Federated Radiogenomics

The foundational principle ensuring that imaging data (DICOM, NIfTI) and genomic data (FASTQ, VCF) remain physically resident within their originating institution's firewall. Only encrypted model gradients or aggregated statistical summaries traverse the network. This architecture satisfies HIPAA and GDPR requirements by design, eliminating the need for data use agreements that mandate central pooling.

Federated algorithms compute correlations between radiomic features and molecular markers without direct data access. Key techniques include:

• Federated canonical correlation analysis (CCA) to find shared latent spaces
• Split learning where the imaging and genomic encoders reside on separate nodes
• Vertical federated learning for matching imaging phenotypes to genomic labels across different institutional databases This enables the identification of non-invasive imaging surrogates for invasive biopsies.

Radiogenomic data is inherently non-independent and identically distributed (non-IID) across sites due to varying scanner vendors, sequencing platforms, and patient demographics. Federated radiogenomics frameworks incorporate:

• FedProx or SCAFFOLD optimizers to handle statistical heterogeneity
• Domain adaptation layers to normalize scanner-specific radiomic features
• Batch effect correction protocols for cross-site genomic harmonization This ensures a globally robust model that does not overfit to a dominant institution's data distribution.

To prevent membership inference attacks or model inversion that could reconstruct patient scans or genotypes, federated radiogenomics systems inject calibrated noise into model updates. Techniques include:

• Gaussian noise addition to gradient updates before aggregation
• Local differential privacy applied to radiomic feature vectors
• Privacy budget accounting to track cumulative epsilon expenditure over training rounds This provides a formal mathematical guarantee against patient re-identification.

A central server orchestrates model training but never sees individual institutional contributions. Secure multi-party computation (SMPC) or homomorphic encryption ensures that gradient updates are summed in an encrypted state. Only the aggregated global model update is decrypted, preventing any honest-but-curious aggregator from isolating a single hospital's radiogenomic patterns.

A critical downstream task linking longitudinal imaging to genomic prognosis. Federated radiogenomics enables Cox proportional hazards models or deep survival machines to be trained across institutions. The model learns to predict time-to-event outcomes (e.g., progression-free survival) from baseline imaging and genomic features without centralizing patient follow-up data, enabling robust multi-institutional prognostic biomarker validation.

The foundational prerequisite for radiogenomics, this technique extracts high-throughput quantitative features (shape, texture, intensity) from medical images in a decentralized manner. Instead of sharing DICOM files, institutions compute feature vectors locally and share only aggregated statistics or model updates. This creates the imaging-derived phenotype data that is subsequently correlated with genomic markers, forming the imaging arm of the radiogenomics pipeline.

The architectural framework that enables radiogenomics by jointly learning from heterogeneous data types—imaging, genomics, and clinical records—without centralizing any modality. Key approaches include:

• Early fusion: Combining feature vectors from local imaging and genomic encoders before passing to a global model
• Intermediate fusion: Cross-modal attention mechanisms that learn correlations between imaging patches and gene expression levels
• Late fusion: Aggregating predictions from independently trained imaging and genomic models

Critical for radiogenomics due to scanner and protocol heterogeneity across institutions. MRI and CT feature distributions vary significantly between vendors and acquisition parameters, confounding genotype-phenotype correlations. Federated domain adaptation learns to map local imaging data into a scanner-invariant latent space without sharing the source images, ensuring that radiomic features reflect underlying biology rather than acquisition artifacts.

The statistical framework for linking radiogenomic biomarkers to clinical outcomes across institutions. Federated survival models—such as decentralized Cox proportional hazards or random survival forests—enable multi-institutional validation of imaging-genomic prognostic signatures. This allows researchers to correlate specific molecular pathway activations visible on imaging with progression-free or overall survival without pooling patient outcome data.

Federated Genome-Wide Association Studies enable the discovery of genetic variants associated with imaging-derived phenotypes across distributed biobanks. By performing variant-phenotype regression locally and sharing only summary statistics, institutions can identify single nucleotide polymorphisms (SNPs) linked to quantitative imaging traits—such as tumor texture or brain volume—without exposing individual-level genotype or image data.

The cryptographic backbone enabling secure radiogenomic correlation. Techniques include:

• Secure Multi-Party Computation (SMPC) for computing cross-modal correlation coefficients without revealing raw data
• Homomorphic Encryption allowing genomic and imaging feature vectors to be mathematically compared while encrypted
• Differential Privacy adding calibrated noise to shared radiogenomic association statistics to prevent patient re-identification