Diagnostic value of diffusion-weighted magnetic resonance imaging in differentiating benign and malignant fetal adrenal tumors.

Objectives: This study aimed to study the minimum and mean ADC values in the differentiation of high and low grade gliomas. Background: Diffusion-weighted imaging (DWI) has greatly enhanced the ability of MRI to differentiate high and low grade gliomas where the ADC values inversely correlated with the tumor grade. Methods: This retrospective study included 50 patients (M/F 23/27) with pathologically proven gliomas (30 patients with high grade and 20 with low grade gliomas) who underwent MRI with diffusion weighted sequence ((b-value 1000 s/mm2) acquired on a 1.5T scanner at menoufia university hospital. Results: Diffusion restriction was found in 93.3% of cases of high grade gliomas (n=28/30) with mean calculated ADC value of calculated mean and minimum ADC values were 0.87±0.3 x 10-3 mm2/sec and 0.82±0.2 x 10-3 mm2/sec respectively. In low grade gliomas diffusion restriction was identified in 7 cases (n=7/20, 35%) with a mean calculated ADC values of 1.3±0.3 x 10-3 mm2/sec and 1.15±0.2 x 10-3 mm2/sec for the mean and minimum ADC respectively. Statistical significance was found between the calculated ADC values of the high and low grade gliomas when using the minimum and mean ADC values between the high and low grade gliomas (p value <0.001) Conclusion: We have demonstrated that both mean and minimum ADC values measurements can be used when trying to differentiate high and low grade gliomas with both showing statistical significance.

To evaluate the diagnostic performance of apparent diffusion coefficient (ADC) parameters by region of interest (ROI) methods in differentiating mass-forming autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC). The institutional review board approved this retrospective study and the requirement for informed consent was waived. Twenty-three patients with mass-forming AIP and 144 patients with PDAC underwent diffusion-weighted imaging with b-values of 0 s/mm2 and 800 s/mm2. The minimum, maximum, and mean ADC values obtained by placing ROIs within lesions and percentile ADC values (10th, 25th, 50th, 75th, and 90th) from entire-lesion histogram analysis were compared between the two groups by using Mann-Whitney U tests. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve analysis. The minimum, maximum, and mean ADC values were significantly different between mass-forming AIP and PDAC groups. ROC curve analysis showed that the maximum ADC had the highest diagnostic performance (0.92), while the minimum ADC value had the lowest diagnostic performance (0.72). The AUC of minimum ADC was significantly lower than that of maximum or mean ADC (P < 0.0001, P < 0.0001). The AUC was lowest in 10th percentile ADC value and highest in 90th percentile value. The AUC increased along with the increase of percentile values. Either the maximum or mean ADC value was effective in differentiating mass-forming AIP from the PDAC group, while the minimum ADC value might not be recommended.

To investigate the correlations between the minimum and mean apparent diffusion coefficient (ADC) values of hepatocellular carcinoma (HCC) and pathological grade. Preoperative magnetic resonance imaging (MRI) images of 241 patients with HCC confirmed by pathology were retrospectively analyzed. All patients underwent preoperative diffusion-weighted imaging (DWI) on a 1.5T MRI scanner. The mean and minimum ADC values of the tumors were measured. The ADC values were compared in tumors with different grades and the correlations between ADC values and pathological grade were analyzed. Receiver operating characteristic (ROC) curves of ADC values were obtained and compared to distinguish poorly and nonpoorly differentiated HCCs. Interobserver agreements were assessed by intraclass correlation coefficient (ICC). The mean and minimum ADC values of poorly differentiated HCCs were lower than those of nonpoorly differentiated HCCs (P = 0.000, 0.000, respectively). The mean and minimum ADC values were negatively correlated with pathological grade (rs = -0.180 and -0.202, respectively) (P = 0.005, 0.002, respectively). For the differentiation between poorly and nonpoorly differentiated HCCs, the mean ADC value provided a sensitivity of 69.57% and a specificity of 73.39% with a cutoff value of 0.96 × 10-3 mm2 /s while the minimum ADC value showed a sensitivity of 78.26% and a specificity of 61.47% with a cutoff value of 0.90 × 10-3 mm2 /s. No significant difference existed between both ROC curves (P = 0.64). The ICC for the measurements of the mean and minimum ADC values was 0.92 (95% confidence interval [CI] 0.90-0.93) and 0.91 (95% CI 0.89-0.93), respectively. DWI of HCC could preoperatively provide quantitative parameters for predicting tumor histological grade. J. Magn. Reson. Imaging 2016;44:1442-1447.

In glioblastoma multiforme, the peritumoral region may be infiltrated with malignant cells in addition to vasogenic edema, whereas in a metastatic deposit, the peritumoral areas comprise predominantly vasogenic edema. The purpose of this study was to determine whether the minimum apparent diffusion coefficient (ADC) can be used to differentiate glioblastoma from solitary metastasis on the basis of cellularity levels in the enhancing tumor and in the peritumoral region. Seventy-three patients underwent conventional MRI and diffusion-weighted imaging (DWI) before undergoing treatment. The minimum ADC was measured in the enhancing tumor, peritumoral region, and contralateral normal white matter. To determine whether there was a statistical difference between metastasis and glioblastoma, we analyzed patient age and sex, minimum ADC value, and ADC ratio of the two groups. A receiver operating characteristic (ROC) curve analysis was used to determine the cutoff value of the minimum ADC that had the best combination of sensitivity and specificity for distinguishing between glioblastoma and metastasis. The mean minimum ADC values and mean ADC ratios in the peritumoral regions of glioblastomas were significantly higher than those in metastases. However, the mean minimum ADC values and mean ADC ratios in enhancing tumors showed no statistically significant difference between the two groups. According to ROC curve analysis, a cutoff value of 1.302 × 10(-3) mm(2)/s for the minimum peritumoral ADC value generated the best combination of sensitivity (82.9%) and specificity (78.9%) for distinguishing between glioblastoma and metastasis. Although the characteristics of solitary metastasis and glioblastoma multiforme may be similar on conventional MRI, DWI can offer diagnostic information to distinguish between the tumors.

The aim of the study was to investigate the value of preoperative diffusion-weighted imaging (DWI) in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC), using and comparing mean and minimum apparent diffusion coefficient (ADC) values.Preoperative MR images of 318 patients with HCC confirmed by surgical pathology were retrospectively analyzed. All patients underwent preoperative DWI on a 1.5 Tesla MRI scanner. The mean and minimum ADC values of the tumors were measured. Interobserver agreements were assessed by the intraclass correlation coefficient (ICC). The ADC values were compared in HCCs between with and without MVI. ROC curves of ADC values were obtained and then compared in distinguishing HCCs with MVI from those without MVI.There were 211 HCCs with MVI and 107 HCCs without MVI. ICC for the measurements of the mean and minimum ADC values between both observers was 0.88 (95% CI 0.85 – 0.90) and 0.88 (95% CI 0.85 – 0.90), respectively. The mean and minimum ADC values of HCCs with MVI were lower than those of HCCs without MVI (P = .00, .00, respectively). With a cut-off value of 0.98 × 10–3 mm2/s, the minimum ADC (MinADC) showed a sensitivity of 62.56% and a specificity of 65.42% in predicting MVI, whereas the mean ADC provided a sensitivity of 79.15% and a specificity of 50.47% with a cut-off value of 1.19 × 10–3 mm2/s. No significant difference existed between MinADC and mean ADC for their diagnostic performances in the prediction of MVI (P = .48).DWI could preoperatively provide quantitative parameters for predicting MVI of HCC.

Accuracy of apparent diffusion coefficient values for distinguishing between pineal germ cell tumour and pineoblastoma

To assess the influence of region of interest (ROI) on tumor apparent diffusion coefficient (ADC) measurements and interobserver variability in pancreatic ductal adenocarcinoma (PDAC). Twenty-two patients recruited with pathology-proven PDAC underwent diffusion-weighted imaging (DWI, 3.0T) prior to the surgical resection. Two independent readers measured tumor ADCs according to three ROI methods: whole-volume, single-slice, and small solid sample of tumor. Minimum and mean ADCs were obtained. The interobserver variability for each of the three methods was analyzed using interclass correlation coefficient (ICC) and Bland-Altman analysis. The minimum and mean ADCs among the ROI methods were compared using nonparametric tests. The single-slice ROI method showed the best reproducibility in the minimum ADC measurements (mean difference ± limits of agreement between two readers were 0.025 ± 0.25 × 10(-3) mm2 /s; ICC, 0.92) among the three ROI methods. For the solid tumor sample ROI, both minimum ADC and mean ADC measurements reproducibility were the worst, with limits of agreement up to ±0.50 × 10(-3) mm2 /s and ±0.32 × 10(-3) mm2 /s, respectively (ICCs, 0.41/0.58). Both the minimum and mean ADCs demonstrated significant differences among the three ROI methods (both P < 0.001). The post-hoc analyses results showed no significant difference with regard to the mean ADCs between whole-volume and single-slice ROI methods (P = 0.14). The ROI method had a considerable influence on both the minimum and mean ADC values and the interobserver variability in PDAC. The worst interobserver variability was observed for both the minimum and mean ADCs derived from small solid-sample ROI.

Purpose: Diffusion weighted imaging (DWI), which is quantified by apparent diffusion coefficient (ADC), can predict tissue microstructure. It has become an essential part of the gynecological magnetic resonance imaging (MRI) protocol. In our study it was aimed to evaluate the value of the maximum, mean, and minimum ADC values of the cervix-parametrium boundary to estimate parametrial invasion for cervix carcinoma. Material and Method: Totally 50 patients with cervical carcinoma, 18 of which had no parametrial invasion (4811-year-old) and 32 had parametrial invasion (5812-year-old) according to conventional T2 weighted imaging were enrolled. Maximum, mean, and minimum ADC values of cervix-parametrium boundary of primary tumors were statistically compared between the groups without and with parametrial invasion. The diagnostic performances of the maximum, mean and minimum ADC values were evaluated by ROC analysis in terms of estimating parametrial invasion. Results: The mean maximum, mean and minimum ADC values were lower for the patients with parametrial invasion. However, only the minimum ADC values had statistically significant differences between the groups. ROC analysis showed an AUC value of 0.726 for minimum ADC in estimating parametrial invasion. A minimum ADC cut-off value of 0.553x10-3 mm2/s had a sensitivity of 63%, specificity of 73%, negative predictive value of 52% and positive predictive value of 80% and accuracy of 66%. Conclusions: ADC values can be applied for the determination of parametrial invasion of cervical carcinoma. Lower minimum ADC values obtained from cervix-parametrium boundary of primary cervical carcinoma may help parametrial invasion. Especially positive predictive value of the cervix-parametrium boundary ADC is remarkable.

Utility of the relative apparent diffusion coefficient for preoperative assessment of low risk endometrial carcinoma

Prediction of Infarct Lesion Volumes by Processing Magnetic Resonance Apparent Diffusion Coefficient Maps in Patients with Acute Ischemic Stroke

The purpose of our study was to assess the utility of the minimum apparent diffusion coefficient (ADC), average ADC, maximum ADC, and ADC difference value and to find optimum ADC parameters for differentiation between benign and malignant lesions in breast diffusion-weighted imaging (DWI). Sixty-seven women with 75 masslike lesions (27 benign, 48 malignant) were examined with 3-T MRI. To assess heterogeneity within the lesion, the difference between minimum and maximum ADCs was recorded as the ADC difference value. Diagnostic performances of these parameters were compared by receiver operating characteristic (ROC) curve analysis. Each ADC parameter showed significant differences between malignant and benign lesions. The optimal cutoff levels for differentiating benign versus malignant lesions were determined by identifying the points where the sensitivity and specificity were equal on the ROC curves. According to ROC analyses, the following sensitivities and specificities were obtained: average ADC, 75.6% and 75.6%; minimum ADC, 85.5% and 85.5%; maximum ADC, 63.5% and 63.5%; ADC difference value, 70.1% and 70.1%. Minimum ADC had the largest area under the ROC curve (AUC) of 0.93. Minimum ADC combined with the ADC difference value improved the AUC to 0.95, with sensitivity and specificity of 89.1% and 89.1%. Minimum ADC may be an optimal DWI single parameter for differentiation between malignant and benign lesions of breast masses. Furthermore, the combination of the minimum ADC and ADC difference value significantly elevated diagnostic performance of breast DWI in comparison with average ADC.

To investigate the accuracy of quantitative diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping for characterizing soft tissue masses (STMs) as cysts or solid masses. This IRB-approved retrospective study included 36 subjects with 37 STMs imaged by conventional MRI (T1-weighted, T2-weighted, contrast-enhanced T1-weighted sequences) and DWI (b-values 50, 400, 800s/mm(2)) with ADC mapping. STMs were defined as non-solid cysts by histology or clinical follow-up, and as solid by histology. For each STM, ADC values (range, mean) were recorded by two observers. Differences between ADC values in cysts and solid STMs were compared using Wilcoxon rank-sum and receiver-operating characteristic (ROC) analysis. There were higher minimum (1.65 vs 0.68, p = 0.003) and mean (2.31 vs 1.45, p = 0.005) ADC values in cysts than solid STMs respectively. Areas under the ROC for minimum and mean ADC values were 0.82 and 0.81 respectively. Using threshold ADC values of 1.8 (minimum) or 2.5 (mean) yielded a sensitivity of 60% and 80% respectively, and a specificity of 100% for classifying a STM as a cyst; for tumors with high fluid-signal intensity, the performance of these threshold values was maintained. Diffusion-weighted imaging with ADC mapping provides a non-contrast MRI alternative for the characterization of STMs as cysts or solid masses. Threshold ADC values exist that provide 100% specificity for differentiating cysts and solid STMs, even for tumors of high fluid-signal intensity on T2-weighted images.

BackgroundDiffusion-weighted imaging (DWI) is a form of magnetic resonance imaging (MRI) based on measuring the random Brownian motion of water molecules within the biological tissues and is particularly useful in tumor characterization.ObjectivesThe purpose of this study was to evaluate the diagnostic value of DW MRI and the apparent diffusion coefficient (ADC) for preoperative grading of astrocytic supratentorial brain tumors.Patients and MethodsTwenty-three patients (14 females, 9 males, mean age 43 years) with astrocytic supratentorial brain tumors underwent preoperative conventional MR imaging and DW MRI. The minimum, maximum and mean ADC values and the minimum, maximum and mean DWI signal intensities of each tumor were taken by placing several regions of interest in the tumor on DWI images and ADC maps. To assess the relationship between these values and the tumor grade, we used the Mann-Whitney U test and the Spearman correlation. Receiver operating characteristic (ROC) analysis was used to determine the cutoff value of the minimum, maximum and mean ADC values and the minimum, maximum and mean DWI signal intensities that had the best composition of sensitivity and specificity for differentiating low-grade and high-grade astrocytic brain tumors.ResultsAccording to the pathology reports, 10 patients had low-grade astrocytomas (grades I, II) and 13 patients had high-grade astrocytomas (grades III, IV). The minimum ADC value showed a significantly inverse correlation with astrocytic tumor grade (P = 0.006). The correlation between the maximum ADC value and the maximum DWI signal intensity with tumor grade was direct (P = 0.013, P = 0.035). According to the ROC analysis, the cutoff values of 0.843 × 10-3 mm2/s, 2.117 × 10-3 mm2/s and 165.2 for the minimum ADC, maximum ADC and maximum DWI respectively, obtained the best combination of sensitivity and specificity for distinguishing low-grade and high-grade astrocytomas.ConclusionMeasuring minimum ADC, maximum ADC and maximum DWI signal intensity can provide valuable information for grading of astrocytic supratentorial brain tumors before surgery.

To evaluate the diagnostic value of diffusion-weighted MRI for differentiating metastatic from non-metastatic retropharyngeal lymph nodes (RLNs) in patients with nasopharyngeal carcinoma (NPC). Untreated patients with NPC (n = 145) were scanned with both morphological MRI and diffusion-weighted imaging (DWI). RLNs (n = 335) were classified as metastatic on the basis of response to therapy as assessed on follow-up MRI. Morphological (short- and long-axial diameters) and functional [mean apparent diffusion coefficient (ADC) and minimum ADC values] parameters of the RLNs were derived from DWI and compared between metastatic and non-metastatic groups. A receiver operating characteristic curve and the area under the curve were used to evaluate the effectiveness of individual criteria and to generate threshold values to diagnose RLN metastases. Statistically significant differences between metastatic and non-metastatic RLNs were found for all four parameters derived from DWI (p < 0.001). At threshold values, accuracies of the ADC-based criteria (0.938 and 0.965 for mean and minimum ADC values, respectively) were greater than that of size-based criteria (0.838 and 0.809 for short- and long-axial diameters). The minimum ADC value at the threshold of 0.89 × 10(-3) mm(2) s(-1) was the most effective of all parameters in differentiating metastatic from non-metastatic RLNs with the sensitivity of 95.7%, specificity of 95.1% and accuracy of 96.5%. DWI is feasible for differentiating metastatic RLNs from non-metastatic nodes in patients with NPC with high accuracy, and the minimum ADC derived from DWI could serve as a standard clinical marker for disease status.

To assess the interobserver reliability of three selective region-of-interest (ROI) measurement protocols for apparent diffusion coefficient (ADC) quantifications in soft tissue masses (STMs) compared with whole tumor volume (WTV) ADC measurements. Institutional review board approval was obtained and informed consent was waived. Three observers independently measured minimum and mean ADCs of 73 benign and malignant musculoskeletal STMs using three selective methods (single-slice [SS], predefined three slices [PD], observer-based [OB]) and WTV measurements at 3.0 Tesla. Minimum and mean ADC values derived from each method were compared with WTV measurements, and inter-reader variation was assessed using the intraclass correlation coefficient (ICC). The time required for each method of ADC measurement was recorded. For the SS, PD, OB, and WTV methods, minimum ADC values ((×10(-3) mm2 /s)) were 0.97, 0.78, 0.73, and 0.67, respectively, and mean ADC values ((×10(-3) mm2 /s)) were 1.49, 1.49, 1.51, and 1.49, respectively. Interobserver agreement was good to excellent for the minimum and mean ADC values for the three readers using the SS, PD, OB, and WTV (ICC range 0.78-0.90). The SS, PD and OB methods required the least amount of measurement time (14 ± 5, 40 ± 17, and 38 ± 15 s, respectively) while the reference WTV method required the longest measurement time (111 ± 54 s) (P < 0.01). While all selective and WTV measurements offer good to excellent interobserver agreement, the selective OB method of ADC measurement results in the closest values to WTV measurements and requires significantly less measurement time than that required for the WTV method.