PURPOSE: This study aimed to study the prognostic value of clinicopathological data, inflammation and nutritional indicators, and to design an effective prognostic nomogram and heat map to predict cancer-specific survival (CSS) and disease-free survival (DFS) of stage II/III GC patients who underwent curative gastrectomy with adjuvant chemotherapy. PATIENTS AND METHODS: We retrospectively analyzed the data of 611 patients with stage II/III GC after curative gastrectomy followed by adjuvant chemotherapy from 3 GC disease centers. Patients were divided into a training cohort (n = 503) and an external validation cohort (n = 108). Nomograms were established based on independent predictors identified by Cox regression analysis in the training cohort. The consistency index (C-index) and the calibration curve were used to evaluate the discriminative ability and accuracy of the nomogram. Heat maps were constructed with the prognostic factors and the corresponding survival probability. We further divided the patients into low-risk and high-risk groups based on the risk score of the nomogram. RESULTS: Through univariate and multivariate survival analysis, the independent risk factors common to CSS and DFS were identified. Then these predictors were incorporated into the nomograms, and the established nomograms used to predict CSS and DFS had high discriminative power in the training cohort. Meanwhile, the calibration curves of CSS and DFS probability also showed good agreement between the prediction based on the nomograms and the actual observation results. The above independent predictors were applied to establish heat maps. Compared with low-risk patients, the high-risk patients calculated according to the nomogram had a shorter survival time and a worse prognosis. CONCLUSION: We established a nomogram and heat map, which could be used to assess the survival rate of stage II/III GC patients who underwent curative gastrectomy with adjuvant chemotherapy. These tools had high prognostic prediction accuracy and provided inspiration for clinical decision-making.
PURPOSE: This study aimed to study the prognostic value of clinicopathological data, inflammation and nutritional indicators, and to design an effective prognostic nomogram and heat map to predict cancer-specific survival (CSS) and disease-free survival (DFS) of stage II/III GC patients who underwent curative gastrectomy with adjuvant chemotherapy. PATIENTS AND METHODS: We retrospectively analyzed the data of 611 patients with stage II/III GC after curative gastrectomy followed by adjuvant chemotherapy from 3 GC disease centers. Patients were divided into a training cohort (n = 503) and an external validation cohort (n = 108). Nomograms were established based on independent predictors identified by Cox regression analysis in the training cohort. The consistency index (C-index) and the calibration curve were used to evaluate the discriminative ability and accuracy of the nomogram. Heat maps were constructed with the prognostic factors and the corresponding survival probability. We further divided the patients into low-risk and high-risk groups based on the risk score of the nomogram. RESULTS: Through univariate and multivariate survival analysis, the independent risk factors common to CSS and DFS were identified. Then these predictors were incorporated into the nomograms, and the established nomograms used to predict CSS and DFS had high discriminative power in the training cohort. Meanwhile, the calibration curves of CSS and DFS probability also showed good agreement between the prediction based on the nomograms and the actual observation results. The above independent predictors were applied to establish heat maps. Compared with low-risk patients, the high-risk patients calculated according to the nomogram had a shorter survival time and a worse prognosis. CONCLUSION: We established a nomogram and heat map, which could be used to assess the survival rate of stage II/III GC patients who underwent curative gastrectomy with adjuvant chemotherapy. These tools had high prognostic prediction accuracy and provided inspiration for clinical decision-making.
Authors: Graeme J K Guthrie; Kellie A Charles; Campbell S D Roxburgh; Paul G Horgan; Donald C McMillan; Stephen J Clarke Journal: Crit Rev Oncol Hematol Date: 2013-04-17 Impact factor: 6.312
Authors: David Cunningham; William H Allum; Sally P Stenning; Jeremy N Thompson; Cornelis J H Van de Velde; Marianne Nicolson; J Howard Scarffe; Fiona J Lofts; Stephen J Falk; Timothy J Iveson; David B Smith; Ruth E Langley; Monica Verma; Simon Weeden; Yu Jo Chua Journal: N Engl J Med Date: 2006-07-06 Impact factor: 91.245