---
license: other
license_name: nlst-data-use-agreement
license_link: https://cdas.cancer.gov/learn/nlst/images/NLST_CDAS_Participant_Data_Use_Agreement.pdf
language:
- en
pretty_name: Radiology Test v2 (OpenM3Chest)
size_categories:
- n<1K
task_categories:
- visual-question-answering
- image-classification
tags:
- medical
- radiology
- chest-ct
- nlst
- openm3chest
- medgemma
- evaluation
dataset_info:
  features:
  - name: keys
    dtype: string
  - name: pids
    dtype: string
  - name: prompt
    dtype: string
  - name: response
    dtype: string
  - name: task_type
    dtype: string
  splits:
  - name: test
    num_bytes: 265112
    num_examples: 154
  download_size: 54296
  dataset_size: 265112
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---

# Radiology Test v2

Evaluation dataset for the **Radiology agent** of an AI Medical Department,
paired with [UngLong/radiology-ready-v2](https://huggingface.co/datasets/UngLong/radiology-ready-v2)
(training set). CT images are in [UngLong/openm3chest-npy-v2](https://huggingface.co/datasets/UngLong/openm3chest-npy-v2).

> ⚠️ **Before using:** CT scans for this test set must be uploaded to `openm3chest-npy-v2` first.
> See `scripts/test_npy_needed.txt` (154 scan keys) for the list to upload via `build_npy_hub.py`.

---

## Dataset Summary

| | |
|---|---|
| **Total rows** | 154 |
| **Screening rows** | 74 (all 8 screening tasks) |
| **Detail rows** | 80 (4 nodule characterization tasks) |
| **Unique CT volumes** | 154 (no overlap between screening & detail pools) |
| **Source** | `*_test.json` splits from NLST/OpenM3Chest |
| **Format** | Multi-task JSON — same as training set |

---

## ⚠️ Known Limitation: Missing Positive Labels in Screening

This is the most important thing to understand about this test set.

### Root Cause

The 8 screening tasks in NLST were **annotated independently** on different scan subsets:

```
chest_abn_54_test.json  → 13,346 scans annotated for atelectasis
chest_abn_55_test.json  → 12,722 scans annotated for pleural thickening
nodule_presence_test.json → 10,406 unique scans
...
```

The multi-task format requires **all 8 labels to be present** for the same scan.
Taking the intersection of all 8 test files leaves only **74 scans** — and these
happen to be predominantly "normal" scans:

```
chest_abn_54 (atelectasis)        :  0/74 positive  ← 0%
chest_abn_55 (pleural thickening) :  0/74 positive  ← 0%
chest_abn_57 (chest wall)         :  0/74 positive  ← 0%
chest_abn_56 (mass/adenopathy)    :  2/74 positive  ← 3%
chest_abn_58 (consolidation)      :  2/74 positive  ← 3%
chest_abn_59 (emphysema)          : 28/74 positive  ← 38% ✅
chest_abn_61 (fibrosis)           : 22/74 positive  ← 30% ✅
nodule_presence                   : 43/74 positive  ← 58% ✅
```

### Why the positive cases are missing

In NLST, rare findings (atelectasis 1%, consolidation 0.8%, chest wall 0.1%) are
unevenly distributed across scan subsets. Scans with `chest_abn_54=Yes` may not
have been included in the `nodule_presence` annotation batch, and vice versa.
This is a structural characteristic of the original NLST annotation process —
not a bug in data processing.

### Evaluation implications

| Task | Evaluable? | Note |
|---|---|---|
| `nodule_presence` | ✅ Yes | 43 positive, 31 negative |
| `chest_abn_59` (emphysema) | ✅ Yes | 28 positive |
| `chest_abn_61` (fibrosis) | ✅ Yes | 22 positive |
| `chest_abn_56` (mass) | ⚠️ Limited | Only 2 positive |
| `chest_abn_58` (consolidation) | ⚠️ Limited | Only 2 positive |
| `chest_abn_54` (atelectasis) | ❌ N/A | 0 positive → recall undefined |
| `chest_abn_55` (pleural) | ❌ N/A | 0 positive → recall undefined |
| `chest_abn_57` (chest wall) | ❌ N/A | 0 positive → recall undefined |
| All 4 detail tasks | ✅ Yes | Balanced distribution |

**Always report F1/Recall per task**, not overall accuracy.
Accuracy will be misleadingly high for tasks with 0 positives (model predicts "No" always → 100% accuracy).

---

## Planned Extension: Single-Task Test Supplements

To properly evaluate the 3 tasks with no positives, supplementary test sets
will be added in future versions. These will relax the "all 8 labels required"
constraint and test each rare finding independently:

| Future dataset | Task | Format |
|---|---|---|
| `radiology-test-abn54` | chest_abn_54 (atelectasis) | Single-task |
| `radiology-test-abn55` | chest_abn_55 (pleural) | Single-task |
| `radiology-test-abn57` | chest_abn_57 (chest wall) | Single-task |

These will be balanced 50/50 and can be used alongside this multi-task set
for comprehensive evaluation.

---

## Label Distribution

### Screening (74 rows)

| Task | Finding | Positive | Negative | % Yes |
|---|---|---|---|---|
| chest_abn_54 | Atelectasis | 0 | 74 | 0% |
| chest_abn_55 | Pleural thickening/effusion | 0 | 74 | 0% |
| chest_abn_56 | Mass/adenopathy ≥10mm | 2 | 72 | 3% |
| chest_abn_57 | Chest wall abnormality | 0 | 74 | 0% |
| chest_abn_58 | Consolidation | 2 | 72 | 3% |
| chest_abn_59 | Emphysema | 28 | 46 | 38% |
| chest_abn_61 | Fibrosis/honeycombing | 22 | 52 | 30% |
| nodule_presence | Lung nodule | 43 | 31 | 58% |

### Detail (80 rows)

| Task | Distribution |
|---|---|
| nodule_location | RUL=18%, LUL=21%, LLL=25%, RLL=30%, RML=6% |
| nodule_attenuation | Solid=74%, Ground Glass=18%, Others=9% ≈ train |
| nodule_margin | Smooth=70%, Poorly defined=14%, Spiculated=11%, Unable=5% ≈ train |
| nodule_size | 4~6mm=46% (⚠️ higher than train 33%), ≤4mm=26%, 8~15mm=11% |

> `nodule_size`: test set skewed toward small nodules (4~6mm overrepresented).
> Size classification metrics may be slightly optimistic.

---

## Schema

Same as `radiology-ready-v2`:

| Field | Type | Description |
|---|---|---|
| `keys` | string | SeriesInstanceUID → `{pids}/{keys}.npy` in NPY repo |
| `pids` | string | Patient ID |
| `prompt` | string | Clinical record + questions + JSON format instruction |
| `response` | string | Ground truth JSON `{task: answer, ...}` |
| `task_type` | string | `"screening"` or `"detail"` |

---

## Usage

```python
import json
import numpy as np
from datasets import load_dataset
from huggingface_hub import hf_hub_download

ds = load_dataset("UngLong/radiology-test-v2", split="train")
row = ds[0]

# Load CT volume (must be uploaded first)
npy_path = hf_hub_download(
    repo_id="UngLong/openm3chest-npy-v2",
    filename=f"{row['pids']}/{row['keys']}.npy",
    repo_type="dataset",
)
volume = np.load(npy_path).astype(np.float32)  # [Z, H, W], HU values

print(row["task_type"])
print(json.loads(row["response"]))
```

## Recommended Evaluation Metrics

```python
from sklearn.metrics import f1_score, recall_score, classification_report

# Per-task F1 and Recall
for task in ["nodule_presence", "chest_abn_59", "chest_abn_61", ...]:
    y_true = [json.loads(r["response"])[task] for r in test_rows]
    y_pred = [model_predict(r)[task] for r in test_rows]
    print(f"{task}: F1={f1_score(y_true, y_pred, average='macro'):.3f}")
    # Do NOT use accuracy for imbalanced tasks
```

---

## Source

Built from [OpenM3Chest](https://openm3chest.org) — NLST-based chest CT dataset.
CT images via [IDC](https://imaging.datacommons.cancer.gov/).
Training set: [UngLong/radiology-ready-v2](https://huggingface.co/datasets/UngLong/radiology-ready-v2).