PET/CT Research Code

Official repository of the paper “Radiogenomic Signatures Derived from Pretreatment PET/CT Predict Pathological Complete Response to Neoadjuvant Chemoimmunotherapy in Non-Small Cell Lung Cancer.”

This repository contains the reproducible training and preprocessing code for a multimodal PET/CT binary-classification study. Python 3.10 or newer is required. The public code deliberately excludes metadata, images, patient-level results, legacy checkpoints, and private absolute paths.

Chinese documentation: README.zh-CN.md

Installation

python -m venv .venv
python -m pip install -e ".[dev,hpo,two-stage]"

Core training installs PyTorch, TorchVision, TorchIO, SimpleITK, and the scientific Python stack. Optuna and boosted-tree libraries are optional extras.

Commands

python -m petct train --config configs/train.yaml
python -m petct hpo --config configs/hpo.yaml
python -m petct crossval --config configs/compare.yaml
python -m petct two-stage --config configs/two_stage.yaml
python -m petct preprocess all --config configs/preprocess.yaml
python -m petct evaluate --config configs/evaluate.yaml
python -m petct split --config configs/split.yaml

Any YAML value can be overridden without editing the file:

python -m petct train --config configs/train.yaml \
  --set training.optimizer.learning_rate=0.0002 --set device=cuda:0

The historical scripts remain as thin wrappers. For example, python train_petct.py --config configs/train.yaml calls the same public API. An HPO best configuration should keep evaluation.external_after_training: false; use configs/evaluate.yaml for the separate final checkpoint evaluation.

Data contract

Metadata may be CSV or XLSX. Configure its ID and binary-label columns in YAML. Manifests contain one case ID per line. Each configured CT and PET root must have matching case directories with exactly 64 naturally sorted grayscale slices of 64 x 64 pixels. Missing modalities, unknown labels, duplicate IDs, invalid image sizes, and invalid slice counts are errors.

Training-only spatial augmentation is applied to CT and PET in a shared TorchIO Subject. Validation and external cohorts are never augmented. The external cohort is not constructed during HPO and is evaluated only after model selection.

Models and outputs

The explicit registry provides fusion3d, densenet, and feature_extractor. TorchVision's weights API is used, and pretrained first-layer weights are expanded to the required input channels.

By default, a run writes resolved_config.yaml, metrics.json, history.csv, run.log, and a pure best.pt state dict. Patient IDs and per-case probabilities are not written. Set output.save_predictions: true only when anonymous paired prediction rows are required for statistical comparison.

Reproducibility checks

pytest
ruff check .
python -m petct --help

See preprocess/README.md for image processing and configs/README.md for the configuration schema.