# Cohort-Aware CT Reproduction

This repository implements the first-stage reproduction scaffold for
`paper.pdf`: CT-first pulmonary lesion characterization with optional PET,
clinical missingness, segmentation support maps, calibration hooks and
discrete-time survival output.

## Current Scope

- Implemented: LIDC/Lung-PET-CT-Dx/NSCLC manifest loaders, DICOM/NIfTI/NPZ
  preprocessing hooks, 3D support network, CT/PET encoders, selective PET gate,
  clinical encoder, survival head, losses, metrics, training and evaluation CLI.
- Reserved: NLST schema and survival interface only. The raw NLST data is not
  downloaded in this phase.
- Deferred: exact TCIA RTSTRUCT/XML parsing details and pathology regularization
  until the corresponding data is present locally.

## Environment

Use the requested conda environment:

```bash
conda activate ct
python -m pip install -r requirements-project.txt
```

## Smoke Test

```bash
conda run -n ct python scripts/create_synthetic_manifest.py --out-dir processed/synthetic --n 8
conda run -n ct pytest -q
```

## Training Entry Points

```bash
conda run -n ct python -m src.train --config configs/lidc_seg.yaml
conda run -n ct python -m src.train --config configs/lung_pet_ct_dx.yaml
conda run -n ct python -m src.train --config configs/nsclc_external.yaml
```

Manifests must point to preprocessed lesion crops. Each row should include
`ct_path`; optional columns are `pet_path`, `mask_path`, `label`, `time`,
`event`, `patient_id`, `lesion_id` and configured clinical columns.