Letter to the Editor: 1.5-T MR imaging of organic laryngotracheal stenosis in a pediatric cohort predominantly younger than 7 years-protocol optimization and diagnostic performance.

To develop a 1.5-T MR imaging protocol tailored for organic laryngotracheal stenosis (LTS) and to assess its performance in visualizing anatomy and pathologies. Presurgical laryngotracheal 1.5-T MR imaging was prospectively conducted in a cohort of 78 children with organic LTS from September 2021 to July 2023. Dedicated sequences were employed to acquire anatomical images of the laryngotracheal region. Image quality was assessed both qualitatively and quantitatively. Using intraoperative findings from laryngotracheal reconstruction (LTR) as the gold standard, the accuracy of preoperative MR imaging measurements of the cricoid cartilage and pathological diagnoses was evaluated. The average age of seventy-eight children was 3.38 ± 2.95 years. Image quality of the larynx and cervical trachea was rated fair or good in 94.9% (74/78) and 96.2% (75/78) of cases, respectively. Interobserver agreement was high (Kappa ≥ 0.81). For 59 children with a score of 3, RTr-FSE-T2WI images presented significantly better signal-to-noise and contrast-to-noise ratios (p < 0.001). Compared with the surgical findings, MR imaging revealed no significant differences in the cricoid plate height, cricoid arch thickness, or short diameter of the narrowest laryngotracheal lumen (p > 0.05), except for the cricoid plate thickness (p = 0.002). Diagnostic accuracies of MR imaging for glottic, subglottic, and tracheal scars were 89.7%, 89.7% and 94.9%, respectively. For tracheomalacia and tracheoesophageal fistula, the accuracies were 85.9% and 97.4%, respectively. Optimized 1.5-T MR imaging provides high-quality anatomical images, serving as a valuable imaging modality for preoperative evaluation of pediatric organic LTS. Question The current imaging techniques used for preoperatively evaluating laryngotracheal conditions in children have notable limitations. Accurate evaluation primarily depends on intraoperative laryngoscopy. Findings The optimized 1.5-T MR sequence provides good contrast between normal and abnormal laryngotracheal tissues, facilitating precise presurgical evaluation of anatomical and structural abnormalities. Clinical relevance The 1.5-T MR sequence package is suitable for the preoperative evaluation of pediatric LTS. It provides detailed information on the cause and severity of stenosis and enables precise measurement of cricoid cartilage dimensions, aiding accurate surgical planning.

To determine the diagnostic performance of a deep learning (DL) model in evaluating myometrial invasion (MI) depth on T2-weighted imaging (T2WI)-based endometrial cancer (EC) MR imaging (ECM). We retrospectively enrolled 530 patients with pathologically proven EC at our institution between January 1, 2013, and December 31, 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both sagittal and coronal T2WI-based MR images were used for lesion area determination. All MR images were divided into two groups: deep (more than 50%) and shallow (less than 50%) MI based on their pathological diagnosis. We trained a detection model based on YOLOv3 algorithm to locate the lesion area on ECM. Then, the detected regions were fed into a classification model based on DL network to identify MI depth automatically. In the testing dataset, the trained model detected lesion regions with an average precision rate of 77.14% and 86.67% in both sagittal and coronal images, respectively. The classification model yielded an accuracy of 84.78%, a sensitivity of 66.67%, a specificity of 87.50%, a positive predictive value of 44.44%, and a negative predictive value of 94.59% in determining deep MI. The radiologists and trained network model together yielded an accuracy of 86.2%, a sensitivity of 77.8%, a specificity of 87.5%, a positive predictive value of 48.3%, and a negative predictive value of 96.3%. In this study, the DL network model derived from MR imaging provided a competitive, time-efficient diagnostic performance in MI depth identification. • The models established with the deep learning method could help improve the diagnostic confidence and performance of MI identification based on endometrial cancer MR imaging. • The models enabled the classification of endometrial cancer MR images to the two categories with a sensitivity of 0.67, a specificity of 0.88, and an accuracy of 0.85. • Using the detected lesion region to evaluate myometrial invasion depth could remove redundant information in the image and provide more effective features.

Non-alcoholic fatty liver disease (NAFLD) is increasing in prevalence and is the most common cause of pediatric chronic liver disease. Objective US-based measures of hepatic steatosis are an unmet clinical need. To evaluate the diagnostic performance of quantitative measurement of liver echogenicity (hepatorenal index, or HRI) for hepatic steatosis in a pediatric cohort. We identified pediatric patients (≤18years old) who underwent both clinically indicated abdominal US and MRI with liver proton-density fat fraction (PDFF) within the 3-month period during the timeframe of July 2015-April 2020 (n=69). Using ImageJ, we drew small circular regions of interest (ROIs) and large freehand ROIs in the liver and right kidney on single longitudinal and transverse images to measure echogenicity (arbitrary units). We calculated four HRIs (liver-to-kidney ratio) as well as liver histogram features. Five pediatric radiologists independently reported the qualitative presence/absence of hepatic steatosis. We used Pearson correlation (r) to assess associations and receiver operating characteristic (ROC) curve analyses to evaluate diagnostic performance. Multivariable logistic regression was used to further assess relationships. Mean patient age was 11.6 (standard deviation [SD] 4.7, range 0.3-18) years; 27/69 (39.1%) were female. Mean PDFF was 12.5% (SD 13.1%, range 1-48%); 34/69 (49.3%) patients were classified as having hepatic steatosis by MRI (PDFF ≥6%). There were significant, positive correlations between all four US HRI methods and PDFF (r=0.51-0.61); longitudinal freehand ROIs exhibited the strongest correlation (r=0.61; P<0.0001). Longitudinal freehand ROI HRI had moderate diagnostic performance for the binary presence of steatosis (area under the curve [AUC]=0.80, P<0.0001), with an optimal cut-off value >1.75 (sensitivity=70.6%, specificity=77.1%). Radiologists' sensitivity for detecting hepatic steatosis ranged from 79.4% to 97.1%, and specificity ranged from 91.2% to 100%. Significant multivariable predictors of PDFF ≥6% included HRI (P=0.002; odds ratio [OR]=34.2), body mass index (BMI) percentile (P=0.005; OR=1.06), and liver gray-scale echogenicity standard deviation (P=0.02; OR=0.79) (receiver operating characteristic AUC = 0.92). Quantitative US HRI has moderate diagnostic performance for detecting liver fat in children and positively correlates with MRI PDFF. Incorporation of BMI-percentile and gray-scale echogenicity standard deviation improved diagnostic performance.

How far is arterial spin labeling MRI from a clinical reality? Insights from arterial spin labeling comparative studies in Alzheimer's disease and other neurological disorders.

MR imaging is useful for the detection and/or confirmation of optic neuritis. The objective of this study was to determine whether a postprocessing algorithm selectively increases the contrast-to-noise ratio of abnormal optic nerves in optic neuritis, facilitating this diagnosis on MR imaging. In this retrospective case-control study, coronal FLAIR images and coronal contrast-enhanced T1WI from 44 patients (31 eyes with clinically confirmed optic neuritis and 28 control eyes) underwent processing using a proprietary postprocessing algorithm designed to detect and visually highlight regions of contiguous increases in signal intensity by increasing the signal intensities of regions that exceed a predetermined threshold. For quantitative evaluation of the effect on image processing, the contrast-to-noise ratio of equivalent ROIs and the contrast-to-noise ratio between optic nerves and normal-appearing white matter were measured on baseline and processed images. The effect of image-processing on diagnostic performance was evaluated by masked reviews of baseline and processed images by 6 readers with varying experience levels. In abnormal nerves, processing resulted in an increase in the median contrast-to-noise ratio from 17.8 to 85.0 (P < .001) on FLAIR and from 19.4 to 93.7 (P < .001) on contrast-enhanced images. The contrast-to-noise ratio for control optic nerves was not affected by processing (P = 0.13). Image processing had a beneficial effect on radiologists' diagnostic performance, with an improvement in sensitivities for 5/6 readers and relatively unchanged specificities. Interobserver agreement improved following processing. Processing resulted in a selective increase in the contrast-to-noise ratio for diseased nerves and corresponding improvement in the detection of optic neuritis on MR imaging by radiologists.

Whole-body magnetic resonance imaging (MRI) has become increasingly popular in oncology. However, the long acquisition time might hamper its widespread application. We sought to assess and compare free-breathing sequences with conventional breath-hold examinations in whole-body MRI using an automated workflow process. This prospective study consisted of 20volunteers and six patients with a variety of pathologies who had undergone whole-body 1.5-T MRI that included T1-weighted radial and Dixon volumetric interpolated breath-hold examination sequences. Free-breathing sequences were operated by using an automated user interface. Image quality, diagnostic confidence, and image noise were evaluated by two experienced radiologists. Additionally, signal-to-noise ratio was measured. Diagnostic performance for the overall detection of pathologies was assessed using the area under the receiver operating characteristics curve (AUC). Study participants were asked to rate their examination experiences in a satisfaction survey. MR free-breathing scans were rated as at least equivalent to conventional MR scans in more than 92% of cases, showing high overall diagnostic accuracy (95% [95% CI 92-100]) and performance (AUC 0.971, 95% CI 0.942-0.988; p< 0.0001) for the assessment of pathologies at simultaneously reduced examination times (25 ±2 vs. 32 ±3min; p< 0.0001). Interrater agreement was excellent for both free-breathing (ϰ =0.96 [95% CI 0.88-1.00]) and conventional scans (ϰ =0.93 [95% CI 0.84-1.00]). Qualitative and quantitative assessment for image quality, image noise, and diagnostic confidence did not differ between the two types of MR image acquisition (all p> 0.05). Scores for patient satisfaction were significantly better for free-breathing compared with breath-hold examinations (p= 0.0145), including significant correlations for the grade of noise (r = 0.79, p< 0.0001), tightness (r = 0.71, p< 0.0001), and physical fatigue (r = 0.52, p= 0.0065). In summary, free-breathing whole-body MRI in tandem with an automated user interface yielded similar diagnostic performance at equivalent image quality and shorter acquisition times compared to conventional breath-hold sequences.

Development and validation of a video-based deep learning model for distinguishing epileptic seizures from non-epileptic events in a pediatric cohort.

To evaluate a CT-based radiomics model for identifying malignant pancreatic intraductal papillary mucinous neoplasms (IPMNs) and compare its performance with the 2017 international consensus guidelines (ICGs). We retrospectively included 194 consecutive patients who underwent surgical resection of pancreatic IPMNs between January 2008 and December 2020. Surgical histopathology was the reference standard for diagnosing malignancy. Using radiomics features from preoperative contrast-enhanced CT, a radiomics model was built with the least absolute shrinkage and selection operator by a five-fold cross-validation. CT and MR images were independently reviewed based on the 2017 ICGs by two abdominal radiologists, and the performances of the 2017 ICGs and radiomics model were compared. The areas under the curve (AUCs) were compared using the DeLong method. A total of 194 patients with pancreatic IPMNs (benign, 83 [43%]; malignant, 111 [57%]) were chronologically divided into training (n = 141; age, 65 ± 8.6years; 88 males) and validation sets (n = 53; age, 66 ± 9.7years; 31 males). There was no statistically significant difference in the diagnostic performance of the 2017 ICGs between CT and MRI (AUC, 0.71 vs. 0.71; p = 0.93) with excellent intermodality agreement (k = 0.86). In the validation set, the CT radiomics model had higher AUC (0.85 vs. 0.71; p = 0.038), specificity (84.6% vs. 61.5%; p = 0.041), and positive predictive value (84.0% vs. 66.7%; p = 0.044) than the 2017 ICGs. The CT radiomics model exhibited better diagnostic performance than the 2017 ICGs in classifying malignant IPMNs. Compared with the radiologists' evaluation based on the 2017 international consensus guidelines, the CT radiomics model exhibited better diagnostic performance in classifying malignant intraductal papillary mucinous neoplasms. • There is a paucity of comparisons between the 2017 international consensus guidelines (ICGs) and radiomics models for malignant intraductal papillary mucinous neoplasms (IPMNs). • The CT radiomics model developed in this study exhibited better diagnostic performance than the 2017 ICGs in classifying malignant IPMNs. • The radiomics model may serve as a valuable complementary tool to the 2017 ICGs, potentially allowing a more quantitative assessment of IPMNs.

There have been no investigations on the association between previous abdominopelvic MRI experience without placental MRI experience and diagnostic accuracy of placenta accreta spectrum (PAS). To evaluate the diagnostic performance of radiologists with different experience levels in interpreting PAS-related MRI findings. This retrospective study included 60 women who underwent MRI for placental assessment between 2016 and 2020. MR images were reviewed by four radiologists who were blinded to the clinical outcomes and had different experience levels in interpreting PAS-related MRI findings. The radiologists' diagnostic performance was evaluated according to the pathologic and surgical outcomes. Simple κ statistics were calculated to determine agreement among the radiologists. Of 60 women, 46 were diagnosed with PAS. The maternal age mean ± SD was 33.0 years ± 5.0 for the PAS absent group and 36.0 ± 4.3 for the PAS present group (p = 0.013). Overall, the most experienced radiologist had the highest sensitivity (100%, 95% confidence interval (CI): 92.3-100%) and NPV (100%, 95% CI: 63.1-100%) in PAS diagnoses. However, the PPV and specificity were independent of experience. The most experienced radiologist had the highest diagnostic accuracy in PAS (90%, 95% CI: 79.5-96.2%) and placenta percreta (95%, 95% CI: 86.1-99.0%). There was a strong association between definitive PAS diagnoses and the highest experience level. The κ values for the interobserver agreement regarding PAS diagnoses were 0.67 for the most experienced radiologist (p < 0.001) and 0.38, 0.40, and 0.43 for the other radiologists (p = 0.001) and regarding placenta percreta diagnoses were 0.87 for the senior radiologist (p < 0.001) and 0.63, 0.57, and 0.62 for the other radiologists (p < 0.001). Previous experience in interpreting PAS-related MRI findings plays a significant role in accurately interpreting such imaging findings. Previous abdominopelvic MRI experience without specific placental MRI experience did not improve diagnostic performance. We believe that our study makes a significant contribution to the literature and that this paper will be of interest to the readership of your journal because to the best of our knowledge, this study is the first in which the correlation between previous experience in abdominopelvic MRI with no specific experience in PAS-related MRI and diagnostic accuracy of radiologists has been explored. Our results could aid in setting up specialized multidisciplinary teams to assist women with PAS disorders.

MR imaging has been applied to determine therapeutic response to glucocorticoid (GC) before treatment in thyroid-associated ophthalmopathy (TAO), while the performance was still poor. To investigate the value of T2 -weighted imaging (T2 WI)-derived radiomics for pretreatment determination of therapeutic response to GC in TAO patients, and compare its diagnostic performance with that of semiquantitative parameters. Retrospective. A total of 110 patients (49 ± 12 years; male/female, n=48/62; responsive/unresponsive, n=62/48), divided into training (n=78) and validation (n=32) cohorts. 3.0 T, T2 -weighted fast spin echo. W.C. and H.H. (6 and 10 years of experience, respectively) performed the measurements. Maximum, mean, and minimum signal intensity ratios (SIRs) of extraocular muscle (EOM) bellies were collected to construct a semiquantitative imaging model. Radiomics features from volumes of interest covering EOM bellies were extracted and three machine learning-based (logistic regression [LR]; decision tree [DT]; support vector machine [SVM]) models were built. The diagnostic performances of models were evaluated using receiver operating characteristic curve analyses, and compared using DeLong test. Two-sided P < 0.05 was considered statistically significant. The responsive group showed higher minimum signal intensity ratio (SIRmin ) of EOMs than the unresponsive group (training: 1.46 ± 0.34 vs. 1.18 ± 0.39; validation: 1.44 ± 0.33 vs. 1.19 ± 0.20). In both cohorts, LR-based radiomics model demonstrated good diagnostic performance (area under the curve [AUC]=0.968, 0.916), followed by DT-based (AUC=0.933, 0.857) and SVM-based models (AUC=0.919, 0.855). All three radiomics models outperformed semiquantitative imaging model (SIRmin : AUC=0.805) in training cohort. In validation cohort, only LR-based radiomics model outperformed that of SIRmin (AUC=0.745). The nomogram integrating LR-based radiomics signature and disease duration further elevated the diagnostic performance in validation cohort (AUC: 0.952 vs. 0.916, P=0.063). T2 WI-derived radiomics of EOMs, together with disease duration, provides a promising noninvasive approach for determining therapeutic response before GC administration in TAO patients. 3 TECHNICAL EFFICACY: Stage 4.

To evaluate the usefulness of adding diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping to conventional 3.0-T MRI to differentiate between benign and malignant superficial soft-tissue masses (SSTMs). The institutional review board approved this study and informed consent was waived. The authors retrospectively analyzed conventional MR images including diffusion-weighted images (b-values: 0, 400, 800 s mm(-2)) in 60 histologically proven SSTMs (35 benign and 25 malignant) excluding lipomas. Two radiologists independently evaluated the conventional MRI alone and again with the additional DWI for the evaluation of malignant masses. The mean ADC values measured within an entire mass and the contrast-enhancing solid portion were used for quantitative analysis. Diagnostic performances were compared using receiver-operating characteristic analysis. For an inexperienced reader, using only conventional MRI, the sensitivity, specificity and accuracy were 84%, 80% and 81.6%, respectively. When combining conventional MRI and DWI, the sensitivity, specificity and accuracy were 96%, 85.7% and 90%, respectively. Additional DWI influenced the improvement of the rate of correct diagnosis by 8.3% (5/60). For an experienced reader, additional DWI revealed the same accuracy of 86.7% without added value on the correct diagnosis. The group mean ADCs of malignant SSTMs were significantly lower than that of benign SSTMs (p < 0.001). The best diagnostic performance with respect to differentiation of SSTMs could be obtained when conventional MRI was assessed in combination with DWI. Adding qualitative and quantitative DWI to conventional MRI can improve the diagnostic performance for the differentiation between benign and malignant SSTMs. Because the imaging characteristics of many malignant superficial soft-tissue lesions overlap with those of benign ones, inadequate surgical resection due to misinterpretation of MRI often occurs. Adding DWI to conventional MRI yields greater diagnostic performances [area under the receiver-operating characteristic curve (AUC), 0.83-0.99] than does the use of conventional MRI alone (AUC, 0.71-0.93) in the evaluation of malignant superficial masses by inexperienced readers.

To evaluate whether a comprehensive image processing method as CAD using CT and MRI can improve the radiologists' diagnosis performance in the differentiation of focal liver lesions. A clinical image database used in this study consists of 14 cases of each lesion including hepatic cysts, hepatocellular carcinoma (HCC), metastatic liver cancer, and hemangioma. This technique by using MR images obtained with various imaging sequences and a series of dynamic MR and dynamic CT images is designed for the enhancement of liver lesions pixel by pixel. In this method, we make the pixel sizes of MR images the same size of CT image by using tri-linear interpolation technique. Then the 3D image registration technique based on mutual information is applied for the matching of images. The image intensity pattern with and without contrast enhancement is determined as the template for the differential detection of each lesion. Pixel-by-pixel cross-correlation coefficient is calculated for the enhancement of each lesion. The radiologists' performance in distinguishing between the liver lesion was evaluated by receiver operating characteristic analysis (ROC) with a continuous rating scale. In free-response ROC analysis, true positive fractions were 75%, 87%, 85%, and 86% for hepatic cysts, HCC, metastatic liver cancer and hemangioma, respectively. Furthermore, average number of false positive and false negatives per image was 3.4 and 0.3, respectively. When radiologists made differential diagnosis of the liver lesions with the images of this technique, diagnostic accuracy was statistically significantly improved compared to the diagnostic accuracy without the images of this technique. The average area under the ROC curve (Az value) improved from 0.881 to 0.964 (p=0.069) for the differential diagnosis of hepatic cysts. Furthermore, the Az value of HCC, metastatic liver cancer, and hemangioma improved from 0.951 to 0.979 (p=0.040), from 0.946 to 0.976 (p=0.226), and from 0.966 to 0.987(p=0.045), respectively. A comprehensive image processing method as CAD using CT and MRI can improve the radiologists' diagnostic performance in the differentiation of focal liver lesions. CLINICAL RELEVANCE/APPLICATION: This method improved the performance of differential detection of liver lesions from a large number of images and it would save radiologists' reading time, and thus could assist their diagnosis.

Two-dimensional and three-dimensional imaging of uterus and fallopian tubes in female infertility

Arterial spin labeling is an MR imaging technique that measures cerebral blood flow (CBF) non-invasively. The aim of the study is to assess the diagnostic performance of arterial spin labeling (ASL) MR imaging for differentiation between high-grade glioma and low-grade glioma. Cochrane Library, Embase, Medline, and Web of Science Core Collection were searched. Study selection ended November 2017. This study was prospectively registered in PROSPERO (CRD42017080885). Two authors screened all titles and abstracts for possible inclusion. Data were extracted independently by 2 authors. Bivariate random effects meta-analysis was used to describe summary receiver operating characteristics. Trial sequential analysis (TSA) was performed. In total, 15 studies with 505 patients were included. The diagnostic performance of ASL CBF for glioma grading was 0.90 with summary sensitivity 0.89 (0.79-0.90) and specificity 0.80 (0.72-0.89). The diagnostic performance was similar between pulsed ASL (AUC 0.90) with a sensitivity 0.85 (0.71-0.91) and specificity 0.83 (0.69-0.92) and pseudocontinuous ASL (AUC 0.88) with a sensitivity 0.86 (0.79-0.91) and specificity 0.80 (0.65-0.87). In astrocytomas, the diagnostic performance was 0.89 with sensitivity 0.86 (0.79 to 0.91) and specificity 0.79 (0.63 to 0.89). Sensitivity analysis confirmed the robustness of the findings. TSA revealed that the meta-analysis was adequately powered. Arterial spin labeling MR imaging had an excellent diagnostic accuracy for differentiation between high-grade and low-grade glioma. Given its low cost, non-invasiveness, and efficacy, ASL MR imaging should be considered for implementation in the routine workup of patients with glioma.

The development of new MR imaging scanners with stronger gradients and improvement in coil technology, allied with emerging fast imaging techniques, has allowed a substantial reduction in MR imaging scan times. Our goal was to develop a 10-minute gadolinium-enhanced brain MR imaging protocol with accelerated sequences and to evaluate its diagnostic performance compared with the standard clinical protocol. Fifty-three patients referred for brain MR imaging with contrast were scanned with a 3T scanner. Each MR image consisted of 5 basic fast precontrast sequences plus standard and accelerated versions of the same postcontrast T1WI sequences. Two neuroradiologists assessed the image quality and the final diagnosis for each set of postcontrast sequences and compared their performances. The acquisition time of the combined accelerated pre- and postcontrast sequences was 10 minutes and 15 seconds; and of the fast postcontrast sequences, 3 minutes and 36 seconds, 46% of the standard sequences. The 10-minute postcontrast axial T1WI had fewer image artifacts (P < .001) and better overall diagnostic quality (P < .001). Although the 10-minute MPRAGE sequence showed a tendency to have more artifacts than the standard sequence (P = .08), the overall diagnostic quality was similar (P = .66). Moreover, there was no statistically significant difference in the diagnostic performance between the protocols. The sensitivity, specificity, and accuracy values for the 10-minute protocol were 100.0%, 88.9%, and 98.1%. The 10-minute brain MR imaging protocol with contrast is comparable in diagnostic performance with the standard protocol in an inpatient motion-prone population, with the additional benefits of reducing acquisition times and image artifacts.