radiology-stats

name

radiology-stats

description

Plan, run, and report imaging biostatistics the way Radiology (RSNA) reviewers expect — diagnostic accuracy with CIs, ROC/AUC comparison (DeLong/bootstrap), reader agreement (Cohen/Fleiss kappa, ICC, Bland-Altman), multi-reader multi-case (MRMC) studies, calibration and decision-curve analysis, multiplicity control for high-dimensional radiomics/omics (Bonferroni vs FDR), survival/prognostic modelling, and sample-size/EPV planning. Use when the user mentions AUC, DeLong, sensitivity/specificity, kappa, ICC, MRMC, calibration, decision curve, p-value, FDR, multiple comparisons, sample size, C-index, or asks how to report a statistic for Radiology. Provides runnable Python/R and a results sentence; never fabricates numbers.

Imaging Biostatistics for Radiology

Use this skill to choose the right test, run it correctly, and report it the way Radiology wants — estimates with 95% CIs, exact p-values, named tests, and multiplicity handled. It covers the statistics that imaging-AI, radiomics, and reader studies live or die on.

Core stance

• Estimate + uncertainty, not just p. Every primary result gets a 95% CI. Report exact p-values (e.g. P = .03, not P < .05); use P < .001 only below that floor.
• The test must match the design. Paired data → paired test (same patients/cases read by both methods); clustered data (multiple lesions per patient) → account for clustering; multiple readers → MRMC, not a naive average.
• Discrimination is not enough for a clinical model. Report calibration and clinical utility (decision-curve) alongside AUC.
• Control multiplicity honestly. Thousands of radiomic/omic features ⇒ FDR or stronger; pre-specify primary vs exploratory.
• No fishing, no fabrication. Pre-specify the primary analysis; never invent a number, a CI, or a p-value. If data are insufficient, say what is needed.
• Reproducible. Return runnable code (Python first; R where it is the field standard) with the software/version and the exact method for CIs.

When to use

• Diagnostic accuracy: sensitivity/specificity/PPV/NPV/accuracy/likelihood ratios + CIs.
• Comparing models/readers/tests: DeLong or bootstrap for AUCs; McNemar for paired sensitivity/specificity.
• Reader studies: kappa / weighted kappa / Fleiss / ICC / Bland-Altman; MRMC design and analysis.
• Prediction models: ROC, calibration (slope/intercept, Brier), decision-curve analysis, threshold selection.
• Radiomics/omics: feature reproducibility (ICC), multiple-testing correction, cross-validation/nested CV, bootstrap optimism.
• Survival/prognosis: Kaplan-Meier + log-rank, Cox, C-index, time-dependent ROC, competing risks.
• Planning: sample size for accuracy / AUC; EPV and Riley minimum sample size for prediction models.

When to open extra files

File	Open when
references/diagnostic-accuracy.md	Sensitivity/specificity/PPV/NPV/LR, the right CI method, paired comparison (McNemar)
references/model-evaluation.md	ROC/AUC, DeLong vs bootstrap, thresholds, calibration, Brier, decision-curve analysis
references/agreement-mrmc.md	Cohen/weighted/Fleiss kappa, ICC model choice, Bland-Altman, MRMC (Obuchowski-Rockette / DBM)
references/high-dimensional-omics.md	Multiple testing (Bonferroni/Holm/BH-FDR/q-values), CV/nested CV, leakage, optimism, harmonisation stats
references/survival-prognostic.md	Kaplan-Meier, Cox PH (+ assumptions), C-index, time-dependent AUC, competing risks
references/sample-size.md	Sample-size for sensitivity/specificity/AUC; EPV; Riley minimum sample size for prediction models

Workflow

1. Restate the design — unit of analysis (patient/lesion/slice), paired vs unpaired, number of readers, prevalence, primary vs secondary endpoints.
2. Pick the estimand and test using the reference files. Name it explicitly.
3. Choose the CI method (e.g. Wilson/Clopper-Pearson for proportions; DeLong or bootstrap for AUC; bootstrap for derived metrics).
4. Handle multiplicity — declare the primary analysis; correct the rest (method + family).
5. Run it — provide runnable code; compute estimate + CI (+ exact p where a test applies).
6. Write the result — a Radiology-style sentence (estimate, CI, p, n) plus a Methods sentence (test, software/version, CI method, multiplicity).
7. Sanity-check — does the CI width match n? is the test paired if the data are? is calibration reported for a clinical model? are subgroups pre-specified?

Reporting templates (fill from real output — never fabricate)

• Accuracy: "Sensitivity was 0.87 (95% CI: 0.81, 0.92; 130/149) and specificity 0.79 (95% CI: 0.72, 0.85; 158/200)."
• AUC comparison: "The model's AUC (0.88; 95% CI: 0.84, 0.92) exceeded the radiologists' (0.81; 95% CI: 0.76, 0.86; difference 0.07; P = .004, DeLong)."
• Agreement: "Inter-reader agreement was substantial (ICC, 0.82; 95% CI: 0.75, 0.87; two-way random-effects, absolute agreement, single rater)."
• Multiplicity: "Of 1218 features, 47 differed after Benjamini-Hochberg control at FDR < 0.05."

Output contract

1. Design read — unit, pairing, readers, prevalence, endpoints.
2. Recommended analysis — estimand, test, CI method, multiplicity plan.
3. Code — runnable Python (and/or R), with library versions noted.
4. Results sentence — Radiology-style, with placeholders only where the user must supply data.
5. Methods sentence — for the statistical-analysis paragraph.
6. Caveats — assumptions, when the test breaks, what the reviewer may ask.

Integrity & handoffs

• Never invent numbers, CIs, or p-values; compute from supplied data or mark as needed.
• Reporting-guideline alignment of the statistics → radiology-reporting.
• Plotting the result (ROC, calibration, DCA, forest, KM) → radiology-figure.
• High-dimensional study design (leakage, batch effects in radiogenomics) → radiology-radiogenomics.
• Sample-size numbers feeding a grant's feasibility section → radiology-grant.
• Analysis plan is locked and results are in; want a harsh pre-submission read → radiology-prereview.
• This skill is statistical guidance, not a substitute for a qualified biostatistician on high-stakes or regulatory work.