radiology-radiogenomics

name

radiology-radiogenomics

description

Design, analyse, report, and submit imaging-multi-omics radiogenomics studies that link radiomic/deep imaging phenotypes to genomic, transcriptomic, single-cell, and spatial-omics data. Use when the user mentions radiogenomics, imaging genomics, imaging-transcriptomics, TCIA/TCGA, GEO, dbGaP, EGA, cBioPortal, multi-omics integration, MOFA, iCluster, SNF, DIABLO, scRNA-seq deconvolution, CIBERSORTx, BayesPrism, spatial transcriptomics, Visium, Xenium, imaging habitats, gene expression, mutations, pathways, biological validation, omics QC, sample-to-image mapping, radiogenomics submission packages, or reviewer comments about batch/leakage/spatial mismatch. Covers the full chain from matched-cohort feasibility and protocol/SAP to omics QC, imaging pipeline, integration, validation, reporting, submission, and rebuttal support. Never fabricates associations, cohort counts, accessions, approvals, metrics, or reviewer actions.

Radiogenomics and Imaging-Multi-Omics

Use this skill to plan, analyse, report, submit, and revise studies that connect imaging phenotypes (radiomics, deep features, or spatial habitats) to molecular data: bulk genomics or transcriptomics, single-cell RNA-seq, deconvolution, and spatial omics. This is one of the highest-difficulty corners of imaging research because the analysable cohort is the matched intersection of imaging and omics, both data spaces are high-dimensional, and scanner/site and sequencing batch effects can masquerade as biology.

Core stance

• Match first, then mine. State the patients with both usable imaging and usable omics first; that matched n drives design, power, claims, and journal tier.
• Map tissue to image. A molecular sample is not automatically the whole tumour. Record timing, lesion, region, treatment interval, and whether the analysis is patient-, lesion-, habitat-, or section-level.
• Separate confirmation from discovery. Pre-specify the primary hypothesis and analysis plan; FDR-control discovery scans and validate independently whenever possible.
• Batch can look like biology. Scanner/site/protocol and sequencing batch/platform/center must be recorded, adjusted or harmonised appropriately, and tested in sensitivity analyses.
• Reproducible imaging and omics. Radiomics must be IBSI/CLEAR-aligned; omics QC, filtering, normalization, batch correction, accessions, and software versions must be explicit.
• Interpret as association unless proven otherwise. Pathways, cell types, and spatial evidence strengthen biological interpretation but usually do not prove mechanism.
• Submission-ready integrity. Never invent cohort counts, accessions, p values, effect sizes, approvals, validation results, or reviewer-response locations.

When to use

• Designing a TCIA-TCGA, GEO, dbGaP/EGA, cBioPortal, in-house, or multi-center radiogenomics study.
• Linking radiomic/deep features with mutations, gene expression, methylation, CNV, proteomics, molecular subtypes, pathway activity, immune/cell-type composition, or prognosis.
• Integrating imaging with multi-omics using MOFA/MOFA+, iCluster, SNF, DIABLO/mixOmics, sparse CCA, multi-block PLS, NMF, or related methods.
• Connecting imaging habitats to scRNA-seq deconvolution or spatial transcriptomics.
• Drafting a protocol, statistical analysis plan, Methods, Results, Discussion, supplement, or submission package for a radiogenomics manuscript.
• Auditing a manuscript or reviewer comments for leakage, batch confounding, small-n optimism, tissue-image mismatch, overclaiming, and incomplete data/code availability.

When to open extra files

File	Open when
references/cohort-design.md	Choosing data sources, matching imaging to omics, estimating matched n, planning validation and ethics
references/sample-to-image-mapping.md	Determining whether tissue, biopsy, histology, spatial omics, or lesion sampling actually matches the imaging ROI/habitat
references/radiomics-pipeline.md	Building the imaging side: segmentation, IBSI features, deep features, habitats, registration, harmonisation
references/omics-qc-preprocessing.md	Preparing RNA-seq, mutation, methylation, CNV, proteomics, or other molecular matrices with QC, normalization, batch handling
references/analysis-plan-sap.md	Writing a protocol/SAP, defining primary versus discovery analyses, covariates, FDR, validation, sensitivity analyses
references/multi-omics-integration.md	Choosing integration strategy and methods: MOFA+, iCluster, SNF, DIABLO/mixOmics, sparse CCA, fusion strategies
references/deep-radiogenomics-fusion-strategies.md	Deep radiomics, foundation-model embeddings, radiopathomics, cross-attention/joint embeddings, pathway-informed fusion, disease-endpoint prioritisation, or modern hybrid fusion strategy
references/single-cell-spatial.md	scRNA-seq deconvolution, pseudobulk, spatial transcriptomics, and habitat linkage
references/association-validation.md

Workflow

1. Define the linkage question: imaging phenotype, molecular layer, endpoint, disease context, discovery versus prediction, and whether the intended claim is association, prediction, or biology.
2. Assemble the matched cohort: sources, eligibility, the imaging-omics intersection, exclusions, and a validation cohort before modelling.
3. Map sample to image: tissue source, lesion/region/habitat, time interval, treatment exposure, and mapping level. Bound claims when mapping is weak.
4. Write the protocol/SAP: primary hypothesis, covariates, multiplicity, validation criterion, missing-data handling, sensitivity analyses, and leakage controls.
5. Build the imaging side: segmentation/annotation, IBSI radiomics or deep features, registration, habitat definitions, stability filtering, and scanner/site harmonisation.
6. Prepare omics: assay-specific QC, filtering, normalization, batch correction, feature definitions, accessions, software versions, and controlled-access constraints.
7. For deep/hybrid radiogenomics, open deep-radiogenomics-fusion-strategies.md and decide whether early fusion, late fusion, joint embedding, pathway graph, radiopathomics, or foundation-model adapter is justified by matched n and validation.
8. Integrate or associate: choose association, pathway analysis, supervised prediction, or formal multi-omics integration. Keep all data-dependent operations inside training or discovery only.
9. Validate and interpret: replicate direction/effect size, add biological corroboration where available, and keep mechanistic language bounded.
10. Report and submit: map to CLEAR/IBSI and the appropriate prediction/diagnostic/observational guidelines, prepare supplement and data/code statements, then run pre-review and journal selection.
11. Revise with traceability: classify reviewer comments, perform feasible analyses, soften unsupported claims, and cite exact manuscript/supplement locations.

Output contract

For design or audit tasks, return as many of these as the task requires:

1. Linkage design: phenotype, omics layer, endpoint, claim type, integration strategy.
2. Matched cohort: sources, intersection n, exclusion logic, validation cohort, limiting count.
3. Sample-to-image map: timing, lesion/region/habitat, tissue source, mapping level, uncertainty.
4. Protocol/SAP: primary hypothesis, covariates, FDR/test family, validation rule, sensitivity analyses.
5. Pipeline: imaging pipeline and omics QC/preprocessing with leakage-prone steps marked train-only.
6. Analysis: association/integration/prediction method, multiplicity control, validation, code/tool route.
7. Fusion strategy: baseline ladder, early/late/joint/pathway/radiopathomics route, matched-n justification, missing-modality and overfitting controls.
8. Biological interpretation: claim level, pathway/cell/spatial evidence, alternative explanations.
9. Reporting map: CLEAR/IBSI plus TRIPOD+AI/STARD/STROBE/REMARK/omics standards as applicable.
10. Submission package: main-manuscript requirements, supplement tables, checklists, data/code/accessions.
11. Reviewer risk list: likely radiogenomics critiques and concrete fixes or response strategy.
12. Author input needed: any missing counts, accessions, approvals, line numbers, software versions, or results.

Handoffs

• Study feasibility, validation strategy, and cohort architecture -> radiology-design.
• Dataset/literature search and accession verification -> radiology-search / radiology-citation.
• Segmentation SOP and reproducibility -> radiology-annotation.
• IBSI/CLEAR/METRICS/RQS, TRIPOD+AI, STARD, STROBE routing -> radiology-reporting.
• ROC, calibration, DCA, survival, DeLong, MRMC, sample size, multiplicity -> radiology-stats.
• Figures: habitats, MOFA factors, heatmaps, deconvolution bars, KM, flow diagrams -> radiology-figure.
• Data/code availability, DICOM de-identification, GEO/dbGaP/EGA/Zenodo/GitHub wording -> radiology-data.
• Ethics, consent, DUA, HIPAA/GDPR/PIPL, genomic re-identification risk -> radiology-ethics.
• Manuscript drafting and claim calibration -> radiology-writing / radiology-polishing.
• Pre-submission mock review -> radiology-prereview; journal ladder -> radiology-journal; rebuttal -> radiology-response.
• Emerging linkage themes (liquid biopsy/ctDNA, pathology-foundation-model fusion) and whether the data can carry them -> radiology-frontier.
• Reframing this research as a funding proposal -> radiology-grant.

This skill guides design, analysis logic, reporting, and submission readiness. It does not replace a genomics/bioinformatics collaborator for production pipelines or institutional legal/ethics review.