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

Lemini, Riccardo; Vico, Tamara Díaz; Trumbull, Denslow; Attwood, Kristopher; Spaulding, Aaron; Elli, Enrique F.; Colibaseanu, Dorin T.; Kukar, Moshim; Gabriel, Emmanuel

doi:10.21037/jgo-20-337

Cited by 4 publications

(4 citation statements)

References 37 publications

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“…Recurrence was often encountered after radical resection of ESCC and greatly affected the prognosis of patients ( 23 ). Unlike the effect of predicting OS and PFS ( 7 , 8 ), we found that TNM staging of primary tumor was not significantly associated with PPS in univariate analysis, suggesting that TNM staging was not effective in predicting PPS in ESCC patients. Similar to our conclusions, many studies on PPS of other cancer types suggested that the TNM staging system was less useful to predict the PPS prognosis ( 24 , 25 ).…”

Section: Discussioncontrasting

confidence: 87%

“…Currently, several models have been showed to predict postoperative OS and progression-free survival (PFS) for EC by using primary tumor characteristics ( 7 , 8 ). Whereas, compared to OS and PFS, the survival after relapse of EC patients may be influenced by characteristics of the primary tumor as well as the recurrence-related features, such as recurrence patterns, metastatic sites, and recurrence interval ( 9 , 10 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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

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Section: Discussioncontrasting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…Lemini R et al. ( 21 ) further externally validated several currently existing prognostic prediction models for the patients with esophageal adenocarcinoma. Unfortunately, none of these models showed satisfactory prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Nomograms for Predicting Disease-Free Survival in Patients With Siewert Type II/III Adenocarcinoma of the Esophagogastric Junction Receiving Neoadjuvant Therapy and Radical Surgery

Guo

Tian

et al. 2022

Front. Oncol.

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ObjectiveThis study aimed to develop prognostic prediction models for patients with Siewert type II/III adenocarcinoma of the esophagogastric junction (AEG) who received neoadjuvant therapy (neoadjuvant chemoradiotherapy or neoadjuvant chemotherapy) and radical surgery. A baseline nomogram and a post-operative nomogram were constructed before neoadjuvant therapy and after surgery. The predictive performance of the constructed nomograms was internally validated and compared to the TNM staging system.Materials and MethodsA total of 245 patients diagnosed with Siewert type II/III AEG and treated with neoadjuvant therapy followed by radical surgery at The Fourth Hospital of Hebei Medical University between January 2011 and December 2017 were enrolled. The variables before neoadjuvant therapy were defined as baseline factors, while the variables of baseline factors along with the variables of treatment and postoperative pathology were defined as post-operative factors. To construct the corresponding nomograms, independent predictors of baseline and post-operative factors were identified. The C-index and a time-dependent receiver operating characteristic curve were used to evaluate the model’s discrimination ability. The calibration ability of the model was determined by comparing the probability of predicted free-recurrence to the actual free-recurrence. Decision curve analysis (DCA) was used to determine the clinical usefulness of the nomogram.ResultsAmong the baseline factors, age, cT stage, cN stage, Borrmann type, and staging laparoscopy were independent prognostic predictors. In contrast, among the post-operative factors, age, cN stage, staging laparoscopy, ypT stage, clinical response, number of positive lymph nodes, number of negative lymph nodes, laurén classification, and lymphatic, or perineural invasion (VELPI) were independent prognostic predictors. The two nomograms were constructed using the independent predictors of prognosis. The C-indexes for the baseline and post-operative nomograms were 0.690 (95% CI, 0.644-0.736) and 0.817 (95% CI, 0.782-0.853), respectively. The AUCs of the baseline nomogram at 3 and 5 years were both greater than cTNM (73.1 vs 58.8, 76.1 vs 55.7). Similarly, the AUCs of the post-operative nomogram were both greater than ypTNM (85.2 vs 69.1, 88.2 vs 71.3) at 3 and 5 years. The calibration curves indicated that both models had a high degree of calibration ability. By comparing the DCA at 3 and 5 years, we determined that the two nomograms constructed had better clinical utility than the TNM staging system.ConclusionsThe constructed nomograms have a more accurate predictive ability than the eighth edition TNM staging system, which can be useful for treatment selection and follow-up monitoring of patients.

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“…In our study, the discrimination of the CoxPH model was slightly improved (0.755), which may be related to the fact that we included more cases. The algorithm proposed by Shapiro ( 33 ) progressed under predicting 1-year survival, with an AUC of 0.63 in the internal validation dataset ( 34 ). Although our DeepSurv model slightly outperformed the Shapiro algorithm in predicting 1-year survival (AUC of DeepSurv: 0.828), what makes our study more significant is that using the deep feed-forward neural network algorithm.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of survival prediction model for gastric adenocarcinoma patients using deep learning: A SEER-based study

Zeng

Cao

et al. 2023

Front. Oncol.

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BackgroundThe currently available prediction models, such as the Cox model, were too simplistic to correctly predict the outcome of gastric adenocarcinoma patients. This study aimed to develop and validate survival prediction models for gastric adenocarcinoma patients using the deep learning survival neural network.MethodsA total of 14,177 patients with gastric adenocarcinoma from the Surveillance, Epidemiology, and End Results (SEER) database were included in the study and randomly divided into the training and testing group with a 7:3 ratio. Two algorithms were chosen to build the prediction models, and both algorithms include random survival forest (RSF) and a deep learning based-survival prediction algorithm (DeepSurv). Also, a traditional Cox proportional hazard (CoxPH) model was constructed for comparison. The consistency index (C-index), Brier score, and integrated Brier score (IBS) were used to evaluate the model’s predictive performance. The accuracy of predicting survival at 1, 3, 5, and 10 years was also assessed using receiver operating characteristic curves (ROC), calibration curves, and area under the ROC curve (AUC).ResultsGastric adenocarcinoma patients were randomized into a training group (n = 9923) and a testing group (n = 4254). DeepSurv showed the best performance among the three models (c-index: 0.772, IBS: 0.1421), which was superior to that of the traditional CoxPH model (c-index: 0.755, IBS: 0.1506) and the RSF with 3-year survival prediction model (c-index: 0.766, IBS: 0.1502). The DeepSurv model produced superior accuracy and calibrated survival estimates predicting 1-, 3- 5- and 10-year survival (AUC: 0.825-0.871).ConclusionsA deep learning algorithm was developed to predict more accurate prognostic information for gastric cancer patients. The DeepSurv model has advantages over the CoxPH and RSF models and performs well in discriminative performance and calibration.

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

Cited by 4 publications

References 37 publications

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

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

Nomograms for Predicting Disease-Free Survival in Patients With Siewert Type II/III Adenocarcinoma of the Esophagogastric Junction Receiving Neoadjuvant Therapy and Radical Surgery

Development and validation of survival prediction model for gastric adenocarcinoma patients using deep learning: A SEER-based study

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