Kan AI het individuele risico op kankerrecidief voorspellen met behulp van tumorgenetische sequencing ?

Kankerrecidief hangt af van een complexe wisselwerking tussen genetische mutaties, de tumoromgeving en de respons op behandeling. Persoonlijke geneeskunde streeft ernaar het risico op terugval te voorspellen door tumorgenomica te analyseren, maar het integreren van enorme datasets blijft een uitdaging voor menselijke clinici. AI zou dit proces kunnen versnellen door patronen te identificeren die verband houden met terugkeer in hoogdimensionale data.

Background

Cancer relapse is shaped by interactions among somatic mutations, the tumor microenvironment, systemic immunity, and therapeutic selection pressures. Personalized oncology seeks to quantify recurrence risk from tumor genomics, but integrating high-dimensional genomic, epigenomic, transcriptomic, and clinical data within a single workflow remains non-trivial for human interpreters.

AI-driven pipelines now fuse whole-exome or whole-transcriptome tumor sequencing with clinical covariates to generate individualized recurrence-risk estimates. Commercial gene-expression assays such as Oncotype DX AR-V7 (prostate cancer) and FoundationOne Hemo (hematologic malignancies) and the breast-cancer panel Oncotype DX Breast Recurrence Score have received regulatory clearance and provide prognostic signatures correlated with distant recurrence and survival endpoints. Deep-learning models trained on TCGA cohorts report AUCs of ≈0.75–0.85 for predicting relapse across several tumor types, outperforming traditional histopathology-based staging in validation splits. Regulatory-cleared tools are currently labeled for prognosis (i.e., outcome prediction) rather than therapy selection (predictive use), and their performance in non-academic, multi-institution cohorts is still being evaluated. Reference: Nature Medicine, enriched May 12 2026.