TY  - JOUR
T1  - Integrating survival analysis and artificial intelligence for personalized risk prediction in precision medicine
A1  - Fredy Heric Villasante-Saravia
A1  - Jose Oscar Huanca-Frias
A1  - Rene Eduardo Huanca-Frias
A1  - Vitaliano Enriquez-Mamani
A1  - Ledú Anali Ferreyros-Calisaya
A1  - Juan Reynaldo Paredes-Quispe
A1  - Alfredo Tumi-Figueroa
JF  - Journal of Advanced Pharmacy Education and Research
JO  - J Adv Pharm Educ Res
SN  - 2249-3379
Y1  - 2026
VL  - 16
IS  - 3
DO  - 10.51847/fWL7k3BFXF
SP  - 142
EP  - 150
N2  - Accurate estimation of disease-specific survival is central to precision oncology because treatment decisions increasingly depend on individualized risk rather than broad population averages. This study developed and compared traditional and artificial intelligence-based survival models for predicting 5-year disease-specific survival among patients with non-small cell lung cancer. The analysis integrated clinical, genomic, and treatment-related variables to evaluate whether modern survival learning methods improve individualized prognostic estimation. The study cohort included 369 patients diagnosed with non-small cell lung cancer and followed for disease-specific survival over a 5-year horizon. Candidate predictors included tumor stage, age, Eastern Cooperative Oncology Group performance status, histological subtype, smoking history, EGFR mutation status, KRAS mutation status, tumor mutational burden, and treatment modality. Three survival models were estimated and compared: Cox proportional hazards regression, random survival forest, and DeepSurv. Model performance was assessed using time-dependent C-index, Brier score, and calibration across clinically relevant time horizons. DeepSurv achieved the highest discrimination, with a 5-year time-dependent C-index of 0.78, outperforming Cox regression and random survival forest. Calibration analysis indicated that DeepSurv produced predicted survival probabilities that were closely aligned with observed disease-specific survival across most risk strata. Explainability analysis was conducted using SHAP values adapted for survival outcomes to identify the most influential predictors in the best-performing model. Tumor mutational burden, performance status, and tumor stage emerged as the strongest predictors of 5-year disease-specific survival. Personalized risk curves demonstrated that patients with similar tumor stages could have substantially different predicted survival trajectories when genomic and functional status variables were jointly considered. The findings suggest that deep survival models can add measurable prognostic value in precision oncology when combined with interpretable model explanations. Although the retrospective design, relatively small sample size, and lack of external validation limit immediate clinical translation, the study provides empirical evidence that AI-based survival prediction can support individualized prognosis in non-small cell lung cancer. Future work should prioritize prospective validation, multi-center transportability assessment, and integration into decision-support workflows‎.
UR  - https://japer.in/article/integrating-survival-analysis-and-artificial-intelligence-for-personalized-risk-prediction-in-precis-o2m8azbgty9tomh
ER  -