Background Until now, several investigators have explored the value of diffusion-weighted magnetic resonance imaging (DWI) for the preoperative tumor grading of endometrial cancer. However, the diagnostic value of DWI with quantitative analysis of apparent diffusion coefficient (ADC) has been controversial. Purpose To explore the role of histogram analysis of ADC maps based on entire tumor volume in determining the grade of endometrial cancer. Material and Methods This study was IRB-approved with waiver of informed consent. Thirty-three patients with endometrial cancer underwent DWI (b = 0, 600, 1000 s/mm2), and corresponding ADC maps were acquired. Regions of interest (ROIs) were drawn on all slices of the ADC map in which the tumor was visualized including areas of necrosis to derive volume-based histographic ADC data. Histogram parameters (5th–95th percentiles, mean, standard deviation, skewness, kurtosis) were correlated with histological grade using one-way ANOVA with Tukey-Kramer test for post hoc comparisons, and were compared between high (grade 3) and low (grades 1/2) grade using Student t-test. ROC curve analysis was performed to determine the optimum threshold value for each parameter, and their corresponding sensitivity and specificity. Results The standard deviation, quartile, 75th, 90th, and 95th percentiles of ADC showed significant differences between grades (P ≤ 0.03 for all) and between high and low grades (P ≤ 0.024 for all). There were no significant correlations between tumor grade and other parameters. ROC curve analysis yielded sensitivities and specificities of 75% and 96%, 62.5% and 92%, 100% and 52%, 100% and 72%, and 100% and 88%, using standard deviation, quartile, 75th, 90th, and 95th percentiles for determining high grade with corresponding areas under the curve (AUCs) of 0.787, 0.792, 0.765, 0.880, and 0.925, respectively. Conclusion Histogram analysis of ADC maps based on entire tumor volume can be useful for predicting the histological grade of endometrial cancer. The 90th and 95th percentiles of ADC were the most promising parameters for differentiating high from low grade.
Background Many imaging methods have been defined for quantification of hepatic steatosis in non-alcoholic fatty liver disease (NAFLD). However, studies comparing the efficiency of magnetic resonance imaging-proton density fat fraction (MRI-PDFF), magnetic resonance spectroscopy (MRS), and liver histology for quantification of liver fat content are limited. Purpose To compare the efficiency of MRI-PDFF and MRS in the quantification of liver fat content in individuals with NAFLD. Material and Methods A total of 19 NAFLD patients underwent MRI-PDFF, MRS, and liver biopsy for quantification of liver fat content. The MR examinations were performed on a 1.5 HDx MRI system. The MRI protocol included T1-independent volumetric multi-echo gradient-echo imaging with T2* correction and spectral fat modeling and MRS with STEAM technique. Results A close correlation was observed between liver MRI-PDFF- and histology- determined steatosis (r = 0.743, P < 0.001) and between liver MRS- and histology-determined steatosis (r = 0.712, P < 0.001), with no superiority between them (ƶ = 0.19, P = 0.849). For quantification of hepatic steatosis, a high correlation was observed between the two MRI methods (r = 0.986, P < 0.001). MRI-PDFF and MRS accurately differentiated moderate/severe steatosis from mild/no hepatic steatosis (P = 0.007 and 0.013, respectively), with no superiority between them (AUCMRI-PDFF = 0.881 ± 0.0856 versus AUCMRS = 0.857 ± 0.0924, P = 0.461). Conclusion Both MRI-PDFF and MRS can be used for accurate quantification of hepatic steatosis.
Background Gallium-68 somatostatin receptor positron emission tomography (PET) has been used in the diagnosis of neuroendocrine tumors (NETs). The compounds often used in molecular imaging of NETs with PET are 68Ga-DOTATOC, 68Ga-DOTATATE, and 68Ga-DOTANOC. There is varying affinity to different somatostatin receptors. Purpose To systematically review and perform a meta-analysis of published data regarding the diagnostic role of 68Ga-DOTATOC and 68Ga-DOTATATE PET in the diagnosis of NETs. Material and Methods A comprehensive literature search of studies published through 30 April 2013 regarding 68Ga-DOTATOC and 68Ga-DOTATATE PET in the diagnosis of NETs was performed using the PubMed/MEDLINE, Embase, and Scopus databases. Pooled sensitivity and specificity of 68Ga-DOTATOC and 68Ga-DOTATATE PET in the diagnosis of NETs were calculated. The area under the receiver-operating characteristic (ROC) curve was calculated to measure the accuracy of 68Ga-DOTATOC and 68Ga-DOTATATE PET in the diagnosis of NETs. Results Ten studies comprising 416 patients with NETs were included in this meta-analysis. The pooled sensitivity of 68Ga-DOTATOC and 68Ga-DOTATATE PET in the diagnosis of NETs calculated on a per-patient-based analysis was 93% (95% confidence interval [CI] 89–96%) and 96% (95% CI 91–99%). The pooled specificity of 68Ga-DOTATOC and 68Ga-DOTATATE PET in diagnosing NETs was 85% (95% CI 74–93%) and 100% (95% CI 82–100%). The area under the ROC curve of 68Ga-DOTATOC and 68Ga-DOTATATE PET was 0.96 and 0.98, respectively, on a per-patient-based analysis. Conclusion The molecular imaging agents 68Ga-DOTATOC and 68Ga-DOTATATE demonstrated high sensitivity and specificity in the diagnosis of NETs on PET scan. Although both are accurate tools in the diagnosis of NETs, 68Ga-DOTATATE PET may be more sensitive and specific than 68Ga-DOTATOC PET scan.
Background Anti-1-amino-3-[18F]fluorocyclobutane-1-carboxylic acid (anti-3-18F-FACBC) positron emission tomography/computed tomography (PET/CT), 11 C-choline PET/CT, 111In–capromab pendetide, and T2-weighted magnetic resonance imaging (MRI) have been used for detecting prostate carcinoma relapse. Purpose To systematically review and perform a meta-analysis of published data regarding the performance of 18F-FACBC PET/CT in the diagnosis of recurrent prostate carcinoma. Material and Methods A comprehensive review of the literature regarding the role of 18F-FACBC PET/CT in the diagnosis of recurrent prostate carcinoma was performed. Pooled sensitivity, specificity, and the area under the receiver-operating characteristic of 18F-FACBC PET/CT in the diagnosis of recurrent prostate carcinoma were calculated based on the included studies. Results Six studies comprising 251 patients, suspicious of prostate carcinoma recurrence, were included in this meta-analysis. 18F-FACBC PET/CT had an 87% pooled sensitivity, 66% pooled specificity, 0.93 the area under the receiver-operating characteristic curve on a per patient-based analysis in detecting prostate carcinoma recurrence. Conclusion 18F-FACBC PET/CT was a non-invasive, metabolic imaging technique in the diagnosis of prostate carcinoma relapse.
Background Digital breast tomosynthesis (DBT) is a promising new technology. Some experimental clinical studies have shown positive results, but the future role and indications of this new technique, whether in a screening or clinical setting, need to be evaluated. Purpose To compare digital mammography and DBT in a side-by-side feature analysis for cancer conspicuity, and to assess whether there is a potential additional value of DBT to standard state-of-the-art conventional imaging work-up with respect to detection of additional malignancies. Material and Methods The study had ethics committee approval. A total of 129 women underwent 2D digital mammography including supplementary cone-down and magnification views and breast ultrasonography if indicated, as well as digital breast tomosynthesis. The indication for conventional imaging in the clinical setting included a palpable lump in 30 (23%), abnormal mammographic screening findings in 54 (42%), and surveillance in 45 (35%) of the women. The women were examined according to present guidelines, including spot-magnification views, ultrasonography, and needle biopsies, if indicated. The DBT examinations were interpreted several weeks after the conventional imaging without knowledge of the conventional imaging findings. In a later session, three radiologists performed a side-by-side feature analysis for cancer conspicuity in a sample of 50 cases. Results State-of-the-art conventional imaging resulted in needle biopsy of 45 breasts, of which 20 lesions were benign and a total of 25 cancers were diagnosed. The remaining 84 women were dismissed with a normal/definitely benign finding and without indication for needle biopsy. The subsequent DBT interpretation found suspicious findings in four of these 84 women, and these four women had to be called back for repeated work-up with knowledge of the tomosynthesis findings. These delayed work-ups resulted in two cancers (increasing the cancer detection by 8%) and two false-positive findings. The side-by-side feature analysis showed higher conspicuity scores for tomosynthesis compared to conventional 2D for cancers presenting as spiculated masses and distortions. Conclusion Tomosynthesis is a promising new technique. Our preliminary clinical experience shows that there is a potential for increasing the sensitivity using this new technique, especially for cancers manifesting as spiculated masses and distortions.
Background: Today, practically all computed tomography (CT) systems are delivered with automatic exposure control (AEC) systems operating with tube current modulation in three dimensions. Each of these systems has different specifications and operates somewhat differently. Purpose: To evaluate AEC systems from four different CT scanner manufacturers: General Electric (GE), Philips, Siemens, and Toshiba, considering their potential for reducing radiation exposure to the patient while maintaining adequate image quality. Material and Methods: The dynamics (adaptation along the longitudinal axis) of tube current modulation of each AEC system were investigated by scanning an anthropomorphic chest phantom using both 16- and 64-slice CT scanners from each manufacturer with the AEC systems activated and inactivated. The radiation dose was estimated using the parameters in the DICOM image information and image quality was evaluated based on image noise (standard deviation of CT numbers) calculated in 0.5 cm2 circular regions of interest situated throughout the spine region of the chest phantom. Results: We found that tube current modulation dynamics were similar among the different AEC systems, especially between GE and Toshiba systems and between Philips and Siemens systems. Furthermore, the magnitude of the reduction in the exposure dose was considerable, in the range of 35-60%. However, in general the image noise increased when the AEC systems were used, especially in regions where the tube current was greatly decreased, such as the lung region. However, the variation in image noise among images obtained along the scanning direction was lower when using the AEC systems compared with fixed mAs. Conclusion: The AEC systems available in modern CT scanners can contribute to a significant reduction in radiation exposure to the patient and the image noise becomes more uniform within any given scan.
Background In patients with non-small-cell lung carcinoma NSCLC the lymph node staging in the mediastinum is important due to impact on management and prognosis. Computed tomography texture analysis (CTTA) is a postprocessing technique that can evaluate the heterogeneity of marked regions in images. Purpose To evaluate if CTTA can differentiate between malignant and benign lymph nodes in a cohort of patients with suspected lung cancer. Material and Methods With tissue sampling as reference standard, 46 lymph nodes from 29 patients were analyzed using CTTA. For each lymph node, CTTA was performed using a research software “TexRAD” by drawing a region of interest (ROI) on all available axial contrast-enhanced computed tomography (CT) slices covering the entire volume of the lymph node. Lymph node CTTA comprised image filtration-histogram analysis undertakes two stages: the first step comprised an application of a Laplacian of Gaussian filter to highlight fine to coarse textures within the ROI, followed by a quantification of textures via histogram analysis using mean gray-level intensity from the entire volume of the lymph nodes. Results CTTA demonstrated a statistically significant difference between the malignant and the benign lymph nodes (P = 0.001), and by binary logistic regression we obtained a sensitivity of 53% and specificity of 97% in the test population. The area under the receiver operating curve was 83.4% and reproducibility was excellent. Conclusion CTTA may be helpful in differentiating between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer, with a low intra-observer variance.
Background Pancreatic neuroendocrine tumors (PNET) include heterogeneous tumors with a variable degree of inherent biologic aggressiveness represented by the histopathologic grade. Although several studies investigated the computed tomography (CT) characteristics which can predict the histopathologic grade of PNET, accurate prediction of the PNET grade by CT examination alone is still limited. Purpose To investigate the important CT findings and CT texture variables for prediction of grade of PNET. Material and Methods Sixty-six patients with pathologically confirmed PNETs (grade 1 = 45, grades 2/3 = 21) underwent preoperative contrast-enhanced CT. Two reviewers determined the presence of predefined CT findings. CT texture was also analyzed on arterial and portal phase using both two-dimensional (2D) and three-dimensional (3D) analysis. Multivariate logistic regression analysis was performed in order to identify significant predictors for tumor grade. Results Among CT findings and CT texture variables, the significant predictors for grade 2/3 tumors were an ill-defined margin (odds ratio [OR] = 7.273), lower sphericity (OR = 0.409) on arterial 2D analysis, higher skewness (OR = 1.972) and lower sphericity (OR = 0.408) on arterial 3D analysis, lower kurtosis (OR = 0.436) and lower sphericity (OR = 0.420) on portal 2D analysis, and a larger surface area (OR = 2.007) and lower sphericity (OR = 0.503) on portal 3D analysis (P < 0.05). Diagnostic performance of texture analysis was superior to CT findings (AUC = 0.774 vs. 0.683). Conclusion CT is useful for predicting grade 2/3 PNET using not only the imaging findings including an ill-defined margin, but also the CT texture variables such as lower sphericity, higher skewness, and lower kurtosis.
Background Magnetic resonance imaging (MRI) is increasingly being used to examine patients with suspected breast cancer. Purpose To determine the diagnostic performance of combined dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) for breast cancer detection. Material and Methods A comprehensive search of the PUBMED, EMBASE, Web of Science, and Cochrane Library databases was performed up to September 2014. Statistical analysis included pooling of sensitivity and specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and diagnostic accuracy using the summary receiver operating characteristic (SROC). All analyses were conducted using STATA (version 12.0), RevMan (version 5.2), and Meta-Disc 1.4 software programs. Results Fourteen studies were analyzed, which included a total of 1140 patients with 1276 breast lesions. The pooled sensitivity and specificity of combined DCE-MRI and DWI were 91.6% and 85.5%, respectively. The pooled sensitivity and specificity of DWI-MRI were 86.0% and 75.6%, respectively. The pooled sensitivity and specificity of DCE-MRI were 93.2% and 71.1%. The area under the SROC curve (AUC-SROC) of combined DCE-MRI and DWI was 0.94, the DCE-MRI of 0.85. Deeks testing confirmed no significant publication bias in all studies. Conclusion Combined DCE-MRI and DWI had superior diagnostic accuracy than either DCE-MRI or DWI alone for the diagnosis of breast cancer.
The noninvasive sensing of the blood glucose concentration is usually based on optical, electrical, or acoustical signals induced by blood glucose; these signals are extremely weak and subject to fluctuations caused by the variation in the body or surroundings. Therefore, it is challenging to detect blood glucose noninvasively with high accuracy, and no successful accurate and noninvasive clinical application has been reported. We found that there are two key measurement issues to be addressed: systematic errors, such as the errors induced by the drifts of devices or by variations in body temperature, among others, are too large to guarantee the trueness of measurement at present; and random disturbances in repeated tests, such as disturbances associated with variations in the human–machine interface, pulses, and the thermal noise of the devices, cause larger repeated measurement errors and compromise precision. Recent novel reference measurements based on differential near-infrared (NIR) spectroscopy are considered promising for solving the systematic error issue by establishing matched references, collected at another detection site or at another time, and subsequently differencing to remove the common systematic errors. However, differencing weakens the signal of interest itself and enlarges the effects of the second issue, random disturbances affecting the precision. It is understood that only reference measurements that can meet the precision requirement will be promising for future applications. Therefore, this study quantitatively evaluates the precision of the main differential NIR spectroscopy measurements considering similar conditions and minimized random disturbances. The precision of the measurements under these conditions should represent their optimal precision levels. After the evaluation, noninvasive glucose-sensing methods that hold promise for future clinical application are proposed. Finally, the evaluation criteria could be a reference for the noninvasive detection of other physiological components.
Background Using imaging techniques to diagnose malignant and inflammatory lesions in the lung can be challenging. Purpose To compare intravoxel incoherent motion (IVIM) and apparent diffusion coefficient (ADC) magnetic resonance imaging (MRI) analysis in their ability to discriminate lung cancer from focal inflammatory lung lesions. Material and Methods Thirty-eight patients with lung masses were included: 30 lung cancers and eight inflammatory lesions. Patients were imaged with 3.0T MRI diffusion weighted imaging (DWI) using 10 b values (range, 0–1000 s/mm2). Tissue diffusivity (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) were calculated using segmented biexponential analysis. ADC (total) was calculated with monoexponential fitting of the DWI data. D, D*, f, and ADC were compared between lung cancer and inflammatory lung lesions. Receiver operating characteristic analysis was performed for all DWI parameters. Results The ADC was significantly higher for inflammatory lesions than for lung cancer ([1.21 ± 0.20] × 10−3 mm2/s vs. [0.97 ± 0.15] × 10−3 mm2/s; P = 0.004). By IVIM, f was found to be significantly higher in inflammatory lesions than lung cancer ([46.10 ± 12.92] % vs. [29.29 ± 10.89] %; P = 0.005). There was no difference in D and D* between lung cancer and inflammatory lesions (P = 0.747 and 0.124, respectively). f showed comparable diagnostic performance with ADC in differentiating lung cancer from inflammatory lung lesions, with areas under the curve of 0.833 and 0.826, sensitivity 80.0% and 73.3%, and specificity 75.0% and 87.5%, respectively. Conclusion The IVIM parameter f value provides comparable diagnostic performance with ADC and could be used as a surrogate marker for differentiating lung cancer from inflammatory lesions.
Background The Bosniak classification was originally based on computed tomographic (CT) findings. Magnetic resonance (MR) and contrast-enhanced ultrasonography (CEUS) imaging may demonstrate findings that are not depicted at CT, and there may not always be a clear correlation between the findings at MR and CEUS imaging and those at CT. Purpose To compare diagnostic accuracy of MR, CEUS, and CT when categorizing complex renal cystic masses according to the Bosniak classification. Material and Methods From February 2011 to June 2012, 46 complex renal cysts were prospectively evaluated by three readers. Each mass was categorized according to the Bosniak classification and CT was chosen as gold standard. Kappa was calculated for diagnostic accuracy and data was compared with pathological results. Results CT images found 27 BII, six BIIF, seven BIII, and six BIV. Forty-three cysts could be characterized by CEUS, 79% were in agreement with CT (κ = 0.86). Five BII lesions were upgraded to BIIF and four lesions were categorized lower with CEUS. Forty-one lesions were examined with MR; 78% were in agreement with CT (κ = 0.91). Three BII lesions were upgraded to BIIF and six lesions were categorized one category lower. Pathologic correlation in six lesions revealed four malignant and two benign lesions. Conclusion CEUS and MR both up- and downgraded renal cysts compared to CT, and until these non-radiation modalities have been refined and adjusted, CT should remain the gold standard of the Bosniak classification.
Background Metal implants may introduce severe artifacts in computed tomography (CT) images. Over the last few years dedicated algorithms have been developed in order to reduce metal artifacts in CT images. Purpose To investigate and compare metal artifact reduction algorithms (MARs) from four different CT vendors when imaging three different orthopedic metal implants. Material and Methods Three clinical metal implants were attached to the leg of an anthropomorphic phantom: cobalt-chrome; stainless steel; and titanium. Four commercial MARs were investigated: SmartMAR (GE); O-MAR (Philips); iMAR (Siemens); and SEMAR (Toshiba). The images were evaluated subjectively by three observers and analyzed objectively by calculating the fraction of pixels with CT number above 500 HU in a region of interest around the metal. The average CT number and image noise were also measured. Results Both subjective evaluation and objective analysis showed that MARs reduced metal artifacts and improved the image quality for CT images containing metal implants of steel and cobalt-chrome. When using MARs on titanium, all MARs introduced new visible artifacts. Conclusion The effect of MARs varied between CT vendors and different metal implants used in orthopedic surgery. Both in subjective evaluation and objective analysis the effect of applying MARs was most obvious on steel and cobalt-chrome implants when using SEMAR from Toshiba followed by SmartMAR from GE. However, MARs may also introduce new image artifacts especially when used on titanium implants. Therefore, it is important to reconstruct all CT images containing metal with and without MARs.
Background Metallic dental prostheses may degrade image quality on head and neck computed tomography (CT). However, there is little information available on the use of dual-energy CT (DECT) and metal artifact reduction software (MARS) in the head and neck regions to reduce metallic dental artifacts. Purpose To assess the usefulness of DECT with virtual monochromatic imaging and MARS to reduce metallic dental artifacts. Material and Methods DECT was performed using fast kilovoltage (kV)-switching between 80-kV and 140-kV in 20 patients with metallic dental prostheses. CT data were reconstructed with and without MARS, and with synthesized monochromatic energy in the range of 40–140-kiloelectron volt (keV). For quantitative analysis, the artifact index of the tongue, buccal, and parotid areas was calculated for each scan. For qualitative analysis, two radiologists evaluated 70-keV and 100-keV images with and without MARS for tongue, buccal, parotid areas, and metallic denture. The locations and characteristics of the MARS-related artifacts, if any, were also recorded. Results DECT with MARS markedly reduced metallic dental artifacts and improved image quality in the buccal area (P < 0.001) and the tongue (P < 0.001), but not in the parotid area. The margin and internal architecture of the metallic dentures were more clearly delineated with MARS (P < 0.001) and in the higher-energy images than in the lower-energy images (P = 0.042). MARS-related artifacts most commonly occurred in the deep center of the neck. Conclusion DECT with MARS can reduce metallic dental artifacts and improve delineation of the metallic prosthesis and periprosthetic region.
Background ShearWaveTM Elastography (SWE) provides a quantitative measurement of tissue stiffness and may improve characterization of breast masses. However, the significance of Young's modulus measurements and appropriate SWE evaluation criteria has not been established yet. Purpose To assess the usefulness of the pattern classification and Young's modulus measurements in the differential diagnosis between benign and malignant solid breast masses. Material and Methods Ninety-six patients (age range 18–84 years, mean 54 years) with 100 solid breast masses who underwent tissue sampling after a US examination were analyzed. We tried to create a visual pattern classification based on the SWE images. After classifying the visual patterns, the Young's modulus of the lesions was measured in every case. Results It was possible to classify the images into four patterns by the visual evaluation: no findings (coded blue homogeneously; Pattern 1), vertical stripe pattern artifacts (Pattern 2), a localized colored area at the margin of the lesion (Pattern 3), and heterogeneously colored areas in the interior of the lesion (Pattern 4). There were 17 Pattern 1 lesions, 14 Pattern 2 lesions, 20 Pattern 3 lesions, and 49 Pattern 4 lesions. When Patterns 1 and 2 were assumed to be benign, and Patterns 3 and 4 were assumed to be malignant, the sensitivity and specificity were 91.3% (63/69) and 80.6% (25/31), respectively. The mean Young's modulus measurements of the benign and the malignant lesions were 42 kPa and 146 kPa, respectively (P < 0.0001). No significant differences were found between benign and malignant lesions in Pattern 3. In Pattern 4, however, the Young's modulus of the benign lesions (50 kPa) was lower than the smallest Young's modulus of malignant lesions (61 kPa). Conclusion The visual pattern classification and adding Young's modulus measurements may improve characterization of solid breast masses.
Background: ShearWave (TM) Elastography (SWE) provides a quantitative measurement of tissue stiffness and may improve characterization of breast masses. However, the significance of Young's modulus measurements and appropriate SWE evaluation criteria has not been established yet. Purpose: To assess the usefulness of the pattern classification and Young's modulus measurements in the differential diagnosis between benign and malignant solid breast masses. Material and Methods: Ninety-six patients (age range 18-84 years, mean 54 years) with 100 solid breast masses who underwent tissue sampling after a US examination were analyzed. We tried to create a visual pattern classification based on the SWE images. After classifying the visual patterns, the Young's modulus of the lesions was measured in every case. Results: It was possible to classify the images into four patterns by the visual evaluation: no findings (coded blue homogeneously; Pattern 1), vertical stripe pattern artifacts (Pattern 2), a localized colored area at the margin of the lesion (Pattern 3), and heterogeneously colored areas in the interior of the lesion (Pattern 4). There were 17 Pattern 1 lesions, 14 Pattern 2 lesions, 20 Pattern 3 lesions, and 49 Pattern 4 lesions. When Patterns 1 and 2 were assumed to be benign, and Patterns 3 and 4 were assumed to be malignant, the sensitivity and specificity were 91.3% (63/69) and 80.6% (25/31), respectively. The mean Young's modulus measurements of the benign and the malignant lesions were 42 kPa and 146 kPa, respectively (P < 0.0001). No significant differences were found between benign and malignant lesions in Pattern 3. In Pattern 4, however, the Young's modulus of the benign lesions (50 kPa) was lower than the smallest Young's modulus of malignant lesions (61 kPa). Conclusion: The visual pattern classification and adding Young's modulus measurements may improve characterization of solid breast masses.
Background Angiomyolipoma (AML) with minimal fat may mimic renal cell carcinoma (RCC) and is difficult to distinguish from RCC with imaging studies alone. Precise diagnostic strategies have been explored to discern AML with minimal fat from RCC. Purpose To compare the morphological and enhancement features of AML with minimal fat with those of size-matched RCC on computed tomography (CT). Material and Methods Our study included 143 pathologically proved renal tumors (29 AML with minimal fat: mean diameter, 2.5 cm; range, 1.2–4 cm; 114 RCC: mean diameter, 2.8 cm; range, 1.3–4 cm). All patients underwent biphasic helical CTs. Two radiologists retrospectively evaluated the morphological (i.e. non-round and round appearances, with or without capsule) and enhancement features (i.e., wash-out, gradual, or prolonged). For the parameters that had statistically significance between the two groups, we calculated the positive and negative predictive values by using the univariate χ2 test. P < 0.05 indicated a significant difference. Results AML with minimal fat showed a non-round appearance without a capsule (n = 24, 83%) and prolonged enhancement (n = 20, 69%). The positive and negative predictive values of the non-round appearance without capsule for differentiating AML with minimal fat from RCC were 82.8% and 95.6%, respectively. The positive and negative predictive values of prolonged enhancement were 62.5% and 90.8%, respectively. These features were valuable predictors for AML with minimal fat from RCC. Conclusion CT images with non-round shape without capsule and prolonged enhancements may be used to differentiate AML with minimal fat from RCC.
Background There has been a growing need for an imaging method for the accurate diagnosis and staging of liver fibrosis as a non-invasive alternative to liver biopsy. Purpose To evaluate the feasibility of intra-voxel incoherent motion (IVIM) imaging for classifying the severity of liver fibrosis. Material and Methods Fifty-seven patients who underwent navigator-triggered, diffusion-weighted imaging (DWI) of the liver on a 1.5-T system using nine b-values and had a reliable reference standard for the diagnosis of liver fibrosis (histopathologic findings [n = 27] or clinical findings for normal [n = 18] or cirrhotic liver [n = 12]), were included in our study. Liver apparent diffusion coefficient (ADC), pure diffusion (Dslow), perfusion fraction (f), and perfusion-related diffusion (Dfast), and the product f · Dfast were compared with the liver fibrosis stages (F). The accuracies of these parameters in diagnosing severe liver fibrosis (F ≥3) were evaluated using the receiver-operating characteristic (ROC) curve analysis. Results The liver fibrosis stages had the strongest negative correlation with f · Dfast (ρ = –0.52). All of the parameters, except for Dslow, were significantly lower in patients with F ≥3 than in those with F ≤2 (P ≤ 0.001). The area under the ROC curve for diagnosing severe fibrosis was the largest for f · Dfast (0.844) with an overall accuracy of 79.0% (45/57) at the optimal cutoff value and followed by f (0.834), Dfast (0.773), ADC (0.762), and Dslow (0.656). Conclusion IVIM imaging is a promising method for classifying the severity of liver fibrosis, with the product f · Dfast being the most accurate parameter.
Background One significant barrier to incorporate Alzheimer’s disease (AD) imaging biomarkers into diagnostic criteria is the lack of standardized methods for biomarker quantification. The European Alzheimer’s Disease Consortium-Alzheimer’s Disease Neuroimaging Initiative (EADC-ADNI) Harmonization Protocol project provides the most authoritative guideline for hippocampal definition and has produced a manually segmented reference dataset for validation of automated methods. Purpose To validate automated hippocampal volumetry using AccuBrain™, against the EADC-ADNI dataset, and assess its diagnostic performance for differentiating AD and normal aging in an independent cohort. Material and Methods The EADC-ADNI reference dataset comprise of manually segmented hippocampal labels from 135 volumetric T1-weighted scans from various scanners. Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), and Pearson’s r were obtained for AccuBrain™ and FreeSurfer. The magnetic resonance imaging (MRI) of a separate cohort of 299 individuals (150 normal controls, 149 with AD) were obtained from the ADNI database and processed with AccuBrain™ to assess its diagnostic accuracy. Area under the curve (AUC) for total hippocampal volumes (HV) and hippocampal fraction (HF) were determined. Results Compared with EADC-ADNI dataset ground truths, AccuBrain™ had a mean DSC of 0.89/0.89/0.89, ICC of 0.94/0.96/0.95, and r of 0.95/0.96/0.95 for right/left/total HV. AccuBrain™ HV and HF had AUC of 0.76 and 0.80, respectively. Thresholds of ≤ 5.71 mL and ≤ 0.38% afforded 80% sensitivity for AD detection. Conclusion AccuBrain™ provides accurate automated hippocampus segmentation in accordance with the EADC-ADNI standard, with great potential value in assisting clinical diagnosis of AD.