Comparison of two region-of-interest placement methods for histogram analysis of apparent diffusion coefficient maps for glioma grading

Objectives

We assessed the value of histogram analysis (HA) of apparent diffusion coefficient (ADC) maps for grading low-grade (LGG) and high-grade (HGG) gliomas.

Methods

We compared the diagnostic performance of two region-of-interest (ROI) placement methods (ROI 1: the entire tumor; ROI 2: the tumor excluding cystic and necrotic portions). We retrospectively evaluated 54 patients with supratentorial gliomas (18 LGG and 36 HGG). All subjects underwent standard 3T contrast-enhanced magnetic resonance imaging. Histogram parameters of ADC maps calculated with the two segmentation methods comprised mean, median, maxi-mum, minimum, kurtosis, skewness, entropy, standard deviation (sd), mean of positive pixels (mpp), uniformity of positive pixels, and their ratios (r) between lesion and normal white matter. They were compared using the independent t-test, chi-square test, or Mann-Whitney U test. For statistically significant results, receiver operating characteristic curves were constructed, and the optimal cutoff value, sensitivity, and specificity were determined by maximizing Youden's index.

Results

The ROI 1 method resulted in significantly higher rADC mean, rADC median, and rADC mpp for LGG than for HGG; these parameters had value for predicting the histological glioma grade with a cutoff (sensitivity, specificity) of 1.88 (77.8%, 61.1%), 2.25 (44.4%, 97.2%), and 1.88 (77.8%, 63.9%), respectively. The ROI 2 method resulted in significantly higher ADC mean, ADC median, ADC mpp, ADC sd, ADC max, rADC median, rADC mpp, rADC mean, rADC sd, and rADC max for LGG than for HGG, while skewness was lower for LGG than for HGG (0.27 [0.98] vs 0.91 [0.81], p = 0.014). In ROI 2, ADC median, ADC mpp, ADC mean, rADC median, rADC mpp, and rADC mean performed well in differentiating glioma grade with cutoffs (sensitivity, specificity) of 1.28 (77.8%, 88.9%), 1.28 (77.8%, 88.9%), 1.25 (77.8%, 91.7%), 1.81 (83.3%, 91.7%), 1.74 (83.3%, 91.7%), and 1.81 (83.3%, 91.7%), respectively.

Conclusions

HA parameters had value for grading gliomas. Ex-cluding cystic and necrotic portions of the tumor for measuring HA parameters was preferable to using the entire tumor as the ROI. In this segmentation, rADC median showed the highest performance in predicting histological glioma grade, followed by rADC mpp, rADC mean, ADC median, ADC mpp, and ADC mean.

A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI

BACKGROUND

Dynamic contrast-enhanced (DCE) MRI commonly outperforms diffusion-weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE-MRI, particularly in patients with chronic kidney disease.

PURPOSE

To develop a novel deep learning model to fully exploit the potential of overall b-value DW-MRI without the need for a contrast agent in predicting breast cancer molecular subtypes and to evaluate its performance in comparison with DCE-MRI.

STUDY TYPE

Prospective.

SUBJECTS

486 female breast cancer patients (training/validation/test: 64%/16%/20%).

FIELD STRENGTH/SEQUENCE

3.0 T/DW-MRI (13 b-values) and DCE-MRI (one precontrast and five postcontrast phases).

ASSESSMENT

The breast cancers were divided into four subtypes: luminal A, luminal B, HER2+, and triple negative. A channel-dimensional feature-reconstructed (CDFR) deep neural network (DNN) was proposed to predict these subtypes using pathological diagnosis as the reference standard. Additionally, a non-CDFR DNN (NCDFR-DNN) was built for comparative purposes. A mixture ensemble DNN (ME-DNN) integrating two CDFR-DNNs was constructed to identify subtypes on multiparametric MRI (MP-MRI) combing DW-MRI and DCE-MRI.

STATISTICAL TESTS

Model performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Model comparisons were performed using the one-way analysis of variance with least significant difference post hoc test and the DeLong test. P < 0.05 was considered significant.

RESULTS

The CDFR-DNN (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.94) demonstrated significantly improved predictive performance than the NCDFR-DNN (accuracies, 0.76 ~ 0.78; AUCs, 0.92 ~ 0.93) on DW-MRI. Utilizing the CDFR-DNN, DW-MRI attained the predictive performance equal (P = 0.065 ~ 1.000) to DCE-MRI (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.95). The predictive performance of the ME-DNN on MP-MRI (accuracies, 0.85 ~ 0.87; AUCs, 0.96 ~ 0.97) was superior to those of both the CDFR-DNN and NCDFR-DNN on either DW-MRI or DCE-MRI.

DATA CONCLUSION

The CDFR-DNN enabled overall b-value DW-MRI to achieve the predictive performance comparable to DCE-MRI. MP-MRI outperformed DW-MRI and DCE-MRI in subtype prediction.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 1.

The Role of Diffusion-Weighted Imaging Based on Maximum-Intensity Projection in Young Patients with Marked Background Parenchymal Enhancement on Contrast-Enhanced Breast MRI

Diffusion-weighted imaging (DWI) utilizing maximum-intensity projection (MIP) was suggested as a cost-effective alternative tool without the risk of gadolinium-based contrast agents. The purpose of this study was to investigate whether DWI MIPs played a supportive role in young (≤60) patients with marked background parenchymal enhancement (BPE) on contrast-enhanced MRI (CE-MRI). The research included 1303 patients with varying degrees of BPE, and correlations between BPE on CE-MRI, the background diffusion signal (BDS) on DWI, and clinical parameters were analyzed. Lesion detection scores were compared between CE-MRI and DWI, with DWI showing higher scores. Among the 186 lesions in 181 patients with marked BPE on CE-MRI, the main lesion on MIPs of CE-MRI was partially or completely seen in 88.7% of cases, while it was not seen in 11.3% of cases. On the other hand, the main lesion on MIPs of DWI was seen in 91.4% of cases, with only 8.6% of cases showing no visibility. DWI achieved higher scores for lesion detection compared to CE-MRI. The presence of a marked BDS was significantly associated with a lower likelihood of a higher DWI score (p < 0.001), and non-mass lesions were associated with a decreased likelihood of a higher DWI score compared with mass lesions (p = 0.196). In conclusion, the inclusion of MIPs of DWI in the preoperative evaluation of breast cancer patients, particularly young women with marked BPE, proved highly beneficial in improving the overall diagnostic process.

Assessment of Prognostic Factors and Molecular Subtypes of Breast Cancer With a Continuous-Time Random-Walk MR Diffusion Model: Using Whole Tumor Histogram Analysis

BACKGROUND

The continuous-time random-walk (CTRW) diffusion model to evaluate breast cancer prognosis is rarely reported.

PURPOSE

To investigate the correlations between apparent diffusion coefficient (ADC) and CTRW-specific parameters with prognostic factors and molecular subtypes of breast cancer.

STUDY TYPE

Retrospective.

POPULATION

One hundred fifty-seven women (median age, 50 years; range, 26-81 years) with histopathology-confirmed breast cancer.

FIELD STRENGTH/SEQUENCE

Simultaneous multi-slice readout-segmented echo-planar imaging at 3.0T.

ASSESSMENT

The histogram metrics of ADC, anomalous diffusion coefficient (D), temporal diffusion heterogeneity (α), and spatial diffusion heterogeneity (β) were calculated for whole-tumor volume. Associations between histogram metrics and prognostic factors (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67 proliferation index), axillary lymph node metastasis (ALNM), and tumor grade were assessed. The performance of histogram metrics, both alone and in combination, for differentiating molecular subtypes (HER2-positive, Luminal or triple negative) was also assessed.

STATISTICAL TESTS

Comparisons were made using Mann-Whitney test between different prognostic factor statuses and molecular subtypes. Receiver operating characteristic curve analysis was used to assess the performance of mean and median histogram metrics in differentiating the molecular subtypes. A P value <0.05 was considered statistically significant.

RESULTS

The histogram metrics of ADC, D, and α differed significantly between ER-positive and ER-negative status, and between PR-positive and PR-negative status. The histogram metrics of ADC, D, α, and β were also significantly different between the HER2-positive and HER2-negative subgroups, and between ALNM-positive and ALNM-negative subgroups. The histogram metrics of α and β significantly differed between high and low Ki-67 proliferation subgroups, and between histological grade subgroups. The combination of αmean and βmean achieved the highest performance (AUC = 0.702) to discriminate the Luminal and HER2-positive subtypes.

DATA CONCLUSION

Whole-tumor histogram analysis of the CTRW model has potential to provide additional information on the prognosis and intrinsic subtyping classification of breast cancer.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Differentiation of Benign From Malignant Sinonasal Masses Using Diffusion Weighted Imaging and Dynamic Contrast Enhanced Magnetic Resonance Imaging

BACKGROUND

The sinonasal region is affected by a variety of neoplasms. A differentiation between benign and malignant masses is essential both for management and prognostication. Morphological analysis often does not allow this differentiation.

OBJECTIVES

This article aims to assess the value of advanced MRI (diffusion [DWI] and dynamic contrast enhanced MRI [DCE-MRI]) in differentiation of benign and malignant sinonasal masses.

METHODS

This prospective study included 40 patients with sinonasal masses who underwent advanced MR on 3T MR scanner. The lesions were analyzed based on morphological characteristics, qualitative, quantitative diffusion parameters, and time signal intensity curves. Apparent diffusion coefficient (ADC) values were acquired using b values of 50 and 1000 s/mm². The accuracy of DWI, DCE-MRI, and combined DWI/DCE-MRI in differentiating benign from malignant sinonasal masses were analyzed.

RESULTS

Perineural extension and growth pattern of the tumor were the best morphological discriminators. Mean ADC values for benign and malignant lesions were 1.675  ±  0.561 and 0.903  ±  0.405  × 10^-3 mm²/sec, ,respectively. ROC revealed that ADC cutoff value of 1.005  × 10^-3 mm²/sec provided an accuracy of 92.5% in differentiating benign from malignant masses (P value <.01). On excluding the benign vascular masses (Juvenile Nasopharyngeal Angiofibroma and hemangioma), the time signal intensity curve showed 78% accuracy (P value <.001). The highest diagnostic performance was achieved by combining DWI and DCE-MRI (95% accuracy).

CONCLUSION

DWI has higher accuracy than DCE-MRI. Quantitative DWI is preferable over qualitative DWI. Accuracy of DCE-MRI can be increased by excluding vascular masses with characteristic imaging features. DWI and DCE-MRI have the highest accuracy when used in combination than either of them alone in differentiating benign from malignant sinonasal masses.

Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer

Objectives: To explore the potential of Radiomics alone and in combination with a diffusion-weighted derived quantitative parameter, namely the apparent diffusion co-efficient (ADC), using supervised classification algorithms in the prediction of outcomes and prognosis. Materials and Methods: Retrospective evaluation of the imaging was conducted for a study cohort of uterine cervical cancer, candidates for radical treatment with chemo radiation. ADC values were calculated from the darkest part of the tumor, both before (labeled preADC) and post treatment (labeled postADC) with chemo radiation. Post extraction of 851 Radiomics features and feature selection analysis—by taking the union of the features that had Pearson correlation >0.35 for recurrence, >0.49 for lymph node and >0.40 for metastasis—was performed to predict clinical outcomes. Results: The study enrolled 52 patients who presented with variable FIGO stages in the age range of 28–79 (Median = 53 years) with a median follow-up of 26.5 months (range: 7–76 months). Disease recurrence occurred in 12 patients (23%). Metastasis occurred in 15 patients (28%). A model generated with 24 radiomics features and preADC using a monotone multi-layer perceptron neural network to predict the recurrence yields an AUC of 0.80 and a Kappa value of 0.55 and shows that the addition of radiomics features to ADC values improves the statistical metrics by approximately 40% for AUC and approximately 223% for Kappa. Similarly, the neural network model for prediction of metastasis returns an AUC value of 0.84 and a Kappa value of 0.65, thus exceeding performance expectations by approximately 25% for AUC and approximately 140% for Kappa. There was a significant input of GLSZM features (SALGLE and LGLZE) and GLDM features (SDLGLE and DE) in correlation with clinical outcomes of recurrence and metastasis. Conclusions: The study is an effort to bridge the unmet need of translational predictive biomarkers in the stratification of uterine cervical cancer patients based on prognosis.

Application of Community Detection Algorithm to Investigate the Correlation between Imaging Biomarkers of Tumor Metabolism, Hypoxia, Cellularity, and Perfusion for Precision Radiotherapy in Head and Neck Squamous Cell Carcinomas

The present study aimed to investigate the correlation at pre-treatment (TX) between quantitative metrics derived from multimodality imaging (MMI), including 18F-FDG-PET/CT, 18F-FMISO-PET/CT, DW- and DCE-MRI, using a community detection algorithm (CDA) in head and neck squamous cell carcinoma (HNSCC) patients. Twenty-three HNSCC patients with 27 metastatic lymph nodes underwent a total of 69 MMI exams at pre-TX. Correlations among quantitative metrics derived from FDG-PET/CT (SUL), FMSIO-PET/CT (K1, k3, TBR, and DV), DW-MRI (ADC, IVIM [D, D*, and f]), and FXR DCE-MRI [Ktrans, ve, and τi]) were investigated using the CDA based on a "spin-glass model" coupled with the Spearman's rank, ρ, analysis. Mean MRI T2 weighted tumor volumes and SULmean values were moderately positively correlated (ρ = 0.48, p = 0.01). ADC and D exhibited a moderate negative correlation with SULmean (ρ ≤ -0.42, p < 0.03 for both). K1 and Ktrans were positively correlated (ρ = 0.48, p = 0.01). In contrast, Ktrans and k3max were negatively correlated (ρ = -0.41, p = 0.03). CDA revealed four communities for 16 metrics interconnected with 33 edges in the network. DV, Ktrans, and K1 had 8, 7, and 6 edges in the network, respectively. After validation in a larger population, the CDA approach may aid in identifying useful biomarkers for developing individual patient care in HNSCC.

Diffusion-Weighted and Dynamic Contrast-Enhanced MRI Derived Imaging Metrics for Stereotactic Body Radiotherapy of Pancreatic Ductal Adenocarcinoma: Preliminary Findings

We aimed to assess longitudinal changes in quantitative imaging metric values obtained from diffusion-weighted (DW-) and dynamic contrast-enhanced magnetic resonance imaging (DCE)-MRI at pre-treatment (TX[0]), immediately after the first fraction of stereotactic body radiotherapy (D1-TX[1]), and 6 weeks post-TX (Post-TX[2]) in patients with pancreatic ductal adenocarcinoma. Ten enrolled patients (n = 10) underwent DW- and DCE-MRI examinations on a 3.0 T scanner. The apparent diffusion coefficient, ADC (mm²/s), was derived from DW imaging data using a monoexponential model. The tissue relaxation rate, R _1t, time-course data were fitted with a shutter-speed model, which provides estimates of the volume transfer constant, K ^trans (min^-1), extravascular extracellular volume fraction, v_e , and mean lifetime of intracellular water protons, τ _i (seconds). Wilcoxon rank-sum test compared the mean values, standard deviation, skewness, kurtosis, and relative percentage (r, %) changes (Δ) in ADC, K ^trans, v_e , and τ _i values between the magnetic resonance examinations. rADC_Δ2-0 values were significantly greater than rADC_Δ1-0 values (P = .009). rK ^trans _Δ2-0 values were significantly lower than rK ^trans _Δ1-0 values (P = .048). rv_{e Δ2-1} and rv_eΔ2-0 values were significantly different (P = .016). rτ _{i Δ2-1} values were significantly lower than rτ _{i Δ2-0} values (P = .008). For group comparison, the pre-TX mean and kurtosis of ADC (P = .18 and P = .14), skewness and kurtosis of K ^trans values (P = .14 for both) showed a leaning toward significant difference between patients who experienced local control (n = 2) and failed early (n = 4). DW- and DCE-MRI-derived quantitative metrics could be useful biomarkers to evaluate longitudinal changes to stereotactic body radiotherapy in patients with pancreatic ductal adenocarcinoma.

Diffusion-weighted double-echo steady-state with a three-dimensional cones trajectory for non-contrast-enhanced breast MRI

The image quality limitations of echo-planar diffusion-weighted imaging (DWI) are an obstacle to its widespread adoption in the breast. Steady-state DWI is an alternative DWI method with more robust image quality but its contrast for imaging breast cancer is not well-understood. The aim of this study was to develop and evaluate diffusion-weighted double-echo steady-state imaging with a three-dimensional cones trajectory (DW-DESS-Cones) as an alternative to conventional DWI for non-contrast-enhanced MRI in the breast. This prospective study included 28 women undergoing clinically indicated breast MRI and six asymptomatic volunteers. In vivo studies were performed at 3 T and included DW-DESS-Cones, DW-DESS-Cartesian, DWI, and CE-MRI acquisitions. Phantom experiments (diffusion phantom, High Precision Devices) and simulations were performed to establish framework for contrast of DW-DESS-Cones in comparison to DWI in the breast. Motion artifacts of DW-DESS-Cones were measured with artifact-to-noise ratio in volunteers and patients. Lesion-to-fibroglandular tissue signal ratios were measured, lesions were categorized as hyperintense or hypointense, and an image quality observer study was performed in DW-DESS-Cones and DWI in patients. Effect of DW-DESS-Cones method on motion artifacts was tested by mixed-effects generalized linear model. Effect of DW-DESS-Cones on signal in phantom was tested by quadratic regression. Correlation was calculated between DW-DESS-Cones and DWI lesion-to-fibroglandular tissue signal ratios. Inter-observer agreement was assessed with Gwet's AC. Simulations predicted hyperintensity of lesions with DW-DESS-Cones but at a 3% to 67% lower degree than with DWI. Motion artifacts were reduced with DW-DESS-Cones versus DW-DESS-Cartesian (p < 0.05). Lesion-to-fibroglandular tissue signal ratios were not correlated between DW-DESS-Cones and DWI (r = 0.25, p = 0.38). Concordant hyperintensity/hypointensity was observed between DW-DESS-Cones and DWI in 11/14 lesions. DW-DESS-Cones improved sharpness, distortion, and overall image quality versus DWI. DW-DESS-Cones may be able to eliminate motion artifacts in the breast allowing for investigation of higher degrees of steady-state diffusion weighting. Malignant breast lesions in DW-DESS-Cones demonstrated hyperintensity with respect to surrounding tissue without an injection of contrast. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 1.

Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics

We evaluated the performance of radiomics and artificial intelligence (AI) from multiparametric magnetic resonance imaging (MRI) for the assessment of breast cancer molecular subtypes. Ninety-one breast cancer patients who underwent 3T dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping were included retrospectively. Radiomic features were extracted from manually drawn regions of interest (n = 704 features per lesion) on initial DCE-MRI and ADC maps. The ten best features for subtype separation were selected using probability of error and average correlation coefficients. For pairwise comparisons with >20 patients in each group, a multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used (70% of cases for training, 30%, for validation, five times each). For all other separations, linear discriminant analysis (LDA) and leave-one-out cross-validation were applied. Histopathology served as the reference standard. MLP-ANN yielded an overall median area under the receiver-operating-characteristic curve (AUC) of 0.86 (0.77–0.92) for the separation of triple negative (TN) from other cancers. The separation of luminal A and TN cancers yielded an overall median AUC of 0.8 (0.75–0.83). Radiomics and AI from multiparametric MRI may aid in the non-invasive differentiation of TN and luminal A breast cancers from other subtypes.

e-ASPECTS derived acute ischemic volumes on non-contrast-enhanced computed tomography images

Background and purpose

Validation of automatically derived acute ischemic volumes (AAIV) from e-ASPECTS on non-contrast computed tomography (NCCT).

Materials and methods

Data from three studies were reanalyzed with e-ASPECTS Version 7. AAIV was calculated in milliliters (ml) in all scored ASPECTS regions of the hemisphere detected by e-ASPECTS. The National Institute of Health Stroke Scale (NIHSS) determined stroke severity at baseline and clinical outcome was measured with the modified Rankin Scale (mRS) between 45 and 120 days. Spearman ranked correlation coefficients (R) of AAIV and e-ASPECTS scores with NIHSS and mRS as well as Pearson correlation of AAIV with diffusion-weighted imaging and CT perfusion-estimated ischemic “core” volumes were calculated. Multivariate regression analysis (odds ratio, OR with 95% confidence intervals, CI) and Bland–Altman plots were performed.

Results

We included 388 patients. Mean AAIV was 11.6 ± 18.9 ml and e-ASPECTS was 9 (8–10: median and interquartile range). AAIV, respectively e-ASPECTS correlated with NIHSS at baseline (R = 0.35, p < 0.001; R = −0.36, p < 0.001) and follow-up mRS (R = 0.29, p < 0.001; R = −0.3, p < 0.001). In subsets of patients, AAIV correlated strongly with diffusion-weighted imaging (n = 37, R = 0.68, p < 0.001) and computed tomography perfusion-derived ischemic “core” (n = 41, R = 0.76, p < 0.001) lesion volume and Bland–Altman plots showed a bias close to zero (−2.65 ml for diffusion-weighted imaging and 0.45 ml forcomputed tomography perfusion “core”). Within the whole cohort, the AAIV (OR 0.98 per ml, 95% CI 0.96–0.99) and e-ASPECTS scores (OR 1.3, 95%CI 1.07–1.57) were independent predictors of good outcome

Conclusion

AAIV on NCCT correlated moderately with clinical severity but strongly with diffusion-weighted imaging lesion and computed tomography perfusion ischemic “core” volumes and predicted clinical outcome.

Diagnostic power of diffusion-weighted magnetic resonance imaging for the presence of lymph node metastasis: A meta-analysis

Present work was designed to quantitatively evaluate the performance of diffusion-weighted magnetic resonance imaging (DWI) in the diagnosis of the presence of metastasis in lymph nodes (LNs). Eligible studies were identified from systematical PubMed and EMBASE searches. Data were extracted. Meta-analyses were performed to generate pooled sensitivity and specificity on the basis of per-node, per-lesion and per-patient, respectively. Fourteen publications (2458 LNs, 404 lesions and 334 patients) were eligible. Per-node basis demonstrated the pooled sensitivity and specificity was 0.82 (P<0.0001) and 0.90 (P<0.0001), respectively. Per-lesion basis illustrated the pooled sensitivity and specificity was 0.73 (P=0.0036) and 0.85 (P<0.0001), respectively. Per-patient basis indicated the pooled sensitivity and specificity was 0.67 (P=0.0909) and 0.86 (P<0.0001), respectively. In conclusion, DWI has rather a negative predictive value for the diagnosis of LN metastasis presence. The difference of the mean apparent diffusion coefficients between benign and malignant LNs is not yet stable. Therefore, the DWI technique has to be further improved.

Feasibility and applicability of diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging in routine assessments of children with high-grade gliomas

Diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) have been used as imaging biomarkers in adults with high-grade gliomas (HGGs). We incorporated free-breathing DW-MRI and DCE-MRI, at a single time point, in the routine follow-up of five children (median age 9 years, range 8-15) with histologically confirmed HGG within a prospective imaging study. It was feasible to incorporate DW-MRI and DCE-MRI in routine assessments of children with HGG. DW and DCE parameters were repeatable in paediatric HGG. Higher median ADC100-1000 significantly correlated with longer survival in our sample.

Multiparametric MRI findings of sinonasal rhabdomyosarcoma in adults with comparison to carcinoma

PURPOSE

To identify magnetic resonance imaging (MRI) features of sinonasal rhabdomyosarcoma in adults, including diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI features as compared with carcinomas.

MATERIALS AND METHODS

Sixty-four patients were included in this study, including 12 sinonasal rhabdomyosarcomas and 52 sinonasal carcinomas. MRI was completed in all 64 patients with a 3T MR scanner. Conventional MR (nonenhanced and static contrast-enhanced) imaging features, DCE-MRI parameters, and the apparent diffusion coefficients (ADCs) were analyzed by two authors independently (X.Y.W. and Y.Z.W.).

RESULTS

Compared with gray matter, sinonasal rhabdomyosarcomas appeared isointense on T₁ -weighted images in 11 cases (91.7%, 11 of 12), and hyperintense on T₂ -weighted images in 9 patients (75%, 9 of 12). After contrast, sinonasal rhabdomyosarcomas showed inhomogeneous enhancement in 10 cases (83.3%, 10 of 12). Skull involvement was found in eight patients (66.7%) with rhabdomyosarcomas. On T₂ -weighted images, sinonasal carcinomas demonstrated isointense in 31 cases (59.6%, 31/52), hyperintense in 14 (26.9%, 14/52), and hypointense in 7 (13.5%, 7/52). Skull involvement was detected in 14 cases (14/52, 26.9%). There were significant differences in T₂ signal intensity (P = 0.005) and skull involvement (P = 0.016) between sinonasal rhabdomyosarcoma and carcinomas. There was a marginal difference in time to peak enhancement (P = 0.061), while no difference in time to maximum enhancement (P = 0.403), maximum contrast index (P = 0.368), and time-intensity curve types (P = 0.138) between rhabdomyosarcoma and carcinomas. The ADCs of sinonasal rhabdomyosarcoma were significantly lower than those of sinonasal carcinomas (P < 0.001).

CONCLUSION

A multiparametric approach using conventional MRI with added ADCs had the potential to improve the diagnostic accuracy of sinonasal rhabdomyosarcoma in adults. Evidence level: 4 J. Magn. Reson. Imaging 2017;45:998-1004.

Distortion correction of echo-planar diffusion-weighted images of uterine cervix

PURPOSE

To investigate the clinical utility of the reverse gradient algorithm in correcting distortions in diffusion-weighted images of the cervix and for increasing diagnostic performance.

MATERIALS AND METHODS

Forty-one patients ages 25-72 years (mean 40 ± 11 years) with suspected or early stage cervical cancer were imaged at 3T using an endovaginal coil. T2 -weighted (W) and diffusion-weighted images with right and left phase-encode gradient directions were obtained coronal to the cervix (b = 0, 100, 300, 500, 800 s mm(-2) ). Differences in angle of the endocervical canal to the x-axis between T2 W and right-gradient, left-gradient, and corrected images were measured. Uncorrected and corrected images were assessed for diagnostic performance when viewed together with T2 W images by two independent observers against subsequent histology.

RESULTS

The angles of the endocervical canal relative to the x-axis were significantly different between the T2 W images and the right-gradient images (P = 0.007), approached significance for left-gradient images (P = 0.055), and were not significantly different after correction (P = 0.95). Corrected images enabled a definitive diagnosis in 34% (n = 14) of patients classified as equivocal on uncorrected images. Tumor volume in this subset was 0.18 ± 0.44 cm(3) (mean ± SD; sensitivity of detection 100% [8/8], specificity 50% [3/6] for an experienced observer). Correction did not improve diagnostic performance for the less-experienced observer.

CONCLUSION

Distortion-corrected diffusion-weighted images improved correspondence with T2 W images and diagnostic performance in a third of cases.

3D high-resolution diffusion-weighted MRI at 3T: Preliminary application in prostate cancer patients undergoing active surveillance protocol for low-risk prostate cancer

PURPOSE

To improve spatial resolution and image quality of diffusion-weighted (DW) MRI in detecting low-risk prostate cancer (lrPC) in patients undergoing active surveillance protocol (AS-PC), we propose the application of a diffusion-prepared balanced steady-state free precession (bSSFP) technique capable of multishot acquisition.

METHODS

Diffusion-prepared bSSFP was compared with single-shot DW echo planar imaging (SS-DW-EPI) at two prescribed resolutions (2.1 × 2.1 × 3.5mm(3) , 0.9 × 0.9 × 3.5 mm(3) ) in nine healthy subjects and nine AS-PC patients. Geometric distortion and susceptibility artifacts were quantitatively assessed in all subjects. In AS-PC patients, lesion detection via blinded multiparametric MRI including T1-weighted, T2-weighted, dynamic contrast-enhanced imaging, and along with either of two DW methods were evaluated against 12-point biopsy.

RESULTS

Geometric distortion and susceptibility artifacts were significantly less for diffusion-prepared bSSFP at both prescribed spatial resolutions than SS-DW-EPI. Apparent diffusion coefficients of healthy prostate tissue were concordant between the two DW methods at both spatial resolutions. In AS-PC patients, multiparametric MRI with diffusion-prepared bSSFP had greater sensitivity (94%, 63%), accuracy (76%, 67%), positive-predictive value (54%, 48%), negative-predictive value (97%, 82%), and area under the curve (0.80, 0.67) than with SS-DW-EPI.

CONCLUSIONS

The proposed diffusion-prepared technique with higher spatial resolution and improved image quality over SS-DW-EPI resulted in better multiparametric MRI detection of lrPC in AS-PC patients.

Diffusion-weighted imaging of the high-risk breast: Apparent diffusion coefficient values and their relationship to breast density

PURPOSE

To document the apparent diffusion coefficient (ADC) of fibroglandular breast tissue in women at high-risk of developing breast cancer and investigate the relationship between ADC and breast density.

MATERIALS AND METHODS

Local research ethics approval was obtained. A total of 33 high-risk women including 17 BRCA1/2 mutation carriers (mean age, 43 years) and 16 women postmantle irradiation (mean age 40 years) underwent diffusion-weighted MRI between days 6 and 16 of their menstrual cycle. ADC histograms from a region of interest in fibroglandular tissue and mammographic breast density measurements were obtained. Mean, percentile ADC values (10th, 25th, 50th, 75th, 90th) and skew were compared for the two groups; ADC and mammographic breast density were correlated.

RESULTS

Mean ADC values (×10(-6) mm(2) /s) were 2017 ± 197 in postmantle irradiated women and 1827 ± 289 in BRCA1/2 mutation carriers (P = 0.035) with significant differences at all percentiles (P < 0.0001) but not skew (P = 0.44). ADC values showed weak positive correlation with mammographic breast density in BRCA1/2 mutation carriers (r = 0.51, P = 0.043) but not in postmantle radiotherapy patients (r = 0.49, P = 0.13).

CONCLUSION

Higher ADC values seen in fibroglandular tissue postmantle irradiation compared with BRCA1/2 mutation carriers has potential to improve tumor detection in these patients. Lack of correlation between ADC and breast density postmantle irradiation may be a result of microstructural changes.

Combining magnetic resonance imaging within six-hours of symptom onset with clinical follow-up at 24 h improves prediction of 'malignant' middle cerebral artery infarction

BACKGROUND

A large diffusion-weighted imaging lesion ≤six-hours of symptom onset was found to predict the development of 'malignant' middle cerebral artery infarction with high specificity, positive predictive value, and negative predictive value, but sensitivity was low.

HYPOTHESIS

We tested the hypothesis that sensitivity can be improved by adding information from clinical follow-up examination after 24 h.

METHODS

We analyzed data from a prospective, multicenter, observational cohort study of patients with acute ischemic stroke and middle cerebral artery occlusion studied by stroke magnetic resonance imaging ≤six-hours of symptom onset. We used the National Institutes of Health Stroke Scale to assess severity of symptoms after 24 h. We used the Classification and Regression Trees analysis to define the optimal thresholds of diffusion-weighted imaging lesion volume and the National Institutes of Health Stroke Scale after 24 h in patients developing 'malignant' middle cerebral artery infarction. We calculated sensitivity, specificity, positive predictive value, and negative predictive value for two simple predictive models based on acute diffusion-weighted imaging lesion volume alone and acute diffusion-weighted imaging lesion volume together with the National Institutes of Health Stroke Scale after 24 h.

RESULTS

Of 135 patients, 27 (20%) developed a 'malignant' middle cerebral artery infarction. The Classification and Regression Trees analysis identified acute diffusion-weighted imaging lesion ≥78 ml and the National Institutes of Health Stroke Scale score after 24 h ≥22 as optimal cut-offs. Inclusion of the National Institutes of Health Stroke Scale score after 24 h in a simple two-step decision tree increased sensitivity from 0·59 to 0·79, while specificity, positive predictive value, and negative predictive value remained largely unchanged.

CONCLUSION

Clinical follow-up examination after 24 h helps identify patients at risk of 'malignant' middle cerebral artery infarction that are missed by predictive algorithms based on early diffusion-weighted imaging lesion volume alone.

Incremental value of magnetic resonance imaging for clinically high risk prostate cancer in 922 radical prostatectomies

PURPOSE

We investigated the incremental value of magnetic resonance imaging in addition to clinical variables for predicting pathological outcomes and disease recurrence in patients with clinically high risk prostate cancer.

MATERIALS AND METHODS

A total of 922 consecutive patients with clinically high risk prostate cancer underwent magnetic resonance imaging before radical prostatectomy. We created multivariate logistic regression and Cox proportional hazards models with clinical variables only or combined with magnetic resonance imaging data to predict pathological outcomes and biochemical recurrence. The models were compared using ROC curves and the Harrell concordance index.

RESULTS

The proportion of patients with pathological extracapsular extension, seminal vesicle invasion and lymph node metastasis was 57.5%, 12.7% and 6.3%, respectively. The sensitivity and specificity of extracapsular extension, seminal vesicle invasion and lymph node metastasis detection were 43% and 84.2%, 34.9% and 93.8%, and 14.0% and 96.9%, respectively. The area under the ROC curve of the model with clinical variable and magnetic resonance imaging data was greater than that of the model with clinical variables alone to predict extracapsular extension and seminal vesicle invasion (0.734 vs 0.697, p=0.001 and 0.750 vs 0.698, p<0.001, respectively). The 5-year biochemical recurrence-free survival rate was 56.1%. To predict biochemical recurrence the concordance index of the multivariate model with clinical variables only and with clinical variables plus magnetic resonance imaging data was 0.563 and 0.599, respectively (p=0.003).

CONCLUSIONS

Magnetic resonance imaging findings have incremental value in addition to clinical variables for predicting pathological outcomes and disease recurrence.

Diffusion tensor tractography of residual fibers in traumatic spinal cord injury: a pilot study

BACKGROUND AND PURPOSE

In clinical studies, evaluating residual fiber tracts in spinal cord injuries poses serious difficulties, whereas diffusion tensor imaging (DTI) can assess alterations in fiber structural integrity. For this reason, this study aimed to determine changes in the structural integrity of residual fiber tracts via fractional anisotropy (FA) variations and fiber-tracking patterns in patients with chronic traumatic spinal cord injury (SCI).

MATERIALS AND METHODS

T2-weighted and diffusion-weighted imaging was performed on four traumatic SCI patients and three healthy volunteers using a 3.0-T MR scanner. After obtaining fiber-tracking maps, FA values were measured and analyzed in residual and remote normal and healthy cords.

RESULTS

Diffusion tensor tractography showed obvious destruction of fiber tracts in injured cords. In the healthy control subjects, averaged FA values ranged from 0.545 to 0.601, whereas all SCI patients had decreased FA values in both residual (0.220 ± 0.121) and remote normal fibers (0.535 ± 0.101). There were also statistically significant differences in FA values between residual and remote normal fibers in patients (P = 0.000) and between their residual and healthy control fibers (P = 0.000). No significant difference was found between remote normal and healthy cords (P = 0.312).

CONCLUSION

Specific FA variations were observed in residual fibers, suggesting that DTI may be a useful tool for evaluating residual tracts in SCI patients.

Current Status and New Developments in Breast MRI

SUMMARY: Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for the detection of breast cancer. Its specificity is equivalent to that of mammography. Nowadays, breast MRI is an absolutely essential breast imaging method. Technical innovations allow dynamic contrast-enhanced (DCE) MRI of both breasts with high image quality. Thereby, DCE breast MRI should always be performed with regard to current standards. New quantitative techniques such as diffusion-weighted MRI are promising. However, they still have potential pitfalls, in particular with regard to the diagnosis of non-mass lesions and small breast lesions. Ongoing technical innovations can possibly help to further optimize breast MRI.

Diffusion-weighted MRI for verification of electroporation-based treatments

Clinical electroporation (EP) is a rapidly advancing treatment modality that uses electric pulses to introduce drugs or genes into, e.g., cancer cells. The indication of successful EP is an instant plasma membrane permeabilization in the treated tissue. A noninvasive means of monitoring such a tissue reaction represents a great clinical benefit since, in case of target miss, retreatment can be performed immediately. We propose diffusion-weighted magnetic resonance imaging (DW-MRI) as a method to monitor EP tissue, using the concept of the apparent diffusion coefficient (ADC). We hypothesize that the plasma membrane permeabilization induced by EP changes the ADC, suggesting that DW-MRI constitutes a noninvasive and quick means of EP verification. In this study we performed in vivo EP in rat brains, followed by DW-MRI using a clinical MRI scanner. We found a pulse amplitude-dependent increase in the ADC following EP, indicating that (1) DW-MRI is sensitive to the EP-induced changes and (2) the observed changes in ADC are indeed due to the applied electric field.