Objective:Accurate prognosis prediction is critical for individualized-therapy making of gastric cancer patients.We aimed to develop and test 6-month,1-,2-,3-,5-,and 10-year overall survival(OS)and cancer-specific sur...Objective:Accurate prognosis prediction is critical for individualized-therapy making of gastric cancer patients.We aimed to develop and test 6-month,1-,2-,3-,5-,and 10-year overall survival(OS)and cancer-specific survival(CSS)prediction models for gastric cancer patients following gastrectomy.Methods:We derived and tested Survival Quilts,a machine learning-based model,to develop 6-month,1-,2-,3-,5-,and 10-year OS and CSS prediction models.Gastrectomy patients in the development set(n=20,583)and the internal validation set(n=5,106)were recruited from the Surveillance,Epidemiology,and End Re-sults(SEER)database,while those in the external validation set(n=6,352)were recruited from the China National Cancer Center Gastric Cancer(NCCGC)database.Furthermore,we selected gastrectomy patients with-out neoadjuvant therapy as a subgroup to train and test the prognostic models in order to keep the accuracy of tumor-node-metastasis(TNM)stage.Prognostic performances of these OS and CSS models were assessed using the Concordance Index(C-index)and area under the curve(AUC)values.Results:The machine learning model had a consistently high accuracy in predicting 6-month,1-,2-,3-,5-,and 10-year OS in the SEER development set(C-index=0.861,0.832,0.789,0.766,0.740,and 0.709;AUC=0.784,0.828,0.840,0.849,0.869,and 0.902,respectively),SEER validation set(C-index=0.782,0.739,0.712,0.698,0.681,and 0.660;AUC=0.751,0.772,0.767,0.762,0.766,and 0.787,respectively),and NCCGC set(C-index=0.691,0.756,0.751,0.737,0.722,and 0.701;AUC=0.769,0.788,0.790,0.790,0.787,and 0.788,respectively).The model was able to predict 6-month,1-,2-,3-,5-,and 10-year CSS in the SEER development set(C-index=0.879,0.858,0.820,0.802,0.784,and 0.774;AUC=0.756,0.827,0.852,0.863,0.874,and 0.884,respectively)and SEER validation set(C-index=0.790,0.763,0.741,0.729,0.718,and 0.708;AUC=0.706,0.758,0.767,0.766,0.766,and 0.764,respectively).In multivariate analysis,the high-risk group with risk score output by 5-year OS model was proved to be a strong survival predictor both in the SEER development set(hazard ratio[HR]=14.59,95%confidence interval[CI]:1.872-2.774,P<0.001),SEER validation set(HR=2.28,95%CI:13.089-16.293,P<0.001),and NCCGC set(HR=1.98,95%CI:1.617-2.437,P<0.001).We further explored the prognostic value of risk score resulted 5-year CSS model of gastrectomy patients,and found that high-risk group remained as an independent CSS factor in the SEER development set(HR=12.81,95%CI:11.568-14.194,P<0.001)and SEER validation set(HR=1.61,95%CI:1.338-1.935,P<0.001).Conclusion:Survival Quilts could allow accurate prediction of 6-month,1-,2-,3-,5-,and 10-year OS and CSS in gastric cancer patients following gastrectomy.展开更多
基金supported by grant from the National Key R&D Program of China(grant number:2017YFC0908300)the Fun-damental Research Funds for the Central Universities(grant number:3332023136).
文摘Objective:Accurate prognosis prediction is critical for individualized-therapy making of gastric cancer patients.We aimed to develop and test 6-month,1-,2-,3-,5-,and 10-year overall survival(OS)and cancer-specific survival(CSS)prediction models for gastric cancer patients following gastrectomy.Methods:We derived and tested Survival Quilts,a machine learning-based model,to develop 6-month,1-,2-,3-,5-,and 10-year OS and CSS prediction models.Gastrectomy patients in the development set(n=20,583)and the internal validation set(n=5,106)were recruited from the Surveillance,Epidemiology,and End Re-sults(SEER)database,while those in the external validation set(n=6,352)were recruited from the China National Cancer Center Gastric Cancer(NCCGC)database.Furthermore,we selected gastrectomy patients with-out neoadjuvant therapy as a subgroup to train and test the prognostic models in order to keep the accuracy of tumor-node-metastasis(TNM)stage.Prognostic performances of these OS and CSS models were assessed using the Concordance Index(C-index)and area under the curve(AUC)values.Results:The machine learning model had a consistently high accuracy in predicting 6-month,1-,2-,3-,5-,and 10-year OS in the SEER development set(C-index=0.861,0.832,0.789,0.766,0.740,and 0.709;AUC=0.784,0.828,0.840,0.849,0.869,and 0.902,respectively),SEER validation set(C-index=0.782,0.739,0.712,0.698,0.681,and 0.660;AUC=0.751,0.772,0.767,0.762,0.766,and 0.787,respectively),and NCCGC set(C-index=0.691,0.756,0.751,0.737,0.722,and 0.701;AUC=0.769,0.788,0.790,0.790,0.787,and 0.788,respectively).The model was able to predict 6-month,1-,2-,3-,5-,and 10-year CSS in the SEER development set(C-index=0.879,0.858,0.820,0.802,0.784,and 0.774;AUC=0.756,0.827,0.852,0.863,0.874,and 0.884,respectively)and SEER validation set(C-index=0.790,0.763,0.741,0.729,0.718,and 0.708;AUC=0.706,0.758,0.767,0.766,0.766,and 0.764,respectively).In multivariate analysis,the high-risk group with risk score output by 5-year OS model was proved to be a strong survival predictor both in the SEER development set(hazard ratio[HR]=14.59,95%confidence interval[CI]:1.872-2.774,P<0.001),SEER validation set(HR=2.28,95%CI:13.089-16.293,P<0.001),and NCCGC set(HR=1.98,95%CI:1.617-2.437,P<0.001).We further explored the prognostic value of risk score resulted 5-year CSS model of gastrectomy patients,and found that high-risk group remained as an independent CSS factor in the SEER development set(HR=12.81,95%CI:11.568-14.194,P<0.001)and SEER validation set(HR=1.61,95%CI:1.338-1.935,P<0.001).Conclusion:Survival Quilts could allow accurate prediction of 6-month,1-,2-,3-,5-,and 10-year OS and CSS in gastric cancer patients following gastrectomy.
