vasari-auto 0.1.0

Automated VASARI featurisation of glioma MRI — fork of the original by Ruffle et al.

VASARI-auto

Note — This is a fork of the original VASARI-auto by Ruffle et al. (2024), maintained by Nikitas Koussis for integration with OncoPrep. All scientific credit belongs to the original authors — see Citation below.

This is the codebase for automated VASARI characterisation of glioma, as detailed in the original article.

Table of Contents

• What is this repository for?
• Usage
• Advantages
  • Stable
  • Efficient
  • Informative
  • Equitable
• Usage queries
• Citation
• Funding

What is this repository for?

The VASARI MRI feature set is a quantitative system designed to standardise glioma imaging descriptions.

Though effective, deriving VASARI is time-consuming to derive manually.

To resolve this, we release VASARI-auto, an automated labelling software applied to open-source lesion masks.

VASARI-auto is a highly efficient and equitable automated labelling system, a favourable economic profile if used as a decision support tool, and offers non-inferior survival prediction.

Usage

VASARI-auto requires only a tumour segmentation file only, which allows users to apply code efficiently and effectively on anonymised lesion masks, for example in using the output of our tumour segmentation model (paper | codebase).

For segmentation files, this code assumes that lesion components are labelled within a NIFTI file as follows:

- Perilesional signal change = 2
- Enhancing tumour = 3
- Nonenhancing tumour = 1

See the Jupyter Notebook tutorial that calls upon the source code.

Advantages

Stable

Relying on tumour segmentation masks and geometry only, VASARI-auto is deterministic, with no variability between inference, in comparison to when cases are reviewed by different neuroradiologists.

Efficient

The time for neuroradiologists to derive VASARI is substantially higher than VASARI-auto (mean time per case 317 vs. 3 s).

A UK hospital workforce analysis forecast that three years of VASARI featurisation would demand 29,777 consultant neuroradiologist workforce hours and >£1.5 ($1.9) million, reducible to 332 hours of computing time (and £146 of power) with VASARI-auto.

Informative

We identify that the best-performing survival model utilised VASARI-auto features instead of those derived by neuroradiologists.

Equitable

VASARI-auto is demonstrably equitable across a diverse patient cohort (panels B and C).

Usage queries

Via github issue log or email to [e-mail]

Citation

If using these works, please cite the following article:

Ruffle JK, Mohinta S, Pegoretti Baruteau K, Rajiah R, Lee F, Brandner S, Nachev P, Hyare H. VASARI-auto: Equitable, efficient, and economical featurisation of glioma MRI. Neuroimage: Clinical, 2024, 44 (103668).

Funding

The Medical Research Council; Wellcome Trust; UCLH NIHR Biomedical Research Centre; Guarantors of Brain; National Brain Appeal; British Society of Neuroradiology.