Metadata-Version: 2.4
Name: radiomics-suite-json9112
Version: 0.1.1
Summary: Advanced radiomics analysis suite for raw segmented imaging and clinical outcome modeling
Author-email: json9112 <json9112@example.com>
Project-URL: Homepage, https://github.com/tablettab/radiomics_suite
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: numpy<2
Requires-Dist: pandas
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: joblib
Requires-Dist: Pillow
Requires-Dist: torch
Requires-Dist: opencv-python
Requires-Dist: scikit-image
Requires-Dist: tqdm
Requires-Dist: openpyxl
Requires-Dist: lifelines

# radiomics-suite-json9112

`radiomics-suite-json9112` wraps advanced radiomics analysis behind a single high-level API.

It accepts raw segmented image folders plus a clinical table, builds traditional and deep radiomics feature spaces internally, synthesizes temporary latent embeddings during analysis, and writes only the final result artifacts by default.

## Install

```bash
pip install radiomics-suite-json9112
```

## Example

```python
from radiomics_suite import EnhancedRadiomicsPipeline

pipeline = EnhancedRadiomicsPipeline()
results = pipeline.run_adv_analysis(
    segmented_folder="/path/to/Segmented images",
    clinical_file="/path/to/master_dataset_normalized_immunotherapy.csv",
    outcome_columns=["OS_12m", "OS_36m", "PFS_12m", "PFS_36m", "DCB", "PNEUMONITIS"],
    light=False,
)
```

Proteomics-style aliasing is also supported for common outcome requests:

```python
from radiomics_suite import EnhancedRadiomicsPipeline

pipeline = EnhancedRadiomicsPipeline()
results = pipeline.run_adv_analysis(
    signal_folder="/path/to/Segmented images",
    clinical_file="/path/to/master_dataset_normalized_immunotherapy.csv",
    outcome_columns=["PNEUMONITIS", "grade >=3"],
    light=True,
)
```

Precomputed features can be reused when needed:

```python
from radiomics_suite import EnhancedRadiomicsPipeline

pipeline = EnhancedRadiomicsPipeline()
results = pipeline.run_adv_analysis(
    clinical_file="/path/to/master_dataset_normalized_immunotherapy.csv",
    traditional_features_file="/path/to/tumor_radiomics_features.csv",
    deep_features_file="/path/to/complete_cool_unet_radiomics.csv",
    outcome_columns=["DCB"],
    light=True,
)
```

## Input Layout

The segmented folder should contain patient subfolders such as `L30 segmented/`, each with paired files like:

- `image_00042.png`
- `image_00042_mask.png`

The clinical table may be `.csv`, `.xlsx`, or `.xls`.

## Notes

- Raw segmented images are the preferred interface.
- Intermediate embeddings are kept in-memory unless `persist_intermediates=True`.
- `light=True` runs a smaller model/search configuration.
- Returned results include `output_dir`, `feature_generation`, and per-outcome best-scenario summaries.
