Literature DB >> 28489249

Validation of two prognostic models for recurrence and survival after radical gastrectomy for gastric cancer.

M Bencivenga1, G Verlato2, D-S Han3, D Marrelli4, F Roviello4, H-K Yang5, G de Manzoni1.   

Abstract

BACKGROUND: Prognostic models from Korea and Italy have been developed that predict overall survival and cancer recurrence respectively after radical gastrectomy for gastric cancer. The aim of this study was to validate the two models in independent patient cohorts, and to evaluate which factors may explain differences in prognosis between Korean and Italian patients with gastric cancer.
METHODS: Patients who underwent radical gastrectomy for gastric cancer between January 2000 and December 2004 at Seoul National University Hospital and at eight centres in Italy were included. Discrimination of the models was tested with receiver operating characteristic (ROC) curves and calculation of area under the curve (AUC). Calibration was evaluated by plotting actual survival probability against predicted survival probability for the Korean nomogram, and actual against predicted risk of recurrence for the Italian score.
RESULTS: Some 2867 and 940 patients from Korea and Italy respectively were included. The Korean nomogram achieved good discrimination in the Italian cohort (AUC 0·80, 95 per cent c.i. 0·77 to 0·83), and the Italian model performed well in the Korean cohort (AUC 0·87, 0·85 to 0·89). The Korean nomogram also achieved good calibration, but this was not seen for the Italian model. Multivariable analyses confirmed that Italian ethnicity was an independent risk factor for cancer recurrence (odds ratio (OR) 1·72, 1·31 to 2·25; P < 0·001), but not for overall survival (OR 1·20, 0·95 to 1·53; P = 0·130).
CONCLUSION: Both prognostic models performed fairly well in independent patient cohorts. Differences in recurrence rates of gastric cancer may be partially explained by ethnicity.
© 2017 BJS Society Ltd Published by John Wiley & Sons Ltd.

Entities:  

Mesh:

Year:  2017        PMID: 28489249     DOI: 10.1002/bjs.10551

Source DB:  PubMed          Journal:  Br J Surg        ISSN: 0007-1323            Impact factor:   6.939


  7 in total

1.  Association Between Compliance to an Enhanced Recovery Protocol and Outcome After Elective Surgery for Gastric Cancer. Results from a Western Population-Based Prospective Multicenter Study.

Authors:  Luca Gianotti; Uberto Fumagalli Romario; Stefano De Pascale; Jacopo Weindelmayer; Valentina Mengardo; Marta Sandini; Andrea Cossu; Paolo Parise; Riccardo Rosati; Lapo Bencini; Andrea Coratti; Giovanni Colombo; Federica Galli; Stefano Rausei; Francesco Casella; Andrea Sansonetti; Dario Maggioni; Andrea Costanzi; Davide P Bernasconi; Giovanni De Manzoni
Journal:  World J Surg       Date:  2019-10       Impact factor: 3.352

2.  External Validation of a Nomogram Developed for Predicting Overall Survival in Gastric Cancer Patients with Insufficient Number of Examined Lymph Nodes.

Authors:  Mehmet Kubat; Mustafa Omer Yazicioglu; Bahadir Osman Bozkirli; Riza Haldun Gundogdu
Journal:  Sisli Etfal Hastan Tip Bul       Date:  2022-03-28

3.  Investigation of analgesic dose of nalbuphine combined with remifentanil after radical gastrectomy.

Authors:  Yang Zhang; Rongfang Zhang; Nannan Ding
Journal:  Exp Ther Med       Date:  2019-06-28       Impact factor: 2.447

4.  Development and validation of a pretreatment nomogram to predict overall survival in gastric cancer.

Authors:  Etsuro Bando; Xinge Ji; Michael W Kattan; Ho Seok Seo; Kyo Young Song; Cho-Hyun Park; Maria Bencivenga; Giovanni de Manzoni; Masanori Terashima
Journal:  Cancer Med       Date:  2020-06-26       Impact factor: 4.452

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

Authors:  Ziyu Li; Xiaolong Wu; Xiangyu Gao; Fei Shan; Xiangji Ying; Yan Zhang; Jiafu Ji
Journal:  Cancer Med       Date:  2020-07-15       Impact factor: 4.452

6.  Utility of Abdominal Drain in Gastrectomy (ADiGe) Trial: study protocol for a multicenter non-inferiority randomized trial.

Authors:  J Weindelmayer; V Mengardo; A Veltri; G L Baiocchi; S Giacopuzzi; G Verlato; G de Manzoni
Journal:  Trials       Date:  2021-02-17       Impact factor: 2.279

Review 7.  Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment.

Authors:  Hema Sekhar Reddy Rajula; Giuseppe Verlato; Mirko Manchia; Nadia Antonucci; Vassilios Fanos
Journal:  Medicina (Kaunas)       Date:  2020-09-08       Impact factor: 2.430

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.