Impact of Automated Segmentation on Radiomics-based Growth Prediction of Vestibular Schwannoma
DOI:
https://doi.org/10.18372/1990-5548.88.20980Keywords:
vestibular schwannoma, automatic segmentation, nnU-Net, radiomics features, reproducibility, IIC, tumor growth prediction, machine learningAbstract
This paper investigates the impact of nnU-Net-based automatic segmentation on the reproducibility of radiomics features and the quality of vestibular schwannoma growth prediction. The nnU-Net model was trained on 317 T1C image pairs with masks from 155 patients from the Vestibular-Schwannoma-MC-RC2 dataset using 5-fold cross-validation, achieving a mean Dice coefficient of 0.862. ICC analysis showed that 88.8% of wavelet features maintain good or excellent agreement (ICC ≥ 0.75) between manual and automatic masks. Comparison on a subset of 96 patients with growth labels showed: the previously published pipeline (Wavelet + Voting ensemble) with manual masks achieves ROC AUC = 0.742, with automatic masks - 0.639 (−14.0%). Pipeline optimization (ICC filtering, ensemble adaptation to LR + LDA) improves the result to 0.687 (−7.4%). The results determine the cost of automating the radiomics pipeline and provide recommendations for ICC filtering for clinical implementation.
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