Performance Comparison of Diffusion Kurtosis Imaging (DKI), Neurite Orientation Dispersion and Density Imaging (NODDI), and Diffusion Microstructure Imaging (DMI) in Predicting Adult-Type Glioma Subtype—A Pilot Study
Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Participants
2.3. MR Imaging
2.4. Image Analysis
2.5. Postoperative Tumor Subtyping
- (i)
- glioblastomas, IDH wildtype
- (ii)
- astrocytomas, IDH mutant
- (iii)
- oligodendrogliomas, IDH mutant
2.6. Statistical Analysis
3. Results
3.1. Patients
3.2. Comparison of DKI, NODDI, and DMI Parameters in Each ROI
3.3. Diagnostic Performance in Differating IDH Mutant from IDH Wildtype Gliomas
3.4. Diagnostic Performance in Differating Astocytomas, IDH Mutant from Oligodendrogliomas, IDH Mutant
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Patients enrolled in the study | 108 |
Patients included in the data analysis | 59 |
Patients excluded due to histopathological/molecular diagnosis | 24 |
Patients excluded due to insufficient MRI quality | 25 |
Mean age of the included patients ± SD | 45.3 ± 15.7 |
Female:male ratio | 1:1.6 |
Glioblastoma, IDH wildtype (WHO grade 4) | 31 (47.4%) |
Astrocytoma, IDH mutant (WHO grade 2) | 12 (18.5%) |
Astrocytoma, IDH mutant (WHO grade 3) | 1 (1.5%) |
Astrocytoma, IDH mutant (WHO grade 4) | 4 (6.2%) |
Oligodendroglioma, IDH mutant (WHO grade 2) | 3 (4.6%) |
Oligodendroglioma, IDH mutant (WHO grade 3) | 8 (12.3%) |
ADC | MK | ODI | fiso | ficvf | v-csf | v-Extra | v-Intra | ||
---|---|---|---|---|---|---|---|---|---|
ROI1 | AUC (95% Confidence interval) | 0.641 (0.452–0.830) | 0.698 (0.522–0.874) | 0.573 (0.397–0.749) | 0.547 (0.377–0.717) | 0.525 (0.348–0.703) | 0.506 (0.319–0.693) | 0.549 (0.379–0.719) | 0.545 (0.367–0.723) |
Cutoff value 1 | 0.536 | 0.561 | 0.638 | 0.164 | 0.624 | 0.283 | 0.416 | 0.619 | |
Sensitivity | 0.933 | 0.867 | 0.667 | 0.767 | 0.767 | 0.700 | 0.733 | 0.767 | |
Specificity | 0.529 | 0.588 | 0.588 | 0.412 | 0.412 | 0.412 | 0.412 | 0.353 | |
ROI2 | AUC (95% Confidence interval) | 0.855 (0.738–0.972) | 0.816 (0.672–0.959) | 0.569 (0.395–0.742) | 0.622 (0.458–0.785) | 0.643 (0.477–0.810) | 0.690 (0.531–0.849) | 0.459 (0.284–0.633) | 0.709 (0.555–0.863) |
Cutoff value 1 | 0.534 | 0.454 | 0.650 | 0.204 | 0.663 | 0.232 | 0.330 | 0.645 | |
Sensitivity | 0.867 | 0.933 | 0.533 | 0.600 | 0.567 | 0.900 | 0.800 | 0.667 | |
Specificity | 0.824 | 0.706 | 0.765 | 0.647 | 0.765 | 0.412 | 0.059 | 0.606 | |
ROI3 | AUC (95% Confidence interval) | 0.902 (0.812–0.992) | 0.882 (0.680–0.963) | 0.674 (0.516–0.831) | 0.558 (0.384–0.731) | 0.655 (0.494–0.816) | 0.654 (0.487–0.821) | 0.529 (0.351–0.708) | 0.678 (0.516–0.841) |
Cutoff value 1 | 0.723 | 0.485 | 0.664 | 0.191 | 0.601 | 0.304 | 0.439 | 0.652 | |
Sensitivity | 0.767 | 0.993 | 0.633 | 0.800 | 0.700 | 0.633 | 0.633 | 0.600 | |
Specificity | 0.941 | 0.665 | 0.765 | 0.412 | 0.588 | 0.765 | 0.647 | 0.606 | |
ROI4 | AUC (95% Confidence interval) | 0.894 (0.797–0.991) | 0.798 (0.638–0.958) | 0.676 (0.520–0.833) | 0.504 (0.328–0.680) | 0.642 (0.481–0.803) | 0.559 (0.387–0.731) | 0.564 (0.387–0.741) | 0.656 (0.489–0.823) |
Cutoff value 1 | 0.737 | 0.627 | 0.690 | 0.226 | 0.668 | 0.262 | 0.429 | 0.626 | |
Sensitivity | 0.833 | 0.800 | 0.533 | 0.367 | 0.533 | 0.767 | 0.633 | 0.633 | |
Specificity | 0.882 | 0.824 | 0.824 | 0.471 | 0.824 | 0.412 | 0.647 | 0.647 | |
ROI5 | AUC (95% Confidence interval) | 0.910 (0.824–0.995) | 0.778 (0.609–0.948) | 0.672 (0.513–0.830) | 0.594 (0.428–0.760) | 0.651 (0.483–0.819) | 0.614 (0.449–0.778) | 0.567 (0.385–0.749) | 0.697 (0.529–0.865) |
Cutoff value 1 | 0.666 | 0.543 | 0.680 | 0.224 | 0.574 | 0.315 | 0.419 | 0.613 | |
Sensitivity | 0.833 | 0.867 | 0.567 | 0.600 | 0.833 | 0.433 | 0.633 | 0.800 | |
Specificity | 0.882 | 0.665 | 0.765 | 0.529 | 0.471 | 0.824 | 0.588 | 0.647 | |
ROI6 | AUC (95% Confidence interval) | 0.840 (0.718–0.962) | 0.775 (0.598–0.951) | 0.637 (0.466–0.808) | 0.629 (0.470–0.789) | 0.572 (0.396–0.4747) | 0.627 (0.468–0.786) | 0.522 (0.349–0.694) | 0.776 (0.640–0.913) |
Cutoff value 1 | 0.711 | 0.581 | 0.597 | 0.245 | 0.637 | 0.317 | 0.397 | 0.598 | |
Sensitivity | 0.700 | 0.833 | 0.767 | 0.533 | 0.533 | 0.422 | 0.733 | 0.833 | |
Specificity | 0.882 | 0.824 | 0.529 | 0.765 | 0.647 | 1.000 | 0.412 | 0.606 |
ADC | MK | ODI | fiso | ficvf | v-csf | v-Extra | v-Intra | ||
---|---|---|---|---|---|---|---|---|---|
ROI1 | AUC (95% Confidence interval) | 0.615 (0.398–0.832) | 0.604 (0.381–0.827) | 0.647 (0.431–0.863) | 0.631 (0.413–0.849) | 0.529 (0.294–0.764) | 0.652 (0.443–0.862) | 0.572 (0.339–0.805) | 0.668 (0.443–0.894) |
Cutoff value 1 | 0.558 | 0.549 | 0.301 | 0.732 | 0.594 | 0.694 | 0.586 | 0.472 | |
Sensitivity | 0.824 | 0.824 | 0.529 | 0.353 | 0.941 | 0.529 | 0.824 | 1.000 | |
Specificity | 0.455 | 0.455 | 0.727 | 0.909 | 0.273 | 0.818 | 0.364 | 0.455 | |
ROI2 | AUC (95% Confidence interval) | 0.802 (0.621–0.984) | 0.802 (0.619–0.986) | 0.625 (0.398–0.853) | 0.636 (0.421–0.852) | 0.652 (0.421–0.884) | 0.759 (0.576–0.942) | 0.540 (0.308–0.772) | 0.749 (0.548–0.950) |
Cutoff value 1 | 0.514 | 0.808 | 0.300 | 0.558 | 0.584 | 0.468 | 0.570 | 0.439 | |
Sensitivity | 0.882 | 0.824 | 0.824 | 0.824 | 0.765 | 0.941 | 0.882 | 0.941 | |
Specificity | 0.455 | 0.818 | 0.455 | 0.455 | 0.555 | 0.455 | 0.273 | 0.555 | |
ROI3 | AUC (95% Confidence interval) | 0.818 (0.649–0.988) | 0.749 (0.547–0.950) | 0.658 (0.440–0.876) | 0.599 (0.374–0.824) | 0.599 (0.364–0.834) | 0.674 (0.475–0.890) | 0.460 (0.229–0.691) | 0.647 (0.423–0.871) |
Cutoff value 1 | 0.606 | 0.909 | 0.290 | 0.587 | 0.590 | 0.621 | 0.611 | 0.467 | |
Sensitivity | 0.824 | 0.765 | 0.882 | 0.824 | 0.706 | 0.765 | 0.412 | 0.941 | |
Specificity | 0.727 | 0.636 | 0.455 | 0.364 | 0.636 | 0.627 | 0.364 | 0.455 | |
ROI4 | AUC (95% Confidence interval) | 0.738 (0.544–0.932) | 0.674 (0.454–0.893) | 0.674 (0.472–0.876) | 0.604 (0.382–0.826) | 0.460 (0.231–0.707) | 0.631 (0.402–0.860) | 0.516 (0.287–0.745) | 0.540 (0.305–0.775) |
Cutoff value1 | 0.535 | 0.907 | 0.339 | 0.584 | 0.617 | 0.514 | 0.636 | 0.579 | |
Sensitivity | 0.824 | 0.765 | 0.529 | 0.765 | 0.059 | 0.941 | 0.294 | 0.765 | |
Specificity | 0.636 | 0.627 | 0.436 | 0.545 | 0.636 | 0.455 | 0.909 | 0.455 | |
ROI5 | AUC (95% Confidence interval) | 0.781 (0.600–0.961) | 0.658 (0.442–0.874) | 0.620 (0.411–0.830) | 0.647 (0.428–0.866) | 0.481 (0.240–0.723) | 0.652 (0.438–0.867) | 0.551 (0.327–0.774) | 0.561 (0.331–0.792) |
Cutoff value 1 | 0.489 | 0.995 | 0.338 | 0.512 | 0.585 | 0.631 | 0.547 | 0.488 | |
Sensitivity | 0.882 | 0.588 | 0.588 | 0.941 | 1.000 | 0.588 | 0.765 | 1–000 | |
Specificity | 0.636 | 0.818 | 0.627 | 0.364 | 0.273 | 0.536 | 0.273 | 0.273 | |
ROI6 | AUC (95% Confidence interval) | 0.797 (0.620–0.974) | 0.631 (0.408–0.854) | 0.508 (0.290–0.726) | 0.556 (0.317–0.795) | 0.503 (0.265–0.740) | 0.567 (0.325–0.809) | 0.561 (0.322–0.801) | 0.548 (0.317–0.779) |
Cutoff value 1 | 0.523 | 0.935 | 0.340 | 0.518 | 0.499 | 0.531 | 0.585 | 0.521 | |
Sensitivity | 0.882 | 0.765 | 0.529 | 1.000 | 1.000 | 1.000 | 0.882 | 1.000 | |
Specificity | 0.727 | 0.545 | 0.182 | 0.273 | 0.273 | 0.364 | 0.364 | 0.182 |
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Share and Cite
Zerweck, L.; Würtemberger, U.; Klose, U.; Reisert, M.; Richter, V.; Nägele, T.; Staber, D.; Han, T.; Shen, M.; Xie, C.; et al. Performance Comparison of Diffusion Kurtosis Imaging (DKI), Neurite Orientation Dispersion and Density Imaging (NODDI), and Diffusion Microstructure Imaging (DMI) in Predicting Adult-Type Glioma Subtype—A Pilot Study. Cancers 2025, 17, 876. https://doi.org/10.3390/cancers17050876
Zerweck L, Würtemberger U, Klose U, Reisert M, Richter V, Nägele T, Staber D, Han T, Shen M, Xie C, et al. Performance Comparison of Diffusion Kurtosis Imaging (DKI), Neurite Orientation Dispersion and Density Imaging (NODDI), and Diffusion Microstructure Imaging (DMI) in Predicting Adult-Type Glioma Subtype—A Pilot Study. Cancers. 2025; 17(5):876. https://doi.org/10.3390/cancers17050876
Chicago/Turabian StyleZerweck, Leonie, Urs Würtemberger, Uwe Klose, Marco Reisert, Vivien Richter, Thomas Nägele, Deborah Staber, Tong Han, Mi Shen, Chuanmiao Xie, and et al. 2025. "Performance Comparison of Diffusion Kurtosis Imaging (DKI), Neurite Orientation Dispersion and Density Imaging (NODDI), and Diffusion Microstructure Imaging (DMI) in Predicting Adult-Type Glioma Subtype—A Pilot Study" Cancers 17, no. 5: 876. https://doi.org/10.3390/cancers17050876
APA StyleZerweck, L., Würtemberger, U., Klose, U., Reisert, M., Richter, V., Nägele, T., Staber, D., Han, T., Shen, M., Xie, C., Hu, H., Yang, S., Cao, Z., Erb, G., Ernemann, U., & Hauser, T.-K. (2025). Performance Comparison of Diffusion Kurtosis Imaging (DKI), Neurite Orientation Dispersion and Density Imaging (NODDI), and Diffusion Microstructure Imaging (DMI) in Predicting Adult-Type Glioma Subtype—A Pilot Study. Cancers, 17(5), 876. https://doi.org/10.3390/cancers17050876