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radiology-figure

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Produce publication-quality white-background academic figures for Radiology (RSNA), Nature-portfolio/npj, European Radiology, NEJM, Science, or Lancet-family venues with Python (matplotlib): ROC curves, calibration plots, decision-curve analysis, forest/SROC plots, Kaplan-Meier curves with numbers-at-risk, Bland-Altman, heatmaps, radiogenomics plots, graphical/visual abstracts, and annotated imaging panels. Uses The Lancet Digital Health guide as the default Lancet-series proxy. Use when the user wants figures, plot cleanup, figure-set planning, journal-specific figure formatting, or overlap/crowding QA. Outputs editable vector (.svg/.pdf) plus 300+ dpi raster, enforces de-identification and journal typography, and never invents data points.

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Source SKILL.md: https://github.com/huang-sir1/radiology-skills/blob/HEAD/radiology-skills/modules/radiology-figure/SKILL.md

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Radiology Publication Figures

Use this skill to build figures that pass Radiology's technical and editorial bar: correct file format and resolution, legible typography, color-blind-safe palettes, honest axes, and the specific chart types imaging-AI reviewers expect (ROC, calibration, decision-curve, forest/SROC, Kaplan-Meier, Bland-Altman), plus de-identified annotated imaging panels.

Core stance

  • Vector first. Primary output is editable .svg (or .pdf); secondary is a ≥ 300 dpi raster (TIFF/PNG). Keep text as text (svg.fonttype='none'), not outlines, so editors can re-typeset.
  • One figure, one message. Each panel answers one question; no two panels duplicate it. Panels are labelled A, B, C (Radiology-family) or a, b, c (Nature-family — the case is venue-dependent, never mixed within one manuscript; see "When to open extra files").
  • Honest graphics. Axes start where the data demand (don't truncate to exaggerate); show uncertainty (CI bands, error bars); state n.
  • De-identify every image. No PHI burned into pixels, no faces/identifiers; scrub DICOM overlays; report windowing (WL/WW) and add a scale bar where size matters.
  • Match the journal. Sans-serif (Arial/Helvetica), figure width to column — Radiology-family single ~85 mm / double ~170 mm, or Nature-family single 89 mm / double 183 mm (max height 170 mm) — adequate font size at final print size (≈ 7–9 pt min). Confirm the target venue before sizing the first figure.
  • Never fabricate data. Plot only supplied/loaded values; mark simulated/example data clearly.

When to use

  • Statistical figures: ROC (+ DeLong annotation), calibration, decision-curve, forest, SROC, Kaplan-Meier (with numbers-at-risk), Bland-Altman, box/violin, heatmaps/clustermaps.
  • Radiogenomics: MOFA/factor plots, deconvolution stacked bars, habitat maps, correlation heatmaps.
  • Imaging panels: multi-row montages, before/after, arrows/insets, windowing labels, scale bars.
  • Flow diagrams: CONSORT / STARD / PRISMA patient-selection diagrams.

When to open extra files

FileOpen when
references/radiology-figure-guidelines.mdFile format, resolution, size, fonts, color, panel labelling, de-identification rules
references/chart-types.mdChoosing/parameterising the right statistical chart (ROC, calibration, DCA, forest, KM, Bland-Altman, heatmap)
references/imaging-panels.mdBuilding montages: windowing, arrows, insets, scale bars, anonymisation, panel layout
references/api.mdThe matplotlib rcParams preamble, color palette, and reusable helper functions (ROC/calibration/forest/KM)
references/design-theory.mdTypography, layout grid, color-blind-safe palettes, anti-redundancy, accessibility
references/color-systems.mdPicking ONE palette (Okabe-Ito / NPG / Morandi) and mapping color→meaning so every figure matches
references/survival-figures.mdKaplan-Meier integrity (curve ↔ numbers-at-risk ↔ censoring), numbers-at-risk done right, time-dependent (IPCW) ROC/calibration/DCA, incremental-value framing
references/figure-set-consistency.mdUnifying palette/fonts/axes across all figures, and cross-validating every figure number against the manuscript tables and data before export
references/nature-figure-spec.mdTarget is a Nature-portfolio venue instead of Radiology — column widths (89/183 mm), lowercase panel letters, RGB, legend word cap, Extended Data/Source Data display-item split
references/figure-intent-and-render-qa.mdFull figure set planning, crowded/colliding labels, DCA/KM/heatmap layout problems, final-size render review, source-data crosswalk, or premium academic visual polish
references/journal-family-visual-style.mdTarget journal family is known, the user supplied author-guide PDFs/classic articles, or the figure set needs Nature/npj or European Radiology visual taste

Workflow

  1. Confirm the target venue (Radiology-family default, or Nature-family → nature-figure-spec.md) before sizing the first figure — column widths and panel-letter case differ and are painful to change after the set is built.
  2. For venue-specific visual taste, open journal-family-visual-style.md and apply the target family's panel lettering, legend density, graphical abstract, table, and source-data conventions.
  3. For full figure sets or layout-sensitive figures, open figure-intent-and-render-qa.md and create the figure intent table plus source-data crosswalk before drawing.
  4. Pick the chart for the message (chart-types.md). Discrimination → ROC; reliability → calibration; clinical value → decision-curve; agreement → Bland-Altman; time-to-event → Kaplan-Meier; meta-analysis → forest/SROC; whole-study summary → graphical abstract.
  5. Start the script with the rcParams preamble and palette from api.md.
  6. Build the panel(s) with helper functions; add CI bands, n, and clear axis labels with units; label panels via panel_letter()/add_panel_letter() (api.md) with the case set for the confirmed venue — never hardcode chr(65+i) per script.
  7. For imaging panels, confirm de-identification, add windowing labels + scale bar + arrows; keep grayscale unless color encodes data.
  8. Export .svg (text-as-text) and a 300–600 dpi raster; check legibility at final print width.
  9. QA (see contract) and inspect the final render for overlap/clipping before returning.

Output contract

  1. Figure plan — what each panel shows and why; the chart type chosen.
  2. Figure intent / source-data crosswalk — for full figure sets or submission figures, show what claim each panel supports and where the data came from.
  3. Script — a single runnable .py starting with the rcParams preamble; data inputs clearly marked (real vs example).
  4. Files — figure.svg (primary) + figure.png/.tiff at ≥ 300 dpi.
  5. QA notes — fonts embedded as text, color-blind check, axis honesty, n shown, de-identification confirmed for any image panel, final-size render checked for no overlap/clipping, and venue-family visual style checked when applicable.

QA checklist (run before returning)

  • rcParams preamble present; output is .svg with svg.fonttype='none' and a ≥ 300 dpi raster.
  • Every axis labelled with units; legend present; panels labelled A/B/C or a/b/c per the confirmed venue, consistently across the whole figure set.
  • Uncertainty shown (CI band/error bars) and n stated.
  • Color-blind-safe; not reliant on red/green alone; sufficient contrast.
  • Imaging panels de-identified; windowing + scale bar present where relevant.
  • No invented data points; example data flagged.
  • Final exported render inspected at target size; no label/tick/legend/number overlap, clipping, or text crossing plot elements.
  • Venue-family rules satisfied when applicable (panel-letter case, background, legend length, graphical abstract blocks, Source Data/table expectations).

Handoffs

  • The statistic behind the plot (AUC CI, DeLong, ICC, calibration metrics, net benefit) → radiology-stats.
  • Whether the figure satisfies a checklist item (flow diagram for STARD/CONSORT), or the Reporting Summary/Nature Portfolio checklist → radiology-reporting.
  • Figure legends/captions prose, display-item plan (main vs Extended Data) → radiology-writing.
  • Source Data files, Extended Data vs Supplementary Information wording → radiology-data.
  • Full figure set finished and ready for a harsh read before submission → radiology-prereview.