Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 2 de 2
Filter
Add more filters










Database
Language
Publication year range
1.
Cancer Sci ; 111(2): 502-512, 2020 Feb.
Article in English | MEDLINE | ID: mdl-31710406

ABSTRACT

The present study was designed to evaluate the dynamic survival and recurrence of remnant gastric cancer (RGC) after radical resection and to provide a reference for the development of personalized follow-up strategies. A total of 298 patients were analyzed for their 3-year conditional overall survival (COS3), 3-year conditional disease-specific survival (CDSS3), corresponding recurrence and pattern changes, and associated risk factors. The 5-year overall survival (OS) and the 5-year disease-specific survival (DSS) of the entire cohort were 41.2% and 45.8%, respectively. The COS3 and CDDS3 of RGC patients who survived for 5 years were 84.0% and 89.8%, respectively. The conditional survival in patients with unfavorable prognostic characteristics showed greater growth over time than in those with favorable prognostic characteristics (eg, COS3, ≥T3: 46.4%-83.0%, Δ36.6% vs ≤T2: 82.4%-85.7%, Δ3.3%; P < 0.001). Most recurrences (93.5%) occurred in the first 3 years after surgery. The American Joint Committee on Cancer (AJCC) stage was the only factor that affected recurrence. Time-dependent Cox regression showed that for both OS and DSS, after 4 years of survival, the common prognostic factors that were initially judged lost their ability to predict survival (P > 0.05). Time-dependent logistic regression analysis showed that the AJCC stage independently affected recurrence within 2 years after surgery (P < 0.05). A postoperative follow-up model was developed for RGC patients. In conclusion, patients with RGC usually have a high likelihood of death or recurrence within 3 years after radical surgery. We developed a postoperative follow-up model for RGC patients of different stages, which may affect the design of future clinical trials.


Subject(s)
Gastric Stump/pathology , Neoplasm Recurrence, Local/mortality , Stomach Neoplasms/mortality , Stomach Neoplasms/surgery , Adult , Aged , Aged, 80 and over , Cohort Studies , Female , Humans , Male , Middle Aged , Neoplasm Recurrence, Local/pathology , Neoplasm Staging , Prognosis , Retrospective Studies , Stomach Neoplasms/pathology , Survival Analysis
2.
J Oncol ; 2019: 6012826, 2019.
Article in English | MEDLINE | ID: mdl-31093283

ABSTRACT

BACKGROUND: Remnant gastric cancer (RGC) is a rare malignant tumor with poor prognosis. There is no universally accepted prognostic model for RGC. METHODS: We analyzed data for 253 RGC patients who underwent radical gastrectomy from 6 centers. The prognosis prediction performances of the AJCC7th and AJCC8th TNM staging systems and the TRM staging system for RGC patients were evaluated. Web-based prediction models based on independent prognostic factors were developed to predict the survival of the RGC patients. External validation was performed using a cohort of 49 Chinese patients. RESULTS: The predictive abilities of the AJCC8th and TRM staging systems were no better than those of the AJCC7th staging system (c-index: AJCC7th vs. AJCC8th vs. TRM, 0.743 vs. 0.732 vs. 0.744; P>0.05). Within each staging system, the survival of the two adjacent stages was not well discriminated (P>0.05). Multivariate analysis showed that age, tumor size, T stage, and N stage were independent prognostic factors. Based on the above variables, we developed 3 web-based prediction models, which were superior to the AJCC7th staging system in their discriminatory ability (c-index), predictive homogeneity (likelihood ratio chi-square), predictive accuracy (AIC, BIC), and model stability (time-dependent ROC curves). External validation showed predictable accuracies of 0.780, 0.822, and 0.700, respectively, in predicting overall survival, disease-specific survival, and disease-free survival. CONCLUSIONS: The AJCC TNM staging system and the TRM staging system did not enable good distinction among the RGC patients. We have developed and validated visual web-based prediction models that are superior to these staging systems.

SELECTION OF CITATIONS
SEARCH DETAIL
...