A competing-risks nomogram and recursive partitioning analysis for cause-specific mortality in patients with esophageal neuroendocrine carcinoma

Lymph node ratio and hematological parameters predict relapse-free survival in patients with high grade rectal neuroendocrine neoplasms after radical resection: a multicenter prognostic study

BACKGROUND

The prognostic nutritional index (PNI), alkaline phosphatase (ALP), and lymph node ratio (LNR) are reportedly related to prognosis. The aim of this study was to elucidate the clinical importance of the LNR and hematological parameters in patients with high grade rectal neuroendocrine neoplasms (HG-RNENs) who were undergoing radical resection.

METHODS

We reviewed the medical records of patients with HG-RNENs from 17 large-scale medical centers in China (January 1, 2010-April 30, 2022). A nomogram was constructed by using a proportional hazard model. Bootstrap method was used to draw calibration plots to validate the reproducibility of the model. Concordance index (C-Index), decision curve analysis (DCA), and time-dependent area under the receiver operating characteristic curve (TD-AUC) analysis were used to compare the prognostic predictive power of the new model with American Joint Committee on Cancer (AJCC) TNM staging and European Neuroendocrine Tumor Society (ENETS) TNM staging.

RESULTS

A total of 85 patients with HG-RNENs were enrolled in this study. In the 45 patients with HG-RNENs who underwent radical resection, PNI ≤ 49.13 (HR: 3.997, 95% CI: 1.379-11.581, P = 0.011), ALP > 100.0 U/L (HR: 3.051, 95% CI: 1.011-9.205, P = 0.048), and LNR > 0.40 (HR: 6.639, 95% CI: 2.224-19.817, P = 0.0007) were independent predictors of relapse-free survival. The calibration plots suggested that the nomogram constructed based on the three aforementioned factors had good reproducibility. The novel nomogram revealed a C-index superior to AJCC TNM staging (0.782 vs 0.712) and ENETS TNM staging (0.782 vs 0.657). Also, the new model performed better compared to AJCC TNM staging and ENETS TNM staging in DCA and TD-AUC analyses.

CONCLUSIONS

LNR, ALP, and PNI were independent prognostic factors in patients with HG-RNENs after radical resection, and the combined indicator had better predictive efficacy compared with AJCC TNM staging and ENETS TNM staging.

Radiomics model based on contrast-enhanced CT texture features for pretreatment prediction of overall survival in esophageal neuroendocrine carcinoma

Background

Limited studies have observed the prognostic value of CT images for esophageal neuroendocrine carcinoma (NEC) due to rare incidence and low treatment experience in clinical. In this study, the pretreatment enhanced CT texture features and clinical characteristics were investigated to predict the overall survival of esophageal NEC.

Methods

This retrospective study included 89 patients with esophageal NEC. The training and testing cohorts comprised 61 (70%) and 28 (30%) patients, respectively. A total of 402 radiomics features were extracted from the tumor region that segmented pretreatment venous phase CT images. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to feature dimension reduction, feature selection, and radiomics signature construction. A radiomics nomogram was constructed based on the radiomics signature and clinical risk factors using a multivariable Cox proportional regression. The performance of the nomogram for the pretreatment prediction of overall survival (OS) was evaluated for discrimination and calibration.

Results

Only the enhancement degree was an independent factor in clinical variable influenced OS. The radiomics signatures demonstrated good predictability for prognostic status discrimination. The radiomics nomogram integrating texture signatures was slightly superior to the nomogram derived from the combined model with a C-index of 0.844 (95%CI: 0.783-0.905) and 0.847 (95% CI: 0.782-0.912) in the training set, and 0.805 (95%CI: 0.707-0.903) and 0.745 (95% CI: 0.639-0.851) in the testing set, respectively.

Conclusion

The radiomics nomogram based on pretreatment CT radiomics signature had better prognostic power and predictability of the overall survival in patients with esophageal NEC than the model using combined variables.

Clinicopathological features, treatment modalities, and prognosis of esophageal neuroendocrine carcinoma: A single-center retrospective study

OBJECTIVES

Esophageal neuroendocrine carcinoma (ENEC) is a rare cancer that is highly malignant and related to a poor prognosis. In this retrospective study we aimed to elucidate the clinical characteristics, diagnosis and management of patients with ENEC and to evaluate the potential prognostic factors.

METHODS

Altogether 82 patients diagnosed with ENEC between January 2009 and December 2020 at the Fudan University Shanghai Cancer Center were retrospectively enrolled. Patients' survival was analyzed using the Kaplan-Meier and log-rank methods. Univariate and multivariate analyses and a Cox regression model were used to identify the prognostic factors.

RESULTS

The median overall survival (mOS) was 13 months in all patients. Multivariate analysis revealed that advanced tumor stage (hazard ratio [HR] 2.67, 95% confidence interval [CI] 1.07-6.66, P = 0.0353), liver (HR 3.36, 95% CI 1.53-7.41, P = 0.0026) and lung metastasis (HR 3.37, 95% CI 1.20-9.51, P = 0.0214) were associated with a poor prognosis. While positive chromogranin A (CgA) expression was related to a favorable outcome (HR 0.21, 95% CI 0.09-0.49, P < 0.001). Also, patients had adjustment of chemotherapy (dose reduction or less than three cycles) were prone to a worse prognosis compared with those did not (HR 4.36, 95% CI 2.10-9.08, P < 0.001).

CONCLUSION

In patients with ENEC, advanced cancer stage, adjustment of chemotherapy, liver and lung metastasis were associated with a poor survival, while CgA expression was related to a favorable prognosis.

MicroRNAs associated with postoperative outcomes in patients with limited stage neuroendocrine carcinoma of the esophagus

Esophageal neuroendocrine carcinoma (E-NEC) is an aggressive disease with a poor prognosis. The present study aimed to assess the role of surgery in the treatment of patients with resectable E-NEC, and identify a microRNA (miRNA/miR) signature in association with positive postoperative outcomes. Between February 2017 and August 2019, 36 patients with E-NEC who underwent curative surgery at the Japan Neuroendocrine Tumor Society partner hospitals were enrolled in the study. A total of 16 (44.4%) patients achieved disease-free survival (non-relapse group), whereas 20 (55.6%) patients developed tumor relapse (relapse group) during the median follow-up time of 36.5 months (range, 1-242) after surgery with a 5-year overall survival rate of 100 and 10.8%, respectively (P<0.01). No clinicopathological parameters, such as histological type or TNM staging, were associated with tumor relapse. Microarray analysis of 2,630 miRNAs in 11 patients with sufficient quality RNA revealed 12 miRNAs (miR-1260a, -1260b, -1246, -4284, -612, -1249-3p, -296-5p, -575, -6805-3p, -12136, -6822-5p and -4454) that were differentially expressed between the relapse (n=6) and non-relapse (n=5) groups. Furthermore, the top three miRNAs (miR-1246, -1260a and -1260b) were associated with overall survival (P<0.01). These results demonstrated that surgery-based multidisciplinary treatment is effective in a distinct subpopulation of limited stage E-NEC. A specific miRNA gene set is suggested to be associated with treatment outcome.

Prognostic value of pretreatment 18F-FDG PET/CT metabolic parameters in esophageal high-grade neuroendocrine carcinoma: A bicenter retrospective study

Objective

The aim of this bicentric retrospective study was to assess the prognostic value of 18F-FDG PET/CT in patients with esophageal high-grade neuroendocrine carcinoma (NECs).

Methods

From the database of two centers, 28 patients affected by esophageal high-grade NECs who underwent 18F-FDG PET/CT before treatment were retrospectively reviewed. Metabolic parameters (SUVmax, SUVmean, tumor-to-blood-pool SUV ratio (TBR), tumor-to-liver SUV ratio (TLR), metabolic tumor volume (MTV), total lesion glycolysis (TLG)) of the primary tumor were measured. Univariate and multivariate analyses were performed for progression-free survival (PFS) and overall survival (OS).

Results

After a median follow-up period of 22 months, disease progression occurred in 11 (39.3%) patients, and death occurred in 8 (28.6%) patients. The median PFS was 34 months, and the median OS was not reached. Univariate analyses revealed that among metabolic parameters, only MTV and TLG were significant prognostic factors, while among clinical variables, only distant metastasis was a significant factor for both PFS and OS (P&lt; 0.05). On multivariate analyses, MTV and TLG were independent prognostic factors for both PFS and OS (P&lt; 0.05).

Conclusions

In patients with esophageal high-grade NEC, MTV and TLG measured on pretreatment 18F-FDG PET/CT are independently prognostic factors for predicting PFS and OS, and might be used as quantitative prognostic imaging biomarkers.

Prediction of Cancer-Specific Survival of Brainstem Glioma in Children Based on Risk Stratification Model

Objective

To develop and authenticate a risk stratification framework and nomogram for ascertaining cancer-specific survival (CSS) among the pediatric brainstem gliomas.

Methods

For patients less than 12 years, according to Surveillance, Epidemiology, and End Results (SEER), information from 1998 to 2016 is found in their databases. The survival outcomes, treatments, and demographic clinicopathologic conditions are scrutinized per the database validation, and training cohorts are divided and validated using multivariate Cox regression analysis. A nomogram was designed, and predominantly, the risk stratification conceptualization engaged selected tenets according to the multivariate analysis. The model's authenticity was substantiated through C-index measure and calibration curves.

Results

There are 806 pediatric concerns of histologically concluded brainstem glioma in the research. According to multivariate analysis, age, grade, radiotherapy, and race (with P value < 0.05) depicted independent prognostic variations of the pediatric gliomas. The nomogram's C-index was approximately 0.75 and an accompanied predictive capability for CSS.

Conclusion

The nomogram constructed in this glioma's context is the primary predictor of using risk stratification. A combination of nomograms with the risk stratification mechanism assists clinicians in monitoring high-risk individuals and engage targeted accessory treatment.

Developmental artificial neural network model to evaluate the preoperative safe limit of future liver remnant volume for HCC combined with clinically significant portal hypertension

Background & aims: Finding a way to comprehensively integrate the presence and grade of clinically significant portal hypertension, amount of preserved liver function and extent of hepatectomy into the guidelines for choosing appropriate candidates to hepatectomy remained challenging. This study sheds light on these issues to facilitate precise surgical decisions for clinicians. Methods: Independent risk factors associated with grade B/C post-hepatectomy liver failure were identified by stochastic forest algorithm and logistic regression in hepatitis B virus-related hepatocellular carcinoma patients. Results: The artificial neural network model was generated by integrating preoperative pre-ALB, prothrombin time, total bilirubin, AST, indocyanine green retention rate at 15 min, standard future liver remnant volume and clinically significant portal hypertension grade. In addition, stratification of patients into three risk groups emphasized significant distinctions in the risk of grade B/C post-hepatectomy liver failure. Conclusion: The authors' artificial neural network model could provide a reasonable therapeutic option for clinicians to select optimal candidates with clinically significant portal hypertension for hepatectomy and supplement the hepatocellular carcinoma surgical treatment algorithm.

Nomogram for the prediction of individualized overall survival of patients diagnosed with small cell esophageal carcinoma

Background

A nomogram was developed for the estimation of individualized overall survival (OS) of patients diagnosed with small cell esophageal carcinoma (SCEC).

Methods

From the SEER dataset, 427 patients diagnosed with SCEC during the period from 2004 to 2015 were selected as training sets. For the establishment of a nomogram capable of estimating the OS possibility of patients diagnosed with SCEC, a group of independent prognostic factors were identified and incorporated. The effectiveness of the nomogram was then both externally and internally verified among 159 patients from Fudan University Shanghai Cancer Center (FUSCC) who were diagnosed with SCEC between 2006 and 2015. The predictive accuracy and discriminative ability of the nomogram were measured by concordance index (C-index). Comparisons between nomogram and the AJCC staging systems (6th and 7th) were performed with calibration plots and area under the curves (AUC) values.

Results

We identified age, gender, primary site, SEER stage, surgery, radiotherapy, and chemotherapy as seven independent risk factors which were then used to set up the nomogram. Calibration curves indicated that the prediction of the nomogram was consistent with real observations for the possibilities of 1-, 3-, and 5-year OS, and applying the nomogram to the cohort for validation led to reproducible results. Moreover, the C-indices and AUC values were higher in the nomogram than those in the AJCC staging system AJCC which is also aimed at the prediction of OS.

Conclusions

This study resulted in the establishment of the first nomogram for the prediction of individualized OS of patients diagnosed with SCEC. The accuracy rate of prediction of this model may be higher than previously established staging systems.

Improved esophageal squamous cell carcinoma screening effectiveness by risk-stratified endoscopic screening: evidence from high-risk areas in China

Background

Risk‐stratified endoscopic screening (RSES), which offers endoscopy to those with a high risk of esophageal cancer, has the potential to increase effectiveness and reduce endoscopic demands compared with the universal screening strategy (i.e., endoscopic screening for all targets without risk prediction). Evidence of RSES in high‐risk areas of China is limited. This study aimed to estimate whether RSES based on a 22‐score esophageal squamous cell carcinoma (ESCC) risk prediction model could optimize the universal endoscopic screening strategy for ESCC screening in high‐risk areas of China.

Methods

Eight epidemiological variables in the ESCC risk prediction model were collected retrospectively from 26,618 individuals aged 40‐69 from three high‐risk areas of China who underwent endoscopic screening between May 2015 and July 2017. The model's performance was estimated using the area under the curve (AUC). Participants were categorized into a high‐risk group and a low‐risk group with a cutoff score having sensitivities of both ESCC and severe dysplasia and above (SDA) at more than 90.0%.

Results

The ESCC risk prediction model had an AUC of 0.80 (95% confidence interval: 0.75–0.84) in this external population. We found that a score of 8 (ranging from 0 to 22) had a sensitivity of 94.2% for ESCC and 92.5% for SDA. The RSES strategy using this threshold score would allow 50.6% of endoscopies to be avoided and save approximately US$ 0.59 million compared to universal endoscopic screening among 26,618 participants. In addition, a higher prevalence of SDA (1.7% vs. 0.9%), a lower number need to screen (60 vs. 111), and a lower average cost per detected SDA (US$ 3.22 thousand vs. US$ 5.45 thousand) could have been obtained by the RSES strategy.

Conclusions

The RSES strategy based on individual risk has the potential to optimize the universal endoscopic screening strategy in ESCC high‐risk areas of China.

Upregulation of PEDF Predicts a Poor Prognosis and Promotes Esophageal Squamous Cell Carcinoma Progression by Modulating the MAPK/ERK Signaling Pathway

Invasion and metastasis represent the primary causes of therapeutic failure in patients diagnosed with esophageal squamous cell carcinoma (ESCC). The lack of effective treatment strategies for metastatic ESCC is the major cause of the low survival rate. Therefore, it is crucial to understand the molecular mechanisms underlying ESCC metastasis and identify potential biomarkers for targeted therapy. Herein, we reported that PEDF is significantly correlated with tumor cell invasion and metastasis in ESCC. The high expression of PEDF is an independent unfavorable prognostic factor for ESCC patients’ overall survival (OS). We successfully developed and verified a nomogram to predict the preoperative OS of ESCC patients, and the actual and nomogram-predicted 1-, 3-, and 5-year survival rates had good consistency. The receiver operating characteristic (ROC) curve showed that the area under the curve (AUC) values for 1-, 3- and 5- survival were 0.764, 0.871, and 0.91, respectively. Overexpression of PEDF significantly promoted the migration and invasion of ESCC cells in vitro, while silencing PEDF yielded the opposite effects. Elevated levels of PEDF altered the expression of proteins involved in epithelial–mesenchymal transition (EMT), as indicated by the upregulation of N-cadherin and the downregulation of α-catenin and E-cadherin in ESCC cells. Mechanistically, PEDF promoted tumor cell motility and EMT by activating the MAPK/ERK signaling pathway. In conclusion, our results reveal that PEDF is involved in ESCC metastasis and could act as a prognostic factor for ESCC. Our research provides a fresh perspective into the mechanism of ESCC metastasis.

Prognostic value of pretreatment contrast-enhanced computed tomography in esophageal neuroendocrine carcinoma: A multi-center follow-up study

BACKGROUND

The rare incidence of esophageal neuroendocrine carcinoma (NEC) and limited treatment experience result in insufficient clinical observations and unsuitable guidelines for its management.

AIM

To investigate the prognostic value of pretreatment contrast-enhanced computed tomography (CT) characteristics in patients with esophageal NEC.

METHODS

Seventy-seven esophageal NEC patients who received contrast-enhanced CT at two hospitals were enrolled in this study from June 2014 to December 2019. The clinical features and image characteristics were recorded accordingly. Univariate survival analysis was performed using the Kaplan-Meier method and log-rank test, and multivariate analysis was carried out with a Cox proportional hazards model.

RESULTS

The multivariate analysis performed using the Cox proportional hazards model showed that N stage, adjuvant chemotherapy, and degree of enhancement were independent prognostic factors for overall survival (OS). Meanwhile, adjuvant chemotherapy was an independent prognostic factor for progression-free survival (PFS). The hazard ratios (HRs) of N stage, adjuvant chemotherapy, and degree of enhancement (mild vs moderate/marked) for OS were 0.426 (P = 0.024), 3.862 (P = 0.006), and 2.169/0.809 (P = 0.037), respectively. The HR of adjuvant chemotherapy for PFS was 6.432 (P < 0.001). Adjuvant chemotherapy was significantly associated with degree of enhancement (P = 0.018).

CONCLUSION

Adjuvant chemotherapy is an independent prognostic factor for OS and PFS. Additionally, N stage and degree of enhancement are prognostic factors for OS in patients with esophageal NEC.