EndoGov uses specialist agents plus a governance layer with hard and soft rule paths to deliver guideline-compliant endometrial cancer risk stratification, reporting 0.943 accuracy and 0.93% logic-violation rate on TCGA-UCEC while outperforming neural baselines on CPTAC-UCEC.
Prediction of recurrence risk in endometrial cancer with multimodal deep learning
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EndoGov: A knowledge-governed multi-agent expert system for endometrial cancer risk stratification
EndoGov uses specialist agents plus a governance layer with hard and soft rule paths to deliver guideline-compliant endometrial cancer risk stratification, reporting 0.943 accuracy and 0.93% logic-violation rate on TCGA-UCEC while outperforming neural baselines on CPTAC-UCEC.