Prediction of Decompensation and Death in Advanced Chronic Liver Disease Using Deep Learning Analysis of Gadoxetic Acid-Enhanced MRI.

OBJECTIVE: This study aimed to evaluate the usefulness of quantitative indices obtained from deep learning analysis of gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and their longitudinal changes in predicting decompensation and death in patients with advanced chronic liver disease (ACLD). MATERIALS AND METHODS: We included patients who underwent baseline and 1-year follow-up MRI from a prospective cohort that underwent gadoxetic acid-enhanced MRI for hepatocellular carcinoma surveillance between November 2011 and August 2012 at a tertiary medical center. Baseline liver condition was categorized as non-ACLD, compensated ACLD, and decompensated ACLD. The liver-to-spleen signal intensity ratio (LS-SIR) and liver-to-spleen volume ratio (LS-VR) were automatically measured on the HBP images using a deep learning algorithm, and their percentage changes at the 1-year follow-up (ΔLS-SIR and ΔLS-VR) were calculated. The associations of the MRI indices with hepatic decompensation and a composite endpoint of liver-related death or transplantation were evaluated using a competing risk analysis with multivariable Fine and Gray regression models, including baseline parameters alone and both baseline and follow-up parameters. RESULTS: Our study included 280 patients (153 male; mean age ± standard deviation, 57 ± 7.95 years) with non-ACLD, compensated ACLD, and decompensated ACLD in 32, 186, and 62 patients, respectively. Patients were followed for 11-117 months (median, 104 months). In patients with compensated ACLD, baseline LS-SIR (sub-distribution hazard ratio [sHR], 0.81; p = 0.034) and LS-VR (sHR, 0.71; p = 0.01) were independently associated with hepatic decompensation. The ΔLS-VR (sHR, 0.54; p = 0.002) was predictive of hepatic decompensation after adjusting for baseline variables. ΔLS-VR was an independent predictor of liver-related death or transplantation in patients with compensated ACLD (sHR, 0.46; p = 0.026) and decompensated ACLD (sHR, 0.61; p = 0.023). CONCLUSION: MRI indices automatically derived from the deep learning analysis of gadoxetic acid-enhanced HBP MRI can be used as prognostic markers in patients with ACLD.

Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI.

OBJECTIVE: We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic acid-enhanced hepatobiliary phase (HBP)-magnetic resonance imaging (MRI) and to evaluate the clinical utility of DLA-assisted assessment of functional liver capacity. MATERIALS AND METHODS: The DLA was developed using HBP-MRI data from 1014 patients. Using an independent test dataset (110 internal and 90 external MRI data), the segmentation performance of the DLA was measured using the Dice similarity score (DSS), and the agreement between the DLA and the ground truth for the volume and SI measurements was assessed with a Bland-Altman 95% limit of agreement (LOA). In 276 separate patients (male:female, 191:85; mean age ± standard deviation, 40 ± 15 years) who underwent hepatic resection, we evaluated the correlations between various DLA-based MRI indices, including liver volume normalized by body surface area (LVBSA), liver-to-spleen SI ratio (LSSR), MRI parameter-adjusted LSSR (aLSSR), LSSR × LVBSA, and aLSSR × LVBSA, and the indocyanine green retention rate at 15 minutes (ICG-R15), and determined the diagnostic performance of the DLA-based MRI indices to detect ICG-R15 ≥ 20%. RESULTS: In the test dataset, the mean DSS was 0.977 for liver segmentation and 0.946 for spleen segmentation. The Bland-Altman 95% LOAs were 0.08% ± 3.70% for the liver volume, 0.20% ± 7.89% for the spleen volume, -0.02% ± 1.28% for the liver SI, and -0.01% ± 1.70% for the spleen SI. Among DLA-based MRI indices, aLSSR × LVBSA showed the strongest correlation with ICG-R15 (r = -0.54, p < 0.001), with area under receiver operating characteristic curve of 0.932 (95% confidence interval, 0.895-0.959) to diagnose ICG-R15 ≥ 20%. CONCLUSION: Our DLA can accurately measure the volume and SI of the liver and spleen and may be useful for assessing functional liver capacity using gadoxetic acid-enhanced HBP-MRI.

Preoperative prediction of postsurgical outcomes in mass-forming intrahepatic cholangiocarcinoma based on clinical, radiologic, and radiomics features.

OBJECTIVES: Current prognostic systems for intrahepatic cholangiocarcinoma (IHCC) rely on surgical pathology data and are not applicable to a preoperative setting. We aimed to develop and validate preoperative models to predict postsurgical outcomes in mass-forming IHCC patients based on clinical, radiologic, and radiomics features. METHODS: This multicenter retrospective cohort study included patients who underwent curative-intent resection for mass-forming IHCC. In the development cohort (single institution data), three preoperative multivariable Cox models for predicting recurrence-free survival (RFS) were constructed, including the clinical-radiologic, radiomics, and clinical-radiologic-radiomics (CRR) models based on clinical and CT findings, CT-radiomics features, and a combination of both, respectively. Model performance was evaluated in the test cohort (data from five institutions) using Harrell's C-index and compared with postoperative prognostic systems. RESULTS: A total of 345 patients (233, development cohort; 112, test cohort) were evaluated. The clinical-radiologic model included five independent CT predictors (infiltrative contour, multiplicity, periductal infiltration, extrahepatic organ invasion, and suspicious metastatic lymph node) and showed similar performance in predicting RFS to the radiomics model (C-index, 0.65 vs. 0.68; p = 0.43 in the test cohort). The CRR model showed significantly improved performance (C-index, 0.71; p = 0.01) than the clinical-radiologic model and demonstrated similar performance to the postoperative prognostic systems in predicting RFS (C-index, 0.71-0.73 vs. 0.70-0.73; p ≥ 0.40) and overall survival (C-index, 0.68-0.71 vs. 0.64-0.74; p ≥ 0.27) in the test cohort. CONCLUSIONS: A model integrating clinical, CT, and radiomics information may be useful for the preoperative assessment of postsurgical outcomes in patients with mass-forming IHCC. KEY POINTS: • The radiomics analysis had incremental value in predicting recurrence-free survival of patients with intrahepatic mass-forming cholangiocarcinoma. • The clinical-radiologic-radiomics model demonstrated similar performance to the postoperatively available prognostic systems (including 8th AJCC system) in predicting recurrence-free survival and overall survival. • The clinical-radiologic-radiomics model may be useful for the preoperative assessment of postsurgical outcomes in patients with mass-forming intrahepatic cholangiocarcinoma.

Radiomics and deep learning in liver diseases.

Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagnostic or predictive tasks using high-dimensional image-derived features and have the potential to expand the capabilities of liver imaging beyond the scope of traditional visual image analysis. Recent research has demonstrated the potential of these techniques in various fields of liver imaging, including staging of liver fibrosis, prognostication of malignant liver tumors, automated detection and characterization of liver tumors, automated abdominal organ segmentation, and body composition analysis. However, because most of the previous studies were preliminary and focused mainly on technical feasibility, further clinical validation is required for the application of radiomics and deep learning in clinical practice. In this review, we introduce the technical aspects of radiomics and deep learning and summarize the recent studies on the application of these techniques in liver radiology.

Combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT.

OBJECTIVES: To develop and validate a combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT. METHODS: One hundred sixty-five patients with vertebral compression fractures were allocated to training (n = 110 [62 acute benign and 48 malignant fractures]) and validation (n = 55 [30 acute benign and 25 malignant fractures]) cohorts. Radiomics features (n = 144) were extracted from non-contrast-enhanced CT images. Radiomics score was constructed by applying least absolute shrinkage and selection operator regression to reproducible features. A combined radiomics-clinical model was constructed by integrating significant clinical parameters with radiomics score using multivariate logistic regression analysis. Model performance was quantified in terms of discrimination and calibration. The model was internally validated on the independent data set. RESULTS: The combined radiomics-clinical model, composed of two significant clinical predictors (age and history of malignancy) and the radiomics score, showed good calibration (Hosmer-Lemeshow test, p > 0.05) and discrimination in both training (AUC, 0.970) and validation (AUC, 0.948) cohorts. Discrimination performance of the combined model was higher than that of either the radiomics score (AUC, 0.941 in training cohort and 0.852 in validation cohort) or the clinical predictor model (AUC, 0.924 in training cohort and 0.849 in validation cohort). The model stratified patients into groups with low and high risk of malignant fracture with an accuracy of 98.2% in the training cohort and 90.9% in the validation cohort. CONCLUSIONS: The combined radiomics-clinical model integrating clinical parameters with radiomics score could predict malignancy in vertebral compression fractures on CT with high discriminatory ability. KEY POINTS: • A combined radiomics-clinical model was constructed to predict malignancy of vertebral compression fractures on CT by combining clinical parameters and radiomics features. • The model showed good calibration and discrimination in both training and validation cohorts. • The model showed high accuracy in the stratification of patients into groups with low and high risk of malignant vertebral compression fractures.

Radiomics and Deep Learning: Hepatic Applications.

Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.

Netrin-1/DCC-mediated PLCγ1 activation is required for axon guidance and brain structure development.

Coordinated expression of guidance molecules and their signal transduction are critical for correct brain wiring. Previous studies have shown that phospholipase C gamma1 (PLCγ1), a signal transducer of receptor tyrosine kinases, plays a specific role in the regulation of neuronal cell morphology and motility in vitro However, several questions remain regarding the extracellular stimulus that triggers PLCγ1 signaling and the exact role PLCγ1 plays in nervous system development. Here, we demonstrate that PLCγ1 mediates axonal guidance through a netrin-1/deleted in colorectal cancer (DCC) complex. Netrin-1/DCC activates PLCγ1 through Src kinase to induce actin cytoskeleton rearrangement. Neuronal progenitor-specific knockout of Plcg1 in mice causes axon guidance defects in the dorsal part of the mesencephalon during embryogenesis. Adult Plcg1-deficient mice exhibit structural alterations in the corpus callosum, substantia innominata, and olfactory tubercle. These results suggest that PLCγ1 plays an important role in the correct development of white matter structure by mediating netrin-1/DCC signaling.

Dynamic contrast-enhanced MRI for oncology drug development.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising tool for evaluating tumor vascularity, as it can provide vasculature-derived, functional, and quantitative parameters. To implement DCE-MRI parameters as biomarkers for monitoring the effect of antiangiogenic or vascular-disrupting treatment, two crucial elements of surrogate endpoint, ie, validation and qualification, should be satisfied. Although early studies have shown the accuracy and reliability of DCE-MRI parameters for evaluating treatment-driven vascular alterations, there have been an increasing number of studies demonstrating the limitations of DCE-MRI parameters as surrogate endpoints. Therefore, in order to improve the application of DCE-MRI parameters in drug development, it is necessary to establish a standardized evaluation method and to determine the correct therapeutics-oriented meaning of individual DCE-MRI parameter. In this regard, this article describes the biophysical background and data acquisition/analysis techniques of DCE-MRI while focusing on the validation and qualification issues. Specifically, the causes of disagreement and confusion encountered in the preclinical and clinical trials using DCE-MRI are presented in detail. Finally, considering these limitations, we present potential strategies to optimize implementation of DCE-MRI. J. Magn. Reson. Imaging 2016;44:251-264.

Dual MRI T1 and T2(*) contrast with size-controlled iron oxide nanoparticles.

Contrast-enhancing magnetic resonance mechanism, employing either positive or negative signal changes, has contrast-specific signal characteristics. Although highly sensitive, negative contrast typically decreases the resolution and spatial specificity of MRI, whereas positive contrast lacks a high contrast-to-noise ratio but offers high spatial accuracy. To overcome these individual limitations, dual-contrast acquisitions were performed using iron oxide nanoparticles and a pair of MRI acquisitions. Specifically, vascular signals in MR angiography were positively enhanced using ultrashort echo (UTE) acquisition, which provided highly resolved vessel structures with increased vessel/tissue contrast. In addition, fast low angle shot (FLASH) acquisition yielded strong negative vessel contrast, resulting in the higher number of discernible vessel branches than those obtained from the UTE method. Taken together, the high sensitivity of the negative contrast delineated ambiguous vessel regions, whereas the positive contrast effectively eliminated the false negative contrast areas (e.g., airways and bones), demonstrating the benefits of the dual-contrast method. FROM THE CLINICAL EDITOR: In this study, the MRI properties of iron oxide nanoparticles were studied in an animal model. These contrast agents are typically considered negative contrast materials, leading to signal loss on T2* weighted images, but they also have known T1 effects as well, which is lower than that of standard positive contrast agents (like gadolinium or manganese) but is still detectable. This dual property was utilized in this study, demonstrating high sensitivity of the negative contrast in delineating ambiguous vessel regions, whereas the positive contrast eliminated false negative contrast areas (areas giving rise to susceptibility effects).

Segmentation of individual renal cysts from MR images in patients with autosomal dominant polycystic kidney disease.

OBJECTIVE: To evaluate the performance of a semi-automated method for the segmentation of individual renal cysts from magnetic resonance (MR) images in patients with autosomal dominant polycystic kidney disease (ADPKD). DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This semi-automated method was based on a morphologic watershed technique with shape-detection level set for segmentation of renal cysts from MR images. T2-weighted MR image sets of 40 kidneys were selected from 20 patients with mild to moderate renal cyst burden (kidney volume < 1500 ml) in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP). The performance of the semi-automated method was assessed in terms of two reference metrics in each kidney: the total number of cysts measured by manual counting and the total volume of cysts measured with a region-based thresholding method. The proposed and reference measurements were compared using intraclass correlation coefficient (ICC) and Bland-Altman analysis. RESULTS: Individual renal cysts were successfully segmented with the semi-automated method in all 20 cases. The total number of cysts in each kidney measured with the two methods correlated well (ICC, 0.99), with a very small relative bias (0.3% increase with the semi-automated method; limits of agreement, 15.2% reduction to 17.2% increase). The total volume of cysts measured using both methods also correlated well (ICC, 1.00), with a small relative bias of <10% (9.0% decrease in the semi-automated method; limits of agreement, 17.1% increase to 43.3% decrease). CONCLUSION: This semi-automated method to segment individual renal cysts in ADPKD kidneys provides a quantitative indicator of severity in early and moderate stages of the disease.

Reproducibility of measurement of apparent diffusion coefficients of malignant hepatic tumors: effect of DWI techniques and calculation methods.

PURPOSE: To evaluate the effect of diffusion-weighted imaging (DWI) methods, apparent diffusion coefficient (ADC) calculation methods, and selection of b-values on the ADCs and the measurement reproducibility of malignant hepatic tumors. MATERIALS AND METHODS: Nineteen patients with pathologically confirmed malignant hepatic tumors underwent breath-hold DWI (b-values = 0, 50, 500 s/mm(2)) and respiratory-triggered DWI (0, 50, 300, 500, 1000 s/mm(2)) twice on a 1.5 T magnetic resonance imaging (MRI) scanner. ADCs were calculated using a two b-value and/or a multiple b-value method. The reproducibility of the ADC measurements was evaluated from the intraclass correlation coefficients (ICCs) and the 95% Bland-Altman limit-of-agreement (LOA). RESULTS: The ADCs were different according to the DWI methods (P = 0.040-0.282), ADC calculation methods (P = 0.003-0.825), and the choice of b-values (P < 0.001). The ADC tended to be more reproducible with use of breath-hold DWI (ICC: 0.898-0.933; LOA, 18.8%-24.0%) than respiratory-triggered DWI (ICC: 0.684-0.928; LOA, 15.0%-31.9%) (P = 0.008-0.122). For respiratory-triggered DWI, the multiple b-value method using five b-values had better reproducibility than the two b-value method for measurement of ADC (P = 0.009-0.221). CONCLUSION: The DWI method, ADC calculation method, and selection of b-values potentially influence the ADCs and the reproducibility of malignant hepatic tumors. ADCs calculated from breath-hold DWI are more reproducible than from respiratory-triggered DWI. A multiple b-value method may improve the reproducibility of respiratory-triggered DWI.

Prostate cancer detection on dynamic contrast-enhanced MRI: computer-aided diagnosis versus single perfusion parameter maps.

OBJECTIVE: The purpose of this article is to assess the value of computer-aided diagnosis (CAD) for prostate cancer detection on dynamic contrast-enhanced MRI (DCE-MRI). MATERIALS AND METHODS: DCE-MRI examinations of 42 patients with prostate cancer were used to generate perfusion parameters, including baseline and peak signal intensities, initial slope, maximum slope within the initial 50 seconds after the contrast injection (slope(50)), wash-in rate, washout rate, time to peak, percentage of relative enhancement, percentage enhancement ratio, time of arrival, efflux rate constant from the extravascular extracellular space to the blood plasma (k(ep)), first-order rate constant for eliminating gadopentetate dimeglumine from the blood plasma (k(el)), and constant depending on the properties of the tissue and represented by the size of the extravascular extracellular space (A(H)). CAD for cancer detection was established by comprehensive evaluation of parameters using a support vector machine. The diagnostic accuracy of single perfusion parameters was estimated using receiver operating characteristic analysis, which determined threshold and parametric maps for cancer detection. The diagnostic performance of CAD for cancer detection was compared with those of T2-weighted imaging (T2WI) and single perfusion parameter maps, using histologic results as the reference standard. RESULTS: The accuracy, sensitivity, and specificity of CAD were 83%, 77%, and 77%, respectively, in the entire prostate; 77%, 91%, and 64%, respectively, in the transitional zone; and 89%, 89%, and 89%, respectively, in the peripheral zone. Values for k(ep), k(el), initial slope, slope(50), wash-in rate, washout rate, and time to peak showed greater area under the curve values (0.803-0.888) than did the other parameters (0.545-0.665) (p < 0.01) and were compared with values for CAD. In the entire prostate, accuracy was greater for CAD than for all perfusion parameters or T2WI (63-77%); sensitivity was greater for CAD than for T2WI, initial slope, wash-in rate, slope(50), and washout rate (38-77%); and specificity was greater for CAD than for T2WI, k(ep), k(el), and time to peak (59-68%) (p < 0.05). CONCLUSION: CAD can improve the diagnostic performance of DCE-MRI in prostate cancer detection, which may vary according to zonal anatomy.

Diagnosis of lymph node metastasis in uterine cervical cancer: usefulness of computer-aided diagnosis with comprehensive evaluation of MR images and clinical findings.

BACKGROUND: Lymph node (LN) status is an important parameter for determining the treatment strategy and for predicting the prognosis for patients with uterine cervical cancer. Computer-aided diagnosis (CAD) can be feasible for differentiating metastatic from non-metastatic lymph nodes in patients with uterine cervical cancer. PURPOSE: To determine the usefulness of CAD that comprehensively evaluates MR images and clinical findings for detecting LN metastasis in uterine cervical cancer. MATERIAL AND METHODS: In 680 LNs from 143 patients who underwent radical hysterectomy for uterine cervical cancer, the CAD system using the Bayesian classifier estimated the probability of metastasis based on MR findings and clinical findings. We compared the diagnostic accuracy for detecting metastatic LNs in the CAD and MR findings. RESULTS: Metastasis was diagnosed in 70 (12%) LNs from 34 (24%) patients. The area under ROC curves of CAD (0.924) was greater than those of the mean ADC (0.854), minimum ADC (0.849), maximum ADC (0.827), short-axis diameter (0.856) and long-axis diameter (0.753) (P < 0.05). The specificity and accuracy of the CAD (86%, 86%) were greater than those of the mean ADC (77%, 77%), maximum ADC (77%, 77%), minimum ADC (68%, 70%), and short-axis diameter (65%, 67%) (P < 0.05). CONCLUSION: CAD system can improve the diagnostic performance of MR for detecting metastatic LNs in uterine cervical cancer.

Feasibility of FAIR imaging for evaluating tumor perfusion.

PURPOSE: To evaluate the feasibility of flow-sensitive alternating inversion recovery (FAIR) for measuring blood flow in tumor models. MATERIALS AND METHODS: In eight mice tumor models, FAIR and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was performed. The reliability for measuring blood flow on FAIR was evaluated using the coefficient of variation of blood flow on psoas muscle. Three regions of interest (ROIs) were drawn in the peripheral, intermediate, and central portions within each tumor. The location of ROI was the same on FAIR and DCE-MR images. The correlation between the blood flow on FAIR and perfusion-related parameters on DCE-MRI was evaluated using the Pearson correlation coefficient. RESULTS: The coefficient of variation for measuring blood flow was 9.8%. Blood flow on FAIR showed a strong correlation with Kep (r = 0.77), percent relative enhancement (r = 0.73), and percent enhancement ratio (r = 0.81). The mean values of blood flow (mL/100 g/min) (358 vs. 207), Kep (sec(-) (1)) (7.46 vs. 1.31), percent relative enhancement (179% vs. 134%), and percent enhancement ratio (42% vs. 26%) were greater in the peripheral portion than in the central portion (P < 0.01). CONCLUSION: As blood flow measurement on FAIR is reliable and closely related with that on DCE-MR, FAIR is feasible for measuring tumor blood flow.

Malignant hepatic tumors: short-term reproducibility of apparent diffusion coefficients with breath-hold and respiratory-triggered diffusion-weighted MR imaging.

PURPOSE: To prospectively evaluate the reproducibility of apparent diffusion coefficient (ADC) measurement in malignant hepatic tumors and to examine the influence of imaging methods, lesion location, and lesion size on the reproducibility of ADC measurement. MATERIALS AND METHODS: The institutional review board approved the study protocol, and informed consent was obtained. Forty-nine patients underwent both breath-hold and respiratory-triggered diffusion-weighted (DW) magnetic resonance imaging on a 1.5-T system twice. Two independent readers measured the ADC of the largest malignant hepatic tumor for each patient on each image sets. Mean ADCs were compared between repeated acquisitions and imaging techniques by using the paired t test. Reproducibility of the ADC measurement and interobsever agreement were determined by using 95% Bland-Altman limits of agreement and intraclass correlation coefficients (ICCs). The effects of the imaging technique, lesion location, and lesion size on the reproducibility of the ADC measurements were assessed by comparing ICCs by using the z test. RESULTS: There were no significant differences in the mean ADC between repeated acquisitions for breath-hold ([1.266-1.275] x 10(-3) mm(2)/sec vs [1.285-1.290] x 10(-3) mm(2)/sec; P = .572-.634) or respiratory-triggered ([1.487-1.502] x 10(-3) mm(2)/sec vs [1.421-1.441] x 10(-3) mm(2)/sec; P = .073-.091) DW MR imaging. The mean ADCs measured by using the respiratory-triggered method ([1.421-1.502] x 10(-3) mm(2)/sec) were significantly higher than those measured by using the breath-hold method ([1.266-1.290] x 10(-3) mm(2)/sec) (P < or = .001). The 95% limits of agreement between ADCs measured on repeated DW images were 28.7%-31.3% of the mean, and those between ADCs measured by two readers were 14.6%-22.5% of the mean. ADC measurement of malignant hepatic tumors tended to be more reproducible for right-lobe than for left-lobe lesions and for larger rather than smaller lesions. CONCLUSION: Changes in ADCs of less than approximately 30% fall into the range of measurement error. Imaging technique significantly affected ADCs of malignant hepatic tumors. Lesion location and size are potentially influential on the reproducibility of ADC measurement.

Slope of emphysema index: an objective descriptor of regional heterogeneity of emphysema and an independent determinant of pulmonary function.

OBJECTIVE: The purpose of this study was to compare quantitative and visual assessments of regional heterogeneity of emphysema and to investigate the influence of regional heterogeneity on pulmonary function in smoking-related emphysema. MATERIALS AND METHODS: We developed an automatic computerized algorithm to quantitatively assess heterogeneity in the upper-lower, anterior-posterior, and central-peripheral directions. The emphysema index was plotted with a linear function (emphysema index slopes: slope of emphysema index in upper-lower direction, slope of emphysema index in anterior-posterior direction, and slope of emphysema index in central-peripheral direction) for consecutive 1-pixel-thick slices using volumetric CT data of 59 patients (58 men and one woman; mean age, 65.7 years). Emphysema index was defined as the percentage area of lung with attenuation values below -950 HU. Visual assessment was performed using a 5-point scoring system. Quantitative and visual assessments were compared. Multiple linear regression was performed to evaluate the influence of emphysema index and emphysema index slopes on the pulmonary function test. RESULTS: Quantitative and visual assessments were significantly correlated in both upper-lower (r(2) = 0.40 and r(2) = 0.67 for observers 1 and 2, respectively) and central-peripheral (r(2) = 0.51 and r(2) = 0.47, respectively) directions. Multiple linear regression revealed that emphysema index, slope of emphysema index in upper-lower direction, and slope of emphysema index in anterior-posterior direction were independent determinants of forced expiratory volume in 1 second (FEV(1)) (r(2) = 0.30; p < 0.001). Emphysema index and slope of emphysema index in upper-lower direction were independent determinants of the ratio of FEV(1) to forced vital capacity (FEV(1)/FVC) (r(2) = 0.32; p < 0.001). In addition to higher emphysema index, lower and posterior lung dominance was associated with a decrease in FEV(1) and FEV(1)/FVC. CONCLUSION: Computerized, quantitative assessment using the emphysema index slope is comparable to visual assessment in the evaluation of regional heterogeneity of emphysema. In addition to the emphysema index, regional heterogeneity of smoking-related emphysema contributes to impairment of pulmonary function.

Node-by-node correlation between MR and PET/CT in patients with uterine cervical cancer: diffusion-weighted imaging versus size-based criteria on T2WI.

The purpose of the study was to perform a node-by-node comparison of an ADC-based diagnosis and various size-based criteria on T2-weighted imaging (T2WI) with regard to their correlation with PET/CT findings in patients with uterine cervical cancer. In 163 patients with 339 pelvic lymph nodes (LNs) with short-axis diameter >5 mm, the minimum apparent diffusion coefficient (ADC), mean ADC, short- and long-axis diameters, and ratio of long- to short-axis diameters (L/S ratio) were compared in PET/CT-positive and -negative LNs. On PET/CT, 118 (35%) LNs in 58 patients were positive. The mean value of minimum and mean ADCs, short- and long-axis diameters, and L/S ratio were different in PET/CT-positive (0.6436 x 10(-3) mm(2)/s, 0.756 x 10(-3) mm(2)/s, 10.3 mm, 13.2 mm, 1.32, respectively) and PET/CT-negative LNs (0.8893 x 10(-3) mm(2)/s, 1.019 x 10(-3) mm(2)/s, 7.4 mm, 11.0 mm, 1.49, respectively) (P < 0.05). The Az value of the minimum ADC (0.864) was greater than those of mean ADC (0.836), short-axis diameter (0.764), long-axis diameter (0.640) and L/S ratio (0.652) (P < 0.05). The sensitivity and accuracy of the minimum ADC (86%, 82%) were greater than those of the short-axis diameter (55%, 74%), long-axis diameter (73%, 58%) and L/S ratio (52%, 66%) (P < 0.05). ADC showed superior correlation with PET/CT compared with conventional size-based criteria on T2WI.

Relative apparent diffusion coefficient: determination of reference site and validation of benefit for detecting metastatic lymph nodes in uterine cervical cancer.

PURPOSE: To determine the reference site for relative apparent diffusion coefficient (rADC) and to evaluate the benefit of rADC for detecting metastatic lymph nodes in uterine cervical cancer. MATERIALS AND METHODS: Two observers independently measured ADCs in the spleen, liver, renal cortex, lumbar spine, lumbar spinal cord, and gluteus maximus on diffusion-weighted images (b value, 0 and 1000 mm/sec(2)) in 50 patients. The reference site for rADC was determined using the intra- and interobserver coefficient of variation (CV) of ADC in these organs. rADC was calculated by ADC(lesion)/ADC(reference site). The benefit of rADC over ADC was validated by comparing the area under the receiver operating curve for identifying metastatic lymph nodes in uterine cervical cancer in 130 patients. RESULTS: The renal cortex was determined to be the reference site for rADC, as its CVs (intraobserver, 5%-7%; interobserver, 5%) were less than those of the other organs (P < 0.05). The ADC and rADC of metastatic lymph nodes (n = 29, ADC, 0.7483 x 10(-3) mm(2)/sec; rADC, 0.3832) were less than those of nonmetastatic lymph nodes (n = 229, ADC, 0.9960 x 10(-3) mm(2)/sec; rADC, 0.5383) (P < 0.05). The area under the receiver operating characteristics curve for differentiating metastatic from nonmetastatic lymph nodes was greater for rADC (0.914; 95% confidence interval [CI], 0.872-0.945) than for ADC (0.872; 95% CI, 0.825-0.910) (P = 0.007). CONCLUSION: The renal cortex is an appropriate reference site for rADC and rADC may improve the accuracy for diagnosing metastatic lymph nodes in uterine cervical cancer.

Apparent diffusion coefficient: prostate cancer versus noncancerous tissue according to anatomical region.

PURPOSE: To evaluate diagnostic performance of apparent diffusion coefficient (ADC) in differentiating prostate cancer from noncancerous tissue according to anatomical region. MATERIALS AND METHODS: In 47 patients with diffusion-weighted-MR (b-value, 0 and 1000 sec/mm2) on a 1.5 T unit, ADCs were measured in prostate cancer and in three noncancerous tissues (transitional zone, peripheral zone, and prostatic base). Diagnostic performance of ADC for differentiating cancer from noncancerous tissue was evaluated using receiver-operating-characteristics (ROC) analysis. RESULTS: Mean ADC of prostate cancer (0.963x10(-3) mm2/s) was lower than those of all noncancerous tissues (P<0.001). In noncancerous tissue, ADC differed according to anatomical region (peripheral zone, 1.572x10(-3) mm2/sec; transitional zone, 1.441x10(-3) mm2/sec; prostatic base, 1.146x10(-3) mm2/sec) (P<0.01). ADC was lower in prostate cancer than in all noncancerous tissues in 34 (72%) patients. Area under the ROC curve for differentiating cancer from noncancerous tissue in prostatic base (0.725) was less than those for differentiating cancer from noncancerous tissue in peripheral (0.952) and transitional zones (0.906) (P<0.05). Sensitivity differed according to anatomical region (peripheral zone, 98%; transitional zone, 82%; prostatic base, 66%) (P<0.05). CONCLUSION: Variable ADC in noncancerous tissue according to anatomical region may limit diagnostic performance of ADC for cancer detection.

Feasibility of diffusion-weighted imaging in the differentiation of metastatic from nonmetastatic lymph nodes: early experience.

PURPOSE: To investigate the feasibility of diffusion-weighted imaging (DWI) in the differentiation of metastatic from nonmetastatic lymph nodes. MATERIALS AND METHODS: In 125 patients who underwent lymph node dissection for uterine cervical cancer, DWI was performed at b value of 0 and 1000 s/mm2. By referring to the surgical maps of the pelvic lymph nodes, the apparent diffusion coefficient (ADC) was compared in the metastatic and nonmetastatic lymph nodes, and receiver-operating-characteristics analysis was performed to evaluate the diagnostic performance of the ADC in differentiating metastatic from nonmetastatic lymph nodes. RESULTS: The ADC were significantly lower in the metastatic lymph nodes (0.7651x10(-3) mm2/s+/-0.1137) than in the nonmetastatic lymph nodes (1.0021x10(-3) mm2/s+/-0.1859; P<0.001). The area-under-the-curve of ADC for differentiating metastatic from nonmetastatic lymph nodes, was 0.902. The sensitivity and specificity of ADC for differentiating metastatic from nonmetastatic lymph nodes, were 87% for the ADC and 80%, respectively. CONCLUSION: DWI is feasible for differentiating metastatic from nonmetastatic lymph nodes in patients with uterine cervical cancer.