An artificial intelligence model integrating clinico-laboratory data and single nucleotide polymorphism-based genomic...

Prostate cancer (PCa) is traditionally diagnosed using prostate-specific antigen (PSA)-based testing together with demographic and clinical factors. Building on this framework, we aimed to develop an AI (artificial intelligence) model for prebiopsy PCa diagnosis by integrating Korean population-relevant risk-associated single nucleotide polymorphisms (SNPs) to improve diagnostic accuracy.

Three models were developed in this study: Korean PCa-specific genomic score (GenPCa-Kor score), electronic medical record (EMR) meta-model, and Geno-EMR meta-model. From genome-wide association study summary statistics, 1,347 PCa-associated SNPs were selected for a deep neural network to derive the GenPCa-Kor score. Thirteen clinico-laboratory EMR parameters were used to build a stacking ensemble (EMR meta-model) with Light Gradient Boosting Machine, and Histogram-based Gradient Boosting Machine, and logistic regression as base learners and logistic regression as the meta-learner, using 10-fold cross-validation and Bayesian hyperparameter optimization. The Geno-EMR meta-model added the GenPCa-Kor score as a 14th feature to the same architecture.

Of 1,590 systematic biopsy-confirmed participants, 1,006 were analyzed; 757 comprised the training cohort and 249 consecutive patients comprised the independent test cohort. In the training cohort, the EMR meta-model and Geno-EMR meta-model achieved area under curves (AUCs) of 0.868 and 0.924, respectively. In the test cohort, their AUCs were 0.859 and 0.892, respectively. For clinically significant PCa (Grade Group ≥2), the Geno-EMR meta-model further improved the AUC from 0.887 to 0.911.

The Geno-EMR meta-model that integrate routine clinico-laboratory parameters with the SNP-based GenPCa-Kor score showed improved discrimination for PCa compared with the EMR meta-model alone.