Routine MR Research Articles

MRI Radiomic Features of Peritumoral Edema May Predict the Recurrence Sites of Glioblastoma Multiforme

Background: The purpose of this study was to investigate whether there is radiological heterogeneity within peritumoral edema and identify the reproducible radiomic features predictive of the sites of recurrence of glioblastoma multiforme (GBM), which may be of value to optimize patients’ management. Methods: We retrospectively evaluated the clinical information and MR images (T1-weighted and fluid-attenuated inversion recovery sequences) of 22 patients with histologically diagnosed GBM. Kaplan-Meier’s method was used for survival analysis. Peritumoral edema regions were manually segmented by an expert into recurrence and non-recurrence subregions. A set of 94 radiomic features were obtained from both subregions using a three-dimensional slicer. Features with significant differences between the two subregions were identified using the paired t test. Subsequently, the data of two patients from The Cancer Genome Atlas (TCGA) database were used to evaluate the clinical value of the selected features. Findings: Ten features with significant differences between the recurrence and non-recurrence subregions were identified and verified on two individual patients from TCGA database with pathologically confirmed diagnosis of GBM. Interpretation: Our results suggested that heterogeneity does exist in peritumoral edema, indicating that the radiomic features of peritumoral edema from routine MR images can be utilized to predict the sites of GBM recurrence. Our findings may further guide the surgical treatment strategy for GBM. Funding Information: This work was supported by grants from the Natural Science Fund of Guangdong Province (No. 2017A030313597), “Climbing Program” Special Fund of Guangdong Province (No. pdjh2019b0100, No. pdjh2020b0112) and Southern Medical University (No. LX2016N006, No. KJ20161102, No.201912121004S, No.201912121013，No. S202012121088, No. X202012121354). Declaration of Interests: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Ethics Approval Statement: Ethics committee approval and patient consent were obtained.

The correlation between magnetic resonance diffusion parameters and Ki-67 and PCNA in hepatic fibrosis and cirrhosis rats.

The aim of this study was to assess the value of different 1.5 T MRI apparent diffusion coefficient (ADC) and exponential apparent diffusion coefficient (EADC) values in diagnosing the stages of liver cirrhosis. Sprague-Dawley (SD) rats were randomly divided into the experimental group and the control group. The experimental group was injected with thioacetamide intraperitoneally 3 times per week. After routine MR scanning, diffusion-weighted imaging (DWI) was processed by spin echo-echo planar imaging (SE-EPI) to generate the ADC value and EADC image. The liver ADC and EADC values of rats were measured in the control and experimental groups, followed by Masson staining and hematoxylin and eosin staining. Furthermore, immunohistochemistry was performed to detect Ki-67 and PCNA expression in liver tissues. In the control group, the differences in ADC and EADC values between the liver fibrosis and cirrhosis group were different. The ADC values of the liver fibrosis stage I-II, III-IV, and cirrhosis rats in the experimental group were lower than the control group, while the EADC values were higher than the control group. The ADC values of the liver fibrosis stage III-IV group and cirrhosis nodules group were lower than the control group. There were significant differences in EADC values between the cirrhotic nodule groups and the control group. DWI-ADC values showed a negative correlation between SD rat liver fibrosis and cirrhosis pathology classification.

Glioblastoma Surgery Imaging-Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations.

Simple SummaryNeurosurgical decisions for patients with glioblastoma depend on tumor characteristics in the preoperative MR scan. Currently, this is based on subjective estimates or manual tumor delineation in the absence of a standard for reporting. We compared tumor features of 1596 patients from 13 institutions extracted from manual segmentations by a human rater and from automated segmentations generated by a machine learning model. The automated segmentations were in excellent agreement with manual segmentations and are practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard reports can be generated by open access software, enabling comparison between surgical cohorts, multicenter trials, and patient registries.Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated or manually delineated. This process is time consuming and subjective. Hence, comparison across cohorts, trials, or registries are subject to assessment bias. In this study, we propose a standardized Glioblastoma Surgery Imaging Reporting and Data System (GSI-RADS) based on an automated method of tumor segmentation that provides standard reports on tumor features that are potentially relevant for glioblastoma surgery. As clinical validation, we determine the agreement in extracted tumor features between the automated method and the current standard of manual segmentations from routine clinical MR scans before treatment. In an observational consecutive cohort of 1596 adult patients with a first time surgery of a glioblastoma from 13 institutions, we segmented gadolinium-enhanced tumor parts both by a human rater and by an automated algorithm. Tumor features were extracted from segmentations of both methods and compared to assess differences, concordance, and equivalence. The laterality, contralateral infiltration, and the laterality indices were in excellent agreement. The native and normalized tumor volumes had excellent agreement, consistency, and equivalence. Multifocality, but not the number of foci, had good agreement and equivalence. The location profiles of cortical and subcortical structures were in excellent agreement. The expected residual tumor volumes and resectability indices had excellent agreement, consistency, and equivalence. Tumor probability maps were in good agreement. In conclusion, automated segmentations are in excellent agreement with manual segmentations and practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard GSI-RADS reports can be generated by open access software.

Assessment of Liver Fibrosis Stage Using Integrative Analysis of Hepatic Heterogeneity and Nodularity in Routine MRI with FIB-4 Index as Reference Standard.

Image-based quantitative methods for liver heterogeneity (LHet) and nodularity (LNod) provide helpful information for evaluating liver fibrosis; however, their combinations are not fully understood in liver diseases. We developed an integrated software for assessing LHet and LNod and compared LHet and LNod according to fibrosis stages in chronic liver disease (CLD). Overall, 111 CLD patients and 16 subjects with suspected liver disease who underwent liver biopsy were enrolled. The procedures for quantifying LHet and LNod were bias correction, contour detection, liver segmentation, and LHet and LNod measurements. LHet and LNod scores among fibrosis stages (F0–F3) were compared using ANOVA with Tukey’s test. Diagnostic accuracy was determined by calculating the area under the receiver operating characteristics (AUROC) curve. The mean LHet scores of F0, F1, F2, and F3 were 3.49 ± 0.34, 5.52 ± 0.88, 6.80 ± 0.97, and 7.56 ± 1.79, respectively (p < 0.001). The mean LNod scores of F0, F1, F2, and F3 were 0.84 ± 0.06, 0.91 ± 0.04, 1.09 ± 0.08, and 1.15 ± 0.14, respectively (p < 0.001). The combined LHet × LNod scores of F0, F1, F2, and F3 were 2.96 ± 0.46, 5.01 ± 0.91, 7.30 ± 0.89, and 8.48 ± 1.34, respectively (p < 0.001). The AUROCs of LHet, LNod, and LHet × LNod for differentiating F1 vs. F2 and F2 vs. F3 were 0.845, 0.958, and 0.954; and 0.619, 0.689, and 0.761, respectively. The combination of LHet and LNod scores derived from routine MR images allows better differential diagnosis of fibrosis subgroups in CLD.

3D-printed models for periacetabular osteotomy surgical planning.

The purpose of this study was to determine the feasibility and clinical benefits of using 3D-printed hemipelvis models for periacetabular osteotomy preoperative planning in the treatment of hip dysplasia. This retrospective study included 28 consecutive cases in 26 patients, with two bilateral cases, who underwent periacetabular osteotomy between January 2017 and February 2020 and had routine radiographs, CT and MR imaging. Of these, 14 cases [mean patient age 30.7 (SD 8.4) years, 11 female] had routine preoperative imaging, and 14 cases [mean patient age 28.0 (SD 8.7) years, 13 female] had routine preoperative imaging and creation of a full-scale 3D-printed hemipelvis model from the CT data. The expected surgical cuts were performed on the 3D-printed models. All patients underwent Bernese periacetabular osteotomy. Operative times, including time to achieve proper acetabular position and total periacetabular osteotomy time, fluoroscopy radiation dose and estimated total blood loss were compiled. ANOVA compared outcome variables between the two patient groups, controlling for possible confounders. On average, patients who had additional preoperative planning using the 3D-printed model had a 5.5-min reduction in time to achieve proper acetabular position and a 14.5-min reduction in total periacetabular osteotomy time; however, these changes were not statistically significant (P = 0.526 and 0.151, respectively). No significant difference was identified in fluoroscopy radiation dose or total blood loss. Detailed surgical planning for periacetabular osteotomy using 3D-printed models is feasible using widely available and affordable technology and shows promise to improve surgical efficiency.

New MR-based measures for the evaluation of age-related lumbar paraspinal muscle degeneration.

Although signal intensity on T2W axial images is sensitive in detection of fatty infiltration to assess paraspinal muscle degeneration, it is affected by inhomogeneities of magnetic fields and individual variabilities. The purpose of this study was to propose reference adjusted signal measures on T2W axial images and determine their capacities in reflecting age-related lumbar paraspinal muscle degeneration. Lumbar MR images of 421 population-based subjects (177 men and 244 women, mean age 53.1years, range 19.8-87.9years) were studied. A custom software Spine Explore (Tulong 2.0) was used to automatically obtain paraspinal measurements of multifidus, erector spinae and psoas major. FCSA/TCSA was defined as functional cross-sectional area relative to total cross-sectional area of paraspinal muscle. Two new signal measures were canal-adjusted and cerebrospinal fluid (CSF)-adjusted signal, defined as the ratio between mean signal measurements and the mean signal of the canal and CSF. The raw signal measurements of the paraspinal muscles were weakly correlated to age (r = 0.28-0.39, P < 0.001). When the signal of canal (r = 0.43-0.59, P < 0.001) or CSF (r = 0.45-0.61, P < 0.001) was used as reference, the correlations substantially increased. Signal measurements of three paraspinal muscles, adjusted or not, were strongly associated with Goutallier score (ρ = 0.60-0.65, P < 0.001) and FCSA/TCSA (r = -0.64 to -0.82, P < 0.001). Greater Goutallier score was associated with greater age (r = 0.38-0.60, P < 0.001), while Lumbar indentation value (LIV) not. On routine T2W axial MR images the adjusted signal measurements using an internal reference of CSF or canal can better reflect age-related degenerative changes in the paraspinal muscles.

Detection of Optic Neuritis on Routine Brain MRI without and with the Assistance of an Image Postprocessing Algorithm

At times, there is a clinical need for using routine brain MR imaging performed close to the time of onset of patients' visual symptoms to firmly establish the diagnosis of optic neuritis. Our aim was to assess the diagnostic performance of radiologists in detecting optic neuritis on routine brain MR images and whether this performance could be enhanced using a postprocessing algorithm. In this retrospective case-control study of 60 patients (37 women, 23 men; mean age, 47.2 [SD, 17.9] years), 2 blinded neuroradiologists evaluated T2-weighted FLAIR and contrast-enhanced T1WI from brain MR imaging for the presence of imaging evidence of optic neuritis. Images were processed using an image-processing algorithm that aimed to selectively accentuate the signal intensity of diseased optic nerves. We assessed the effect of image processing on the contrast-to-noise ratio between the optic nerves and normal-appearing white matter and on the diagnostic performance of the neuroradiologists, including the interobserver reliability. The average sensitivity of readers was 55%, 56.5%, and 30.0% on FLAIR, coronal contrast-enhanced T1WI, and axial contrast-enhanced T1WI, respectively. Sensitivities were lower in the absence of fat saturation on FLAIR (P = .001) and coronal contrast-enhanced T1WI (P = .04). Processing increased the contrast-to-noise ratio of diseased (P value range = .03 to <.001) but not of control optic nerves. Processing did not improve the sensitivity but improved the specificity and positive predictive value. Interobserver agreement improved from slight to good. Detection of optic neuritis on routine brain MR imaging is challenging. Specificity, positive predictive value, and interobserver agreement can be improved by postprocessing of MR images.

Role of adding T2 mapping sequence to the routine MR imaging protocol in the assessment of articular knee cartilage in osteoarthritis

BackgroundThis work aims at elucidating the role of adding complementary T2 mapping to the routine 1.5 Tesla MRI protocol in the articular knee cartilage assessment for early detection of osteoarthritis, and also, comparing the articular cartilage thickness and T2 relaxation times between the case and control groups regarding knee compartments affection.ResultsBoth sensitivities and specificities were 73.3% and 100%, respectively, for the standard MR protocol alone and 96.7% and 90% after adding the T2 mapping to the standard MR protocol that leads to significant sensitivity improvement. A comparison between patients and controls as regards T2 values showed a highly statistically significant difference (independent T test, p <0.001).ConclusionA combination of both morphological and T2 mapping MRI, together with clinical evaluation represents a desirable multimodal approach to the diagnosis of osteoarthritis. In the early detection of osteoarthritis, adding T2 mapping sequence to the standard MR protocol at 1.5 Tesla improved sensitivity from 73.3 to 96.7%.

Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women

BackgroundNon-mass enhancement (NME) is a diagnostic dilemma and highly reliant on the experience of the radiologists. Texture analysis (TA) could serve as an objective method to quantify lesion characteristics. However, it remains unclear what role TA plays in a predictive model based on routine MRI characteristics. The purpose of this study was to explore the value of TA in distinguishing between benign and malignant NME in premenopausal women.MethodsWomen in whom NME was histologically proven (n = 147) were enrolled (benign: 58; malignant: 89) was retrospective. Then, 102 and 45 patients were classified as the training and validation groups, respectively. Scanning sequences included Fat-suppressed T2-weighted and fat-suppressed contrast-enhanced T1-weighted which were acquired on a 1.5T MRI system. Clinical and routine MR characteristics (CRMC) were evaluated by two radiologists according to the Breast Imaging and Reporting and Data system (2013). Texture features were extracted from all post-contrast sequences in the training group. The combination model was built and then assessed in the validation group. Pearson’s chi-square test and Mann–Whitney U test were used to compare categorical variables and continuous variables, respectively. Logistic regression analysis and receiver operating characteristic curve were employed to assess the diagnostic performance of CRMC, TA, and their combination model in NME diagnosis.ResultsThe combination model showed superior diagnostic performance in differentiating between benign and malignant NME compared to that of CRMC or TA alone (AUC, 0.887 vs 0.832 vs 0.74). Moreover, compared to CRMC, the model showed high specificity (72.5% vs 80%). The results obtained in the validation group confirmed the model was promising.ConclusionsWith the combined use of TA and CRMC could afford an improved diagnostic performance in differentiating between benign and malignant NME.

DOES QUANTIFICATION OF DIFFUSION (ADC VALUE) AID IN IDENTIFYING MALIGNANT VERTEBRAL LESIONS? A CROSS SECTIONAL OBSERVATIONAL STUDY.

Background: Characterization of vertebral pathologies as benign or malignant is a frequent dilemma that often requires invasive procedures for diagnosis. Today, imaging is sufcient for diagnosis of most benign vertebral pathologies with further conrmation on response to therapy. With better understanding of diffusion characteristics of tissues, attempt is being made to assess various imaging characteristics to make a denite diagnosis of malignant lesions as well. In this study we quantied apparent diffusion coefcient (ADC) of lesions in an effort to determine whether ADC values are different for benign or malignant vertebral body lesions. Consecutive patients that reported to the department of Radiodiagnosis, during 1st November 2017 to 31st March 2019, were included in the study. In this cross sectional observational study, for 32, that is, 22 benign and ten malignant vertebral lesions, diffusion weighted (DW) MRI sequence was done and ADC values were recorded. Conrmation was done with post treatment follow up or wherever feasible, tissue diagnosis. All malignant cases had histopathology conrmation from the site of primary lesion. Quantitative variables using independent t-test were used for comparison of ADC values between two groups. Results: The difference in mean ADC values of benign and malignant lesions were statistically signicant (P&lt;0.0001). The optimal cutoff of ADC -3 2 value for differentiating benign from malignant vertebral body lesion was 0.950 x 10 mm /s with sensitivity of 80% and specicity of 95.45%. Conclusion:In all cases, DWI/ ADC, along with routine MR sequences, were able to characterize the lesion either as benign or malignant except in two cases of tubercular infection of spine and one each of spindle cell sarcoma &amp; metastasis from cancer lung where there was overlap of ADC values.

MR vessel wall imaging in tubercular meningitis.

Tubercular meningitis (TBM) has the propensity to cause secondary vasculitis through various mechanisms leading to development of cerebrovascular complications. These vascular involvements can be detected by vessel wall imaging (VWI). In this study, we aimed to study detailed findings of vessel wall imaging in cases of tubercular meningitis. All consecutive patients of suspected tubercular meningitis in whom diagnosis of TBM could be made according to diagnostic criteria given by Ahuja et al. were included in thestudy. High-resolution MR VWI and time of flight (TOF) magnetic resonance angiography (MRA) were done along with routine MRI sequences. Arteries up to second-order branches were studied, and statistical analyses were done with respect to stage of tubercular meningitis, infarctions and TOF MRA findings. Out of all 101 cases of TBM, infarctions were found in 49 cases (48.5%), and vessel wall enhancement was seen in 67 cases (66.3%). With increasing severity of disease, more severe vascular involvement was seen on VWI. There was significant association between enhancement of individual arteries and infarctions in their territories. VWI had better sensitivity than the MRA, while MRA had better specificity than VWI for detection of vascular complications. Tubercular vasculitis can be detected by VWI in the form of nodular or smooth segmental enhancement of vessel wall with or without stenosis. Incorporation of VWI in routine MR imaging can play a greater role in early detection and management of cerebrovascular complications which can help to improve prognosis of the disease.

Merits of adding coronal-STIR sequence in a routine lumbo-sacral spine MR imaging protocol to investigate sciatica and sciatica-like symptoms

Merits of adding coronal-STIR sequence in a routine lumbo-sacral spine MR imaging protocol to investigate sciatica and sciatica-like symptoms

Automated Detection and Segmentation of Brain Metastases in Malignant Melanoma: Evaluation of a Dedicated Deep Learning Model.

Malignant melanoma is an aggressive skin cancer in which brain metastases are common. Our aim was to establish and evaluate a deep learning model for fully automated detection and segmentation of brain metastases in patients with malignant melanoma using clinical routine MR imaging. Sixty-nine patients with melanoma with a total of 135 brain metastases at initial diagnosis and available multiparametric MR imaging datasets (T1-/T2-weighted, T1-weighted gadolinium contrast-enhanced, FLAIR) were included. A previously established deep learning model architecture (3D convolutional neural network; DeepMedic) simultaneously operating on the aforementioned MR images was trained on a cohort of 55 patients with 103 metastases using 5-fold cross-validation. The efficacy of the deep learning model was evaluated using an independent test set consisting of 14 patients with 32 metastases. Manual segmentations of metastases in a voxelwise manner (T1-weighted gadolinium contrast-enhanced imaging) performed by 2 radiologists in consensus served as the ground truth. After training, the deep learning model detected 28 of 32 brain metastases (mean volume, 1.0 [SD, 2.4] cm3) in the test cohort correctly (sensitivity of 88%), while false-positive findings of 0.71 per scan were observed. Compared with the ground truth, automated segmentations achieved a median Dice similarity coefficient of 0.75. Deep learning-based automated detection and segmentation of brain metastases in malignant melanoma yields high detection and segmentation accuracy with false-positive findings of <1 per scan.

Diffusion-Weighted Imaging: An Exciting and Problem-Solving Tool in Patients with Hepatic Metastases.

Background The diffusion-weighted imaging (DWI) is based on the random Brownian motion of water molecules that influences image contrast depending on different pathological conditions. Objective The purpose of this study was to evaluate the efficacy of various magnetic resonance imaging (MRI) sequences including diffusion-weighted and gadobenate-enhanced MRI in the detection and characterization of liver lesions in a patient of known primary malignancy and to compare MRI with contrast-enhanced computed tomography (CECT) and ultrasonography (USG) in the detection of liver metastases. Methods All patients underwent a multiphase MRI. The final diagnosis was established by histopathological examination. Results A total of 43 patients of known primary malignancy were enrolled. MRI gave a provisional diagnosis of liver metastases in 21 patients and benign disease in 22 patients with histopathological correlation revealing two false-negative and one false-positive result. In the detection of lesions, DWI outscored other sequences (92.9 vs. 83.5% in hepatobiliary phase vs. 55.0% in T 2 -weighted sequences) with a statistically significant difference noted only in comparison with T 2 -weighted sequences ( p < 0.001). In 16 patients, MRI added new lesions that were not detected by CECT/USG. The sensitivity and specificity of MRI for detecting metastases were 90.9%/95.2% and 97.9%/96.8% for per-patient and per-lesion basis, respectively. Conclusion Multiphase MRI improved both the detection and characterization of liver metastases. Adding DWI to the routine MR sequences helped in detecting small liver metastases (<10 mm) not detected by other sequences.

Tumor Habitat-derived Radiomic Features at Pretreatment MRI That Are Prognostic for Progression-free Survival in Glioblastoma Are Associated with Key Morphologic Attributes at Histopathologic Examination: A Feasibility Study.

To identify radiomic features extracted from the tumor habitat on routine MR images that are prognostic for progression-free survival (PFS) and to assess their morphologic basis with corresponding histopathologic attributes in glioblastoma (GBM). In this retrospective study, 156 pretreatment GBM MR images (gadolinium-enhanced T1-weighted, T2-weighted, and fluid-attenuated inversion recovery [FLAIR] images) were curated. Of these 156 images, 122 were used for training (90 from The Cancer Imaging Archive and 32 from the Cleveland Clinic, acquired between December 1, 2011, and May 1, 2018) and 34 were used for validation. The validation set was obtained from the Ivy Glioblastoma Atlas Project database, for which the percentage extent of 11 histologic attributes was available on corresponding histopathologic specimens of the resected tumor. Following expert annotations of the tumor habitat (necrotic core, enhancing tumor, and FLAIR-hyperintense subcompartments), 1008 radiomic descriptors (eg, Haralick texture features, Laws energy features, co-occurrence of local anisotropic gradient orientations [CoLIAGe]) were extracted from the three MRI sequences. The top radiomic features were obtained from each subcompartment in the training set on the basis of their ability to risk-stratify patients according to PFS. These features were then concatenated to create a radiomics risk score (RRS). The RRS was independently validated on a holdout set. In addition, correlations (P < .05) of RRS features were computed, with the percentage extent of the 11 histopathologic attributes, using Spearman correlation analysis. RRS yielded a concordance index of 0.80 on the validation set and constituted radiomic features, including Laws (capture edges, waves, ripple patterns) and CoLIAGe (capture disease heterogeneity) from enhancing tumor and FLAIR hyperintensity. These radiomic features were correlated with histopathologic attributes associated with disease aggressiveness in GBM, particularly tumor infiltration (P = .0044) and hyperplastic blood vessels (P = .0005). Preliminary findings demonstrated significant associations of prognostic radiomic features with disease-specific histologic attributes, with implications for risk-stratifying patients with GBM for personalized treatment decisions. Supplemental material is available for this article. © RSNA, 2020.

Relationship Between Magnetic Resonance T2-Mapping and Matrix Metalloproteinase 1,3 in Knee Osteoarthritis

ObjectiveTo investigate the relationship between quantitative analysis of MRI (T2-mapping) and the expression of matrix metalloproteinase (MMP-1, MMP-3) in osteoarthritis of the knee joint and the role of MMP-1,3 in the pathogenesis of osteoarthritis.MethodsThirty cases of knee osteoarthritis (KOA) patients with total knee arthroplasty (TKA) (lesion group) and 30 healthy adult volunteers (control group) were scanned with 1.5 T routine MR and T2-mapping, and their T2 values were measured and statistically analyzed. The pathological examination of the knee cartilage that was replaced during the operation and the immunohistochemical assay were used to measure the expression of MMP-1,3. The correlation between the T2 value of magnetic resonance imaging and the expression of MMP-1,3 was analyzed.Results(1) According to the Recht grading standard for magnetic resonance, the T2 value of magnetic resonance increased significantly with the increase of cartilage degeneration. The differences in T2 values between each level and the normal group were statistically significant (P < 0.05). (2) The T2 value of magnetic resonance imaging increased with the severity of the cartilage degeneration pathological Mankin grading, and the difference was statistically significant (P < 0.05). (3) The expression of MMP-1,3 increased with cartilage degeneration. (4) The T2 value and the expression of MMP-1 in cartilage showed a linear trend. The result of Spearman correlation analysis showed that the expression of MMP-1,3 increased as the cartilage T2 value increased. There was a positive linear correlation between the two.ConclusionThe T2 value of magnetic resonance increased with the degeneration of KOA cartilage. The expression of MMP-1,3 increased with the severity of articular cartilage destruction. The T2 value of KOA magnetic resonance was positively correlated with the expression of MMP-1,3.

Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging.

There currently lacks a noninvasive and accurate method to distinguish benign and malignant ovarian lesion prior to treatment. This study developed a deep learning algorithm that distinguishes benign from malignant ovarian lesion by applying a convolutional neural network on routine MR imaging. Five hundred forty-five lesions (379 benign and 166 malignant) from 451 patients from a single institution were divided into training, validation, and testing set in a 7:2:1 ratio. Model performance was compared with four junior and three senior radiologists on the test set. Compared with junior radiologists averaged, the final ensemble model combining MR imaging and clinical variables had a higher test accuracy (0.87 vs 0.64, p < 0.001) and specificity (0.92 vs 0.64, p < 0.001) with comparable sensitivity (0.75 vs 0.63, p = 0.407). Against the senior radiologists averaged, the final ensemble model also had a higher test accuracy (0.87 vs 0.74, p = 0.033) and specificity (0.92 vs 0.70, p < 0.001) with comparable sensitivity (0.75 vs 0.83, p = 0.557). Assisted by the model's probabilities, the junior radiologists achieved a higher average test accuracy (0.77 vs 0.64, Δ = 0.13, p < 0.001) and specificity (0.81 vs 0.64, Δ = 0.17, p < 0.001) with unchanged sensitivity (0.69 vs 0.63, Δ = 0.06, p = 0.302). With the AI probabilities, the junior radiologists had higher specificity (0.81 vs 0.70, Δ = 0.11, p = 0.005) but similar accuracy (0.77 vs 0.74, Δ = 0.03, p = 0.409) and sensitivity (0.69 vs 0.83, Δ = -0.146, p = 0.097) when compared with the senior radiologists. These results demonstrate that artificial intelligence based on deep learning can assist radiologists in assessing the nature of ovarian lesions and improve their performance. • Artificial Intelligence based on deep learning can assess the nature of ovarian lesions on routine MRI with higher accuracy and specificity than radiologists. • Assisted by the deep learning model's probabilities, junior radiologists achieved better performance that matched those of senior radiologists.

Experience of application of T2-mapping MRI assessment of inflammatory myopathies

Specialized quantitative MRI techniques allow identifying structural changes in individual muscles in inflammatory myopathies.Aim. To evaluate the role of T2 mapping MRI and to evaluate specific changes in T2 relaxation time in patients with inflammatory myopathies at different stages of the disease process.Methods. T2 mapping technique for assessing structural changes in muscle tissues was optimized for the Philips “Ingenia” 3.0T MR scanner. T2 relaxation (T2rt) time of the thigh and lower leg muscles (muscule rectus femoris, m. vastus lateralis, m. adductor magnus, m. soleus, m. tibialis anterior, medial and lateral head of m. gastrocnemius) was assessed in the control group (n = 9) and the study group (n = 6). Patients recruited in the study group suffered from dermatomyositis and polymyositis of various severity.Results. The following values of T2rt were obtained in the control group: m.rectus femoris – 78.2±6.9 ms, m.vastus lateralis – 79.5±4.8 ms, m.adductor magnus – 77.1±4.4 ms, m. soleus – 92.9±4.3 ms, m. tibialis anterior – 89.4±1.3 ms, m.gastrocnemius medial head – 93.7±2.4 ms, m.gastrocnemius lateral head – 92.9±3.5 ms. Patients with polymyositis and dermatomyositis showed an increase in the signal intensity on STIR and PD-SPAIR in the structure of m.soleus, m. gastrocmenius, m.rectus femoris, as well as a significant increase in T2rt of 40–49% (p&lt;0.01). T2rt values did not differ in patients with polymyositis who received drug therapy and had clinical remission. Patients with dermatomyositis who had laboratory remission along with the deterioration reported an increase in T2rt in the structure of long spine muscles, m. rectus femoris and m. vastus lateralis equal to 15–25% (p&lt;0.05). However, none significant changes in the signal characteristics on STIR and PDSPAIR were reported.Conclusion. The sensitivity of the reported technique in the detection of sub-acute swelling of muscle tissue in the absence of significant changes in signal characteristics on routine MR sequences had been shown.

Updates on Deep Learning and Glioma: Use of Convolutional Neural Networks to Image Glioma Heterogeneity.

Deep learning represents end-to-end machine learning in which feature selection from images and classification happen concurrently. This articles provides updates on how deep learning is being applied to the study of glioma and its genetic heterogeneity. Deep learning algorithms can detect patterns in routine and advanced MR imaging that elude the eyes of neuroradiologists and make predictions about glioma genetics, which impact diagnosis, treatment response, patient management, and long-term survival. The success of these deep learning initiatives may enhance the performance of neuroradiologists and add greater value to patient care by expediting treatment.

Preoperative Determination of Isocitrate Dehydrogenase Mutation in Gliomas Using Spectral Editing MRS: A Prospective Study.

The edited magnetic resonance spectroscopy (MRS) technique has not yet been formally evaluated for the in vivo detection of 2-hydroxyglutarate (2-HG) in patients with gliomas of various grades. To evaluate the diagnostic accuracy of edited MRS in the preoperative identification of the isocitrate dehydrogenase (IDH) mutation status in patients with gliomas. Prospective. Fifty-eight subjects (31 glioblastomas, 27 grade II and III gliomas). Mescher-Garwood (MEGA)-PRESS and routine clinical brain tumor MR sequences were used at 3T. Data were analyzed using an advanced method for accurate, robust, and efficient spectral fitting (AMARES) from jMRUI software. The amplitudes of the 2-HG, N-acetyl-aspartate (NAA), choline (Cho), and creatine/phosphocreatine (Cr) resonances were calculated with their associated Cramer-Rao lower bound (CRLB). The IDH1 R132H mutation status was assessed by immunohistochemistry for all patients. Patients with grades II and III gliomas with negative immunohistochemistry underwent DNA sequencing to further interrogate IDH mutation status. The differences in 2-HG amplitudes, 2-HG/NAA, 2-HG/Cho, and 2-HG/Cr between IDH-mutant and IDH-wildtype gliomas were assessed using Mann-Whitney U-tests. Receiver operating characteristic curve analysis was performed to evaluate the diagnostic accuracy of each parameter. The 2-HG amplitudes, 2-HG/NAA, and 2-HG/Cho were higher for IDH-mutant gliomas than IDH-wildtype gliomas (P < 0.007). Using a CRLB threshold <30%, a 2-HG cutoff greater than 0 had a sensitivity of 80% (95% confidence interval [CI]: 52-96%) and a specificity of 81% (95% CI: 54-96%) in identifying IDH-mutant gliomas. In the subset of patients with grades II and III gliomas, the sensitivity was 80% (95% CI: 52-96%) and specificity was 100% (95% CI: 40-100%). Among 2-HG ratios, the highest AUC for the identification of IDH mutant status was achieved using the 2-HG/NAA (AUC = 0.8, 95% CI 0.67-.89). Preoperative edited MRS appears to be able to help identify IDH-mutant gliomas with high specificity. Level of Evidence 1 Technical Efficacy Stage 2 J. MAGN. RESON. IMAGING 2021;53:416-426.