radiology-crossmodal-mapping

Radiology Cross-Modal Mapping

Use this skill when the central problem is how to align an imaging phenotype or habitat with single-cell, spatial-omics, or pathology-derived cellular states. Make the mapping unit and its uncertainty explicit before choosing a method.

Core stance

• Map at the finest common unit with verified correspondence; coarsen until defensible. Never promote cohort concordance to patient-, lesion-, region-, or cell-level evidence.
• Classify each link as direct, weak, unpaired, or unresolved; separately classify cohort completeness as fully or partially paired.
• Treat registration, sampling, timing, treatment, and spatial-scale mismatch as analysis variables, not footnotes.
• Audit identity, eligibility, and provenance metadata before splitting; do not use outcomes, biological measurements, or apparent correspondence to resolve links. Then split by patient and, when applicable, center, keeping every fitted step inside training data.

Required intake

Collect the phenotype, assay, anatomy, endpoint, cohorts/centers, hierarchy identifiers, dates, intervening treatment, registration evidence, modality missingness, batch/site variables, intended claim, and validation material. Mark unknown or conflicting links as unresolved.

Create a mapping-unit table before analysis:

Imaging record	Verified patient	Lesion	Imaging/time	Specimen/section	Region/cell state	Link correspondence	Finest verified common unit	Uncertainty
one row per proposed link	ID/none	ID	date/phase	ID(s)	ID/label	direct/weak/unpaired/unresolved	patient/lesion/region	source/magnitude

Add a cohort summary stating eligible counts, modality availability, and whether completeness is fully or partially paired. See alignment and pairing for definitions, scale mismatch, and permitted inference.

Workflow

1. Define the estimand. State the imaging feature or habitat, cellular state or spatial neighborhood, shared unit, endpoint, direction of mapping, and whether the aim is discovery, prediction, annotation transfer, or biological corroboration.
2. Audit metadata before splitting. Using identity, provenance, modality availability, dates, and prespecified eligibility only, trace patient -> lesion -> specimen -> section -> region -> cell. Assign link correspondence and cohort completeness; freeze unresolved links. Do not inspect outcomes, expression, cell states, imaging features, or biological plausibility.
3. Split, then align. Split eligible patients and reserve centers when applicable. Apply the prespecified finest common unit with verified metadata correspondence and coarsen until defensible; learn any image-, omics-, or biology-driven alignment in training only.
4. Choose the mapping route. Match pseudobulk, deconvolution, canonical correlation, contrastive mapping, graph alignment, or habitat linkage to the link status, scale, and sample size. Transfer labels across imaging and omics only through a paired bridge, shared measured features, or an independently validated cross-modal mapper; otherwise transfer within omics and validate the imaging association separately.
5. Lock leakage-safe validation. Keep feature selection, habitat discovery, normalization, anchor learning, label transfer, deconvolution tuning, embedding, graph construction, and threshold selection inside training. Add held-out-center or external validation when transportability is claimed.
6. Run controls. Include mapping permutations, biologically implausible or negative regions, null features, method-specific nulls, and site/batch-aware baselines.
7. Run sensitivity analyses. Vary registration tolerance, temporal window, aggregation level, habitat definition, cell-state reference, preprocessing, covariates, and borderline links.
8. Validate biology. Prefer an independent cohort and orthogonal IHC, multiplex immunofluorescence, in situ hybridization, pathology, or separately measured spatial evidence.
9. Bound claims. Tie each conclusion to its link status, cohort completeness, shared unit, validation, and unresolved alternative explanations.