HistoAtlas: Pan-Cancer Quantitative Histomics

38 interpretable morphometric features from 6,745 TCGA diagnostic H&E slides across 21 solid-tumor cancer types

From raw H&E whole-slide images to quantitative histomics: tissue and cell segmentation, compartment-resolved feature extraction, pan-cancer statistical analysis, and an interactive web atlas.

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Overview

HistoAtlas is a pan-cancer computational histopathology atlas that quantifies tumor morphology from routine H&E-stained diagnostic whole-slide images. This dataset contains 38 interpretable, compartment-resolved histomic features extracted from 6,745 TCGA slides spanning 21 solid-tumor cancer types, representing 6,745 unique patients.

Each feature captures a specific, biologically interpretable aspect of tumor architecture: tissue composition, cell densities, nuclear morphology, spatial organization of immune and stromal cells, and intra-tumoral heterogeneity. Features are computed from automated tissue and cell segmentation at single-cell resolution, then aggregated at the slide level.

The full precomputed statistical analysis (survival associations, molecular correlations, mutation associations, morphological clusters) is available through the interactive web atlas and the companion arXiv paper.

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Dataset Description

Source Data

Formalin-fixed, paraffin-embedded (FFPE) H&E-stained diagnostic whole-slide images were obtained from The Cancer Genome Atlas (TCGA) via the Genomic Data Commons (GDC) portal. One slide per patient was retained (primary tumor diagnostic slide with the largest tissue area), yielding 6,745 slides across 6,745 unique patients.

Inclusion criteria:

• Viable tissue area above 1 mm^2
• No severe processing artifacts (pen marks covering >20% of tissue, out-of-focus regions)
• Essential clinical metadata available (vital status, follow-up time)

Twelve additional TCGA cancer types were excluded because their dominant cell morphologies fall outside the training domain of the cell detection model.

Segmentation Pipeline

Feature extraction used a two-stage segmentation pipeline:

1. Tissue segmentation: A CellViT-inspired architecture (Horst et al., 2024) with a Phikon self-supervised ViT-B backbone, trained on the PanopTILs crowdsourced annotation dataset. Inference at 0.5 um/px on 224x224 pixel tiles classified each region into five effective tissue compartments: cancerous epithelium, stroma, necrosis, normal epithelium, and blood.

2. Cell segmentation and classification: The HistoPLUS model detected and classified individual cells into nine morphological types: tumor cells, lymphocytes, fibroblasts, plasmocytes, neutrophils, eosinophils, red blood cells, apoptotic bodies, and mitotic figures. Inference at 40x magnification (0.25 um/px) with overlap deduplication via a union-find algorithm.

Spatial Feature Computation

All spatial features were computed on compartment masks resampled to 8 um/px. Five spatial bands were defined using the signed Euclidean distance transform from the tumor boundary:

Band	Definition	Distance
Tumor front	Outer rim of tumor	0-50 um inside tumor boundary
Tumor core	Deep tumor interior	>50 um inside
Peritumoral stroma (near)	Stroma adjacent to tumor	0-50 um outside
Peritumoral stroma (far)	Distant stroma	50-200 um outside
Necrosis ring	Perinecrotic zone	0-100 um from necrosis

Spatial interpretability: tissue compartment maps, top-scoring tiles with cell-type overlays, and the link between spatial features and survival associations.

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Cancer Types

Code	Cancer Type	N slides
BRCA	Breast invasive carcinoma	1,037
LUAD	Lung adenocarcinoma	511
THCA	Thyroid carcinoma	473
HNSC	Head and neck squamous cell carcinoma	471
UCEC	Uterine corpus endometrial carcinoma	459
COAD	Colon adenocarcinoma	441
BLCA	Bladder urothelial carcinoma	417
STAD	Stomach adenocarcinoma	400
LIHC	Liver hepatocellular carcinoma	365
LUSC	Lung squamous cell carcinoma	357
PRAD	Prostate adenocarcinoma	353
CESC	Cervical squamous cell carcinoma	279
ACC	Adrenocortical carcinoma	227
THYM	Thymoma	180
ESCA	Esophageal carcinoma	158
READ	Rectum adenocarcinoma	157
PAAD	Pancreatic adenocarcinoma	146
OV	Ovarian serous cystadenocarcinoma	107
UCS	Uterine carcinosarcoma	87
MESO	Mesothelioma	82
CHOL	Cholangiocarcinoma	38
Total		6,745

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Features

Each slide has 51 *_value columns (quantitative measurements) and 51 matching *_status columns (QC flags: "ok", "warn", or "fail"). The 38 core histomic features are organized into five categories:

Tissue Composition (7 features)

Feature	Description
tumor_area_fraction	Fraction of tissue area classified as cancerous epithelium
stroma_area_fraction	Fraction of tissue area classified as stroma
normal_epithelium_area_fraction	Fraction classified as normal epithelium
tumor_front_fraction	Proportion of tumor area within the 0-50 um front band
largest_tumor_component_share	Share of total tumor area in the largest connected component
tumor_region_solidity	Convex hull solidity of the tumor mask
tissue_coverage	Fraction of the slide covered by tissue

Cell Densities (6 features)

Feature	Description
intratumoral_lymphocyte_density	Lymphocyte count per mm^2 within tumor epithelium
stromal_lymphocyte_density	Lymphocyte count per mm^2 within stroma
intratumoral_cancer_cell_density	Cancer cell count per mm^2 within tumor
fibroblast_density_stroma	Fibroblast count per mm^2 within stroma
intratumoral_eosinophil_density	Eosinophil count per mm^2 within tumor
intratumoral_neutrophil_density	Neutrophil count per mm^2 within tumor

Nuclear Morphology and Kinetics (8 features)

Feature	Description
tumor_nuclear_area_median	Median nuclear area of tumor cells (um^2)
tumor_nuclear_eccentricity_median	Median nuclear eccentricity (0 = circle, 1 = line)
tumor_nuclear_irregularity_median	Median nuclear contour irregularity
tumor_nuclear_irregularity_iqr	IQR of nuclear irregularity (morphological heterogeneity)
tumor_pleomorphism_index	Composite nuclear pleomorphism score
mitotic_index_tumor	Mitotic figure count per mm^2 of tumor
apoptotic_index_tumor	Apoptotic body count per mm^2 of tumor
apoptosis_mitosis_ratio_tumor	Ratio of apoptotic to mitotic events

Spatial Organization (18 features)

Feature	Description
lymphocyte_infiltration_ratio_front	Lymphocyte density ratio: tumor front vs. core
myeloid_infiltration_ratio_front	Myeloid cell density ratio: tumor front vs. core
tumor_lymphocyte_nn_distance_front	Mean nearest-neighbor distance from tumor cells to lymphocytes at the front
tumor_fibroblast_coupling_front	Fibroblast density ratio at the tumor front
tumor_stroma_interface_density	Length of tumor-stroma boundary per unit tumor area
interface_normalized_immune_pressure	Immune cell density normalized by interface length
invasion_depth_p75	75th percentile of tumor invasion depth (um)
peritumoral_immune_richness	Shannon diversity of immune cell types in peritumoral stroma
peritumoral_fibroblast_enrichment	Fibroblast enrichment in peritumoral vs. distal stroma
immune_desert_fraction	Fraction of tumor area devoid of immune cells
deep_intratumoral_lymphocyte_fraction	Fraction of intratumoral lymphocytes in tumor core (>50 um)
fibroblast_lymphocyte_proximity_stroma	Mean distance between fibroblasts and lymphocytes in stroma
intratumoral_myeloid_lymphoid_tilt	Log-ratio of myeloid to lymphoid cells within tumor
stromal_inflammatory_tilt	Log-ratio of inflammatory to fibroblast cells in stroma
eosinophil_neutrophil_ratio_peritumoral	Eosinophil-to-neutrophil ratio in peritumoral stroma
perinecrotic_lymphocyte_enrichment	Lymphocyte enrichment near necrosis
perinecrotic_neutrophil_enrichment	Neutrophil enrichment near necrosis
perinecrotic_myeloid_tilt	Myeloid-to-lymphoid tilt near necrosis

Spatial Heterogeneity (3 features)

Feature	Description
lymphocyte_density_heterogeneity_tumor	Coefficient of variation of lymphocyte density across tumor tiles
tumor_cell_density_heterogeneity	Coefficient of variation of cancer cell density across tumor tiles
stromal_cellularity_heterogeneity	Coefficient of variation of total cellularity across stroma tiles

Additional Features

Feature	Description
necrosis_in_tumor_fraction	Fraction of necrosis within tumor regions
necrosis_heterogeneity	Spatial heterogeneity of necrosis distribution
necrosis_rbc_enrichment	RBC enrichment near necrosis (hemorrhagic necrosis)
necrosis_contact_fraction_stroma	Fraction of necrosis boundary in contact with stroma
tumor_contact_fraction_stroma	Fraction of tumor boundary in contact with stroma
tumor_contact_fraction_necrosis	Fraction of tumor boundary in contact with necrosis
tumor_contact_fraction_normal	Fraction of tumor boundary in contact with normal epithelium
tumor_necrosis_proximity	Mean distance from tumor to nearest necrosis
artifact_fraction	Fraction of tissue area flagged as artifact

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Key Findings

HistoAtlas overview: (a) pipeline, (b) feature correlation structure, (c) UMAP embedding colored by cancer type, (d) morphological cluster composition, (e) cluster feature profiles.

Survival and molecular associations: (a) pan-cancer forest plot of immune density hazard ratios, (b) Kaplan-Meier curves in BRCA, (c-d) gene expression correlations.

Pathway-level associations: (a) heatmap of mean Spearman correlations between Hallmark pathways and histomic features, (b) distribution of significant associations across molecular data types.

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Usage

import pandas as pd

# Load the dataset
df = pd.read_parquet("hf://datasets/PABannier/HistoAtlas/data.parquet")

print(f"Shape: {df.shape}")  # (6745, 106)
print(f"Cancer types: {df['cancer_type'].nunique()}")  # 21

# Get feature values for a specific cancer type
brca = df[df["cancer_type"] == "BRCA"]
print(f"BRCA slides: {len(brca)}")  # 1037

# Extract all value columns (quantitative measurements)
value_cols = [c for c in df.columns if c.endswith("_value")]
features = df[["cancer_type", "slide_name"] + value_cols]

# Check QC status for a feature
ok_mask = df["intratumoral_lymphocyte_density_status"] == "ok"
print(f"Slides with OK lymphocyte density: {ok_mask.sum()}")

With Hugging Face datasets

from datasets import load_dataset

dataset = load_dataset("PABannier/HistoAtlas")
df = dataset["data"].to_pandas()

Linking to TCGA Clinical and Molecular Data

Slide names follow the TCGA barcode convention. Extract the case barcode (first 12 characters) to match with clinical, genomic, and transcriptomic data:

# Extract TCGA case barcode for linking
df["case_id"] = df["slide_name"].str[:12]

# Now join with TCGA-CDR clinical data, MC3 mutations, RNA-seq, etc.

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Preprocessing Notes

The values in this dataset are raw (untransformed) measurements. The statistical analyses in the paper applied the following preprocessing:

1. Log-transform: 22 features with heavy right-skew were transformed using log(1 + x)
2. Winsorization: All features clipped at the 0.5th and 99.5th percentiles (pan-cohort)
3. Z-score standardization: Zero mean, unit variance (scope varies by analysis)

The *_status columns encode per-slide QC flags:

• "ok": Feature passed all quality checks
• "warn": Minor quality concern (e.g., small compartment area)
• "fail": Feature unreliable for this slide (e.g., insufficient tumor area)

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Validation

Independent validation was performed on 1,095 CPTAC slides from 817 cases across five matched cancer types (BRCA, COAD, LUAD, LUSC, UCEC):

• 4/5 cancer-type matches passed feature-level concordance
• Prespecified histomic-transcriptomic associations replicated in direction (10/10) and significance (9/10)
• Matched mRNA-protein associations were directionally concordant in 98.8% of doubly-significant pairs

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Citation

If you use this dataset, please cite:

@article{bannier2025histoatlas,
  title={HistoAtlas: a pan-cancer histomics atlas linking quantitative tissue morphology to transcriptomic programs, somatic alterations, and clinical outcomes},
  author={Bannier, Pierre-Antoine},
  journal={arXiv preprint arXiv:2603.16587},
  year={2025},
  url={https://arxiv.org/abs/2603.16587}
}

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Links

• Interactive Web Atlas: histoatlas.com
• Paper: arXiv:2603.16587
• Code: github.com/histoatlas/histoatlas

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License

This dataset is released under CC BY-NC 4.0. The underlying TCGA whole-slide images are governed by GDC Data Use Policies.