To investigate whether molecular information from primary tumor regions can identify prostate cancer likely to spread, potentially reducing unnecessary lymph node testing.
Approach:
Study Design: Analyzed tissue from 51 prostate cancer patients using spatial transcriptomics to assess gene activity in primary tumor regions and matched lymph node metastases.
Machine Learning: Trained machine learning models on molecular profiles to recognize patterns linked to lymph node involvement.
Key Findings:
Primary tumor regions associated with lymph node metastases exhibited a distinct molecular profile.
Machine learning models demonstrated promising performance in identifying patients at higher risk of lymph node metastasis.
Limitations:
The work is exploratory and requires further validation before clinical application.
The study does not yet incorporate morphology-based machine learning approaches.
