Yu Sub Sung

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.

Feasibility of Automated Quantification of Regional Disease Patterns Depicted on High-Resolution Computed Tomography in Patients with Various Diffuse Lung Diseases

Objective This study was designed to develop an automated system for quantification of various regional disease patterns of diffuse lung diseases as depicted on high-resolution computed tomography (HRCT) and to compare the performance of the automated system with human readers. Materials and Methods A total of 600 circular regions-of-interest (ROIs), 10 pixels in diameter, were utilized. The 600 ROIs comprised 100 ROIs that represented six typical regional patterns (normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). The ROIs were used to train the automated classification system based on the use of a Support Vector Machine classifier and 37 features of texture and shape. The performance of the classification system was tested with a 5-fold cross-validation method. An automated quantification system was developed with a moving ROI in the lung area, which helped classify each pixel into six categories. A total of 92 HRCT images obtained from patients with different diseases were used to validate the quantification system. Two radiologists independently classified lung areas of the same CT images into six patterns using the manual drawing function of dedicated software. Agreement between the automated system and the readers and between the two individual readers was assessed. Results The overall accuracy of the system to classify each disease pattern based on the typical ROIs was 89%. When the quantification results were examined, the average agreement between the system and each radiologist was 52% and 49%, respectively. The agreement between the two radiologists was 67%. Conclusion An automated quantification system for various regional patterns of diffuse interstitial lung diseases can be used for objective and reproducible assessment of disease severity.

Intravoxel incoherent motion diffusion-weighted MRI of the abdomen: The effect of fitting algorithms on the accuracy and reliability of the parameters.

To evaluate the influence of fitting methods on the accuracy and reliability of intravoxel incoherent motion (IVIM) parameters, with a particular emphasis on the constraint function.

Perfusion Assessment Using Intravoxel Incoherent Motion-Based Analysis of Diffusion-Weighted Magnetic Resonance Imaging: Validation Through Phantom Experiments.

ObjectivesThe aims of this study were to demonstrate the theoretical meaning of intravoxel incoherent motion (IVIM) parameters and to compare the robustness of 2 biexponential fitting methods through magnetic resonance experiments using IVIM phantoms. Materials and MethodsIntravoxel incoherent motion imaging was performed on a 3 T magnetic resonance imaging scanner using 15 b values (0–800 s/mm2) for 4 phantoms with different area fractions of the flowing water compartment (FWC%), at the infusion flow rates of 0, 1, 2, and 3 mL/min. Images were quantitatively analyzed using monoexponential free biexponential, and segmented biexponential fitting models. ResultsThere were some inconsistent variations in Dslow with changing flow rates. The perfusion fraction, f, showed a significant positive correlation with the flow rate for both the free and segmented fitting methods (&rgr; = 0.838 to 0.969; P < 0.001). The fast diffusion coefficient, Dfast, had a significant positive correlation with the flow rate for segmented fitting (&rgr; = 0.745 to 0.969; P < 0.001), although it showed an inverse correlation with the flow rate for free fitting (&rgr; = −0.527 to −0.791; P ⩽ 0.017). Significant positive correlations with the FWC% of the phantoms were noted for f (P = 0.510 for free fitting and P = 0.545 for segmented fitting, P < 0.001). ConclusionsThe IVIM model allows for an approximate segmentation of molecular diffusion and perfusion, with a minor contribution of the perfusion effect on Dslow. The f and Dfast can provide a rough estimation of the flow fraction and flow velocity. Segmented fitting may be a more robust method than free fitting for calculating the IVIM parameters, especially for Dfast.

Does Computer-Aided Diagnosis Permit Differentiation of Angiomyolipoma Without Visible Fat From Renal Cell Carcinoma on MDCT?

OBJECTIVE The purpose of this study was to evaluate the diagnostic value of computer-aided diagnosis (CADx) in differentiating angiomyolipoma without visible fat from renal cell carcinoma (RCC) on MDCT. MATERIALS AND METHODS The study included 406 patients who had 47 angiomyolipomas without visible fat and 359 RCCs smaller than 4 cm, all of which were diagnosed on the basis of findings from nephrectomy or percutaneous biopsy performed at our institution between 2000 and 2011. MDCT (slice thickness, 2.5 mm for corticomedullary phase image or 5 mm for the other phase images) and clinical findings were blindly reviewed by two radiologists in a single session. At the time the study was performed, radiologist 1 had 8 years of experience, and radiologist 2 had 18 years of experience. On the basis of the MDCT and clinical findings, CADx classified renal tumors as angiomyolipoma and RCC, and each radiologist independently recorded the probability score (0-5) for angiomyolipoma. The accuracy of CADx versus radiologists in diagnosing angiomyolipoma was compared using ROC analysis. Interobserver agreement between the two radiologists was evaluated. RESULTS CADx yielded an area under the curve (Az) value of 0.949, which was greater than the Az values yielded by radiologists 1 and 2 (0.872 and 0.782, respectively; p < 0.05). In addition, the Az value for radiologist 1 was greater than that for radiologist 2 (p = 0.01). CADx with a threshold of -1.0085 showed greater sensitivity than radiologist 1 and greater sensitivity, specificity, and accuracy than radiologist 2 (p < 0.05). The interobserver agreement for the differentiation was fair (κ = 0.289). CONCLUSION CAD can improve diagnostic performance in differentiating angiomyolipoma from RCC. The diagnostic performance of radiologists is variable according to the clinical experience and physical and emotional states of the radiologists.

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.

Intravoxel incoherent motion diffusion‐weighted imaging of the pancreas: Characterization of benign and malignant pancreatic pathologies

To evaluate the diagnostic value of apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) parameters in differentiating patients with either a normal pancreas (NP), pancreatic ductal adenocarcinoma (PDAC), neuroendocrine tumor (NET), solid pseudopapillary tumor (SPT), acute pancreatitis (AcP), vs. autoimmune pancreatitis (AIP).

Effect of various binning methods and ROI sizes on the accuracy of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of texture features at HRCT

To find optimal binning, variable binning size linear binning (LB) and non-linear binning (NLB) methods were tested. In case of small binning size (Q ≤ 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. To find optimal binning method and ROI size of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of textural analysis at HRCT Six-hundred circular regions of interest (ROI) with 10, 20, and 30 pixel diameter, comprising of each 100 ROIs representing six regional disease patterns (normal, NL; ground-glass opacity, GGO; reticular opacity, RO; honeycombing, HC; emphysema, EMPH; and consolidation, CONS) were marked by an experienced radiologist from HRCT images. Histogram (mean) and co-occurrence matrix (mean and SD of angular second moment, contrast, correlation, entropy, and inverse difference momentum) features were employed to test binning and ROI effects. To find optimal binning, variable binning size LB (bin size Q: 4~30, 32, 64, 128, 144, 196, 256, 384) and NLB (Q: 4~30) methods (K-means, and Fuzzy C-means clustering) were tested. For automated classification, a SVM classifier was implemented. To assess cross-validation of the system, a five-folding method was used. Each test was repeatedly performed twenty times. Overall accuracies with every combination of variable ROIs, and binning sizes were statistically compared. In case of small binning size (Q ≤ 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. In case of 30x30 ROI size and most of binning size, the K-means method showed better than other NLB and LB methods. When optimal binning and other parameters were set, overall sensitivity of the classifier was 92.85%. The sensitivity and specificity of the system for each class were as follows: NL, 95%, 97.9%; GGO, 80%, 98.9%; RO 85%, 96.9%; HC, 94.7%, 97%; EMPH, 100%, 100%; and CONS, 100%, 100%, respectively. We determined the optimal binning method and ROI size of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of texture features at HRCT.

Quantitative Computed Tomography Features for Predicting Tumor Recurrence in Patients with Surgically Resected Adenocarcinoma of the Lung

Purpose The purpose of this study was to determine if preoperative quantitative computed tomography (CT) features including texture and histogram analysis measurements are associated with tumor recurrence in patients with surgically resected adenocarcinoma of the lung. Methods The study included 194 patients with surgically resected lung adenocarcinoma who underwent preoperative CT between January 2013 and December 2013. Quantitative CT feature analysis of the lung adenocarcinomas were performed using in-house software based on plug-in package for ImageJ. Ten quantitative features demonstrating the tumor size, attenuation, shape and texture were extracted. The CT parameters obtained from 1-mm and 5-mm data were compared using intraclass correlation coefficients. Univariate and multivariable logistic regression methods were used to investigate the association between tumor recurrence and preoperative CT findings. Results The 1-mm and 5-mm data were highly correlated in terms of diameter, perimeter, area, mean attenuation and entropy. Circularity and aspect ratio were moderately correlated. However, skewness and kurtosis were poorly correlated. Multivariable logistic regression analysis revealed that area (odds ratio [OR], 1.002 for each 1-mm2 increase; P = 0.003) and mean attenuation (OR, 1.005 for each 1.0-Hounsfield unit increase; P = 0.022) were independently associated with recurrence. The receiver operating curves using these two independent predictive factors showed high diagnostic performance in predicting recurrence (C-index = 0.81, respectively). Conclusion Tumor area and mean attenuation are independently associated with recurrence in patients with surgically resected adenocarcinoma of the lung.

Detection of Local Tumor Recurrence After Definitive Treatment of Head and Neck Squamous Cell Carcinoma: Histogram Analysis of Dynamic Contrast-Enhanced T1-Weighted Perfusion MRI

OBJECTIVE This study aimed to explore the added value of histogram analysis of the ratio of initial to final 90-second time-signal intensity AUC (AUCR) for differentiating local tumor recurrence from contrast-enhancing scar on follow-up dynamic contrast-enhanced T1-weighted perfusion MRI of patients treated for head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS AUCR histogram parameters were assessed among tumor recurrence (n = 19) and contrast-enhancing scar (n = 27) at primary sites and compared using the t test. ROC analysis was used to determine the best differentiating parameters. The added value of AUCR histogram parameters was assessed when they were added to inconclusive conventional MRI results. RESULTS Histogram analysis showed statistically significant differences in the 50th, 75th, and 90th percentiles of the AUCR values between the two groups (p < 0.05). The 90th percentile of the AUCR values (AUCR90) was the best predictor of local tumor recurrence (AUC, 0.77; 95% CI, 0.64-0.91) with an estimated cutoff of 1.02. AUCR90 increased sensitivity by 11.7% over that of conventional MRI alone when added to inconclusive results. CONCLUSION Histogram analysis of AUCR can improve the diagnostic yield for local tumor recurrence during surveillance after treatment for HNSCC.