radiology-figure

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

File	Open when
references/radiology-figure-guidelines.md	File format, resolution, size, fonts, color, panel labelling, de-identification rules
references/chart-types.md	Choosing/parameterising the right statistical chart (ROC, calibration, DCA, forest, KM, Bland-Altman, heatmap)
references/imaging-panels.md	Building montages: windowing, arrows, insets, scale bars, anonymisation, panel layout
references/api.md	The matplotlib rcParams preamble, color palette, and reusable helper functions (ROC/calibration/forest/KM)
references/design-theory.md	Typography, layout grid, color-blind-safe palettes, anti-redundancy, accessibility
references/color-systems.md	Picking ONE palette (Okabe-Ito / NPG / Morandi) and mapping color→meaning so every figure matches
references/survival-figures.md	Kaplan-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.md	Unifying palette/fonts/axes across all figures, and cross-validating every figure number against the manuscript tables and data before export
references/nature-figure-spec.md	Target 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.md	Full 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.md	Target journal family is known, the user supplied author-guide PDFs/classic articles, or the figure set needs Nature/npj or European Radiology visual taste
references/backend-and-geometry-qa.md	Choosing Python vs R, using survival/nomogram/ComplexHeatmap conventions, or auditing white background, excessive whitespace, edge clipping, and export geometry

Workflow

Choose the requested scope first: advisory explains/designs a figure, audit inspects existing figures and reports findings, and create-edit produces or changes artifacts. Audit may render a temporary preview for inspection; it does not authorize rewriting originals. Generate scripts and exports only for create-edit or an explicitly requested artifact. Use only applicable QA items: conceptual diagrams and flowcharts need not have numerical axes, confidence intervals or sample n.

0. 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.
1. 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.
2. 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.
3. Pick the chart for the message (chart-types.md). Discrimination → ROC; reliability → calibration; net benefit at justified risk thresholds linked to a clinical action → decision-curve; agreement → Bland-Altman; time-to-event → Kaplan-Meier; meta-analysis → forest/SROC; whole-study summary → graphical abstract.
4. Resolve the backend with backend-and-geometry-qa.md: reuse the project's existing Python/R stack; otherwise default to Python for general imaging panels and consider R for survival/nomogram/ComplexHeatmap workflows where it is the established project route.
5. Start the script with the backend theme and palette from api.md (Python) or backend-and-geometry-qa.md (R).
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/.pdf (editable text where possible) and a 300–600 dpi raster on an explicit opaque white canvas; check legibility at final print width.
9. QA (see contract): run scripts/audit_figure_export.py on raster exports, then visually inspect the final render for text/number/legend overlap and semantic errors before returning.

Output contract

For advisory/audit scope, return only the requested plan or evidence-linked findings and applicable QA status. The script and file requirements below apply to artifact creation/editing. A diagnostic render is not proof that statistical, de-identification or submission checks passed.

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 or .R using the selected backend theme; data inputs clearly marked (real vs example).
4. Files — editable figure.svg/figure.pdf + 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)

• Selected backend theme/preamble is present; output is editable .svg/.pdf with text retained where possible and a ≥ 300 dpi raster on an opaque white canvas.
• 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).
• Raster geometry audit recorded (white canvas, DPI metadata when available, occupied area, edge-ink/clipping risk); visual inspection still required for actual text collisions.

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.
• Reuse or adapt a frozen figure inside an audience-specific scientific deck → radiology-paper2ppt with the editable asset, source locator and transformation limits.
• Full figure set finished and ready for a harsh read before submission → radiology-prereview.
• Figure/table value reconciliation and project state → radiology-table / radiology-pipeline; upload package → radiology-submission.