Noninvasive prediction of Ki-67 expression level in IDH-wildtype glioblastoma using MRI histogram analysis: comparison and combination of MRI morphological features DOI Creative Commons
Liang Qiang, Qiang Li, Xianwang Liu

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: May 16, 2025

Purpose To assess and compare the effectiveness of magnetic resonance imaging (MRI) morphological features MRI histogram analysis in noninvasively predicting Ki-67 expression levels patients with IDH-wildtype glioblastoma. Methods Forty-six cases glioblastoma measured from January 2022 to July 2024 were retrospectively collected. They divided into low-level group (Ki-67<20%, n=20) high-level (Ki-67≥20%, n=26) according level. assessed recorded. performed on contrast-enhanced T1-weighted images. Differences between these parameters compared two groups. The diagnostic performance was by area under receiver operating characteristic curve (AUC). Spearman correlation used evaluate relationship Results Hemorrhage more prone occur ( P =0.017). min, P01, P50, P75 higher than those <0.00357). There a significant positive min r =0.774), P01 =0.729), P50 =0.625), =0.591), level <0.05). optimal obtained combining parameters, an AUC 0.867. Conclusion Both could predict glioblastoma, combined model integrating can be excellent biomarker for

Language: Английский

Noninvasive prediction of Ki-67 expression level in IDH-wildtype glioblastoma using MRI histogram analysis: comparison and combination of MRI morphological features DOI Creative Commons
Liang Qiang, Qiang Li, Xianwang Liu

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: May 16, 2025

Purpose To assess and compare the effectiveness of magnetic resonance imaging (MRI) morphological features MRI histogram analysis in noninvasively predicting Ki-67 expression levels patients with IDH-wildtype glioblastoma. Methods Forty-six cases glioblastoma measured from January 2022 to July 2024 were retrospectively collected. They divided into low-level group (Ki-67<20%, n=20) high-level (Ki-67≥20%, n=26) according level. assessed recorded. performed on contrast-enhanced T1-weighted images. Differences between these parameters compared two groups. The diagnostic performance was by area under receiver operating characteristic curve (AUC). Spearman correlation used evaluate relationship Results Hemorrhage more prone occur ( P =0.017). min, P01, P50, P75 higher than those <0.00357). There a significant positive min r =0.774), P01 =0.729), P50 =0.625), =0.591), level <0.05). optimal obtained combining parameters, an AUC 0.867. Conclusion Both could predict glioblastoma, combined model integrating can be excellent biomarker for

Language: Английский

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