Unlocking Multimodal Cancer Insights: How HONeYBEE Transforms Oncology AI with Foundation Models

Revolutionizing Oncology Research with Scalable AI Infrastructure

In the rapidly evolving field of oncology, researchers and clinicians face significant challenges in integrating diverse data types to gain comprehensive patient insights. HONeYBEE emerges as a groundbreaking framework specifically designed to overcome these obstacles through foundation model-driven embeddings that enable scalable multimodal artificial intelligence. This innovative platform represents a paradigm shift in how we approach cancer data analysis, moving beyond traditional single-modality tools toward integrated, comprehensive patient profiling.

Seamless Integration with Biomedical Data Ecosystems

What sets HONeYBEE apart is its exceptional interoperability with established biomedical data repositories and machine learning environments. The framework supports direct data ingestion from critical resources including:

• NCI Cancer Research Data Commons (CRDC)
• Proteomics Data Commons (PDC)
• Genomic Data Commons (GDC)
• Imaging Data Commons (IDC)
• The Cancer Imaging Archive (TCIA)

This comprehensive compatibility ensures researchers can leverage existing data investments while benefiting from HONeYBEE’s advanced analytical capabilities. The framework’s full compatibility with PyTorch, Hugging Face, and FAISS creates a familiar environment for data scientists, while its pretrained foundation models and extensible pipelines future-proof research initiatives.

Comprehensive Multimodal Data Processing Capabilities

HONeYBEE’s evaluation using The Cancer Genome Atlas (TCGA) dataset demonstrates its robust handling of real-world clinical data challenges. The analysis incorporated data from 11,428 patients across 33 cancer types, including:

• Clinical text from 11,428 patients
• Molecular profiles from 13,804 samples representing 10,938 patients
• Pathology reports from 11,108 patients
• Whole-slide images (WSIs) from 8,060 patients
• Radiologic images from 1,149 patients

This heterogeneous dataset, with its inherent missing data patterns, provided the perfect testing ground for HONeYBEE’s ability to handle real-world clinical constraints while maintaining analytical rigor.

Advanced Foundation Models for Each Data Modality

HONeYBEE’s strength lies in its sophisticated selection of foundation models tailored to specific data types:

Clinical Text and Pathology Reports

The framework supports multiple language models including GatorTron, Qwen3, Med-Gemma, and Llama-3.2. For primary analyses, GatorTron embeddings demonstrated superior performance due to specialized training on clinical text, though the framework maintains flexibility to incorporate any Hugging Face models as needed.

Whole-Slide Image Analysis

For digital pathology, HONeYBEE integrates three powerful models: UNI (ViT-L/16, 307M parameters), UNI2-h (ViT-g/14, 632M parameters), and Virchow2 (DINOv2, 1.1B parameters). These models offer varying balances of computational efficiency and feature extraction capability, with UNI providing optimal performance for large-scale processing scenarios.

Radiological Imaging

The framework employs RadImageNet, a convolutional neural network pre-trained on over four million medical images across CT, MRI, and PET modalities. This extensive pretraining enables the model to handle diverse imaging protocols commonly encountered in oncology practice., as comprehensive coverage

Molecular Data Processing

For complex multi-omics data, HONeYBEE incorporates SeNMo, a self-normalizing deep learning encoder specifically designed for high-dimensional data including gene expression, DNA methylation, somatic mutations, miRNA, and protein expression. The model’s self-normalizing properties ensure stable training despite the diverse scales and distributions inherent in multi-omics datasets.

Innovative Multimodal Fusion Strategies

HONeYBEE implements three sophisticated fusion approaches to integrate heterogeneous data from patients with at least two available modalities:

• Concatenation: Preserves modality-specific information while combining embeddings
• Mean pooling: Averages embeddings after dimension standardization
• Kronecker product: Captures pairwise interactions between modalities

Surprisingly, evaluation using normalized mutual information (NMI) and adjusted mutual information (AMI) revealed that clinical embeddings alone achieved the strongest cancer-type clustering performance (NMI: 0.7448, AMI: 0.702), outperforming all multimodal fusion strategies. This finding highlights the curated nature of clinical documentation in TCGA, where expert-extracted diagnostic variables effectively summarize dispersed information across raw data types.

Robust Performance Across Core Oncology Tasks

HONeYBEE-generated embeddings demonstrated exceptional performance across four critical downstream applications:

Cancer Type Classification

Clinical embeddings achieved remarkable 90.21% classification accuracy using simple random forest classifiers, establishing a strong baseline for cancer-type differentiation. Molecular embeddings showed structured but overlapping clusters reflecting biological similarities among related cancers, while other modalities demonstrated varying degrees of clustering effectiveness.

Patient Similarity Retrieval

The framework enabled efficient identification of similar patient profiles across multiple data types, supporting comparative analysis and cohort identification for clinical trials and research studies.

Cancer-Type Clustering

While clinical embeddings dominated clustering performance, multimodal fusion approaches consistently outperformed weaker single modalities such as molecular, radiology, and WSI embeddings. Concatenation emerged as the most effective fusion method, achieving NMI of 0.4440 and AMI of 0.347.

Overall Survival Prediction

HONeYBEE supported comprehensive survival analysis across all 33 TCGA cancer types, with models trained individually for each cancer type using stratified cross-validation approaches that accounted for survival outcomes and censoring patterns.

Accessible Data and Model Resources

To accelerate oncology research, HONeYBEE’s patient-level feature vectors and associated metadata are publicly available through multiple Hugging Face repositories including TCGA, CGCI, Foundation Medicine, CPTAC, and TARGET. This commitment to open science ensures that researchers worldwide can build upon HONeYBEE’s foundation to advance cancer understanding and treatment.

Transforming Cancer Research Through Integrated AI

HONeYBEE represents a significant leap forward in multimodal AI for oncology, offering researchers a flexible, scalable platform that accommodates real-world data constraints while maintaining analytical rigor. By providing standardized embedding workflows, minimal-code implementation of state-of-the-art techniques, and seamless integration with existing biomedical infrastructure, the framework lowers barriers to advanced AI adoption in cancer research. As the field continues to evolve, HONeYBEE’s modular design ensures it will remain at the forefront of multimodal cancer analytics, enabling new discoveries and improved patient outcomes through comprehensive data integration.

References

• https://huggingface.co/datasets/Lab-Rasool/TCGA
• https://huggingface.co/datasets/Lab-Rasool/CGCI
• https://huggingface.co/datasets/Lab-Rasool/FM
• https://huggingface.co/datasets/Lab-Rasool/CPTAC
• https://huggingface.co/datasets/Lab-Rasool/TARGET

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