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LH2-data-labs/multimodal-oncology-atlas

LH2 Data — Multimodal Oncology Dataset A large-scale, multimodal oncology dataset built around a principle rare in the field: placing non-Caucasian patient populations at the centre, not the periphery. Dataset Summary The vast majority of oncology datasets used to train diagnostic, prognostic, and treatment AI models are drawn overwhelmingly from Caucasian, Western populations — a well-documented limitation that undermines model generalisability and equity in… See the full description on the dataset page: https://huggingface.co/datasets/LH2-data-labs/multimodal-oncology-atlas.

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Dataset Card

LH2 Data — Multimodal Oncology Dataset

A large-scale, multimodal oncology dataset built around a principle rare in the field: placing non-Caucasian patient populations at the centre, not the periphery.

Dataset Summary

The vast majority of oncology datasets used to train diagnostic, prognostic, and treatment AI models are drawn overwhelmingly from Caucasian, Western populations — a well-documented limitation that undermines model generalisability and equity in real-world deployment. This dataset addresses this directly, offering a deeply multimodal oncology resource — genomic, clinical, imaging, and biospecimen — anchored across the Global South.

This sample contains structured clinical records for 20 de-identified cancer patients demonstrating the full schema. The complete dataset includes ~25,000 enrolled patients (target: 50,000 within 12 months) across 15 countries spanning the Indian Subcontinent, Southeast Asia, Latin America, and the Middle East.

Cohort at a Glance

Dimension	Scale
Patients Enrolled	~25,000 (sample: 20)
12-Month Target	50,000 patients sequenced
Geographic Coverage	Indian Subcontinent, Southeast Asia, Latin America, Middle East
Cancer Types	Multiple solid tumour types (prostate, colon, lung, and others)
Clinical Data Categories	13 per patient
Data Modalities	Genomic (WES) + Digital Pathology (WSI) + Clinical + Biobank

Data Modalities

Modality	Description	Format
Genomic Sequencing	Whole exome sequencing (WES) and targeted panel data. Variant calls available.	FASTQ, BAM, VCF
Digital Pathology	Whole slide images (WSIs) linked to genomic and clinical data across multiple cancer types.	SVS, TIFF, DICOM
Clinical Records	Structured longitudinal data across 13 categories: staging (AJCC pTNM), surgery, chemotherapy, targeted therapy, immunotherapy, radiation, PET-CT, molecular pathology (NGS, IHC), serum biomarkers, haematology, biochemistry, and performance status.	Parquet, CSV, JSON
Biobank	Residual FFPE tissue and extracted DNA/RNA for re-analysis and future modality generation.	Physical specimens
Multimodal Linked	Every modality joined at the patient level — DNA, tissue imaging, and clinical trajectories as linked records.	Patient-level joins

Clinical Record Schema (28 fields)

Field	Type	Description
`patient_id`	string	De-identified patient identifier
`region`	string	Geographic region (Indian Subcontinent / Southeast Asia / Latin America / Middle East)
`cancer_type`	string	Primary cancer diagnosis (named types: Prostate, Colon, Lung; others anonymised)
`histology_grade`	string	Tumour differentiation grade (G1–G4)
`ajcc_stage_ptnm`	string	AJCC pTNM staging (I through IVB)
`age_at_diagnosis`	int	Patient age at diagnosis
`sex`	string	Male / Female
`surgery`	string	Surgical intervention performed
`chemotherapy`	string	Chemotherapy regimen
`chemotherapy_cycles`	int	Number of chemotherapy cycles administered
`targeted_therapy`	string	Targeted therapy agent
`immunotherapy`	string	Immunotherapy agent
`radiation`	string	Radiation modality and intent
`msi_status`	string	Microsatellite instability status (MSI-High / MSI-Low / MSS)
`molecular_pathology_ngs`	string	NGS mutation panel result
`molecular_pathology_ihc`	string	IHC expression result
`pet_ct`	string	PET-CT metabolic activity findings
`serum_biomarkers`	string	Serum biomarker status
`haematology_status`	string	Haematology assessment
`biochemistry_status`	string	Biochemistry assessment
`performance_status_ecog`	int	ECOG performance status (0–4)
`treatment_response`	string	Best response (Complete / Partial / Stable / Progressive)
`recurrence`	bool	Whether recurrence was detected
`survival_months`	int	Survival duration in months
`vital_status`	string	Alive / Deceased
`has_wes_data`	bool	Whether WES data is available for this patient
`has_wsi_data`	bool	Whether WSI data is available for this patient
`has_biobank_specimen`	bool	Whether biobank specimen is available

Key Differentiators

—Non-Caucasian patient populations placed at the centre of dataset design — a principle rare in oncology AI
—Multimodal from day one: genomic + imaging + clinical + biospecimen, all linked per patient
—13 clinical data categories per patient including staging, surgery, chemotherapy, targeted therapy, molecular pathology (NGS + IHC), PET-CT, serum biomarkers, haematology, and biochemistry
—15 countries across the Indian Subcontinent, Southeast Asia, Latin America, and the Middle East — populations systematically absent from TCGA, ICGC, and most major oncology training datasets
—Physical biobank enables generation of future modalities (spatial transcriptomics, single-cell RNA-seq)
—Scalable cohort: 25,000 today → 50,000 target in 12 months

IP & Compliance

—Full commercial and AI/ML training rights secured under exclusive licensing agreement
—Ethics and informed consent frameworks aligned with source jurisdictions
—De-identified patient data — no personally identifiable information (PII)
—Compliant with applicable data protection and cross-border transfer regulations (DPDP Act 2023, HIPAA, GDPR as applicable)

Citation

bibtex

@dataset{lh2_oncology_2025,
  title={LH2 Data — Multimodal Oncology Dataset},
  author={LH2 Data Labs},
  year={2025},
  publisher={Hugging Face},
  note={Multimodal oncology dataset: 25K+ patients across the Global South}
}