Ultrasmall iron oxide nanoparticles with MRgFUS for enhanced magnetic resonance imaging of orthotopic glioblastoma.

Ultrasmall iron oxide nanoparticles (USIO NPs) are expected to become the next generation T1 contrast agents; however, their diagnostic and therapeutic potential for primary brain tumors (such as glioblastoma multiforme, GBM) is yet to be explored. At present, the main challenge is the effective hindering of biological barriers, including the blood-brain barrier (BBB) and the blood-brain tumor barrier (BBTB). Herein, we aimed to investigate whether the USIO NPs, in combination with MR-guided focused ultrasound (MRgFUS), could intensify MR imaging of GBM. In this study, we presented zwitterionic USIO NPs for enhanced MR imaging of both xenografted and orthotopic GBM mouse models. We first synthesized citric-stabilized USIO NPs with a size of 3.19 ± 0.76 nm, modified with ethylenediamine, and decorated with 1,3-propanesultone (1,3-PS) to form USIO NPs-1,3-PS. The obtained USIO NPs-1,3-PS exhibited good cytocompatibility and cellular uptake efficiency. MRgFUS, in combination with microbubbles, provided a non-invasive and safe technique for BBB opening, which, in turn, promoted the delivery of USIO NPs-1,3-PS in orthotopic GBM. This developed USIO NP nanoplatform may improve the precision imaging of solid tumors and therapeutic efficacy in the central nervous system.

AMPK, a key molecule regulating aging-related myocardial ischemia-reperfusion injury.

Aging leads to the threat of more diseases to the biological anatomical structure and the decline of disease resistance, increasing the incidence and mortality of myocardial ischemia-reperfusion injury (MI/RI). Moreover, MI/RI promotes damage to an aging heart. Notably, 5'-adenosine monophosphate-activated protein kinase (AMPK) regulates cellular energy metabolism, stress response, and protein metabolism, participates in aging-related signaling pathways, and plays an essential role in ischemia-reperfusion (I/R) injury diseases. This study aims to introduce the aging theory, summarize the interaction between aging and MI/RI, and describe the crosstalk of AMPK in aging and MI/RI. We show how AMPK can offer protective effects against age-related stressors, lifestyle factors such as alcohol consumption and smoking, and hypertension. We also review some of the clinical prospects for the development of interventions that harness the effect of AMPK to treat MI/RI and other age-related cardiovascular diseases.

HPV prevalence and distribution characteristics in postmenopausal women from Nanjing, China.

BACKGROUND: Cervical cancer is strongly associated with human papillomavirus (HPV) infection. In this retrospective study, we analyzed the data of postmenopausal women who were tested for HPV in Nanjing First Hospital from 2019 to 2021. METHODS: We retrospectively analyzed the data of 14,608 postmenopausal women aged 45-90 years, who underwent HPV examination in Nanjing First Hospital between January 2019 and December 2021. All participants were tested for 23 HPV genotypes. We subsequently analyzed the infection rate and evaluated the distribution of HPV using the chi-square test. RESULTS: Our results showed that the HPV infection rate in postmenopausal women in Nanjing, China was 22.36%. In terms of age group, the infection rate was 19.54%, 24.30%, 26.58%, and 14.99% in those aged ≤ 50, 51-60, 61-70, and ≥ 71 years, respectively. The most common HPV subtypes were HPV52 (22.1 3%), HPV58 (15.86%), HPV53 (14.17%), HPV16 (12.61%), and HPV81 (11.66%), in that order. The single-HPV infection rate was 14.23%, and the multiple-genotype infection rate was 8.14% (1189/14,608). CONCLUSIONS: This study showed that in Nanjing, China, the different age groups of post-menopausal women could have different rates of HPV infection, and the most common types were HPV52, HPV58, HPV53, HPV16 and HPV81. These findings highlighted the importance of understanding the epidemiology of HPV infection in specific populations, such as postmenopausal women in Nanjing, China. The results could provide valuable information for healthcare professionals and policymakers to develop targeted prevention and screening strategies for reducing the burden of HPV-related diseases in this population.

Application value of a computer-aided diagnosis and management system for the detection of lung nodules.

Background: Computer-aided diagnosis (CAD) systems can help reduce radiologists' workload. This study assessed the value of a CAD system for the detection of lung nodules on chest computed tomography (CT) images. Methods: The study retrospectively analyzed the CT images of patients who underwent routine health checkups between August 2019 and November 2019 at 3 hospitals in China. All images were first assessed by 2 radiologists manually in a blinded manner, which was followed by assessment with the CAD system. The location and classification of the lung nodules were determined. The final diagnosis was made by a panel of experts, including 2 associate chief radiologists and 1 chief radiologist at the radiology department. The sensitivity for nodule detection and false-positive nodules per case were calculated. Results: A total of 1,002 CT images were included in the study, and the process was completed for 999 images. The sensitivity of the CAD system and manual detection was 90.19% and 49.88% (P<0.001), respectively. Similar sensitivity was observed between manual detection and the CAD system in lung nodules >15 mm (P=0.08). The false-positive nodules per case for the CAD system were 0.30±0.84 and those for manual detection were 0.24±0.68 (P=0.12). The sensitivity of the CAD system was higher than that of the radiologists, but the increase in the false-positive rate was only slight. Conclusions: In addition to reducing the workload for medical professionals, a CAD system developed using a deep-learning model was highly effective and accurate in detecting lung nodules and did not demonstrate a meaningfully higher the false-positive rate.

Systematic radiomics analysis based on multiparameter MRI to preoperatively predict the expression of Ki67 and histological grade in patients with bladder cancer.

OBJECTIVES: Bladder cancer is among the most prevalent urothelial malignancies. Radiomics-based preoperative prediction of Ki67 and histological grade will facilitate clinical decision-making. METHODS: This retrospective study recruited 283 bladder cancer patients between 2012 and 2021. Multiparameter MRI sequences included: T1WI, T2WI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The radiomics features of intratumoral and peritumoral regions were extracted simultaneously. Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms were employed to select the features. Six machine learning-based classifiers were adopted to construct the radiomics models, and the best was chosen for the model construction. RESULTS: The mRMR and LASSO algorithms were more suitable for Ki67 and histological grade, respectively. Additionally, Ki67 had a higher proportion of intratumoral features, while peritumoral features accounted for a greater proportion of the histological grade. Random forests performed the best in predicting both pathological outcomes. Consequently, the multiparameter MRI (MP-MRI) models achieved area under the curve (AUC) values of 0.977 and 0.852 for Ki67 in training and test sets, respectively, and 0.972 and 0.710 for the histological grade. CONCLUSION: Radiomics holds the potential to predict multiple pathological outcomes of bladder cancer preoperatively and are expected to provide clinical decision-making guidance. Furthermore, our work inspired the process of radiomics research. ADVANCES IN KNOWLEDGE: This study demonstrated that different feature selection techniques, segmentation regions, classifiers, and MRI sequences will affect the performance of the model. We systematically demonstrated that radiomics can predict histological grade and Ki67.

Pleural Effusions on MRI in Autosomal Dominant Polycystic Kidney Disease.

Autosomal dominant polycystic kidney disease (ADPKD) has cystic fluid accumulations in the kidneys, liver, pancreas, arachnoid spaces as well as non-cystic fluid accumulations including pericardial effusions, dural ectasia and free fluid in the male pelvis. Here, we investigate the possible association of ADPKD with pleural effusion. ADPKD subjects (n = 268) and age-gender matched controls without ADPKD (n = 268) undergoing body magnetic resonance imaging from mid-thorax down into the pelvis were independently evaluated for pleural effusion by 3 blinded expert observers. Subjects with conditions associated with pleural effusion were excluded from both populations. Clinical and laboratory data as well as kidney, liver and spleen volume, pleural fluid volume, free pelvic fluid and polycystic kidney disease genotype were evaluated. Pleural effusions were observed in 56 of 268 (21%) ADPKD subjects compared with 21 of 268 (8%) in controls (p < 0.0001). In a subpopulation controlling for renal function by matching estimated glomerular filtration rate (eGFR), 28 of 110 (25%) ADPKD subjects had pleural effusions compared to 5 of 110 (5%) controls (p < 0.001). Pleural effusions in ADPKD subjects were more prevalent in females (37/141; 26%) than males (19/127,15%; p = 0.02) and in males were weakly correlated with the presence of free pelvic fluid (r = 0.24, p = 0.02). ADPKD subjects with pleural effusions were younger (48 ± 14 years old vs. 43 ± 14 years old) and weighed less (77 vs. 70 kg; p ≤ 0.02) than those without pleural effusions. For ADPKD subjects with pleural effusions, the mean volume of fluid layering dependently in the posterior−inferior thorax was 19 mL and was not considered to be clinically significant. Pleural effusion is associated with ADPKD, but its role in the pathogenesis of ADPKD requires further evaluation.

Associations of hiatus hernia with CT-based interstitial lung changes: the MESA Lung Study.

BACKGROUND: Hiatus hernia (HH) is prevalent in adults with pulmonary fibrosis. We hypothesised that HH would be associated with markers of lung inflammation and fibrosis among community-dwelling adults and stronger among MUC5B (rs35705950) risk allele carriers. METHODS: In the Multi-Ethnic Study of Atherosclerosis, HH was assessed from cardiac and full-lung computed tomography (CT) scans performed at Exam 1 (2000-2002, n=3342) and Exam 5 (2010-2012, n=3091), respectively. Percentage of high attenuation areas (HAAs; percentage of voxels with attenuation between -600 and -250 HU) was measured from cardiac and lung scans. Interstitial lung abnormalities (ILAs) were examined from Exam 5 scans (n=2380). Regression models were used to examine the associations of HH with HAAs, ILAs and serum matrix metalloproteinase-7 (MMP-7), and adjusted for age, sex, race/ethnicity, educational attainment, smoking, height, weight and scanner parameters for HAA analysis. RESULTS: HH detected from Exam 5 scans was associated with a mean percentage difference in HAAs of 2.23% (95% CI 0.57-3.93%) and an increase of 0.48% (95% CI 0.07-0.89%) per year, particularly in MUC5B risk allele carriers (p-value for interaction=0.02). HH was associated with ILAs among those <80 years of age (OR for ILAs 1.78, 95% CI 1.14-2.80) and higher serum MMP-7 level among smokers (p-value for smoking interaction=0.04). CONCLUSIONS: HH was associated with more HAAs over time, particularly among MUC5B risk allele carriers, and ILAs in younger adults, and may be a risk factor in the early stages of interstitial lung disease.

Peritumoral and Intratumoral Texture Features Based on Multiparametric MRI and Multiple Machine Learning Methods to Preoperatively Evaluate the Pathological Outcomes of Pancreatic Cancer.

BACKGROUND: Radiomics-based preoperative evaluation of lymph node metastasis (LNM) and histological grade (HG) might facilitate the decision-making for pancreatic cancer and further efforts are needed to develop effective models. PURPOSE: To develop multiparametric MRI (MP-MRI)-based radiomics models to evaluate LNM and HG. STUDY TYPE: Retrospective. POPULATION: The pancreatic cancer patients from the main center (n = 126) were assigned to the training and validation sets at a 4:1 ratio. The patients from the other center (n = 40) served as external test sets. FIELD STRENGTH/SEQUENCE: A 3.0 T and 1.5 T/T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast enhancement T1-weighted imaging. ASSESSMENT: A total of 10,686 peritumoral and intratumoral radiomics features were extracted which contained first-order, shape-based, and texture features. The following three-step method was applied to reduce the feature dimensionality: SelectKBest (a function from scikit-learn package), least absolute shrinkage and selection operator (LASSO), and recursive feature elimination based on random forest (RFE-RF). Six classifiers (random forest, logistic regression, support vector machine, K-nearest neighbor, decision tree, and XGBOOST) were trained and selected based on their performance to construct the clinical, radiomics, and combination models. STATISTICAL TESTS: Delong's test was used to compare the models' performance. P value less than 0.05 was considered significant. RESULTS: Twelve significant features for LNM and 11 features for HG were obtained. Random forest and logistic regression performed better than the other classifiers in evaluating LNM and HG, respectively, according to the surgical pathological results. The best performance was obtained with the models that combined peritumoral and intratumoral features with area under curve (AUC) values of 0.944 and 0.892 in the validation and external test sets for HG and 0.924 and 0.875 for LNM. DATA CONCLUSION: Radiomics holds the potential to evaluate LNM and HG of pancreatic cancer. The combination of peritumoral and intratumoral features will make models more accurate. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 2.

Preoperative Extrapancreatic Extension Prediction in Patients with Pancreatic Cancer Using Multiparameter MRI and Machine Learning-Based Radiomics Model.

RATIONALE AND OBJECTIVES: Pancreatic cancer is a common malignant tumor with a dismal prognosis. Preoperative differentiation of extrapancreatic extension (EPE) based on radiomics will facilitate treatment decision-making. MATERIALS AND METHODS: This research retrospectively recruited 156 patients from two medical centers. 122 patients from the center A were randomly divided into the training set and the internal test set in a 4:1 ratio. Additionally, 34 patients from the center B served as the external test set. Radiomics features were extracted from multiparametric MRI (MP-MRI), containing axial T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), and dynamic contrast enhancement (DCE) sequences. The three-step method was used for feature extraction: SelecteKBest, least absolute shrinkage and selection operator (LASSO) algorithm, and recursive feature elimination based on random forest (RFE-RF). The model was constructed using six classifiers based on machine learning, and the classifier with the best performance was chosen. Finally, clinical factors associated with EPE were incorporated into the combined model. RESULTS: The classifier with the best performance was XGBoost, which obtained area under curve (AUC) values of 0.853 and 0.848 in the internal and external test sets, respectively. Through SelectKBest, the most relevant clinical factor for EPE was determined to be platelet, which was then added to the combined model, yielding AUC values of 0.880 and 0.848 in the internal and external test sets, respectively. CONCLUSION: Radiomics models had the potential to noninvasively and accurately predict EPE before surgery. Additionally, it would add value to personalized precision treatment.

Mid-term efficacy grading evaluation and predictive factors of magnetic resonance-guided focused ultrasound surgery for painful bone metastases: a multi-center study.

OBJECTIVES: MR imaging-guided focused ultrasound surgery (MRgFUS) is an emerging non-invasive treatment. It is helpful in investigating the mid-term grading efficacy and safety of MRgFUS, and possible risk factors in participants with painful bone metastases. METHODS: This four-center prospective study enrolled 96 participants between June 2016 and May 2019 with painful bone metastases. The Numerical Rating Scale (NRS), Brief Pain Inventory-Quality of Life (BPI-QoL) score, morphine equivalent daily dose (MEDD), and the adverse events (AEs) were recorded before and at 1 week, 1 month, 2 months, and 3 months after MRgFUS. The repeated ANOVA tests were used to analyze the change in NRS and BPI-QoL, and logistic regression analysis was used to analyze the possible risk factors. RESULTS: A total of 82 participants completed the 3-month follow-up period. And 16 (19.5%) participants were complete responders (CR), 46 (56.1%) participants were effective responders (ER), and the other 20 (24.4%) participants were non-responders (NR). The NRS (2.67 ± 2.47 at 3 months compared to 6.38 ± 1.70 before treatment) and BPI-QoL score (3.11 ± 2.51 at 3 months compared to 5.40 ± 1.85 before treatment) significantly decreased after the treatment at all time points (p < 0.001). Eleven adverse events were recorded and they were all cured within 1 to 52 days after treatment. The non-perfused volume (NPV) ratio (p = 0.001) and the bone metastases lesion type (p = 0.025) were the key risk factors. CONCLUSIONS: MRgFUS can be used as a non-invasive, effective, and safe modality to treat painful bone metastases. NPV ratio and the lesion type may be used as affecting factors to predict the mid-term efficacy of MRgFUS. KEY POINTS: • MRgFUS can be considered a non-invasive, effective, and safe modality to treat painful bone metastases. • The NRS and BPI-QoL score at 1 week, 1 month, 2 months, and 3 months all decreased significantly (p < 0.001) after receiving MRgFUS. Among 82 participants, 16 (19.5%) were complete responders, 46 (56.1%) were effective responders, and the other 20 (24.4%) were non-responders. • According to logistic regression analysis, non-perfused volume ratio and the bone metastases lesion type were the affecting factors to predict the mid-term efficacy of MRgFUS. The adjusted OR of non-perfused volume ratio was 0.86 (p = 0.001), and osteoblastic lesion type was 0.06 (p = 0.025).

Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer.

OBJECTIVES: This study aims to develop and evaluate multiparametric MRI (MP-MRI)-based radiomic models as a noninvasive diagnostic method to predict several biological characteristics of prostate cancer. METHODS: A total of 252 patients were retrospectively included who underwent radical prostatectomy and MP-MRI examinations. The prediction characteristics of this study were as follows: Ki67, S100, extracapsular extension (ECE), perineural invasion (PNI), and surgical margin (SM). Patients were divided into training cohorts and validation cohorts in the ratio of 4:1 for each group. After lesion segmentation manually, radiomic features were extracted from MP-MRI images and some clinical factors were also included. Max relevance min redundancy (mRMR) and recursive feature elimination (RFE) based on random forest (RF) were adopted to select features. Six classifiers were included (SVM, KNN, RF, decision tree, logistic regression, XGBOOST) to find the best diagnostic performance among them. The diagnostic efficiency of the construction models was evaluated by ROC curves and quantified by AUC. RESULTS: RF performed best among the six classifiers for the four groups according to AUC values (Ki67 = 0.87, S100 = 0.80, ECE = 0.85, PNI = 0.82). The performance of SVM was relatively the best for SM (AUC = 0.77). The number and importance of DCE features ranked first in the models of each group. The combined models of MP-MRI and clinical characteristics showed no significant difference compared with MP-MRI models according to Delong's tests. CONCLUSIONS: Radiomics models based on MP-MRI have the potential to predict biological characteristics and are expected to be a noninvasive method to evaluate the risk stratification of prostate cancer.

Chronic exposure to perfluorohexane sulfonate leads to a reproduction deficit by suppressing hypothalamic kisspeptin expression in mice.

BACKGROUND: Perfluorohexane sulfonate (PFHxS) is a six-carbon perfluoroalkyl sulfonic acid found as an environmental contaminant. This study aims to investigate the effects of PFHxS exposure on female reproduction and the underlying mechanism in mice. METHODS: Eight-week-old ICR mice were divided randomly into four groups administered corn oil (vehicle) and PFHxS at doses of 0.5, 5, and 50 mg/kg/day for 42 days by intragastric administration. Body weight, ovarian weight, estrous cycle, follicle counts, and serum sex hormone levels were evaluated. The expression of kisspeptin and gonadotropin releasing hormone (GnRH) in the hypothalamus was also detected. RESULTS: Compared to vehicle exposure, 5 mg/kg/day PFHxS treatment prolonged the estrous cycle, especially the duration of diestrus, after 42 days of treatment. The numbers of secondary follicles, antral follicles and corpus lutea were significantly reduced in the PFHxS-treated mice. Moreover, compared with the control mice, the PFHxS-treated mice showed decreases in the serum levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH), and estrogen (E2), and reduced GnRH mRNA levels, along with the lack of an LH surge. Furthermore, the PFHxS-treated mice had lower levels of kisspeptin immunoreactivity and kiss-1 mRNA in the arcuate nucleus (ARC) and anteroventral periventricular nucleus (AVPV) than the control mice. After intraventricular administration of kisspeptin-10, the numbers of secondary follicles, antral follicles and corpus lutea recovered, along with the levels of GnRH mRNA, FSH, and LH in the mice treated with 5 mg/kg/day PFHxS. CONCLUSION: These results indicate that chronic exposure of mice to 5 mg/kg/day PFHxS affects reproductive functions by inhibiting kisspeptin expression in the ARC and AVPV regions, leading to deficits in follicular development and ovulation.

Prevalence of Inferior Vena Cava Compression in ADPKD.

INTRODUCTION: Kidney and liver cysts in autosomal dominant polycystic kidney disease (ADPKD) can compress the inferior vena cava (IVC), but IVC compression prevalence and its risk factors are unknown. METHODS: Patients who have ADPKD (n = 216) with abdominal magnetic resonance imaging (MRI) studies and age-/sex-matched controls (n = 216) were evaluated for IVC compression as well as azygous vein diameter (a marker of collateral blood flow) and IVC aspect ratio (left-to-right dimension divided by anterior-to-posterior dimension with a value of 1 corresponding to a circular (high pressure) IVC caudal to compression. RESULTS: Severe IVC compression (≥70%) was observed in 33 (15%) ADPKD subjects and mild compression (≥50% to <70%) was observed in 33 (15%) subjects; whereas controls had no IVC compression (P < 0.001). Severe IVC compression was associated with larger azygous vein (4.0 ± 1.3 mm versus 2.3 ± 0.8 mm without IVC compression; P < 0.001) and a more circular IVC cross-section upstream (mean IVC aspect ratio: 1.16 ± 0.27 vs. 1.69 ± 0.67, P < 0.001), suggesting higher pressure upstream from the compression. IVC compression was associated with older age, lower estimated glomerular filtration rate (eGFR), greater height-adjusted total kidney volumes, greater height-adjusted liver volume (ht-LV), and greater liver and renal cyst fractions (P < 0.001). No subject younger than 30 years had IVC compression, but ADPKD subjects ≥40 years old had 12-fold higher risk of IVC compression (95% confidence interval [CI]: 4.2-42.4), with highest predicted probability for Mayo Clinic classes 1D (59%; 95% CI: 39%-76%) and 1E (74%; 95% CI: 49%-90%) after adjustment (P < 0.001). Women with ht-LV ≥ 2000 ml/m had 83% (95% CI: 59%-95%) prevalence of IVC compression. Complications of IVC compression included deep vein thrombosis (DVT) and symptomatic hypotension. CONCLUSIONS: IVC compression is common in ADPKD patients >40 years old, with Mayo Clinic class 1D/E, and in females with ht-LV > 2000 ml/m.

Utilization of radiomics to predict long-term outcome of magnetic resonance-guided focused ultrasound ablation therapy in adenomyosis.

OBJECTIVE: To develop and evaluate a T2 MR-based radiomics prediction model incorporating radiomics features and clinical parameters to predict the response to magnetic resonance-guided focused ultrasound surgery (MRgFUS) in patients with adenomyosis. MATERIALS AND METHODS: Sixty-nine patients (mean age, 38.6 years; age range, 26-50 years) with adenomyosis treated by MRgFUS were reviewed and allocated to training (n = 48) and testing cohorts (n = 21). One thousand one hundred eighteen radiomics features were extracted from T2-weighted imaging before MRgFUS. The radiomics features' dimension was reduced by Pearson correlation coefficient after normalization. Analysis of variance and logistical regression were used for feature selection by fivefold cross-validation in the training cohort, and the machine learning model was constructed for comparing the clinical model, radiomics model, and radiomics-clinical model which combined survived radiomics features and clinical parameters. The discrimination result of the model was obtained by bootstrap; receiver operating characteristic curve, area under the curve (AUC), and decision curve analyses were performed to illustrate the model performance in both the training and testing cohorts. RESULTS: Good response was achieved in 47 patients (68.1%) and failed in 22 patients (38.9%). The radiomics model comprised four selected features and demonstrated a degree of prediction capability of patients' poor response to MRgFUS treatment. The radiomics-clinical model showed good discrimination, with an AUC of 0.81 (95% confidence interval, 0.592-0.975) in the testing cohort. The decision curve analysis also showed favorable performance of the radiomics-clinical model. CONCLUSIONS: A prediction model composed of T2WI-based radiomics features and clinical parameters could be applied to guide the radiologist to evaluate MRgFUS for patients with adenomyosis who will achieve good response. KEY POINTS: • Magnetic resonance imaging-guided focused ultrasound surgery represents an alternative treatment for adenomyosis, but nearly one third of patients remain symptomatic 6 months after MRgFUS. • Combining four radiomics features of T2-weighted MRI with eight clinical features further improves prediction of poor responders to MR-guided focused ultrasound treatment of uterine adenomyosis (AUC = 0.81 in the testing cohort). • The radiomics model based on T2-weighted imaging combined with clinical parameters can help predict which patients are likely to have a good response to MRgFUS for adenomyosis.

Hemorrhagic Cysts and Other MR Biomarkers for Predicting Renal Dysfunction Progression in Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND: Screening for rapidly progressing autosomal dominant polycystic kidney disease (ADPKD) is necessary for assigning and monitoring therapies. Height-adjusted total kidney volume (ht-TKV) is an accepted biomarker for clinical prognostication, but represents only a small fraction of information on abdominal MRI. PURPOSE: To investigate the utility of other MR features of ADPKD to predict progression. STUDY TYPE: Single-center retrospective. POPULATION: Longitudinal data from 186 ADPKD subjects with baseline serum creatinine, PKD gene testing, abdominal MRI measurements, and ≥2 follow-up serum creatinine were reviewed. FIELD STRENGTH/SEQUENCE: 1.5T, T2 -weighted single-shot fast spin echo, T1 -weighted 3D spoiled gradient echo (liver accelerated volume acquisition) and 2D cine velocity encoded gradient echo (phase contrast MRA). ASSESSMENT: Ht-TKV, renal blood flow (RBF), number and fraction of renal and hepatic cysts, bright T1 hemorrhagic renal cysts, and liver and spleen volumes were independently assessed by three observers blinded to estimated glomerular filtration rate (eGFR) data. STATISTICAL TESTS: Linear mixed-effect models were applied to predict eGFR over time using MRI features at baseline adjusted for confounders. Validation was performed in 158 patients who had follow-up MRI using receiver operator characteristic, sensitivity, and specificity. RESULTS: Hemorrhagic cysts, fraction of renal and hepatic cysts, height-adjusted liver and spleen volumes were significant independent predictors of future eGFR (final prediction model R2 = 0.88 P < 0.05). The number of hemorrhagic cysts significantly improved the prediction compared to ht-TKV in predicting future eGFR (area under the curve [AUC] = 0.94, 95% confidence interval [CI]: 0.9-0.94 vs. R2 = 0.9, 95% CI: 0.85-0.9, P = 0.045). For baseline eGFR ≥60 ml/min/1.73m2 , sensitivity for predicting eGFR<45 ml/min/1.73m2 by ht-TKV alone was 29%. Sensitivity increased to 72% with all MRI variables in the model (P < 0.05 = 0.019), whereas specificity was unchanged, 100% vs. 99%. DATA CONCLUSION: Combining multiple MR features including hemorrhagic renal cysts, renal cyst fraction, liver and spleen volume, hepatic cyst fraction, and renal blood flow enhanced sensitivity for predicting eGFR decline in ADPKD compared to the standard model including only ht-TKV. Level of Evidence 2 Technical Efficacy Stage 2 J. MAGN. RESON. IMAGING 2021;53:564-576.

Internal Carotid Artery and its Relationship with Structures in Sellar Region: Anatomic Study and Clinical Applications.

BACKGROUND: Complications of the internal carotid artery (ICA) in surgery are rare but severe. This study aimed to locate the ICA with 5 stationary bony structures in the sellar region: the anterior clinoid process, the tuberculum sellae, the bottom of the hypophyseal fossa, the posterior edge of the hypophyseal fossa, and the postclinoid process and to do measurements to determine their anatomic relationship with the ICA. METHODS: After multiple planar reconstructions on computer tomographic angiography images of 120 ICA in 60 individuals, we defined the 5 bony structures as 5 origins in the horizontal, sagittal, and vertical planes with the 3D coordinate system and got the cross-sections of bilateral ICA on the coronal plane passing through each origin. We measured the distances between the cross-sections and the origins and angles between the horizontal plane and the line passing through the origin and the cross-sections on each coronal plane. We also measured the distances between the bilateral ICA on the 5 coronal planes. Besides, we measured the coordinate of the anterior edge of bilateral ICA, taking the anterior clinoid process as the origin. RESULTS: With the 3-dimensional coordinate system, we located the ICA to the bony structures in skull base. The distance between the bilateral ICA on coronal planes were 18.8 ± 2.9 mm, 23.6 ± 3.7 mm, 19.9 ± 3.4 mm, 24.7 ± 4.3 mm, and 23.5 ± 3.5 mm, from anterior to posterior, respectively. CONCLUSION: The 3D coordinate system used in this study is of value in preoperational assessment, and data we obtained indicate the safety ranges avoiding damage to the ICA in surgery.

Three-Dimensional Imaging Anatomic Study and Clinical Application of the Third Ventricle Transcallosal-Transforniceal Approach.

The third ventricle is a narrow, funnel-shaped, unilocular, midline cavity located between the 2 thalami, under the body of the lateral ventricle, in the center of the head. Damaging of brain tissue in the third ventricle when conducting operation under the microscope will lead to serious consequence. The study aimed to precisely detect the relative location of specific structures on the approach to the third ventricle. The authors rebuilt a 3-dimensional reconstruction of the brain and selected specific sections and then measured several crucial distance, angle to precisely assure the approaching pathway and localize the hypothalamic sulcus, interthalamic adhesion, anterior commissure, optic chiasm, and pineal body. In the study, canthomeatal line was used as base line to measure angle. Parameters were obtained from 58 samples (22 males and 36 females) between 21 and 76 years old. Means and standard deviation were calculated as well as the 95% confidence interval for the mean value of the measured data. The data were analyzed by SPSS, statistical software with the comparison between sexes and sides. The results could be reference for clinical and anatomic utilities.

Comparative study of DTS and CT in the skeletal trauma imaging diagnosis evaluation and radiation dose.

PURPOSE: To compare the differences between CT (Computed tomography) and DTS (digital tomosynthesis) in the X-ray radiation dose, and to analyze the sensitivity and specificity of each examination to check the suspected fractures and dislocations cases. METHODS: From May 2010 to February 2012, 46 patients suspected with fractures and/or dislocations were prospectively enrolled to do the CT and DTS examination on the basis of the results of DR examination. Patients were considered ineligible if they were pregnant or lactating (female subjects only). The Yangzhou Regional Ethical Review Board in Jiangsu province, China approved this study. All 46 patients signed the informed consent. To contrast radiation dose between CT and DTS group of the patients and make statistical analysis. Two experienced experts assess the image quality by I-III grade and make statistical analysis. Draw the ROC of reader A and B according to the golden standard of clinical diagnosis. RESULTS: The average effective dose of CT Group is 5.46 msv, while average effective dose of DTS group is 0.07 msv.There is significant statistical difference (t=22.89, p<0.01) between them. Calculate and compare the area under the ROC curve of reader A and B. The area of reader A by CT and DTS is 0.999 ± 0.005, 0.973 ± 0.022. The area of reader B is 0.961 ± 0.026, 0.944 ± 0.032. Both of them perform z test, and P>0.05. There is no significant difference. Assess the uniformity of two readers with Kappa test. The Kappa value of reader A and B is respectively 0.757 and 0.783. When the value is greater than 0.75, two readers' uniformity is considered well. The two readers' sensitivity and specificity in diagnosing hidden skeletal trauma with CT and DTS are respectively 97.06% (reader A), 96.97% (reader B); 91.67% (reader A), 84.62% (reader B). Use the χ(2) test to them, then get the same result: P>0.05. There is no statistical significant. CONCLUSION: Radiation dose of VolumnRAD DTS only account for 1.5% of CT, but its image can meet the requirements for clinical diagnosis. When fracture and/or dislocation is suspected with DR, VolumnRAD DTS can be recommended firstly as further checks.