Apparent Diffusion Coefficient Threshold Research Articles

Evaluation of Large Ischemic Cores to Predict Outcomes of Thrombectomy: A Proposal of a Novel Treatment Phase

Background Endovascular treatment of large ischemic cores is challenging. The severity of ischemic stress is assessed using the apparent diffusion coefficient (ADC). We aimed to evaluate the ADC in patients with a low Alberta Stroke Program Early CT [Computed Tomography] Score using diffusion‐weighted imaging and whether it correlates with clinical outcomes. Methods This study included consecutive patients with acute large ischemic stroke (Alberta Stroke Program Early CT Score‐diffusion‐weighted imaging ≤5) who underwent endovascular treatment with successful recanalization between April 2014 and March 2023. The most frequent ADC (peak ADC) and diffusion‐weighted imaging lesion volumes were assessed. The primary outcome was the 3‐month modified Rankin Scale (mRS) score. Good (mRS score, 0–3) and poor clinical outcomes (mRS score, 4–6) were compared to confirm whether ADC was associated with clinical outcomes. Results In total, 78 patients were enrolled in this study; 30 had an mRS score of 0 to 3 at 3 months. The peak ADC in these patients was significantly higher than that in patients with mRS scores of 4 to 6 ( P = 0.0002). In multivariate analysis, peak ADC was strongly associated with good clinical outcomes (odds ratio, 1.231; P = 0.0135) rather than onset‐to‐recanalization time and ischemic core volume. The optimal peak ADC threshold for discriminating between the mRS groups was 520×10 −6 mm 2 /s with a sensitivity of 75% and a specificity of 73%. Good clinical outcomes were more frequently observed in patients with peak ADC ≥520×10 −6 mm 2 /s ( P <0.0001). Conclusion In large ischemic cores, diffusion‐weighted imaging lesions with peak ADCs ≥520×10 −6 mm 2 /s are associated with favorable outcomes. Evaluation of the ischemic core is necessary to confirm endovascular treatment.

Overestimation of ischemic core on baseline MRI in acute stroke.

In patients with acute ischemic stroke (AIS), overestimation of ischemic core on MRI-DWI has been described primarily in regions with milder reduced diffusion. We aimed to assess the possibility of ischemic core overestimation on pretreatment MRI despite using more restricted reduced diffusion (apparent diffusion coefficient (ADC) ≤620 × 10-6 mm2/s) in AIS patients with successful reperfusion. In this retrospective single institutional study, AIS patients who had pretreatment MRI underwent successful reperfusion and had follow-up MRI to determine the final infarct volume were reviewed. Pretreatment ischemic core and final infarction volumes were calculated. Ghost core was defined as overestimation of final infarct volume by baseline MRI of >10 mL. Baseline clinical, demographic, and treatment-related factors in this cohort were reviewed. A total of 6/156 (3.8%) patients had overestimated ischemic core volume on baseline MRI, with mean overestimation of 65.6 mL. Three out of six patients had pretreatment ischemic core estimation of >70 mL, while the final infarct volume was <70 mL. All six patients had last known well-to-imaging <120 min, median (IQR): 65 (53-81) minutes. Overestimation of ischemic core, known as ghost core, is rare using severe ADC threshold (≤620 × 10-6 mm2/s), but it does occur in nearly 1 of every 25 patients, confined to hyperacute patients imaged within 120 min of symptom onset. Awareness of this phenomenon carries implications for treatment and trial enrollment.

How to manage type 2 curve dilemma in dynamic contrast-enhanced magnetic resonance imaging of the breast: diffusion-weighted imaging or early phase enhancement kinetics?

Type 2 time-intensity curves can indicate both malignant and benign breast lesions in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). To investigate whether diffusion-weighted imaging (DWI) or early phase kinetics of DCE-MRI is practical to discriminate breast masses that depict type 2 curve in DCE-MRI. We retrospectively included 107 lesions in 97 patients with type 2 curves in DCE-MRI. Morphological characteristics, early phase dynamic parameters on DCE-MRI, and apparent diffusion coefficient (ADC) values on DWI were evaluated. Diagnostic thresholds of ADC and early phase maximum enhancement ratio (EPMER) to distinguish between benign and malignant masses were calculated. Strongest predictors of malignancy were determined to build the most effective diagnostic model. DWI, EPMER, and all morphological features were found statistically significant to discriminate malignancy (P <0.05). The thresholds of ADC and EPMER were assigned as 1.0 ×10-3 mm2/s and 72%, respectively. The sensitivity and specificity were 80% and 97% for ADC, and 93% and 60% for EPMER, respectively. Two models were established. Model 1 comprised ADC and the lesion margin. Model 2 consisted of ADC, margin, and EPMER with a high specificity (99%) and positive predictive value (97%). When combined with DWI, early phase wash-in data provide diagnostic improvement of breast masses presenting type 2 curve in the late phase of DCE-MRI, especially for specificity. Future studies are required to support our findings for the need of a cross-validation.

The value of dynamic contrast-enhanced MRI and diffusion-weighted sequence in the evaluation of endometrial lesions

Background/Aim: Endometrial abnormalities represent a diagnostic challenge due to overlapping imaging features with normal endometrium. Aim of this study was to assess accuracy of dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging (MRI) in evaluation of endometrial lesions in comparison with T2 and to assess local staging validity and degree of myometrial invasion in malignancy. Methods: Forty patients with abnormal vaginal bleeding or sonographic thickened endometrial were recruited. MRI examination of pelvis was per-formed using 1.5 T scanner with a pelvic array coil. Conventional T1-and T2, dynamic contrast-enhanced (DCE) sequences and diffusion-weighted image (DWI) were performed. Results: Mean age of patients was 53.2 years and 60 % of patients COM-plained of post-menopausal bleeding. Irregular margin, type III enhancement curve, a high signal in T2WI and DWI and low signal of apparent diffusion coefficient (ADC) were significantly associated with malignancy. The optimum ADC threshold value for distinguishing benign from malignant endometrial lesions was 0.905 × 10 -3 mm 2 /S, with 95.5 % sensitivity and 92.9 % specificity. DWI was most sensitive to malignant endometrial lesions, followed by DCE (89.6 %, 98.4 %) and T2 (86.7 %, 91.4 %). DWI and DCE staging correlated with FIGO staging (p = 0.0001 and p = 0.019, respectively). DWI had the best sensitivity for myometrial invasion (95.6 %), followed by DCE (91.9 %) and T2WI (90.1 %). All three sequences had 89.7 % specificity. Conclusion: DWI and DCE MRI were superior to conventional MRI at distinguishing malignant from benign endometrial lesions and can improve myometrial invasion depth evaluation and therapy planning when COM-bined with morphological T2WI. ADC cutoff at a high b value improved MRI diagnostic sensitivity and specificity.

Apparent diffusion coefficient measurements of bone marrow infiltration patterns in multiple myeloma for the assessment of tumor burden - a feasibility study.

The purpose of our study was to explore and compare the tumor burden of different bone marrow infiltration patterns and evaluate the feasibility of apparent diffusion coefficient (ADC) value to identify patterns in multiple myeloma (MM). Ninety-three patients with newly diagnosed multiple myeloma and 23 controls had undergone routine magnetic resonance imaging (MRI) and diffusion-weighted MRI (DWI) from January 2019 to November 2020. Five bone marrow (BM) infiltration patterns were allocated according to routine MRI. The laboratory data and ADC values of patterns were analyzed and compared. ROC analysis was used to establish the best diagnostic ADC threshold value for identifying these patterns and distinguishing normal pattern from controls. Besides, the correlation between the ADC values of diffuse pattern and the plasma cells ratio was assessed. The values of hemoglobin, beta-2 microglobulin (β2-MG), plasma cell, M protein, the percentages of stage, high-risk fluorescence in situ hybridization, and ADC values showed significant difference among patterns. ADCmean at a specific value (368.5×10-6 mm2/s) yielded a maximum specificity (95.5%) and sensitivity (92.0%) in diagnosing MM. A specific value (335.5×10-6mm2/s) yielded a maximum specificity (84.7%) and sensitivity (88.0%) in discriminating visually normal pattern in MM from controls. There was a moderate positive correlation between the plasma cells ratio and ADCs of diffuse infiltration patterns (r = 0.648, P < 0.001). The bone marrow infiltration patterns in MM patients can indicate the tumor burden and ADC value has the ability to discriminate these patterns objectively.

Abstract 103: Quantitative Analysis of Apparent Diffusion Coefficients to Predict Neurological Prognosis in Cardiac Arrest Survivors: An Observational Derivation and Internal-External Validation Study

Aim: Our study aimed to develop apparent diffusion coefficient (ADC) values and thresholds to predict poor outcomes in out-of-hospital cardiac arrest (OHCA) survivors by quantitatively analyzing ADC values in brain MRI. Methods: This observational study utilized prospectively collected data two tertiary academic hospitals. The derivation cohort comprised 70% of patients randomly selected from one hospital, whereas the internal validation cohort comprised the remaining 30%. The external validation cohort used data from the other hospital. MRI data were restricted to scans conducted at 3T within 72-96 h after OHCA. We analyzed the % voxels of ADC thresholds (percentage of voxels below ADC thresholds per total voxel [PV]) at 50-step intervals ranging from 200 to 1200 х 10 -6 mm/s. Poor neurological outcomes were defined as cerebral performance categories 3-5 at 6 months after OHCA. Results: In total, 448 brain MRI scans were evaluated, including a derivation cohort (n=224) and internal/external validation cohorts (n=96 and 128, respectively). The PV values of 500, 550, 600, 650 and 700 х 10 -6 mm/s were determined using the area under the curve (AUC) and demonstrated better performance than higher values in predicting poor neurological outcomes across the derivation (AUC 0.90-0.91), internal validation (AUC 0.87-0.91), and external validation cohorts (AUC 0.91-0.92), showing similar performance levels. A PV of 600х10 -6 mm/s predicted a poor outcome with 100% specificity at a threshold of &gt;13.2% in the derivation cohort, with a sensitivity of 74% (95% CI 66-81) at 100% specificity. In both the internal and external validation cohorts, when using the same threshold, a specificity of 100% corresponded to sensitivities of 75% (95% CI 63-85) and 78% (95% CI 66-87), respectively (Figure). Conclusions: Quantitative analysis of ADC values in brain MRI is a promising tool for predicting poor neurological outcomes in OHCA survivors, with high sensitivity at a specificity of 100%.

Abstract 296: Comparison of Prognostic Performance Using Quantitative Analysis of Head Computed Tomography and Apparent Diffusion Coefficient Imaging Before Target Temperature Management in Out-of-Hospital Cardiac Arrest Survivors

Aim: We compared head computed tomography (HCT) and diffusion-weighted magnetic resonance imaging (DW-MRI) findings before target temperature management (TTM) to predict neurological outcomes in out-of-hospital cardiac arrest survivors. Methods: Using prospectively collected data from a TTM registry, we included patients who underwent HCT and DW-MRI within 6 h of return of spontaneous circulation (ROSC). We examined the gray-to-white matter ratio (GWR) on HCT and apparent diffusion coefficient (ADC) values obtained through voxel-based analysis of DW-MRI. We analyzed the mean ADC value and the % voxels of ADC thresholds (percentage of voxels below ADC thresholds per total voxel) at 50-step intervals ranging from 200 to 1200 х 10 -6 mm/s, identifying thresholds that differentiate between good and poor neurological outcomes. Additionally, we analyzed the combination of pupillary light reflex (PLR) and neuron-specific enolase (NSE) values acquired at the same time points to predict the neurological outcomes. The primary outcome measure was the dichotomized cerebral performance category (CPC) at six months, defined as good (CPC 1-2) or poor (CPC 3-5). Results: Of 131 patients (26% female), 74 (57%) had poor outcomes. The time from ROSC to HCT and DW-MRI scans was 1.7h and 3.2h, respectively. The group with a good outcome had higher GWR (1.26 vs. 1.20) and mean ADC (787.10 vs. 697.49) values, but lower values in all ranges (250-1150) of ADC values (all P&lt;0.001). Mean ADC and ADC values in the range of 350-650 demonstrated better predictive performance for poor outcomes than the GWR (P &lt; 0.05). The mean ADC value had the highest sensitivity of 51.3% (95% CI 39.4-63.1; cutoff value ≤ 739.2 х 10 –6 mm 2 /s) when the false positive rate (FPR) was 0%. Furthermore, when combined with PLR and NSE values, the prognostic performance was significantly improved compared to using Mean ADC alone (0.83 vs 0.92; P&lt;0.01). Conclusions: In this cohort study, early voxel-based quantitative ADC analysis after ROSC (i.e., before TTM) was associated with poor neurological outcomes 6 months after cardiac arrest. We found that the Mean ADC value showed the highest sensitivity when the FPR was 0% and that combining the NSE and PLR values resulted in an even better prognostic performance.

JS09.6.A ASSESSMENT OF RESPONSE TO REGORAFENIB IN RECURRENT GLIOMA PATIENTS USING FET PET AND MRI INCLUDING ADC VALUES

Abstract BACKGROUND The phase 2 REGOMA trial suggested that glioblastoma patients treated with the multikinase inhibitor regorafenib at first progression have significantly longer overall survival than patients treated with lomustine. Here, we evaluated the value of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET and MRI for the evaluation of response to regorafenib in recurrent glioma patients. MATERIAL AND METHODS Twenty patients (age range, 30-72 years) with CNS WHO grade 3 or 4 gliomas (glioblastoma, 85%) at recurrence were treated according to the REGOMA trial with regorafenib at 160 mg/d for the first three weeks of each 4-week cycle, with individual dose adjustment according to toxicity. FET PET and MRI were performed at baseline and follow-up after the second cycle. Static FET PET parameters comprised tumor-to-brain ratios (TBR) and metabolic tumor volumes (MTV). Parameters derived from dynamic FET PET acquisition were time-to-peak and slope. MRI response assessment was based on RANO criteria. Additionally, mean and minimum apparent diffusion coefficients (ADC) were obtained from diffusion-weighted MRI. FET PET parameters and ADC thresholds were defined using ROC analyses to predict an overall survival of ≥ 6 months. The predictive values of FET PET parameters, ADC values, and RANO criteria were subsequently evaluated using univariate and multivariate survival estimates. RESULTS Patients received a median of three regorafenib cycles (range, 2-10 cycles). After treatment initiation, the median follow-up was 8.6 months (range, 3.2-27.3 months). The median number of previous treatment lines was two (range, 1-4 lines). At the time of data analysis, 17 patients (85%) had died. After two cycles of regorafenib, a regorafenib-induced reduction of mean TBR values by ≥ 10% predicted a longer overall survival (9.9 vs. 5.3 months; P=0.023). Interestingly, absolute mean TBR values ≤ 2.0 at follow-up were prognostic and associated with longer OS (10.6 vs. 4.5 months; P=0.007). In contrast, MTV, dynamic PET parameters, RANO criteria, and ADC values were not predictive for response or prognostic (all P&amp;gt;0.05). Multivariate survival analyses revealed that relative mean TBR changes were most potent in predicting response to regorafenib (P=0.038; HR, 0.246) and absolute mean TBR values for prognostication (P&amp;lt;0.001; HR, 0.005). CONCLUSION Data suggest that FET PET parameters are clinically valuable for identifying responders to regorafenib early after treatment initiation and helpful for prognostication. MRI parameters, including ADC values, do not appear to add valuable information in this context.

Automated acute ischemic stroke lesion delineation based on apparent diffusion coefficient thresholds.

Automated lesion segmentation is increasingly used in acute ischemic stroke magnetic resonance imaging (MRI). We explored in detail the performance of apparent diffusion coefficient (ADC) thresholding for delineating baseline diffusion-weighted imaging (DWI) lesions. Retrospective, exploratory analysis of the prospective observational single-center 1000Plus study from September 2008 to June 2013 (clinicaltrials.org; NCT00715533). We built a fully automated lesion segmentation algorithm using a fixed ADC threshold (≤620 × 10-6 mm2/s) to delineate the baseline DWI lesion and analyzed its performance compared to manual assessments. Diagnostic capabilities of best possible ADC thresholds were investigated using receiver operating characteristic curves. Influential patient factors on ADC thresholding techniques' performance were studied by conducting multiple linear regression. 108 acute ischemic stroke patients were selected for analysis. The median Dice coefficient for the algorithm was 0.43 (IQR 0.20-0.64). Mean ADC values in the DWI lesion (β = -0.68, p < 0.001) and DWI lesion volumes (β = 0.29, p < 0.001) predicted performance. Optimal individual ADC thresholds differed between subjects with a median of ≤691 × 10-6 mm2/s (IQR ≤660-750 × 10-6 mm2/s). Mean ADC values in the DWI lesion (β = -0.96, p < 0.001) and mean ADC values in the brain parenchyma (β = 0.24, p < 0.001) were associated with the performance of individual thresholds. The performance of ADC thresholds for delineating acute stroke lesions varies substantially between patients. It is influenced by factors such as lesion size as well as lesion and parenchymal ADC values. Considering the inherent noisiness of ADC maps, ADC threshold-based automated delineation of very small lesions is not reliable.

Prospective validation of diffusion-weighted MRI as a biomarker of tumor response and oncologic outcomes in head and neck cancer: Results from an observational biomarker pre-qualification study

PurposeTo determine DWI parameters associated with tumor response and oncologic outcomes in head and neck (HNC) patients treated with radiotherapy (RT). MethodsHNC patients in a prospective study were included. Patients had MRIs pre-, mid-, and post-RT completion. We used T2-weighted sequences for tumor segmentation which were co-registered to respective DWIs for extraction of apparent diffusion coefficient (ADC) measurements. Treatment response was assessed at mid- and post-RT and was defined as: complete response (CR) vs. non-complete response (non-CR). The Mann-Whitney U test was used to compare ADC between CR and non-CR. Recursive partitioning analysis (RPA) was performed to identify ADC threshold associated with relapse. Cox proportional hazards models were done for clinical vs. clinical and imaging parameters and internal validation was done using bootstrapping technique. ResultsEighty-one patients were included. Median follow-up was 31 months. For patients with post-RT CR, there was a significant increase in mean ADC at mid-RT compared to baseline ((1.8 ± 0.29) × 10–3 mm2/s vs. (1.37 ± 0.22) × 10–3 mm2/s, p < 0.0001), while patients with non-CR had no significant increase (p > 0.05). RPA identified GTV-P delta (Δ)ADCmean < 7% at mid-RT as the most significant parameter associated with worse LC and RFS (p = 0.01). Uni- and multi-variable analysis showed that GTV-P ΔADCmean at mid-RT ≥ 7% was significantly associated with better LC and RFS. The addition of ΔADCmean significantly improved the c-indices of LC and RFS models compared with standard clinical variables (0.85 vs. 0.77 and 0.74 vs. 0.68 for LC and RFS, respectively, p < 0.0001 for both). ConclusionΔADCmean at mid-RT is a strong predictor of oncologic outcomes in HNC. Patients with no significant increase of primary tumor ADC at mid-RT are at high risk of disease relapse.

Comparison of apparent diffusion coefficient and relative apparent diffusion coefficient values for differential diagnosis of breast lesions

Objectives: The purpose the study was to evaluate the role of diffusion weighted magnetic resonance imaging (DW-MRI) in diagnosis of benign and malignant breast lesions, to calculate a cut-off apparent diffusion coefficient (ADC) value and to explore use of relative ADC (r ADC) for improving sensitivity and specificity of MRI in diagnosis of breast cancer. Methods: This retrospective study based on a cohort of patients who underwent dynamic contrast enhanced (DCE)-MRI having suspicious breast mass by ultrasonography and mammography to whom DWI sequence was added to the routine diagnostic MRI. ADC and r ADC (lesion/normal breast tissue) values of breast masses were calculated. The threshold ADC values used to differentiate benign and malignant lesions were determined using receiver operating characteristic analysis, sensitivity, specificity, positive predictive value and negative predictive value were calculated. Results: Malignant masses had significantly lower ADC (mean: 1.03 ± 0.36 × 10-3 mm2/s) and r ADC (mean: 0.66 ± 0.22 × 10-3 mm2/s) values than those of benign masses with ADC (mean: 1.50 ± 0.56 × 10−3 mm2/s) and r ADC (mean: 0.97 ± 0.31 × 10-3 mm2/s) values, respectively (p = 0.001 for both). The best cut-off value for the lesion ADC was 1.09 × 10-3 mm2/s with a sensitivity of 72.73%, and specificity of 79.17%. The best cut-off value for r ADC was 0.83 with sensitivity of 78.79% and specificity of 70.83%. Conclusions: DWI has high diagnostic value with high sensitivity and specificity differentiating benign and malignant breast lesions. ADC and r ADC values can improve the diagnostic accuracy of differentiating benign and malignant breast lesions.

Prospective Assessment of Diffusion-Weighted-MRI as a Biomarker of Treatment Response and Disease Control in Head and Neck Cancer

<h3>Purpose/Objective(s)</h3> To determine diffusion-weighted MRI (DWI) parameters associated with tumor response and local control (LC) in head and neck (HNC) patients treated with definitive radiation therapy (RT). <h3>Materials/Methods</h3> Eighty-seven HNC patients enrolled in an active prospective imaging study at MD Anderson Cancer Center were included in the analysis. Patients had MRIs prior to, at mid-RT, and post-treatment completion. Inclusion criteria include adult with histologic evidence of malignant head and neck neoplasm indicated for curative-intent treatment with RT with/without chemotherapy; good performance status (ECOG score 0-2); and with no contraindications to MRI. Patients were scanned using a full-body MRI scanner with two large four-channel flex phased-array coils. We used fat-suppressed T2-weighted turbo spin echo for tumor segmentation which was co-registered to respective DWIs for extraction of apparent diffusion coefficient (ADC) measurements. Sequence parameters were (TR/TE (ms); pixel size/slice thickness (mm); b-values (s/mm2)): T2-weighted (5280/80; 0.5/2; N/A), BLADE (5900/50; 2/4; 0 & 800), RESOLVE (9380/63; 2/4; 0 & 800). Treatment response was assessed at mid-RT and at 8-12 weeks post-RT using RECIST 1.1 criteria and was defined as: complete response (CR) vs non-complete response (non-CR). Pre-RT ADC was correlated with RT response (CR vs non-CR) at mid- and post-RT time points. Mann-Whitney test was used to compare ADC values between the mid-treatment CR group and the non-CR group. Recursive partitioning analysis (RPA) was done to identify ADC threshold associated with relapse. <h3>Results</h3> Fifty-nine patients with grossly visible primary tumor volume prior to RT start were included in this analysis. Patients' disease and treatment criteria are shown in Table 1. Pre-treatment ADC was not correlated with tumor response or oncologic outcomes (P>0.05). For patients with CR at the end of RT, there was a significant increase in mean ADC at mid-RT compared to baseline ((1.8 ± 0.29) × 10^–3 mm²/s <i>vs</i> (1.37 ± 0.22) × 10^–3 mm²/s, <i>p</i> <0.0001). However, patients with no-CR had no statistically significant increase (p>0.05). Median follow-up was 32 months. Patients with ≤106% delta ADC at mid-RT had significantly worse local control at 2-year compared to patients with higher delta ADC (48% vs 96%, <i>p</i> <0.0001). <h3>Conclusion</h3> Delta ADC changes is a strong predictor of local recurrence in head and neck cancer patients. Multi-institutional data is needed for validation of our results.

Safely reducing unnecessary benign breast biopsies by applying non-mass and DWI directional variance filters to ADC thresholding

BackgroundThresholding apparent diffusion coefficient (ADC) maps obtained from Diffusion-Weighted-Imaging (DWI) has been proposed for identifying benign lesions that can safely avoid biopsy. The presence of malignancies with high ADC values leads to high thresholds, limiting numbers of avoidable biopsies.PurposeWe evaluate two previously reported methods for identifying avoidable biopsies: using case-set dependent ADC thresholds that assure 100% sensitivity and using negative likelihood ratio (LR-) with a fixed ADC threshold of 1.50 × 10–3 mm2/s. We evaluated improvements in efficacy obtained by excluding non-mass lesions and lesions with anisotropic intra-lesion morphologic characteristics.Study typeProspective.Population55 adult females with dense breasts with 69 BI-RADS 4 or 5 lesions (38 malignant, 31 benign) identified on ultrasound and mammography and imaged with MRI prior to biopsy.Field strength/sequence1.5 T and 3.0 T. DWI.AssessmentAnalysis of DWI, including directional images was done on an ROI basis. ROIs were drawn on DWI images acquired prior to biopsy, referencing all available images including DCE, and mean ADC was measured. Anisotropy was quantified via variation in ADC values in the lesion core across directional DWI images.Statistical testsImprovement in specificity at 100% sensitivity was evaluated with exact McNemar test with 1-sided p-value < 0.05 indicating statistical significance.ResultsUsing ADC thresholding that assures 100% sensitivity, non-mass and directional variance filtering improved the percent of avoidable biopsies to 42% from baseline of 10% achieved with ADC thresholding alone. Using LR-, filtering improved outcome to 0.06 from baseline 0.25 with ADC thresholding alone. ADC thresholding showed a lower percentage of avoidable biopsies in our cohort than reported in prior studies. When ADC thresholding was supplemented with filtering, the percentage of avoidable biopsies exceeded those of prior studies.Data conclusionSupplementing ADC thresholding with filters excluding non-mass lesions and lesions with anisotropic characteristics on DWI can result in an increased number of avoidable biopsies.

Limited value of multiparametric MRI with dynamic contrast-enhanced and diffusion-weighted imaging in non-mass enhancing breast tumors

PurposeTo investigate the diagnostic value of multiparametric MRI (mpMRI) including dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in non-mass enhancing breast tumors. MethodPatients who underwent mpMRI, who were diagnosed with a suspicious non-mass enhancement (NME) on DCE-MRI (BI-RADS 4/5), and who subsequently underwent image-guided biopsy were retrospectively included. Two radiologists independently evaluated all NMEs, on both DCE-MR images and high-b-value DW images. Different mpMRI reading approaches were evaluated: 1) with a fixed apparent diffusion coefficient (ADC) threshold (<1.3 malignant, ≥1.3 benign) based on the recommendation by the European Society of Breast Imaging (EUSOBI); 2) with a fixed ADC threshold (<1.5 malignant, ≥1.5 benign) based on recently published trial data; 3) with an ADC threshold adapted to the assigned BI-RADS classification using a previously published reading method; and 4) with individually determined best thresholds for each reader. Results: The final study sample consisted of 66 lesions in 66 patients. DCE-MRI alone had the highest sensitivity for breast cancer detection (94.8–100 %), outperforming all mpMRI reading approaches (R1 74.4–87.1 %, R2 71.7–94.8 %) and DWI alone (R1 74.4 %, R2 79.4 %). The adapted approach achieved the best specificity for both readers (85.1 %), resulting in the best diagnostic accuracy for R1 (86.5 %) but a moderate diagnostic accuracy for R2 (77.2 %). ConclusionmpMRI has limited added diagnostic value to DCE-MRI in the assessment of NME.

Association Between Quantitative Diffusion-Weighted Magnetic Resonance Neuroimaging and Outcome After Pediatric Cardiac Arrest.

Diffusion MRI can quantify the extent of hypoxic-ischemic brain injury after cardiac arrest. Our objective was to determine the association between the adult-derived threshold of apparent diffusion coefficient (ADC) <650 × 10-6 mm2/s in >10% of brain tissue and an unfavorable outcome after pediatric cardiac arrest. Since ADC decreases exponentially as a function of increasing age, we determined the association between (1) having >10% of brain tissue below a novel age-dependent ADC threshold, and (2) age-normalized whole-brain mean ADC and unfavorable outcome. This was a retrospective study of patients aged ≤18 years who had cardiac arrest and a clinically obtained brain MRI within 7 days. The primary outcome was unfavorable neurologic status at hospital discharge based on the Pediatric Cerebral Performance Category score. ADC images were extracted from 3-direction diffusion imaging. We determined whether each patient had >10% of voxels with an ADC below prespecified thresholds. We computed the whole-brain mean ADC for each patient. One hundred thirty-four patients were analyzed. Patients with ADC <650 × 10-6 mm2/s in >10% of voxels had 15 times higher odds (95% CI 5-65) of an unfavorable outcome compared with patients with ADC <650 × 10-6 mm2/s (area under the receiver operating characteristic curve [AUROC] 0.72 [95% CI 0.63-0.80]). These ADC criteria had a sensitivity and specificity of 0.49 and 0.94, respectively, and positive and negative predictive values of 0.93 and 0.52, respectively, for an unfavorable outcome. The age-dependent ADC threshold that yielded optimal sensitivity and specificity for unfavorable outcomes was <300 × 10-6 mm2/s below each patient's predicted whole-brain mean ADC. The sensitivity, specificity, and positive and negative predictive values for this ADC threshold were 0.53, 0.96, 0.96, and 0.54, respectively (odds ratio [OR] 26.4 [95% CI 7.5-168.3]; AUROC 0.74 [95% CI 0.66-0.83]). Lower age-normalized whole-brain mean ADC was also associated with an unfavorable outcome (OR 0.42 [0.24-0.64], AUROC 0.76 [95% CI 0.66-0.82]). Quantitative diffusion thresholds on MRI within 7 days after cardiac arrest were associated with an unfavorable outcome in children. The age-independent ADC threshold was highly specific for predicting an unfavorable outcome. However, the specificity and sensitivity increased when using age-dependent ADC thresholds. Age-dependent ADC thresholds may improve prognostic accuracy and require further investigation in larger cohorts. This study provides Class III evidence that quantitative diffusion-weighted imaging within 7 days postarrest can predict an unfavorable clinical outcome in children.

Multi-parametric arterial spin labeling and diffusion-weighted imaging of paranasal sinuses masses.

To evaluate arterial spin labeling (ASL) and diffusion-weighted imaging (DWI) in discrimination of benign from malignant paranasal sinus (PNS) tumors. A prospective study was done upon 42 cases of PNS masses that underwent magnetic resonance ASL and DWI of the head. Tumor blood flow (TBF) and apparent diffusion coefficient (ADC) of the masses were calculated by two observers. The pathological diagnosis was malignant (n = 28) and benign (n = 14) cases. For both observers, the malignant PNS masses had significantly higher TBF (P < 0.001, 0.001) and lower ADC (P < 0.001, 0.001) than in benign masses. The ROC curve analysis of TBF, The threshed TBF was (121.45, 122.68mL/100g/min) used for differentiation between benign and malignant PNS masses, revealed sensitivity (92.9%, 89.3%), specificity (85.7%, 85.7%), accuracy (90.5%, 88.1%) and the AUC was 0.87 and 0.86 by both observers. the ROC curve analysis of ADC, The threshold ADC (1.215, 1.205 X10-3mm2/s) was used for differentiation between benign and malignant PNS masses, revealed sensitivity (96.4%, 89.3%), specificity (78.6%, 78.6%), accuracy of (90.5%, 85.7%) and the AUC was 0.93 and 0.92 by both observers. Combined analysis of TBF and ADC used for differentiation between benign and malignant PNS masses had revealed sensitivity (96.4%, 89.3%), specificity (92.9%, 85.7%) accuracy of (95.2%, 88.1%) and AUC. (0.995, 0.985) for both observers. Combined using of TBF and ADC have a role in differentiation malignant from benign PNS masses.

Accuracy of 2D BLADE Turbo Gradient- and Spin-Echo Diffusion Weighted Imaging for the Diagnosis of Primary Middle Ear Cholesteatoma.

To evaluate the diagnostic accuracy of 2D BLADE turbo gradient- and spin-echo diffusion weighted imaging (TGSE BLADE DWI) for primary middle ear cholesteatoma diagnosis, using qualitative and quantitative methods. Retrospective case series. University hospital. Participants included those with suspected primary middle ear cholesteatoma after assessment by clinical otorhinolaryngologists combined with magnetic resonance imaging (MRI) examination. Finally, of the 85 ears from 65 patients enrolled in the study, 73 had cholesteatoma, and 12 had otitis media. Two radiologists independently assessed images and measured apparent diffusion coefficient (ADC) values. Sensitivity, specificity and accuracy were evaluated. Kappa (k) statistics, the intraclass correlation coefficient (ICC), the Kolmogorov-Smirnov normality test, the independent t test, and receiver operating characteristic (ROC) analysis were used for statistical analysis. Pair-wise comparison of the area under the ROC curve (AUC) was also performed using the Delong test. Imaging and histopathologic findings. The mean ADC value of cholesteatoma group (mean, 0.923 ± 0.246 × 10 -3 mm 2 /s) was significantly lower than that of noncholesteatoma group (mean, 1.744 ± 0.205 × 10 -3 mm 2 /s; p < 0.001). In ≤3 mm cholesteatoma group, the AUC of qualitative DWI was 0.846; the sensitivity, specificity, and accuracy for diagnosing cholesteatoma were 69.23%, 100%, and 84%, respectively; while the AUC of quantitative diagnosis was significantly increased to 1.0 ( p = 0.0209); and based on the optimal threshold of ADC, ≤1.352 × 10 -3 mm 2 /s, the sensitivity, specificity and accuracy improved to 100%. For >3 mm cholesteatoma group, there were no significant differences in diagnostic performance. Excellent interobserver agreement and ICC for the qualitative and quantitative evaluations (k = 0.90 and ICC = 0.80, respectively) was noted between reviewers. TGSE BLADE DWI is useful for the detection of primary middle ear cholesteatomas, especially ≤3 mm lesions.

Contribution of Diffusion-Weighted Imaging and ADC Values to Papillary Breast Lesions.

This study aimed to evaluate the role of apparent diffusion coefficient (ADC) values obtained from diffusion-weighted imaging (DWI) in the differentiation of malignant from benign papillary breast lesions. The magnetic resonance imaging (MRI) data of 94 breast papillary lesions confirmed by pathology were retrospectively analyzed. The differences in ADC values of papillary lesions under different enhancements in MRI and different pathological types were investigated, and the ADC threshold was determined by the receiver operating characteristic curve for its potential diagnostic value. The mean ADC values in borderline and malignant lesions (1.01 ± 0.20 × 10-3 mm2/s) were significantly lower compared to benign lesions (1.21 ± 0.27 × 10-3 mm2/s) (P < 0.05). The optimal threshold of the ADC value could be 1.00 × 10-3 mm2/s. The ADC values were statistically significant in differentiating between benign and malignant papillary lesions whether in mass or non-mass enhancement (P < 0.05). However, there were no statistical differences in the ADC values among borderline or any other histological subtypes of malignant lesions (P > 0.05). Measuring ADC values from DWI can be used to identify benign and malignant breast papillary lesions. The diagnostic performance of the ADC value in identifying benign and malignant breast lesions is not affected by the way of lesion enhancement. However, it shows no use for differential diagnosis among malignant lesion subtypes for now. The ADC value of 1.00 × 10-3 mm2/s can be used as the most appropriate threshold for distinguishing between benign and malignant breast papillary lesions.

Predictive accuracy of an ADC map for hemorrhagic transformation in acute ischemic stroke patients after successful recanalization with endovascular therapy.

BackgroundHemorrhagic transformation (HT) of acute ischemic stroke (AIS) is associated with poor outcome. Previous studies only reported the association of mean ischemic severity or total infarct volume with HT after endovascular therapy (EVT). We aimed to investigate the predictive value of preoperative apparent diffusion coefficient (ADC) map for HT by combinated ischemic severity and corresponding volume in AIS after successful recanalization with EVT.MethodsWe retrospectively analyzed 119 consecutive cases of AIS with large vessel occlusion of anterior circulation within 24 hours after symptom onset and successful recanalization after EVT. All cases had baseline magnetic resonance imaging (MRI), follow-up computed tomography (CT), and magnetic resonance angiography (MRA) or computed tomography angiography (CTA). Volumes of ADC <0.6×10−3, 0.5×10−3, 0.4×10−3, and 0.3×10−3 mm2/s, baseline characteristics and outcomes of patients with and without HT identified by European Collaborative Acute Stroke Study (ECASS) were compared. The optimal ADC and volume threshold for predicting HT were analyzed using receiver operating characteristic (ROC) curve, and multivariate logistic regression analysis were performed with clinical characteristics and volumes of optimal ADC threshold to determine risk factors for HT.ResultsAmong 119 patients, 42 patients had HT on follow-up CT, including 24 hemorrhagic infarct (HI) cases and 18 parenchymal hematoma (PH) cases. The optimal volumes were 6.46 mL with ADC <0.4×10−3 mm2/s for predicting both HT and PH, with a larger area under curve (AUC) of 83.3% for HT than that for PH of 80%. In logistic regression analysis, intravenous tissue plasminogen activator (IV tPA) treatment, atrial fibrillation, and volume of ADC <0.4×10−3 mm2/s were identified as independent predictors for HT and volume of ADC <0.4×10−3 mm2/s had the highest odds ratio (OR) value.ConclusionsThe combination of ischemic severity and corresponding volume in ADC map may predict HT after thrombectomy. In addition to the total infarct volume, volume with severe ischemia should be taken into consideration in preoperative patient selection.

Preoperative Apparent Diffusion Coefficient Values for Differentiation between Low and High Grade Meningiomas: An Updated Systematic Review and Meta-Analysis.

The meta-analysis aimed to compare the preoperative apparent diffusion coefficient (ADC) values between low-grade meningiomas (LGMs) and high-grade meningiomas (HGMs). Medline, Cochrane, Scopus, and Embase databases were screened up to January 2022 for studies investigating the ADC values of meningiomas. The study endpoint was the reported ADC values for LGMs and HGMs. Further subgroup analyses between 1.5T and 3T MRI scanners, ADC threshold values, ADC in different histological LGMs, and correlation coefficients (r) between ADC and Ki-67 were also performed. The quality of studies was evaluated by the quality assessment of diagnostic accuracy studies (QUADAS-2). A χ2-based test of homogeneity was performed using Cochran’s Q statistic and inconsistency index (I2). Twenty-five studies with a total of 1552 meningiomas (1102 LGMs and 450 HGMs) were included. The mean ADC values (×10−3 mm2/s) were 0.92 and 0.79 for LGMs and HGMs, respectively. Compared with LGMs, significantly lower mean ADC values for HGMs were observed with a pooled difference of 0.13 (p < 0.00001). The results were consistent in both 1.5T and 3T MRI scanners. For ADC threshold values, pooled sensitivity of 69%, specificity of 82%, and AUC of 0.84 are obtained for differentiation between LGMs and HGMs. The mean ADC (×10−3 mm2/s) in different histological LGMs ranged from 0.87 to 1.22. Correlation coefficients (r) of mean ADC and Ki-67 ranged from −0.29 to −0.61. Preoperative ADC values are a useful tool for differentiating between LGMs and HGMs. Results of this study provide valuable information for planning treatments in meningiomas.