# Pam50 Subtype Classification Eval

> Evaluates a deep learning model's ability to classify breast cancer into four PAM50 molecular subtypes (Basal-like, HER2-enriched, Luminal A, Luminal B) using H&E-stained histopathology images. It probes the model's discriminative capability, robustness to domain shifts between institutional cohorts, and the necessity of preprocessing steps like stain normalization and multi-objective patch selection. Use when the user wants to benchmark on TCGA-BRCA, CPTAC-BRCA, or asks about evaluating this task. Reports Macro-averaged F1-score.

- Skill: `qhjqhj00/pam50-subtype-classification-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/pam50-subtype-classification-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/pam50-subtype-classification-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/pam50-subtype-classification-eval

---


# pam50-subtype-classification-eval

> A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selection — Borji et al. (2026) (arXiv:2604.01798, 2026)

## What this evaluates

Evaluates a deep learning model's ability to classify breast cancer into four PAM50 molecular subtypes (Basal-like, HER2-enriched, Luminal A, Luminal B) using H&E-stained histopathology images. It probes the model's discriminative capability, robustness to domain shifts between institutional cohorts, and the necessity of preprocessing steps like stain normalization and multi-objective patch selection.

## Datasets

- **TCGA-BRCA** — total 627; splits: internal_validation (627)
- **CPTAC-BRCA** — total 122; splits: external_validation (122)

## Metrics

- `Accuracy` — range: [0, 1]
  - Proportion of correctly classified samples out of the total number of samples.
- `Macro-averaged F1-score` **(primary)** — range: [0, 1]
  - Harmonic mean of precision and recall computed per subtype, then averaged across all four PAM50 subtypes to balance class performance.
- `AUC` — range: [0, 1]
  - Area under the Receiver Operating Characteristic (ROC) curve, measuring the model's ability to distinguish between classes across all classification thresholds.

## Input / output format

**Input**: H&E-stained histopathology whole-slide images, processed into a curated subset of patches via multi-objective optimization (NSGA-II) and stain normalization.

**Output**: Predicted PAM50 subtype label (one of four classes: Basal-like, HER2-enriched, Luminal A, Luminal B).

## Scoring recipe

```python
def compute_metrics(gold_labels, pred_labels, pred_probs, subtypes):
    macro_f1 = 0
    for subtype in subtypes:
        tp = sum(p == subtype and g == subtype for p, g in zip(pred_labels, gold_labels))
        fp = sum(p == subtype and g != subtype for p, g in zip(pred_labels, gold_labels))
        fn = sum(p != subtype and g == subtype for p, g in zip(pred_labels, gold_labels))
        prec = tp / (tp + fp) if (tp + fp) > 0 else 0
        rec = tp / (tp + fn) if (tp + fn) > 0 else 0
        f1 = 2 * prec * rec / (prec + rec) if (prec + rec) > 0 else 0
        macro_f1 += f1
    macro_f1 /= len(subtypes)
    auc = roc_auc_score(gold_labels, pred_probs, multi_class='ovr')
    accuracy = sum(p == g for p, g in zip(pred_labels, gold_labels)) / len(gold_labels)
    return accuracy, macro_f1, auc
```

## Common pitfalls

- Ablation studies were conducted as single-run experiments without multiple random seeds, making performance degradation claims indicative rather than statistically definitive.
- Significant domain shift between internal (TCGA) and external (CPTAC) cohorts causes expected but substantial performance drops, particularly for Basal-like and HER2-enriched subtypes.
- Stain normalization is critical; omitting it severely degrades feature extraction due to color discrepancies across histopathology slides.

## Evidence (verbatim from paper)

> We first evaluated the performance of our proposed method on the internal validation set of the TCGA-BRCA dataset. The model achieved a classification accuracy of 0.8995, a macro-averaged F1-score of 0.8812, and an exceptionally high AUC of 0.9841, underscoring its strong discriminative capability across the four PAM50 breast cancer subtypes.

## Citation

```bibtex
@misc{borji2026pam50,
  title={A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selection},
  author={Borji et al. (2026)},
  year={2026},
  note={arXiv:2604.01798}
}
```

- arXiv: 2604.01798