Background:Prostate cancer (PCa) spans indolent localized to lethal metastatic castration-resistant disease, underscoring the need for biologically grounded risk tools. ArteraAI multimodal artificial intelligence (MMAI), one of only two NCCN guideline–supported biomarkers for localized PCa and backed by Simon Level 1B evidence, has been validated as a prognostic marker across the spectrum of PCa. As an unsupervised AI model, MMAI remains non–human-interpretable limiting clinical trust as it is unknown what the AI is detecting. We therefore aimed to “open the black box”. For this unsupervised AI tool, “does biology matter?” If so, which pathways are important as mediators of metastatic progression that may be leveraged for novel therapeutic strategies.

Methods:MMAI scores among patients with oligometastatic Pca were computed from digitized H&E images. A self-supervised model produced a 128- image-feature vector, which was used as input with clinical features (age, PSA, T stage) for MMAI scoring. Using the same prostate tissue, DNA panel and whole transcriptome sequencing (Tempus xT + xR) was performed. Pathogenic genomic alterations, differential gene expression, and gene set enrichment were analyzed across the MMAI spectrum. For select representative cases, AI attention heatmaps localized slide regions driving the MMAI score and were co-registered with spatial transcriptomic (ST) maps (10x Genomics Visium) to align heatmap foci with spatial gene-expression.