Development and Validation of a Clinical Prognostic Nomogram for Esophageal Adenocarcinoma Patients

Design Appropriate Incision Length for Uniportal Video-Assisted Thoracoscopic Lobectomy: Take into Account Safety and Minimal Invasiveness

BACKGROUND

 There is no criterion on the length of the uniportal video-assisted thoracoscopic surgery (UVATS) incision when performing lobectomy. We aimed to develop a nomogram to assist surgeons in designing incision length for different individuals.

METHODS

 A cohort consisting of 290 patients were enrolled for nomogram development. Univariate and multivariate logistic regression analyses were performed to identify candidate variables among perioperative characteristics. C-index and calibration curves were utilized for evaluating the performance of the nomogram. Short-term outcomes of nomogram-predicted high-risk patients were compared between long incision group and conventional incision group.

RESULTS

 Of 290 patients, 150 cases (51.7%) were performed incision extension during the surgery. Age, tumor size, and tumor location were identified as candidate variables related with intraoperative incision extension and were incorporated into the nomogram. C-index of the nomogram was 0.75 (95% confidence interval: 0.6961-0.8064), indicating the good predictive performance. Calibration curves presented good consistency between the nomogram prediction and actual observation. Of high-risk patients identified by the nomogram, the long incision group (n = 47) presented shorter duration of operation (p = 0.03), lower incidence of total complications (p = 0.01), and lower incidence of prolonged air leak (p = 0.03) compared with the conventional incision group (n = 55).

CONCLUSION

 We developed a novel nomogram for predicting the risk of intraoperative incision extension when performing uniportal video-assisted thoracoscopic lobectomy. This model has the potential to assist clinicians in designing the incision length preoperatively to ensure both safety and minimal invasiveness.

Prognosis prediction in esophageal signet-ring-cell carcinoma: a competing risk analysis

OBJECTIVE

This study aims to construct and validate a competing risk nomogram model to predict 1-year, 3-year, and 5-year cancer-specific survival (CSS) for patients with esophageal signet-ring-cell carcinoma.

METHODS

Patients diagnosed with esophageal signet-ring-cell carcinoma (ESRCC) between 2010 and 2015 were abstracted from the Surveillance, Epidemiology, and End Results (SEER) database. We performed the competing risk model to select significant variables to build a competing risk nomogram, which was used to estimate 1-year, 3-year, and 5-year CSS probability. The C-index, receiver operating characteristic (ROC) curve, calibration plot, Brier score, and decision curve analysis were performed in the internal validation.

RESULTS

A total of 564 patients with esophageal signet-ring-cell carcinoma fulfilled the eligibility criteria. The competing risk nomogram identified 4 prognostic variables, involving the gender, lung metastases, liver metastases, and receiving surgery. The C indexes of nomogram were 0.61, 0.75, and 0.70, respectively for 5-year, 3-year, and 1-year CSS prediction. The calibration plots displayed high consistency. The Brier scores and decision curve analysis respectively favored good prediction ability and clinical utility of the nomogram.

CONCLUSIONS

A competing risk nomogram for esophageal signet-ring-cell carcinoma was successfully constructed and internally validated. This model is expected to predict 1-year, 3-year, and 5-year CSS, and help oncologists and pathologists in clinical decision making and health care management for esophageal signet-ring-cell carcinoma patients.

Neoadjuvant versus definitive chemoradiation in locally advanced esophageal cancer for patients of advanced age or significant comorbidities

The addition of surgery to chemoradiation for esophageal cancer has not shown a survival benefit in randomized trials. Patients with more comorbidities or advanced age are more likely to be given definitive chemoradiation due to surgical risk. We aimed to identify subsets of patients in whom the addition of surgery to chemoradiation does not provide an overall survival (OS) benefit. The National Cancer Database was queried for patients with locally advanced esophageal cancer who received either definitive chemoradiation or neoadjuvant chemoradiation followed by surgery. Bivariate analysis was used to assess the association between patient characteristics and treatment groups. Log-rank tests and Cox proportional hazards models were performed to assess for differences in survival. A total of 15,090 with adenocarcinoma and 5,356 with squamous cell carcinoma met the inclusion criteria. Patients treated with neoadjuvant chemoradiation and surgery had significantly improved survival by Cox proportional hazards model regardless of histology if <50, 50-60, 61-70, or 71-80 years old. There was no significant benefit or detriment in patients 81-90 years old. Survival advantage was also significant with a Charlson/Deyo comorbidity condition score of 0, 1, 2, and ≥3 in adenocarcinoma squamous cell carcinoma with scores of 2 or ≥3 had no significant benefit or detriment. Patients 81-90 years old or with squamous cell carcinoma and a Charlson/Deyo comorbidity score ≥ 2 lacked an OS benefit from neoadjuvant chemoradiation followed by surgery compared with definitive chemoradiation. Careful consideration of esophagectomy-specific surgical risks should be used when recommending treatment for these patients.

A clinical prognostic model for patients with esophageal squamous cell carcinoma based on circulating tumor DNA mutation features

Background

Few predictive models have included circulating tumor DNA (ctDNA) indicators to predict prognosis of esophageal squamous cell carcinoma (ESCC) patients. Here, we aimed to explore whether ctDNA can be used as a predictive biomarker in nomogram models to predict the prognosis of patients with ESCC.

Methods

We included 57 patients who underwent surgery and completed a 5-year follow-up. With next-generation sequencing, a 61-gene panel was used to evaluate plasma cell-free DNA and white blood cell genomic DNA from patients with ESCC. We analyzed the relationship between the mutation features of ctDNA and the prognosis of patients with ESCC, identified candidate risk predictors by Cox analysis, and developed nomogram models to predict the 2- and 5-year disease-free survival (DFS) and overall survival (OS). The area under the curve of the receiver operating characteristic (ROC) curve, concordance index (C-index), calibration plot, and integrated discrimination improvement (IDI) were used to evaluate the performance of the nomogram model. The model was compared with the traditional tumor-nodes-metastasis (TNM) staging system.

Results

The ROC curve showed that the average mutant allele frequency (MAF) of ctDNA variants and the number of ctDNA variants were potential biomarkers for predicting the prognosis of patients with ESCC. The predictors included in the models were common candidate predictors of ESCC, such as lymph node stage, angiolymphatic invasion, drinking history, and ctDNA characteristics. The calibration curve demonstrated consistency between the observed and predicted results. Moreover, our nomogram models showed clear prognostic superiority over the traditional TNM staging system (based on C-index, 2-year DFS: 0.82 vs. 0.64; 5-year DFS: 0.78 vs. 0.65; 2-year OS: 0.80 vs. 0.66; 5-year OS: 0.77 vs. 0.66; based on IDI, 2-year DFS: 0.33, p <0.001; 5-year DFS: 0.18, p = 0.04; 2-year OS: 0.28, p <0.001; 5-year OS: 0.15, p = 0.04). The comprehensive scores of the nomogram models could be used to stratify patients with ESCC.

Conclusions

The novel nomogram incorporating ctDNA features may help predict the prognosis of patients with resectable ESCC. This model can potentially be used to guide the postoperative management of ESCC patients in the future, such as adjuvant therapy and follow-up.

Genetic characteristics and clinical-specific survival prediction in elderly patients with gallbladder cancer: a genetic and population-based study

Background

Biliary system cancers are most commonly gallbladder cancers (GBC). Elderly patients (≥ 65) were reported to suffer from an unfavorable prognosis. In this study, we analyzed the RNA-seq and clinical data of elderly GBC patients to derive the genetic characteristics and the survival-related nomograms.

Methods

RNA-seq data from 14 GBC cases were collected from the Gene Expression Omnibus (GEO) database, grouped by age, and subjected to gene differential and enrichment analysis. In addition, a Weighted Gene Co-expression Network Analysis (WGCNA) was performed to determine the gene sets associated with age grouping further to characterize the gene profile of elderly GBC patients. The database of Surveillance, Epidemiology, and End Results (SEER) was searched for clinicopathological information regarding elderly GBC patients. Nomograms were constructed to predict the overall survival (OS) and cancer-specific survival (CSS) of elderly GBC patients. The predictive accuracy and capability of nomograms were evaluated through the concordance index (C-index), calibration curves, time-dependent operating characteristic curves (ROC), as well as area under the curve (AUC). Decision curve analysis (DCA) was performed to check out the clinical application value of nomograms.

Results

Among the 14 patients with GBC, four were elderly, while the remaining ten were young. Analysis of gene differential and enrichment indicated that elderly GBC patients exhibited higher expression levels of cell cycle-related genes and lower expression levels of energy metabolism-related genes. Furthermore, the WGCNA analysis indicated that elderly GBC patients demonstrated a decrease in the expression of genes related to mitochondrial respiratory enzymes and an increase in the expression of cell cycle-related genes. 2131 elderly GBC patients were randomly allocated into the training cohort (70%) and validation cohort (30%). Our nomograms showed robust discriminative ability with a C-index of 0.717/0.747 for OS/CSS in the training cohort and 0.708/0.740 in the validation cohort. Additionally, calibration curves, AUCs, and DCA results suggested moderate predictive accuracy and superior clinical application value of our nomograms.

Conclusion

Discrepancies in cell cycle signaling and metabolic disorders, especially energy metabolism, were obviously observed between elderly and young GBC patients. In addition to being predictively accurate, the nomograms of elderly GBC patients also contributed to managing and strategizing clinical care.

A novel risk classification system based on the eighth edition of TNM frameworks for esophageal adenocarcinoma patients: A deep learning approach

Objective

To develop and validate a deep learning predictive model with better performance in survival estimation of esophageal adenocarcinoma (EAC).

Method

Cases diagnosed between January 2010 and December 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. A deep learning survival neural network was developed and validated based on 17 variables, including demographic information, clinicopathological characteristics, and treatment details. Based on the total risk score derived from this algorithm, a novel risk classification system was constructed and compared with the 8th edition of the tumor, node, and metastasis (TNM) staging system.

Results

Of 7,764 EAC patients eligible for the study, 6,818 (87.8%) were men and the median (interquartile range, IQR) age was 65 (58-72) years. The deep learning model generated significantly superior predictions to the 8th edition staging system on the test data set (C-index: 0.773 [95% CI, 0.757-0.789] vs. 0.683 [95% CI, 0.667-0.699]; P < 0.001). Calibration curves revealed that the deep learning model was well calibrated for 1- and 3-year OS, most points almost directly distributing on the 45° line. Decision curve analyses (DCAs) showed that the novel risk classification system exhibited a more significant positive net benefit than the TNM staging system. A user-friendly and precise web-based calculator with a portably executable file was implemented to visualize the deep learning predictive model.

Conclusion

A deep learning predictive model was developed and validated, which possesses more excellent calibration and discrimination abilities in survival prediction of EAC. The novel risk classification system based on the deep learning algorithm may serve as a useful tool in clinical decision making given its easy-to-use and better clinical applicability.

Nomogram for prognosis of patients with esophageal squamous cell cancer after minimally invasive esophagectomy established based on non-textbook outcome

BACKGROUND

Non-textbook outcome (non-TO) represents a new prognostic evaluation index for surgical oncology. The present study aimed to develop new nomograms based on non-TO to predict the mortality and recurrence rate in patients with esophageal squamous cell cancer (ESCC) after minimally invasive esophagectomy (MIE).

METHODS

The study involved a retrospective analysis of 613 ESCC patients, from the prospectively maintained database from January 2011 to December 2018. All the included ESCC patients underwent MIE, and they were randomly (1:1) assigned to the training cohort (307 patients) and the validation cohort (306 patients). Kaplan-Meier survival analysis was used to analyze the differences recorded between overall survival (OS) and disease-free survival (DFS). In the case of the training cohort, the nomograms based on non-TO were developed using Cox regression, and the performance of these nomograms was calibrated and evaluated in the validation cohort.

RESULTS

Significant differences were recorded for 5-year OS and DFS between non-TO and TO groups (p < 0.05). Multivariate cox analysis revealed that non-TO, intraoperative bleeding, T stage, and N stage acted as independent risk factors that affected OS and DFS (p < 0.05). The results for multivariate regression were used to build non-TO-based nomograms to predict OS and DFS of patients with ESCC, the t-AUC curve analysis showed that the nomograms predicting OS and DFS were more accurate as compared to TNM staging, during the follow-up period in the training cohort and validation cohort. Further, the nomogram score was used to divide ESCC patients into low-, middle-, and high-risk groups and significant differences were recorded for OS and DFS between these three groups (p < 0.001).

CONCLUSIONS

Non-TO was identified as an independent prognostic factor for ESCC patients. The nomograms based on non-TO could availably predict OS and DFS in ESCC patients after MIE.

Construction and Validation of Prognosis Nomogram for Metastatic Lung Squamous Cell Carcinoma: A Population-Based Study

Purpose: This study aimed to establish a nomogram to predict overall survival in lung squamous cell carcinoma patients with metastasis for clinical decision-making. Methods: We investigated lung squamous cell carcinoma patients diagnosed with stage M1 in the Surveillance, Epidemiology, and Final Results database between 2010 and 2015. They were divided into training cohort and validation cohort. In the training cohort, statistically significant prognostic factors were identified using univariate and multivariate Cox regression analysis, and an individualized nomogram model was developed. The model was evaluated by C-index, area under the curve, calibration plot, decision curve analysis, and risk group stratification. Results: In total, 9910 patients were included in our study, including 6937 in the training cohort and 2937 in the validation cohort. Factors containing age, T stage, N stage, bone metastasis, brain metastasis, liver metastasis, surgery, chemotherapy, and radiotherapy were independent prognostic factors for overall survival and were used in the construction of the nomogram. The C-index in the training cohort and validation cohort were 0.711 (95% confidenc interval: 0.705-0.717) and 0.707 (95% confidenc interval: 0.697-0.717), respectively. The time-dependent area under the curve of both groups was higher than 0.7 within 5 years. Calibration plots indicated that the nomogram-predicted survival was consistent with the recorded 6-month, 1-year, and 2-year prognoses. Furthermore, decision curve analysis revealed that the nomogram was clinically useful and had a better discriminative ability to recognize patients at high risk than the TNM criteria-based tumor staging. And then we developed an overall survival risk classification system based on the nomogram total points for each patient, which divided all patients into a high-risk group and a low-risk group. Finally, we implemented this nomogram in a free online tool. Conclusion: We constructed a nomogram and a corresponding risk classification system predicting the overall survival of lung squamous cell carcinoma patients with metastasis. These tools can assist in patients’ counseling and guide treatment decision-making.

Development and validation of a deep learning model to predict survival of patients with esophageal cancer

Objective

To compare the performance of a deep learning survival network with the tumor, node, and metastasis (TNM) staging system in survival prediction and test the reliability of individual treatment recommendations provided by the network.

Methods

In this population-based cohort study, we developed and validated a deep learning survival model using consecutive cases of newly diagnosed stage I to IV esophageal cancer between January 2004 and December 2015 in a Surveillance, Epidemiology, and End Results (SEER) database. The model was externally validated in an independent cohort from Fujian Provincial Hospital. The C statistic was used to compare the performance of the deep learning survival model and TNM staging system. Two other deep learning risk prediction models were trained for treatment recommendations. A Kaplan–Meier survival curve was used to compare survival between the population that followed the recommended therapy and those who did not.

Results

A total of 9069 patients were included in this study. The deep learning network showed more promising results in predicting esophageal cancer-specific survival than the TNM stage in the internal test dataset (C-index=0.753 vs. 0.638) and external validation dataset (C-index=0.687 vs. 0.643). The population who received the recommended treatments had superior survival compared to those who did not, based on the internal test dataset (hazard ratio, 0.753; 95% CI, 0.556-0.987; P=0.042) and the external validation dataset (hazard ratio, 0.633; 95% CI, 0.459-0.834; P=0.0003).

Conclusion

Deep learning neural networks have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with esophageal cancer.

Construction, Validation, and Visualization of Two Web-Based Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Elderly Patients with Primary Osseous Spinal Neoplasms

Background

Primary osseous spinal neoplasms (POSNs) are the rarest tumor type in the spine. Very few studies have presented data on elderly patients with POSNs specifically. The present study was aimed at exploring the prognostic factors and developing two web-based nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for this population.

Method

The data of elderly patients with POSNs was extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. Cox regression analyses were performed to determine independent prognostic factors for OS and CSS, these prognostic factors were incorporated to establish nomograms. The discrimination of the nomograms was evaluated by the receiver operating characteristic (ROC) curve and the value of area under the curve (AUC). Calibration curve was plotted to assess the predictive accuracy of model. Decision curve analysis (DCA) was conducted to determine the net clinical benefit. Furthermore, two web-based survival rate calculators were developed.

Result

A total of 430 patients were finally selected into this study and were randomly assigned to the training set (302 cases) and validation set (128 cases). Of these, 289 patients were further considered for the analysis of CSS and were randomized into training set (205 cases) and validation set (84 cases). Based on the results of univariate and multivariate Cox analyses, variables that significantly correlated with survival outcomes were used to establish nomograms for OS and CSS prediction. Two established nomograms demonstrated good predictive performance. In the training set, the AUCs of the nomogram for predicting 12-, 24-, and 36-month OS were 0.849, 0.903, and 0.889, respectively, and those for predicting 12-, 24-, and 36-month CSS were 0.890, 0.880, and 0.881, respectively. Two web-based survival rate calculators were developed to estimate OS (https://research1.shinyapps.io/DynNomappOS/) and CSS (https://research1.shinyapps.io/DynNomappCSS/).

Conclusion

Novel nomograms based on identified clinicopathological factors were developed and can be used as a tool for clinicians to predict OS and CSS in elderly patients with POSNs. These models could help facilitate a personalized survival evaluation for this population.