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Study Design: Retrospective study. Objectives: Giant cell tumors (GCTs) of the mobile spine can be locally aggressive. This study described and classified the typical and atypical appearance of aggressive spinal GCTs according to imaging findings to help the imaging diagnosis, especially for patients with rapid neurological deficit that may require emergent surgery without biopsy. Methods: Computed tomography (CT) and magnetic resonance imaging (MRI) scans of patients diagnosed with aggressive spinal GCTs at single center were reviewed. Results: Overall, 101 patients with 100 CT images and 94 MR images were examined. All lesions were osteolytic with cortical destruction; 95 lesions showed epidural extension; 90 were centered in the vertebral body; 82 showed pathological fracture and/or collapse of the vertebral body; 78 had pseudotrabeculation on CT; 80 showed low-to-iso signal intensity or heterogeneous high-signal intensity with cystic areas on the T2-weighted images; 9 showed fluid–fluid level on T2-weighted images; and 61 patients showed marked enhancement on contrast-enhanced CT and/or MRI. Forty-one lesions (40.6%) had at least 1 atypical radiographic feature: 19 involved ≥2 segments; 11 were centered in the posterior neural arch; 10 had a paravertebral mass over 2 segments; 16 showed partial margin sclerosis with partial cortical destruction on CT scans; and 3 showed mineralization within the tumor on CT. Eighty-eight patients underwent CT-guided biopsy with a diagnostic accuracy rate of 94.3%. Conclusions: Spinal GCTs might appear more radiologically atypical, and about 40% of the lesions may have at least 1 atypical feature. CT-guided biopsies are recommended for definitive diagnosis.

To analyze the clinical and radiographic characteristics of thoracic disk disease associated with myelopathy (TDM). This is a retrospective clinical review of prospectively collected imaging data based at a single institute. Based on preoperative CT and MRI, we classified TDM as thoracic disk herniation (TDH), THD with ossification (THDO), TDH with posterior bony avulsions of the vertebrae (TDH with PBA), TDH with posterior vertebral osteophytes (TDH with PVO), giant thoracic osteophyte and calcific discitis with herniation (CDH). Patient characteristics and radiographic data were compared between different types of TDM. Among the 257 patients included, 12.06% of patients presented with symptoms after traumatic events. The most frequent complaint at onset and preoperative was back pain (29.2%) and subjective lower limb weakness (75.5%), respectively. All TDH with PBA is distributed at the lower thoracic segments, while CDH predominantly in the middle and lower thoracic segments. TDH with PBA was more frequent in men than TDH and CDH. Compared with TDH, TDHO, and TDH with PVO, TDH with PBA was younger in surgery age, and TDH and CDH had lower preoperative JOA scores than TDH with PBA. CDH had a larger ventral occupying ratio than TDH, TDHO, and TDH with PBA. The onset of TDM was generally insidious but may be triggered acutely by apparently trivial events. With a low prevalence, TDM varied clinical symptoms. Different types of TDM had various clinical features, which might indicate different pathological mechanisms.

Background Spine osteoblastomas (OBs) are relatively rare. In contrast to osteoid osteoma, radiologic and clinical manifestations of OB can be varied and atypical. Typical radiographic features in spinal OB include peritumoral bone sclerosis, bone marrow edema, and soft tissue edema. Atypical radiographic features include lesions involving ≥3 segments, lesions with extensive (≥3 segments) bone sclerosis, excessive edema (≥3 segments) of soft tissue and bone marrow, no intralesional calcification, and location in the vertebral body only. The aim of this study was to identify typical and atypical features of OB. Methods Pretreatment computed tomography scans and magnetic resonance imaging were reviewed retrospectively. Percutaneous biopsies were performed to confirm pathology in atypical cases. Results A total of 50 images from patients with diagnosed OB were reviewed. Atypical radiographic features were found in 18 cases (36%). Pathologic diagnosis was confirmed as OB in 86.2% (25/29) cases after percutaneous computed tomography–guided biopsy. Conclusions Our results show that >30% of spinal OB cases might have atypical radiographic features. In cases with atypical radiographic features, computed tomography–guided biopsies are recommended.

The classification of calcaneofibular ligament (CFL) injuries on magnetic resonance imaging (MRI) is time-consuming and subject to substantial interreader variability. This study explores the feasibility of classifying CFL injuries using deep learning methods by comparing them with the classifications of musculoskeletal (MSK) radiologists and further examines image cropping screening and calibration methods.The imaging data of 1,074 patients who underwent ankle arthroscopy and MRI examinations in our hospital were retrospectively analyzed. According to the arthroscopic findings, patients were divided into normal (class 0, n=475); degeneration, strain, and partial tear (class 1, n=217); and complete tear (class 2, n=382) groups. All patients were divided into training, validation, and test sets at a ratio of 8:1:1. After preprocessing, the images were cropped using Mask region-based convolutional neural network (R-CNN), followed by the application of an attention algorithm for image screening and calibration and the implementation of LeNet-5 for CFL injury classification. The diagnostic effects of the axial, coronal, and combined models were compared, and the best method was selected for outgroup validation. The diagnostic results of the models in the intragroup and outgroup test sets were compared with those results of 4 MSK radiologists of different seniorities.The mean average precision (mAP) of the Mask R-CNN using the attention algorithm for the left and right image cropping of axial and coronal sequences was 0.90-0.96. The accuracy of LeNet-5 for classifying classes 0-2 was 0.92, 0.93, and 0.92, respectively, for the axial sequences and 0.89, 0.92, and 0.90, respectively, for the coronal sequences. After sequence combination, the classification accuracy for classes 0-2 was 0.95, 0.97, and 0.96, respectively. The mean accuracies of the 4 MSK radiologists in classifying the intragroup test set as classes 0-2 were 0.94, 0.91, 0.86, and 0.85, all of which were significantly different from the model. The mean accuracies of the MSK radiologists in classifying the outgroup test set as classes 0-2 were 0.92, 0.91, 0.87, and 0.85, with the 2 senior MSK radiologists demonstrating similar diagnostic performance to the model and the junior MSK radiologists demonstrating worse accuracy.Deep learning can be used to classify CFL injuries at similar levels to those of MSK radiologists. Adding an attention algorithm after cropping is helpful for accurately cropping CFL images.

Quantitative comparison of diffusion parameters from various models of diffusion-weighted (DWI) and diffusion kurtosis (DKI) imaging for distinguishing spinal metastases and chordomas.DWI and DKI examinations were performed in 31 and 13 cases of spinal metastases and chordomas, respectively. DWI derived apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), water molecular distributed diffusion coefficient (DDC), and intravoxel water diffusion heterogeneity (α). DKI derived mean diffusivity (MD) and mean kurtosis (MK). Independent sample t-testing compared statistical differences among parameters. Sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were determined. Pearson correlation analysis evaluated the parameters' correlations.ADC, D, f, DDC, α, and MD were significantly lower in spinal metastases than chordomas (all P 0.05). MK was significantly higher in spinal metastases than chordomas (P 0.05). D had the highest area under the ROC curve (AUC) of 0.886, greater than MD (AUC = 0.706) or DDC (AUC = 0.742) in differentiating the two tumors (both P 0.05). Combining D with f and α statistically significantly increased the AUC for diagnosis (to 0.995) relative to D alone (P 0.05). There was a certain correlation among DDC, ADC, and D (all P 0.05).Monoexponential, biexponential, and stretched-exponential models of DWI and DKI can potentially differentiate spinal metastases and chordomas. D combined with f and α performed best.

Background: Hypertension is one of the most challenging public health problems worldwide. Previous studies suggested that the Songling Xuemaikang capsule (SXC)—a Chinese herbal formula—was effective for essential hypertension. However, the efficacy of SXC monotherapy for hypertension remains unclear. We aimed to compare the blood pressure (BP)–lowering efficacy and safety of SXC versus losartan in patients with essential hypertension. Methods: In this multicenter, randomized, double-blind, noninferiority trial in China, patients 18 to 65 years of age with mild essential hypertension were randomly allocated to receive either SXC or losartan for 8 weeks. The primary outcome was the change in sitting diastolic BP from baseline to 8 weeks, with a predefined noninferiority margin of −2.5 mm Hg. Results: Of the 755 patients who entered a 2-week run-in period, 628 patients (327 women and 301 men; mean [SD] age, 52.6 [9.2] years) were randomly assigned to the SXC (n=314) or losartan (n=314) group. The primary analysis based on the intention-to-treat principle showed that the change in diastolic BP from baseline to 8 weeks was similar between the SXC and losartan groups (−7.9 [8.0] versus −8.1 [7.9]). The lower boundary of 95% CI (mean difference, −0.24 [95% CI, −1.51 to 1.03]) was above the margin of −2.5 mm Hg, showing noninferiority. Results were consistent with per-protocol analysis. SXC produced greater improvements in total hypertension symptom score (−5.7 [4.2] versus −5.0 [4.0]; P =0.020) and total cholesterol (−0.1 [1.0] versus 0.1 [1.2]; P =0.025). There were no differences between groups in the other BP and patient-reported outcomes. Incidence and severity of adverse events were similar between groups. Conclusions: SXC was well tolerated and demonstrated noninferior to losartan in BP lowering in patients with mild hypertension. SXC might be an alternative for mild hypertension, particularly for patients with a preference for natural medicine. Registration: URL: www.chictr.org.cn ; Unique identifier: ChiCTR-IPR-16008108.

Sacral cysts are classically divided into Tarlov cysts and meningeal diverticula. However, the pathogenesis of sacral cysts remains unclear. This study aimed to clarify a novel type of sacral extradural spinal meningeal cyst with a specific arachnoidal structure.Nine patients with prophylactic diverticula were included in the study. All patients underwent MRI preoperative reconstruction and traditional neck transfixation.All patients presented with more than one symptom. The major symptom was lower extremity pain, followed by lower extremity numbness (77.8%, 7/9), lower extremity weakness (55.6%, 5/9), bowel/bladder and sexual dysfunction (55.6%, 5/9), and tenesmus (22.2%, 2/9). After long-term follow-up, the outcome was classified as improved in 9 patients (100%).The clinical findings of this study illustrate a special subtype and may help explain the mechanism of sacral cyst formation.

Background Subspine impingement is considered a source of residual hip symptoms after primary hip arthroscopy, and the role of the subspine space and soft tissue is not clear. The purpose of this study was to analyze the relationship between the subspine space and labrum size in subspine impingement patients. Methods We performed a retrospective study of patients with femoroacetabular impingement between July 2016 and July 2020. Sixteen patients without hip symptom relief after primary hip arthroscopic treatment of femoroacetabular impingement and undergoing revision surgery for anterior inferior iliac spine compression were included as the study group. Forty-eight matched patients who underwent only primary surgery and whose hip discomfort was relieved without a diagnosis of subspine impingement were included as the control group. The patients’ preoperative computerized tomography data were reviewed, and the anterior inferior iliac spine dimensions and the size of the subspine space were measured. The size of the labrum at the 11:30, 1:30, and 3 o’clock positions was measured with the use of magnetic resonance imaging. The ratio of the subspine space to the labrum was also calculated. Results There was no significant difference in anterior inferior iliac spine dimensions between these two groups (p > 0.05). A relatively narrow subspine space was found in the study group, especially in the direction of the anterior inferior iliac spine. Compared with the control group, subspine impingement patients were identified with larger labrums at 11:30 (8.20 ± 1.95 mm vs. 6.81 ± 0.50 mm, p = 0.016), 1:30 (7.83 ± 1.61 mm and 6.25 ± 0.78 mm, p = 0.001) and 3:00 (9.50 ± 1.73 mm vs. 7.48 ± 0.99 mm, p = 0.001). A relative mismatch between the subspine space and the labrum was also identified in the study group. The ratios of the labrum width to the subspine area were significantly larger in the study group than in the control group. Conclusion This study reported potential additional criteria for subspine impingement—a large labrum and a relatively narrow subspine space—instead of abnormal anterior inferior iliac spine dimensions. For those with a large labrum and narrow subspine space, the diagnosis of subspine impingement should be carefully made, and arthroscopic anterior inferior iliac spine decompression may be important.

The diagnosis of labral injury on MRI is time-consuming and potential for incorrect diagnoses.To explore the feasibility of applying deep learning to diagnose and classify labral injuries with MRI.Retrospective.A total of 1016 patients were divided into normal (n = 168, class 0) and abnormal labrum (n = 848) groups. The abnormal group consisted of n = 111 with class 1 (degeneration), n = 437 with class 2 (partial or complete tear), and n = 300 with unclassified injury. Patients were randomly divided into training, validation, and test cohort according to the ratio of 55%:15%:30%.Fat-saturation proton density-weighted fast spin-echo sequence at 3.0 T.Convolutional neural network-6 (CNN-6) was used to extract, discriminate, and detect oblique coronal (OCOR) and oblique sagittal (OSAG) images. Mask R-CNN was used for segmentation. LeNet-5 was used to diagnose and classify labral injuries. The weighting method combined the models of OCOR and OSAG. The output-input connection was used to correlate the whole diagnosis/classification system. Four radiologists performed subjective diagnoses to obtain the diagnosis results.CNN-6 and LeNet-5 were evaluated by area under the receiver operating characteristic (ROC) curve and related parameters. The mean average precision (MAP) evaluated the Mask R-CNN. McNemar's test was used to compare the radiologists and models. A P value 0.05 was considered statistically significant.The area under the curve (AUC) of CNN-6 was 0.99 for extraction, discrimination, and detection. MAP values of Mask R-CNN for OCOR and OSAG image segmentation were 0.96 and 0.99. The accuracies of LeNet-5 in the diagnosis and classification were 0.94/0.94 (OCOR) and 0.92/0.91 (OSAG), respectively. The accuracy of the weighted models in the diagnosis and classification were 0.94 and 0.97, respectively. The accuracies of radiologists in the diagnosis and classification of labrum injuries ranged from 0.85 to 0.92 and 0.78 to 0.94, respectively.Deep learning can assist radiologists in diagnosing and classifying labrum injuries.3 TECHNICAL EFFICACY: Stage 2.

Objectives To analyze the radiologic and clinical changes after denosumab treatment in patients with giant cell tumors (GCTs) in the mobile spine. Methods Clinical data and images by computed tomography and magnetic resonance imaging at a single center were retrospectively reviewed before and after denosumab treatment. Results Pre- and post-treatment data from 24 patients were evaluated. On imaging, marginal ossification and/or bone formation was observed in 22 patients (91.7%). The median maximum diameter of the GCT reduced from 52.5 to 48.2 mm (p p p = 0.001). On clinical symptoms, the mean visual analog scale scores were reduced from 5.3 to 2.0 (p p p = 0.003), and the neurological function of four patients improved according to Frankel grade (p = 0.046). Conclusions Bone formation, tumor reduction, regression of epidural lesion and the decrease in SI ratio on T2-weighted image should be considered as the effectiveness of denosumab in the treatment of spinal GCT. In clinical application, denosumab can relieve pain, improve neurological function, and improve the quality of life of spinal GCT patients.

Background The preoperative prediction of lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) is essential in prognosis and treatment strategy formulation. Purpose To compare the performance of computed tomography (CT) and magnetic resonance imaging (MRI) radiomics models for the preoperative prediction of LNM in PDAC. Material and Methods In total, 160 consecutive patients with PDAC were retrospectively included, who were divided into the training and validation sets (ratio of 8:2). Two radiologists evaluated LNM basing on morphological abnormalities. Radiomics features were extracted from T2-weighted imaging, T1-weighted imaging, and multiphase contrast enhanced MRI and multiphase CT, respectively. Overall, 1184 radiomics features were extracted from each volume of interest drawn. Only features with an intraclass correlation coefficient ≥0.75 were included. Three sequential feature selection steps—variance threshold, variance thresholding and least absolute shrinkage selection operator—were repeated 20 times with fivefold cross-validation in the training set. Two radiomics models based on multiphase CT and multiparametric MRI were built with the five most frequent features. Model performance was evaluated using the area under the curve (AUC) values. Results Multiparametric MRI radiomics model achieved improved AUCs (0.791 and 0.786 in the training and validation sets, respectively) than that of the CT radiomics model (0.672 and 0.655 in the training and validation sets, respectively) and of the radiologists’ assessment (0.600–0.613 and 0.560–0.587 in the training and validation sets, respectively). Conclusion Multiparametric MRI radiomics model may serve as a potential tool for preoperatively evaluating LNM in PDAC and had superior predictive performance to multiphase CT-based model and radiologists’ assessment.

BackgroundRecently, the Turing test has been used to investigate whether machines have intelligence similar to humans. Our study aimed to assess the ability of an artificial intelligence (AI) system for spine tumor detection using the Turing test.MethodsOur retrospective study data included 12179 images from 321 patients for developing AI detection systems and 6635 images from 187 patients for the Turing test. We utilized a deep learning-based tumor detection system with Faster R-CNN architecture, which generates region proposals by Region Proposal Network in the first stage and corrects the position and the size of the bounding box of the lesion area in the second stage. Each choice question featured four bounding boxes enclosing an identical tumor. Three were detected by the proposed deep learning model, whereas the other was annotated by a doctor; the results were shown to six doctors as respondents. If the respondent did not correctly identify the image annotated by a human, his answer was considered a misclassification. If all misclassification rates were >30%, the respondents were considered unable to distinguish the AI-detected tumor from the human-annotated one, which indicated that the AI system passed the Turing test.ResultsThe average misclassification rates in the Turing test were 51.2% (95% CI: 45.7%–57.5%) in the axial view (maximum of 62%, minimum of 44%) and 44.5% (95% CI: 38.2%–51.8%) in the sagittal view (maximum of 59%, minimum of 36%). The misclassification rates of all six respondents were >30%; therefore, our AI system passed the Turing test.ConclusionOur proposed intelligent spine tumor detection system has a similar detection ability to annotation doctors and may be an efficient tool to assist radiologists or orthopedists in primary spine tumor detection.

ObjectiveAutism spectrum disorder (ASD) is associated with altered brain development, but it is unclear which specific structural changes may serve as potential diagnostic markers. This study aimed to identify and model brain-wide differences in structural connectivity using MRI diffusion tensor imaging (DTI) in young ASD and typically developing (TD) children (3·5-6 years old).MethodsNinety-three ASD and 26 TD children were included in a discovery dataset and 12 ASD and 9 TD children from different sites included as independent validation datasets. Brain-wide (294 regions) structural connectivity was measured using DTI (fractional anisotropy, FA) under sedation together with symptom severity and behavioral and cognitive development. A connection matrix was constructed for each child for comparisons between ASD and TD groups. Pattern classification was performed and the resulting model tested on two independent datasets.ResultsThirty-three structural connections showed increased FA in ASD compared to TD children and associated with both symptom severity and general cognitive development. The majority (29/33) involved the frontal lobe and comprised five different networks with functional relevance to default mode, motor control, social recognition, language and reward. Overall, classification accuracy is very high in the discovery dataset 96.77%, and 91·67% and 88·89% in the two independent validation datasets.ConclusionsIdentified structural connectivity differences primarily involving the frontal cortex can very accurately distinguish individual ASD from TD children and may therefore represent a robust early brain biomarker.

High-resolution CT (HRCT) imaging features of idiopathic interstitial pneumonia (IIP) patients are related to glucocorticoid sensitivity. This study aimed to develop an artificial intelligence model to assess glucocorticoid efficacy according to the HRCT imaging features of IIP. The medical records and chest HRCT images of 150 patients with IIP were analyzed retrospectively. The U-net framework was used to create a model for recognizing different imaging features, including ground glass opacities, reticulations, honeycombing, and consolidations. Then, the area ratio of those imaging features was calculated automatically. Forty-five patients were treated with glucocorticoids, and according to the drug efficacy, they were divided into a glucocorticoid-sensitive group and a glucocorticoid-insensitive group. Models assessing the correlation between imaging features and glucocorticoid sensitivity were established using the k-nearest neighbor (KNN) algorithm. The total accuracy (ACC) and mean intersection over union (mIoU) of the U-net model were 0.9755 and 0.4296, respectively. Out of the 45 patients treated with glucocorticoids, 34 and 11 were placed in the glucocorticoid-sensitive and glucocorticoid-insensitive groups, respectively. The KNN-based model had an accuracy of 0.82. An artificial intelligence model was successfully developed for recognizing different imaging features of IIP and a preliminary model for assessing the correlation between imaging features and glucocorticoid sensitivity in IIP patients was established.

Introduction Predicting the postoperative neurological function of cervical spondylotic myelopathy (CSM) patients is generally based on conventional magnetic resonance imaging (MRI) patterns, but this approach is not completely satisfactory. This study utilized radiomics, which produced advanced objective and quantitative indicators, and machine learning to develop, validate, test, and compare models for predicting the postoperative prognosis of CSM. Materials and methods In total, 151 CSM patients undergoing surgical treatment and preoperative MRI was retrospectively collected and divided into good/poor outcome groups based on postoperative modified Japanese Orthopedic Association (mJOA) scores. The datasets obtained from several scanners (an independent scanner) for the training (testing) cohort were used for cross‐validation (CV). Radiological models based on the intramedullary hyperintensity and compression ratio were constructed with 14 binary classifiers. Radiomic models based on 237 robust radiomic features were constructed with the same 14 binary classifiers in combination with 7 feature reduction methods, resulting in 98 models. The main outcome measures were the area under the receiver operating characteristic curve (AUROC) and accuracy. Results Forty‐one (11) radiomic models were superior to random guessing during CV (testing), with significant increased AUROC and/or accuracy (P AUROC, This study builds and analysis 98 radiomic machine learning models to predict postoperative spinal cord function in CSM, and finds out that the optimal radiomic model is better than the optimal radiological model.

BACKGROUND: The weightings of iterative reconstruction algorithm can affect CT radiomic quantification. But, the effect of ASiR-V levels on the reproducibility of CT radiomic features between ultra-low-dose computed tomography (ULDCT) and low-dose computed tomography (LDCT) is still unknown. The purpose of study is to investigate whether adaptive statistical iterative reconstruction-V (ASiR-V) levels affect radiomic feature quantification using ULDCT and to assess the reproducibility of radiomic features between ULDCT and LDCT. METHODS: Sixty-three patients with pulmonary nodules underwent LDCT (0.70±0.16 mSv) and ULDCT (0.15±0.02 mSv). LDCT was reconstructed with ASiR-V 50%, and ULDCT with ASiR-V 50%, 70%, and 90%. Radiomics analysis was applied, and 107 features were extracted. The concordance correlation coefficient (CCC) was calculated to describe agreement among ULDCTs and between ULDCT and LDCT for each feature. The proportion of features with CCC >0.9 among ULDCTs and between ULDCT and LDCT, and the mean CCC for all features between ULDCT and LDCT were also compared. RESULTS: Sixty-three solid nodules (SNs) and 48 pure ground-glass nodules (pGGNs) were analyzed. There was no difference for the proportion of features in SNs among ULDCTs and between ULDCT and LDCT (P>0.05). The proportion of features in pGGNs were highest for ULDCT(70%) (vs.) (90%) (78.5%) and ULDCT(90%) vs. LDCT(50%) (50.5%). In SNs, the mean CCC for ULDCT(90%) vs. LDCT(50%) was 0.67±0.26, not different with that for ULDCT(50%) vs. LDCT(50%) (0.68±0.24) and ULDCT(70%) vs. LDCT(50%) (0.64±0.21) (P>0.05). In pGGNs, the mean CCC for ULDCT(90%) vs. LDCT(50%) was 0.79±0.19, higher than that for ULDCT(50%) vs. LDCT(50%) (0.61±0.28) and ULDCT(70%) vs. LDCT(50%) (0.76±0.24) (P

Background: To investigate the value of an intravoxel incoherent motion (IVIM) MRI for discriminating spinal metastasis from tuberculous spondylitis.Methods: This study included 50 patients with spinal metastasis (32 lung cancer, 7 breast cancer, 11 renal cancer), and 20 tuberculous spondylitis. All patients underwent IVIM MRI at 3.0T before treatment. The IVIM parameters including single-index model ( Apparent diffusion coefficient (ADC)-stand), double exponential model (ADCslow，ADCfast and f) and stretched-exponential model parameters (distributed diffusion coefficient (DDC) and α) were acquired. Two radiologists separately measured these parameters for each lesion through drawing region of interest. Receiver operating characteristic (ROC) and the area under the ROC curve analysis was used to evaluate the diagnostic performance. Each parameter was substituted into the Logistic regression model to determine the meaningful parameters, and the combined diagnostic performance was evaluated.Results: The ADCfast and f showed significant differences between spinal metastasis and tuberculous spondylitis. (for all, p < 0.05). The Logistic regression model results showed that ADCfast and f were independent factors affecting the conclusion (Pfast and f were 0.823 (95%CI:0.719 to 0.927) and 0.876 (95%CI: 0.782 to 0.969), respectively. ADCfast combined with f showed the highest AUC value of 0.925 (95%CI: 0.858 to 0.992). Additional significant differences were found in ADCstand, ADCslow, DDC and α among different metastasis type.Conclusions: IVIM MR imaging may be helpful for differentiating spinal metastasis from tuberculous spondylitis and may be used to detect the origin tumor for those patients who could not identify primary tumors, and provide help for clinical treatment.

This study aimed to use the most frequent features to establish a vertebral MRI-based radiomics model that could differentiate multiple myeloma (MM) from metastases and compare the model performance with different features number. We retrospectively analyzed conventional MRI (T1WI and fat-suppression T2WI) of 103 MM patients and 138 patients with metastases. The feature selection process included four steps. The first three steps defined as conventional feature selection (CFS), carried out 50 times (ten times with 5-fold cross-validation), included variance threshold, SelectKBest, and least absolute shrinkage and selection operator. The most frequent fixed features were selected for modeling during the last step. The number of events per independent variable (EPV) is the number of patients in a smaller subgroup divided by the number of radiomics features considered in developing the prediction model. The EPV values considered were 5, 10, 15, and 20. Therefore, we constructed four models using the top 16, 8, 6, and 4 most frequent features, respectively. The models constructed with features selected by CFS were also compared. The AUCs of 20EPV-Model, 15EPV-Model, and CSF-Model (AUC = 0.71, 0.81, and 0.78) were poor than 10EPV-Model (AUC = 0.84, p < 0.001). The AUC of 10EPV-Model was comparable with 5EPV-Model (AUC = 0.85, p = 0.480). The radiomics model constructed with an appropriate small number of the most frequent features could well distinguish metastases from MM based on conventional vertebral MRI. Based on our results, we recommend following the 10 EPV as the rule of thumb for feature selection. • The developed radiomics model could distinguish metastases from multiple myeloma based on conventional vertebral MRI. • An accurate model based on just a handful of the most frequent features could be constructed by utilizing multiple feature reduction techniques. • An event per independent variable value of 10 is recommended as a rule of thumb for modeling feature selection.

Objective To compare the differences of brain apparent diffusion coefficient (ADC) values in surviving fetuses, twin controls and single fetus controls using diffusion weighted imaging (DWI) sequence, and to perform follow-up study to reveal the underlying cerebral microstructure changes. Materials and methods Thirty-two twins with demise of one fetus, 25 twins, and 20 single fetuses were included. DWI was performed and ADC map was reconstructed automatically. ADC values of certain regions were compared among three-groups, and between left- and right-hemisphere in surviving fetuses. ROC was generated to identify ADC values in surviving fetuses and twin controls. Results ADC values were lower in bilateral white matter of frontal lobes (left: 1.68 ± 0.19 vs. 1.91 ± 0.26, 1.88 ± 0.19, P P = 0.002), parietal lobes (left: 1.80 ± 0.25 vs. 2.00 ± 0.21, 1.92 ± 0.28, P = 0.012; right: 1.81 ± 0.19 vs. 1.99 ± 0.24, 1.92 ± 0.26, P = 0.012) and occipital lobes (left: 1.72 ± 0.24 vs. 1.87 ± 0.20, 1.70 ± 0.24, P = 0.017; right: 1.73 ± 0.23 vs. 1.91 ± 0.22, 1.70 ± 0.27, P = 0.005) in surviving fetuses compared with twin controls and single fetus, respectively. In discriminating surviving fetuses and twin controls, the area-under-the-curve of ADC values in frontal, parietal and occipital lobes were range from 0.677 to 0.737. The combination of frontal and parietal lobes and gestational age had highest area-under-the-curve (0.771, 95% CI 0.648–0.894). Conclusions DWI is a very useful sequence for detecting underlying changes. ADC values might be effective indicators of subtle anomalies in surviving fetuses. Trial registration: None.

BACKGROUND Radiomics has shown promising results in the diagnosis, efficacy, and prognostic assessments of multiple myeloma (MM). However, little evidence exists on the utility of radiomics in predicting a high-risk cytogenetic (HRC) status in MM. PURPOSE To develop and test a magnetic resonance imaging (MRI)-based radiomics model for predicting an HRC status in MM patients. STUDY TYPE Retrospective. POPULATION Eighty-nine MM patients (HRC [n: 37] and non-HRC [n: 52]). FIELD STRENGTH/SEQUENCE A 3.0 T; fast spin-echo (FSE): T1-weighted image (T1WI) and fat-suppression T2WI (FS-T2WI). ASSESSMENT Overall, 1409 radiomics features were extracted from each volume of interest drawn by radiologists. Three sequential feature selection steps-variance threshold, SelectKBest, and least absolute shrinkage selection operator-were repeated 10 times with 5-fold cross-validation. Radiomics models were constructed with the top three frequency features of T1 WI/T2 WI/two-sequence MRI (T1 WI and FS-T2 WI). Radiomics models, clinical data (age and visually assessed MRI pattern), or radiomics combined with clinical data were used with six classifiers to distinguish between HRC and non-HRC statuses. Six classifiers used were support vector machine, random forest, logistic regression (LR), decision tree, k-nearest neighbor, and XGBoost. Model performance was evaluated with area under the curve (AUC) values. STATISTICAL TESTS Mann-Whitney U-test, Chi-squared test, Z test, and DeLong method. RESULTS The LR classifier performed better than the other classifiers based on different data (AUC: 0.65-0.82; P

Magnetic resonance imaging (MRI) has been shown to be associated with prognosis in some tumors; however, the correlation in pancreatic ductal adenocarcinoma (PDAC) remains inconclusive. In this retrospective study, we ultimately included 136 patients and analyzed quantitative MRI parameters that are associated with prognosis and recurrence patterns in PDAC using survival analysis and competing risks models; all the patients have been operated on with histopathology and immunohistochemical staining for further evaluation. In intravoxel incoherent motion diffusion-weighted imaging (DWI), we found that pure-diffusion coefficient D value was an independent risk factor for overall survival (OS) (HR: 1.696, 95% CI: 1.003–2.869, p = 0.049) and recurrence-free survival (RFS) (HR: 2.066, 95% CI: 1.252–3.409, p = 0.005). A low D value (≤1.08 × 10−3 mm2/s) was significantly associated with a higher risk of local recurrence (SHR: 5.905, 95% CI: 2.107–16.458, p = 0.001). Subgroup analysis revealed that patients with high D and f values had significantly better outcomes with adjuvant chemotherapy. Distant recurrence patients in the high-D value group who received chemotherapy may significantly improve their OS and RFS. It was found that preoperative multiparametric quantitative MRI correlates with prognosis and recurrence patterns in PDAC. Diffusion coefficient D value can be used as a noninvasive biomarker for predicting prognosis and recurrence patterns in PDAC.

Objectives:Symptomatic vertebral haemangioma (SVH) can present with atypical imaging features. Thus, this study analysed the imaging features of SVH using CT and MRI to improve SVH awareness.Methods:We retrospectively analysed CT and MRI characteristics of 118 patients with clinically and pathologically confirmed SVH.Results:Overall, 118 patients were diagnosed with SVH, including 79 females and 39 males (mean age, 45.76 ± 16.36 years). The thoracic spine (n = 86) was the most common location of SVH, followed by the lumbar spine (n = 17). Involvement of multiple spinal segments was observed in 15 patients (12.71%). A total of 101 lesions (85.59%) were centred in the vertebral body, 15 lesions (12.71%) were centred in the posterior attachment, and two lesions (2%) were centred in the paraspinal region. CT showed 39 lesions (33.05%) without a typical honeycomb or polka-dot pattern. Compression fracture was observed in 23 patients (19.49%). Extraosseous extension was present in 111 patients (94.1%), and 17 lesions (14.41%) presented with foraminal extension. Epidural bony compression was observed in 46 patients (38.98%). 20 lesions (16.95%) had atypical T2weighted MRI signals, and 8 lesions (10.26%) showed atypical enhancement.Conclusion:SVH was predominantly located in the thoracic spine. Involvement of multiple segments, posterior attachment localisation, absence of honeycomb or polka-dot signs, compression fracture, and atypical T2weighted imaging signals and enhancement were uncommon. Epidural bony compression was not uncommon and has important clinical significance.Advances in knowledge:The imaging features of SVHs are not fully understood. We examined the largest series of SVH cases reported to date.

We aimed to investigate the feasibility of predicting high-risk cytogenetic abnormalities (HRCAs) in patients with multiple myeloma (MM) using a spinal MRI-based radiomics method. In this retrospective study, we analyzed the radiomic features of 248 lesions (HRCA [n = 111] and non-HRCA [n = 137]) using T1WI, T2WI, and fat suppression T2WI. To construct the radiomics model, the top nine most frequent radiomic features were selected using logistic regression (LR) machine-learning processes. A combined LR model incorporating radiomic features and basic clinical characteristics (age and sex) was also built. Fivefold external cross-validation was performed, and a comparative analysis of 10 random fivefold cross-validation sets was used to verify result stability. Model performance was compared by plotting receiver operating characteristic curves and the area under the curve (AUC). Comparable AUC values were observed between the radiomics model and the combined model in validation cohorts (AUC: 0.863 vs. 0.870, respectively, p = 0.206). The radiomics model had an AUC of 0.863, with a sensitivity of 0.789, a specificity of 0.787, a positive predictive value of 0.753, a negative predictive value of 0.824, and an accuracy of 0.788 in the validation cohort, which were comparable with the performance in the training cohorts. Radiomic features of routine spinal MRI reflect differences between HRCAs and non-HRCAs in patients with MM. This MRI-based radiomics model might be a useful and independent tool to predict HRCAs in patients MM.

AIM To investigate whether whole-lesion histogram analysis of apparent diffusion coefficient (ADC) values derived from mono-exponential and bi-exponential diffusion-weighted imaging (DWI) can differentiate lung cancer from benign pulmonary lesions. MATERIALS AND METHODS Thirty-two patients with lung cancer and 17 patients with benign pulmonary lesions were included retrospectively. All patients underwent DWI before surgery or biopsy. ADC histogram parameters, including mean, percentile values (10th and 90th), kurtosis, and skewness, were calculated independently by two radiologists. The histogram parameters were compared between patients with lung cancer and benign lesions. Receiver operating characteristic curves were constructed to evaluate the diagnostic performance. RESULTS The ADCMean, ADC10th, DMean, D10th were significantly lower in lung cancer (1.187 ± 0.144 × 10−3; 0.440 ± 0.062 × 10−3; 1.068 ± 0.108 × 10−3; and 0.422 ± 0.049 × 10−3 mm/s) compared to benign lesions (1.418 ± 0.274 × 10−3; 0.555 ± 0.113 × 10−3; 1.216 ± 0.149 × 10−3; and 0.490 ± 0.044 × 10−3 mm/s; p CONCLUSION Whole-lesion histogram analysis of ADC values derived by mono-exponential and bi-exponential DWI using 3 T magnetic resonance imaging helps distinguish lung cancer from benign pulmonary lesions.

Objective To investigate the values of multimodal imaging approaches in the diagnosis of spinal osteoblastomas with an emphasis on MRI findings. Materials and methods We retrospectively evaluated the imaging findings of 35 patients with spinal osteoblastomas. The imaging methods included radiography, whole-body bone scintigraphy (WBBS), CT and MRI. Results Radiography detected 87.1% (27/31) of the lesions; WBBS demonstrated increased radionuclide activity in all the lesions. CT could precisely show and localize all niduses, and calcification was always detected. MRI usually could adequately delineate the niduses of osteoblastomas, especially on T2WI (88.2%; 30/34). 71.9% (23/32) of osteoblastomas were surrounded with moderate or extensive bone marrow edema (BME) with soft tissue edema (STE). STE always extended along the muscle bundle adjacent to the lesion; there was no subcutaneous fat involvement. BME was eccentrically distributed in the vertebral body and spread inward from the sides of the nidus. The extent of BME in the vertebral body tended to be inversely proportional to the distance from the nidus. In addition, rare magnifications of osteoblastoma including multifocal diseases (n = 2), vertebra plana (n = 1) or with aneurysmal bone cysts (n = 6) were also observed in our study. Conclusions In patients showing moderate or extensive BME together with STE on MRI, both CT and MRI should be used to confirm nidus presence. The above-mentioned characteristics of edema on MRI of patients with spinal osteoblastoma are helpful in not only localizing the nidus, but also enhancing the diagnostic confidence.

With an aging society, osteoporosis is one of the most common diseases threatening the health of China's elderly population and is an issue that is raising increasing concern. Osteoporosis is characterized by bone loss and increased susceptibility to fragility fractures. Various imaging modalities such as X-ray, CT, MRI and nuclear medicine along with assessment of bone mineral density (BMD) play an important role in its diagnosis and management, and the treatment requires multidisciplinary teamwork. A lack of consensus in the approach to imaging and BMD measurement is hampering the quality of service and patient care in China. Therefore a panel of Chinese experts from the fields of radiology, orthopedics, endocrinology and nuclear medicine reviewed the international guidelines, consensus and literature with the most recent data from China and, taking account of current clinical practice in China, the panel reached this consensus to help guide the diagnosis of osteoporosis using imaging and BMD. This consensus report provides guidelines and standards for the imaging and BMD assessment of osteoporosis and criteria for the diagnosis of osteoporosis in China.

Objective: The aim of this study was to predict response to neoadjuvant chemotherapy (NAC) in patients with locally advanced hypopharyngeal cancer by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods: A retrospective study enrolled 46 diagnosed locally advanced hypopharyngeal cancer. DCE-MRI were performed prior to and after two cycles of NAC. The volume transfer constant (Ktrans), extracellular extravascular volume fraction (Ve), and plasma volume fraction (Kep) were computed from primary tumors. DCE-MRI parameters were used to measure tumor response according to the Response Evaluation Criteria in Solid Tumors criteria (RECIST). Results： After 2 NAC cycles, 30 out of 46 patients were categorized into the responder group, whereas the other 16 were categorized into non-responder group. Compared with the pretreatment value, the post-treatment Ktrans and Kep was significantly lower (P < 0.05), but no significant change in Ve (P > 0.05). Compared with non-responders, a notably higher pretreatment Ktrans, Kep, lower post-treatment Ktrans, higher ΔKtrans and ΔKep were observed in responders (all P < 0.05). While the pretreatment Ve, post-treatment Ve, and ΔVe did not differ significantly (P>0.05) between the two groups. The receiver operating characteristic curve analysis revealed that pretreatment Ktrans of 0.202/min is the most optimal cut-off in predicting response to chemotherapy, resulting in an AUC of 0.837 and corresponding sensitivity and specificity of 76.7%, and 81.1%, respectively. Conclusion: DCE-MRI especially pretreatment Ktrans can potentially predict the treatment response to neoadjuvant chemotherapy for hypopharyngeal cancer. Advances in knowledge: Few studies of DCE-MRI on hypopharyngeal cancer treated with chemoradiation reported. The results demonstrate that DCE-MRI especially pretreatment Ktrans may be more potential value in predicting the treatment response to neoadjuvant chemotherapy for hypopharyngeal cancer.

BACKGROUND The relationship between plaque characteristics and their predictive value for perioperative cerebral blood flow (CBF) are unknown. PURPOSE To investigate the relationship between carotid plaque characteristics and perioperative CBF utilizing MRI. STUDY TYPE Prospective. POPULATION In all, 131 patients with carotid moderate-to-severe stenosis referred for carotid endarterectomy (CEA). FIELD STRENGTH/SEQUENCE 3T, black-blood T1 - and T2 -weighted, 3D time-of-flight, and simultaneous noncontrast angiography intraplaque hemorrhage. ASSESSMENT The relative CBF (rCBF = CBFindex-hemisphere /CBFcontralateral-hemisphere ) and the CBF difference ratio (DRCBF = [CBFpost-CEA - CBFpre-CEA ]/CBFpre-CEA ) in the middle cerebral artery territory were measured. The pre- and post-CEA CTP data were used as the assessment standard for CBF change. Carotid lipid-rich necrotic core (LRNC), intraplaque hemorrhage, calcification, fibrous cap rupture, maximum wall thickness, normalized wall index (NWI), and stenosis were determined. STATISTICAL TESTS Pearson or Spearman correlation, Mann-Whitney U-test, and linear regression. RESULTS Patients with LRNC had higher rCBFpre-CEA than those without (1.0 ± 0.1 vs. 0.9 ± 0.1, P

Objective: To explore the value of related parameters in monoexponential, biexponential, and stretched-exponential models of diffusion-weighted imaging (DWI) in differentiating metastases and myeloma in the spine. Methods: 53 metastases and 16 myeloma patients underwent MRI with 10 b-values (0–1500 s/mm2). Parameters of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), the distribution diffusion coefficient (DDC), and intravoxel water diffusion heterogeneity (α) from DWI were calculated. The independent sample t test and the Mann–Whiney U test were used to compare the statistical difference of the parameter values between the two. Receiver operating characteristics (ROC) curve analysis was used to identify the diagnostic efficacy. Then substituted each parameter into the decision tree model and logistic regression model, identified meaningful parameters, and evaluated their joint diagnostic performance. Results: The ADC, D, and α values of metastases were higher than those of myeloma, whereas the D* value was lower than that of myeloma, and the difference was significant (p < 0.05); the area under the ROC curve for the above parameters was 0.661, 0.710, 0.781, and 0.743, respectively. There was no significant difference in the f and DDC values (p > 0.05). D and α were found to conform to the decision tree model, and the accuracy of model diagnosis was 84.1%. ADC and α were found to conform to the logistic regression model, and the accuracy was 87.0%. Conclusion: The 3 models of DWI have certain values indifferentiating metastases and myeloma in spine, and the diagnostic performance of ADC, D, α and D*was better. Combining ADC with α may markedly aid in the differential diagnosis of the two. Advances in knowledge: Monoexponential, biexponential, and stretched-exponential models can offer additional information in the differential diagnosis of metastases and myeloma in the spine. Decision tree model and logistic regression model are effective methods to help further distinguish the two.

AIM To evaluate the accuracy of ultra-low-dose computed tomography (ULDCT) with ASiR-V using a noise index (SmartmA) for pulmonary nodule detection and Lung CT Screening Reporting And Data System (Lung-RADS) classifications compared with low-dose CT (LDCT). MATERIALS and METHODS Two-hundred and ten patients referred for lung cancer screening underwent conventional chest LDCT (0.80 ± 0.28 mSv) followed immediately by ULDCT (0.16 ± 0.03 mSv). ULDCT was scanned using 120 kV/SmartmA with a noise index of 28 HU and reconstructed with ASiR-V70%. The types and diameters of all nodules were recorded. The attenuation of pure ground-glass nodules (pGGNs) was measured on LDCT. All nodules were further classified using Lung-RADS. Sensitivities of nodule detection on ULDCT were analysed using LDCT as the reference standard. Logistic regression was used to establish a prediction model for the sensitivity of nodules. RESULTS LDCT revealed 362 nodules and the overall sensitivity on ULDCT was 90.1%. The sensitivity for solid nodules (SNs) of ≥1 mm diameter was 96.6% (228/236) and 100% (26/26) for SNs of ≥6 mm diameter. For pGGNs of ≥6 mm, the overall sensitivity was 93% (40/43) and 100% (29/29) for nodules with a attenuation value −700 HU or more. The agreement of Lung-RADS classification between two scans was good. On logistic regression, diameter was the only independent predictor for sensitivity of SNs (p CONCLUSION ULDCT with ASiR-V using SmartmA is suitable for lung-cancer screening in people with a BMI ≤35 kg/m2 as it has a low radiation dose of 0.16 mSv, high sensitivity for nodule detection and good performance of Lung-RADS classifications.

The present study aimed to explore the effect of computerized multi-domain cognitive training (MDCT) on brain gray matter volume and neuropsychological performance in patients with amnestic mild cognitive impairment (amnestic MCI). Twenty-one patients with amnestic MCI participated in a computerized MDCT program. The program targeted a broad set of cognitive domains via programs focused on reasoning, memory, visuospatial, language, calculation, and attention. Seventeen Participants completed the intervention and all completed a battery of neuropsychological tests to evaluate cognitive function while 12 out of 17 underwent 3 T MRI scanning before and after the intervention to measure gray matter (GM) volume. We examined correlations between the changes in neuropsychological scores and GM volumes across participants after the intervention. After training, we observed significant increases in GM volume in the right angular gyrus (AG) and other parietal subareas near the intraparietal sulcus (p p > 0.05). A correlation analysis revealed positive correlations between the changes in GM volume in the right AG and scores in the immediate recall component of the Hopkins Verbal Learning Test-Revised (HVLT-R) (r = 0.64, p = 0.024) and the Brief Visuospatial Memory Test–Revised (BVMT-R) (r = 0.67, p = 0.016). Our findings indicate that a computerized MDCT program may protect patients with amnestic MCI against brain GM volume loss and has potential in preserving general cognition. Thus, our non-pharmacological intervention may slow the rate of disease progression.

We aimed to determine the ability of contrast-enhanced computed tomography (CECT) texture and histogram analyses to differentiate between benign and malignant thyroid nodules. The clinical data from 49 patients with 60 thyroid nodules were retrospectively analyzed. Nodules were classified as malignant or benign based on their histological results. Five texture and histogram parameters of thyroid nodules from CECT images, including entropy, mean, standard deviation, skewness, and kurtosis, were compared and analyzed between the two groups. Regions of interest in axial CECT images were delineated manually by two radiologists. Interobserver agreement in texture and histogram parameters between the two radiologists was assessed using the intraclass correlation coefficient (ICC). The Mann–Whitney U test and receiver operating characteristic curve analysis were conducted to estimate the diagnostic capability of texture parameters. Interobserver reproducibility (ICC = 0.919–0.969) was excellent. Among the 60 nodules, 36 were malignant and 24 were benign. Entropy of malignant thyroid nodules was significantly higher compared with benign thyroid nodules (P = 0.005). A trend toward a higher kurtosis value was observed in malignant thyroid nodules (P = 0.062). When an entropy value of 6.55 was used as a cutoff for differentiating benign from malignant thyroid nodules, the optimal area under the curve, sensitivity, and specificity were 0.716 (0.585–0.847, 95% confidence interval, P = 0.005), 75.0%, and 62.5%, respectively. CECT texture and histogram analyses can be used to differentiate benign from malignant thyroid nodules.

To primarily evaluate the diagnostic performance of the monoexponential and intravoxel incoherent motion (IVIM) diffusion weighted imaging (DWI) models for differentiating between nonhypervascular pancreatic neuroendocrine tumors (PNETs) and pancreatic ductal adenocarcinomas (PDACs).63 patients with PNETs (35 nonhypervascular PNETs and 28 hypervascular PNETs) and 164 patients with PDACs were retrospectively enrolled in the study and underwent multiple b-value DWI. Intraobserver and interobserver reliabilities of DWI parameters were assessed by using the intraclass correlation coefficient (ICC). The parameters of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) of nonhypervascular PNETs were compared with PDACs and hypervascular PNETs using the independent sample t test or the Mann-Whitney U test. The diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis.All DWI parameters values showed good to excellent intra- and interobserver agreements (ICC = 0.743-0.873). Nonhypervascular PNETs had significantly lower ADC and D, but significantly higher f than PDACs (P = 0.005, P 0.001 and P 0.001, respectively). ADC, D and f of nonhypervascular PNETs were lower than hypervascular PNETs (P = 0.001,0.001 and 0.093, respectively). D* of nonhypervascular PNETs showed no statistically significant differences with PDACs and hypervascular PNETs (P = 0.809 and 0.420). D showed a higher area under the curve (AUC), followed by ADC and f (AUC = 0.885, 0.665 and 0.740, respectively) in differentiating nonhypervascular PNETs from PDACs.Monoexponential and IVIM diffusion models are valuable to differentiate nonhypervascular PNETs from PDACs. D showed better performance than f and ADC.

Langerhans cell histiocytosis (LCH) of the spine is a relatively rare condition with unknown etiology. The diagnosis and treatment protocols for spine LCH remain controversial.In this study, we evaluated the efficacy and safety of our proposed diagnosis and treatment protocol introduced in 2009.This is a retrospective study.A total of 110 patients with spine LCH who had been diagnosed and treated in our hospital from October 1997 to November 2015 were included in this study.The age, gender, symptoms, neurologic function, lesion distribution, radiological features, pathology, treatment, outcome, and treatment complications of the patients were collected. Visual analog scale (VAS) for pain and Frankel scale for neurologic status were also documented.We retrospectively reviewed 110 patients with spine LCH who had been diagnosed and treated in our hospital from October 1997 to November 2015. The indications for computed tomography (CT)-guided biopsy and surgery for spine LCH have become more stringent since 2009. In cases of a solitary spinal lesion, immobilization and observation were usually first suggested. Chemotherapy was suggested for cases with multifocal LCH lesions, and low-dosage radiotherapy was restricted to recurrent solitary lesion. This project was supported by our hospital (No. Y71508-01) (¥ 400,000).This series included 69 male and 41 female patients (age range, 1-52 years). Pain was the most common symptom (93.6%, 103/110). Pathologic diagnosis was achieved in 72 cases (65.5%). CT-guided biopsies were performed in 91.3% (42/46) and 73.2% (41/56) of cases before and after 2009, respectively (p=.02). Ninety-eight cases (89.1%) were followed up for a mean 66.3 (range, 24-159) months. Immobilization and observation were performed in 25.9% (14/54) and 75.0% (42/56) of cases before and after 2009, respectively (p.001). Approximately 35.2% (19/54) and 10.7% (6/56) of cases had surgery (p=.002) before and after 2009, respectively. During the follow-up, no significant difference was found in the outcomes between the two groups treated before and after 2009 (p=.64).Biopsy is not mandatory for typical spine lesions of LCH. Given the self-healing tendency of spine LCH, immobilization and observation remain the first-choice treatments for LCH lesions. Conservative biopsy and treatment protocols might be more appropriate for spinal LCH.

Objectives Cellular schwannoma that occurs in the spine is relatively rare. Herein, we retrospectively analyzed the clinical and imaging data from nine cases of spinal cellular schwannoma. Materials and Methods The clinical, imaging data and pathological manifestations were retrospectively analyzed from nine cases of pathologically confirmed spinal cellular schwannoma. Results All nine cases were treated with surgical excision; seven cases were solitary lesions characterized by a dumbbell-shaped soft tissue mass growing out across the intervertebral foramen and two cases were multiple nodular lesions in the spinal canal. Six cases had compressive bony destruction, one case displayed osteolytic and compressive bony destruction, and two cases were without apparent bony destruction. Computed Tomography (CT)scans showed three cases were isodense and two cases were miscellany density. Magnetic Resonance Imaging (MRI) scans showed that the solid part of the tumor was isointense on the T1-weighted imaging (T1WI); five cases were isointense and three cases were slightly hyperintense on T2-weighted imaging (T2WI). Five cases exhibited hyperintense cystic regions. MRI enhanced scanning showed that 3/7 cases showed moderately to markedly heterogeneous enhancement and the remaining 4/7 cases showed markedly homogeneous enhancement. Except for one recurrence, 8/9 cases had a good prognosis. Conclusion Spinal cellular schwannoma should be diagnosed combining the imaging manifestation with clinical performance and pathological examination in order to distinguish it from other tumors, such as classical schwannoma and malignant peripheral nerve sheath tumors.

To explore the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for evaluating early outcomes of CyberKnife radiosurgery for spinal metastases.Patients with spinal metastases who were treated with CyberKnife radiosurgery from July 2018 to December 2020 were enrolled. Conventional MRI and DCE-MRI were performed before treatment and at 3 months after treatment. Patients showing disease progression were defined as the progressive disease (PD) group and those showing complete response, partial response, and stable disease were defined as the non-PD group. The haemodynamic parameters (volume transfer constant [Ktrans], rate constant [Kep], and extravascular space [Ve]) before and after treatment between the groups were analysed. Area under the curve (AUC) values were calculated.A total of 27 patients with 39 independent spinal lesions were included. The median follow-up time was 18.6 months (6.2-36.4 months). There were 27 lesions in the non-PD group and 12 lesions in the PD group. Post-treatment Kep, ΔKtrans and ΔKep in the non-PD group (0.959/min, - 32.6% and -41.1%, respectively) were significantly lower than the corresponding values in PD group (1.429/min, 20.4% and -6%; p0.05). Post-treatment Ve and ΔVe (0.223 and 27.8%, respectively) in the non-PD group were significantly higher than that of the PD group (0.165 and -13.5%, p0.05). ΔKtrans showed the highest diagnostic efficiency, with an AUC of 0.821.DCE-MRI parameters change significantly at an early stage after CyberKnife stereotactic radiosurgery for spinal metastases. DCE-MRI may be of value in determining the early treatment response.

Rationale and Objectives This study aimed to evaluate the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiation of four spinal lesions by using heuristic and pharmacokinetic parameters analyzed from DCE signal intensity time course. Materials and Methods DCE-MRI of 62 subjects with confirmed myeloma (n = 9), metastatic cancer (n = 22), lymphoma (n = 7), and inflammatory tuberculosis (TB) (n = 24) in the spine were analyzed retrospectively. The region of interest was placed on strongly enhanced tissues. The DCE time course was categorized as the “wash-out,” “plateau,” or “persistent enhancement” pattern. The maximum enhancement, steepest wash-in enhancement, and wash-out slope using the signal intensity at 67 seconds after contrast injection as reference were measured. The Tofts 2-compartmental pharmacokinetic model was applied to obtain Ktrans and kep. Pearson correlation between heuristic and pharmacokinetic parameters was evaluated, and receiver operating characteristic curve analysis was performed for pairwise group differentiation. Results The mean wash-out slope was −22% ± 10% for myeloma, 1% ± 0.4% for metastatic cancer, 3% ± 3% for lymphoma, and 7% ± 10% for TB, and it could significantly distinguish myeloma from metastasis (area under the curve [AUC] = 0.884), lymphoma (AUC = 1.0), and TB (AUC = 1.0) with P = .001, and distinguish metastasis from TB (AUC = 0.741) with P = .005. The kep and wash-out slope were highly correlated (r = 0.92), and they showed a similar diagnostic performance. The Ktrans was significantly correlated with the maximum enhancement (r = 0.71) and the steepest wash-in enhancement (r = 0.85), but they had inferior diagnostic performance compared to the wash-out slope. Conclusions DCE-MRI may provide additional diagnostic information, and a simple wash-out slope had the best diagnostic performance. The heuristic and pharmacokinetic parameters were highly correlated.

Purpose To measure the porosity index (PI) by ultrashort echo-time magnetic resonance imaging (UTE MRI) of the femoral neck and tibia; assess its correlations with age, gender, and body mass index (BMI); and analyze the PI correlations between both sites to assess whether tibial PI can reflect changes of femoral neck PI. Materials and Methods In all, 68 healthy men and women (mean age, 45.7 ± 15.9 years) underwent 3D UTE MRI (3.0T) of the hip and mid-shaft tibia. PI of the inferior femoral neck cortex and the whole cortex of the tibia were analyzed. Associations between parameters and differences of PIs between men and women, pre- and postmenopausal women were tested. Results Femoral neck PI was negatively correlated with age (r = –0.385, P = 0.043) and curvilinearly correlated with BMI (R2 = 0.225, P = 0.041) in men. Tibial PI was correlated with BMI (r = –0.477, P = 0.002) in women and age (r = 0.469, P = 0.043) in postmenopausal women, although P = 0.097 (r = 0.403) after adjustment for BMI. Femoral PI was significantly higher in men than in women (P < 0.001). No significant difference in femoral and tibial PI was observed between pre- and postmenopausal women. The femoral neck and tibial PIs were not significantly correlated in any group. Conclusion PIs had some correlations with age, gender, and BMI. Since femoral neck PI was not correlated with tibial PI, the tibia cannot substitute the femoral neck for estimating bone quality. Direct assessment of the femoral neck cortex is needed. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2017.

Background: Numerous studies have investigated the influence of osseous factors on patellofemoral joint instability, but research on the influence of dynamic muscle factors in vivo is still in the exploratory stage. This study aimed to use magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) to evaluate vastus medialis oblique (VMO) fiber bundles in patients with recurrent patellar dislocation to explore the changes in muscle morphology and function. Methods: This prospective study involved 30 patients (7 males and 23 females; average age, 21.4 ± 3.8 years) clinically diagnosed with recurrent patellar dislocation in Peking University Third Hospital and 30 healthy volunteers matched for age, sex, and body mass index in our medical school between January 2014 and October 2014. None of the patients had a recent history of traumatic patellar dislocation or transient patellar dislocation. All patients underwent conventional MRI and DTI of the knee. The cross-sectional area of the VMO on MRI and the fractional anisotropy (FA), apparent diffusion coefficient (ADC), and primary (λ1), secondary (λ2), and three-level characteristic (λ3) values on DTI were measured. The independent-samples t-test was used to compare these parameters between the two groups. Results: Compared with the control group, the patient group showed significantly higher FA values (0.39 ± 0.05 vs. 0.33 ± 0.03) and significantly lower ADC (1.51 ± 0.13 vs. 1.58 ± 0.07), λ2 (4.96 ± 0.13 vs. 5.04 ± 0.07), and λ3 values (4.44 ± 0.14 vs. 4.58 ± 0.07; t = 5.99, t = –2.58, t = –3.02, and t = –4.88, respectively; all P < 0.05). Cross-sectional VMO area and λ1 values did not differ between the two groups (t = –1.82 and t = 0.22, respectively; both P > 0.05). Conclusions: The functional status of the VMO is closely associated with recurrent patellar dislocation. MRI, especially DTI (FA, ADC, λ2, and λ3), can detect early changes in VMO function and might facilitate the noninvasive monitoring of the functional status of the VMO in patients with recurrent patellar dislocation.

Background: Compared with computed tomography (CT), magnetic resonance imaging (MRI) might overestimate the condition of osteochondral lesions of the talus (OLTs) owing to subchondral bone marrow edema and the overlying cartilage defect. However, no study has compared MRI and CT directly in evaluating OLTs with subchondral cysts. Purpose: To compare the reliability and validity of MRI and CT in evaluating OLTs with subchondral cysts. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: An institutional radiology database was queried for inpatients diagnosed with OLTs with subchondral cysts who had undergone surgical treatment between May 2015 and October 2019. A total of 48 patients met the inclusion criteria. Based on our measurement method, 2 experienced observers who were blinded to the study independently measured the length, width, and depth of the cysts using MRI and CT. The classification of cystic lesions was also performed based on MRI and CT findings. Results: Interobserver reliability was almost perfect, with intraclass correlation coefficients (ICCs) ranging from 0.935 to 0.999. ICCs for intraobserver reliability ranged from 0.944 to 0.976. The mean size of cysts measured on MRI (length, 13.38 ± 4.23 mm; width, 9.28 ± 2.28 mm; depth, 11.54 ± 3.69 mm) was not significantly different to that evaluated on CT (length, 13.40 ± 4.08 mm; width, 9.25 ± 2.34 mm; depth, 11.32 ± 3.54 mm). The size of subchondral cysts was precisely estimated on both MRI and CT. The MRI classification and CT classification revealed almost perfect agreement (kappa = 0.831). Conclusion: With our measurement method, both MRI and CT were deemed to be reliable and valid in evaluating the size of subchondral cysts of OLTs, and the MRI classification was well-correlated with the CT classification. The presented measurement method and classification systems could provide more accurate information before surgery.

Vertebral hemangioma (VH) is one of the most common benign spinal tumors and can be aggressive in some cases. While most aggressive VHs have typical radiographic features, including vertical striations, a honeycomb appearance, and/or a "polka-dot sign" in computed tomography (CT) scans, cases with atypical features might complicate diagnosis. This study aimed to determine the range and frequency of these atypical features.In this retrospective study, to identify the typical and atypical features of aggressive VH, pretreatment CT and magnetic resonance imaging (MRI) were reviewed retrospectively by 1 radiologist and 1 orthopaedic surgeon. Percutaneous biopsies were performed to confirm the VH in atypical cases.A total of 95 patients with aggressive VHs were treated in our hospital from January 2005 to December 2017. Thirty-four (36%) of the lesions showed at least 1 atypical radiographic feature: 16 patients (17%) had a vertebral compression fracture, 11 patients (12%) had expansive and/or osteolytic bone destruction without a honeycomb appearance and/or "polka-dot sign", 11 patients (12%) had obvious epidural osseous compression of the spinal cord, 12 patients (13%) had involvement of1 segment, 9 patients (10%) had a VH centered in the pedicle and/or lamina, and 8 patients (8%) had atypical MRI signals. Forty-three patients underwent percutaneous biopsies, which had an accuracy of 86%.Based on radiographic analysis, aggressive VH can be classified as typical or atypical. More than one-third of aggressive VH lesions may have at least 1 atypical feature. CT-guided biopsies are indicated for these atypical cases.

To investigate the correlation of parameters measured by dynamic-contrast-enhanced MRI (DCE-MRI) and 18F-FDG PET/CT in spinal tumors, and their role in differential diagnosis. A total of 49 patients with pathologically confirmed spinal tumors, including 38 malignant, six benign and five borderline tumors, were analyzed. The MRI and PET/CT were done within 3 days, before biopsy. On MRI, the ROI was manually placed on area showing the strongest enhancement to measure pharmacokinetic parameters Ktrans and kep. On PET, the maximum standardized uptake value SUVmax was measured. The parameters in different histological groups were compared. ROC was performed to differentiate between the two largest subtypes, metastases and plasmacytomas. Spearman rank correlation was performed to compare DCE-MRI and PET/CT parameters. The Ktrans, kep and SUVmax were not statistically different among malignant, benign and borderline groups (P = 0.95, 0.50, 0.11). There was no significant correlation between Ktrans and SUVmax (r = − 0.20, P = 0.18), or between kep and SUVmax (r = − 0.16, P = 0.28). The kep was significantly higher in plasmacytoma than in metastasis (0.78 ± 0.17 vs. 0.61 ± 0.18, P = 0.02); in contrast, the SUVmax was significantly lower in plasmacytoma than in metastasis (5.58 ± 2.16 vs. 9.37 ± 4.26, P = 0.03). In differential diagnosis, the AUC of kep and SUVmax was 0.79 and 0.78, respectively. The vascular parameters measured by DCE-MRI and glucose metabolism measured by PET/CT from the most aggressive tumor area did not show a significant correlation. The results suggest they provide complementary information reflecting different aspects of the tumor, which may aid in diagnosis of spinal lesions. These slides can be retrieved under Electronic Supplementary Material.

Purpose To evaluate the clinical value of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) in the detection of pulmonary nodules in a Chinese population. Method One hundred eighty-eight patients (16.41 ≤ BMI ≤ 29.87 kg/m2) with pulmonary nodules detected on low-dose chest CT (LDCT) underwent local ULDCT at the center of the chosen nodule with a scan length of 3 cm. LDCT was performed using the Assist kV (120/100 kV)/Smart mA mode and at 120 kV/2.8 mAs for ULDCT. After scanning, CT images were reconstructed with ASiR-V 50%. For both scans, nodule diameters were measured and reference standards were established for the presence and types of lung nodules found on LDCT. The sensitivity of ULDCT was compared against the standard, and logistic regression analysis was used to determine the independent predictors for nodule detection. Results Compared with LDCT (0.93 ± 0.32 mSv), a 89.7% dose decrease was seen with ULDCT, for which the calculated effective dose was 0.096 ± 0.006 mSv (P 0.05). Conclusions ULDCT can be used in the management of pulmonary nodules for people with BMI ≤ 30 kg/m2 at 10% radiation dose of LDCT.

To determine the imaging characteristics of SAPHO (synovitis, acne, pustulosis, hyperostosis, and osteitis) syndrome and seronegative spondyloarthropathies (SpAs) on whole-spine magnetic resonance imaging (WS-MRI) and evaluate the role of WS-MRI in the diagnosis and differentiation of the two diseases.Twenty-eight patients with SAPHO and 44 with SpAs were included. All patients were symptomatic and clinically diagnosed with SAPHO or SpAs, and all underwent WS-MRI for comparison of imaging characteristics.The mean age of the SAPHO patients was 48.7 ± 12.7 years, while that of the SpA patients was 34.7 ± 12.3 years (p0.001). WS-MRI showed that the frequency of cervical, thoracic, and lumbar spine involvement was 53.6% versus 52.3%, 75% versus 88.6%, and 60.7% versus 63.6%, respectively (p=0.70, 0.13, and 0.80). The frequency of sacroiliac joint involvement was 7.1% and 100% (p0.001). Continuous spinal involvement accounted for 50% versus 43.2%, 60.7% versus 84.1%, and 39.3% versus 40.9% in the cervical, thoracic, and lumbar vertebrae, respectively (p=0.03). WS-MRI showed that bone marrow oedema of spinal anterior corner was observed in 50% versus 75% (p=0.03). Vertebral body and posterior attachment involvement accounted for 85.7% versus 93.2% and 14.3% versus 34.1% (p=0.3, 0.06). The frequency of bone erosion in mobile spine was 75% and 36.4%, respectively (p=0.02). The frequency of intervertebral disc, endplate, anterior thoracic wall, and paraspinal soft-tissue swelling was 42.9% versus 18.2%, 53.6% versus 22.7%, 85.7% versus 42.2%, and 50% versus 11.4% (p=0.02, 0.00).Factors differentiating the two groups at WS-MRI were bone marrow oedema of the spinal anterior corner, bone erosion, and swelling of the intervertebral disc, endplate, anterior thoracic wall, and paraspinal soft-tissue.

Background: Langerhans cell histiocytosis (LCH) may affect atlas and axis, and there were very few published cases describing a characteristic of LCH of atlantoaxial. Objective: The objective of the study is to investigate the image manifestations of atlantoaxial LCH to improve the in-depth comprehension on it. Materials and Methods: A retrospective study was done of computed tomography (CT) and magnetic resonance imaging in atlas and axis and prognosis was analyzed. Results: The study included 41 patients (average age 12.9 years and median age 8 years) diagnosed with LCH, with 75.6% under 15 years old. Eighty-four lesions of LCH were identified including 47 in the atlas and 37 in the axis. The osteolytic bone destructions in the atlas and axis were characterized, 22% accompanied by sclerotic margins. Thirteen patients had a compression fracture, 11 in the lateral mass of the atlas and 2 in the C2 vertebral body. Sixteen and three patients had atlantoaxial malalignment and dislocation, respectively. On T2-weighted images, 68.9% showed iso- or low-signal intensity, 27.6% showed hyperintensity signal, and 3.4% showed heterogeneous signal. On postcontrast images, 81.9% showed significant enhancement, 12.5% showed moderate enhancement, and 6.3% showed mild enhancement. CT reexamination of 14 patients indicated atlantoaxial bone destruction relatively repaired in 12 patients. Thirty-three patients were a follow-up, 81.8% had no significant symptoms and 18.2% with remaining symptoms. Conclusions: The atlas and axis were affected by LCH, mainly in children. The lateral mass was easily affected and compressed, destruction of the atlas and axis could lead to atlantoaxial joint instability. The prognosis was good in most of the patients.

Purpose To characterize the morphological and dynamic-contrast-enhanced (DCE) MRI features of chordoma and giant cell tumor (GCT) of bone occurring in the axial skeleton. Materials and Methods A total of 13 patients with chordoma and 26 patients with GCT who received conventional T1, T2, and DCE-MRI on 3 Tesla MR scanners were retrospectively identified and analyzed. Two radiologists evaluated morphological features independently, including the lesion location, expansile bone changes, vertebral compression, presence of paraspinal soft tissue mass, fibrous septa, and the signal intensity on T1WI and T2WI. The inter-observer agreement was evaluated by kappa test. The DCE kinetics was measured to obtain the initial area under curve (IAUC) and the wash-out slope; also the two-compartmental pharmacokinetic model was applied to obtain Ktrans and kep. The diagnostic accuracy was evaluated by CHAID decision tree and ROC analysis. Results Chordomas were more likely to show soft tissue mass than GCTs (13/13 = 100% versus 15/26 = 58%; P = 0.007), as well as fibrous septa (9/13 = 69% versus 0; P

Background This study was designed to quantitatively investigate differences in lumbar dural sac dimensions between the lateral and supine positions in late pregnancy. Methods Ten healthy volunteers with singleton pregnancies at 28–39weeks of gestation were included. Magnetic resonance imaging was performed in random order while subjects were in the left lateral and supine positions. Lumbosacral axial scans were obtained at the L1–2, L2–3, L3–4, L4–5 and L5–S1 intervertebral disc levels. The axial section area, anteroposterior maximum diameter and transversal maximum diameter of the dural sac were measured and differences between these parameters in the lateral and supine positions were compared. Results The axial section areas of the dural sac at L1–2 ( P P =0.001), L3–4 ( P P =0.005) and the transversal maximum diameter of the dural sac at L1–2 ( P P P P =0.001) were greater in the lateral position compared with the supine position. The anteroposterior maximum diameter of the dural sac at L4–5 was greater in the lateral position compared with the supine position ( P =0.019) but there were no significant differences at other levels. The magnitude of the differences in axial section area and transverse maximal diameter were similar among the levels studied. Conclusions The axial section area and the transversal maximum diameter of the dural sac in the lumbar area are reduced in the supine compared with the lateral position in late pregnancy.

Purpose To differentiate metastatic lesions in the spine originated from primary lung cancer and other cancers using radiomics and deep learning, compared to traditional hot-spot ROI analysis. Methods In a retrospective review of clinical spinal MRI database with a dynamic contrast enhanced (DCE) sequence, a total of 61 patients without prior cancer diagnosis and later confirmed to have metastases (30 lung; 31 non-lung cancers) were identified. For hot-spot analysis, a manual ROI was placed to calculate three heuristic parameters from the wash-in, maximum, and wash-out phases in the DCE kinetics. For each case, the 3D tumor mask was generated by using the normalized-cut algorithm. Radiomics analysis was performed to extract histogram and texture features from three DCE parametric maps. Deep learning was performed using these maps as inputs into a conventional convolutional neural network (CNN), as well as using all 12 sets of DCE images into a convolutional long short term memory (CLSTM) network. Results For hot-spot ROI analysis, mean wash-out slope was 0.25 ± 10% for lung metastases and −9.8 ± 12.9% for other tumors. CHAID classification using a wash-out slope of −6.6% followed by wash-in enhancement ratio of 98% achieved a diagnostic accuracy of 0.79. Radiomics analysis using features representing tumor heterogeneity only reached the highest accuracy of 0.71. Classification using CNN achieved a mean accuracy of 0.71 ± 0.043, whereas a CLSTM improved accuracy to 0.81 ± 0.034. Conclusions DCE-MRI machine-learning analysis methods have potential to predict lung cancer metastases in the spine, which may be used to guide subsequent workup for confirmed diagnosis.

We report the case of a 54-year-old man with 6 months of progressive sacral pain. Computed tomography showed a large osteolytic mass in the sacrum with multiple osteolytic foci in the ilium and lumbar spine. Magnetic resonance imaging revealed multiple fluid-fluid levels in the sacral lesion as well as in the lumbar foci. The multiple bone lesions were initially diagnosed as a primary sacral malignancy with multiple bone metastases. A biopsy of the sacral lesion under the guidance of computed tomography was performed and a metastatic adenocarcinoma from the stomach or lung was found pathologically. Finally, a gastric tubular adenocarcinoma was detected by endoscopic biopsy and the diagnosis of the patient was gastric cancer with uncommon multiple bone metastases.