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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, maximum, 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. Excluding 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. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

Abstract Objective The purpose of this meta‐analysis was to compare the efficiency of high‐resolution computed tomography (HRCT) and diffusion‐weighted magnetic resonance imaging (DWI) in guiding the diagnosis of middle ear cholesteatoma in clinical practice. Materials and methods Cochrane Library, Medline, Embase, PubMed, and Web of Science were searched for studies that evaluated the sensitivity and specificity of HRCT or DWI in detecting middle ear cholesteatoma. A random‐effects model was used to calculate and summarize the pooled estimates of sensitivity, specificity, and diagnostic odds ratios. Postoperative pathological results were considered as the diagnostic gold standard for middle ear cholesteatoma. Results Fourteen published articles (860 patients) met the inclusion criteria. The sensitivity and specificity of DWI when diagnosing cholesteatoma (regardless of type) were 0.88 (95% confidence interval [CI], 0.80–0.93) and 0.93 (95% CI, 0.86–0.97), respectively, while those of HRCT were 0.68 (95% CI, 0.57–0.77) and 0.78 (95% CI, 0.60–0.90), respectively. Notably, the sensitivity and specificity levels of DWI were similar to those of HRCT (p = .1178 for sensitivity, p = .2144 for specificity; pair‐sampled t tests). The sensitivity and specificity of DWI or HRCT for the diagnosis of primary cholesteatoma were 0.78 (95% CI, 0.65–0.88) and 0.84 (95% CI, 0.69–0.93), respectively, while that for recurrent cholesteatoma were 0.93 (95% CI, 0.61–0.99) and 0.94 (95% CI, 0.82–0.98), respectively. Conclusion DWI and HRCT have similar levels of high sensitivity and specificity in detecting various cholesteatomas. Also, the diagnostic efficiency of HRCT or DWI for recurrent cholesteatoma is identical to that of primary cholesteatoma. Therefore, HRCT may be used in clinical settings to reduce the use of DWI and save clinical resources. Lay summary Data on the use of diffusion‐weighted magnetic resonance imaging and high‐resolution computed tomography in the diagnosis of cholesteatoma were obtained through a literature search. They were analyzed to guide the clinical diagnosis and treatment of cholesteatoma. Level of evidence NA

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. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

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.

PURPOSE: To compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping. PROCEDURES: In this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naïve breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification with leave-one-out cross-validation was applied for pairwise differentiation of receptor status and molecular subtyping. Histopathologic results were considered the gold standard. RESULTS: For lesion that were segmented on DWI and segmentation ROIs were propagated to ADC maps the following classification accuracies > 90% were obtained: luminal B vs. HER2-enriched, 94.7 % (based on COM features); luminal B vs. others, 92.3 % (COM, HIS); and HER2-enriched vs. others, 90.1 % (RLM, COM). For lesions that were segmented directly on ADC maps, better results were achieved yielding the following classification accuracies: luminal B vs. HER2-enriched, 100 % (COM, WAV); luminal A vs. luminal B, 91.5 % (COM, WAV); and luminal B vs. others, 91.1 % (WAV, ARM, COM). CONCLUSIONS: Radiomic signatures from DWI with ADC mapping allows evaluation of breast cancer receptor status and molecular subtyping with high diagnostic accuracy. Better classification accuracies were obtained when breast tumor segmentations could be performed on ADC maps.

Abstract Background In the scenario of lung lesions, the differential diagnosis is important, since the treatment is determined by the characteristics of the lesion. The goal in the evaluation of pulmonary lesions is to distinguish malignant lesions from benign lesions in a non-invasive manner as possible. Since, CT is not sufficient to accurately distinguish malignant nodules from benign nodules and patients with benign nodules might undergo invasive diagnostic methods, such as lung biopsy or video-assisted thoracoscopic surgery, to rule out a malignancy. Now, MRI performed by using diffusion-weighted (DW) can offer both qualitative and quantitative information that can be helpful for tumour assessment. Moreover, lesion‐to‐spinal cord signal intensity ratio (LSR) has also been shown to be useful for the differentiation of lung lesions. Quantitative tumour assessment is possible by the calculation of ADC. Results A total of 30 patients were eligible for inclusion in our final analysis; with male/female case number about 10/20 (33.3%/ 66.7%) and age range from 20 to 74 (46.8 ± 14.9) were subjected to MRI study and MRI diffusion. Sensitivity analysis showed that ADC mean, and ADC min value can significantly predict malignant lung lesions using cutoff point 502.8, with sensitivity 92.9% and specificity 68.7%, AUC 89.3% and p value 0.0001. Lesion-to-spinal cord signal intensity ratio (LSR) can significantly predict malignant lung lesions using cutoff point > 1.3, with sensitivity 85.7% and specificity 75%, AUC 79.7% and p value 0.006. Conclusions This study confirmed that the DWI combined with ADC value and LSR is effective and valuable tool in differentiation of pulmonary lesions whether benign or malignant which is considered to be noninvasive alternative tool for the characterization of pulmonary lesions. We recommend before invasive intervention to perform diffusion MRI and LSR as and important aid for proper diagnosis.

Background: Surgical exploration and histologic evaluation are often required to determine the nature of the ovarian tumors being benign or malignant. Therefore, it may be impossible to determine beforehand whether minimally invasive or extensive surgery is required. Better preoperative planning might be accomplished with the use of a trustworthy approach that could distinguish malignant and benign masses. MRI is beneficial for identifying ovarian masses and their origins due to its sensitivity and improved characters by the application of diffusion-weighted imaging. This research aimed to assess the characterization of ovarian lesions using MRI with DWI.Methods: Forty patients diagnosed to have ovarian masses were evaluated in this cross-sectional analytical study after being referred to the University Hospital at the radiology department. All patients were exposed to pelvic MRI with DWI, and 7 underwent DWIBS. Surgery was performed with pathologic correlation on 39 individuals. Only one patient was given a 3-month course of routine US monitoring.Results: MRI's sensitivity was 99.9 percent, whereas DWI's was one hundred percent. When comparing DWI with traditional MRI, the former has a greater specificity (78.1%) while the latter has a better accuracy (86.0%). On average, malignant lesions had an ADC of (0.93x 10-3 ± 0.43 SD mm2/s), whereas benign ones had 1.3 x 10-3 ± 0.6 SD mm2/s with a p-value = 0.005. Because of the teratoma's mixed cellularity, mature teratomas exhibited restricted diffusion with ADC values of 0.5x10-3mm2/s (false positive).Conclusion: The specificity of conventional MRI was enhanced when combined with DWI, boosting radiologist confidence in picture interpretation and, in turn, improving patients' outcomes and prognoses.

Objective: The purpose of this meta‐analysis was to compare the efficiency of high‐resolution computed tomography (HRCT) and diffusion‐weighted magnetic resonance imaging (DWI) in guiding the diagnosis of middle ear cholesteatoma in clinical practice. Materials and methods: Cochrane Library, Medline, Embase, PubMed, and Web of Science were searched for studies that evaluated the sensitivity and specificity of HRCT or DWI in detecting middle ear cholesteatoma. A random‐effects model was used to calculate and summarize the pooled estimates of sensitivity, specificity, and diagnostic odds ratios. Postoperative pathological results were considered as the diagnostic gold standard for middle ear cholesteatoma. Results: Fourteen published articles (860 patients) met the inclusion criteria. The sensitivity and specificity of DWI when diagnosing cholesteatoma (regardless of type) were 0.88 (95% confidence interval [CI], 0.80–0.93) and 0.93 (95% CI, 0.86–0.97), respectively, while those of HRCT were 0.68 (95% CI, 0.57–0.77) and 0.78 (95% CI, 0.60–0.90), respectively. Notably, the sensitivity and specificity levels of DWI were similar to those of HRCT (p =.1178 for sensitivity, p =.2144 for specificity; pair‐sampled t tests). The sensitivity and specificity of DWI or HRCT for the diagnosis of primary cholesteatoma were 0.78 (95% CI, 0.65–0.88) and 0.84 (95% CI, 0.69–0.93), respectively, while that for recurrent cholesteatoma were 0.93 (95% CI, 0.61–0.99) and 0.94 (95% CI, 0.82–0.98), respectively. Conclusion: DWI and HRCT have similar levels of high sensitivity and specificity in detecting various cholesteatomas. Also, the diagnostic efficiency of HRCT or DWI for recurrent cholesteatoma is identical to that of primary cholesteatoma. Therefore, HRCT may be used in clinical settings to reduce the use of DWI and save clinical resources. Lay summary: Data on the use of diffusion‐weighted magnetic resonance imaging and high‐resolution computed tomography in the diagnosis of cholesteatoma were obtained through a literature search. They were analyzed to guide the clinical diagnosis and treatment of cholesteatoma. Level of evidence: NA [ABSTRACT FROM AUTHOR]

Background: In the scenario of lung lesions, the differential diagnosis is important, since the treatment is determined by the characteristics of the lesion. The goal in the evaluation of pulmonary lesions is to distinguish malignant lesions from benign lesions in a non-invasive manner as possible. Since, CT is not sufficient to accurately distinguish malignant nodules from benign nodules and patients with benign nodules might undergo invasive diagnostic methods, such as lung biopsy or video-assisted thoracoscopic surgery, to rule out a malignancy. Now, MRI performed by using diffusion-weighted (DW) can offer both qualitative and quantitative information that can be helpful for tumour assessment. Moreover, lesion‐to‐spinal cord signal intensity ratio (LSR) has also been shown to be useful for the differentiation of lung lesions. Quantitative tumour assessment is possible by the calculation of ADC. Results: A total of 30 patients were eligible for inclusion in our final analysis; with male/female case number about 10/20 (33.3%/ 66.7%) and age range from 20 to 74 (46.8 ± 14.9) were subjected to MRI study and MRI diffusion. Sensitivity analysis showed that ADC mean, and ADC min value can significantly predict malignant lung lesions using cutoff point < 1.53 and < 1.34 respectively, with sensitivity 75%, 56.3%, and specificity 92.9%, 100% with p values 0.001, and 0.005 respectively. SI lesion can significantly predict malignant lung lesions using cutoff point > 502.8, with sensitivity 92.9% and specificity 68.7%, AUC 89.3% and p value 0.0001. Lesion-to-spinal cord signal intensity ratio (LSR) can significantly predict malignant lung lesions using cutoff point > 1.3, with sensitivity 85.7% and specificity 75%, AUC 79.7% and p value 0.006. Conclusions: This study confirmed that the DWI combined with ADC value and LSR is effective and valuable tool in differentiation of pulmonary lesions whether benign or malignant which is considered to be noninvasive alternative tool for the characterization of pulmonary lesions. We recommend before invasive intervention to perform diffusion MRI and LSR as and important aid for proper diagnosis. [ABSTRACT FROM AUTHOR]

Background: The use of diffusion-weighted (DW) sequences to distinguish recurrent cholesteatoma from chronic otitis media with granulations has shown to be quite effective. The differential in water molecule diffusion in various biological tissues is used in DW MRI. Objectives: The goal of this research was to evaluate the analytic exactness of DWI and the change in ADCs of the middle ear cleft cholesteatoma and non-cholesteatomatous tissue, particularly recurrent otitis media single-shot multi echo planar DW images. In the event of cholesteatoma , intraoperative findings and histological data would be used to validate the outcomes. In the event of otitis media, we will either follow up with the patients clinically to see whether their symptoms improve or if they need a CT scan. Patients and methods: At South Valley University Hospitals, this research was performed on patients with supposed cholesteatoma (supposed clinically or by CT scan) or branded those, as well as patients with suspected recurrence of otitis media who had previously had operation or were preparing for surgery. Results: Range of Mean ADC values of cases enrolled in this study which calculated by 3D synapse, even cholesteatoma or otitis media varies from 0.4 to 2.35 with mean average 1.028 Conclusion: DWI is a useful technique in the assessment of cholesteatoma because it distinguishes it from otitis media with high specificity, reducing needing next stare procedures in many instances.

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.

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.

Abstract Background Diffusion-weighted (DW) and blood oxygen level-dependent (BOLD) magnetic resonance imaging are classical sequences of functional MR, but the exploration in non-transplanted kidney disease is limited. Objects: To analyze the characteristics of apparent diffusion coefficient (ADC) and R2* value using DW and BOLD imaging in tubulointerstitial nephritis (TIN). Methods Four acute TIN, thirteen chronic TIN patients, and four controls were enrolled. We used multiple gradient-echo sequences to acquire 12 T2*-weighted images to calculate the R2* map. DW imaging acquired ADC values by combining a single-shot spin-echo echo-planar imaging pulse sequence and the additional motion probing gradient pulses along the x,y, z-axes with two b values:0 and 200, as well as 0 and 800 s/mm2. ATIN patients performed DW and BOLD magnetic resonance at renal biopsy(T0) and the third month(T3). We assessed the pathological changes semiquantitatively, and conducted correlation analyses within functional MR, pathological and clinical indexes. Results In ATIN, ADCs were significantly lower(b was 0,200 s/mm2, 2.86 ± 0.19 vs. 3.39 ± 0.11, b was 0,800 s/mm2, 1.76 ± 0.12 vs. 2.16 ± 0.08, P

Background: Diffusion magnetic resonance imaging (MRI) abnormalities in multiple sclerosis (MS) are not limited to lesions, but have also been observed in the white matter that appears normal on conventional MRI sequences, known as normal-appearing white matter (NAWM). There is evidence of microstructural processes occurring in the NAWM. Objective: To assess the correlation between NAWM apparent diffusion coefficient (ADC) and fractional anisotropy (FA) with brain volume and clinical disability in MS. Methods: Brain MRI from 33 MS patients were included. ADC and FA measurements of the genu, body, and splenium of corpus callosum (CC) were done. ADC and FA values were analyzed to measure their correlation with brain volume from MR volumetry and clinical disability represented by Expanded Disability Status Scale (EDSS). Results: The mean ADC of CC NAWM was.93 ×10−3 mm2/s (±.13 SD), and the mean FA.72 (±.12 SD). ADC and FA of CC NAWM were significantly correlated with the ratio of brain volume to intracranial volume (R = −0,70 and 0,78 respectively), and with EDSS (R =.52 and −.59 respectively). Conclusion: There were significant correlations between ADC and FA of NAWM with brain volume and EDSS of MS patients. Further longitudinal studies were needed to evaluate the potential of diffusion MRI in the evaluation of MS. [ABSTRACT FROM AUTHOR]

[Display omitted] • Plasma levels of IL-1 β , IL-7, CCL3, and GM-CSF were elevated from days 3 to 60 post-injury. • Day 7 plasma marker panels were associated with trans-hemispheric cortical map transfer outcome. • High IL-1 β and low IL-7, CCL3, and GM-CSF plasma levels at day 60 associated with worse pathology. The process of neuroinflammation occurring after traumatic brain injury (TBI) has received significant attention as a potential prognostic indicator and interventional target to improve patients' outcomes. Indeed, many of the secondary consequences of TBI have been attributed to neuroinflammation and peripheral inflammatory changes. However, inflammatory biomarkers in blood have not yet emerged as a clinical tool for diagnosis of TBI and predicting outcome. The controlled cortical impact model of TBI in the rodent gives reliable readouts of the dynamics of post-TBI neuroinflammation. We now extend this model to include a panel of plasma cytokine biomarkers measured at different time points post-injury, to test the hypothesis that these markers can predict brain microstructural outcome as quantified by advanced diffusion-weighted magnetic resonance imaging (MRI). Fourteen 8–10-week-old male rats were randomly assigned to sham surgery (n = 6) and TBI (n = 8) treatment with a single moderate-severe controlled cortical impact. We collected blood samples for cytokine analysis at days 1, 3, 7, and 60 post-surgery, and carried out standard structural and advanced diffusion-weighted MRI at day 60. We then utilized principal component regression to build an equation predicting different aspects of microstructural changes from the plasma inflammatory marker concentrations measured at different time points. The TBI group had elevated plasma levels of IL-1 β and several neuroprotective cytokines and chemokines (IL-7, CCL3, and GM-CSF) compared to the sham group from days 3 to 60 post-injury. The plasma marker panels obtained at day 7 were significantly associated with the outcome at day 60 of the trans-hemispheric cortical map transfer process that is a frequent finding in unilateral TBI models. These results confirm and extend prior studies showing that day 7 post-injury is a critical temporal window for the reorganisation process following TBI. High plasma level of IL-1 β and low plasma levels of the neuroprotective IL-7, CCL3, and GM-CSF of TBI animals at day 60 were associated with greater TBI pathology. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNN) can accurately predict chronological age in healthy individuals from structural MRI brain scans. Potentially, these models could be applied during routine clinical examinations to detect deviations from healthy ageing, including early-stage neurodegeneration. This could have important implications for patient care, drug development, and optimising MRI data collection. However, existing brain-age models are typically optimised for scans which are not part of routine examinations (e.g., volumetric T1-weighted scans), generalise poorly (e.g., to data from different scanner vendors and hospitals etc.), or rely on computationally expensive pre-processing steps which limit real-time clinical utility.Here, we sought to develop a brain-age framework suitable for use during routine clinical head MRI examinations. Using a deep learning-based neuroradiology report classifier, we generated a dataset of 23,302 ‘radiologically normal for age’ head MRI examinations from two large UK hospitals for model training and testing (age range = 18–95 years), and demonstrate fast (< 5 s), accurate (mean absolute error [MAE] < 4 years) age prediction from clinical-grade, minimally processed axial T2-weighted and axial diffusion-weighted scans, with generalisability between hospitals and scanner vendors (Δ MAE < 1 year). The clinical relevance of these brain-age predictions was tested using 228 patients whose MRIs were reported independently by neuroradiologists as showing atrophy ‘excessive for age’. These patients had systematically higher brain-predicted age than chronological age (mean predicted age difference = +5.89 years, 'radiologically normal for age' mean predicted age difference = +0.05 years, p < 0.0001).Our brain-age framework demonstrates feasibility for use as a screening tool during routine hospital examinations to automatically detect older-appearing brains in real-time, with relevance for clinical decision-making and optimising patient pathways.

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. [ABSTRACT FROM AUTHOR]

Background: Numerous endometrial disorders can create several difficulties for the radiologist due to the overlapping of imaging characteristics and diverse endometrial pathologies. The most frequently utilized imaging tool for diagnosing and characterizing endometrial focal lesions is magnetic resonance imaging (MRI) with diffusion weighted images (DWI). Objective: We conducted this study to determine the efficacy of MRI with DWI in improving the diagnostic accuracy of endometrial focal lesions, especially in the differential diagnosis of benign and malignant focal endometrial masses. Patients and methods: This study recruited 36 women (21 postmenopausal and 15 premenopausal) who experienced vaginal bleeding and had endometrial thickness and focal endometrial lesions with a distinct echo pattern on ultrasound (US) examination. The age of patients was between 27 to 85 years, with an average of 45.2 years. Ethics Committee approval was obtained in addition to written informed consent from all included patients. Results: The 36 patients included in this study, were classified according to their lesions histopathological results; Benign group (15 lesions; 41.67%) and malignant group (21 lesions; 58.33%). The most common benign lesion was endometrial polyp (9/15) while the most common malignant lesion was endometrial carcinoma (21/21). In the current study MRI with diffusion could correctly diagnose 33 lesions out of 36 lesions, achieving (91.6%) sensitivity, (100%) specificity, (100%) positive predictive value (PPV), (95.6%) negative predictive value (NPV) and accuracy (97.05 %). Conclusion: Integrating DWI and ADC mapping at a high b value in pelvic MRI examination improves the sensitivity, specificity, and precision of diagnosing endometrial focal lesions. [ABSTRACT FROM AUTHOR]

Objectives: To explore the potential of Radiomics alone and in combination with a diffusionweighted 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. [ABSTRACT FROM AUTHOR]

Acute encephalopathy with biphasic seizure and late reduced diffusion (AESD) is a clinico-radiologic syndrome of acute encephalopathy characterized by biphasic seizure and altered consciousness in acute phase followed by restricted diffusion in bilateral cerebral parenchyma on magnetic resonance imaging (MRI) in the subacute stage. Here, we present the MRI and magnetic resonance spectroscopy (MRS) findings in a case of AESD presenting in 4-year child and diagnosed based on clinico-radiological correlation.

Background: Diffusion-weighted (DW) and blood oxygen level-dependent (BOLD) magnetic resonance imaging are classical sequences of functional MR, but the exploration in non-transplanted kidney disease is limited.Objects: To analyze the characteristics of apparent diffusion coefficient (ADC) and R2* value using DW and BOLD imaging in tubulointerstitial nephritis (TIN).Methods: Four acute TIN, thirteen chronic TIN patients, and four controls were enrolled. We used multiple gradient-echo sequences to acquire 12 T2*-weighted images to calculate the R2* map. DW imaging acquired ADC values by combining a single-shot spin-echo echo-planar imaging pulse sequence and the additional motion probing gradient pulses along the x,y, z-axes with two b values:0 and 200, as well as 0 and 800 s/mm2. ATIN patients performed DW and BOLD magnetic resonance at renal biopsy(T0) and the third month(T3). We assessed the pathological changes semiquantitatively, and conducted correlation analyses within functional MR, pathological and clinical indexes.Results: In ATIN, ADCs were significantly lower(b was 0,200 s/mm2, 2.86 ± 0.19 vs. 3.39 ± 0.11, b was 0,800 s/mm2, 1.76 ± 0.12 vs. 2.16 ± 0.08, P < 0.05) than controls, showing an obvious remission at T3. Cortical and medullary R2* values (CR2*,MR2*) were decreased, significant difference was only observed in MR2*(T0 24.3 ± 2.1vs.T3 33.1 ± 4.1,P < 0.05). No relationship was found between functional MR and histopathological indexes.MR2* had a close relationship with eGFR (R = 0.682,P = 0.001) and serum creatinine(R = -0.502,P = 0.012). Patients with lower ADC when b was 0,200 s/mm2 showed more increase of ADC(R = -0.956,P = 0.044) and MR2*(R = -0.949,P = 0.05) after therapy. In CTIN group, lowered MR2* and MR2*/CR2* provided evidence of intrarenal ischemia. CTIN with advanced CKD (eGFR< 45) had significantly lower ADCb200 value.Conclusions: We observed the reduction and remission of ADC and R2* values in ATIN case series. ATIN patients had concurrently decreased ADCb800 and MR2*. The pseudo normalization of CR2* with persistently low MR2* in CTIN suggested intrarenal hypoxia. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background. The 2010 revision of the McDonald criteria, widely used for the diagnosis of multiple sclerosis (MS), has established that dissemination in time (DIT) can be demonstrated by the simultaneous presence of asymptomatic gadolinium-enhancing and non-enhancing plaques on a single magnetic resonance imaging (MRI). When the use of gadolinium contrast agents is contraindicated, diffusion-weighted imaging (DWI) is utilized to confirm diffusion alterations in active inflammatory plaques. This study intended to examine whether DWI can be a viable alternative to contrast-enhanced T1-weighted imaging for demonstrating DIT in MS. Material and methods.We assessed 30 previously diagnosed MS patients during acute relapse (based on the 2010 McDonald criteria) and evaluated their brain MRI via DWI‚ contrast-enhanced T1-weighted imaging, and FLAIR sequences. Asymptomatic plaques were defined as either hyperintense or non-hyperintense in DWI and enhancing or non-enhancing in T1GAD-MRI. Statistical indices for the prediction of plaque enhancement in T1 GAD-MRI via DWI-MRI were calculated and compared. Results. The 30 participants in our study had a total of 925 demyelinating plaques that were larger than 3mm in size and presented to be hyperintense in FLAIR-MRI. Diffusion hyperintensity and plaque enhancement were significantly correlated. The sensitivity‚ specificity, positive predictive value‚ negative predictive value, and accuracy of DWI were calculated to be 69.66%‚ 99.76%‚ 96.88%‚ 96.86%, and 96.86%, respectively. Conclusions. Hyperintense DWI findings do not necessarily overlap with contrast enhancements in T1 GAD-MRI. DWI was shown to produce a higher rate of false-positive results. Our study concludes that although T1 GAD-MRI should not be replaced by DWI to determine DIT due to its lower specificity, DWI’s continued use as a surrogate screening imaging sequence whenever the use of T1GAD-MRI is of concern is not without its merits. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

Introduction: Diffusion-weighted (DW) is an evolving technique that provides both qualitative and quantitative data about the diffusion of water molecules and b-value plays a significant role in DW images. The aim of this study is to find the optimal b-value in diffusion-weighted MRI for the detection of breast lesions. Methods: The study included 124 subjects. T2-FS sequences were acquired for lesion measurement. DW was acquired with b-values 300, 600 and 1000 s/mm². DW images were evaluated by two radiologists under the criteria's - signal-to-noise ratio (SNR), contrast differentiation of lesion from the glandular tissue, and contrast differentiation of glandular tissue from fatty tissues. Relative Signal intensity ratio (SIR), SNR and contrast-to-noise ratio (CNR) were calculated. Results: Kappa value is highest for a b-value of 600 for all the three criteria, for subjective SNR the kappa value was 0.906 which indicates that the inter-rater reliability rate is an almost perfect agreement. Differentiation of lesion from the fibro-glandular tissues, the kappa coefficient is 0.904 which interprets that the agreement between the readers is almost perfect and the kappa coefficient to differentiate the fibro-glandular from fatty tissues is 0.875 which shows that there is substantial agreement. Relative SIR, SNR, and CNR is highest for a b-value of 600 with a mean value of 4.6, 14.7, and 44 respectively. Conclusion: Optimal b-value for good image quality is 0, 600 s/mm². Standardizing an optimal b-value is highly significant since it improves the overall image quality and enhances multi- center diagnosing with an accurate report. [ABSTRACT FROM AUTHOR]

We aimed to assess longitudinal changes in quantitative imaging metric values obtained from diffusionweighted (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 DWand 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, R1t, time-course data were fitted with a shutter-speed model, which provides estimates of the volume transfer constant, Ktrans (min1), extravascular extracellular volume fraction, ve, and mean lifetime of intracellular water protons, t i (seconds). Wilcoxon rank-sum test compared the mean values, standard deviation, skewness, kurtosis, and relative percentage (r, %) changes (D) in ADC, Ktrans, ve, and t i values between the magnetic resonance examinations. rADCΔ2-0 values were significantly greater than rADCD1-0 values (P = .009). rKtrans D2-0 values were significantly lower than rKtrans D1-0 values (P = .048). rveD2-1 and rveD2-0 values were significantly different (P = .016). rt iD2-1 values were significantly lower than rt iD2-0 values (P = .008). For group comparison, the pre-TX mean and kurtosis of ADC (P=.18 and P =.14), skewness and kurtosis of Ktrans 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. [ABSTRACT FROM AUTHOR]

Aim: To determine whether whole-body magnetic resonance imaging is valuable in staging of neuroendocrine tumors by comparison with the conventional imaging defined by the combination of computed tomography and somatostatin receptor scintigraphy. Methods: This study concerned the patients included in the multicenter prospective study NCT02786303 with the following inclusion criteria: well-differentiated gastroenteropancreatic neuroendocrine tumors or of unknown primary, and computed tomography, whole-body magnetic resonance imaging and somatostatin receptor scintigraphy performed within 6 weeks. Results of the conventional imaging were compared with those of magnetic resonance imaging. Discrepancies between the conventional imaging and magnetic resonance imaging were evaluated by reviewing medical records. Results: Thirty-one patients (17 men and 14 women) were prospectively included. Complete concordance between the magnetic resonance imaging and the conventional imaging results was observed in 25 patients and discrepancies in 6. Whole-body magnetic resonance imaging detected more liver lesions than the conventional imaging did but standard imaging set was more effective in the detection of bone and peritoneum lesions than magnetic resonance imaging. Detecting more lesions had no impact on therapeutic management. Conclusions: Whole-body magnetic resonance imaging including diffusion weighted may be a valuable alternative to computed tomography and somatostatin receptor scintigraphy. Further studies should compare whole-body MRI to the 68Ga PET/CT. [ABSTRACT FROM AUTHOR]

Objective: study the role of Dynamic contrast-enhanced (DCE), and Diffusion-weighted (DWI) MRI for preoperative early staging of urinary bladder cancer and compare their diagnostic accuracy with histopathological diagnosis. Materials and Method: Thirty-seven patients with bladder cancer included in this study. All patients underwent Magnetic Resonance Imaging (MRI) on a 1.5-T scanner. MR images were evaluated and assigned a stage which compared with the histopathological staging depending on transurethral resection of bladder tumor (TURBT). Results: There was substantial agreement between DCE- and DWI-MRI for T staging (kappa = 0.943, p <0.001), both MRI modalities had similar specificity for diagnosing stage T1 bladder CA with DWI offered slightly higher sensitivity (SN), positive predictive value (PPV), negative predictive values (NPV) and accuracy. Both MRI modalities show fair specificity for stage T2 with DWI offered slightly higher specificity and similar sensitivity for both with good accuracy. Conclusion: MRI is an excellent modality for preoperative early staging as well as grading of the bladder cancer. Both Diffusion-weighted MR imaging and dynamic contrast-enhanced MRI offer excellent agreement for T-staging of bladder cancer with DWI as the preferred modality without the use of contrast media. [ABSTRACT FROM AUTHOR]

Abstract Background Disruption in the thalamus, such as volume, shape, and cortical connectivity, is regarded as an important pathophysiological mechanism in schizophrenia. However, there is little evidence of nuclei-specific structural alterations in the thalamus during early-stage psychosis, mainly because of the methodological limitations of conventional structural imaging in identifying the thalamic nuclei. Methods A total of 37 patients with first-episode psychosis and 36 matched healthy control subjects underwent diffusion tensor imaging, diffusion kurtosis imaging, and T1-weighted magnetic resonance imaging. Connectivity-based segmentation of the thalamus was performed using diffusion tensor imaging, and averages of the diffusion kurtosis values, which represent microstructural complexity, were estimated using diffusion kurtosis imaging and were compared in each thalamic nucleus between the groups. Results The mean kurtosis values in the thalamic regions with strong connections to the orbitofrontal cortex (F 1,70 = 8.40, p <.01) and the lateral temporal cortex (F 1,70 = 8.46, p <.01) were significantly reduced in patients with first-episode psychosis compared with those of the healthy control subjects. The mean kurtosis values in the thalamic region with strong connection to the orbitofrontal cortex showed a significant correlation with spatial working memory accuracy in patients with first-episode psychosis (r =.36, p <.05), whereas no significant correlation between these variables was observed in the healthy control subjects. Conclusions The observed pattern of reduced microstructural complexity in the nuclei not only highlights the involvement of the thalamus but also emphasizes the role of the higher-order nuclei in the pathophysiology beginning in the early stage of schizophrenia. [ABSTRACT FROM AUTHOR]

Background: Disrupted thalamic connectivity system, which encompasses the deficits in the thalamus and thalamocortical connectivity, is regarded to contribute to the pathophysiology of schizophrenia. Recent reports suggest the possible genetic contribution to the disrupted thalamo-prefrontal connectivity, however, research on elucidating thalamic connectivity system components, specifically the thalamic nuclei, associated with the genetic predisposition to schizophrenia has been limited. Here, we investigated the genetic aspects of thalamic nuclei-specific microstructural integrities in schizophrenia.Methods: A total of 34 asymptomatic relatives of schizophrenia patients with high genetic loading and 33 healthy control subjects underwent diffusion tensor imaging, diffusion kurtosis imaging, and T1-weighted magnetic resonance imaging. The thalamus was segmented via a connectivity-based segmentation method using the region-of-interest masks. The microstructural integrity of each thalamic nucleus, measured by averages of the diffusion kurtosis values, was then compared between the groups.Results: The volumetric and mean kurtosis values of the thalamic nuclei were intact in asymptomatic relatives of schizophrenia patients with high genetic loading.Conclusions: Our results revealed that, in the thalamic connectivity system, the genetics may hold different weights of effects on different components, and that more is given on the thalamo-prefrontal connectivity than on the thalamus. Further, the current results may add further evidence to the current literature that thalamic nuclei microstructural abnormalities present in psychosis may have state marker characteristics. [ABSTRACT FROM AUTHOR]

Objective: To evaluate diagnostic performance of diffusion weighted imaging (DWI) in evaluating ovarian masses with suspicious features on magnetic resonance imaging (MRI). Patients and methods: Pelvic MRI and DWI assessed 235 complex and solid ovarian masses of suspicious MRI features. On DWI, scanning acquired by b values: 0, 500, 1000 and 1500. Analysis considered signal intensity (SI) at b1000 and the mean ADC values for the solid components of the masses. Results: Included masses proved benign in 75(32%), borderline (low potential malignancy) in 55(23.4%) and malignant in 105(44.6%). Restricted diffusion was observed in all of the invasive malignancy (57.1%, n = 105/184). Benign and borderline tumors with high DWI SI presented 15.2% and 27.7% respectively (P

Background: Differentiating benign from malignant sinonsal lesions is essential for treatment planning as well as determining the patient′s prognosis, but the differentiation is often difficult in clinical practice. The study aimed to determine whether the combination of diffusion-weighted (DW) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can improve the performance in differentiating benign from malignant sinonasal tumors. Methods: This retrospective study included 197 consecutive patients with sinonasal tumors (116 malignant tumors and 81 benign tumors). All patients underwent both DW and DCE-MRI in a 3-T magnetic resonance scanner. Two different settings of b values (0,700 and 0,1000 s/mm 2 ) and two different strategies of region of interest (ROI) including whole slice (WS) and partial slice (PS) were used to calculate apparent diffusion coefficients (ADCs). A DW parameter with WS ADCs b0,1000 and two DCE-MRI parameters (time intensity curve [TIC] and time to peak enhancement [Tpeak]) were finally combined to use in differentiating the benign from the malignant tumors in this study. Results: The mean ADCs of malignant sinonasal tumors (WS ADCs b0,1000 = 1.084 × 10−3 mm 2 /s) were significantly lower than those of benign tumors (WS ADCs b0,1000 = 1.617 × 10−3 mm 2 /s, P < 0.001). The accuracy using WS ADCs b0,1000 alone was 83.7% in differentiating the benign from the malignant tumors (85.3% sensitivity, 81.2% specificity, 86.4% positive predictive value [PPV], and 79.5% negative predictive value [NPV]). The accuracy using DCE with Tpeak and TIC alone was 72.1% (69.1% sensitivity, 74.1% specificity, 77.5% PPV, and 65.1% NPV). Using DW-MRI parameter was superior than using DCE parameters in differentiation between benign and malignant sinonasal tumors (P < 0.001). The accuracy was 87.3% (90.5% sensitivity, 82.7% specificity, 88.2% PPV, and 85.9% NPV) using DW-MRI combined with DCE-MRI, which was superior than that using DCE-MRI alone or using DW-MRI alone (both P < 0.001) in differentiating the benign from the malignant tumors. Conclusions: Diffusion-weighted combined with DCE-MRI can improve imaging performance in differentiating benign from malignant sinonasal tumors, which has the potential to improve diagnostic accuracy and to provide added value in the management for these tumors.

Introduction: The aim of this study was to analyze the sensitivity and specificity of non-echoplanar (non-EPI) diffusion-weighted (DW) magnetic resonance imaging (MRI) for the detection of cholesteatoma, with a focus on its value as an adjunct to clinical examination. Methods: In a prospective cohort study, 92 cases were divided into 2 groups: “clinically cholesteatoma” (n = 79) and “clinically no cholesteatoma” (n = 13). Non-EPI DW MRI was performed preoperatively in all cases. The presence of a cholesteatoma was assessed by clinicians otoscopically, by neuroradiologists on non-EPI DW MRI, by the surgeon intraoperatively, and finally by the pathologist postoperatively. Data analysis was performed for specificity, sensitivity, positive predictive value, negative predictive value, and interrater variability. Results: The sensitivity and specificity were 89.3% and 75%, respectively, in the “clinically cholesteatoma” group and 0% and 100% in the “clinically no cholesteatoma” group. Non-EPI DW MRI had a positive predictive value of 98.5% when cholesteatoma was suspected clinically and a negative predictive value of 84.6% when cholesteatoma was not suspected clinically. Conclusion: If cholesteatoma is suspected clinically, non-EPI DW MRI is not necessary. If there is no clinical suspicion of cholesteatoma in second-look situations, sensitivity is low and serial follow-up MRI with long intervals is advised. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • MC simulation of restricted diffusion is performed using Octree structure & binary marker. • These strategies are validated against known theoretical results. • Using binary marker strategy is more efficient than mesh-based representation. • GPU-based implementation of these approaches are demonstrated in realistic astrocytes. • Simulation of diffusion-weighted signal in astrocyte takes only a few minutes. Abstract The primary goal of this work is to develop an efficient Monte-Carlo simulation of diffusion-weighted signal in complex cellular structures, such as astrocytes, directly derived from confocal microscopy. In this study, we first use an octree structure for spatial decomposition of surface meshes. Octree structure and radius-search algorithm help to quickly identify the faces that particles can possibly encounter during the next time step, thus speeding up the Monte-Carlo simulation. Furthermore, we propose to use a three-dimensional binary marker to describe the complex cellular structure and optimize the particle trajectory simulation. Finally, a GPU-based version of these two approaches is implemented for more efficient modeling. It is shown that the GPU-based binary marker approach yields unparalleled performance, opening up new possibilities to better understand intracellular diffusion, validate diffusion models, and create dictionaries of intracellular diffusion signatures. [ABSTRACT FROM AUTHOR]

Background: Correct staging and treatment initiation in malignant lymphoma depends on accurate lymph node characterization. However, nodal assessment based on conventional and diffusion-weighted (DWI) MRI remains challenging, particularly in smaller nodes.Purpose: To evaluate first-order apparent diffusion coefficient (ADC) texture parameters compared to mean ADC for lymph node characterization in non-Hodgkin lymphoma (NHL) using whole-body DWI (WB-DWI).Study Type: Retrospective.Population: Twenty-eight patients with NHL.Field Strength/sequence: 3T whole-body DWI using two b-values (0-1000 s/mm2 ).Assessment: Regions of interest were drawn on the three most hyperintense lymph nodes on b1000-images, irrespective of size, in all nodal body regions. Diagnostic performance of mean ADC (ADCmean ) was compared with first-order ADC texture parameters: standard deviation (ADCstdev ), kurtosis (ADCkurt ), and skewness (ADCskew ). Additional subanalyses focused on the accuracy of ADCmean and ADC texture parameters in different lymph node volumes and nodal regions.Statistical Tests: Benign and malignant nodes were compared using Mann-Whitney U-tests with 18-Fluoro-deoxyglucose positron emission tomography computed tomography and bone marrow biopsy as reference standard. Receiver operating characteristic analyses were performed to determine cutoff values and calculate sensitivity, specificity, accuracy, and positive and negative predictive value (PPV, NPV).Results: ADCmean (P = 0.008), ADCskew and ADCkurt differed significantly between benign and malignant nodes (P < 0.001), while ADCstdev didn't (P = 0.21). ADCskew was the best discriminating parameter, with 79% sensitivity, 86% specificity, 83% accuracy, 85% PPV, and 81% NPV. In every volume category, ADCskew yielded the highest accuracy (88% in 0-25th percentile volume, 75% in 25th -75th percentile, 93% in 75-100th percentile). On a per-region basis, ADCskew accuracy varied 13.6% between nodal regions, while ADCmean , ADCkurt , and ADCstdev showed interregional variation of 17.4%, 20.3%, and 14.9%, respectively.Data Conclusion: First-order ADC texture analysis with WB-DWI improved lymph node characterization compared to ADCmean . ADCskew was the most accurate and robust discriminatory parameter over all lymph node volumes and nodal body regions.Level Of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:897-906. [ABSTRACT FROM AUTHOR]

Background: Diffusion‐weighted (DW) imaging is a functional magnetic resonance imaging (MRI) technique that detects lesions with high cellularity, such as malignant tumours. This prospective study was performed to compare the accuracy of DW‐MRI with multidetector computed tomography (MDCT) in staging of colorectal cancer. Methods: Thirty patients with histologically proven colorectal cancer were prospectively recruited. Each patient underwent both MDCT and DW‐MRI of the abdomen–pelvis for primary staging. Images were evaluated for nodal and distant metastases. The reference standard was histopathological findings for nodal involvement and surveillance imaging for suspected hepatic metastases. Results: The primary cancers were located in the rectum (n = 16, 53.3%), sigmoid colon (n = 9, 30%) and right colon (n = 5, 16.6%). For nodal metastases, the sensitivity and specificity of DW‐MRI were 84.6% (95% confidence interval (CI): 54.6–98.1%) and 20.0% (95% CI: 2.5–55.6%) compared with 84.6% (95% CI: 54.6–98.1%) and 40.0% (95% CI: 12.2–73.8%) for MDCT. For liver metastases, the sensitivity and specificity for DW‐MRI were 100.0% (95% CI: 63.1–100.0%) and 100% (95% CI: 84.6–100%) compared with 87.5% (95% CI: 47.4–99.7%) and 95.5% (95% CI: 77.2–99.9%) for MDCT. DW imaging altered the clinical management in three (10.0%) patients by detecting missed hepatic metastases in two patients and accurately diagnosing another patient with a hepatic cyst, mistaken for metastasis on MDCT. Conclusion: DW‐MRI is more accurate for detecting hepatic metastases in colorectal cancer compared with MDCT. [ABSTRACT FROM AUTHOR]

MRI has transformed from the theoretical, investigative realm to mainstream clinical medicine over the past four decades and has become a core component of the diagnostic toolbox in the practice of gastroenterology (GI). Its success is attributable to exquisite contrast and the ability to isolate specific proton species through the use of different pulse sequences (i.e., T1-weighted, T2-weighted, diffusion-weighted) and exploiting extracellular and hepatobiliary contrast agents. Consequently, MRI has gained preeminence in various GI clinical applications: liver and pancreatic lesion evaluation and detection, liver transplantation evaluation, pancreatitis evaluation, Crohn's disease evaluation (using MR enterography) rectal cancer staging and perianal fistula evaluation. MR elastography, in concert with technical innovations allowing for fat and iron quantification, provides a noninvasive approach, or "MRI virtual liver biopsy" for diagnosis and management of chronic liver diseases. In the future, the arrival of ultra-high-field MR systems (7 T) and the ability to perform magnetic resonance spectroscopy in the abdomen promise even greater diagnostic insight into chronic liver disease. [ABSTRACT FROM AUTHOR]

The usefulness of diffusion-weighted magnetic resonance imaging (DWI) in the evaluation of scrotal pathology has recently been reported. A standard reference of normal testicular apparent diffusion coefficient (ADC) values and their variations with age is necessary when interpreting normal testicular anatomy and pathology. We evaluated 147 normal testes using DWI, including 71 testes from 53 men aged 20-39 years (group 1), 67 testes from 42 men aged 40-69 years (group 2) and nine testes from six men older than 70 years (group 3). DWI was performed along the axial plane, using a single shot, multislice spin-echo planar diffusion pulse sequence and b-values of 0 and 900 s mm−2 . The mean and standard deviation of the ADC values of normal testicular parenchyma were calculated for each age group separately. Analysis of variance (ANOVA) followed by post hoc analysis (Dunnett T3) was used for statistical purposes. The ADC values (× 10−3 mm 2 s−1 ) of normal testicular tissue were different among age groups (group 1: 1.08 ± 0.13; group 2: 1.15 ± 0.15 and group 3: 1.31 ± 0.22). ANOVA revealed differences in mean ADC among age groups (F = 11.391, P < 0.001). Post hoc analysis showed differences between groups 1 and 2 (P = 0.008) and between groups 1 and 3 (P = 0.043), but not between groups 2 and 3 (P = 0.197). Our findings suggest that ADC values of normal testicular tissue increase with advancing age.

Background: Diffusion-weighted (DW)-MRI is invaluable in detecting prostate cancer. We determined its sensitivity and specificity and established interobserver agreement for detecting tumour in men with a family history of prostate cancer stratified by genetic risk. Methods: 51 men with a family history of prostate cancer underwent T2-W + DW-endorectal MRI at 3.0 T. Presence of tumour was noted at right and left apex, mid and basal prostate sextants by 2 independent observers, 1 experienced and the other inexperienced in endorectal MRI. Sensitivity and specificity against a 10-core sampling technique (lateral and medial cores at each level considered together) in men with >2× population risk based on 71 SNP analysis versus those with lower genetic risk scores was established. Interobserver agreement was determined at a subject level. Results: Biopsies indicated cancer in 28 sextants in 13/51 men; 32 of 51 men had twice the population risk (>0.25) based on 71 SNP profiling. Sensitivity/specificity per-subject for patients was 90.0%/86.4% (high-risk) vs. 66.7%/100% (low-risk, observer 1) and 60.0%/86.3% (high-risk) vs. 33.3%/93.8% (low-risk, observer 2) with moderate overall inter-observer agreement (kappa = 0.42). Regional sensitivities/specificities for high-risk vs. low-risk for observer 1 apex 72.2%/100% [33.3%/100%], mid 100%/93.1% [100%/97.3%], base 16.7%/98.3% [0%/100%] and for observer 2 apex 36.4%/98.1% [0%/100%], mid 28.6%/96.5% [100%/100%], base 20%/100% [0%/97.3%] were poorer as they failed to detect multiple lesions. Conclusion: Endorectal T2W + DW-MRI at 3.0 T yields high sensitivity and specificity for tumour detection by an experienced observer in screening men with a family history of prostate cancer and increased genetic risk.

Objectives: To compare the image quality and diagnostic performance of reduced field-of-view (rFOV) versus conventional full field-of-view (fFOV) diffusion-weighted (DW) imaging of endometrial cancer.Methods: Fifty women with endometrial cancer underwent preoperative rFOV and fFOV DW imaging. Two radiologists compared the image qualities of both techniques, and five radiologists assessed superficial and deep myometrial invasion using both techniques. The statistical analysis included the Wilcoxon signed-rank test and paired t-test for comparisons of image quality and mean diagnostic values.Results: Distortion, tumour delineation, and overall image quality were significantly better with rFOV DW imaging, compared to fFOV DW imaging (P < 0.05); however, the former was inferior in noise (P < 0.05). Regarding superficial invasion, the mean accuracies of the techniques did not differ statistically (rFOV, 58.0% versus fFOV, 56.0%; P = 0.30). Regarding deep myometrial invasion, rFOV DW imaging yielded significantly better mean accuracy, specificity, and positive predictive values (88.4%, 97.8%, and 91.7%, respectively), compared with fFOV DW imaging (84.8%, 94.1%, and 77.4%, respectively; P = 0.009, 0.005, and 0.011, respectively).Conclusions: Compared with fFOV DW imaging, rFOV DW imaging yielded less distortion, improved image quality and, consequently, better diagnostic performance for deep myometrial invasion of endometrial cancer.Key Points: • rFOV DWI yields better assessment of deep myometrial invasion in endometrial cancer. • rFOV DWI could not sufficiently evaluate superficial invasion in endometrial cancer. • Distortion, tumour delineation, and overall image quality were improved with rFOV DWI. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose To analyze diffusion-weighted magnetic resonance imaging (DW-MRI) for treatment response assessment in locally advanced rectal cancer (LARC). Patients and methods Patients with histologically proven rectal adenocarcinoma, stage II–III disease, were enrolled and underwent surgery following neoadjuvant chemoradiotherapy (nCRT). All patients were referred for a DW-MRI protocol on a 3 Tesla MR-system, consisting of axial T2-weighted and DWI sequences prior (I), during (II) and after (III) nCRT. Corresponding apparent diffusion coefficient (ADC) values were calculated. Results Between February 2011 and June 2015, 37 patients participated in the study. All patients completed programmed treatment. Overall, 11 patients (29.7%) had pathologic complete response (pCR). No correlation between the mean pre- (ADC-I), during (ADC-II), post- (ADC-III) ADC and the reduction in tumor size after nCRT was recorded. No substantial difference in the ADC distribution was found between pCR and no-pCR patients. The ADC-II level significantly increased in the pCR cases (T = 1.675; p < 0.05). Conclusion ADC value could be useful for discriminating between the pCR patients and the no-pCR patients. Further studies are necessary to identify the optimal MRI parameters combination to predict tumor response to nCRT. It is hoped that these data will provide the basis for a more solid scientific evidence. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background This study investigated diffusion-weighted (DWI) magnetic resonance imaging (MRI) as an alternative to fat-suppressed T2-weighted imaging (FS-T2WI) for assessment of partial-thickness rotator cuff tears (RCTs). Methods Patients with arthroscopy proven partial-thickness RCTs who also received MRI (FS-T2WI and DWI) before surgery were prospectively included. Receiver operating characteristic curves were used to compare DWI vs. FS-T2WI using lesion-to-muscle signal intensity ratios. A cutoff point for predicting partial-thickness tears was determined using the Youden index. Results Included were 146 patients, with a mean age of 48.3 years (range, 19-86 years), of whom 43 had full-thickness RCTs, 67 had partial-thickness RCTs, and 36 had no tears. Areas under receiver operating characteristic curves for diagnosing partial-thickness tears were significantly higher for DWI (0.910) than for FS-T2WI (0.822, P  = .016). Lesion-to-muscle signal intensity ratio cutoff values were 1.06 for DWI vs. 1.65 for FS-T2WI, respectively. The sensitivity and accuracy of DWI (89.1% [98 of 110] and 87.7% [128 of 146], respectively) for diagnosing partial-thickness and full-thickness tears were higher than for FS-T2WI (65.5% [72 of 110] and 72.6% [106 of 146], respectively). FS-T2WI, however, had higher specificity (94.4% [34 of 36]) than DWI (83.3% [30 of 36]). Conclusions DWI is more accurate and sensitive than FS-T2WI for diagnosing partial-thickness RCTs. [ABSTRACT FROM AUTHOR]