Artificial intelligence-based prognostic model for urologic cancers: A seer-based study - TİNAY, İLKER (My University, 2022)
Simple Summary We describe a risk profile reconstruction model for cancer-specific survival estimation for continuous time points after urologic cancer diagnosis. We used artificial intelligence (AI)-based algorithms, a national cancer registry data, and accessible clinical parameters for the risk-profile reconstruction. We derived a risk stratification model and estimated the minimum follow-up duration and the likelihood for risk stability in prostate, kidney, and testicular cancers. The estimated follow-up duration was in alignment with recognized clinical guidelines for these cancers. Moreover, the estimated follow-up duration was differed by the cancer origin and the disease dissemination status. Overall, the reconstruction of the population\"s risk profile for the cancer-specific prognostic score estimation is feasible using AI and has potential application in clinical settings to improve risk stratification and surveillance management. 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.
Keywords: Tıp; Sağlık Bilimleri; Dahili Tıp Bilimleri; İç Hastalıkları; Onkoloji; Medicine; Health Sciences; Internal Medicine Sciences; Internal Diseases; Oncology; ONKOLOJİ; Klinik Tıp; Klinik Tıp (MED); ONCOLOGY; CLINICAL MEDICINE; Clinical Medicine (MED); surveillance management; machine learning; artificial intelligence; urologic cancers; data-driven solution; survival modeling; PROSTATE-CANCER; EAU GUIDELINES; FOLLOW-UP; RISK STRATIFICATION; SURVIVAL; EPIDEMIOLOGY; CARCINOMA; ISSUES; TUMORS; CARE