Evaluating the impact of segmentation strategies on radiomic feature stability for paediatric brain tumour diagnosis using diffusion weighted imaging.
Authors
Affiliations (5)
Affiliations (5)
- Aston Institute of Health and Neurodevelopment, Aston University, Birmingham, United Kingdom.
- Department of Oncology, Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, United Kingdom.
- Institute of Cancer & Genomics Science, University of Birmingham, Birmingham, United Kingdom.
- Medical Physics Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom.
- School of Medicine, University of Nottingham, Nottingham, United Kingdom.
Abstract
This research aims to comprehensively assess the impact of both conservatively and extensively delineated Regions of Interest (ROI) on subsequent quantitative in-vivo characterisation of paediatric brain tumours using diffusion-weighted MRI. Utilising a retrospective cohort of 106 paediatric brain tumour patients, ground truth (GT) ROIs delineating tumour boundaries were eroded or dilated, simulating conservative and extensive ROI drawing strategies respectively. Stability was evaluated for 19 first-order radiomic features extracted from Apparent Diffusion Coefficient (ADC) maps within each ROI. Further, these features were used to train a series of machine learning models to evaluate the impact of ROI boundaries on downstream diagnostic classification. For 18/19 first-order features, ROI dilation introduced significantly (p < 0.01) greater feature variability than erosion, with large effect size (d > 0.8) for 11 features. This relationship was variable between diagnoses, and strongest amongst pilocytic astrocytomas. Diagnostic models trained using features from GT ROIs were negatively impacted with classification accuracy reduced by 3.8 ± 0.8% and 5.6 ± 0.9% for low-level erosion and dilation respectively. Inclusion of eroded/dilated ROI features into the training dataset combined with stable-feature selection partially mitigated the impact of erosion/dilation on model accuracy with 1.4 ± 0.7% and 2.9 ± 0.3% accuracy drop compared model accuracy on features extracted from GT ROIs. The consistently reduced impact of conservative boundaries over extensive ones suggests that, in terms of segmentation strategies, exclusion of ambiguous boundary regions may be preferable over their inclusion. Additionally, diagnostic models exhibited improved robustness to variable ROI drawing strategies through training augmentation and selection of stable features.