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Background <p>Determining isocitrate dehydrogenase (IDH) mutation is crucial for glioma clinical management. MR diffusion-derived ‘vessel density’ (DDVD) offers non-invasive tissue perfusion evaluation within the tumor microenvironment. The study attempts to distinguish IDH genotypes of diffuse gliomas using DDVD in whole tumor parenchyma and its habitats.</p> Methods <p>This study enrolled 63 patients with diffuse gliomas (30 IDH-mutant and 33 IDH-wildtype) who underwent diffusion-weighted (DW) imaging at 3T. DDVD<sub>b0b10</sub> was the signal difference between the <i>b</i> = 0 and <i>b</i> = 10&#xa0;s/mm<sup>2</sup> DW images. DDVD<sub>b0b10_b10b20</sub> is DDVD<sub>b0b10</sub> minus DDVD<sub>b10b20</sub>. nDDVD was DDVD divided by signal intensity at <i>b</i> = 0&#xa0;s/mm<sup>2</sup> DW image. Correlations between DDVD metrics/intravoxel incoherent motion (IVIM) imaging metrics (<i>D</i> and <i>f</i>) and IDH genotypes/Ki-67 status were studied.</p> Results <p>In tumor parenchyma, DDVD<sub>b0b10_b10b20</sub> and nDDVD<sub>b0b10_b10b20</sub> were lower, whereas <i>D</i> was higher in IDH-mutant gliomas [median (interquartile range): 12.76 (9.79–14.60); 15.14 (11.61–19.29); 1.31 (1.19–1.39)] compared to IDH-wildtype gliomas [14.48 (2.93–18.60), <i>p</i> = 0.008; 20.55 (15.89–24.02), <i>p</i> &lt; 0.001; 1.16 (0.98–1.27), <i>p</i> = 0.003]. Habitat analysis improved the diagnostic performance for IDH genotyping, with the highest AUC of 0.823 found for the nDDVD<sub>b0b10_b10b20</sub> derived from the high DDVD<sub>b0b10</sub> value habitat. Diagnostic efficacy of the combined model of nDDVD<sub>b0b10_b10b20</sub> with <i>D</i> was superior to that of combined model of <i>f</i> with <i>D</i>. The habitat model incorporating age, sex, and Karnofsky Performance Status further significantly enhanced the diagnostic efficacy, with an AUC reaching 0.979. Additionally, DDVD and <i>f</i> showed a positive correlation with Ki-67, while <i>D</i> exhibited a negative correlation with Ki-67 (all <i>p</i> &lt; 0.05).</p> Conclusion <p>DDVD, as a novel biomarker of microvascular perfusion, effectively differentiates IDH genotypes in gliomas. The habitat analysis improves the diagnostic accuracy for IDH genotyping.</p>

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Magnetic resonance diffusion-derived vessel density (DDVD) as a valuable tissue perfusion biomarker for isocitrate dehydrogenase genotyping in diffuse gliomas

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  • 发表日期：

    2025年3月6日

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  • Chen-Xi Ni,
  • Ruo-Lan Lin,
  • Dian-Qi Yao,
  • Fu-Zhao Ma,
  • Yu-Ting Shi,
  • Ying-Ying He,
  • Yang Song,
  • Guang Yang,
  • Ri-Feng Jiang,
  • Yì Xiáng J. Wáng

摘要

Background

Determining isocitrate dehydrogenase (IDH) mutation is crucial for glioma clinical management. MR diffusion-derived ‘vessel density’ (DDVD) offers non-invasive tissue perfusion evaluation within the tumor microenvironment. The study attempts to distinguish IDH genotypes of diffuse gliomas using DDVD in whole tumor parenchyma and its habitats.

Methods

This study enrolled 63 patients with diffuse gliomas (30 IDH-mutant and 33 IDH-wildtype) who underwent diffusion-weighted (DW) imaging at 3T. DDVDb0b10 was the signal difference between the b = 0 and b = 10 s/mm2 DW images. DDVDb0b10_b10b20 is DDVDb0b10 minus DDVDb10b20. nDDVD was DDVD divided by signal intensity at b = 0 s/mm2 DW image. Correlations between DDVD metrics/intravoxel incoherent motion (IVIM) imaging metrics (D and f) and IDH genotypes/Ki-67 status were studied.

Results

In tumor parenchyma, DDVDb0b10_b10b20 and nDDVDb0b10_b10b20 were lower, whereas D was higher in IDH-mutant gliomas [median (interquartile range): 12.76 (9.79–14.60); 15.14 (11.61–19.29); 1.31 (1.19–1.39)] compared to IDH-wildtype gliomas [14.48 (2.93–18.60), p = 0.008; 20.55 (15.89–24.02), p < 0.001; 1.16 (0.98–1.27), p = 0.003]. Habitat analysis improved the diagnostic performance for IDH genotyping, with the highest AUC of 0.823 found for the nDDVDb0b10_b10b20 derived from the high DDVDb0b10 value habitat. Diagnostic efficacy of the combined model of nDDVDb0b10_b10b20 with D was superior to that of combined model of f with D. The habitat model incorporating age, sex, and Karnofsky Performance Status further significantly enhanced the diagnostic efficacy, with an AUC reaching 0.979. Additionally, DDVD and f showed a positive correlation with Ki-67, while D exhibited a negative correlation with Ki-67 (all p < 0.05).

Conclusion

DDVD, as a novel biomarker of microvascular perfusion, effectively differentiates IDH genotypes in gliomas. The habitat analysis improves the diagnostic accuracy for IDH genotyping.