Use of MRI-based deep learning radiomics to diagnose sacroiliitis related to axial spondyloarthritis.

OBJECTIVES: This study aimed to evaluate the performance of a deep learning radiomics (DLR) model, which integrates multimodal MRI features and clinical information, in diagnosing sacroiliitis related to axial spondyloarthritis (axSpA). MATERIAL & METHODS: A total of 485 patients diagnosed with sacroiliitis related to axSpA (n = 288) or non-sacroiliitis (n = 197) by sacroiliac joint (SIJ) MRI between May 2018 and October 2022 were retrospectively included in this study. The patients were randomly divided into training (n = 388) and testing (n = 97) cohorts. Data were collected using three MRI scanners. We applied a convolutional neural network (CNN) called 3D U-Net for automated SIJ segmentation. Additionally, three CNNs (ResNet50, ResNet101, and DenseNet121) were used to diagnose axSpA-related sacroiliitis using a single modality. The prediction results of all the CNN models across different modalities were integrated using a stacking method based on different algorithms to construct ensemble models, and the optimal ensemble model was used as DLR signature. A combined model incorporating DLR signature with clinical factors was developed using multivariable logistic regression. The performance of the models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS: Automated deep learning-based segmentation and manual delineation showed good correlation. ResNet50, as the optimal basic model, achieved an area under the curve (AUC) and accuracy of 0.839 and 0.804, respectively. The combined model yielded the highest performance in diagnosing axSpA-related sacroiliitis (AUC: 0.910; accuracy: 0.856) and outperformed the best ensemble model (AUC: 0.868; accuracy: 0.825) (all P < 0.05). Moreover, the DCA showed good clinical utility in the combined model. CONCLUSION: We developed a diagnostic model for axSpA-related sacroiliitis by combining the DLR signature with clinical factors, which resulted in excellent diagnostic performance.

Automatic Image Segmentation and Grading Diagnosis of Sacroiliitis Associated with AS Using a Deep Convolutional Neural Network on CT Images.

Ankylosing spondylitis (AS) is a chronic inflammatory disease that causes inflammatory low back pain and may even limit activity. The grading diagnosis of sacroiliitis on imaging plays a central role in diagnosing AS. However, the grading diagnosis of sacroiliitis on computed tomography (CT) images is viewer-dependent and may vary between radiologists and medical institutions. In this study, we aimed to develop a fully automatic method to segment sacroiliac joint (SIJ) and further grading diagnose sacroiliitis associated with AS on CT. We studied 435 CT examinations from patients with AS and control at two hospitals. No-new-UNet (nnU-Net) was used to segment the SIJ, and a 3D convolutional neural network (CNN) was used to grade sacroiliitis with a three-class method, using the grading results of three veteran musculoskeletal radiologists as the ground truth. We defined grades 0-I as class 0, grade II as class 1, and grades III-IV as class 2 according to modified New York criteria. nnU-Net segmentation of SIJ achieved Dice, Jaccard, and relative volume difference (RVD) coefficients of 0.915, 0.851, and 0.040 with the validation set, respectively, and 0.889, 0.812, and 0.098 with the test set, respectively. The areas under the curves (AUCs) of classes 0, 1, and 2 using the 3D CNN were 0.91, 0.80, and 0.96 with the validation set, respectively, and 0.94, 0.82, and 0.93 with the test set, respectively. 3D CNN was superior to the junior and senior radiologists in the grading of class 1 for the validation set and inferior to expert for the test set (P < 0.05). The fully automatic method constructed in this study based on a convolutional neural network could be used for SIJ segmentation and then accurately grading and diagnosis of sacroiliitis associated with AS on CT images, especially for class 0 and class 2. The method for class 1 was less effective but still more accurate than that of the senior radiologist.

Ultrashort echo time magnetization transfer imaging of knee cartilage and meniscus after long-distance running.

OBJECTIVE: To assess the detection of changes in knee cartilage and meniscus of amateur marathon runners before and after long-distance running using a 3D ultrashort echo time MRI sequence with magnetization transfer preparation (UTE-MT). METHODS: We recruited 23 amateur marathon runners (46 knees) in this prospective cohort study. MRI scans using UTE-MT and UTE-T2* sequences were performed pre-race, 2 days post-race, and 4 weeks post-race. UTE-MT ratio (UTE-MTR) and UTE-T2* were measured for knee cartilage (eight subregions) and meniscus (four subregions). The sequence reproducibility and inter-rater reliability were also investigated. RESULTS: Both the UTE-MTR and UTE-T2* measurements showed good reproducibility and inter-rater reliability. For most subregions of cartilage and meniscus, the UTE-MTR values decreased 2 days post-race and increased after 4 weeks of rest. Conversely, the UTE-T2* values increased 2 days post-race and decreased after 4 weeks. The UTE-MTR values in lateral tibial plateau, central medial femoral condyle, and medial tibial plateau showed a significant decrease at 2 days post-race compared to the other two time points (p < 0.05). By comparison, no significant UTE-T2* changes were found for any cartilage subregions. For meniscus, the UTE-MTR values in medial posterior horn and lateral posterior horn regions at 2 days post-race were significantly lower than those at pre-race and 4 weeks post-race (p < 0.05). By comparison, only the UTE-T2* values in medial posterior horn showed a significant difference. CONCLUSIONS: UTE-MTR is a promising method for the detection of dynamic changes in knee cartilage and meniscus after long-distance running. KEY POINTS: • Long-distance running causes changes in the knee cartilage and meniscus. • UTE-MT monitors dynamic changes of knee cartilage and meniscal non-invasively. • UTE-MT is superior to UTE-T2* in monitoring dynamic changes in knee cartilage and meniscus.

Dual-energy CT Virtual Non-Calcium Image is Beneficial for the Detection of Non-displaced Knee Fractures.

BACKGROUND: Early and accurate diagnosis is vital for avoiding the development of nondisplaced fractures to displaced fractures. Dual-energy CT (Computed Tomography) can detect bone marrow edema (BME), which may help to detect non-displaced fractures. AIM: To evaluate the value of DECT (Dual-Energy Computed Tomography) VNCa (Virtual noncalcium) images for improving diagnostic performance and confidence in acute non-displaced knee fractures. METHODS: 125 patients with clinical suspicion of knee fractures underwent both DECT and MR. Conventional linear-blended CT and VNCa images were obtained from DECT. First, five readers with varying levels of experience evaluated the presence of fractures on conventional linear-blended CT and graded their diagnostic confidence on a scale of 1 to 10. Then BME with VNCa images was evaluated and compared with MR. Finally, the VNCa images combined with conventional linear-blended CT images were used to reassess the presence of fractures and diagnostic confidence. Diagnostic performance and matched pair analyses were performed. RESULTS: 20 non-displaced knee fractures were detected. The consistency test of VNCa images and MR by five radiologists showed Kappa values are 0.76, 0.79, 0.81,0.85,and 0.90,respectively. The diagnostic performance of all readers was improved when using VNCa images combined with conventional linear-blended CT compared with that with conventional linear-blended CT alone. Diagnostic confidence was improved with combined conventional linear-blended CT and VNCa images (median score:8,8,9,9, and 10, respectively) compared with conventional linear-blended CT alone (median score:7,7,8,9, and 9). CONCLUSION: DECT VNCa images could improve the radiologists' diagnostic performance and confidence with varying levels of experience in the detection of non-displaced knee fractures.

Synthetic MRI in the detection and quantitative evaluation of sacroiliac joint lesions in axial spondyloarthritis.

Objective: Our primary objective was to verify the hypothesis that synthetic magnetic resonance imaging (MRI) is similar to conventional MRI in detecting sacroiliac joint lesions in patients with axial spondyloarthritis (axSpA). A secondary objective was to assess the quantitative value of synthetic mapping in bone marrow edema (BME) and fat metaplasia. Methods: A total of 132 axSpA patients who underwent synthetic and conventional MRI from October 2019 to March 2021 were included in this prospective study. Two independent readers visually evaluated active inflammatory (BME, capsulitis, enthesitis, and inflammation at site of erosion) and structural lesions (erosion, sclerosis, ankylosis, and fat metaplasia) of the sacroiliac joints on conventional and synthetic magnetic resonance (MR) images. In addition, T1, T2, and proton density (PD) values, which were generated by synthetic mapping, were used to further quantitatively evaluate BME and fat metaplasia. A McNemar test was used to compare the differences between the two methods in the detection of sacroiliac joint lesions. Intraclass correlation coefficients (ICCs) were used to assess the inter-reader consistency of quantitative values. Mann-Whitney tests were performed, and receiver operating characteristic (ROC) curves were created for all quantitative analyses. Results: There were no statistical difference between synthetic and conventional MRI in the detection of sacroiliac joint lesions (all p-values > 0.05). A total of 103 images of BME and 111 images of fat metaplasia were quantitatively evaluated using T1, T2, and PD values. The consistency of quantitative values among readers was good (ICC 0.903-0.970). T1 and T2 values were consistently higher in BME than in normal marrow (p < 0.001), but PD values were not significantly different (p = 0.830). T2 and PD values were higher in fat metaplasia than in normal marrow, but T1 values were lower (p < 0.001). In the case of BME, T1 values had greater diagnostic efficiency [area under the curve (AUC) 0.99] than T2 values (AUC 0.78). There were no significant differences in the diagnostic efficiency of T1 (AUC 0.88), T2 (AUC 0.88), and PD (AUC 0.88) values in the case of fat metaplasia. Conclusion: Synthetic MRI is as effective as conventional MRI in detecting sacroiliac joint lesions in patients with axSpA. Furthermore, synthetic mapping can accurately quantify BME and fat metaplasia.

Magnetic resonance image compilation sequence to quantitatively detect active sacroiliitis with axial spondyloarthritis.

Background: To evaluate the diagnostic value of quantitative parameters [T1, T2, and proton density (PD) value] generated from magnetic resonance image compilation (MAGiC) sequence for active sacroiliitis in the patients with axial spondyloarthritis (ax-SpA). Methods: A total of 90 consecutive ax-SpA patients were recruited and divided into an active group (n=48) and inactive group (n=42) based on the Spondyloarthritis Research Consortium Canada (SPARCC) score in this prospective study. In addition, 47 healthy volunteers were recruited as the control group. All participants underwent magnetic resonance (MR) scanning (including MAGiC sequence and T2 mapping sequence) to obtain the T1 value, T2 value, PD value of MAGiC sequence (MAGiC T1 value, T2 value, PD value), and the T2 value of T2 mapping sequence (T2 map T2 value). Intraclass correlation coefficients (ICC) were calculated to assess the inter­ and intra­observer agreement. The correlation between the MAGiC T2 value and the T2 map T2 value was analyzed using Spearman's Rho. One-way analysis of variance (ANOVA) and receiver operating characteristic (ROC) analysis were performed for all parameters. Results: For the active group, inactive group, and control group, the MAGiC T1 value, T2 value, PD value, and T2 map T2 value were (1,700.91±725.40, 546.58±59.49, 640.25±95.79 ms), (129.37±23.85, 117.16±20.37, 90.52±12.05 ms), (76.47±15.92, 82.69±9.51, 75.51±9.17 pu), and (96.75±16.06, 87.96±9.27, 82.03±10.17 ms), respectively. The difference of the MAGiC T1 value and the MAGiC T2 value in the three groups was statistically significant (P<0.05). The MAGiC PD value was only statistically significant between inactive and control groups (P=0.001). When comparing the ROC curves of quantitative values among the three groups, MAGiC T1 value showed higher diagnostic efficacy than MAGiC T2 value between the active and inactive groups (MAGiC T1AUC: 0.971, MAGiC T2AUC: 0.655, P<0.0001), and the MAGiC T2 value showed higher diagnostic efficacy than T2 map T2 value between the active group and control group, and the inactive group and control group (MAGiC T2AUC: 0.940, T2 map T2AUC: 0.784, P=0.0021; MAGiC T2AUC: 0.877, T2 map T2AUC: 0.644, P=0.0011). The consistency of measurements was excellent (ICC =0.972-0.998). The MAGiC T2 value was positively correlated with the T2 map T2 value, but with a low correlation (r=0.402; P<0.001). Conclusions: A significant difference was detected between the MAGiC T1 and T2 values among the three groups, while MAGiC PD value had limited diagnostic value. MAGiC T1 value was better at differentiating the active group and inactive group than MAGiC T2 value. MAGiC T2 value was better at differentiating the active group and control group, the inactive group and control group than T2 map T2 value.

An all-in-one biomimetic iron-small interfering RNA nanoplatform induces ferroptosis for cancer therapy.

Iron-dependent ferroptosis is a promising therapeutic strategy for cancers. However, the sustained overexpression of the antioxidant glutathione (GSH) in cancer cells substantially limits its therapeutic effect. Seeking efficient approaches that can perform high GSH depletion efficiency remains a significant task. Herein, we construct an all-in-one nanoplatform with functions of tumor targeting, monitoring and treatment for cancer ferroptosis therapy by constructing a homotypic cancer cell membrane-camouflaged iron-small interfering RNA nanohybrid (CM-Fe-siR). The SLC7A11-targeted siRNA in the nanohybrid inhibits the biosynthesis of GSH by cutting off the supply of intracellular cystine, an essential ingredient in GSH synthesis, which subsequently results in the accumulation of reactive oxygen species (ROS) that are generated from Fenton reaction induced by iron. Meanwhile, the intracellular deficiency of GSH inactivates glutathione peroxidase 4 (GPX4, a lipid repair enzyme), which further increases the accretion of lipid peroxides to enhance iron-induced ferroptosis. This biomimetic nanohybrid shows a remarkable anti-cancer effect by triggering sustainable and efficient ferroptosis via these multiple synergistic actions. Besides, the nanohybrids enable in vivo magnetic resonance imaging (MRI) monitoring of therapy. The biomimetic CM-Fe-siR all-in-one nanoplatform may provide an efficient means of ferroptosis therapy for cancers. STATEMENT OF SIGNIFICANCE: Ferroptosis therapy based on the Fenton reaction of iron nanomaterials has aroused much attention in cancer treatment; however, the therapeutic efficacy is greatly inhibited by the sustained overexpression of the antioxidant GSH in cancer cells. It is of great importance to exploit more reagents or techniques performing high GSH depletion efficiency. Here, we facilely construct an all-in-one cancer cell membrane-camouflaged iron-siRNA nanoplatform, which possesses good biosafety, tumor-targeting, and noninvasive MRI monitoring capabilities. It effectively inhibits the GSH synthesis, and further simultaneously promotes the ROS accumulation and GPX4 inactivation, leading to enhanced cancer ferroptosis. This work highlights that the biomimetic iron-siRNA nanohybrids have a high potential in clinical application for imaging-guided cancer ferroptosis therapy.

Performance of adding hepatobiliary phase image in magnetic resonance imaging for detection of hepatocellular carcinoma: a meta-analysis.

OBJECTIVES: To determine the performance of diagnostic algorithm of adding hepatobiliary phase (HBP) images in Gd-EOB-DTPA-enhanced MRI for the detection of hepatocellular carcinoma (HCC) measuring up to 3 cm in patients with chronic liver disease. METHODS: We searched multiple databases from inception to April 10, 2020, to identify studies on using Gd-EOB-DTPA-enhanced MRI for the diagnostic accuracy of HCC (≤ 3 cm) in patients with chronic liver disease. The diagnostic algorithm of Gd-EOB-DTPA-enhanced MRI with HBP for HCC was defined as a nodule showing hyperintensity during arterial phase and hypointensity during the portal venous, delayed, or hepatobiliary phases. For gadoxetic acid-enhanced MRI without HBP, the diagnostic criteria were a nodule showing arterial enhancement and hypointensity on the portal venous or delayed phases. The data were extracted to calculate summary estimates of sensitivity, specificity, diagnostic odds ratio, likelihood ratio, and summary receiver operating characteristic (sROC) by using a bivariate random-effects model. RESULTS: Twenty-nine studies with 2696 HCC lesions were included. Overall Gd-EOB-DTPA-enhanced MRI with HBP had a sensitivity of 87%, specificity of 92%, and the area under the sROC curve of 95%. The summary sensitivity of Gd-EOB-DTPA-enhanced MRI with HBP was significantly higher than that without HBP (84% vs 68%, p = 0.01). CONCLUSION: Gd-EOB-DTPA-enhanced MRI with HBP showed higher sensitivity than that without HBP and had comparable specificity for diagnosis of HCC in patients with chronic liver disease. KEY POINTS: • Hypointensity on HBP is a major feature for diagnosis of HCC. • Extending washout appearance to the transitional or hepatobiliary phase on Gd-EOB-DTPA provides favorable sensitivity and comparable specificity for diagnosis HCC. • The summary sensitivity of gadoxetic acid-enhanced MRI with HBP was significantly higher than that without HBP (84% vs 68%, p = 0.01) for diagnosis of HCC in patients with chronic liver disease.

A comparative study of effective atomic number calculations for dual-energy CT.

PURPOSE: Several new formalisms of Effective Atomic Number ( Z eff ) have emerged recently, deviating from the widely accepted Mayneord's definition. This comparative study aims to reexamine their theories, reveal their connections, and apply them to material differentiation on dual-energy computed tomography (DECT). METHODS: The first part of this paper is an in-depth review of several highly cited Z eff formalisms. This part includes (1) refuting the claim in Taylor's study that the classic Mayneord's formalism was inaccurate, (2) showing that Mayneord's, Rutherford's, and Bourque's formalisms were equivalent, and (3) explaining the fundamental difference between Taylor's and Bourque's formalisms. The second part of this paper explains how we translated the theories into software implementation and added an open-source Z eff calculation engine to our free research software 3D Quantitative Imaging (3DQI). The work includes developing an interpolation method based on radial basis function to make Taylor's formalism applicable to DECT, and devising a table lookup method to generate Z eff map with high efficiency for all appropriate formalisms. RESULTS: Comparing Bourque's and Taylor's formalisms for six common materials over 40 ∼ 100 keV energy range, it was found that Bourque's Z eff values had a weak energy dependence by 0.18% ∼ 3.10%, but for Taylor's results this variation increased by a factor of 10. Further comparison showed that at 61 keV, different formalisms fall into two categories-Bourque, Mayneord, Van Abbema (a derivative of Rutherford) for the first category, and Taylor and Manohara for the second. Formalisms within each category produced similar Z eff values. For a material consisting of two elements, the two categories of formalisms tended to show a greater discrepancy if the constituent elements had larger difference in Z . The developed Z eff calculation engine was successfully applied to kidney stone classification and colon electronic cleansing. CONCLUSIONS: We renewed the understanding of several popular Z eff formalisms: Contrary to the conclusion of Taylor's study, Mayneord's power-law formula is well grounded in theory; Bourque's formalism (based on the average electron microscopic cross-section) is considered numerically equivalent to Rutherford's, but with the advantage of being mathematically rigorous and physically meaningful; Taylor's formalism (based on the average atomic microscopic cross-section) is theoretically not suitable for DECT but a workaround still exists; Manohara's formalism should be used with caution due to a problem in its definition of electron cross-sections. The developed Z eff engine in the 3DQI software facilitated accurate and efficient Z eff estimate for various DECT applications.

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

BACKGROUND: Differentiating chondrosarcoma from enchondroma using conventional MRI remains challenging. An effective method for accurate preoperative diagnosis could affect the management and prognosis of patients. PURPOSE: To validate and evaluate radiomics nomograms based on non-enhanced MRI and clinical risk factors for the differentiation of chondrosarcoma from enchondroma. STUDY TYPE: Retrospective. POPULATION: 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. FIELD STRENGTH/SEQUENCE: Axial non-contrast-enhanced T1-weighted images (T1WI) and fat-suppressed T2-weighted images (T2WI-FS) were acquired at 3.0 T. ASSESSMENT: 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. STATISTICAL TESTS: The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomics signatures and clinical radiomics nomograms. RESULTS: 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). DATA CONCLUSION: The proposed clinical radiomics nomogram showed promising performance in differentiating chondrosarcoma from enchondroma. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

CT-derived abdominal adiposity: Distributions and better predictive ability than BMI in a nationwide study of 59,429 adults in China.

BACKGROUND: Although abdominal adiposity is associated with an altered cardiometabolic risk profile, the specific contribution of abdominal adipose tissue distribution remains not fully understood. Computed tomography (CT) is a well-established and precise method to measure abdominal adipose tissue distribution. The present study investigated abdominal adiposity assessed by CT in a large-scale Chinese population. METHOD: A total of 59,429 adults who underwent a low dose chest CT for lung cancer screening at one of 13 health checkup centers throughout China were evaluated. Abdominal visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) areas were measured at the center of the 2nd lumbar vertebra with Mindways quantitative CT software using the existing CT dataset without any additional radiation exposure. The ratio of visceral to total adipose tissue (TAT) areas (VAT/TAT ratio) was calculated and expressed as a percentage. Anthropometric indices including body mass index (BMI) and waist circumference were also obtained. RESULTS: BMI, waist circumference, VAT area, SAT area, and the VAT/TAT ratio were 25.0 ±â€¯3.0 kg/m2, 90 ±â€¯8 cm, 194 ±â€¯77 cm2, 85 ±â€¯41 cm2, and 69.5 ±â€¯9.1%, respectively, in men and 23.3 ±â€¯3.1 kg/m2, 79 ±â€¯8 cm, 120 ±â€¯57 cm2, 123 ±â€¯53 cm2, and 48.9 ±â€¯9.7% in women. With increasing age, VAT area and the VAT/TAT ratio increased in both sexes whereas SAT area decreased in men (P < 0.001 for all). After adjustment for BMI and waist circumference, older individuals showed higher VAT area and higher VAT/TAT ratio than younger subjects (P < 0.001 for all). Adjusted VAT areas in participants aged 75 or older was 45 cm2 (95% confidence interval [CI]: 41 cm2, 50 cm2) higher in men and 43 cm2 (95% CI: 37 cm2, 49 cm2) higher in women compared with participants aged 31-44 years. Additionally, differences in VAT area across age groups increased as BMI or waist circumference increased. VAT and SAT areas, but not the VAT/TAT ratio, were positively associated with BMI and waist circumference in every age group. CONCLUSION: In a nationwide study conducted in China, distributions of CT-derived measures of visceral and subcutaneous adiposity were found to vary significantly between sex and age groups. Our study also revealed that the proportion of VAT (an important driver of cardiometabolic risk) could not be predicted from BMI in a Chinese population.

Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks.

OBJECTIVE: To explore the application of deep learning in patients with primary osteoporosis, and to develop a fully automatic method based on deep convolutional neural network (DCNN) for vertebral body segmentation and bone mineral density (BMD) calculation in CT images. MATERIALS AND METHODS: A total of 1449 patients were used for experiments and analysis in this retrospective study, who underwent spinal or abdominal CT scans for other indications between March 2018 and May 2020. All data was gathered from three different CT vendors. Among them, 586 cases were used for training, and other 863 cases were used for testing. A fully convolutional neural network, called U-Net, was employed for automated vertebral body segmentation. The manually sketched region of vertebral body was used as the ground truth for comparison. A convolutional neural network, called DenseNet-121, was applied for BMD calculation. The values post-processed by quantitative computed tomography (QCT) were identified as the standards for analysis. RESULTS: Based on the diversity of CT vendors, all testing cases were split into three testing cohorts: Test set 1 (n = 463), test set 2 (n = 200), and test set 3 (n = 200). Automated segmentation correlated well with manual segmentation regarding four lumbar vertebral bodies (L1-L4): the minimum average dice coefficients for three testing sets were 0.823, 0.786, and 0.782, respectively. For testing sets from different vendors, the average BMDs calculated by automated regression showed high correlation (r > 0.98) and agreement with those derived from QCT. CONCLUSIONS: A deep learning-based method could achieve fully automatic identification of osteoporosis, osteopenia, and normal bone mineral density in CT images. KEY POINTS: • Deep learning can perform accurate fully automated segmentation of lumbar vertebral body in CT images. • The average BMDs obtained by deep learning highly correlates with ones derived from QCT. • The deep learning-based method could be helpful for clinicians in opportunistic osteoporosis screening in spinal or abdominal CT scans.

Opportunistic Screening Using Low-Dose CT and the Prevalence of Osteoporosis in China: A Nationwide, Multicenter Study.

Opportunistic screening for osteoporosis can be performed using low-dose computed tomography (LDCT) imaging obtained for other clinical indications. In this study we explored the CT-derived bone mineral density (BMD) and prevalence of osteoporosis from thoracic LDCT in a large population cohort of Chinese men and women. A total of 69,095 adults (40,733 men and 28,362 women) received a thoracic LDCT scan for the purpose of lung cancer screening between 2018 and 2019, and data were obtained for analysis from the China Biobank Project, a prospective nationwide multicenter population study. Lumbar spine (L1 -L2 ) trabecular volumetric bone mineral density (vBMD) was derived from these scans using quantitative computed tomography (QCT) software and the American College of Radiology QCT diagnostic criteria for osteoporosis were applied. Geographic regional differences in the prevalence of osteoporosis were assessed and the age-standardized, population prevalence of osteoporosis in Chinese men and women was estimated from the 2010 China census. The prevalence of osteoporosis by QCT for the Chinese population aged >50 years was 29.0% for women and 13.5% for men, equating to 49.0 million and 22.8 million, respectively. In women, this rate is comparable to estimates from dual-energy X-ray absorptiometry (DXA), but in men, the prevalence is double. Prevalence varied geographically across China, with higher rates in the southwest and lower rates in the northeast. Trabecular vBMD decreased with age in both men and women. Women had higher peak trabecular vBMD (185.4 mg/cm3 ) than men (176.6 mg/cm3 ) at age 30 to 34 years, but older women had lower trabecular vBMD (62.4 mg/cm3 ) than men (92.1 mg/cm3 ) at age 80 years. We show that LDCT-based opportunistic screening could identify large numbers of patients with low lumbar vBMD, and that future cohort studies are now required to evaluate the clinical utility of such screening in terms of fracture prevention and supporting national health economic analyses. © 2020 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR)..

Energy-converting biomaterials for cancer therapy: Category, efficiency, and biosafety.

Energy-converting biomaterials (ECBs)-mediated cancer-therapeutic modalities have been extensively explored, which have achieved remarkable benefits to overwhelm the obstacles of traditional cancer-treatment modalities. Energy-driven cancer-therapeutic modalities feature their distinctive merits, including noninvasiveness, low mammalian toxicity, adequate therapeutic outcome, and optimistical synergistic therapeutics. In this advanced review, the prevailing mainstream ECBs can be divided into two sections: Reactive oxygen species (ROS)-associated energy-converting biomaterials (ROS-ECBs) and hyperthermia-related energy-converting biomaterials (H-ECBs). On the one hand, ROS-ECBs can transfer exogenous or endogenous energy (such as light, radiation, ultrasound, or chemical) to generate and release highly toxic ROS for inducing tumor cell apoptosis/necrosis, including photo-driven ROS-ECBs for photodynamic therapy, radiation-driven ROS-ECBs for radiotherapy, ultrasound-driven ROS-ECBs for sonodynamic therapy, and chemical-driven ROS-ECBs for chemodynamic therapy. On the other hand, H-ECBs could translate the external energy (such as light and magnetic) into heat for killing tumor cells, including photo-converted H-ECBs for photothermal therapy and magnetic-converted H-ECBs for magnetic hyperthermia therapy. Additionally, the biosafety issues of ECBs are expounded preliminarily, guaranteeing the ever-stringent requirements of clinical translation. Finally, we discussed the prospects and facing challenges for constructing the new-generation ECBs for establishing intriguing energy-driven cancer-therapeutic modalities. This article is categorized under: Nanotechnology Approaches to Biology >Nanoscale Systems in Biology.

Mean Apparent Propagator MRI Is Better Than Conventional Diffusion Tensor Imaging for the Evaluation of Parkinson's Disease: A Prospective Pilot Study.

BACKGROUND AND PURPOSE: Mean apparent propagator (MAP) MRI is a novel diffusion imaging method to map tissue microstructure. The purpose of this study was to evaluate the diagnostic value of the MAP MRI in Parkinson's disease (PD) in comparison with conventional diffusion tensor imaging (DTI). METHODS: 23 PD patients and 22 age- and gender-matched healthy controls were included. MAP MRI and DTI were performed on a 3T MR scanner with a 20-channel head coil. The MAP metrics including mean square displacement (MSD), return to the origin probability (RTOP), return to the axis probability (RTAP), and return to the plane probability (RTPP), and DTI metrics including fractional anisotropy (FA), and mean diffusivity (MD), were measured in subcortical gray matter and compared between the two groups. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic performance of all the metrics. The association between the diffusion metrics and disease severity was assessed by Pearson correlation analysis. RESULTS: For MAP MRI, the mean values of MSD in the bilateral caudate, pallidum, putamen, thalamus and substantia nigra (SN) were higher in PD patients than in healthy controls (p FDR ≤ 0.001); the mean values of the zero displacement probabilities (RTOP, RTAP, and RTPP) in the bilateral caudate, pallidum, putamen and thalamus were lower in PD patients (p FDR < 0.001). For DTI, only FA in the bilateral SN was significantly higher in PD patients than those in the controls (p FDR < 0.001). ROC analysis showed that the areas under the curves of MAP MRI metrics (MSD, RTOP, RTAP, and RTPP) in the bilateral caudate, pallidum, putamen and thalamus (range, 0.85-0.94) were greater than those of FA and MD of DTI (range, 0.55-0.69) in discriminating between PD patients and healthy controls. RTAP in the ipsilateral pallidum (r = -0.56, p FDR = 0.027), RTOP in the bilateral and contralateral putamen (r = -0.58, p FDR = 0.019; r = -0.57, p FDR = 0.024) were negatively correlated with UPDRS III motor scores. CONCLUSION: MAP MRI outperformed the conventional DTI in the diagnosis of PD and evaluation of the disease severity.

Diagnostic accuracy of dual-energy CT virtual non-calcium images with different related contrast material values for the detection of bone marrow edema in knee.

PURPOSE: The purpose of this study was to evaluate the diagnostic accuracy of different related contrast material (Rel.CM) values in dual-energy computed tomography (DECT) virtual non-calcium (VNCa) images for the detection of bone marrow edema (BME) in knee. METHOD: This prospective study was approved by the institutional research ethics board, and written informed consent was obtained from all participants. Twenty-three patients (24 knees) who underwent dual-energy CT and MRI within three weeks from July 2018 to June 2019 with a definite history of trauma were enrolled. Each knee was divided into 12 regions. First, MR images served as the reference standard, Receiver operating characteristic (ROC) curve was used and diagnostic accuracy of VNCa images corresponding to different Rel.CM values (1.25, 1.35, 1.45, 1.55, 1.65, 1.75) were analyzed, aimed to select an optimal Rel.CM value of VNCa images for detecting BME. Then, CT values of the normal areas and BME areas were measured on the VNCa images corresponding to the optimal Rel.CM value for preliminary quantitative analysis. The rank-sum test was used to compare the differences of CT values between BME areas and normal bone marrow areas on the VNCa images. RESULTS: The 24 knees were divided into 288 areas. MR Imaging showed BME in 121 areas. The areas under the ROC curve with different Rel.CM values (1.25, 1.35, 1.45, 1.55, 1.65, and 1.75) were 0.633, 0.674, 0.882, 0.684, 0.651, and 0.649, respectively. On the VNCa images of Rel.CM = 1.45, the diagnostic accuracy was the highest (up to 89.2 %), the CT values of the BME area and the normal area were -67.9 (1.7∼-100.1) HU and -94.5 (-69.7∼-144.9) HU, respectively, with statistical significance (Z=-9.804, P < 0.05). CONCLUSIONS: The VNCa images with a Rel.CM value of 1.45 is optimal for the detection of BME in knee.

[Radiomics models based on non-enhanced MRI can differentiate chondrosarcoma from enchondroma].

OBJECTIVE: To develop and validate radiomics models based on non-enhanced magnetic resonance (MR) imaging for differentiating chondrosarcoma from enchondroma. METHODS: We retrospectively evaluated a total of 68 patients (including 27 with chondrosarcoma and 41 with enchondroma), who were randomly divided into training group (n=46) and validation group (n=22). Radiomics features were extracted from T1WI and T2WI-FS sequences of the whole tumor by two radiologists independently and selected by Low Variance, Univariate feature selection, and least absolute shrinkage and selection operator (LASSO). Radiomics models were constructed by multivariate logistic regression analysis based on the features from T1WI and T2WI-FS sequences. The receiver-operating characteristics (ROC) curve and intraclass correlation coefficient (ICC) analyses of the radiomics models and conventional MR imaging were performed to determine their diagnostic accuracy. RESULTS: The ICC value for interreader agreement of the radiomics features ranged from 0.779 to 0.923, which indicated good agreement. Ten and 11 features were selected from the T1WI and T2WI-FS sequences to construct radiomics models, respectively. The areas under the curve (AUCs) of T1WI and T2WI-FS models were 0.990 and 0.925 in training group and 0.915 and 0.855 in the validation group, respectively, showing no significant differences between the two sequence-based models (P>0.05). In all the cases, the AUCs of the two radiomics models based on T1WI and T2WI-FS sequences and conventional MR imaging were 0.955, 0.901 and 0.569, respectively, demonstrating a significantly higher diagnostic accuracy of the two sequence-based radiomics models than conventional MR imaging (P<0.01). CONCLUSIONS: The radiomics models based on T1WI and T2WI-FS non-enhanced MR imaging can be used for the differentiation of chondrosarcoma from enchondroma.

Ultrasmall Ag2Te Quantum Dots with Rapid Clearance for Amplified Computed Tomography Imaging and Augmented Photonic Tumor Hyperthermia.

With the fast development of nanomedicine, the imaging-guided and photo-induced cancer monotherapies can efficiently eliminate tumor lesions, which are strongly dependent on the construction of versatile theranostic nanoplatforms. Among diverse photo-converting nanoplatforms, silver chalcogenide nanoparticles feature high biocompatibility, narrow band gaps, and tunable optical properties, yet Ag2Te-based nanosystems are still at a proof-of-concept stage, and the exploration of Ag2Te-based nanosystems suitable for photonic tumor hyperthermia is challenging. Herein, we report on the construction of versatile ultrasmall Ag2Te quantum dots (QDs) via a facile biomineralization strategy. Especially, these Ag2Te QDs with negligible toxicity and excellent biocompatibility were developed for X-ray computed tomography (CT) imaging-guided photonic tumor hyperthermia by near-infrared (NIR) activation. The fabricated Ag2Te QDs exhibited a high tumor suppression rate (94.3%) on 4T1 breast tumor animal models due to the high photothermal-conversion efficiency (50.5%). Mechanistically, Ag2Te QDs were promising potential CT imaging agents for imaging guidance and monitoring during photonic hyperthermia. Importantly, Ag2Te QDs were rapidly eliminated from the body via feces and urine because of their ultrasmall sizes. This work not only broadens the biomedical applications of silver chalcogenide-based theranostic nanosystems but also provides the paradigm of theranostic nanosystems with a photonic tumor hyperthermia effect and outstanding contrast enhancement of high-performance CT imaging.

Clinicoradiologic characteristics of endolymphatic sac tumors.

PURPOSE: Endolymphatic sac tumor (ELST) is a rare, slow-growing, and low-grade malignant tumor arising from the endolymphatic sac in the posterior petrous bone. The purpose of this study is to describe the clinical and radiologic features, and investigate the clinicoradiologic correlation of ELST. METHODS: We retrospectively reviewed the clinical, computed tomography (CT), magnetic resonance imaging (MRI), and pathologic findings of 14 patients with 15 ELSTs. RESULTS: Patients comprised of eight women and six men with a mean age of 42.3 years at the time of diagnosis and 35.2 years at the time of initial symptoms. The mean interval between initial symptoms and diagnosis was 84.7 months. The most frequent cochleovestibular symptom was hearing loss in 14 patients (100%); other cochleovestibular symptoms were tinnitus in eight patients (57.1%), vertigo in three patients (21.4%), and aural fullness in three patients (21.4%). Ten patients (71.4%) presented with facial paralyses and five patients (14.3%) presented lower cranial nerve deficits. CT findings revealed spiculated, stippled, or reticular high density within the tumors. The lesions involved mastoid cells, vertical facial nerve canal, semicircular canal, cochlea, tympanum, jugular foramen, internal auditory canal, or petrous apex. On the available MRI, all the eight lesions showed patchy and/or speckled hyperintensity on unenhanced T1WI. Five lesions showed flow voids on T2WI and T1WI. Three lesions had blood fluid levels within cysts. CONCLUSION: CT and MRI findings of ELSTs are associated with clinical features. Imaging tests should be performed to identify ELSTs early and ensure greater potential for hearing preservation in patients with cochleovestibular symptoms.