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.

The paper is published in Neuro-Oncology Advances. https://doi.org/10.1093/noajnl/vdag115

The workflow is being developed into a software. So stay tuned!

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.

2026

Ghimire P, Li J, Yaou L, Modat M, Booth T. Predictive radiomics for evaluation of cancer immune SignaturE in glioblastoma: the PRECISE-GBM study. Neuro-Oncology Advances. Published May 2, 2026. https://doi.org/10.1093/noajnl/vdag115

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

https://huggingface.co/PrazNeuro/PRECISE_GBM

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

Email: prajwal.1.ghimire@kcl.ac.uk