Anticancer research · 2025
Radiomics for Growth Prediction of Vestibular Schwannomas in Neurofibromatosis Type 2.
СтатьяНазвание и аннотация: Перевод в очереди
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Boe Nina, Mautner Victor F, Friedrich Reinhard E, Farschtschi Said C, Dührsen Lasse, Meyer Hanno S, Koeppen Johannes A
Аннотация
BACKGROUND/AIM: NF2-related schwannomatosis, formerly known as Neurofibromatosis type 2 (NF2) is characterized by bilateral vestibular schwannomas (VS). Managing NF2 requires balancing watchful waiting with surgical intervention, each carrying inherent risks. While these risks are acknowledged, they have not yet been subjected to systematic investigation. Accurate prognosis of tumor growth is crucial for clinical decision-making. This study investigated radiomics features from longitudinal magnetic resonance imaging (MRI) data to predict VS growth.
PATIENTS AND METHODS: Radiomics features were extracted from cranial MRIs of 32 NF2 patients, each with at least two or more imaging time points. The association between these features and tumor growth was analyzed through correlation, visual inspection, and the Boruta algorithm.
RESULTS: Correlations between growth rates and radiomics features were weak (ρ≤0.23, p<0.016). Three features exhibited a bimodal distribution, with cluster affiliation linked to tumor growth rate [cluster A: 7.9%/month, cluster B: 2.0%/month; Fisher exact odds ratio (OR)=2.55, p=0.010]. When considering only the first tumors in the MRI series, the Fisher exact OR was 2.29 (p=0.223). Boruta analysis identified wavelet.HLH_glcm_InverseVariance as a key feature, also relevant in the bimodal distribution. The Fisher exact OR of wavelet.HLH_glcm_InverseVariance for tumor growth was 2.64 (p=0.011) for all tumors and 2.21 (p=0.229) for initial tumors in the MRI series.
CONCLUSION: Bimodally distributed radiomics features from initial MRIs did not reliably predict rapid tumor growth (error probability: 23%) but may aid in planning MRI follow-up intervals.
PATIENTS AND METHODS: Radiomics features were extracted from cranial MRIs of 32 NF2 patients, each with at least two or more imaging time points. The association between these features and tumor growth was analyzed through correlation, visual inspection, and the Boruta algorithm.
RESULTS: Correlations between growth rates and radiomics features were weak (ρ≤0.23, p<0.016). Three features exhibited a bimodal distribution, with cluster affiliation linked to tumor growth rate [cluster A: 7.9%/month, cluster B: 2.0%/month; Fisher exact odds ratio (OR)=2.55, p=0.010]. When considering only the first tumors in the MRI series, the Fisher exact OR was 2.29 (p=0.223). Boruta analysis identified wavelet.HLH_glcm_InverseVariance as a key feature, also relevant in the bimodal distribution. The Fisher exact OR of wavelet.HLH_glcm_InverseVariance for tumor growth was 2.64 (p=0.011) for all tumors and 2.21 (p=0.229) for initial tumors in the MRI series.
CONCLUSION: Bimodally distributed radiomics features from initial MRIs did not reliably predict rapid tumor growth (error probability: 23%) but may aid in planning MRI follow-up intervals.