PrazNeuro/PRECISE_GBM

Predictive Radiomics for Evaluation of Cancer Immune SignaturE in Glioblastoma | PRECISE-GBM

This repository contains an AI-based training and retraining pipeline for Predictive Radiomics for Evaluation of Cancer Immune SignaturE in Glioblastoma (PRECISE-GBM). It is the multimodal radiogenomic framework that integrates MRI radiomics, genomics, and immune signatures for patient stratification.

Project: PRECISE-GBM - Model training & retraining helpers

📝 Overview

This repository contains code to train models (Gaussian Mixture labelling + SVM and ensemble classifiers) and to persist all artifacts required to reproduce or retrain models on new data. It includes:

• Scenario_heldout_final_PRECISE.py — training pipeline producing .joblib models and metadata JSONs (selected features, best params, CV results).
• retrain_helper.py — CLI utility to rebuild pipelines, set best params and retrain using saved selected-features and params JSONs. Supports JSON/YAML config files and auto-detection of model type.
• README_RETRAIN.md — detailed retrain examples and a notebook cell.

This repo also includes helper files to make it ready for GitHub:

• requirements.txt — Python dependencies
• .gitignore — recommended ignores (models, caches, logs)
• LICENSE — MIT license
• GitHub Actions workflow for CI (pytest smoke test)

📝 Getting started (Windows PowerShell)

1. Create and activate a virtual environment

python -m venv .venv
.\.venv\Scripts\Activate.ps1

2. Install dependencies

pip install --upgrade pip
pip install -r requirements.txt

3. Run training (note: the training script reads data from absolute paths configured in the script — adjust them or run from an environment where those files are present)

python Scenario_heldout_final_PRECISE.py

The training script will create model files under models_LM22/ and models_GBM/ and write metadata JSONs next to each joblib model (selected features, params, cv results) as well as group-level JSON summaries.

📝 Retraining

See README_RETRAIN.md for detailed CLI and notebook examples. Short example:

python retrain_helper.py \
  --model-prefix "models_GBM/scenario_1/GBM_scen1_Tcell" \
  --train-csv "data\new_train.csv" \
  --label-col "label"

📝Notes

• The training script contains hard-coded absolute paths to data files. Before running on another machine, update the scenarios_* file paths or place the datasets in the same paths.
• Retrain helper auto-detects model type when --model-type is omitted by looking for {prefix}_svm_params.json or {prefix}_ens_params.json.
• YAML config support for retrain requires PyYAML (pip install pyyaml).

📝 CI

A basic GitHub Actions workflow runs a smoke pytest to ensure the retrain helper imports and basic pipeline construction works. It does not run heavy training.

📝 Contributing

See CONTRIBUTING.md for guidance on opening issues and PRs.

📝 License

This project is released under the MIT License — see LICENSE. MIT License.

📝 Citation

Please use the following citations when using the repository.

2025

Ghimire P, Modat M, Booth T. Predictive radiogenomic AI Model for patient stratification in brain tumor immunotherapy trials. Neuro-oncology. Oct 2025; 26(Suppl_3): iii58–iii59. doi: https://doi.org/10.1093/neuonc/noaf193.188

Ghimire P, Modat M, Booth T. Radiogenomic AI model predicts immune status in IDH wildtype glioblastoma: PRECISE-GBM study. RCR open. Jan 2025; 3(1): 100234

2024

Ghimire P, Modat M, Booth T. A machine Learning bases predictive radiomics for evaluation of cancer immune signature in glioblastoma: the PRECISE-GBM study. Neuro-Oncology. Oct 2024; 26(suppl_5): v25.

Ghimire P, Modat M, Booth T. A radiogenomic machine learning based study to identify Predictive Radiomics for Evaluation of Cancer Immune SignaturE in IDHw Glioblastoma. Neuro-Oncology. Oct 2024; 26(suppl_7): vii3

Contact:

Dr Prajwal Ghimire

MBBS MRCSEd MSc PhD'26

School of Biomedical Engineering & Imaging Sciences, King's College London

Email: [email protected]