Cancers · 2022
Artificial Intelligence-Based Prognostic Model for Urologic Cancers: A SEER-Based Study.
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Eminaga Okyaz, Shkolyar Eugene, Breil Bernhard, Semjonow Axel, Boegemann Martin, Xing Lei, Tinay Ilker, Liao Joseph C
Аннотация
BACKGROUND: Prognostication is essential to determine the risk profile of patients with urologic cancers.
METHODS: We utilized the SEER national cancer registry database with approximately 2 million patients diagnosed with urologic cancers (penile, testicular, prostate, bladder, ureter, and kidney). The cohort was randomly divided into the development set (90%) and the out-held test set (10%). Modeling algorithms and clinically relevant parameters were utilized for cancer-specific mortality prognosis. The model fitness for the survival estimation was assessed using the differences between the predicted and observed Kaplan-Meier estimates on the out-held test set. The overall concordance index (c-index) score estimated the discriminative accuracy of the survival model on the test set. A simulation study assessed the estimated minimum follow-up duration and time points with the risk stability.
RESULTS: We achieved a well-calibrated prognostic model with an overall c-index score of 0.800 (95% CI: 0.795-0.805) on the representative out-held test set. The simulation study revealed that the suggestions for the follow-up duration covered the minimum duration and differed by the tumor dissemination stages and affected organs. Time points with a high likelihood for risk stability were identifiable.
CONCLUSIONS: A personalized temporal survival estimation is feasible using artificial intelligence and has potential application in clinical settings, including surveillance management.
METHODS: We utilized the SEER national cancer registry database with approximately 2 million patients diagnosed with urologic cancers (penile, testicular, prostate, bladder, ureter, and kidney). The cohort was randomly divided into the development set (90%) and the out-held test set (10%). Modeling algorithms and clinically relevant parameters were utilized for cancer-specific mortality prognosis. The model fitness for the survival estimation was assessed using the differences between the predicted and observed Kaplan-Meier estimates on the out-held test set. The overall concordance index (c-index) score estimated the discriminative accuracy of the survival model on the test set. A simulation study assessed the estimated minimum follow-up duration and time points with the risk stability.
RESULTS: We achieved a well-calibrated prognostic model with an overall c-index score of 0.800 (95% CI: 0.795-0.805) on the representative out-held test set. The simulation study revealed that the suggestions for the follow-up duration covered the minimum duration and differed by the tumor dissemination stages and affected organs. Time points with a high likelihood for risk stability were identifiable.
CONCLUSIONS: A personalized temporal survival estimation is feasible using artificial intelligence and has potential application in clinical settings, including surveillance management.