---
language:
  - en
license: other
license_name: lh2-data-labs-commercial
license_link: LICENSE.md
task_categories:
  - tabular-classification
  - text-classification
tags:
  - medical
  - oncology
  - cancer
  - genomics
  - pathology
  - clinical-records
  - multimodal
  - global-south
  - wes
  - wsi
  - biobank
  - non-caucasian
  - diversity
  - ai-training
pretty_name: "LH2 Data — Multimodal Oncology Dataset"
size_categories:
  - 10K<n<100K
---

# 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}
}
```