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Augmenting radiological assessment of imaging evident dementias with radiomic analysis

This work is published in npj Dementia (https://doi.org/10.1038/s44400-025-00031-1).

Overview

This repository provides a comprehensive pipeline for preprocessing, feature extraction, modeling, and evaluation of neuroradiology imaging data. It integrates FreeSurfer-based volumetric analysis, white matter hyperintensity (WMH) burden estimation, and machine learning models (XGBoost) for Alzheimer’s disease (AD) and Other Imaging Evident Dementia (OIED) predictions.

Table of Contents

Environment Setup

  1. Create a Conda environment: 
    conda env create -f environment.yml
# or for macOS specific
conda env create -f environment-macos.yml
  2. Activate the environment: 
    conda activate neurorad-radiomics

Data Structure

  • data/
    • ml_data/: preprocessed tabular datasets (ml_data_filtered.csv, ml_test_data_filtered.csv, etc.)
    • dev-model/: saved XGBoost models and tuning parameters
    • feature_config.json: definitions of volumetric, WMH, imaging, and demographic features
    • lobe_mapping.json, feature_names_map.json, etc.

Preprocessing

Scripts under preprocessing/ handle MRI data conversion and feature extraction:

  • fs_scripts.py: filter and convert FreeSurfer outputs, parse volume statistics
  • lst_scripts.py: run LST (Lesion Segmentation Tool) for WMH estimation
  • NACC_mris.py & prep_array_job_csv_.py: generate batch arguments for array jobs on NACC datasets

Model Training

Training scripts are in train/:

  • model.py: preprocessing and label generation
  • wandb_sweep.py: hyperparameter sweep setup with Weights & Biases
  • cv.py: cross-validation with GroupKFold
  • final_train.py: train final AD, OIED models with hyperparmeters defined from sweeps

Results & Analysis

Misc. analyses and plotting scripts are in results/

Utilities

  • utils/load_data.py: load and derive feature lists from JSON config
  • utils/dump_dkt_atlas.py: export region names grouped by lobes