The development of a prediction model based on deep learning for prognosis prediction of gastrointestinal stromal tumor: a SEER-based study

Abstract

Accurately predicting the prognosis of Gastrointestinal stromal tumor (GIST) patients is an important task. The goal of this study was to create and assess models for GIST patients' survival patients using the Surveillance, Epidemiology, and End Results Program (SEER) database based on the three different deep learning models. Four thousand five hundred thirty-eight patients were enrolled in this study and divided into training and test cohorts with a 7:3 ratio; the training cohort was used to develop three different models, including Cox regression, RSF, and DeepSurv model. Test cohort was used to evaluate model performance using c-index, Brier scores, calibration, and the area under the curve (AUC). The net benefits at risk score stratification of GIST patients based on the optimal model was compared with the traditional AJCC staging system using decision curve analysis (DCA). The clinical usefulness of risk score stratification compared to AJCC tumor staging was further assessed using the Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI). The DeepSurv model predicted cancer-specific survival (CSS) in GIST patients showed a higher c-index (0.825), lower Brier scores (0.142), and greater AUC of receiver operating characteristic (ROC) analysis (1-year ROC:0.898; 3-year:0.853, and 5-year ROC: 0.856). The calibration plots demonstrated good agreement between the DeepSurv model's forecast and actual results. The NRI values ( training cohort: 0.425 for 1-year, 0.329 for 3-year and 0.264 for 5-year CSS prediction; test cohort:0.552 for 1-year,0.309 for 3-year and 0.255 for 5-year CSS prediction) and IDI (training cohort: 0.130 for 1-year,0.141 for 5-year and 0.155 for 10-year CSS prediction; test cohort: 0.154 for 1-year,0.159 for 3-year and 0.159 for 5-year CSS prediction) indicated that the risk score stratification performed significantly better than the AJCC staging alone (P < 0.001). DCA demonstrated the risk score stratification as more clinically beneficial and discriminatory than AJCC staging. Finally, an interactive native web-based prediction tool was constructed for the survival prediction of GIST patients. This study established a high-performance prediction model for projecting GIST patients based on deep learning, which has advantages in predicting each person's prognosis and risk stratification.

Keywords: And end results program (SEER); Deep learning; DeepSurv; Epidemiology; Gastrointestinal stromal tumor; Machine learning; The surveillance.

Figure 1

The flowchart of data filtering.

Figure 2

Diagram of the deep learning procedure.

Figure 3

Prediction error curve. A useful model will have a Brier score less than 0.25 as a standard.

Figure 4

The receiver operating curves (ROC) and calibration curves for 1-, 3-, 5-year survival predictions. ROC curves for (A) 1-, (C) 3-, (E) 5-survival predictions. Calibration curves for (B) 1-, (D) 3-, (F) 5-year survival predictions. In (B), (D), and (F), each set of images is arranged in the order of Cox model, RSF model, and DeepSurv model.

Figure 5

Kaplan–Meier curves of cancer-specific survival for new risk classification and the AJCC tumor staging (A) The AJCC stage in the test cohort; (B) The deepsurv risk stratification in the test cohort.

Figure 6

Decision curve analysis of the DeepSurv risk stratification and AJCC tumor staging for the survival prediction of GIST patients. (A,C,E) 1‐year, 3‐year and 5‐year survival benefit in the train cohort. (B,D,F) 1‐year, 3‐year and 5‐year survival benefit in the test cohort.

Figure 7

Feature importance for DeepSurv model, only the top 10 variables in importance are shown in the Figure.

Figure 8

The manual interactive interface based on Deepsurv model for predicting the survival probabilities of GIST patients.