project-idea

Tumor Board Prep Assistant: LLM-Powered Variant Briefing Generator

The CIViC MCP server enables natural language queries to expert-curated cancer variant interpretations — meaning clinicians can now ask plain-English questions and get synthesized answers about a patient's tumor mutations.

Build a clinical workflow tool that takes a patient's somatic mutation report (VCF or structured text) as input and automatically generates a structured tumor board briefing document. The tool would use the CIViC MCP server to look up each actionable variant, then synthesize the results into a one-page summary per patient: variant significance, associated therapies, relevant trials, and confidence level.

The output would be formatted for tumor board use — concise, cited, and organized by clinical actionability tier. A 'confidence' flag would surface variants where CIViC evidence is strong vs. where the LLM is extrapolating, reducing the hallucination risk that makes clinicians distrust AI tools.

This directly addresses a real bottleneck: preparing tumor board materials currently requires a molecular pathologist or bioinformatician to manually look up each variant. Automating the first draft could save hours per case and make precision oncology more accessible at community hospitals that lack dedicated genomics staff.

Who Is This For?

Oncology informaticists, clinical software developers, and hospital IT teams serving tumor board workflows.

Skills & Tools Needed

CIViC MCP API integration (from civicdb.org)
VCF/genomics file parsing (PyVCF or similar)
LLM prompt engineering for structured clinical output
PDF/Word report generation (ReportLab, python-docx)
Basic understanding of cancer genomics variant classification (VICC/AMP tiers)

Feasibility

high — CIViC MCP is live and documented, VCF parsing is well-understood, and the output is a structured document — a working prototype could be built in a weekend hackathon.

Inspired by: CIViC MCP: Integrating Large Language Models with the Clinical Interpretations of Variants in Cancer

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