Minimum Apparent Diffusion Coefficient Research Articles

The relationship between the apparent diffusion coefficient and the Ki-67 proliferation index in intracranial solitary fibrous tumor/hemangiopericytoma.

This study evaluated the value of the apparent diffusion coefficient (ADC) in distinguishing grade II and III intracranial solitary fibrous tumors/hemangiopericytomas and explored the correlation between ADC and Ki-67. The preoperative MRIs of 37 patients treated for solitary fibrous tumor/hemangiopericytoma (grade II, n = 15 and grade III, n = 22) in our hospital from 2011 to October 2020 were retrospectively analyzed. We compared the difference between the minimum, average, maximum, and relative ADCs based on tumor grade and examined the correlation between ADC and Ki-67. Receiver operating characteristic curve analysis was used to analyze the diagnostic efficiency of the ADC. There were significant differences in the average, minimum, and relative ADCs between grade II and III patients. The optimal cutoff value for the relative ADC value to differentiate grade II and III tumors was 0.998, which yielded an area under the curve of 0.879. The Ki-67 proliferation indexes of grade II and III tumors were significantly different, and the average (r = - 0.427), minimum (r = - 0.356), and relative (r = - 0.529) ADCs were significantly negatively correlated with the Ki-67 proliferation index. ADC can be used to differentiate grade II and III intracranial solitary fibrous tumors/hemangiopericytomas. Our results can be used to formulate a personalized surgical treatment plan before surgery.

Shear Wave Ultrasound Elastography and Diffusion-Weighted Magnetic Resonance Imaging Findings of Pleural-Based Masses with Histopathologic Correlation

Objective: The study aims to evaluate the usefulness of non-invasive diagnostic methods, shear wave elastography (SWE), and diffusion-weighted magnetic resonance imaging (DWI) to differentiate benign and malignant lesions in the thoracic pleural based masses by comparing them with histopathological findings.Materials and Methods: Sixty-three patients having a pleural-based peripheral mass on CT, admitted to the interventional radiology department for transthoracic biopsy, were included in the study. All patients underwent DWI, and ADC values of the groups were measured. Transthoracic biopsy was performed with the guidance of US from the area where the highest shear wave velocity (SWV) value was calculated. ADC and SWV values of histopathologically proven benign and malignant lesions were statistically compared.Results: Fifty-six patients were male, and seven were female. The mean age was 64.68±10.13 years (41-85 years). Fourty-four patients were malignant, and 19 were benign. The maximum SWV was found to be 4.13±0.59 m/s in malignant cases and 3.55±0.71 m/s in benign cases, and the difference was significant (p = 0.001). Mean ADC value was measured as 1.04±0.30 x 10-3 mm2/s in malignant cases and 1.32±0.33 x 10-3 mm2/s in benign cases on DWI and the difference was significant (p = 0.002). In malignant cases, the minimum ADC was 0.73±0.29 x 10-3 mm2/s, and 0.99±0.44 x 10-3 mm2/s in benign cases, the difference was significant (p = 0.024). ROC analysis revealed a cut-off value of ≥4.08 m/s for SWVmax, ≤1.01x10-3 mm2/s for mean ADC, and ≤0.8x10-3 mm2/s for minimum ADC showed a significant performance in distinguishing malignant and benign lesions. Conclusions: Transthoracic US elastography and DWI are useful in differentiating malignant and benign lesions in appropriate cases. Both SWE and DWI are useful in routine use because they are non-invasive and do not contain radiation. In particular, SWE is suitable for biopsy guidance and may prevent the possibility of insufficient material.

Stromal tumor-infiltrating lymphocytes level as a prognostic factor for resected intrahepatic cholangiocarcinoma and its prediction by apparent diffusion coefficient.

Tumor-infiltrating lymphocytes (TILs) are a prognostic factor or an indicator of chemotherapy response for various malignancies. The aim of this study was to investigate the prognostic impact of TILs in resected intrahepatic cholangiocarcinoma (IHCC). We also investigated the usefulness of the apparent diffusion coefficient (ADC) in diffusion-weighted magnetic resonance imaging (DW-MRI) to predict TILs. We enrolled 23 patients with IHCC who underwent initial hepatic resection in Tokushima University Hospital from 2006 to 2017. We evaluated stromal TILs in the tumor marginal area and central area in surgical specimens. Patients were divided into low vs high stromal TILs groups. We analyzed the patients' clinicopathological factors, including prognosis, according to the degree of stromal TILs. We also analyzed the correlation between stromal TILs and the minimum ADC value. Stromal TILs in the marginal area reflected overall survival more accurately than that in the central area. Additionally, marginal low TILs was significantly associated with lymph node metastasis and portal vein invasion. Both overall- and disease-free survival rates in the marginal low TILs group were significantly worse than those in the marginal high TILs group (P < 0.05). In the multivariate analysis, marginal low TILs were an independent prognostic factor for both overall- and disease-free survival (P < 0.05), and marginal low TILs were significantly associated with lower minimum ADC values (P < 0.02). Stromal TILs, especially in the marginal area, might demonstrate prognostic impact in patients with IHCC. Moreover, the ADC values from MRI may predict TILs in IHCC tumor tissue.

The role of apparent diffusion coefficient as a predictive factor for tumor recurrence in patients with cerebellopontine angle epidermoid tumor.

Intracranial epidermoid tumors are slowly growing benign tumors, but due to adjacent critical neurovascular structures, surgical resection is challenging, with the risk of recurrence. The apparent diffusion coefficient (ADC) has been used to evaluate the characteristics of brain tumors, but its utility for intracranial epidermoid tumors has not been specifically explored. This study analyzed the utility of preoperative ADC values in predicting tumor recurrence for patients with intracranial epidermoid tumors. Between 2008 and 2019, 21 patients underwent surgery for cerebellopontine angle (CPA) epidermoid tumor, and their preoperative ADC data were analyzed. The patients were divided into two groups: the recurrence group, defined by regrowth of the remnant tumor or newly developed mass after gross total resection on magnetic resonance imaging (MRI); and the stable group, defined by the absence of growth or evidence of tumor on MRI. Receiver operating characteristic (ROC) analysis was used to obtain the ADC cutoff values for predicting tumor recurrence. The prognostic value of the ADC was assessed using Kaplan-Meier curves. The minimum ADC values were significantly lower in the recurrence group than in the stable tumor group (P = 0.020). ROC analysis showed that a minimum ADC value lower than 804.5 × 10-6 mm2/s could be used to predict higher recurrence risk of CPA epidermoid tumors. Non-total resection and mean and minimum ADC values lower than the respective cutoffs were negative predictors of recurrence-free survival. Minimum ADC values could be useful in predicting the recurrence of CPA epidermoid tumors.

Whole-lesion ADC histogram analysis versus single-slice ADC measurement for the differentiation of benign and malignant soft tissue tumors

PurposeTo evaluate and compare the diagnostic performances of whole-lesion apparent diffusion coefficient (ADC) histogram analysis and single-slice ADC measurement in the differentiation of benign and malignant soft tissue tumors. MethodsFifty-three patients (mean age: 48.5 ± 21.4) with soft tissue tumors (27 benign and 26 malignant) were evaluated with diffusion-weighted MRI. Whole-lesion ADC histogram parameters (mean, median, 10th percentile, 90th percentile, minimum, maximum, range, mean absolute deviation, interquartile range, kurtosis, skewness, root mean squared, variance and inhomogeneity) of the lesions were measured using the whole solid tumor volume region of interest (ROI). In other sessions, five ROIs were manually drawn on the tumor slices, and mean ADC and minimum ADC of the measurements were calculated. Diagnostic accuracies of the two methods were assessed and compared. ResultsMean, median, minimum, 10th percentile, 90th percentile, range, root mean squared and inhomogeneity of ADC histogram analysis, and mean ADC and minimum ADC of single-slice ADC measurement were significantly different between malignant and benign lesions (p < 0.001 – p = 0.002). Among the ADC histogram parameters, 10th percentile had the highest diagnostic performance (AUC = 0.825) followed by mean (AUC = 0.792) and median (AUC = 0.789). For the single-slice ADC measurement, the AUC of mean ADC and minimum ADC were 0.842 and 0.786, respectively. Mean ADC of single-slice measurement had a similar diagnostic performance with the 10th percentile, mean, and median of ADC histogram analysis (p = 0.070–1.000). ConclusionsBoth whole-lesion ADC histogram analysis and single-slice ADC measurement can differentiate benign and malignant soft tissue tumors with similar diagnostic performances.

The Benefit of Apparent Diffusion Coefficient in Evaluating the Invasiveness of Hepatocellular Carcinoma.

BackgroundThis study aimed to evaluate hepatocellular carcinoma (HCC) invasiveness using the apparent diffusion coefficient (ADC).MethodsEighty-one patients with HCC confirmed by pathology and examined by preoperative magnetic resonance imaging diffusion-weighted imaging from January 2015 to September 2020 were retrospectively analyzed. Clinical and pathological data were recorded. The minimum ADC (ADCmin), average ADC (ADCmean), and the ratio of ADCmean to normal-appearing hepatic parenchyma ADC (ADCnahp) were assessed. The associations between clinical information, ADC value, and HCC invasiveness (microvascular invasion [MVI], tumor differentiation, and Ki-67 expression) were evaluated statistically. Independent risk factors related to HCC invasiveness were screened using binary logistic regression, and the diagnostic efficiency was evaluated by the receiver operating characteristic curve and its area under the curve (AUC) value.ResultsTumor size was related to HCC MVI and tumor differentiation (P < 0.05). HCC MVI was associated with ADCmin, ADCmean, and the ADCmean-to-ADCnahp ratio (all P < 0.05) with AUC values of 0.860, 0.860, and 0.909, respectively. If these were combined with tumor size, the AUC value increased to 0.912. The degree of tumor differentiation was associated with ADCmin, ADCmean, and the ADCmean-to-ADCnahp ratio (all P < 0.05) with AUC values of 0.719, 0.708, and 0.797, respectively. If these were combined with tumor size, the AUC value increased to 0.868. Ki-67 expression was associated with ADCmin, ADCmean, and the ADCmean-to-ADCnahp ratio (all P < 0.05) with AUC values of 0.731, 0.747, and 0.746, respectively. Combined them, the AUC value increased to 0.763.ConclusionsThe findings indicated that the ADC value has significant potential for the non-invasive preoperative evaluation of HCC invasiveness.

Assessment of Apparent Diffusion Coefficients and SUVs as Predicators of Histological Differentiation in Anal Squamous Cell Carcinoma.

The study aims to assess minimal apparent diffusion coefficient (ADCmin) and SUVmax as predictors of histological differentiation in patients with anal squamous cell carcinoma (ASCC) and to determine cutoff values for each histopathological tumor grade. A retrospective study of 41 ASCC patients (14 males, 27 females; mean age, 65 ± 13 years) staged with FDG PET/CT and MRI (mean scan time interval, 21 ± 11 days). SUVmax and ADCmin values were measured and compared with histopathological tumor grading obtained from biopsy. The mean size and tumor volume were 3 ± 2 cm and 16.5 ± 27.3 cm3, respectively. The mean ADCmin values for well-, moderately, and poorly differentiated ASCC were 935 ± 179, 896 ± 123, and 637 ± 114, respectively. The mean SUVmax for well-, moderately, and poorly differentiated ASCC were 6.9 ± 1.8, 11.5 ± 4.1, and 13.4 ± 2.6, respectively. The difference in mean ADCmin values between poorly and moderately/well-differentiated tumors was statistically significant, whereas this was not significant between moderately and well-differentiated tumors. Differences in SUVmax values were statistically significant between poorly/moderately and well-differentiated tumors, whereas there was no statistical significance between poorly and moderately differentiated tumors. By combining the 2 modalities using cutoff values of 675 × 10-6 mm2·s-1 for ADCmin and 8.5 for SUVmax, it was possible to differentiate the tumor categories with a sensitivity, specificity, positive predictive value, and negative predictive value, respectively, of 84.6%, 96.4%, 91.7%, and 93.1% for well-differentiated ASCC, 76.5%, 87.5%, 81.3%, and 84% for moderately, and 90.9%, 89.3%, 76.9%, and 96.2% for poorly differentiated ASCC, respectively. ADCmin and SUVmax values correlated with the degree of differentiation in ASCC and can be used as predictors of tumor grading and aggressiveness. Combined ADCmin and SUVmax cutoff values can therefore be used for early patient risk stratification and treatment decision making.

High-Resolution Diffusion-Weighted Imaging of C6 Glioma on a 7T BioSpec MRI Scanner: Correlation of Tumor Cellularity and Nuclear-to-Cytoplasmic Ratio with Apparent Diffusion Coefficient

To determine the association of the apparent diffusion coefficient (ADC) with quantitative cellularity and the nuclear-to-cytoplasmic ratio in C6 glioma. Animal models bearing C6 gliomas underwent MR scans with T1 rapid acquisition with relaxation enhancement (RARE), T2 RARE, and high-resolution diffusion-weighted imaging sequences. For each model, three consecutive sections were used to draw regions of interest (ROIs) and measure ADC values; the middle section was localized in the plane with the maximal solid tumor area. The minimal, mean, and maximal ADC values were recorded for each ROI. GFAP-immunostained sections coregistered with ADC measurements were used to calculate tumor cellularity and the nuclear-to-cytoplasmic (N/C) ratio. Spearman's correlation was used to assess the relationship between ADC values and quantitative tumor cellularity as well as N/C ratios with a significance level of p < 0.05. Thirty-three sections from 11 glioma-bearing rats were analyzed. The median values of the minimal, mean, and maximal ADC were 0.443 × 10-3, 0.744 × 10-3, and 1.140 × 10-3 mm2/s, respectively. The median cellularity and N/C ratio were 2151.234 per 0.025 mm2 and 0.857, respectively. The minimal, mean, and maximal ADCs were all significantly associated with cellularity, with correlation coefficients of -0.712 (p < 0.001), -0.631 (p < 0.001), and -0.460 (p = 0.007), respectively. The minimal and mean ADC had significant negative relationships with the N/C ratio, with correlation coefficients of -0.565 (p = 0.001) and -0.426 (p = 0.013), respectively. The minimal ADC correlated well with cellularity and N/C ratios in C6 glioma and may be used as a biomarker of these two pathological features.

Added Value of Quantitative Apparent Diffusion Coefficients for Identifying Small Hepatocellular Carcinoma from Benign Nodule Categorized as LI-RADS 3 and 4 in Cirrhosis.

Background and AimsCorrect identification of small hepatocellular carcinomas (HCCs) and benign nodules in cirrhosis remains challenging, quantitative apparent diffusion coefficients (ADCs) have shown potential value in characterization of benign and malignant liver lesions. We aimed to explore the added value of ADCs in the identification of small (≤3 cm) HCCs and benign nodules categorized as Liver Imaging Reporting and Data System (LI-RADS) 3 (LR-3) and 4 (LR-4) in cirrhosis.MethodsNinety-seven cirrhosis patients with 109 small nodules (70 HCCs, 39 benign nodules) of LR-3 and 4 LR-4 based on major and ancillary magnetic resonance imaging features were included. Multiparametric quantitative ADCs of the lesions, including the mean ADC (ADCmean), minimum ADC (ADCmin), maximal ADC (ADCmax), ADC standard deviation (ADCstd), and mean ADC value ratio of lesion-to-liver parenchyma (ADCratio) were calculated. Regarding the joint diagnosis, a nomogram model was plotted using multivariate logistic regression analysis. The performance was assessed using the area under the receiver operating characteristic curve (AUC).ResultsThe ADCmean, ADCmin, ADCratio, and ADCstd were significantly associated with the identification of small HCC and benign nodules (p<0.001). For the joint diagnosis, the LI-RADS category (odds ratio [OR]=12.50), ADCmin (OR=0.14), and ADCratio (OR=0.12) were identified as independent factors for distinguishing HCCs from benign nodules. The joint nomogram model showed good calibration and discrimination, with a C-index of 0.947. Compared with the LI-RADS category alone, this nomogram model demonstrated a significant improvement in diagnostic performance, with AUC increasing from 0.820 to 0.967 (p=0.001).ConclusionsThe addition of quantitative ADCs could improve the identification of small HCC and benign nodules categorized as LR-3 and 4 LR-4 in patients with cirrhosis.

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study.

PurposeTo evaluate isocitrate dehydrogenase (IDH) status in clinically diagnosed grade II~IV glioma patients using the 2016 World Health Organization (WHO) classification based on MRI parameters.Materials and MethodsOne hundred and seventy-six patients with confirmed WHO grade II~IV glioma were retrospectively investigated as the study set, including lower-grade glioma (WHO grade II, n = 64; WHO grade III, n = 38) and glioblastoma (WHO grade IV, n = 74). The minimum apparent diffusion coefficient (ADCmin) in the tumor and the contralateral normal-appearing white matter (ADCn) and the rADC (ADCmin to ADCn ratio) were defined and calculated. Intraclass correlation coefficient (ICC) analysis was carried out to evaluate interobserver and intraobserver agreement for the ADC measurements. Interobserver agreement for the morphologic categories was evaluated by Cohen’s kappa analysis. The nonparametric Kruskal-Wallis test was used to determine whether the ADC measurements and glioma subtypes were related. By univariable analysis, if the differences in a variable were significant (P<0.05) or an image feature had high consistency (ICC >0.8; κ >0.6), then it was chosen as a predictor variable. The performance of the area under the receiver operating characteristic curve (AUC) was evaluated using several machine learning models, including logistic regression, support vector machine, Naive Bayes and Ensemble. Five evaluation indicators were adopted to compare the models. The optimal model was developed as the final model to predict IDH status in 40 patients with glioma as the subsequent test set. DeLong analysis was used to compare significant differences in the AUCs.ResultsIn the study set, six measured variables (rADC, age, enhancement, calcification, hemorrhage, and cystic change) were selected for the machine learning model. Logistic regression had better performance than other models. Two predictive models, model 1 (including all predictor variables) and model 2 (excluding calcification), correctly classified IDH status with an AUC of 0.897 and 0.890, respectively. The test set performed equally well in prediction, indicating the effectiveness of the trained classifier. The subgroup analysis revealed that the model predicted IDH status of LGG and GBM with accuracy of 84.3% (AUC = 0.873) and 85.1% (AUC = 0.862) in the study set, and with the accuracy of 70.0% (AUC = 0.762) and 70.0% (AUC = 0.833) in the test set, respectively.ConclusionThrough the use of machine-learning algorithms, the accurate prediction of IDH-mutant versus IDH-wildtype was achieved for adult diffuse gliomas via noninvasive MR imaging characteristics, including ADC values and tumor morphologic features, which are considered widely available in most clinical workstations.

Correlations between apparent diffusion coefficient values and histopathologic factors in breast cancer.

Breast cancer is the most common malignancy dia-gnosed in women, and the incidence gradually increases. Magnetic resonance imaging (MRI) is become widely used to identify benign and malignant breast tumors. Objective: The aim of this study was to evaluate the relationships between apparent diffusion coefficient (ADC) values and histopathologic prognostic factors in breast cancer. Forty-nine breast carcinoma patients were included evaluated for prognostic factors, including histological type, histo-logical grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and molecular subtype. Minimum (ADCmin) and mean (ADCmean) ADC values were compared among prognostic factor groups by Mann-Whitney U test and Kruskal-Wallis test. Lower mean ADCmin and ADCmean values were observed for no special type (NST) than for invasive lobular carcinoma (ILC) type (0.81 ± 0.03 × 10-3 and 0.96 ± 0.03 × 10-3 mm2/s, P= 0.002 and 0.03, respectively). The mean ADCmin and ADCmean values for the high-level Ki-67 group were significantly lower than those for the low-level Ki-67 group (P = 0.001 and 0.008, respectively). No correlations were observed between ADC values and histological grades, ER, PR, HER2, and molecular subtypes. ADCmin and ADCmean values correlated with the pro-liferation marker Ki-67 and histological grade. ADC values can serve as noninvasive indicators of cell proliferation in breast cancer.

Clinical usefulness of the fast protocol of breast diffusion-weighted imaging using 3T magnetic resonance imaging with a 16-channel breast coil

We aimed to evaluate the usefulness of a fast protocol of diffusion-weighted imaging (DWI) with one excitation using 3T magnetic resonance imaging (MRI) and a 16-channel breast coil. We analyzed 30 lesions from 27 women between February 2020 and June 2020. The visibility score (from 1 = extremely poor to 5 = excellent) and apparent diffusion coefficient (ADC) value between one and four excitations were evaluated by two readers. The image acquisition time was 40 s for one excitation and 1 min 52 s for four excitations. The visibility scores were 4.630 ± 0.718 and 4.267 ± 1.015 for one excitation and 4.730 ± 0.691 and 4.200 ± 1.000 for four excitations by the two readers. There was no significant difference in the visibility (P = 0.184 and P = 0.423), mean ADC value (P = 0.918 and P = 0.417), and minimum ADC value (P = 0.936 and P = 0.443) between one and four excitations by the two readers. Despite the short acquisition time, the visibility score and ADC values of one-excitation DWI were comparable to that with four excitations. Our fast DWI protocol could provide reproducible visibility and ADC value, potentially helping radiologists to efficiently diagnose patients.

Role of diffusion-weighted MRI in differentiating between benign and malignant bone lesions: a prospective study

To evaluate the ability of diffusion-weighted magnetic resonance imaging (DW-MRI) to differentiate between benign and malignant bony tumours. This prospective study was conducted from October, 2018 to December, 2019. The study included 62 patients (37 male and 25 female) with clinically suspected bony lesions referred to the Radiology Department. Patients underwent clinical examination, radiography, computed tomography (CT), and ultrasonography examinations. MRI studies were conducted using a 1.5-T MRI machine, and post-processing analysis was done using a Philips Extended MRI workspace workstation. The mean apparent diffusion coefficient (ADC) value of benign lesions ranged between 0.85×10-3 and 2.44×10-3 mm2/s. The lowest ADC values were measured in a giant cell tumour and in an inclusion epidermoid cyst (0.85×10-3 and 0.93×10-3 mm2/s, respectively). The highest measurement was in bony cysts (2.44×10-3 mm2/s) followed by osteoid osteoma (2.2×10-3 mm2/s) and osteochondroma (1.85×10-3 mm2/s). Amongst malignant lesions, ADC values ranged from 0.42×10-3 to 2.4×10-3 mm2/s. The lowest value was measured in malignant round cell tumour Ewing's/primitive neuroectodermal tumour (PNET), and the highest was measured in conventional chondrosarcoma. Metastatic lesions were observed in 11 patients with a mean ADC value of 0.71×10-3 mm2/s, followed by osteosarcoma in six patients with a mean ADC value of 0.74×10-3 mm2/s. There was a significant difference between the mean, minimum, and maximum ADC values of benign and malignant tumours. The present findings indicate that the best cut-off ADC range to predict malignancy is 0.78-0.86×10-3 mm2/s, with a sensitivity of 89.47%, specificity of 97.22%, and accuracy of 94.55%.

Non-invasive assessment of heterogeneity of gliomas using diffusion and perfusion MRI: correlation with spatially co-registered PET.

Heterogeneity of gliomas challenges the neuronavigated biopsy and oncological therapy. Diffusion and perfusion magnetic resonance imaging (MRI) can reveal the cellular and hemodynamic heterogeneity of tumors. Integrated positron emission tomography (PET)/MRI is expected to be a non-invasive imaging approach to characterizing glioma. To evaluate the value of apparent diffusion coefficient (ADC), cerebral blood volume (CBV), and spatially co-registered maximal standard uptake value (SUVmax) for tissue characterization and glioma grading. Thirty-seven consecutive patients with pathologically confirmed gliomas were retrospectively investigated. The relative minimum ADC (rADCmin), relative maximal ADC (rADCmax), relative maximal rCBV (rCBVmax), the relative minimum rCBV (rCBVmin), and the corresponding relative SUVmax (rSUVmax) were measured. The paired t-test was used to compare the quantitative parameters between different regions to clarify tumor heterogeneity. Imaging parameters between WHO grade IV and grade II/III gliomas were compared by t-test. The diagnostic efficiency of multiparametric PET/MRI was analyzed by receiver operating characteristic (ROC) curve. The values of rSUVmax were significantly different between maximal diffusion/perfusion area and minimum diffusion/perfusion area (P < 0.001/P < 0.001) within tumor. The values of rADCmin (P < 0.001), rCBVmax (P = 0.002), and corresponding rSUVmax (P = 0.001/P < 0.001) could be used for grading gliomas. The areas under the ROC curves of rSUVmax defined by rADCmin and rCBVmax were 0.89 and 0.91, respectively. Diffusion and perfusion MRI can detect glioma heterogeneity with excellent molecular imaging correlations. Regions with rCBVmax suggest tissues with the highest metabolism and malignancy for guiding glioma grading and tissue sampling.

The Added Value of Inflow-Based Vascular-Space-Occupancy and Diffusion-Weighted Imaging in Preoperative Grading of Gliomas

Objectives: The present study aimed to study whether combined inflow-based vascular-space-occupancy (iVASO) MR imaging (MRI) and diffusion-weighted imaging (DWI) improve the diagnostic accuracy in the preoperative grading of gliomas. Methods: Fifty-one patients with histopathologically confirmed diffuse gliomas underwent preoperative structural MRI, iVASO, and DWI. We performed 2 qualitative consensus reviews: (1) structural MR images alone and (2) structural MR images with iVASO and DWI. Relative arteriolar cerebral blood volume (rCBVa) and minimum apparent diffusion coefficient (mADC) were compared between low-grade and high-grade gliomas. Receiver operating characteristic (ROC) curve analysis was performed to compare the tumor grading efficiency of rCBVa, mADC, and the combination of the two parameters. Results: Two observers diagnosed accurate tumor grade in 40 of 51 (78.4%) patients in the first review and in 46 of 51 (90.2%) in the second review. Both rCBVa and mADC showed significant differences between low-grade and high-grade gliomas. ROC analysis gave a threshold value of 1.52 for rCBVa and 0.85 × 10⁻³ mm²/s for mADC to provide a sensitivity and specificity of 88.0 and 81.2% and 100.0 and 68.7%, respectively. The area under the ROC curve (AUC) was 0.87 and 0.85 for rCBVa and mADC, respectively. The combination of rCBVa and mADC values increased the AUC to 0.92. Conclusion: The combined application of iVASO and DWI may improve the diagnostic accuracy of glioma grading.

Dynamic contrast enhanced-MRI and diffusion-weighted image as predictors of lymphovascular invasion in node-negative invasive breast cancer

BackgroundLymphovascular invasion (LVI) is an important risk factor for prognosis of breast cancer and an unfavorable prognostic factor in node-negative invasive breast cancer patients. The purpose of this study was to evaluate the association between LVI and pre-operative features of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in node-negative invasive breast cancer.MethodsData were collected retrospectively from 132 cases who had undergone pre-operative MRI and had invasive breast carcinoma confirmed on the last surgical pathology report. MRI and DWI data were analyzed for the size of tumor, mass shape, margin, internal enhancement pattern, kinetic enhancement curve, high intratumoral T2-weighted signal intensity, peritumoral edema, DWI rim sign, and apparent diffusion coefficient (ADC) values. We calculated the relationship between presence of LVI and various prognostic factors and MRI features.ResultsPathologic tumor size, mass margin, internal enhancement pattern, kinetic enhancement curve, DWI rim sign, and the difference between maximum and minimum ADC were significantly correlated with LVI (p < 0.05).ConclusionsWe suggest that DCE-MRI with DWI would assist in predicting LVI status in node-negative invasive breast cancer patients.

RCBV and ADC based grading of gliomas with glimpse into radiogenomics

Purpose. The present study was carried out to study the role of relative cerebral blood volume (rCBV), apparent diffusion coefficient (ADC) and MR spectroscopy in grading gliomas to help the surgeon plan the approach and extent of surgery as well as judge the need for any adjuvant radio/chemotherapy.&#x0D; Methods. 65 patients with glioma were prospectively studied with MRI. Basic MR sequences (T1W, T2W, T2W/FLAIR) were followed by diffusion-weighted (DW) imaging with b value of 1000 (minimum ADC values used for analysis). Then the patients were administered Gadobenate Dimeglumine/ Meglumine Gadoterate in a dose of 0.1mmol/kg at a rate of 4ml/sec after which 20ml of saline was flushed at a rate of 4ml/sec and T2*W/FFE dynamic images were acquired; dynamics showing maximum fall in the intensity were used for creating rCBV and rCBF maps and calculating rCBV. Single voxel spectroscopy (SVC) was done using the PRESS sequence with intermediate TE of 144ms. NAA/Cr, Cho/Cr, Cho/NAA, Cho+Cr/NAA and NAA/Crn ratios (NAA from the tumour, Crn from the normal side) were calculated.&#x0D; Results. Grade I gliomas showed minimum ADC&gt;0.84x10-3mm2/s and maximum rCBV&lt;1.9ml/100gm, grade II gliomas showed min ADC 0.75-0.84x10-3mm2/s and max rCBV of 1.9-2.6ml/100gm, grade III had min ADC of 0.70-0.75x10-3mm2/s and max rCBV of 2.7-3.0ml/100gm, while grade IV tumors showed min ADC&lt;0.70x10-3mm2/s and max rCBV&gt;3.0ml/100gm. rCBV values were better than ADC values in differentiating grade I from II and grade II from III. The ADC values were better than rCBV values in differentiating grade III from grade IV.&#x0D; Conclusions. Both minimum ADC and maximum rCBV within the tumour were significant but these parameters within peritumoral oedema were not significant in grading gliomas. Though lipid and lactate (especially lipid) peaks were found more frequently in higher-grade tumours, various spectroscopy parameters were not significant in grading gliomas. Preoperative grading of gliomas with the help of advanced MR parameters like ADC and rCBV can help the surgeon plan the approach and extent of surgery as well as judge the need for any adjuvant radio/chemotherapy. Advancing radio-genomic and radiomic technologies can supplement the current radiologic methods of diagnosis and prognosis.

Intratumoral and Peritumoral Apparent Diffusion Coefficient and MGMT mRNA Expression in Different Meningioma Histopathological Grade

BACKGROUND: Histopathological examination is the gold standard for diagnosing meningioma and determining the treatments. However, it is invasive in nature. This study was conducted to identify intratumoral and peritumoral apparent diffusion coefficient (ADC) value and mRNA O6-methylguanine-DNA methyltransferase (MGMT) expression in meningioma.METHODS: Data were collected from 39 patients who were clinically diagnosed with meningioma. However, only 37 patients met the inclusion criteria. These subjects then underwent examinations and received treatment from October 2017 to September 2018. Magnetic resonance imaging (MRI) data with diffusion-weighted imaging-apparent diffusion coefficient (DWI-ADC) sequence, histopathological diagnosis of meningioma, and results of MGMT mRNA expression were obtained.RESULTS: The most frequent type of low-grade and overall tumor was meningioma not otherwise specified (56.8%). For high-grade tumor, there were two atypical cases: atypical meningioma (2.7%) and rhabdoid meningioma (2.7%). Meningothelial meningioma had the highest mean value of minimum intratumoral ADC at 864.57±219 x10-3 mm2/s, whereas rhabdoid meningioma had the lowest at 417 x10-3 mm2/s. For minimum peritumoral ADC, rhabdoid meningioma had the highest mean value at 1,651 x10-3 mm2/s, while atypical meningioma has the lowest at 1,281 x10-3 mm2/s. For MGMT mRNA, meningothelial meningioma had the highest mean value at 10±1.2 fold change, whereas rhabdoid meningioma had the lowest mean at 6.18 fold change.CONCLUSION: WHO grade I meningiomas had higher minimum intratumoral ADC values and higher MGMT mRNA expression than the high-grade tumors. Minimum peritumoral ADC values differed across the histopathological grades.KEYWORDS: meningioma, RNA, messenger, MRI, methyltransferases, RT-PCR, ADC, MGMT mRNA

Does the SUVmax of FDG-PET/CT Correlate with the ADC Values of DWI in Musculoskeletal Malignancies?

Purpose:To evaluate the correlation of maximum standardized uptake values (SUVmax) of 18F-Fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) with the apparent diffusion coefficient (ADC) of diffusion weighted imaging (DWI) in musculoskeletal malignancies.Methods:Institutional ethics committee approved this retrospective study. Twenty-seven patients (mean age: 44.85 ± 24.07; 17 men and 10 women) with a total of 29 musculoskeletal tumors underwent both FDG-PET/CT and DWI between January 2017 and March 2020. Region-of-interest (ROI)-based maximal standardized uptake values (SUVmax) of the tumors were measured on FDG-PET/CT images. Two radiologists measured lesions’ mean and minimum apparent diffusion coefficient (ADCmean and ADCmin) using five distinct ROIs on DWI images. Pearson correlation analysis was used to assess the correlation between SUVmax and ADC values.Results:There were 18 soft tissue tumors (62.1%) and 11 bone tumors (37.9%) with a mean maximum diameter of 9.4 ± 6.2 cm. The mean SUVmax, ADCmean and ADCmin of the whole lesions were 12.93 ± 9.63, 0.85 ± 0.28 × 10–3mm2/s and 0.61 ± 0.27 × 10–3mm2/s, respectively. SUVmax had a weak correlation with tumor maximum diameter (r = 0.378, p = 0.043), whereas ADCmean and ADCmin had none. There was strong inverse correlation between SUVmax and both ADCmean (r = –0.616, p < 0.001) and ADCmin (r = –0.638, p < 0.001).Conclusion:In musculoskeletal tumors, quantitative markers of FDG uptake and diffusion restriction strongly correlate.

Conventional and advanced MR imaging insights of synovial sarcoma

ObjectivesSynovial sarcomas commonly involve extremities. The purpose of this study was to systematically assess and describe the appearance of pathologically proven synovial sarcomas on conventional MR sequences, diffusion weighted imaging and dynamic contrast enhanced imaging. MethodsIn this cross-sectional retrospective study, fifteen pre-operative MRIs were analyzed separately by two musculoskeletal radiologists and a fellow. MRI features of synovial sarcomas were evaluated in a systematic fashion on conventional and advanced MR sequences. ResultsThe tumors demonstrated heterogeneous appearance on conventional MR sequences. Peritumoral edema was absent in four of 15 (27%) lesions including grade 2 and grade 3 tumors. Average minimum ADC was 0.8 × 10–3 mm2/s and average mean ADC was 1.2 × 10–3 mm2/s. There was avid early arterial phase enhancement on contrast imaging. Average relative enhancement of the tumors was 5.7 times compared to the adjacent skeletal muscle. ConclusionSynovial sarcomas demonstrate avid early arterial phase post-contrast enhancement on contrast images, low ADC values, and heterogeneous appearance on conventional MRI sequences. Peritumoral edema may be absent in such tumors despite being high grade tumors.