Evaluating the impact of segmentation strategies on radiomic feature stability for paediatric brain tumour diagnosis using diffusion weighted imaging.
In a retrospective cohort of 106 children with brain tumours, simulated ROI erosion produced more stable ADC radiomic features and smaller diagnostic-accuracy losses than ROI dilation, while training augmentation and stable-feature selection partially improved model robustness.
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In a retrospective cohort of 106 children with brain tumours, simulated ROI erosion produced more stable ADC radiomic features and smaller diagnostic-accuracy losses than ROI dilation, while training augmentation and stable-feature selection partially improved model robustness.
Research significance
The study provides evidence that conservative tumour segmentation and robustness-oriented model training can reduce segmentation-related degradation of imaging classifiers; it is an inference, not demonstrated here, that more reliable classifiers could eventually improve diagnostic or treatment-selection decisions.
Source abstract
OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.