Survival prediction tools for esophageal and gastroesophageal junction cancer: A systematic review. J Thorac Cardiovasc Surg. 2018;156:847-856. [PMID: 30011772 DOI: 10.1016/j.jtcvs.2018.03.146]

Enhancing cisplatin drug sensitivity through PARP3 inhibition: The influence on PDGF and G-coupled signal pathways in cancer

Drug resistance poses a significant challenge in cancer treatment despite the clinical efficacy of cisplatin. Identifying and targeting biomarkers open new ways to improve therapeutic outcomes. In this study, comprehensive bioinformatic analyses were employed, including a comparative analysis of multiple datasets, to evaluate overall survival and mutation hotspots in 27 base excision repair (BER) genes of more than 7,500 tumors across 23 cancer types. By using various parameters influencing patient survival, revealing that the overexpression of 15 distinct BER genes, particularly PARP3, NEIL3, and TDG, consistently correlated with poorer survival across multiple factors such as race, gender, and metastasis. Single nucleotide polymorphism (SNP) analyses within protein-coding regions highlighted the potential deleterious effects of mutations on protein structure and function. The investigation of mutation hotspots in BER proteins identified PARP3 due to its high mutation frequency. Moving from bioinformatics to wet lab experiments, cytotoxic experiments demonstrated that the absence of PARP3 by CRISPR/Cas9-mediated knockdown in MDA-MB-231 breast cancer cells increased drug activity towards cisplatin, carboplatin, and doxorubicin. Pathway analyses indicated the impact of PARP3 absence on the platelet-derived growth factor (PDGF) and G-coupled signal pathways on cisplatin exposure. PDGF, a critical regulator of various cellular functions, was downregulated in the absence of PARP3, suggesting a role in cancer progression. Moreover, the influence of PARP3 knockdown on G protein-coupled receptors (GPCRs) affects their function in the presence of cisplatin. In conclusion, the study demonstrated a synthetic lethal interaction between GPCRs, PDGF signaling pathways, and PARP3 gene silencing. PARP3 emerged as a promising target.

Prediction of hepatic metastasis in esophageal cancer based on machine learning

This study aimed to establish a machine learning (ML) model for predicting hepatic metastasis in esophageal cancer. We retrospectively analyzed patients with esophageal cancer recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2020. We identified 11 indicators associated with the risk of liver metastasis through univariate and multivariate logistic regression. Subsequently, these indicators were incorporated into six ML classifiers to build corresponding predictive models. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A total of 17,800 patients diagnosed with esophageal cancer were included in this study. Age, primary site, histology, tumor grade, T stage, N stage, surgical intervention, radiotherapy, chemotherapy, bone metastasis, and lung metastasis were independent risk factors for hepatic metastasis in esophageal cancer patients. Among the six models developed, the ML model constructed using the GBM algorithm exhibited the highest performance during internal validation of the dataset, with AUC, accuracy, sensitivity, and specificity of 0.885, 0.868, 0.667, and 0.888, respectively. Based on the GBM algorithm, we developed an accessible web-based prediction tool (accessible at https://project2-dngisws9d7xkygjcvnue8u.streamlit.app/ ) for predicting the risk of hepatic metastasis in esophageal cancer.

Gastro-Esophageal Cancer: Can Radiomic Parameters from Baseline 18F-FDG-PET/CT Predict the Development of Distant Metastatic Disease?

We aimed to determine if clinical parameters and radiomics combined with sarcopenia status derived from baseline 18F-FDG-PET/CT could predict developing metastatic disease and overall survival (OS) in gastroesophageal cancer (GEC). Patients referred for primary staging who underwent 18F-FDG-PET/CT from 2008 to 2019 were evaluated retrospectively. Overall, 243 GEC patients (mean age = 64) were enrolled. Clinical, histopathology, and sarcopenia data were obtained, and primary tumor radiomics features were extracted. For classification (early-stage vs. advanced disease), the association of the studied parameters was evaluated. Various clinical and radiomics models were developed and assessed. Accuracy and area under the curve (AUC) were calculated. For OS prediction, univariable and multivariable Cox analyses were performed. The best model included PET/CT radiomics features, clinical data, and sarcopenia score (accuracy = 80%; AUC = 88%). For OS prediction, various clinical, CT, and PET features entered the multivariable analysis. Three clinical factors (advanced disease, age ≥ 70 and ECOG ≥ 2), along with one CT-derived and one PET-derived radiomics feature, retained their significance. Overall, 18F-FDG PET/CT radiomics seems to have a potential added value in identifying GEC patients with advanced disease and may enhance the performance of baseline clinical parameters. These features may also have a prognostic value for OS, improving the decision-making for GEC patients.

Prognostic factors in clinicopathology of oesophagogastric adenocarcinoma: a single-centre longitudinal study of 347 cases over a 20-year period

Oesophagogastric adenocarcinoma (EGA) includes oesophageal (EA), gastro-oesophageal junctional (GEJA), and gastric (GA) adenocarcinomas. The prognostic values of clinicopathological factors in these tumours remain obscure, especially for GEJA that has been inconsistently classified and staged. We studied the prognosis of EGA patients among the three geographic groups in 347 consecutive patients with a median age of 70 years (range 47-94). All patients were male, and 97.1% were white. Based on tumour epicentre location, EGAs were sub-grouped into EA (over 2 cm above the GEJ; n=3, 18.1%), GEJA (within 2 cm above and 3 cm below the GEJ; n=231, 66.6%), and GA (over 3 cm below the GEJ; n=53, 15.3%). We found that the median overall survival (OS) was the longest in EA (62.9 months), compared to GEJA (33.4), and GA (38.1) (p<0.001). Significant risk factors for OS included tumour location (p=0.018), size (p<0.001), differentiation (p<0.001), adenocarcinoma subtype (p<0.001), and TNM stage (p<0.001). Independent risk factors for OS comprised low-grade papillary adenocarcinoma [odds ratio (OR) 0.449, 95% confidence interval (CI) 0.214-0.944, p<0.05), mixed adenocarcinoma (OR 1.531, 95% CI 1.056-2.218, p<0.05), adenosquamous carcinoma (OR 2.206, 95% CI 1.087-4.475, p<0.05), N stage (OR 1.505, 95% CI 1.043-2.171, p<0.05), and M stage (OR 10.036, 95% CI 2.519-39.993, p=0.001)]. EGA was further divided into low-risk (common well-moderately differentiated tubular and low-grade papillary adenocarcinomas) and high-risk (uncommon adenocarcinoma subtypes, adenosquamous carcinoma) subgroups. In this grouping, the median OS was significantly longer in the low-risk (83 months) than in the high-risk (10 months) subgroups (p<0.001). In conclusion, the prognosis of EGA patients was significantly better in EA than in GEJA or GA and could be stratified into low and high-risk subgroups with significantly different outcomes.

Combination of clinical, radiomic, and "delta" radiomic features in survival prediction of metastatic gastroesophageal adenocarcinoma

Objectives

To identify combined clinical, radiomic, and delta-radiomic features in metastatic gastroesophageal adenocarcinomas (GEAs) that may predict survival outcomes.

Methods

A total of 166 patients with metastatic GEAs on palliative chemotherapy with baseline and treatment/follow-up (8-12 weeks) contrast-enhanced CT were retrospectively identified. Demographic and clinical data were collected. Three-dimensional whole-lesional radiomic analysis was performed on the treatment/follow-up scans. "Delta" radiomic features were calculated based on the change in radiomic parameters compared to the baseline. The univariable analysis (UVA) Cox proportional hazards model was used to select clinical variables predictive of overall survival (OS) and progression-free survival (PFS) (p-value <0.05). The radiomic and "delta" features were then assessed in a multivariable analysis (MVA) Cox model in combination with clinical features identified on UVA. Features with a p-value <0.01 in the MVA models were selected to assess their pairwise correlation. Only non-highly correlated features (Pearson's correlation coefficient <0.7) were included in the final model. Leave-one-out cross-validation method was used, and the 1-year area under the receiver operating characteristic curve (AUC) was calculated for PFS and OS.

Results

Of the 166 patients (median age of 59.8 years), 114 (69%) were male, 139 (84%) were non-Asian, and 147 (89%) had an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1. The median PFS and OS on treatment were 3.6 months (95% CI 2.86, 4.63) and 9 months (95% CI 7.49, 11.04), respectively. On UVA, the number of chemotherapy cycles and number of lesions at the end of treatment were associated with both PFS and OS (p < 0.001). ECOG status was associated with OS (p = 0.0063), but not PFS (p = 0.054). Of the delta-radiomic features, delta conventional HUmin, delta gray-level zone length matrix (GLZLM) GLNU, and delta GLZLM LGZE were incorporated into the model for PFS, and delta shape compacity was incorporated in the model for OS. Of the treatment/follow-up radiomic features, shape compacity and neighborhood gray-level dependence matrix (NGLDM) contrast were used in both models. The combined 1-year AUC (Kaplan-Meier estimator) was 0.82 and 0.81 for PFS and OS, respectively.

Conclusions

A combination of clinical, radiomics, and delta-radiomic features may predict PFS and OS in GEAs with reasonable accuracy.

Integration of IDPC Clustering Analysis and Interpretable Machine Learning for Survival Risk Prediction of Patients with ESCC

Precise forecasting of survival risk plays a pivotal role in comprehending and predicting the prognosis of patients afflicted with esophageal squamous cell carcinoma (ESCC). The existing methods have the problems of insufficient fitting ability and poor interpretability. To address this issue, this work proposes a novel interpretable survival risk prediction method for ESCC patients based on extreme gradient boosting improved by whale optimization algorithm (WOA-XGBoost) and shapley additive explanations (SHAP). Given the imbalanced nature of the data set, the adaptive synthetic sampling (ADASYN) is first used to generate the samples with high survival risk. Then, an improved clustering by fast search and find of density peaks (IDPC) algorithm based on cosine distance and K nearest neighbors is used to cluster the patients. Next, the prediction model for each cluster is obtained by WOA-XGBoost and the constructed model is visualized with SHAP to uncover the factors hidden in the structured model and improve the interpretability of the black-box model. Finally, the effectiveness of the proposed scheme is demonstrated by analyzing the data collected from the First Affiliated Hospital of Zhengzhou University. The results of the analysis reveal that the proposed methodology exhibits superior performance, as indicated by the area under the receiver operating characteristic curve (AUROC) of 0.918 and accuracy of 0.881.

The AUGIS Survival Predictor: Prediction of Long-Term and Conditional Survival After Esophagectomy Using Random Survival Forests

OBJECTIVE

The aim of this study was to develop a predictive model for overall survival after esophagectomy using pre/postoperative clinical data and machine learning.

SUMMARY BACKGROUND DATA

For patients with esophageal cancer, accurately predicting long-term survival after esophagectomy is challenging. This study investigated survival prediction after esophagectomy using a RandomSurvival Forest (RSF) model derived from routine data from a large, well-curated, national dataset.

METHODS

Patients diagnosed with esophageal adenocarcinoma or squamous cell carcinoma between 2012 and 2018 in England and Wales who underwent an esophagectomy were included. Prediction models for overall survival were developed using the RSF method and Cox regression from 41 patient and disease characteristics. Calibration and discrimination (time-dependent area under the curve) were validated internally using bootstrap resampling.

RESULTS

The study analyzed 6399 patients, with 2625 deaths during follow-up. Median follow-up was 41 months. Overall survival was 47.1% at 5 years. The final RSF model included 14 variables and had excellent discrimination with a 5-year time-dependent area under the receiver operator curve of 83.9% [95% confidence interval (CI) 82.6%-84.9%], compared to 82.3% (95% CI 81.1%-83.3%) for the Cox model. The most important variables were lymph node involvement, pT stage, circumferential resection margin involvement (tumor at < 1 mm from cut edge) and age. There was a wide range of survival estimates even within TNM staging groups, with quintiles of prediction within Stage 3b ranging from 12.2% to 44.7% survival at 5 years.

CONCLUSIONS

An RSF model for long-term survival after esophagectomy exhibited excellent discrimination and well-calibrated predictions. At a patient level, it provides more accuracy than TNM staging alone and could help in the delivery of tailored treatment and follow-up.

Prognostic impact of examined lymph-node count for patients with esophageal cancer: development and validation prediction model

Esophageal cancer (EC) is a malignant tumor with high mortality. We aimed to find the optimal examined lymph node (ELN) count threshold and develop a model to predict survival of patients after radical esophagectomy. Two cohorts were analyzed: the training cohort which included 734 EC patients from the Chinese registry and the external testing cohort which included 3208 EC patients from the Surveillance, Epidemiology, and End Results (SEER) registry. Cox proportional hazards regression analysis was used to determine the prognostic value of ELNs. The cut-off point of the ELNs count was determined using R-statistical software. The prediction model was developed using random survival forest (RSF) algorithm. Higher ELNs count was significantly associated with better survival in both cohorts (training cohort: HR = 0.98, CI = 0.97-0.99, P < 0.01; testing cohort: HR = 0.98, CI = 0.98-0.99, P < 0.01) and the cut-off point was 18 (training cohort: P < 0.01; testing cohort: P < 0.01). We developed the RSF model with high prediction accuracy (AUC: training cohort: 87.5; testing cohort: 79.3) and low Brier Score (training cohort: 0.122; testing cohort: 0.152). The ELNs count beyond 18 is associated with better overall survival. The RSF model has preferable clinical capability in terms of individual prognosis assessment in patients after radical esophagectomy.

Development and validation of a machine learning model for survival risk stratification after esophageal cancer surgery

Background

Prediction of prognosis for patients with esophageal cancer(EC) is beneficial for their postoperative clinical decision-making. This study's goal was to create a dependable machine learning (ML) model for predicting the prognosis of patients with EC after surgery.

Methods

The files of patients with esophageal squamous cell carcinoma (ESCC) of the thoracic segment from China who received radical surgery for EC were analyzed. The data were separated into training and test sets, and prognostic risk variables were identified in the training set using univariate and multifactor COX regression. Based on the screened features, training and validation of five ML models were carried out through nested cross-validation (nCV). The performance of each model was evaluated using Area under the curve (AUC), accuracy(ACC), and F1-Score, and the optimum model was chosen as the final model for risk stratification and survival analysis in order to build a valid model for predicting the prognosis of patients with EC after surgery.

Results

This study enrolled 810 patients with thoracic ESCC. 6 variables were ultimately included for modeling. Five ML models were trained and validated. The XGBoost model was selected as the optimum for final modeling. The XGBoost model was trained, optimized, and tested (AUC = 0.855; 95% CI, 0.808-0.902). Patients were separated into three risk groups. Statistically significant differences (p < 0.001) were found among all three groups for both the training and test sets.

Conclusions

A ML model that was highly practical and reliable for predicting the prognosis of patients with EC after surgery was established, and an application to facilitate clinical utility was developed.

The Evaluation of a SEER-Based Nomogram in Predicting the Survival of Patients Treated with Neoadjuvant Therapy Followed by Esophagectomy

Background

A novel nomogram based on the Surveillance, Epidemiology, and End Results (SEER) database has been developed to predict the survival of patients with esophageal carcinoma who received neoadjuvant therapy followed by surgery. We aimed to evaluate the accuracy and value of the nomogram with an external validation cohort.

Methods

A total of 2,224 patients in SEER database were divided into the training cohort (n = 1556) and the internal validation cohort (n = 668), while 77 patients in our institute were enrolled in the external validation cohort. A Cox proportional hazards regression model was used to develop a nomogram based on the training cohort, while the C-indexes, the calibration curves, receiver operating characteristics curve (ROC), and Kaplan-Meier survival curve were applied in the internal and external validation cohort.

Results

Five independent risk factors were identified and integrated into the nomogram (C-index = 0.645, 95%CI 0.627–0.663). The nomogram exhibited good prognostic value in the internal validation cohort (C-index = 0.648 95%CI 0.622–0.674). However, the C-index, calibration plot, receiver operating characteristics curve (ROC) analysis, Kaplan-Meier survival curve of the nomogram in the external validation cohort were not as good as the training and internal validation cohort (C-index = 0.584 95%CI 0.445–0.723). Further analysis demonstrated that the resection margin involvement (R0, R1, or R2 resection) was an independent risk factor for the patients, which was not included in the SEER cohort.

Conclusions

the nomogram based on the SEER database fails to accurately predict the prognosis of the patients in the external validation cohort, which can be caused by the absence of essential information from the SEER database.

Development of an alarm symptom-based risk prediction score for localized oesophagogastric adenocarcinoma (VIOLA score)

BACKGROUND

Gastroesophageal adenocarcinoma is a major contributor to global disease burden with poor prognosis even in resectable, regionally limited stages. Feasible prognostic tools are crucial to improve patient management, yet scarce.

PATIENTS AND METHODS

Disease-related symptoms, patient, tumour, treatment as well as laboratory parameters at initial diagnosis and overall survival (OS) of patients with stage II and III gastroesophageal adenocarcinoma, who were treated between 1990 and 2020 at the Medical University of Vienna, were evaluated in a cross-validation model to develop a feasible risk prediction score.

RESULTS

In total, 628 patients were included in this single-centre analysis. The final score ranked from 0 to 10 and included the factors sex (female +1), age, years (30-59 +1, >60 +2), underweight classified by body mass index (+2), location of the tumour (stomach +1), stage (III +2), stenosis in endoscopy (+1) and weight loss (+1). The score was grouped into low- (0-3), medium- (4-6) and high-risk (7+) subgroups. The median OS were 70.3 [95% confidence interval (CI) 51.2-111.8], 23.4 (95% CI 21.2-26.7) and 12.6 (7.0-16.1) months, respectively. The 1-year survival probabilities were 0.88 (95% CI 0.83-0.93), 0.75 (95% CI 0.70-0.79) and 0.54 (95% CI 0.39-0.74), whereas the 5-year survival probabilities were 0.57 (95% CI 0.49-0.66), 0.24 (95% CI 0.20-0.28) and 0.09 (95% CI 0.03-0.28), respectively.

CONCLUSIONS

The VIennese risk prediction score for Oesophagogastric Localized Adenocarcinoma (VIOLA) risk prediction score poses a feasible tool for the estimation of OS in patients with regionally limited gastroesophageal adenocarcinoma and, thus, may improve patient management in clinical routine. Prospective analyses should be carried out to confirm our findings.

Survival benefit of adjuvant chemotherapy following neoadjuvant therapy and oesophagectomy in oesophageal adenocarcinoma

BACKGROUND

The evidence assessing the additional benefits of adjuvant chemotherapy (AC) following neoadjuvant therapy (NAT; i.e. chemotherapy or chemoradiotherapy) and oesophagectomy for oesophageal adenocarcinoma (EAC) are limited. This study aimed to determine whether AC improves long-term survival in patients receiving NAT and oesophagectomy.

METHODS

Patients receiving oesophagectomy for EAC following NAT from 2004 to 2016 were identified from the National Cancer Data Base (NCDB). To account for immortality bias, patients with survival ≤3 months were excluded to account for immortality bias. Propensity score matching (PSM) and Cox regression was performed to account for selection bias and analyze impact of AC on overall survival.

RESULTS

Overall, 12,972 (91%) did not receive AC and 1,255 (9%) received AC. After PSM there were 2,485 who did not receive AC and 1,254 who did. After matching, AC was associated with improved survival (median: 38.5 vs 32.3 months, p < 0.001), which remained after multivariable adjustment (HR: 0.78, CI_95%: 0.71-0.87). On multivariable interaction analyses, this benefit persisted in subgroup analysis for nodal status: N0 (HR: 0.85, CI_95%: 0.69-0.96), N1 (HR: 0.66, CI_95%: 0.56-0.78), N2/3 (HR: 0.80, CI_95%: 0.66-0.97) and margin status: R0 (HR: 0.77, CI_95%: 0.69-0.86), R1 (HR: 0.60, CI_95%: 0.43-0.85). Further, patients with stable disease following NAT (HR: 0.60, CI_95%: 0.59-0.80) or downstaged (HR: 0.80, CI_95%: 0.68-0.95) disease had significant survival benefit after AC, but not patients with upstaged disease.

CONCLUSION

AC following NAT and oesophagectomy is associated with improved survival, even in node-negative and margin-negative disease. NAT response may be crucial in identifying patients who will benefit maximally from AC, and thus future research should be focused on identifying molecular phenotype of tumours that respond to chemotherapy to improve outcomes.

Tumor microenvironment characterization in esophageal cancer identifies prognostic relevant immune cell subtypes and gene signatures

Esophageal cancer (ESCA) is a common malignancy in the digestive system with a high mortality rate and poor prognosis. Tumor microenvironment (TME) plays an important role in the tumorigenesis, progression and therapy resistance of ESCA, whereas its role in predicting clinical outcomes has not been fully elucidated. In this study, we comprehensively estimated the TME infiltration patterns of 164 ESCA patients using Gene Set Variation Analysis (GSVA) and identified 4 key immune cells (natural killer T cell, immature B cell, natural killer cell, and type 1 T helper cell) associated with the prognosis of ESCA patients. Besides, two TME groups were defined based on the TME patterns with different clinical outcomes. According to the expression gene set between two TME groups, we built a model to calculate TMEscore based on the single-sample gene-set enrichment analysis (ssGSEA) algorithm. TMEscore systematically correlated the TME groups with genomic characteristics and clinicopathologic features. In conclusion, our data provide a novel TMEscore which can be regarded as a reliable index for predicting the clinical outcomes of ESCA.

Imputing pre-diagnosis health behaviour in cancer registry data and investigating its relationship with oesophageal cancer survival time

BACKGROUND

As oesophageal cancer has short survival, it is likely pre-diagnosis health behaviours will have carry-over effects on post-diagnosis survival times. Cancer registry data sets do not usually contain pre-diagnosis health behaviours and so need to be augmented with data from external health surveys. A new algorithm is introduced and tested to augment cancer registries with external data when one-to-one data linkage is not available.

METHODS

The algorithm is to use external health survey data to impute pre-diagnosis health behaviour for cancer patients, estimate misclassification errors in these imputed values and then fit misclassification corrected Cox regression to quantify the association between pre-diagnosis health behaviour and post-diagnosis survival. Data from US cancer registries and a US national health survey are used in testing the algorithm.

RESULTS

It is demonstrated that the algorithm works effectively on simulated smoking data when there is no age confounding. But age confounding does exist (risk of death increases with age and most health behaviours change with age) and interferes with the performance of the algorithm. The estimate of the hazard ratio (HR) of pre-diagnosis smoking was HR = 1.32 (95% CI 0.82,2.68) with HR = 1.93 (95% CI 1.08,7.07) in the squamous cell sub-group and pre-diagnosis physical activity was protective of survival with HR = 0.25 (95% CI 0.03, 0.81). But the method failed for less common behaviours (such as heavy drinking).

CONCLUSIONS

Further improvements in the I2C2 algorithm will permit enrichment of cancer registry data through imputation of new variables with negligible risk to patient confidentiality, opening new research opportunities in cancer epidemiology.

Prognostic models for stage I-III esophageal cancer: a comparison between existing calculators

Background

Determining the best approach for esophageal cancer and predicting accurate prognosis are critical. Multiple studies evaluated characteristics associated with overall survival, and several prediction models have been developed. This study aimed to evaluate existing models and perform external validation of selected models.

Methods

A retrospective investigation of a multi-site institutional enterprise for patients with a diagnosis of esophageal cancer between 2013-2014 was performed. Selected survival prediction models included the Roswell Park Comprehensive Cancer Center (RPCCC) calculator, Oregon Health & Science University (OHSU) calculator, and two nomograms published by Shapiro et al. and Sun et al. One-year overall survival, level of agreement, and performance for each model were evaluated.

Results

A total of 104 patients were included and used to assess the prediction models. One-year overall survival was 0.76. Different calculators tended to rank patients similarly; however, they did not agree on predicted overall survival. The least disparity in correlation was observed between OHSU and Shapiro calculators. Shapiro's model achieved the highest performance [area under the curve (AUC) =0.63].

Conclusions

Selected models showed fair results in estimating individual overall survival, although none achieved a high performance. While these tools may support the decision-making process for esophageal cancer patients, their implementation in clinical practice requires improved refinement to optimize their clinical utility.

Comprehensive analysis of prognostic value of lymph node classifications in esophageal squamous cell carcinoma: a large real-world multicenter study

Background:

We aim to assess the prognostic ability of three common lymph node–based staging algorithms, namely, the number of positive lymph nodes (pN), the lymph node ratio (LNR), and log odds of positive lymph nodes (LODDS) in patients with esophageal squamous cell carcinoma (ESCC).

Methods:

A total of 3902 ESCC patients treated at 10 Chinese institutions between 2003 and 2013 were included, along with 2465 patients from the Surveillance, Epidemiology, and End Results (SEER) database. The prognostic ability of the aforementioned algorithms was evaluated using time-dependent receiver operating characteristic (tdROC) curves, R², Harrell’s concordance index (C-index), and the likelihood ratio chi-square score. The primary outcomes included cancer-specific survival (CSS), overall survival (OS), and CSS with a competing risk of death by non-ESCC causes.

Results:

LODDS had better prognostic performance than pN or LNR in both continuous and stratified patterns. In the multicenter cohort, the multivariate analysis showed that the model based on LODDS classification was superior to the others in predictive accuracy and discriminatory capacity. Two nomograms integrating LODDS classification and other clinicopathological risk factors associated with OS as well as cancer-specific mortality were constructed and validated in the SEER database. Finally, a novel TN_LODDS classification which incorporates the LODDS classification was built and categorized patients in to three new stages.

Conclusion:

Among the three lymph node–based staging algorithms, LODDS demonstrated the highest discriminative capacity and prognostic accuracy for ESCC patients. The nomograms and novel TN_LODDS classification based on LODDS classification could serve as precise evaluation tools to assist clinicians in estimating the survival time of individual patients and improving clinical outcomes postoperatively in the future.

Next-Generation Sequencing of 487 Esophageal Adenocarcinomas Reveals Independently Prognostic Genomic Driver Alterations and Pathways

PURPOSE

To delineate recurrent oncogenic driver alterations and dysregulated pathways in esophageal adenocarcinoma and to assess their prognostic value.

EXPERIMENTAL DESIGN

We analyzed a large cohort of patients with lower esophageal and junctional adenocarcinoma, prospectively sequenced by MSK-IMPACT with high-quality clinical annotation. Patients were subdivided according to treatment intent, curative versus palliative, which closely mirrored clinical staging. Genomic features, alterations, and pathways were examined for association with overall survival using Cox proportional hazard models, adjusted for relevant clinicopathologic factors knowable at the time of diagnosis.

RESULTS

Analysis of 487 patients revealed 16 oncogenic driver alterations, mostly amplifications, present in ≥5% of patients. Patients in the palliative-intent cohort, compared with those in the curative-intent cohort, were more likely to have metastatic disease, ERBB2 amplifications, Cell-cycle and RTK-RAS pathway alterations, as well as a higher fraction of genome altered and rate of whole-genome doubling. In multivariable analyses, CDKN2A alterations, SMAD4 alterations, KRAS amplifications, Cell-cycle and TGFβ pathways, and overall number of oncogenic drivers were independently associated with worse overall survival. ERBB2 amplification was associated with improved survival, presumably due to trastuzumab therapy.

CONCLUSIONS

Our study suggests that higher levels of genomic instability are associated with more advanced disease in esophageal adenocarcinoma. Furthermore, CDKN2A, KRAS, and SMAD4 represent prognostic biomarkers, given their strong association with poor survival.

Conditional survival in patients with esophageal or gastroesophageal junction cancer after receiving various treatment modalities

Background

To estimate the adjusted conditional overall survival (COS) in patients with esophageal cancer after receiving various treatment modalities via a national population‐based database, and to investigate the possible time‐dependent effects.

Materials and Methods

Eligible patients diagnosed with esophageal cancer between 2000 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) registry. The Kaplan‐Meier method was used to calculate conventional survival time. The inverse probability of treatment weighting method was used to estimate the adjusted COS in patients receiving different treatment modalities. Landmark analysis was employed to investigate the possible time‐dependent effects of different treatment modalities in patients who had survived a certain period of time.

Results

A total of 25,232 patients were included in the final analysis. The conventional 5‐year overall survival was 19.3%. The 5‐year adjusted COS increased most for the first 3 years, and increased slightly afterwards. In patients with regional esophageal or gastroesophageal junction cancer, stage‐specific analysis showed that surgery only and preoperative radiation therapy benefited most for patients with localized disease, preoperative radiation therapy plus surgery benefited regional, and preoperative radiation therapy plus surgery benefited distant disease, with the 5‐year adjusted COS given patients had survived 3 years being 67.0% (95% CI 65.2%–68.7%), 59.9% (95% CI 58.3%–61.5%), 58.4% (95% CI 56.3%–60.5%), and 61.8% (95% CI 59.5%–64.1%), respectively. In time‐dependent analysis, the benefits of surgery only in localized cases were prominent within 48 months after diagnosis. Preoperative radiation therapy showed long‐lasting benefits in patients with regional disease. In patients with distant disease, all treatment modalities showed similar and short‐term effects.

Conclusions

The adjusted COS in patients with esophageal cancer increased as time accrued after receiving various treatment modalities. The time‐dependent effects in specific tumor stage provided a dynamic view on optimization of treatment strategies.

Impact of palliative therapies in metastatic esophageal cancer patients not receiving chemotherapy

BACKGROUND

Palliative therapy has been associated with improved overall survival (OS) in several tumor types. Not all patients with metastatic esophageal cancer receive palliative chemotherapy, and the roles of other palliative therapies in these patients are limited.

AIM

To investigate the impact of other palliative therapies in patients with metastatic esophageal cancer not receiving chemotherapy.

METHODS

The National Cancer Database was used to identify patients between 2004-2015. Patients with M1 disease who declined chemotherapy and had known palliative therapy status [palliative therapies were defined as surgery, radiotherapy (RT), pain management, or any combination thereof] were included. Cases with unknown chemotherapy, RT, or nonprimary surgery status were excluded. Kaplan-Meier estimates of OS were calculated. Cox proportional hazards regression models were employed to examine factors influencing survival.

RESULTS

Among 140234 esophageal cancer cases, we identified 1493 patients who did not receive chemotherapy and had complete data. Median age was 70 years, most (66.3%) had a Charlson Comorbidity Index (CCI) of 0, and 37.1% were treated at an academic center. The majority (72.7%) did not receive other palliative therapies. On both univariate and multivariable analyses, there was no difference in OS between those receiving other palliative therapy (median 2.83 mo, 95%CI: 2.53-3.12) vs no palliative therapy (2.37 no, 95%CI: 2.2-2.56; multivariable P = 0.290). On univariate, but not multivariable analysis, treatment at an academic center was predictive of improved OS [Hazard ratio (HR) 0.90, 95%CI: 0.80-1.00; P = 0.047]. On multivariable analysis, female sex (HR 0.81, 95%CI: 0.71-0.92) and non-black, other race compared to white race (HR 0.72, 95%CI: 0.56-0.93) were associated with reduced mortality, while South geographic region relative to West region (HR 1.23, 95%CI: 1.04-1.46) and CCI of 1 relative to CCI of 0 (HR 1.17, 95%CI: 1.03-1.32) were associated with increased mortality. Higher histologic grade and T-stage were also associated with worse OS (P < 0.05).

CONCLUSION

Palliative therapies other than chemotherapy conferred a numerically higher, but not statistically significant difference in OS among patients with metastatic esophageal cancer not receiving chemotherapy. Quality of life metrics, inpatient status, and subgroup analyses are important for examining the role of palliative therapies other than chemotherapy in metastatic esophageal cancer and future studies are warranted.

Risk Factors for Nonhome Discharge After Esophagectomy for Neoplastic Disease

BACKGROUND

Esophagectomies are known to be technically challenging operations that create significant physiologic changes. These patients often require assisted care postoperatively that necessitates a nonhome discharge. The purpose of this study was to assess factors associated with nonhome discharge after esophagectomy for neoplastic disease.

METHODS

The 2016 to 2017 American College of Surgeons National Surgical Quality Improvement Program Esophagectomy database was queried to identify patients who underwent esophagectomy for a neoplasm. Patients were excluded if they died within 30 days of their operation, the index operation was considered emergent, or had missing data for the variables of interest. Multivariable analysis was performed to identify which factors were predictive of nonhome discharge.

RESULTS

One thousand seven patients were included. Of those, 121 (12.0%) had a nonhome discharge. Multivariable analysis showed that the following factors were associated with nonhome discharge: Modified Charlson comorbidity index (adjusted odds ratio [aOR], 2.04; 95% confidence interval [CI], 1.49-2.86), partially dependent preoperative functional status (aOR, 13.18; 95% CI, 1.07-315.67), urinary tract infection (aOR, 5.25; 95% CI, 1.32-20.41), and length of stay (aOR, 1.12; 95% CI, 1.08-1.16).

CONCLUSIONS

We identified various factors associated with nonhome discharge. Early identification of patients who are at risk for nonhome discharge is important for early discharge planning, which may decrease nonmedical delays and healthcare costs.

A multinational review: Oesophageal cancer in low to middle-income countries

Oesophageal cancer (OC) is an aggressive neoplasm that manifests in the gastrointestinal tract and is the result of numerous factors that can contribute to the development of the disease. These may include old age, nutritional deficiencies, oesophageal obstruction and food ingestion difficulties. Environmental factors serve a large role in increasing the risk of developing OC. Two factors that serve an increasing risk of developing OC are the use of tobacco and the consumption of alcohol. Genetic factors also exhibit a large effect on the risk of developing OC, for example, the causative genes in Black Africans differ from other races. OC is 3–4 times more common among men than women. OC has been previously reported in >450 000 individuals worldwide, and its incidence is increasing. The current review compares OC in low to middle-income countries with developed countries. The incidence of OC, particularly squamous cell carcinoma (SCC) is high in low and middle-income countries. In developed countries, the incidence of SCC is low compared with adenocarcinoma. The majority of OC cases are diagnosed in the late stages of the disease, leading to high mortality rates. The current review aimed to discuss factors that contribute to the development of this disease in different geographical areas and genetic mechanisms governing these findings. The current review also aims to discuss the preventative treatment options for the disease, and also discusses the diagnosis and surveillance in five LMICs, including South Africa, China, Tanzania, India and Brazil.

Keratin 17 Expression Predicts Poor Clinical Outcome in Patients With Advanced Esophageal Squamous Cell Carcinoma

The major roles of keratin 17 (K17) as a prognostic biomarker have been highlighted in a range of human malignancies. However, its relevance to esophageal squamous cell carcinoma (ESCC) remains unexplored. In this study, the relationship between K17 expression and clinicopathologic parameters and survival were determined by RNA sequencing (RNA-Seq) in 90 ESCCs and by immunohistochemistry (IHC) in 68 ESCCs. K17 expression was significantly higher in ESCC than in paired normal tissues at both the messenger RNA and protein levels. K17 messenger RNA and staining by IHC were significantly correlated with aggressive characteristics, including advanced clinical stage, invasion depth, and lymph node metastases; and were predictive of poor prognosis in advanced disease patients. Furthermore, K17 expression was detected by IHC in high-grade premalignant lesions of the esophageal mucosa, suggesting that K17 could also be a biomarker of dysplasia of the esophageal mucosa. Overall, this study established that K17 is a negative prognostic biomarker for the most common subtype of esophageal cancer.

Population Registry of Esophageal and Stomach Tumours in Ontario (PRESTO): protocol for a multicentre clinical and pathological database including 25 000 patients

INTRODUCTION

Oesophagogastric cancers carry a high mortality, economic burden and rising incidence. There is a need to monitor and improve care for this disease. Pathologic information is a cornerstone of cancer diagnosis, treatment and prognosis. Few population-based studies combine pathology information and clinical outcomes. The objective of this study is to develop a clinical and pathological database of oesophagogastric cancers to study practice patterns, resource utilisation and clinical outcomes.

METHODS AND ANALYSIS

The Population Registry of Esophageal and Stomach Tumours in Ontario (PRESTO) will include all patients with oesophagogastric cancer diagnosed from 2002 onwards within the province of Ontario. We estimate that the sample over the first 14 years of the study will include 26 000 patients. Pathologic information from diagnostic procedures, endomucosal resection specimens and surgical resection specimens is being abstracted into a purpose-built database. Pathology information will be linked to administrative data, which capture baseline demographics, patient-reported symptoms, physician billings, hospital visits, hospital characteristics, geography and vital statistics. The registry will be updated prospectively.

ETHICS AND DISSEMINATION

Ethics approval for this study was obtained from the Sunnybrook Health Sciences Centre Research Ethics Board. The PRESTO database will enable the study of oesophagogastric cancer in Ontario under six themes of inquiry: treatment, surgical outcomes, pathology, survival, health system and resource utilisation and cost. This information will be a valuable addition to the global efforts to understand ways to optimise care for these diseases.

Deep Convolutional Neural Network-Based Positron Emission Tomography Analysis Predicts Esophageal Cancer Outcome

In esophageal cancer, few prediction tools can be confidently used in current clinical practice. We developed a deep convolutional neural network (CNN) with 798 positron emission tomography (PET) scans of esophageal squamous cell carcinoma and 309 PET scans of stage I lung cancer. In the first stage, we pretrained a 3D-CNN with all PET scans for a task to classify the scans into esophageal cancer or lung cancer. Overall, 548 of 798 PET scans of esophageal cancer patients were included in the second stage with an aim to classify patients who expired within or survived more than one year after diagnosis. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. In the pretrain model, the deep CNN attained an AUC of 0.738 in identifying patients who expired within one year after diagnosis. In the survival analysis, patients who were predicted to be expired but were alive at one year after diagnosis had a 5-year survival rate of 32.6%, which was significantly worse than the 5-year survival rate of the patients who were predicted to survive and were alive at one year after diagnosis (50.5%, p < 0.001). These results suggest that the prediction model could identify tumors with more aggressive behavior. In the multivariable analysis, the prediction result remained an independent prognostic factor (hazard ratio: 2.830; 95% confidence interval: 2.252–3.555, p < 0.001). We conclude that a 3D-CNN can be trained with PET image datasets to predict esophageal cancer outcome with acceptable accuracy.

Patient-reported outcomes in lung and esophageal cancer

Carcinomas of the lung and esophagus are associated with significant disease and treatment related morbidity. Measuring patients' self-perceived notion of their health-related quality of life (HRQOL), throughout the course of illness, is central to the delivery of comprehensive, patient-centered care. This article reviews commonly used HRQOL instruments in thoracic surgery and discusses the integral role of patient-reported outcomes (PROs) in comparative effectiveness research and prognostication in the realm of lung and esophageal cancer. We also highlight challenges and future directions for widespread implementation of PROs into clinical and research practice.

Does anti-reflux surgery disrupt the pathway of Barrett's esophagus progression to cancer?

In patients with Barrett's esophagus (BE), anti-reflux surgery aims to sustainable control reflux symptoms and heal reflux induced esophageal mucosal inflammation and prevent progression of BE to adenocarcinoma. There is growing evidence that beside gastric acid, bile salts in refluxed duodenal juice are responsible for the development and progression of BE. However, the pathogenesis of BE progression and the metaplasia-dysplasia-carcinoma sequence of the adenocarcinoma of the esophagus (EAC) is multifactorial and occurs over long natural time course. After anti-reflux surgery significant levels of regression from metaplastic Barrett's to non-metaplastic epithelium as well as from dysplastic to non-dysplastic BE have been observed and a randomized trial showed that sufficient surgical reflux control reduces the risk of Barrett's progression significantly when compared to medical treatment. Thus, large cohort studies show significant reduced risk of EAC in patients suffering from gastroesophageal reflux disease (GERD) with and without BE after anti-reflux surgery. Even after anti-reflux surgery the risk for EAC remains elevated in patients with BE and the right moment of intercepting the progressive nature of GERD has to be discussed in future. The paper also addresses the impact of anti-reflux surgery, endoscopic ablation and life style therapies for the management of GERD, BE and cancer prevention.