記述言語：日本語   出版者・発行元：Japanese Journal of Radiology  

Background: This study aimed to investigate the usefulness of time-dependent diffusion magnetic resonance imaging (MRI) parameters compared with the conventional apparent diffusion coefficient (ADC) in distinguishing tumor grade and isocitrate dehydrogenase (IDH) genotypes of adult-type diffuse gliomas. Methods: This retrospective study included 102 patients with adult-type diffuse gliomas. ADC maps obtained using diffusion-weighted imaging at short (7.1 ms) and long (44.5 ms) diffusion times (ADC7.1ms and ADC44.5ms) and maps of ADC changes (cADC) and relative ADC changes (rcADC) between the two diffusion times were generated. The mean, 5th, and 95th percentile values of each parameter were compared between low-grade (LGGs) and high-grade gliomas (HGGs) and between IDH-mutant and IDH-wildtype gliomas. The discriminative performance was assessed using receiver operating characteristic (ROC) analysis, and correlation with Ki-67 labeling index (Ki-67LI) was assessed using Spearman’s rank correlation. Multivariable logistic regression analyses were conducted to predict HGGs and IDH-wildtype gliomas. Results: In HGGs, the mean and 5th percentile values of ADC44.5ms and ADC7.1ms were significantly lower, whereas cADC and rcADC indices were significantly higher than those in LGGs. Performance of the mean rcADC (area under the ROC curve: 0.925; 95% confidence interval: 0.855–0.967) was significantly better than any index of conventional ADCs for tumor grade classification. The mean rcADC demonstrated the strongest correlation with Ki-67LI (ρ = 0.542, p < 0.0001). Moreover, the 95th percentile of rcADC was an independent predictor of IDH-wildtype gliomas after adjustment for age and sex, was useful for distinguishing IDH-wildtype from IDH-mutant gliomas Conclusions: The mean rcADC showed the strongest correlation with the Ki-67 LI and achieved better diagnostic performance than conventional PGSE-based ADC for differentiating LGGs from HGGs. In multivariable analyses, the mean and 95th percentile of rcADC were identified as independent predictors of HGGs and IDH-wildtype gliomas, respectively.

DOI： 10.1007/s11604-025-01936-w

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PubMed

記述言語：日本語   出版者・発行元：Japanese Journal of Radiology  

Purpose: This study aimed to investigate the difference in T1, T2, and proton density (PD) values derived from synthetic magnetic resonance imaging (MRI) across various meningioma subtypes and their potential as imaging biomarkers for the World Health Organization (WHO) grade classification of meningioma. Materials and methods: This study retrospectively analyzed the data of 100 consecutive meningioma cases. Across subtypes and WHO grades, intratumoral T1, T2, PD, and apparent diffusion coefficient (ADC) values, along with tumor volume, were measured and compared, and the discriminative performance of each parameter was evaluated using the area under the receiver operating characteristic curve (AUC). Simple and multiple regression analyses were conducted to assess the associations between quantitative imaging parameters and atypical pathological features (4 ≥ mitotic findings/high power field; brain invasion; increased cellularity; small cells with high nuclear-to-cytoplasmic ratio; prominent nucleoli; sheeting; necrosis). Results: Among all subtypes, angiomatous, microcystic, and metaplastic meningioma demonstrated remarkable high T1 and T2 values. The AUCs for differentiating these subtypes from other subtypes were 0.97 for T1 value and 0.99 for T2 value. Volume (p < 0.001), T1 value (p < 0.001), T2 value (p = 0.002), and ADC value (p = 0.035) were significantly higher in high grade meningioma than low grade meningioma, with an AUC of 0.83 achieved by combining volume, T1, and ADC value. Multiple regression analysis revealed that volume was significantly associated with necrosis (p = 0.018), whereas T1 and T2 values were significantly associated with sheeting (p = 0.047 and 0.025 for T1 and T2). Conclusion: T1 and T2 mappings may serve as useful quantitative imaging biomarkers for meningioma grading.

DOI： 10.1007/s11604-026-02014-5

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PubMed

記述言語：日本語   出版者・発行元：Tomography  

Objectives: The potential use of electron density (ED) and effective atomic number (Zeff) derived from dual-energy computed tomography (DECT) as novel quantitative imaging biomarkers for differentiating malignant brain tumors was investigated. Methods: Data pertaining to 136 patients with a pathological diagnosis of brain metastasis (BM), glioblastoma, and primary central nervous system lymphoma (PCNSL) were retrospectively reviewed. The 10th percentile, mean and 90th percentile values of conventional 120-kVp CT value (CTconv), ED, Zeff, and relative apparent diffusion coefficient derived from diffusion-weighted magnetic resonance imaging (rADC: ADC of lesion divided by ADC of normal-appearing white matter) within the contrast-enhanced tumor region were compared across the three groups. Furthermore, machine learning (ML)-based diagnostic models were developed to maximize diagnostic performance for each tumor classification using the indices of DECT parameters and rADC. Machine learning models were developed using the AutoGluon-Tabular framework with rigorous patient-level data splitting into training (60%), validation (20%), and independent test sets (20%). Results: The 10th percentile of Zeff was significantly higher in glioblastomas than in BMs (p = 0.02), and it was the only index with a significant difference between BMs and glioblastomas. In the comparisons including PCNSLs, all indices of CTconv, Zeff, and rADC exhibited significant differences (p < 0.001–0.02). DECT-based ML models exhibited high area under the receiver operating characteristic curves (AUC) for all pairwise differentiations (BMs vs. Glioblastomas: AUC = 0.83; BMs vs. PCNSLs: AUC = 0.91; Glioblastomas vs. PCNSLs: AUC = 0.82). Combined models of DECT and rADC demonstrated excellent diagnostic performance between BMs and PCNSLs (AUC = 1) and between Glioblastomas and PCNSLs (AUC = 0.93). Conclusion: This study suggested the potential of DECT-derived ED and Zeff as novel quantitative imaging biomarkers for differentiating malignant brain tumors.

DOI： 10.3390/tomography11110120

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PubMed

記述言語：日本語   出版者・発行元：Tomography  

Objectives: Few studies have reported in vivo measurements of electron density (ED) and effective atomic number (Z<inf>eff</inf>) in normal brain tissue. To address this gap, dual-energy computed tomography (DECT)-derived ED and Z<inf>eff</inf> maps were used to characterize normal-appearing adult brain tissues, evaluate age-related changes, and investigate correlations with myelin partial volume (V<inf>my</inf>) from synthetic magnetic resonance imaging (MRI). Materials and Methods: Thirty patients were retrospectively analyzed. The conventional computed tomography (CT) value (CT<inf>conv</inf>), ED, Z<inf>eff</inf>, and V<inf>my</inf> were measured in the normal-appearing gray matter (GM) and white matter (WM) regions of interest. V<inf>my</inf> and DECT-derived parameters were compared between WM and GM. Correlations between V<inf>my</inf> and DECT parameters and between age and DECT parameters were analyzed. Results: V<inf>my</inf> was significantly greater in WM than in GM, whereas CT<inf>conv</inf>, ED, and Z<inf>eff</inf> were significantly lower in WM than in GM (all p < 0.001). Z<inf>eff</inf> exhibited a stronger negative correlation with V<inf>my</inf> (ρ = −0.756) than CT<inf>conv</inf> (ρ = −0.705) or ED (ρ = −0.491). ED exhibited weak to moderate negative correlations with age in nine of the 14 regions. In contrast, Z<inf>eff</inf> exhibited weak to moderate positive correlations with age in nine of the 14 regions. CT<inf>conv</inf> exhibited negligible to insignificant correlations with age: Conclusions: This study revealed distinct GM–WM differences in ED and Z<inf>eff</inf> along with opposing age-related changes in these quantities. Therefore, myelin may have substantially contributed to the lower Z<inf>eff</inf> observed in WM, which underlies the GM–WM contrast observed on non-contrast-enhanced CT.

DOI： 10.3390/tomography11090095

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PubMed

記述言語：日本語   出版者・発行元：Japanese Journal of Radiology  

Purpose: To clarify the usefulness of electron density (ED) using dual-energy CT (DECT) parameters for predicting treatment response in patients with pulmonary embolism (PE). Materials and methods: The study population comprised 30 patients with PE (49 thrombi) who underwent pretreatment DECT. The study coordinator diagnosed PE using contrast-enhanced CT (CECT) as the gold standard and annotated the location of thrombi on CECT prior to the DECT image analyses. CT attenuation values on conventional 120 kVp, 40 keV, and 70 keV virtual monochromatic (VM) images; effective atomic number; and ED of pretreatment pulmonary thrombi were measured on unenhanced CT. Thrombi were classified into dissolved and residual groups according to the findings of posttreatment follow-up CT. DECT parameters were compared between the two groups using the Mann–Whitney U test. For statistically significant parameters, receiver-operating characteristic (ROC) analysis was used to evaluate their performance for differentiating two groups. Diagnostic accuracy for predicting treatment response in patients with PE was determined by calculating the area under the ROC curve (AUC). Results: ED values, CT values on conventional 120 kVp imaging, and those on 70 keV VM imaging were significantly higher in thrombi in the dissolved group than the residual group (p < 0.001, p = 0.012, p = 0.009, respectively). AUC values for predicting dissolution response by ED, conventional 120 kVp imaging, and 70 keV VM imaging (cut-off value, 3.49 × 10²³/cm³, 53.4 HU, and 50.7 HU, respectively) were 0.856, 0.744, and 0.755, respectively. AUC was significantly higher for ED than for conventional 120 kVp imaging and 70 keV VM imaging (p = 0.032, p = 0.016). Conclusions: ED derived from unenhanced DECT may help predict therapeutic efficacy in patients with PE.

DOI： 10.1007/s11604-025-01747-z

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PubMed

記述言語：英語  

記述言語：英語  

記述言語：英語  

記述言語：英語  

記述言語：英語