Development and External Validation of a Simplified Nomogram Predicting Individual Survival After R0 Resection for Gastric Cancer: An International, Multicenter Study

Zheng, Zhi; Lü, Jun; Wang, Wei; Desiderio, Jacopo; Li, Ping; Xie, Jian; Wang, Jia Bin; Lin, Jian; Parisi, Amilcare; Zhou, Zhiwei; Changming, Huang; Zheng, Chao

doi:10.1245/s10434-018-6551-1

Cited by 32 publications

(24 citation statements)

References 22 publications

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“…Adjuvant chemotherapy: according to the patient's wishes and their physical condition, fluoride-based adjuvant chemotherapy was recommended for most patients with pathological stage II and III disease [20] in our center, as previously described [21,22]. A total of 256 patients (63.8%) received adjuvant chemotherapy, with the median number of cycles being 5 (range 1-12), similar to a previous study [23].…”

Section: Definitionsmentioning

confidence: 65%

The predictive value of the preoperative C-reactive protein–albumin ratio for early recurrence and chemotherapy benefit in patients with gastric cancer after radical gastrectomy: using randomized phase III trial data

Lü

Zheng

et al. 2019

Gastric Cancer

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Background The definition and predictors of early recurrence (ER) for gastric cancer (GC) patients after radical gastrectomy are unclear. Methods A minimum-p value approach was used to evaluate the optimal cutoff value of recurrence-free survival to determine ER and late recurrence (LR). Receiver operating characteristic curves were generated for inflammatory indices. Potential risk factors for ER were assessed with a Cox regression model. A decision curve analysis was performed to evaluate the clinical utility. Results A total of 401 patients recruited in a clinical trial (NCT02327481) from January 2015 to April 2016 were included in this study. The optimal length of recurrence-free survival to distinguish between ER (n = 44) and LR (n = 52) was 12 months. Factors associated with ER included a preoperative C-reactive protein-albumin ratio (CAR) ≥ 0.131, stage III and postoperative adjuvant chemotherapy (PAC) > 3 cycles. The risk model consisting of both the CAR and TNM stage had a higher predictive ability and better clinical utility than TNM stage alone. Further stratification analysis of the stage III patients found that for the patients with a CAR < 0.131, both PAC with 1-3 cycles (p = 0.029) and > 3 cycles (p < 0.001) could reduce the risk of ER. However, for patients with a CAR ≥ 0.131, a benefit was observed only if they received PAC > 3 cycles (54.2% vs 16.0%, p = 0.004), rather than 1-3 cycles (58.3% vs 54.2%, p = 0.824). Conclusions A recurrence-free interval of 12 months was found to be the optimal threshold for differentiating between ER and LR. Preoperative CAR was a promising predictor of ER and PAC response. PAC with 1-3 cycles may not exert a protective effect against ER for stage III GC patients with CAR ≥ 0.131.

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Section: Definitionsmentioning

confidence: 65%

The predictive value of the preoperative C-reactive protein–albumin ratio for early recurrence and chemotherapy benefit in patients with gastric cancer after radical gastrectomy: using randomized phase III trial data

Lü

Zheng

et al. 2019

Gastric Cancer

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“…A nomogram is a graphical calculation or algorithm that combines several continuous variables to predict a specific end point using traditional statistical methods (for example, Logistic or Cox regression model) (Kattan, 2002). It has been constructed and verified in a variety of cancers to help clinicians quickly and accurately judge the prognosis of patients (He et al, 2018;Kim et al, 2018;Zheng et al, 2018). Many studies (Bunduc et al, 2018;Kang et al, 2017;He et al, 2013;Ntala et al, 2018;Ansari et al, 2019;Beeghly-Fadiel et al, 2016;Eguchi et al, 2016) have confirmed that the clinical characteristics and prognosis of young patients with PC are different from older patients, but the nomogram of PC in previous studies (He et al, 2018;Song, Miao & Chen, 2018;Li & Liu, 2019) cannot accurately predict the prognosis of young patients with PC.…”

Section: Introductionmentioning

confidence: 99%

Nomograms for predicting overall survival and cancer-specific survival in young patients with pancreatic cancer in the US based on the SEER database

Shi

Zhou

Yang

2020

PeerJ

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Background The incidence of young patients with pancreatic cancer (PC) is on the rise, and there is a lack of models that could effectively predict their prognosis. The purpose of this study was to construct nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of young patients with PC. Methods PC patients younger than 50 years old from 2004 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were selected and randomly divided into training set and validation set. Univariable and forward stepwise multivariable Cox analysis was used to determine the independent factors affecting OS. The Fine and Gray competing risk regression model was used to determine the independent factors affecting CSS. We used significant variables in the training set to construct nomograms predicting prognosis. The discrimination and calibration power of models were evaluated by concordance index (C-index), calibration curve and 10-flod cross-validation. Results A total of 4,146 patients were selected. Multivariable Cox analysis showed that gender, race, grade, pathological types, AJCC stage and surgery were independent factors affecting OS. The C-index of the nomogram predicting OS in training and validation was 0.733 (average = 0.731, 95% CI [0.724–0.738]) and 0.742 (95% CI [0.725–0.759]), respectively. Competing risk analysis showed that primary site, pathological types, AJCC stage and surgery were independent factors affecting CSS. The C-index of the nomogram predicting CSS in training and validation set was 0.792 (average = 0.765, 95% CI [0.742–0.788]) and 0.776 (95% CI [0.773–0.779]), respectively. C-index based on nomogram was better in training and validation set than that based on AJCC stage. Calibration curves showed that these nomograms could accurately predict the 1-, 3- and 5-year OS and CSS both in training set and validation set. Conclusions The nomograms could effectively predict OS and CSS in young patients with PC, which help clinicians more accurately and quantitatively judge the prognosis of individual patients.

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“…Both models of Kattan et al10 and Kim et al 38 were developed from Western centers; in the SEER cohort, the AUC of these two models were not significantly different with that of our ANN model (Table 2). However, our ANN model was superior to the models developed from Eastern centers including the model by Zheng et al 39 from China and the models by Han et al, 40 Song et al, 17 and Woo et al 9 from Korea. In the CIAH cohort, the Western models did not perform as well as our ANN model.…”

Section: Resultsmentioning

confidence: 56%

“…After reviewing previous published articles, five nomograms and one scoring model were identified as available for all the three cohorts 9,10,17,38‐40 . Referring to the involved variables for these six models, comparisons among the six models and our ANN model were performed in 3613 patients of the SEER cohort, 2676 patients of the CIAH cohort, and all patients of the PUCHI cohort.…”

Section: Resultsmentioning

confidence: 99%

Development and validation of an artificial neural network prognostic model after gastrectomy for gastric carcinoma: An international multicenter cohort study

Gao

et al. 2020

Cancer Medicine

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Background Recently, artificial neural network (ANN) methods have also been adopted to deal with the complex multidimensional nonlinear relationship between clinicopathologic variables and survival for patients with gastric cancer. Using a multinational cohort, this study aimed to develop and validate an ANN‐based survival prediction model for patients with gastric cancer. Methods Patients with gastric cancer who underwent gastrectomy in a Chinese center, a Japanese center, and recorded in the Surveillance, Epidemiology, and End Results database, respectively, were included in this study. Multilayer perceptron neural network was used to develop the prediction model. Time‐dependent receiver operating characteristic (ROC) curves, area under the curves (AUCs), and decision curve analysis (DCA) were used to compare the ANN model with previous prediction models. Results An ANN model with nine input nodes, nine hidden nodes, and two output nodes was constructed. These three cohort's data showed that the AUC of the model was 0.795, 0.836, and 0.850 for 5‐year survival prediction, respectively. In the calibration curve analysis, the ANN‐predicted survival had a high consistency with the actual survival. Comparison of the DCA and time‐dependent ROC between the ANN model and previous prediction models showed that the ANN model had good and stable prediction capability compared to the previous models in all cohorts. Conclusions The ANN model has significantly better discriminative capability and allows an individualized survival prediction. This model has good versatility in Eastern and Western data and has high clinical application value.

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Development and External Validation of a Simplified Nomogram Predicting Individual Survival After R0 Resection for Gastric Cancer: An International, Multicenter Study

Abstract: The novel simplified nomogram simplifies the assessment of individual survival after R0 resection for GC without sacrificing predictive ability. It also has potential for use with other databases and for clinical applications.

Cited by 32 publications

References 22 publications

The predictive value of the preoperative C-reactive protein–albumin ratio for early recurrence and chemotherapy benefit in patients with gastric cancer after radical gastrectomy: using randomized phase III trial data

The predictive value of the preoperative C-reactive protein–albumin ratio for early recurrence and chemotherapy benefit in patients with gastric cancer after radical gastrectomy: using randomized phase III trial data

Nomograms for predicting overall survival and cancer-specific survival in young patients with pancreatic cancer in the US based on the SEER database

Development and validation of an artificial neural network prognostic model after gastrectomy for gastric carcinoma: An international multicenter cohort study

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