Fat-suppressed T2-weighted Images Research Articles

Microsurgical Anatomy of the Inferior Petroclival Vein and its Relation to Surrounding Structures: A Cadaveric and Radiological Study.

The inferior petroclival vein (IPV) courses along the extracranial surface of the petroclival fissure. It is occasionally involved in vascular diseases and has recently been used for vascular access to the cavernous sinus. However, detailed descriptions of its anatomy are currently lacking. To define the anatomic relationship between the IPV and its surrounding structures based on cadaveric dissection and radiological analysis. A dry skull and an injected cadaver head were examined to reveal the relationships between the IPV and its surrounding structures. The existence of the IPV and its relationships with other venous structures were also examined by contrast-enhanced, fat-suppressed T1-weighted magnetic resonance imaging in 26 patients (51 sides). The entire course of the IPV was shown via stepwise cadaver dissection from below. Its relationships with surrounding structures, such as the jugular bulb, sigmoid sinus, inferior petrosal sinus, petrosal venous confluence, and the posterior, lateral, and anterior condylar veins, were also shown. In the radiological analysis, the IPV was identified on all sides. The rostral end of the vein was connected to the venous plexus around the carotid artery on all sides. The vein drained into the caudal end of the inferior petrosal sinus (49/51 sides, 96.1%) or into the anterior condylar vein (2/51 sides, 3.9%). A precise understanding of the anatomy of the IPV will enable endovascular and skull base surgeons to achieve diagnoses and gain safe access to lesions involving the IPV.

Water-Fat Separation in MR Fingerprinting for Quantitative Monitoring of the Skeletal Muscle in Neuromuscular Disorders.

Background Quantitative MRI is increasingly proposed in clinical trials related to neuromuscular disorders (NMDs). Purpose To investigate the potential of an MR fingerprinting sequence for water and fat fraction (FF) quantification (MRF T1-FF) for providing markers of fatty replacement and disease activity in patients with NMDs and to establish the sensitivity of water T1 as a marker of disease activity compared with water T2 mapping. Materials and Methods Data acquired between March 2018 and March 2020 from the legs of patients with NMDs were retrospectively analyzed. The MRI examination comprised fat-suppressed T2-weighted imaging, mapping of the FF measured with the three-point Dixon technique (FFDixon), water T2 mapping, and MRF T1-FF, from which the FF measured with MRF T1-FF (FFMRF) and water T1 were derived. Data from the legs of healthy volunteers were prospectively acquired between January and July 2020 to derive abnormality thresholds for FF, water T2, and water T1 values. Kruskal-Wallis tests and receiver operating characteristic curve analysis were performed, and linear models were used. Results A total of 73 patients (mean age ± standard deviation, 47 years ± 12; 45 women) and 15 healthy volunteers (mean age, 33 years ± 8; three women) were evaluated. A linear correlation was observed between FFMRF and FFDixon (R2 = 0.97, P < .001). Water T1 values were higher in muscles with high signal intensity at fat-suppressed T2-weighted imaging than in muscles with low signal intensity (mean value, 1281 msec [95% CI: 1165, 1604] vs 1198 msec [95% CI: 1099, 1312], respectively; P < .001), and a correlation was found between water T1 and water T2 distribution metrics (R2 = 0.66 and 0.79 for the median and 90th percentile values, respectively; P < .001). Water T1 classified the patients' muscles as abnormal based on quantitative water T2, with high sensitivity (93%; 68 of 73 patients) and specificity (80%; 53 of 73 patients) (area under the receiver operating characteristic curve, 0.92 [95% CI: 0.83, 0.97]; P < .001). Conclusion Water-fat separation in MR fingerprinting is robust for deriving quantitative imaging markers of intramuscular fatty replacement and disease activity in patients with neuromuscular disorders. © RSNA, 2021 Online supplemental material is available for this article.

Assessment of tumor depth in oral tongue squamous cell carcinoma with multiparametric MRI: correlation with pathology

ObjectivesTo compare the correlation of depth of invasion (DOI) measured on multiple magnetic resonance imaging (MRI) sequences and pathological DOI, in order to determine the optimal MRI sequence for measurement.MethodsA total of 122 oral tongue squamous cell carcinoma (OTSCC) patients were retrospectively analyzed, who had received preoperative MRI and surgical resection. DOIs measured on fat-suppressed T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), dynamic enhanced-T1 high-resolution insotropic volume examination (e-THRIVE), and contrast-enhanced fat-suppressed T1WI (CE-T1WI) were respectively compared to those measured in pathologic specimens. The cutoff value of the best correlated MRI sequence was determined, and the T staging accuracy of MRI-derived DOI was evaluated.ResultsDOI derived from e-THRIVE showed the best correlation (r = 0.936, p < 0.001) with pathological DOI. The area under the curve values of MRI-derived DOI distinguishing T1 stage from T2 stage and distinguishing T2 stage from T3 stage were 0.969 and 0.974, respectively. The T staging criteria of MRI-derived DOI were 6.2 mm and 11.4 mm, with a staging accuracy of 86.9% compared to pathological DOI criteria of 5 mm and 10 mm.ConclusionE-THRIVE was the optimal MR sequence to measure the MR-derived DOI, and DOI derived from e-THRIVE could serve as a potential cut-off value as a clinical T staging indicator of OTSCC.Key Points• Multiparametric MRI helps radiologists to assess the neoplasm invasion in patients with oral tongue squamous cell carcinoma.• Retrospective study indicated that measurement was most accurate on enhanced-T1 high-resolution insotropic volume examination dynamic contrast enhancement images.• T staging of oral tongue squamous cell carcinoma was accurate according to the dynamic contrast enhancement MRI-derived depth of invasion.

Assessment of squamous cell carcinoma of the floor of the mouth with magnetic resonance imaging

PurposeWe aimed to use magnetic resonance imaging (MRI) to determine the relationship between the pathological depth of invasion (DOI), undetectability, and tumor thickness of squamous cell carcinoma of the floor of the mouth.Materials and methodsWe retrospectively evaluated the relationship between pathological DOI and MRI detectability, as well as the relationship between pathological DOI and tumor thickness on coronal fat-suppressed contrast-enhanced T1-weighted imaging or coronal T2-weighted imaging.ResultsWe analyzed 30 patients with squamous cell carcinoma of the floor of the mouth; MRI revealed that the pathological DOI of the 11 undetectable lesions (median 2 mm) was smaller than that of the 19 detectable lesions (median 14 mm) (p < 0.001), and the cut-off value was 3 mm (sensitivity, 0.84; specificity, 0.91; area under the curve, 0.89). Tumor thickness on coronal fat-suppressed contrast-enhanced T1-weighted imaging was assessed in all 19 detectable lesions; however, tumor thickness on coronal T2-weighted imaging could not be assessed in eight cases. Tumor thickness on coronal fat-suppressed contrast-enhanced T1-weighted imaging was found to be significantly associated with the pathological DOI.ConclusionsUndetectability on MRI indicates superficial lesions with a pathological DOI value that is less than 3 mm. In detectable lesions, tumor thickness on coronal fat-suppressed contrast-enhanced T1-weighted imaging is associated with pathological DOI.

Effect of Portal Vein and Hepatic Artery Occlusion on Radiofrequency Ablation: Animal Experiment Comparing MR and Histology Measurements of the Ablation Zones.

This animal experimental study evaluated how hepatic artery and portal vein transient occlusion affects the ablation zone of hepatic radiofrequency ablation (RFA). Twenty-one rabbits were divided into three groups of seven each: (1) control, (2) hepatic artery occlusion, and (3) portal vein occlusion by a balloon catheter. For each rabbit, two or three RFA sessions were performed using an electrode needle. Ablation time, temperature around the tip of RFA needle at the end of RFA, ablation volume on fat-suppressed T1-weighted image in the hepatobiliary phase, and coagulative necrosis area on histopathology were measured and compared between the three groups using the Kruskal-Wallis paired Mann-Whitney U tests. In 43 RFA sessions (group 1, 15; group 2, 14; group 3, 14), mean tissue temperature in group 3 (77.0°C ± 7.7°C) was significantly higher compared to groups 1 (59.2°C ± 18.8°C; P = 0.010) and 2 (67.5°C ± 9.9°C; P = 0.010). In addition, mean ablation volume and coagulative necrosis in group 3 (2.10 ± 1.37 mm3 and 0.86 ± 0.28 mm2, respectively) were larger compared to groups 1 (0.84 ± 0.30 mm3; P < 0.001 and 0.55 ± 0.26 mm2; P = 0.020, respectively) and 2 (0.89 ± 0.59 mm3; P = 0.002 and 0.60 ± 0.22 mm2; P = 0.024, respectively). Portal vein occlusion potentially boosts tissue temperature, ablation volume, and area of histopathologically proven coagulative necrosis during hepatic RFA in the non-cirrhotic liver.

Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer.

Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer.Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer.Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2–: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811).Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.

LIMITED INTER-RATER RELIABILITY IN ANTEROLATERAL LIGAMENT DETECTION IN PEDIATRIC KNEE MRIS

Background:Despite the rapidly expanding anterolateral ligament (ALL) literature base, little has focused on the reliability of its detection on MRI. This is especially important in the pediatric population where the scarcity of pediatric cadaveric specimens hinders the accurate estimation of its prevalence.Purpose:The purpose of this study was to assess the visibility of ALL on common MRI pulse sequences and to calculate the inter-rater reliability among multiple expert readers in a cohort of normal pediatric knee MRIs.Methods:A series of 3T pediatric knee MRIs obtained from 8/2018-4/2019 were reviewed to identify patients without significant knee pathology. Knee laterality, age, gender, and regional physeal status were also recorded. One orthopedic surgeon, 1 pediatric musculoskeletal (MSK) radiologist, and 1 adult MSK radiologist, independently reviewed each MRI pulse sequence (T1-weighted, PD-weighted, and PD-weighted fat-suppressed images) separately, to determine the presence or absence of the 3 parts of the ALL (femoral, tibial, and meniscal proper). Inter-rater reliability was calculated using Fleiss’ Kappa (K).Results:Inclusion criteria were met for 75 patient knees with a mean age of 12.7±3.42y (3-19) and a greater proportion of females (57.3%). The distribution of regional physeal status was: Open-48.0%, Closing-29.3%, and Closed-22.7%. Inter-rater reliability was slight to fair (K: 0.141-0.340) for visualizing any specific ALL component on a unique MRI sequence (Table 1). For visualizing a specific attachment on any MRI sequence, there was slight to moderate agreement (K: 0.018-0.411). Visualizing any ALL attachment on any MRI sequence also yielded slight agreement (K: 0.190). Inter-rater reliability between specialities (i.e. Ortho vs. MSK Radiology) did not differ substantially from group findings. No single study sequence demonstrated enhanced reliability for all ALL components.Conclusion:The reliability of ALL detection on MRI across multiple clinicians was slight to moderate for all components in this cohort of normal 3T pediatric knee MRIs. No single sequence was consistently better for ALL detection agreement. This is in contrast with previous work showing substantial agreement among raters and describing PD-weighted imaging as preferable for viewing the ALL. Experience in musculoskeletal imaging did not confer improved agreement. Establishing diagnostic reliability should be a prerequisite for use of an imaging finding in the research or clinical setting. Existing literature drawing conclusions from ALL findings on MRI may need to be interpreted with caution if reliability testing is not described.

Method for Diagnosing the Bone Marrow Edema of Sacroiliac Joint in Patients with Axial Spondyloarthritis Using Magnetic Resonance Image Analysis Based on Deep Learning.

Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the sacroiliac joints. In this study, we develop a method for detecting bone marrow edema by magnetic resonance (MR) imaging of the sacroiliac joints and a deep-learning network. A total of 815 MR images of the sacroiliac joints were obtained from 60 patients diagnosed with axSpA and 19 healthy subjects. Gadolinium-enhanced fat-suppressed T1-weighted oblique coronal images were used for deep learning. Active sacroiliitis was defined as bone marrow edema, and the following processes were performed: setting the region of interest (ROI) and normalizing it to a size suitable for input to a deep-learning network, determining bone marrow edema using a convolutional-neural-network-based deep-learning network for individual MR images, and determining sacroiliac arthritis in subject examinations based on the classification results of individual MR images. About 70% of the patients and normal subjects were randomly selected for the training dataset, and the remaining 30% formed the test dataset. This process was repeated five times to calculate the average classification rate of the five-fold sets. The gradient-weighted class activation mapping method was used to validate the classification results. In the performance analysis of the ResNet18-based classification network for individual MR images, use of the ROI showed excellent detection performance of bone marrow edema with 93.55 ± 2.19% accuracy, 92.87 ± 1.27% recall, and 94.69 ± 3.03% precision. The overall performance was additionally improved using a median filter to reflect the context information. Finally, active sacroiliitis was diagnosed in individual subjects with 96.06 ± 2.83% accuracy, 100% recall, and 94.84 ± 3.73% precision. This is a pilot study to diagnose bone marrow edema by deep learning based on MR images, and the results suggest that MR analysis using deep learning can be a useful complementary means for clinicians to diagnose bone marrow edema.

DE-MR simulation imaging for prone radiotherapy after breast-conserving surgery: assessing its application in lumpectomy cavity delineation based on deformable image registration

BackgroundThe application of delayed-enhancement magnetic resonance (DE-MR) simulation imaging in lumpectomy cavity (LC) delineation for prone radiotherapy in patients with an invisible seroma or a low seroma clarity score (SCS) after breast-conserving surgery (BCS) based on deformable image registration (DIR) was assessed.MethodsTwenty-six patients who were suitable for radiotherapy in prone positions after BCS were enrolled, and both computed tomography (CT) and DE-MR simulation scans were acquired. The LC delineated based on titanium surgical clips on CT images was denoted as LCCT. The LC delineated based on the signal of cavity boundaries on fat-suppressed T2-weighted imaging (T2WI) and multiphase delayed-enhancement T1-weighted imaging (DE-T1WI), which was performed at 2 min, 5 min and 10 min postinjection, were denoted as LCT2, LC2T1, LC5T1 and LC10T1, respectively. Afterwards, DIR was performed to compare the volumes and locations of the LCs with MIM software. The generalized conformity index (CIgen) of inter (intra) observer (Inter-CIgen and Intra-CIgen) was also used to explore the inter(intra) observer variation for LC delineation on each image modality.ResultsLCCT–LC10T1 provided the best conformal index (CI) and degree of inclusion (DI), increasing by 2.08% and 4.48% compared to LCCT–LCT2, 11.36% and 2.94% for LCCT–LC2T1, and 8.89% and 7.69% for LC5T1–LCCT, respectively. The center of mass (COM) of LCCT–LC10T1 decreased by 17.86%, 6.12% and 13.21% compared with that of LCCT–LCT2, LCCT–LC2T1 and LCCT–LC5T1, respectively. The agreement of LC delineation was strongest for 10th min DE-TIWI (coefficient of variation, COV = 2.30%, Inter-CIgen = 87.06%, Intra-CIgen = 92.64%).ConclusionFor patients with a low SCS (SCS ≤ 2) after BCS, it is feasible to contour the LC based on prone DE-MR simulation images. Furthermore, the LC derived from prone DE-T1WI at 10 min was found to be most similar to that derived from prone CT simulation scans using titanium surgical clips regardless of the volume and location of the LC. Inter (intra) variability was minimal for the delineation of the LC based on 10th min DE-TIWI.

Development and Evaluation of Deep Learning-Accelerated Single-Breath-Hold Abdominal HASTE at 3 T Using Variable Refocusing Flip Angles.

Deep learning (DL) reconstruction enables substantial acceleration of image acquisition while maintaining diagnostic image quality. The aims of this study were to overcome the drawback of specific absorption rate (SAR)-related limitations at 3 T and to develop a DL-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTE) sequence for 2-dimesional T2-weighted fat-suppressed magnetic resonance imaging of the abdomen at 3 T using a variable flip angle (FA) evolution for the refocusing radiofrequency pulses, as well as to evaluate its feasibility and image quality in comparison to state-of-the-art T2-weighted fat-suppressed imaging technique (BLADE). First, a suitable FA evolution with low cardiac motion-related signal loss (CRSL) and low SAR was determined through a prospective volunteer study with 11 participants. Image quality and diagnostic confidence with 5 different FA evolutions of a HASTEDL were assessed to identify the most suitable FA evolution. Second, the identified FA evolution was implemented clinically and evaluated in 51 patients undergoing a clinically indicated liver magnetic resonance imaging at 3 T. Two radiologists assessed the HASTEDL and standard sequences regarding overall image quality, noise, contrast, sharpness, artifacts, CRSL, and diagnostic confidence using a Likert scale ranging from 1 to 4, with 4 being the best. Comparative analyses were conducted to assess the differences between HASTEDL (acquisition time, 21 seconds; single breath-hold) and the routinely used T2-weighted BLADE sequence (acquisition time, 4 minutes; respiratory triggering). From the volunteer study, the FA evolution characterized by the control points 130-90-110-130 degrees (HASTEDL) was identified as optimal among the 5 evolutions evaluated and was implemented in our clinical protocol. In all 51 patients, HASTEDL was successfully acquired at 3 T and showed excellent image quality (median, 4; interquartile range, 3-4). Although BLADE was rated significantly higher for overall image quality, noise, contrast, sharpness, artifacts, CRSL, and diagnostic confidence than HASTEDL, no differences were found concerning the number (n = 102) and measured diameter of the detected hepatic lesions between the 2 sequences BLADE and HASTEDL. The proposed single-breath-hold abdominal HASTEDL with variable refocusing FAs is feasible at 3 T within SAR limits and yields high image quality and diagnostic confidence as compared with a standard T2-weighted acquisition technique, at a 10th of the acquisition time.

Radiomics Nomograms Based on Non-enhanced MRI and Clinical Risk Factors for the Differentiation of Chondrosarcoma from Enchondroma.

Differentiating chondrosarcoma from enchondroma using conventional MRI remains challenging. An effective method for accurate preoperative diagnosis could affect the management and prognosis of patients. To validate and evaluate radiomics nomograms based on non-enhanced MRI and clinical risk factors for the differentiation of chondrosarcoma from enchondroma. Retrospective. A total of 103 patients with pathologically confirmed chondrosarcoma (n= 53) and enchondroma (n= 50) were randomly divided into training (n= 68) and validation (n= 35) groups. Axial non-contrast-enhanced T1-weighted images (T1WI) and fat-suppressed T2-weighted images (T2WI-FS) were acquired at 3.0 T. Clinical risk factors (sex, age, and tumor location) and diagnosis assessment based on morphologic MRI by three radiologists were recorded. Three radiomics signatures were established based on the T1WI, T2WI-FS, and T1WI + T2WI-FS sequences. Three clinical radiomics nomograms were developed based on the clinical risk factors and three radiomics signatures. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomics signatures and clinical radiomics nomograms. Tumor location was an important clinical risk factor (P< 0.05). The radiomics signature based on T1WI and T1WI + T2WI-FS features performed better than that based on T2WI-FS in the validation group (AUC in the validation group: 0.961, 0.938, and 0.833, respectively; P< 0.05). In the validation group, the three clinical radiomics nomograms (T1WI, T2WI-FS, and T1WI + T2WI-FS) achieved AUCs of 0.938, 0.935, and 0.954, respectively. In all patients, the clinical radiomics nomogram based on T2WI-FS (AUC=0.967) performed better than that based on T2WI-FS (AUC=0.901, P< 0.05). The proposed clinical radiomics nomogram showed promising performance in differentiating chondrosarcoma from enchondroma. 4 TECHNICAL EFFICACY: Stage 2.

Radiomics based on multisequence magnetic resonance imaging for the preoperative prediction of peritoneal metastasis in ovarian cancer.

To develop a radiomics signature based on multisequence magnetic resonance imaging (MRI) to preoperatively predict peritoneal metastasis (PM) in ovarian cancer (OC). Eighty-nine patients with OC were divided into a training cohort including patients (n = 54) with a single lesion and a validation cohort including patients (n = 35) with bilateral lesions. Radiomics features were extracted from the T2-weighted images (T2WIs), fat-suppressed T2WIs, multi-b-value diffusion-weighted images (DWIs), and corresponding parametric maps. A radiomics signature and nomogram incorporating the radiomics signature and clinical predictors were developed and validated on the training and validation cohorts, respectively. The radiomics signature generated by 6 selected features showed a favorable discriminatory ability to predict PM in OC with an area under the curve (AUC) of 0.963 in the training cohort and an AUC of 0.928 in the validation cohort. The nomogram, comprising the radiomics signature, pelvic fluid, and CA-125 level, showed more favorable discrimination with an AUC of 0.969 in the training cohort and 0.944 in the validation cohort. Net reclassification index with values of 0.548 in the training cohortand 0.500 in the validation cohort. Radiomics signature based on multisequence MRI serves as an effective quantitative approach to predict PM in OC patients. A nomogram of radiomics signature and clinical predictors could further improve the prediction ability of PM in patients with OC. • Multisequence MRI-based radiomics showed a favorable discriminatory ability to predict PM in OC. • The nomogram incorporating the radiomics signature and clinical predictors was clinically useful to preoperatively predict PM in patients with OC.

Differentiating Liver Hemangioma from Metastatic Tumor Using T2-enhanced Spin-echo Imaging with a Time-reversed Gradient-echo Sequence in the Hepatobiliary Phase of Gadoxetic Acid-enhanced MR Imaging.

Purpose: To evaluate the utility of T2-enhanced spin-echo imaging using the time-reversed gradient echo sequence (T2FFE imaging) in the hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI (Gd-EOB-MRI) for differentiating hemangiomas from metastatic tumors.Methods: A total of 61 patients with 133 liver lesions, including 37 hemangiomas and 96 metastatic tumors, were scanned by Gd-EOB-MRI. Four data sets were independently analyzed by two readers: (1) 3D fat-suppressed T2-weighted imaging (FS-T2WI) alone; (2) the combination of 3D FS-T2WI and T2FFE imaging in the HBP of Gd-EOB-MRI; (3) the combination of 3D FS-T2WI, diffusion-weighted imaging (DWI) with the b-value of 1000 s/mm2 and the apparent diffusion coefficient (ADC); and (4) a dynamic study of Gd-EOB-MRI. After classifying the lesion sizes as ≤ 10 mm or > 10 mm, we conducted a receiver-operating characteristic analysis to compare diagnostic accuracies among the four data sets for differentiating hemangiomas from metastatic tumors.Results: The areas under the curves (AUCs) of the four data sets of two readers were: (1) ≤ 10 mm (0.85 and 0.91) and > 10 mm (0.88 and 0.97), (2) ≤ 10 mm (0.94 and 0.94) and > 10 mm (0.96 and 0.95), (3) ≤ 10 mm (0.90 and 0.87) and > 10 mm (0.89 and 0.95), and (4) ≤ 10 mm (0.62 and 0.67) and > 10 mm (0.76 and 0.71), respectively. Data sets (2) and (3) showed no significant differences in AUCs, but both showed significantly higher AUCs compared to that of (4) regardless of the lesion size (P < 0.05).Conclusion: The combination of 3D FS-T2WI and T2FFE imaging in the HBP of Gd-EOB-MRI achieved an accuracy equivalent to that of the combination of 3D FS-T2WI, DWI, and ADC and might be helpful in differentiating hemangiomas from metastatic tumors.

Qualitative and Quantitative Analysis of a Spiral Gradient Echo Sequence for Contrast-Enhanced Fat-Suppressed T1-Weighted Spine Magnetic Resonance Imaging.

Pulse sequences with non-Cartesian k-space sampling enable improved imaging in anatomical areas with high degrees of motion artifacts. We analyzed a novel spiral 3-dimensional (3D) gradient echo (GRE) magnetic resonance imaging (MRI) sequence ("spiral," 114.7 ± 11 seconds) and compared it with a radial 3D GRE ("vane," 216.7 ± 2 seconds) and a conventional Cartesian 2D turbo spin echo (TSE) sequence ("TSE," 266.7 ± 82 seconds) for contrast-enhanced fat-suppressed T1-weighted spine imaging. Forty consecutive patients referred for contrast-enhanced MRI were prospectively scanned with all 3 sequences. A qualitative analysis was performed by 3 readers using 4- or 5-point Likert scales to independently grade images in terms of overall image quality, occurrence of artifacts, lesion conspicuity, and conspicuity of nerve roots. The numbers of visible nerve roots per sequence and patient were counted in consensus. Coefficient of variation measurements were performed for the paravertebral musculature (CVPM) and the spinal cord (CVSC). Spiral (median [interquartile range], 5 [4-5]) exhibited improved overall image quality in comparison to TSE (3 [3-4]) and vane (4 [4-5]; both P < 0.001). Vane surpassed TSE in terms of overall image quality (P < 0.001). Spiral (4 [3.75-4]) and vane (3.5 [3-4]) presented with less artifacts than TSE (3 [2.75-3.25]; both P < 0.001). Spiral (4 [4-5]) outperformed vane (4 [3-5]; P = 0.01) and TSE (4 [3-4]; P = 0.04) in terms of lesion conspicuity. Conspicuity of nerve roots was superior on spiral (3 [3-4]) and vane (4 [3-4]) when compared with TSE (1.5 [1-2]; both P < 0.001). Readers discerned significantly more nerve roots on spiral (4 [2.75-8]) and vane (4 [3.75-7.25]) images when compared with TSE (2 [0-4]; both P < 0.001). Interreader agreement ranged from moderate (α = 0.639) to almost perfect (α = 0.967). CVPM and CVSC were significantly lower on spiral as compared with vane and TSE (P < 0.001, P = 0.04). Vane exhibited lower CVPM and CVSC than TSE (P < 0.001, P = 0.01). A novel spiral 3D GRE sequence improves contrast-enhanced fat-suppressed T1-weighted spinal imaging qualitatively and quantitatively in comparison with a conventional Cartesian 2D TSE sequence and to a lesser extent with a radial 3D GRE sequence at shorter scan times.

MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study.

To develop and validate a radiomics signature based on magnetic resonance imaging (MRI) from multicenter datasets for preoperative prediction of pathologic response to neoadjuvant chemotherapy (NAC) in patients with osteosarcoma. We retrospectively enrolled 102 patients with histologically confirmed osteosarcoma who received chemotherapy before treatment from 4 hospitals (68 in the primary cohort and 34 in the external validation cohort). Quantitative imaging features were extracted from contrast-enhanced fat-suppressed T1-weighted images (CE FS T1WI). Four classification methods, i.e., the least absolute shrinkage and selection operator logistic regression (LASSO-LR), support vector machine (SVM), Gaussian process (GP), and Naive Bayes (NB) algorithm, were compared for feature selection and radiomics signature construction. The predictive performance of the radiomics signatures was assessed with the area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis (DCA). Thirteen radiomics features selected based on the LASSO-LR classifier were adopted to construct the radiomics signature, which was significantly associated with the pathologic response. The prediction model achieved the best performance between good and poor responders with an AUC of 0.882 (95% CI, 0.837-0.918) in the primary cohort. Calibration curves showed good agreement. Similarly, findings were validated in the external validation cohort with good performance (AUC, 0.842 [95% CI, 0.793-0.883]) and good calibration. DCA analysis confirmed the clinical utility of the selected radiomics signature. The constructed CE FS T1WI-radiomics signature with excellent performance could provide a potential tool to predict pathologic response to NAC in patients with osteosarcoma. • The radiomics signature based on multicenter contrast-enhanced MRI was useful to predict response to NAC. • The prediction model obtained with the LASSO-LR classifier achieved the best performance. • The baseline clinical characteristics were not associated with response to NAC.

Value of conventional magnetic resonance imaging texture analysis in the differential diagnosis of benign and borderline/malignant phyllodes tumors of the breast

BackgroundThe purpose of this study was to determine the potential value of magnetic resonance imaging (MRI) texture analysis (TA) in differentiating between benign and borderline/malignant phyllodes tumors of the breast.MethodsThe preoperative MRI data of 25 patients with benign phyllodes tumors (BPTs) and 19 patients with borderline/malignant phyllodes tumors (BMPTs) were retrospectively analyzed. A gray-level histogram and gray-level cooccurrence matrix (GLCM) were used for TA with fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) images, and 2- and 7-min postcontrast T1W images on dynamic contrast-enhanced MRI (DCE-T1WI2min and DCE-T1WI7min) between BPTs and BMPTs. Independent sample t-test and Mann-Whitney U test were performed for intergroup comparison. A regression model was established by using binary logistic regression analysis, and receiver operating characteristic (ROC) curve analysis was carried out to evaluate diagnostic efficiency.ResultsFor ADC images, the texture parameters angular second moment (ASM), correlation, contrast, entropy and the minimum gray values of ADC images (ADCMinimum) showed significant differences between the BPT group and BMPT group (all p<0.05). The parameter entropy of FS-T2WI and the maximum gray values and kurtosis of the tumor solid region of DCE-T1WI7min also showed significant differences between these two groups. Except for ADCMinimum, angular second moment of FS-T2WI (FS-T2WIASM), and the maximum gray values of DCE-T1WI7min (DCE-T1WI7min-Maximum) of the tumor solid region, the AUC values of other positive texture parameters mentioned above were greater than 0.75. Binary logistic regression analysis demonstrated that the contrast of ADC images (ADCContrast) and entropy of FS-T2WI (FS-T2WIEntropy) could be considered independent texture variables for the differential diagnosis of BPTs and BMPTs. Combined, the AUC of these parameters was 0.891 (95% CI: 0.793–0.988), with a sensitivity of 84.2% and a specificity of up to 89.0%.ConclusionTexture analysis could be helpful in improving the diagnostic efficacy of conventional MR images in differentiating BPTs and BMPTs.

A dermoid cyst misdiagnosed as a lipoma due to atypical magnetic resonance images: a case report

BackgroundDermoid cysts are well-known lesions that manifest as subcutaneous tumors around the lateral sides of the eyebrows in young patients. Computed tomography or magnetic resonance imaging (MRI) is often performed to confirm the diagnosis. On the other hand, a lipoma is usually a circular lesion, which is sometimes observed in the upper part of the face. The signals of both T1-weighted and T2-weighted images of MRI of a lipoma are, in general, relatively highly homogenous, and the signals decrease in fat-suppressed images. Therefore, differential diagnosis between a dermoid cyst and a lipoma is usually made with MRI, especially based on fat-suppressed images. Here, we present a case of misdiagnosis of a dermoid cyst as a lipoma because of atypical magnetic resonance images.Case presentationWe report a case of a 24-year-old Japanese woman with a dermoid cyst around the lateral edge of the eyebrow. The cyst had been gradually increasing in size for the past 2 years. On MRI, it showed high internal signals on T1- and T2-weighted images. However, the signal intensity decreased homogeneously in the fat-suppressed T2-weighted images. The observed tumor had a yellowish appearance under the endoscope. On the basis of these findings, the lesion was considered a lipoma until it ruptured intraoperatively. The pathological diagnosis confirmed it to be a dermoid cyst.ConclusionSome dermoid cysts contain lipid-rich liquid, and these may be misdiagnosed as lipomas by MRI. When a tumor is located at a common site for a dermoid cyst, the MRI images should be validated carefully if it appears like a lipoma, and the differential diagnosis should be considered carefully.

Magnetic Resonance Imaging-Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two-Center Study.

Preoperative prediction of soft tissue sarcoma (STS) grade is important for treatment decisions. Therefore, formulation an STS grade model is strongly needed. To develop and test an magnetic resonance imaging (MRI)-based radiomics nomogram for predicting the grade of STS (low-grade vs. high grade). Retrospective POPULATION: One hundred and eighty patients with STS confirmed by pathologic results at two independent institutions were enrolled (training set, N=109; external validation set, N=71). Unenhanced T1-weighted (T1WI) and fat-suppressed T2-weighted images (FS-T2WI) were acquired at 1.5T and 3.0T. Clinical-MRI characteristics included age, gender, tumor-node-metastasis (TNM) stage, American Joint Committee on Cancer (AJCC) stage, progression-free survival (PFS), and MRI morphological features (ie, margin). Radiomics feature extraction were performed on T1WI and FS-T2WI images by minimum redundancy maximum relevance (MRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm. The selected features constructed three radiomics signatures models (RS-T1， RS-FST2, and RS-Combined). Univariate and multivariate logistic regression analysis were applied for screening significant risk factors. Radiomics nomogram was constructed by incorporating the radiomics signature and risk factors. Clinical-MRI characteristics were performed by a univariate analysis. Model performances (discrimination, calibration, and clinical usefulness) were validated in the external validation set. The RS-T1 model, RS-FST2 model, and RS-Combined model had an area under curves (AUCs) of 0.645, 0.641, and 0.829, respectively, in the external validation set. The radiomics nomogram, incorporating significant risk factors and the RS-Combined model had AUCs of 0.916 (95%CI, 0.866-0.966, training set) and 0.879 (95%CI, 0.791-0.967, external validation set), and demonstrated good calibration and good clinical utility. The proposed noninvasive MRI-based radiomics models showed good performance in differentiating low-grade from high-grade STSs. 3 TECHNICAL EFFICACY STAGE: 2.

"Anterior-shift sign": a novel MRI finding of adult hip dysplasia.

In adult hip dysplasia, methods for direct evaluation of hip instability have not been established. The present study aimed to determine findings suggestive of hip instability on magnetic resonance imaging (MRI) and to evaluate their correlations with clinical and radiological factors. We retrospectively reviewed 72 hips in 50 patients with hip dysplasia (45 females, 5 males; mean age: 40.0years; age range: 15-59years; Kellgren-Lawrence grade: ≤ 2). Hip dysplasia was defined as a lateral center-edge angle < 25°. Among the hips, 50 had pain (symptomatic dysplasia group) and 22 were asymptomatic (asymptomatic dysplasia group). As controls, 12 normal hips in 12 patients who underwent screening for asymptomatic osteonecrosis of the femoral head by MRI were evaluated. Using an oblique axial view on fat-suppressed T2-weighted images, we evaluated the presence of a gap between the posterior part of the femoral head and the corresponding acetabular surface, indicating hip instability (anterior-shift sign). The correlations of anterior-shift sign with clinical and radiographical factors were examined. Anterior-shift sign was observed in 92.0% in the symptomatic dysplasia group, 9.1% in the asymptomatic dysplasia group, and 0% in the control group. In adult hip dysplasia, cases with anterior-shift sign had significantly more pain and labrum tear occurrence than cases without anterior-shift sign. Anterior-shift sign was correlated with Kellgren-Lawrence grade and degree of acetabular coverage. This study suggested that hip instability can be observed as the anterior-shift sign on MRI. This sign is useful when considering indications for periacetabular osteotomy in adult hip dysplasia.

Evaluating Heterogeneity of Primary Lung Tumor Using Clinical Routine Magnetic Resonance Imaging and a Tumor Heterogeneity Index.

ObjectiveTo improve the assessment of primary tumor heterogeneity in magnetic resonance imaging (MRI) of non-small cell lung cancer (NSCLC), we proposed a method using basic measurements from T1- and T2-weighted MRI.MethodsOne hundred and four NSCLC patients with different T stages were studied. Fifty-two patients were analyzed as training group and another 52 as testing group. The ratios of standard deviation (SD)/mean signal value of primary tumor from T1-weighted (T1WI), T1-enhanced (T1C), T2-weighted (T2WI), and T2 fat suppression (T2fs) images were calculated. In the training group, correlation analyses were performed between the ratios and T stages. Then an ordinal regression model was built to generate the tumor heterogeneous index (THI) for evaluating the heterogeneity of tumor. The model was validated in the testing group.ResultsThere were 11, 32, 40, and 21 patients with T1, T2, T3, and T4 disease, respectively. In the training group, the median SD/mean on T1WI, T1C, T2WI, and T2fs sequences was 0.11, 0.19, 0.16, and 0.15 respectively. The SD/mean on T1C (p=0.003), T2WI (p=0.000), and T2fs sequences (p=0.002) correlated significantly with T stages. Patients with more advanced T stage showed higher SD/mean on T2-weighted, T2fs, and T1C sequences. The median THI in the training group was 2.15. THI correlated with T stage significantly (p=0.000). In the testing group, THI was also significantly related to T stages (p=0.001). Higher THI had relevance to more advanced T stage.ConclusionsThe proposed ratio measurements and THI based on MRI can serve as functional radiomic markers that correlated with T stages for evaluating heterogeneity of lung tumors.