Structuring Radiology Reports

Structuring radiology reports

A radiology report is prose, but its meaning is structured: a technique, a comparison, a list of findings (each with anatomy, laterality, and a measurement), and an impression that may carry an assessment category (BI-RADS, Lung-RADS) and a follow-up recommendation. This skill turns the narrative into that structure so findings are trackable — especially incidental findings that need downstream follow-up.

OpenMed extracts the anatomy, disease/finding, and measurement spans on-device; this skill organizes them into sectioned, coded findings. It is decision-support, not a diagnostic device — every structured finding must be attributable back to its source sentence for radiologist review.

When to use

• You have a CT/MRI/X-ray/US/mammography report and need {technique, comparison, findings[], impression} with measurements and laterality.
• You must capture BI-RADS (breast) or Lung-RADS (lung screening) assessment categories and the recommended action.
• You need to track incidental findings and the follow-up interval/modality the report recommends.
• You are mapping findings toward RadLex terms or a DICOM-SR structured report.

Quick start

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", "")),
}

_split_findings / _extract_followup are your sentence splitter and a recommendation matcher ("recommend …", "follow-up in N months"); keep each finding tied to its source sentence offsets.

Workflow

1. De-identify first. openmed.deidentify(report, policy=...); structure from deidentified_text. Patient name, MRN, accession, and dates go before anything is stored or shared.
2. Split sections by the standard headers (TECHNIQUE, COMPARISON, FINDINGS, IMPRESSION; also HISTORY/INDICATION). Reports vary — fall back to position if headers are missing.
3. Run analyze_text for anatomy and finding entities; capture measurements ("8 mm", "1.2 cm") and laterality ("right", "left", "bilateral") near each finding.
4. Build one structured finding per observation: {anatomy, finding, laterality, measurement, change_vs_prior, source_offsets}. "Unchanged", "stable", "increased", "new" capture temporal change against the comparison.
5. Pull the assessment category (BI-RADS 0-6, Lung-RADS 1-4X) from the impression and the recommended follow-up (modality + interval).
6. Flag incidental findings — findings unrelated to the exam indication — and route them to a follow-up tracker so they aren't lost.
7. Map toward RadLex / DICOM-SR if you need coded interoperability, and surface the whole structure to a radiologist for verification.

Hand-off to / from OpenMed

OpenMed's analyze_text returns a dict; result["entities"] items carry text, label, confidence, start, end.

• From extracting-clinical-entities: Anatomy and Disease/finding entities populate each structured finding; keep offsets so every field traces to a source sentence.
• From extracting-lab-tables / OCR: if the report is a scan, OCR it first (openmed.multimodal.ocr.ocr), then run NER on the recognized text.
• From segmenting-clinical-sections: reuse section detection if your reports don't use canonical headers.
• To building-patient-timelines: dated findings + change-vs-prior feed a longitudinal view (e.g. nodule size over time).
• To extracting-dicom-metadata: pair the structured findings with the study's DICOM metadata when assembling a DICOM-SR object.
• De-identify with deidentifying-clinical-text (openmed.deidentify) before any export. Everything runs on-device.

Edge cases & gotchas

• Negation and uncertainty change meaning. "No pleural effusion" and "cannot exclude metastasis" are findings about absence/uncertainty — don't record them as positive findings. Use resolving-clinical-context (openmed.clinical) for negation/hedging before asserting a finding.
• Laterality errors are clinically dangerous. "Right" vs "left" must bind to the correct finding; a misattributed side can drive wrong-site decisions. Tie laterality to the nearest anatomy span by offset, not document-wide.
• Measurements need their unit and axis. "8 mm" vs "0.8 cm" are equal; a bare "8" is ambiguous. Capture the unit; for masses, capture all reported dimensions ("2.1 x 1.4 cm"), not just the first.
• Assessment categories have controlled value sets. BI-RADS 0-6 and Lung-RADS 1, 2, 3, 4A, 4B, 4X each map to a defined management action — don't invent or round categories; pull the literal value from the impression.
• Incidental findings get lost. A renal cyst mentioned in a chest CT is the classic missed follow-up. Explicitly separate incidental from indication-related findings and push incidentals to a tracker.
• The impression is the actionable summary, but findings may contain detail the impression omits — structure both, and prefer the impression for follow-up/assessment.
• Decision-support disclaimer. This is not a diagnostic medical device; it organizes text a radiologist authored. Every structured field must be reviewable against its source. Do not auto-act on a derived category or follow-up without clinician sign-off.

Standards & references

• RadLex (RSNA radiology lexicon): https://radlex.org/
• DICOM Structured Reporting (PS3.16 templates): https://www.dicomstandard.org/
• ACR BI-RADS Atlas: https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Bi-Rads
• ACR Lung-RADS: https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Lung-Rads
• RSNA Radiology Reporting templates: https://www.rsna.org/practice-tools/data-tools-and-standards/radreport-template-library
• ACR Incidental Findings white papers: https://www.acr.org/Clinical-Resources/Incidental-Findings
• HL7 FHIR R4 DiagnosticReport (imaging): https://hl7.org/fhir/R4/diagnosticreport.html