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Multicenter Study
. 2021 Nov 11;108(11):1341-1350.
doi: 10.1093/bjs/znab237.

Prediction of long-term survival after gastrectomy using random survival forests

S A Rahman  1   2 , N Maynard  3 , N Trudgill  4 , T Crosby  5 , M Park  2 , H Wahedally  2 , T J Underwood  1 , D A Cromwell  2 ; NOGCA Project Team and AUGIS
Affiliations
Multicenter Study

Prediction of long-term survival after gastrectomy using random survival forests

S A Rahman et al. Br J Surg. .

Abstract

Background: No well validated and contemporaneous tools for personalized prognostication of gastric adenocarcinoma exist. This study aimed to derive and validate a prognostic model for overall survival after surgery for gastric adenocarcinoma using a large national dataset.

Methods: National audit data from England and Wales were used to identify patients who underwent a potentially curative gastrectomy for adenocarcinoma of the stomach. A total of 2931 patients were included and 29 clinical and pathological variables were considered for their impact on survival. A non-linear random survival forest methodology was then trained and validated internally using bootstrapping with calibration and discrimination (time-dependent area under the receiver operator curve (tAUC)) assessed.

Results: The median survival of the cohort was 69 months, with a 5-year survival of 53.2 per cent. Ten variables were found to influence survival significantly and were included in the final model, with the most important being lymph node positivity, pT stage and achieving an R0 resection. Patient characteristics including ASA grade and age were also influential. On validation the model achieved excellent performance with a 5-year tAUC of 0.80 (95 per cent c.i. 0.78 to 0.82) and good agreement between observed and predicted survival probabilities. A wide spread of predictions for 3-year (14.8-98.3 (i.q.r. 43.2-84.4) per cent) and 5-year (9.4-96.1 (i.q.r. 31.7-73.8) per cent) survival were seen.

Conclusions: A prognostic model for survival after a potentially curative resection for gastric adenocarcinoma was derived and exhibited excellent discrimination and calibration of predictions.

Plain language summary

In this study the authors used a large nationwide dataset from England and Wales and tried to make a predictive model that estimated how long patients would survive after surgery for gastric cancer. They found that using a machine learning methodology provided excellent results and accuracy in predictions, significantly in excess of any other published model and traditional staging methods. The model will be useful to provide individualized prediction of survival to patients and in the future could be used to stratify treatments.

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Figures

Fig. 1
Fig. 1
Kaplan–Meier survival of study cohort . The vertical dotted line represents the median survival, 69 months
Fig. 2
Fig. 2
Calibration of predictions a 3 years and b 5 years after surgery. The dotted line represents the ideal and the solid line the model’s performance on internal validation
Fig. 3
Fig. 3
Variable effects on survival Average predicted survival is shown from 0–60 months for a pN stage, b pT stage and c resection margin
Fig. 4
Fig. 4
Predicted 5-year survival for combinations of selected variables Colours represent differing prognosis, with green more favourable, and red less favourable
Fig. 5
Fig. 5
Predicted survival a Stage 3a and b stage 1a example cases and mean survival
See this image and copyright information in PMC

References

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