Improvement of magnetic resonance imaging diffusion metric ‘slow diffusion coefficient (SDC)’ over apparent diffusion coefficient (ADC) for isocitrate dehydrogenase (IDH) genotyping in diffuse gliomas

Though the diffusion metric apparent diffusion coefficient (ADC) had been considered to reflect diffusion and tissue cellularity, recent work suggests ADC measure is also heavily contributed by tissue T2 relaxation time (1,2). This is partly due to that a tissue on high b-value imaging has a shorter ‘measured’ T2 than the low b-value imaging, and thus shorter T2 tissues (particularly those with T2 <60 ms) have a faster T2-related signal decay between low b-value imaging and high b-value imaging (3,4). To mitigate this, a new metric ‘slow diffusion coefficient (SDC)’ has been proposed (5). In its basic form, SDC is derived from the signal subtraction of a high b-value diffusion weighted image and a higher b-value diffusion weighted image.

Recently, we explored the potential application of SDC to distinguish isocitrate dehydrogenase (IDH) genotypes of diffuse gliomas (6). The study enrolled 63 patients with diffuse gliomas (30 IDH-mutant n=30, 33 IDH-wildtype n=33) who underwent diffusion-weighted imaging at 3 T. SDC pixelwise maps were computed using the following equation:

SDC=[S(b1)–S(b2)]/(b2−b1)[unit:arbitraryunit(au)/s][1]

where b₁ and b₂ refer to a high b-value (500 mm²/s in the study) and a higher b-value respectively (750 mm²/s in the study), S(b₁) and S(b₂) denote the image signal-intensity acquired at the high b-value and the higher b-value respectively. The results showed that IDH gene testing mutant negative tumors had SDC value of 0.339±0.055 au/s, IDH mutant gene testing positive tumors had SDC value of 0.437±0.097 au/s, with area under receiver operating characteristic curve (AUROC) of 0.828 for separation (6). SDC was positively and moderately correlated with ADC (calculated with b-values =0 and 1,000 s/mm²), with Pearson r of 0.705 (P<0.0001). The best AUROC of 0.897 in separating IDH mutant −/+ tumors was achieved by a combination of SDC, diffusion-derived ‘vessel density’ (DDVD) (calculated with b-values =0 and 10 mm²/s) (7,8), and ADC.

In this letter, using the same data (6), we further analyzed the relative performance of the traditional metric ADC, compared to that of SDC, in separating IDH mutant −/+ tumors and tumor grades. The results are shown in Figures 1,2. IDH mutant negative tumors had ADC value of 0.985±0.235 mm²/s, IDH mutant positive tumors had ADC value of 1.290±0.381 ×10⁻³ mm²/s, with AUROC of 0.760 for separation. There was an improvement in separation of IDH mutant positive and IDH mutant negative diffuse gliomas by SDC over ADC. Separation of tumor histology grades was also better by SDC than by ADC particularly for grade-3 tumors. Statistical significance among the histology groups improved from P=0.0022 for ADC to P<0.0001 for SDC. ADC was negatively correlated with Ki-67 labeling index with Pearson r of −0.332 (95% confidence interval: −0.535 to −0.091, P=0.008), while SDC was negatively correlated with Ki-67 labeling index with Pearson r of −0.382 (95% confidence interval: −0.5756 to −0.1487, P=0.002). Thus, Figures 1,2 support the clinical usefulness of the SDC metric for diffusion glioma IDH mutation evaluation.

Figure 1 Relative performance of ADC (A,B) and SDC (C,D) in separating IDH mutant −/+ tumors. IDH mutation is based on gene testing. ADC analysis offers an AUROC of 0.7601 (B), while SDC analysis offers an AUROC of 0.8283 (D). Statistical significance between the groups improves from P=0.0003 for ADC to P<0.0001 for SDC (Mann-Whitney test). IDHm (−): IDH mutant negative; IDHm (+): IDH mutant positive. ADC unit: ×10⁻³ mm²/s. SDC unit: au/s. ADC, apparent diffusion coefficient; AUROC, area under receiver operating characteristic curve; IDH, isocitrate dehydrogenase; SDC, slow diffusion coefficient.

Figure 2 ADC and SDC differences according to glioma histology grading. Glioma ADC values (A) and SDC values (B) box and whiskers plots. Histological higher-grade tumors exhibiting lower ADC value and lower SDC value. (C) The mean values of ADC and SDC for grade 2 is normalized to be 1, and proportional decrease of mean ADC and SDC for grade 3 and grade 4 tumors are presented. Separation is better by SDC particularly for grade-3 tumors (arrows). Statistical significance among the groups improves from P=0.0022 for ADC to P<0.0001 for SDC (Kruskal-Wallis test). IDHm (−): IDH mutant negative; IDHm (+): IDH mutant positive. ADC unit: ×10⁻³ mm²/s. SDC unit: au/s. ADC, apparent diffusion coefficient; IDH, isocitrate dehydrogenase; SDC, slow diffusion coefficient.

For abdominal magnetic resonance imaging, with the conventional ADC approach, the spleen has been reported to have a much lower ADC than liver, hepatocellular carcinomas (HCCs) have a lower ADC than liver parenchyma. On the other hand, with SDC analysis, the spleen has faster diffusion than liver and HCCs have faster diffusion than liver parenchyma (5). The liver and spleen have a similar amount of blood perfusion (9), the spleen is waterier than the liver. HCCs are mostly associated with increased blood supply and increased proportion of arterial blood supply and with edema. Perfusion computed tomography also shows HCCs have a shorter mean transit time (MTT) than adjacent liver parenchyma. It is more reasonable with SDC results that spleen and HCC have faster diffusion than liver parenchyma. SDC has been shown to be highly sensitive and specific in identifying liquid lesions. Hu et al. reported that a combination of DDVD and SDC offers an accuracy of >95% in separating liver hemangioma and liver solid mass-forming lesions (10). Recently, it was shown that a typical SDC semi-quantitative score feature had an odd ratio of around 38 suggesting the diagnosis of liver focal nodular hyperplasia (FNH) against liver malignant tumors (HCC, metastasis, and Intrahepatic cholangiocarcinoma), while an ADC semi-quantitative score feature had an odd ratio of around 8 suggesting the diagnosis of liver FNH against liver malignant tumors, suggesting better differential diagnosis potential by SDC than by ADC (11).

The results described in this letter suggest that, while the roles of SDC and ADC for lesion classification can be complementary, in some clinical scenarios SDC as a biomarker offers a better lesion differentiation power than ADC (11-13).

Acknowledgments

None.

Footnote

Funding: This work was funded by the grants from the Fujian Medical University’s Startup Fund for Scientific Research (Nos. 2023QH1039 and 2024QH1060), Guidance Project of Fujian Science and Technology Program (No. 2022Y0024) and Hong Kong GRF Project (No. 14112521).

Conflicts of Interest: The authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2603/coif). Y.X.J.W. serves as the Editor-In-Chief of Quantitative Imaging in Medicine and Surgery. He is the founder of Yingran Medicals Ltd., which develops medical image-based diagnostics software. There is a Chinese patent pending related to this article (Y.X.J.W.). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Ethical approval was granted by the Ethics Committee of Fujian Medical University Union Hospital, and all participants provided informed consent. All methods were carried out according to relevant guidelines and regulations.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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