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Objectives: To validate prospectively the ADNEX magnetic resonance (MR) scoring system to assess adnexal masses and to evaluate a new, modified ADNEX MR scoring system that incorporates diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping.Methods: Between January 2015 and September 2018, 323 consecutive women with adnexal masses diagnosed on transvaginal ultrasound (TVS) underwent standardized MR imaging (MRI) including diffusion and dynamic contrast-enhanced sequences. Of these, 131 underwent subsequent surgery. For interpretation of the MRI examinations, we applied the five-category ADNEX MR scoring system, along with a modified scoring system including DWI with ADC mapping. For both scoring systems, a score was given for all adnexal masses. Histological diagnosis was considered as the gold standard and lesions were classified as benign or malignant. The difference between the predictive values for diagnosing malignancy of the classical and modified scoring systems was assessed on the basis of the areas under the receiver-operating-characteristics (AUC) curves. The sensitivity and specificity for diagnosing malignancy of each score were also calculated.Results: Among the 131 women with adnexal mass(es) diagnosed on TVS who underwent MRI and subsequent surgery, the surgery revealed 161 adnexal masses in 126 women; five women had no mass. Histological examination confirmed 161 adnexal masses, of which all had been detected on MRI: 32 malignant tumors, 15 borderline tumors, which were classified as part of the malignant group (n = 47), and 114 benign lesions. The AUC for prediction of a malignant lesion was 0.938 (95% CI, 0.902-0.975) using the classical ADNEX MR scoring system and 0.974 (95% CI, 0.953-0.996) using the modified scoring system. Pairwise comparison of these AUCs revealed a significant difference (P = 0.0032). The sensitivity and specificity for diagnosing malignancy with an ADNEX MR score of 4 or more were 95.5% and 86.6%, respectively, using the classic scoring system, and 95.7% and 93.3%, respectively, using the modified scoring system.Conclusion: DWI with ADC mapping could be integrated into the ADNEX MR scoring system to improve specificity, thereby potentially optimizing clinical management by avoiding unnecessary surgery. © 2020 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

Introduction: An appropriate surgical approach for posterior fossa lesions is to start tumor removal from areas with a defined plane to where tumor is infiltrating the brainstem or peduncles. This surgical approach minimizes risk of damage to eloquent areas. Although magnetic resonance imaging (MRI) is the current standard preoperative imaging obtained for diagnosis and surgical planning of pediatric posterior fossa tumors, it offers limited information on the infiltrative planes between tumor and normal structures in patients with medulloblastomas. Because medulloblastomas demonstrate diffusion restriction on apparent diffusion coefficient map (ADC map) sequences, we investigated the role of ADC map in predicting infiltrative and non-infiltrative planes along the brain stem and/or cerebellar peduncles by medulloblastomas prior to surgery. Methods: Thirty-four pediatric patients with pathologically confirmed medulloblastomas underwent surgical resection at our facility from 2004 to 2012. An experienced pediatric neuroradiologist reviewed the brain MRIs/ADC map, assessing the planes between the tumor and cerebellar peduncles/brain stem. An independent evaluator documented surgical findings from operative reports for comparison to the radiographic findings. The radiographic findings were statistically compared to the documented intraoperative findings to determine predictive value of the test in identifying tumor infiltration of the brain stem cerebellar peduncles. Results: Twenty-six patients had preoperative ADC mapping completed and thereby, met inclusion criteria. Mean age at time of surgery was 8.3 ± 4.6 years. Positive predictive value of ADC maps to predict tumor invasion of the brain stem and cerebellar peduncles ranged from 69 to 88 %; negative predictive values ranged from 70 to 89 %. Sensitivity approached 93 % while specificity approached 78 %. Conclusions: ADC maps are valuable in predicting the infiltrative and non-infiltrative planes along the tumor and brain stem interface in medulloblastomas. Inclusion and evaluation of ADC maps in preoperative evaluation can assist in surgical resection planning in patients with medulloblastoma. [ABSTRACT FROM AUTHOR]

Objectives To investigate the accuracy of quantitative diffusion-weighted imaging with apparent diffusion coefficient (ADC) mapping for characterizing bone lesions as benign or malignant. Methods At 3 T, 31 subjects with intramedullary lesions imaged by DWI ( b -values 50, 400, 800 s/mm 2 ) were included. ADC values (minimum, mean, maximum) were recorded by three observers independently. Interobserver variability and differences between ADC values in benign and malignant lesions were assessed (unpaired t -test, receiver operating characteristic (ROC) analysis). Results Of 31 lesions, 18 were benign (osteoblastic ( n = 1), chondroid ( n = 6), cysts ( n = 4), hemangiomatosis ( n = 1), fibrous ( n = 3), eosinophilic granuloma ( n = 1), giant cell tumor ( n = 1), osteomyelitis ( n = 1)) and 13 were malignant (primary ( n = 5), metastases ( n = 8)). Overall, there were higher minimum (1.27 × 10 −3 mm 2 /s vs 0.68 × 10 −3 mm 2 /s, p < 0.001), mean (1.68 × 10 −3 mm 2 /s vs 1.13 × 10 −3 mm 2 /s, p < 0.001), and maximum (2.09 × 10 −3 mm 2 /s vs 1. 7 × 10 −3 mm 2 /s, p = 0.03). ADC values in benign lesions compared with those in malignancies. ROC analysis revealed areas under the curve for minimum, mean, and maximum ADC values of 0.91, 0.85, and 0.71, respectively. ADC measurements were made with high inter-observer concordance ( ρ = 0.83–0.96). Conclusion Quantitative ADC maps may have predictive value for the characterization of bone lesions. Benign lesions generally have higher minimum, mean, and maximum ADC values than malignancies, with the minimum value offering the highest accuracy for characterization. [ABSTRACT FROM AUTHOR]

Background: Sinonasal lesions are a heterogeneous group of lesions that span from a tumor to tumor-like nature. Characterization of such cases preoperatively can improve the surgical control and the overall outcome of these patients. Objective: In this prospective study, we aimed at evaluation of the role of apparent diffusion coefficient (ADC) in the differentiation between benign and malignant sinonasal lesions. Subjects and methods: All patients scheduled to have sinonasal surgical intervention at Ain Shams University Hospitals, Cairo, Egypt, were enrolled. Diffusion-weighted (DW) magnetic resonance imaging (MRI) with calculation of ADC were done for all cases. Radiologic findings were then compared with histologic findings, and the sensitivity, specificity, negative and positive predictive values (PPVs) of the conventional MRI, DW-MRI, and ADC value in differentiation of benign from malignant sinonasal lesions were then calculated. Results: There were 59 patients with median age of 43 years old. There were 20 cases of inflammatory lesions, 16 cases of benign tumors, and 23 cases of malignant lesions. The ADC values of all cases ranged from 0.4 × 10-3 to 2 × 10-3 (median = 1.5 × 10-3). The median ADC value for the malignant lesions was 0.6 × 10-3, whereas that for the inflammatory conditions was 1.6 × 10-3 and that for the benign tumors was 1.5 × 10-3 with a highly significant difference (p < .001). Analysis of the conventional MRI and DW-MRI to differentiate between malignant and benign lesions showed that the sensitivity, specificity, PPV, and negative predictive value (NPV) were 100%, 97%, 96%, and 100% and 91%, 97%, 95%, and 95%, respectively. Conclusion: DW-MRI did not add significantly to the information gained from conventional MRI. It should be considered complimentary only to standard MRI in uncertain cases when malignancy is still a concern. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the comparison between diffusion-weighted imaging (DWI), T2-weighted imaging, (T2WI) and contrast T1-weighted imaging (cT1WI) in uterine leiomyoma following treatment by magnetic resonance imaging-guided, high intensity focused ultrasound surgery (MRg-HIFUS). Methods: Twenty one patients (45±5 yrs) with clinical symptoms of uterine leiomyoma (fibroids) were treated by MRg-HIFUS using an integrated 1.5T MRI-HIFUS system. MRI parameters consisted of DWI, T2WI, and T1-weighted fast spoiled gradient echo before and after contrast. The post-MRg-HIFUS treatment volume in the fibroid was assessed by cT1WI and DWI. Trace apparent diffusion coefficient maps were constructed for quantitative analysis. The regions of the treated uterine tissue were defined by a semisupervised segmentation method called the “eigenimage filter,” using both cT1WI and DWI. Signal-to-noise ratios were determined for the T2WI pretreatment images. Segmented regions were tested by a similarity index for congruence. Descriptive, regression, and Bland–Altman statistics were calculated. Results: All the patients exhibited heterogeneously increased DWI signal intensity localized in the treated fibroid regions and were colocalized with the cT1WI defined area. The mean pretreatment T2WI signal intensity ratios were T2WI/muscle=1.8±0.7 and T2WI/myometrium=0.7±0.4. The congruence between the regions was significant, with a similarity of 84% and a difference of 8% between the regions. Regression analyses of the cT1WI and DWI segmented treatment volumes were found to be significantly correlated (r2=0.94, p<0.05) with the linear equation, (cT1WI)=1.1(DWI)-0.66. There is good agreement between the regions defined by cT1WI and DWI in most of the cases as shown from the Bland–Altman plots. Conclusions: Diffusion-weighted imaging exhibited excellent agreement, congruence, and correlation with the cT1WI-defined region of treatment in uterine fibroid. Therefore, DWI could be useful as an adjunct for assessing treatment of uterine fibroids by MRg-HIFUS. [ABSTRACT FROM AUTHOR]

In patients with mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke-like episodes (MELAS), T2-, FLAIR, and diffusion-weighted imaging reveal lesions as hyperintense areas after stroke-like episodes. It has been speculated that the lesions occur by vasogenic edema. However, we describe a case of MELAS which presented both normal and restricted water diffusion after stroke-like episodes, as detected by apparent diffusion coefficient map and values. Both cytotoxic and vasogenic edema can occur at acute stage after stroke-like episodes. [Copyright &y& Elsevier]

Simple Summary: Artificial Intelligence methods using machine learning and radiomics is an emerging area of research for radiological and oncological applications for patient management. Recent evidence from breast cancer suggests that different breast cancer subtypes may respond differently to adjuvant therapies. The use of a 21-gene array assay called OncotypeDX can predict potential recurrence of cancer in patients with estrogen positive breast cancer after treatment, however, there are potential cost disadvantages that hamper its widespread use. Multiparametric magnetic resonance imaging can simultaneously identify key functional parameters and provide unique imaging phenotypes of breast cancer, which is used in radiomic analysis. Radiomics provide quantitative information of different tissue types. We have developed a new machine learning radiomic informatics tool that integrates clinical and imaging variables, single, and multiparametric radiomics to compare with the OncotypeDX test to stratify patients into three risk groups: low, medium, and high risk of breast cancer recurrence. Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained using the mpMRI, clinical, pathologic, and radiomic descriptors for prediction of the OncotypeDX risk score. The trained mpRad IRIS model had a 95% and specificity was 83% with an Area Under the Curve (AUC) of 0.89 for classifying low risk patients from the intermediate and high-risk groups. The lesion size was larger for the high-risk group (2.9 ± 1.7 mm) and lower for both low risk (1.9 ± 1.3 mm) and intermediate risk (1.7 ± 1.4 mm) groups. The lesion apparent diffusion coefficient (ADC) map values for high- and intermediate-risk groups were significantly (p < 0.05) lower than the low-risk group (1.14 vs. 1.49 × 10−3 mm2/s). These initial studies provide deeper insight into the clinical, pathological, quantitative imaging, and radiomic features, and provide the foundation to relate these features to the assessment of treatment response for improved personalized medicine. [ABSTRACT FROM AUTHOR]