CoFe2O4 on Mica Substrate as Flexible Ethanol Gas Sensor in Self-Heating Mode.

In this study, a novel flexible ethanol gas sensor was created by the deposition of a CoFe2O4 (CFO) thin film on a thin mica substrate using the pulsed laser deposition technique. Transition electron microscopy (TEM) investigations clearly demonstrated the successful growth of CFO on the mica, where a well-defined interface was observed. Ethanol gas-sensing studies showed optimal performance at 200 °C, with the highest response of 19.2 to 100 ppm ethanol. Operating the sensor in self-heating mode under 7 V applied voltage, which corresponds to a temperature of approximately 200 °C, produced a maximal response of 19.2 to 100 ppm ethanol. This aligned with the highest responses observed during testing at 200 °C, confirming the sensor's accuracy and sensitivity to ethanol under self-heating conditions. In addition, the sensor exhibited good selectivity to ethanol and excellent flexibility, maintaining its high performance after bending and tilting up to 5000 times. As this is the first report on flexible self-heated CFO gas sensors, we believe that this research holds great promise for the future development of high-quality sensors based on this approach.

Fat Loss in Patients with Metastatic Clear Cell Renal Cell Carcinoma Treated with Immune Checkpoint Inhibitors.

The purpose of this study was to determine the prognostic impact of fat loss after immune checkpoint inhibitor (ICI) treatment in patients with metastatic clear cell renal cell carcinoma (ccRCC). Data from 60 patients treated with ICI therapy for metastatic ccRCC were retrospectively analyzed. Changes in cross-sectional areas of subcutaneous fat (SF) between the pre-treatment and post-treatment abdominal computed tomography (CT) images were expressed as percentages and were divided by the interval between the CT scans to calculate ΔSF (%/month). SF loss was defined as ΔSF < -5%/month. Survival analyses for overall survival (OS) and progression-free survival (PFS) were performed. Patients with SF loss had shorter OS (median, 9.5 months vs. not reached; p < 0.001) and PFS (median, 2.6 months vs. 33.5 months; p < 0.001) than patients without SF loss. ΔSF was independently associated with OS (adjusted hazard ratio (HR), 1.49; 95% confidence interval (CI), 1.07-2.07; p = 0.020) and PFS (adjusted HR, 1.57; 95% CI, 1.17-2.12; p = 0.003), with a 5%/month decrease in SF increasing the risk of death and progression by 49% and 57%, respectively. In conclusion, Loss of SF after treatment initiation is a significant and independent poor prognostic factor for OS and PFS in patients with metastatic ccRCC who receive ICI therapy.

Brain signatures based on structural MRI: Classification for MCI, PMCI, and AD.

Structural MRI (sMRI) provides valuable information for understanding neurodegenerative illnesses such as Alzheimer's Disease (AD) since it detects the brain's cerebral atrophy. The development of brain networks utilizing single imaging data-sMRI is an understudied area that has the potential to provide a network neuroscientific viewpoint on the brain. In this paper, we proposed a framework for constructing a brain network utilizing sMRI data, followed by the extraction of signature networks and important regions of interest (ROIs). To construct a brain network using sMRI, nodes are defined as regions described by the brain atlas, and edge weights are determined using a distance measure called the Sorensen distance between probability distributions of gray matter tissue probability maps. The brain signatures identified are based on the changes in the networks of disease and control subjects. To validate the proposed methodology, we first identified the brain signatures and critical ROIs associated with mild cognitive impairment (MCI), progressive MCI (PMCI), and Alzheimer's disease (AD) with 60 reference subjects (15 each of control, MCI, PMCI, and AD). Then, 200 examination subjects (50 each of control, MCI, PMCI, and AD) were selected to evaluate the identified signature patterns. Results demonstrate that the proposed framework is capable of extracting brain signatures and has a number of potential applications in the disciplines of brain mapping, brain communication, and brain network-based applications.

Prediction of epithelial-to-mesenchymal transition molecular subtype using CT in gastric cancer.

OBJECTIVES: To develop a prediction model with computed tomography (CT) images and to build a nomogram incorporating known clinicopathologic variables for individualized estimation of epithelial-to-mesenchymal transition (EMT) subtype gastric cancer. METHODS: Patients who underwent primary resection of gastric cancer (GC) and molecular subgroup analysis (n = 451) were reviewed. Multivariable analysis using a stepwise variable selection method was performed to build a predictive model for EMT subtype GC. A nomogram using the results of the multivariable analysis was constructed. An optimal cutoff value of total prognostic points of the nomogram for the prediction of EMT subtype was determined. The predictive model for the EMT subtype was internally validated by bootstrap resampling method. RESULTS: There were 88 patients with EMT subtype and 363 patients with non-EMT subtype based on transcriptome analysis. The patient's age, Lauren classification, and mural stratification on CT were variables selected for the predictive model. The area under the curve (AUC) of the model was 0.865, and the validated AUC of the bootstrap sample was 0.860. The optimal cutoff value of total prognostic points for the prediction of EMT subtype was 94.622, with 90.9% sensitivity, 67.2% specificity, and 71.8% accuracy. CONCLUSION: A predictive model using patient's age, Lauren classification, and mural stratification on CT for EMT molecular subtype GC was made. A nomogram was built which would serve as a useful screening tool for an individualized estimate of EMT subtype. KEY POINTS: • A predictive model for epithelial-to-mesenchymal transition (EMT) subtype incorporating patient's age, Lauren classification, and mural stratification on CT was built. • The predictive model had high diagnostic accuracy (area under the curve (AUC) = 0.865) and was validated (bootstrap AUC = 0.860). • Adding CT findings to clinicopathologic variables increases the accuracy of the predictive model than using only.

Preoperative Helical Dynamic Enhanced Multidetector Row Computed Tomography: Can It Be a Prognostic Indicator in Early-Stage Non-small Cell Lung Cancer?

OBJECTIVE: This study aimed to investigate the prognostic significance of dynamic contrast-enhanced computed tomography in patients with stage IA non-small cell lung cancer (NSCLC). METHODS: We retrospectively enrolled 139 patients (77 men, 62 women; mean age, 59 years) with stage IA NSCLC who underwent dynamic contrast-enhanced computed tomography. Data on age, pathologic subtype, peak enhancement, and net enhancement of primary lung cancer were collected and correlated with 5-year survival. RESULTS: Peak enhancement had a significant correlation with overall survival in the univariable analysis (hazard ratio [HR], 1.18, confidence interval [CI], 1.01-1.38; P = 0.04) and in the multivariable analysis (HR, 1.19; CI, 1.01-1.39; P = 0.04). Patients with peak enhancement of 90 Hounsfield unit or higher had a significantly increased risk of death compared with patients with less enhancement after curative surgery (HR, 4.15; CI, 1.23-13.95; P = 0.02). CONCLUSIONS: Our study confirmed the prognostic significance of peak enhancement as an indicator for the overall survival of stage IA NSCLC.

Texture analysis using T1-weighted images for muscles in Charcot-Marie-Tooth disease patients and volunteers.

OBJECTIVES: To explore whether texture features using T1-weighted images correlate with fat fraction, and whether they differ between Charcot-Marie-Tooth (CMT) disease patients and volunteers. METHODS: The institutional review board approved this retrospective study, and the requirement for informed consent was waived; data of eighteen CMT patients and eighteen healthy volunteers from a previous study was used. Texture features of the muscles including mean, standard deviation (SD), skewness, kurtosis, and entropy of the signal intensity were derived from T1-weighted images. Spearman's correlation analysis was used to assess the relationship between texture features and fat fraction measured by 3D multiple gradient echo Dixon-based sequence. Mann-Whitney U test was used to compare the texture features between CMT patients and volunteers. Intraobserver and interobserver agreements for the texture features were assessed using the intraclass correlation coefficient. RESULTS: The SD (ρ = 0.256, p < 0.001) and entropy (ρ = 0.263, p < 0.001) were significantly and positively correlated with fat fraction; skewness (ρ = - 0.110, p = 0.027) and kurtosis (ρ = - 0.149, p = 0.003) were significantly and inversely correlated with fat fraction. The CMT patients showed a significantly higher SD (63.45 vs. 49.26; p < 0.001), skewness (1.06 vs. 0.56; p < 0.001), kurtosis (4.00 vs. 1.81; p < 0.001), and entropy (3.20 vs. 3.02; p < 0.001) than did the volunteers. Intraobserver and interobserver agreements were almost perfect for mean, SD, and entropy. CONCLUSIONS: Texture features using T1-weighted images correlated with fat fraction and differed between CMT patients and volunteers. KEY POINTS: • Standard deviation and entropy of muscles derived from T1-weighted images were significantly and positively correlated with the muscle fat fraction. • Mean, standard deviation, and entropy were considered highly reliable in muscle analyses. • Texture features may have the potential to diagnose early stage of intramuscular fatty infiltration.

Prognostic significance of sarcopenia in microsatellite-stable gastric cancer patients treated with programmed death-1 inhibitors.

BACKGROUND: Sarcopenia has been underscored as a significant predictor of poor prognosis in cancer patients undergoing immunotherapy with programmed death-1 (PD-1) inhibitors. We aimed to investigate the prognostic significance of computed tomography (CT)-determined sarcopenia in patients with microsatellite-stable (MSS) gastric cancer (GC) treated with PD-1 inhibitors. METHODS: We retrospectively assessed patients with MSS GC who had been treated with PD-1 inhibitors from March 2016 to June 2019. Pre-treatment sarcopenic status was determined by analyzing L3 skeletal muscle index with abdominal CT. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method, and the differences in survival probability according to sarcopenic status were compared using the log-rank test. Cox proportional hazards regression analyses were performed to identify predictors of PFS and OS. RESULTS: Of 149 patients with MSS GC (mean age, 57.0 ± 12.3 years; 93 men), 79 (53.0%) had sarcopenia. Patients with sarcopenia had significantly shorter PFS than patients without sarcopenia (median, 1.4 months vs. 2.6 months; P = 0.026). Sarcopenia was independently associated with shorter PFS (adjusted hazard ratio [HR], 1.79; 95% confidence interval [CI], 1.10-2.93; P = 0.020). Patients with sarcopenia had shorter OS than patients without sarcopenia (median, 3.6 months vs. 4.9 months; P = 0.052), but sarcopenia itself was not a significant prognostic factor for OS (adjusted HR, 1.01; 95% CI, 0.58-1.75; P = 0.974). CONCLUSIONS: CT-determined sarcopenia is an independent prognostic factor for PFS in patients with MSS GC treated with PD-1 inhibitors.

Utility of diffusion-weighted imaging in association with pathologic upgrading in biopsy-proven grade I endometrial cancer.

BACKGROUND: Prediction of pathologic upgrading is clinically meaningful to identify the optimal candidate of fertility-preserving hormonal treatment in the young patients with biopsy-proven grade I endometrial cancer. PURPOSE: To investigate the utility of diffusion-weighted imaging (DWI) in association with pathologic upgrading in endometrial cancer. STUDY TYPE: Retrospective. SUBJECTS: Preoperative MRI datasets of 221 patients with grade I endometrial cancer on endometrial biopsy (n = 146), dilatation and curettage (n = 66), or either (n = 9). FIELD STRENGTH/SEQUENCE: 3.0T, including T2 -weighted imaging, DWI with a b-value of 1000 s/mm2 , and dynamic contrast enhanced imaging. ASSESSMENT: The tumor size was determined as the longest diameter of the lesion. The minimum apparent diffusion coefficient (ADCmin ) was calculated using histogram analysis of the entire tumor. STATISTICAL TESTS: Mann-Whitney U-test, Pearson's chi-square test, Fisher's exact test, intraclass correlation coefficient (ICC) analysis, receiver operating characteristic (ROC) curve analysis, univariate and multivariate logistic regression analysis. RESULTS: Pathologic upgrading was identified in 42 patients (19.0%). Patients with pathologic upgrading had larger tumors and showed lower ADCmin values than those without pathologic upgrading (both P < 0.001). The area under the ROC curve of ADCmin and tumor size was 0.812 and 0.758, respectively. On multivariate analysis, tumor ADCmin ≤0.600 × 10-3 mm2 /s (odds ratio [OR], 11.8; P < 0.001) and tumor size on MRI >3 cm (OR, 3.24; P = 0.009) were independently associated with pathologic upgrading. Upgrading occurred in 23 of 31 patients (74.2%) with ADCmin ≤0.600 × 10-3 mm2 /s and tumor size >3 cm, and in 7 of 114 patients (6.1%) with ADCmin >0.600 × 10-3 mm2 /s and tumor size ≤3 cm. DATA CONCLUSION: Tumor ADC and tumor size on MRI may be useful parameters in association with pathologic upgrading in biopsy-proven grade I endometrial cancer. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:117-123.

The effects of sarcopenia and sarcopenic obesity after pancreaticoduodenectomy in patients with pancreatic head cancer.

BACKGROUND: Recently, several studies have reported that sarcopenia and sarcopenic obesity (SO) could worsen postoperative complications after PD. This study aims to evaluate the effects of preoperative sarcopenia and SO following PD in pancreatic head cancer (PHD). METHODS: Preoperative sarcopenia and SO were assessed in 548 patients undergoing PD for PHC at Samsung Medical Centre between 2007 and 2016. The visceral adipose tissue-to-skeletal muscle ratio was calculated from cross-sectional visceral fat and muscle areas on preoperative CT images. The overall survival (OS) and rate of clinically relevant postoperative pancreatic fistula (CR-POPF) among postoperative complications were extracted from prospectively maintained databases. RESULTS: Preoperative sarcopenia was present in 252 patients (45.9%). The 5-year survival rates of patients with non-sarcopenia and sarcopenia were 28.4% and 23.4% (p = 0.046). Preoperative SO was present in 202 patients (36.9%). After multivariable analysis, the presence of SO was the only independent risk factor for CR-POPF (p = 0.018). CONCLUSION: Sarcopenia can be a risk factor affecting decreased OS after PD in patients with PHC. SO is the only predictive factor for CR-POPF after PD in patients with PHC. More observational studies are needed to evaluate the effects of sarcopenia and SO on survival after PD.

Diffusion tensor imaging of the sciatic nerve in Charcot-Marie-Tooth disease type I patients: a prospective case-control study.

OBJECTIVES: This study aimed to evaluate whether diffusion tensor imaging (DTI) parameters and cross-sectional area (CSA) can differentiate between the sciatic nerve of Charcot-Marie-Tooth (CMT) disease type I (demyelinating form) patients and that of controls. METHODS: This prospective comparison study included 18 CMT type I patients and 18 age/sex-matched volunteers. Magnetic resonance imaging including DTI and axial T2-weighted Dixon sequence was performed for each subject. Region of interest analysis was independently performed by two radiologists on each side of the sciatic nerve at four levels: hamstring tendon origin (level 1), lesser trochanter of the femur (level 2), gluteus maximus tendon insertion (level 3), and mid-femur (level 4). Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated. The CSA of the sciatic nerve bundle was measured using axial water-only image at each level. Comparisons of DTI parameters between the two groups were performed using the two-sample t test and Mann-Whitney U test. Interobserver agreement analysis was also conducted. RESULTS: Interobserver agreement was excellent for all DTI parameter analyses. FA was significantly lower at all four levels in CMT patients than controls. RD, MD, and CSA were significantly higher at all four levels in CMT patients. AD was significantly higher at level 2 in CMT patients. CONCLUSION: DTI assessment of the sciatic nerve is reproducible and can discriminate the demyelinating nerve pathology of CMT type I patients from normal nerves. The CSA of the sciatic nerve is also a potential parameter for diagnosing nerve abnormality in CMT type I patients. KEY POINTS: • Diffusion tensor imaging parameters of the sciatic nerve at proximal to mid-femur level revealed significant differences between the Charcot-Marie-Tooth disease patients and controls. • The cross-sectional area of the sciatic nerve was significantly larger in the Charcot-Marie-Tooth disease patients. • Interobserver agreement was excellent (intraclass coefficient > 0.8) for all diffusion tensor imaging parameter analyses.

Enhanced Hydrogen Detection in ppb-Level by Electrospun SnO2-Loaded ZnO Nanofibers.

High-performance hydrogen sensors are important in many industries to effectively address safety concerns related to the production, delivering, storage and use of H2 gas. Herein, we present a highly sensitive hydrogen gas sensor based on SnO2-loaded ZnO nanofibers (NFs). The xSnO2-loaded (x = 0.05, 0.1 and 0.15) ZnO NFs were fabricated using an electrospinning technique followed by calcination at high temperature. Microscopic analyses demonstrated the formation of NFs with expected morphology and chemical composition. Hydrogen sensing studies were performed at various temperatures and the optimal working temperature was selected as 300 °C. The optimal gas sensor (0.1 SnO2 loaded ZnO NFs) not only showed a high response to 50 ppb hydrogen gas, but also showed an excellent selectivity to hydrogen gas. The excellent performance of the gas sensor to hydrogen gas was mainly related to the formation of SnO2-ZnO heterojunctions and the metallization effect of ZnO.

Gas Sensing Properties of Mg-Incorporated Metal-Organic Frameworks.

The gas sensing properties of two novel series of Mg-incorporated metal-organic frameworks (MOFs), termed Mg-MOFs-I and -II, were assessed. The synthesized iso-reticular type Mg-MOFs exhibited good crystallinity, high thermal stability, needle-shape morphology and high surface area (up to 2900 m2·g-1), which are promising for gas sensing applications. Gas-sensing studies of gas sensors fabricated from Mg-MOFs-II revealed better sensing performance, in terms of the sensor dynamics and sensor response, at an optimal operating temperature of 200 °C. The MOF gas sensor with a larger pore size and volume showed shorter response and recovery times, demonstrating the importance of the pore size and volume on the kinetic properties of MOF-based gas sensors. The gas-sensing results obtained in this study highlight the potential of Mg-MOFs gas sensors for the practical monitoring of toxic gases in a range of environments.

ppb-Level Selective Hydrogen Gas Detection of Pd-Functionalized In2O3-Loaded ZnO Nanofiber Gas Sensors.

Pd nanoparticle-functionalized, xIn2O3 (x = 0.05, 0.1, and 0.15)-loaded ZnO nanofibers were synthesized by an electrospinning and ultraviolet (UV) irradiation method and assessed for their hydrogen gas sensing properties. Morphological and chemical analyses revealed the desired morphology and chemical composition of the synthesized nanofibers. The optimal gas sensor namely Pd-functionalized, 0.1In2O3-loaded ZnO nanofibers showed a very strong response to 172-50 ppb hydrogen gas at 350 °C, which is regarded as the optimal sensing temperature. Furthermore, the gas sensors showed excellent selectivity to hydrogen gas due to the much lower response to CO and NO2 gases. The enhanced gas response was attributed to the excellent catalytic activity of Pd to hydrogen gas, and the formation of Pd/ZnO and In2O3/ZnO heterojunctions, ZnO-ZnO homojunction, as well as the formation of PdHx. Overall, highly sensitive and selective hydrogen gas sensors can be produced based on a simple methodology using a synergistic effect from Pd functionalization and In2O3 loading in ZnO nanofibers.

Sub-ppm Formaldehyde Detection by n-n TiO2@SnO2 Nanocomposites.

Formaldehyde (HCHO) is an important indicator of indoor air quality and one of the markers for detecting lung cancer. Both medical and air quality applications require the detection of formaldehyde in the sub-ppm range. Nanocomposites SnO2/TiO2 are promising candidates for HCHO detection, both in dark conditions and under UV illumination. Nanocomposites TiO2@SnO2 were synthesized by ALD method using nanocrystalline SnO2 powder as a substrate for TiO2 layer growth. The microstructure and composition of the samples were characterized by ICP-MS, TEM, XRD and Raman spectroscopy methods. The active surface sites were investigated using FTIR and TPR-H2 methods. The mechanism of formaldehyde oxidation on the surface of semiconductor oxides was studied by in situ DRIFTS method. The sensor properties of nanocrystalline SnO2 and TiO2@SnO2 nanocomposites toward formaldehyde (0.06-0.6 ppm) were studied by in situ electrical conductivity measurements in dark conditions and under periodic UV illumination at 50-300 °C. Nanocomposites TiO2@SnO2 exhibit a higher sensor signal than SnO2 and a decrease in the optimal measurement temperature by 50 °C. This result is explained based on the model considering the formation of n-n heterocontact at the SnO2/TiO2 interface. UV illumination leads to a decrease in sensor response compared with that obtained in dark conditions because of the photodesorption of oxygen involved in the oxidation of formaldehyde.

Clinically insignificant prostate cancer suitable for active surveillance according to Prostate Cancer Research International: Active surveillance criteria: Utility of PI-RADS v2.

BACKGROUND: Active surveillance (AS) is an important treatment strategy for prostate cancer (PCa). Prostate Imaging-Reporting and Data System (PI-RADS) v2 has been addressed, but few studies have reported the value of PI-RADS v2 for assessing risk stratification in patients with PCa, especially on selecting potential candidates for AS. PURPOSE: To investigate the utility of PI-RADS v2 and apparent diffusion coefficient (ADC) in evaluating patients with insignificant PCa, who are suitable for AS. STUDY TYPE: Retrospective. SUBJECTS: In all, 238 patients with PCa who met the Prostate Cancer Research International: Active Surveillance criteria underwent radical prostatectomy. FIELD STRENGTH/SEQUENCE: 3.0T, including T2 -weighted, diffusion-weighted, and dynamic contrast-enhanced imaging. ASSESSMENT: Insignificant cancer was defined histopathologically as an organ-confined disease with a tumor volume <0.5 cm3 without Gleason score 4-5. Patients were divided into two groups based on the PI-RADS v2 and tumor ADC: A, PI-RADS score ≤3 and ADC ≥1.095 × 10-3 mm2 /s; and B, PI-RADS score 4-5 or ADC <1.095 × 10-3 mm2 /s. Preoperative clinical and imaging variables were evaluated regarding the associations with insignificant cancer. RESULTS: Of the 238 patients, 101 (42.8%) were diagnosed with insignificant cancer on pathological findings. The number of positive cores, prostate-specific antigen density (PSAD), PI-RADS v2 and tumor ADC were significantly associated with insignificant cancer on univariate analysis (P < 0.05). However, multivariate analysis indicated tumor ADC (odds ratio [OR] = 4.57, P < 0.001) and PI-RADS v2 (OR = 3.60, P < 0.001) were independent predictors of insignificant cancer. Area under the receiver operating characteristics curve (AUC) reached 0.803 when PI-RADS v2 (AUC = 0.747) was combined with tumor ADC (AUC = 0.786). DATA CONCLUSION: The PI-RADS v2 together with tumor ADC may be a useful marker for predicting patients with insignificant PCa when considering AS. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1072-1079.

Prebiopsy Multiparametric MRI With Cancer-Negative Findings in Men With Suspected Prostate Cancer: Evaluation Using Prostate Imaging Reporting and Data System Version 2.

OBJECTIVE: The purpose of this study is to investigate the rates and characteristics of missed cancers at prebiopsy multiparametric MRI with cancer-negative findings according to the Prostate Imaging Reporting and Data System (PI-RADS) version 2 in men with suspected prostate cancer (PCa). MATERIALS AND METHODS: A total of 584 consecutive men (biopsy naive, n = 392; repeated biopsy, n = 192) with suspected PCa who underwent prebiopsy 3-T multipara-metric MRI, followed by subsequent biopsies, were enrolled. Cancer-positive findings were confirmed at systemic biopsies and cognitive MRI-targeted biopsies, whereas cancer-negative findings were confirmed at systemic biopsies performed during subsequent follow-up. Missing and detection rates of all PCa and clinically significant cancer according to five biopsy-based definitions were determined. The likelihood of PCa at multiparametric MRI was evaluated according to PI-RADS version 2, and the results were compared. RESULTS: Pathologically confirmed cancers were found in 25% of patients. Cancer-positive MRI findings were seen in 99 men (17%) and, of these, 85.9% had PCa. Of 485 men with cancer-negative MRI findings, a total of 61 (12.6%) had PCa, including 46 men in the biopsy-naive group and 15 men in the repeated-biopsy group. For clinically significant cancers, the rate of missed cancers at MRI was 0.1-6.0%, and the detection rate was 21.2-83.5%. For detecting PCa, multiparametric MRI had 96.8% specificity, 87.2% accuracy, and 87.4% negative predictive value. CONCLUSION: Prebiopsy 3-T multiparametric MRI with cancer-negative findings missed approximately 12.6% of cases of PCa, including 0.1-6.0% of clinically significant cancers in a cohort of biopsy-naive men and those who had undergone repeated biopsy.

Breast Cancer Heterogeneity: MR Imaging Texture Analysis and Survival Outcomes.

Purpose To determine the relationship between tumor heterogeneity assessed by means of magnetic resonance (MR) imaging texture analysis and survival outcomes in patients with primary breast cancer. Materials and Methods Between January and August 2010, texture analysis of the entire primary breast tumor in 203 patients was performed with T2-weighted and contrast material-enhanced T1-weighted subtraction MR imaging for preoperative staging. Histogram-based uniformity and entropy were calculated. To dichotomize texture parameters for survival analysis, the 10-fold cross-validation method was used to determine cutoff points in the receiver operating characteristic curve analysis. The Cox proportional hazards model and Kaplan-Meier analysis were used to determine the association of texture parameters and morphologic or volumetric information obtained at MR imaging or clinical-pathologic variables with recurrence-free survival (RFS). Results There were 26 events, including 22 recurrences (10 local-regional and 12 distant) and four deaths, with a mean follow-up time of 56.2 months. In multivariate analysis, a higher N stage (RFS hazard ratio, 11.15 [N3 stage]; P = .002, Bonferroni-adjusted α = .0167), triple-negative subtype (RFS hazard ratio, 16.91; P < .001, Bonferroni-adjusted α = .0167), high risk of T1 entropy (less than the cutoff values [mean, 5.057; range, 5.022-5.167], RFS hazard ratio, 4.55; P = .018), and T2 entropy (equal to or higher than the cutoff values [mean, 6.013; range, 6.004-6.035], RFS hazard ratio = 9.84; P = .001) were associated with worse outcomes. Conclusion Patients with breast cancers that appeared more heterogeneous on T2-weighted images (higher entropy) and those that appeared less heterogeneous on contrast-enhanced T1-weighted subtraction images (lower entropy) exhibited poorer RFS. © RSNA, 2016 Online supplemental material is available for this article.

Evaluation of extracapsular extension in prostate cancer using qualitative and quantitative multiparametric MRI.

PURPOSE: To investigate the value of multiparametric magnetic resonance imaging (mpMRI) for extracapsular extension (ECE) in prostate cancer (PCa). MATERIALS AND METHODS: In all, 292 patients who received radical prostatectomy and underwent preoperative mpMRI at 3T were enrolled retrospectively. For determining the associations with ECE, the likelihood of ECE was assessed qualitatively on T2 -weighted imaging (T2 WI) and combined T2 WI and diffusion-weighted imaging (DWI) or dynamic contrast-enhanced imaging (DCEI). Quantitative MRI parameters were measured in PCa based on histopathological findings. Two models for detecting ECE including imaging and clinical parameters were developed using multivariate analysis: Model 1 excluding combined T2 WI and DWI and DCEI and Model 2 excluding combined T2 WI and DWI, and combined T2 WI and DCEI. Diagnostic performance of imaging parameters and models was evaluated using the area under the receiver operating characteristics curve (Az). RESULTS: For detecting ECE, the specificity, accuracy, and Az of combined T2 WI and DWI or DCEI were statistically better than those of T2 WI (P < 0.05), and all quantitative MRI parameters showed a statistical difference between the patients with and without ECE (P < 0.05). On multivariate analysis, significant independent markers in Model 1 were combined T2 WI and DWI, combined T2 WI and DCEI, and Ktrans (P < 0.05). In Model 2, significant markers were combined T2 WI and DWI and DCEI, Ktrans , Kep , and Ve (P < 0.05). The Az values of models 1 and 2 were 0.944 and 0.957, respectively. CONCLUSION: mpMRI may be useful to improve diagnostic accuracy of the models for determining the associations with ECE in PCa. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;45:1760-1770.

Differentiation of mass-forming focal pancreatitis from pancreatic ductal adenocarcinoma: value of characterizing dynamic enhancement patterns on contrast-enhanced MR images by adding signal intensity color mapping.

OBJECTIVES: To evaluate the value of dynamic enhancement patterns on contrast-enhanced MR images by adding signal intensity colour mapping (SICM) to differentiate mass-forming focal pancreatitis (MFFP) from pancreatic ductal adenocarcinoma (PDAC). METHODS: Forty-one clinicopathologically proven MFFPs and 144 surgically confirmed PDACs were enrolled. Laboratory and MR imaging parameters were used to differentiate MFFP from PDAC. In particular, enhancement patterns on MR images adding SICM were evaluated. By using classification tree analysis (CTA), we determined the predictors for the differentiation of MFFP from PDAC. RESULTS: In the CTA, with all parameters except enhancement pattern on SICM images, ductal obstruction grade and T1 hypointensity grade of the pancreatic lesion were the first and second splitting predictor for differentiation of MFFP from PDAC, in order. By adding an enhancement pattern on the SICM images to CTA, the enhancement pattern was the only splitting predictor to differentiate MFFP from PDAC. The CTA model including enhancement pattern on SICM images has sensitivity of 78.0 %, specificity of 99.3 %, and accuracy of 94.6 % for differentiating MFFP from PDAC. CONCLUSION: The characterization of enhancement pattern for pancreatic lesions on contrast-enhanced MR images adding SICM would be helpful to differentiate MFFP from PDAC. KEY POINTS: • SICM was useful to characterize enhancement pattern. • Enhancement pattern on SICM was the only splitting predictor on CTA. • This model may be useful for differentiating MFFP from PDAC.

Effects of long-term treatment on brain volume in patients with obstructive sleep apnea syndrome.

We assessed structural brain damage in obstructive sleep apnea syndrome (OSA) patients (21 males) and the effects of long-term continuous positive airway pressure (CPAP) treatment (18.2 ± 12.4 months; 8-44 months) on brain structures and investigated the relationship between severity of OSA and effects of treatment. Using deformation-based morphometry to measure local volume changes, we identified widespread neocortical and cerebellar atrophy in untreated patients compared to controls (59 males; Cohen's D = 0.6; FDR < 0.05). Analysis of longitudinally scanned magnetic resonance imaging (MRI) scans both before and after treatment showed increased brain volume following treatment (FDR < 0.05). Volume increase was correlated with longer treatment in the cortical areas that largely overlapped with the initial atrophy. The areas overlying the hippocampal dentate gyrus and the cerebellar dentate nucleus displayed a volume increase after treatment. Patients with very severe OSA (AHI > 64) presented with prefrontal atrophy and displayed an additional volume increase in this area following treatment. Higher impairment of working memory in patients prior to treatment correlated with prefrontal volume increase after treatment. The large overlap between the initial brain damage and the extent of recovery after treatment suggests partial recovery of nonpermanent structural damage. Volume increases in the dentate gyrus and the dentate nucleus possibly likely indicate compensatory neurogenesis in response to diminishing oxidative stress. Such changes in other brain structures may explain gliosis, dendritic volume increase, or inflammation. This study provides neuroimaging evidence that revealed the positive effects of long-term CPAP treatment in patients with OSA.