Structuring Radiology Reports

What it does

When your agent uses it

• The user has a CT/MRI/X-ray/ultrasound/mammography report and needs the sections split (technique
• Lesion measurements and laterality captured
• BI-RADS / Lung-RADS assessment categories pulled
• Incidental findings and recommended follow-up tracked

Example prompts

• “Use the structuring-radiology-reports skill to convert free-text radiology narratives into structured findings and impression — with measurements…”
• “/structuring-radiology-reports”

Requirements

• Python 3

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

1. 1De-identify first. openmed.deidentify(report, policy=...); structure
2. 2Split sections by the standard headers (TECHNIQUE, COMPARISON, FINDINGS,
3. 3Run analyze_text for anatomy and finding entities; capture measurements
4. 4Build one structured finding per observation: `{anatomy, finding,
5. 5Pull the assessment category (BI-RADS 0-6, Lung-RADS 1-4X) from the
6. 6Flag incidental findings — findings unrelated to the exam indication — and
7. 7Map toward RadLex / DICOM-SR if you need coded interoperability, and

What it can do on your machine

Read from SKILL.md and the folder at commit 6b1bb2c. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • acr.org
  • radlex.org
  • dicomstandard.org
  • rsna.org
  • hl7.org

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Structuring Radiology Reports loads about 2.2k tokens when it runs. Until then it costs about 227 tokens; SKILL.md has 702 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~227

When it runs · the whole SKILL.md, loaded when a task matches

~2.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from maziyarpanahi/openmed at commit 6b1bb2c, republished under its Apache-2.0 licence (© maziyarpanahi). 702 words, ~2,196 tokens.

name

structuring-radiology-reports

description

Converts free-text radiology narratives into structured findings and impression — with measurements, laterality, anatomy, and follow-up recommendations — after OpenMed NER. Use when the user has a CT/MRI/X-ray/ultrasound/mammography report and needs the sections split (technique, comparison, findings, impression), lesion measurements and laterality captured, BI-RADS / Lung-RADS assessment categories pulled, or incidental findings and recommended follow-up tracked. Trigger keywords: radiology report, findings, impression, RadLex, DICOM-SR, BI-RADS, Lung-RADS, ACR, laterality, measurement, nodule, incidental finding, follow-up, structured reporting. Pairs after OpenMed: run openmed.analyze_text on the report (Anatomy/Disease/measurement entities), then assemble structured findings. De-identify the report first. Decision-support only — not a diagnostic medical device.

license

Apache-2.0

metadata.project

OpenMed

metadata.category

imaging-ocr

metadata.pairs

after

metadata.version

1.0

import openmed

report = (
    "TECHNIQUE: CT chest without contrast.\n"
    "COMPARISON: CT 2023-11-02.\n"
    "FINDINGS: A 8 mm solid nodule is noted in the right upper lobe, "
    "unchanged. No pleural effusion.\n"
    "IMPRESSION: 8 mm right upper lobe nodule, stable. Lung-RADS 2. "
    "Recommend annual low-dose CT screening."
)

# 1) De-identify the report on-device first (synthetic example shown).
deid = openmed.deidentify(report, policy="hipaa_safe_harbor")
text = deid.deidentified_text

# 2) Run NER for anatomy / finding / measurement spans.
ents = openmed.analyze_text(
    text,
    model_name="anatomy_detection_superclinical",   # Anatomy category
    output_format="dict",
)["entities"]

# 3) Split sections by header, then attach entities + measurements per finding.
import re
SECTION = re.compile(r"(?im)^(TECHNIQUE|COMPARISON|FINDINGS|IMPRESSION)\s*:")
sections, last, name = {}, 0, None
for m in SECTION.finditer(text):
    if name: sections[name] = text[last:m.start()].strip()
    name, last = m.group(1).upper(), m.end()
if name: sections[name] = text[last:].strip()

structured = {
    "technique": sections.get("TECHNIQUE"),
    "comparison": sections.get("COMPARISON"),
    "findings": _split_findings(sections.get("FINDINGS", "")),   # one per sentence
    "impression": sections.get("IMPRESSION"),
    "measurements": re.findall(r"\b\d+(?:\.\d+)?\s?(?:mm|cm)\b", text),
    "laterality": sorted({w for w in ("right", "left", "bilateral")
                          if re.search(rf"\b{w}\b", text, re.I)}),
    "assessment": (re.search(r"\b(?:BI-RADS|Lung-RADS)\s*\d[A-C]?\b", text, re.I)
                   or [None])[0] if re.search(r"RADS", text, re.I) else None,
    "follow_up": _extract_followup(sections.get("IMPRESSION", "")),
}

© maziyarpanahi, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/structuring-radiology-reports of maziyarpanahi/openmed.

Open the folder on GitHubat commit 6b1bb2c