.

Results

The prognostic nomogram of PPS was established by integrating independent prognostic factors, including age, body mass index, number of lymph node dissection, recurrence pattern, and recurrence treatment. The nomogram demonstrated good performance, with C-index values of 0.756, 0.817, and 0.730 for the training and two validation cohorts. The 1-year AUC values were 0.773, 0.798, and 0.735 and 3-year AUC values were 0.832, 0.871, and 0.791, respectively. Furthermore, we found that patients with bone metastasis displayed the worst PPS compared to other isolated recurrence patterns.

Conclusion

We constructed a nomogram to reliably predict PPS, which would be valuable to provide individual managements for ESCC patients after radical surgery.

Treatment paradigms and survival outcomes in esophageal adenocarcinoma with liver metastasis: a retrospective cohort study using the SEER database

Background

Esophageal adenocarcinoma with liver metastasis (EACLM) at the time of diagnosis has a poor prognosis and few therapeutic options. The best treatment options and prognostic factors for EACLM patients are unclear. The present study sought to explore the optimal treatment modalities for and the prognosis of these patients.

Methods

Patients diagnosed with EACLM at the time of diagnosis were identified from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2015. The last follow-up date was December 31, 2018. Treatment patterns were divided into four groups: local therapy (surgery/radiation), systemic therapy [chemotherapy (CT)], combination therapy (surgery/radiation + CT), and no treatment. The Kaplan-Meier (K-M) method and log-rank test were used for overall survival (OS) and disease-specific survival (DSS). Univariable and multivariable Cox regression were performed to identify the prognostic factors. Propensity score-matching (PSM) analyses were performed for sensitive analyses.

Results

A total of 925 patients diagnosed with EACLM were included in the study. The median OS was 12, 10, 3, and 2 months for combination therapy, systemic therapy, local therapy, and no treatment, respectively (P<0.001). After PSM, the patients who received systemic treatment had a better OS (median 9 vs. 2 months; P<0.001) and DSS (median 9 vs. 3 months; P<0.001) than those who received no treatment. Compared to systemic therapy, combination therapy did not increase patients' OS (median 13 vs. 12 months, P=0.069) but did improve their DSS (median 19 vs. 13 months, P=0.048).

Conclusions

EACLM patients might benefit the most from systemic therapy and combination therapy. For patients who are well-tolerated, combination therapy should be considered as a preferable option.

Prognostic Nomogram for Predicting Long-Term Overall Survival of Esophageal Cancer Patients Receiving Neoadjuvant Chemoradiotherapy Plus Surgery: A Population-Based Study

Objective

Neoadjuvant chemoradiotherapy (nCRT) plays an important role in patients with locally advanced esophageal cancer (EC). We aim to determine the prognostic risk factors and establish a reliable nomogram to predict overall survival (OS) based on SEER population.

Methods

Patients with EC coded by 04–15 in the SEER database were included. The data were divided into training group and verification group (7:3). The Cox proportional-risk model was evaluated by using the working characteristic curve (receiver operating characteristic curve, ROC) and the area under the curve (AUC), and a nomogram was constructed. The calibration curve was used to measure the consistency between the predicted and the actual results. Decision curve analysis (DCA) was used to evaluate its clinical value. The best cut-off value of nomogram score in OS was determined by using X-tile software, and the patients were divided into low-risk, medium-risk, and high-risk groups.

Results

A total of 2,209 EC patients who underwent nCRT were included in further analysis, including 1,549 in the training cohort and 660 in the validation group. By Cox analysis, sex, marital status, T stage, N stage, M stage, and pathological grade were identified as risk factors. A nomogram survival prediction model was established to predict the 36-, 60-, and 84-month survival. The ROC curve and AUC showed that the model had good discrimination ability. The correction curve was in good agreement with the prediction results. DCA further proved the effective clinical value of the nomogram model. The results of X-tile analysis showed that the long-term prognosis of patients in the low-risk subgroup was better in the training cohort and the validation cohort (p &lt; 0.001).

Conclusion

This study established an easy-to-use nomogram risk prediction model consisting of independent prognostic factors in EC patients receiving nCRT, helping to stratify risk, identify high-risk patients, and provide personalized treatment options.