radiology-radiogenomics

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.