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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 (LV BSA), liver-to-spleen SI ratio (LSSR), MRI parameter-adjusted LSSR (aLSSR), LSSR x LV BSA, and aLSSR x LV BSA, 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 BlandAltman 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 x LV BSA 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.
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Objective@#Preoperative differentiation between inverted papilloma (IP) and its malignant transformation to squamous cell carcinoma (IP-SCC) is critical for patient management. We aimed to determine the diagnostic accuracy of conventional imaging features and histogram parameters obtained from whole tumor apparent diffusion coefficient (ADC) values to predict IP-SCC in patients with IP, using decision tree analysis. @*Materials and Methods@#In this retrospective study, we analyzed data generated from the records of 180 consecutive patients with histopathologically diagnosed IP or IP-SCC who underwent head and neck magnetic resonance imaging, including diffusion-weighted imaging and 62 patients were included in the study. To obtain whole tumor ADC values, the region of interest was placed to cover the entire volume of the tumor. Classification and regression tree analyses were performed to determine the most significant predictors of IP-SCC among multiple covariates. The final tree was selected by cross-validation pruning based on minimal error. @*Results@#Of 62 patients with IP, 21 (34%) had IP-SCC. The decision tree analysis revealed that the loss of convoluted cerebriform pattern and the 20th percentile cutoff of ADC were the most significant predictors of IP-SCC. With these decision trees, the sensitivity, specificity, accuracy, and C-statistics were 86% (18 out of 21; 95% confidence interval [CI], 65–95%), 100% (41 out of 41; 95% CI, 91–100%), 95% (59 out of 61; 95% CI, 87–98%), and 0.966 (95% CI, 0.912–1.000), respectively. @*Conclusion@#Decision tree analysis using conventional imaging features and histogram analysis of whole volume ADC could predict IP-SCC in patients with IP with high diagnostic accuracy.
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Objective@#Preoperative differentiation between inverted papilloma (IP) and its malignant transformation to squamous cell carcinoma (IP-SCC) is critical for patient management. We aimed to determine the diagnostic accuracy of conventional imaging features and histogram parameters obtained from whole tumor apparent diffusion coefficient (ADC) values to predict IP-SCC in patients with IP, using decision tree analysis. @*Materials and Methods@#In this retrospective study, we analyzed data generated from the records of 180 consecutive patients with histopathologically diagnosed IP or IP-SCC who underwent head and neck magnetic resonance imaging, including diffusion-weighted imaging and 62 patients were included in the study. To obtain whole tumor ADC values, the region of interest was placed to cover the entire volume of the tumor. Classification and regression tree analyses were performed to determine the most significant predictors of IP-SCC among multiple covariates. The final tree was selected by cross-validation pruning based on minimal error. @*Results@#Of 62 patients with IP, 21 (34%) had IP-SCC. The decision tree analysis revealed that the loss of convoluted cerebriform pattern and the 20th percentile cutoff of ADC were the most significant predictors of IP-SCC. With these decision trees, the sensitivity, specificity, accuracy, and C-statistics were 86% (18 out of 21; 95% confidence interval [CI], 65–95%), 100% (41 out of 41; 95% CI, 91–100%), 95% (59 out of 61; 95% CI, 87–98%), and 0.966 (95% CI, 0.912–1.000), respectively. @*Conclusion@#Decision tree analysis using conventional imaging features and histogram analysis of whole volume ADC could predict IP-SCC in patients with IP with high diagnostic accuracy.
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Objective@#Muscle quantity and quality can be measured with an automated system on CT. However, the effects of contrast phases on the muscle measurements have not been established, which we aimed to investigate in this study. @*Materials and Methods@#Muscle quantity was measured according to the skeletal muscle area (SMA) measured by a convolutional neural network-based automated system at the L3 level in 89 subjects undergoing multiphasic abdominal CT comprising unenhanced phase, arterial phase, portal venous phase (PVP), or delayed phase imaging. Muscle quality was analyzed using the mean muscle density and the muscle quality map, which comprises normal and low-attenuation muscle areas (NAMA and LAMA, respectively) based on the muscle attenuation threshold. The SMA, mean muscle density, NAMA, and LAMA were compared between PVP and other phases using paired t tests. Bland-Altman analysis was used to evaluate the inter-phase variability between PVP and other phases. Based on the cutoffs for low muscle quantity and quality, the counts of individuals who scored lower than the cutoff values were compared between PVP and other phases. @*Results@#All indices showed significant differences between PVP and other phases (p < 0.001 for all). The SMA, mean muscle density, and NAMA increased during the later phases, whereas LAMA decreased during the later phases. Bland-Altman analysis showed that the mean differences between PVP and other phases ranged -2.1 to 0.3 cm2 for SMA, -12.0 to 2.6 cm2 for NAMA, and -2.2 to 9.9 cm2 for LAMA.The number of patients who were categorized as low muscle quantity did not significant differ between PVP and other phases (p ≥ 0.5), whereas the number of patients with low muscle quality significantly differed (p ≤ 0.002). @*Conclusion@#SMA was less affected by the contrast phases. However, the muscle quality measurements changed with the contrast phases to greater extents and would require a standardization of the contrast phase for reliable measurement.
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Objective@#Although the liver-to-spleen volume ratio (LSVR) based on CT reflects portal hypertension, its prognostic role in cirrhotic patients has not been proven. We evaluated the utility of LSVR, automatically measured from CT images using a deep learning algorithm, as a predictor of hepatic decompensation and transplantation-free survival in patients with hepatitis B viral (HBV)-compensated cirrhosis. @*Materials and Methods@#A deep learning algorithm was used to measure the LSVR in a cohort of 1027 consecutive patients (mean age, 50.5 years; 675 male and 352 female) with HBV-compensated cirrhosis who underwent liver CT (2007–2010).Associations of LSVR with hepatic decompensation and transplantation-free survival were evaluated using multivariable Cox proportional hazards and competing risk analyses, accounting for either the Child-Pugh score (CPS) or Model for End Stage Liver Disease (MELD) score and other variables. The risk of the liver-related events was estimated using Kaplan-Meier analysis and the Aalen-Johansen estimator. @*Results@#After adjustment for either CPS or MELD and other variables, LSVR was identified as a significant independent predictor of hepatic decompensation (hazard ratio for LSVR increase by 1, 0.71 and 0.68 for CPS and MELD models, respectively; p < 0.001) and transplantation-free survival (hazard ratio for LSVR increase by 1, 0.8 and 0.77, respectively; p < 0.001). Patients with an LSVR of < 2.9 (n = 381) had significantly higher 3-year risks of hepatic decompensation (16.7% vs. 2.5%, p < 0.001) and liver-related death or transplantation (10.0% vs. 1.1%, p < 0.001) than those with an LSVR ≥ 2.9 (n = 646). When patients were stratified according to CPS (Child-Pugh A vs. B–C) and MELD (< 10 vs. ≥ 10), an LSVR of < 2.9 was still associated with a higher risk of liver-related events than an LSVR of ≥ 2.9 for all Child-Pugh (p ≤ 0.045) and MELD (p ≤ 0.009) stratifications. @*Conclusion@#The LSVR measured on CT can predict hepatic decompensation and transplantation-free survival in patients with HBV-compensated cirrhosis.
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Objective@#Measurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT)volumetry is considered to be the most reliable noninvasive method for liver and spleen volume measurement, it has limitedapplication in clinical practice due to its time-consuming segmentation process. We aimed to develop and validate a deeplearning algorithm (DLA) for fully automated liver and spleen segmentation using portal venous phase CT images in variousliver conditions. @*Materials and Methods@#A DLA for liver and spleen segmentation was trained using a development dataset of portal venousCT images from 813 patients. Performance of the DLA was evaluated in two separate test datasets: dataset-1 which included150 CT examinations in patients with various liver conditions (i.e., healthy liver, fatty liver, chronic liver disease, cirrhosis,and post-hepatectomy) and dataset-2 which included 50 pairs of CT examinations performed at ours and other institutions.The performance of the DLA was evaluated using the dice similarity score (DSS) for segmentation and Bland-Altman 95%limits of agreement (LOA) for measurement of the volumetric indices, which was compared with that of ground truth manualsegmentation. @*Results@#In test dataset-1, the DLA achieved a mean DSS of 0.973 and 0.974 for liver and spleen segmentation, respectively,with no significant difference in DSS across different liver conditions (p = 0.60 and 0.26 for the liver and spleen, respectively).For the measurement of volumetric indices, the Bland-Altman 95% LOA was -0.17 ± 3.07% for liver volume and -0.56 ± 3.78%for spleen volume. In test dataset-2, DLA performance using CT images obtained at outside institutions and our institutionwas comparable for liver (DSS, 0.982 vs. 0.983; p = 0.28) and spleen (DSS, 0.969 vs. 0.968; p = 0.41) segmentation. @*Conclusion@#The DLA enabled highly accurate segmentation and volume measurement of the liver and spleen using portalvenous phase CT images of patients with various liver conditions.
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OBJECTIVE: A widely applicable, non-invasive screening method for non-alcoholic fatty liver disease (NAFLD) is needed. We aimed to develop and validate an index combining computed tomography (CT) and routine clinical data for screening for NAFLD in a large cohort of adults with pathologically proven NAFLD.MATERIALS AND METHODS: This retrospective study included 2218 living liver donors who had undergone liver biopsy and CT within a span of 3 days. Donors were randomized 2:1 into development and test cohorts. CT(L-S) was measured by subtracting splenic attenuation from hepatic attenuation on non-enhanced CT. Multivariable logistic regression analysis of the development cohort was utilized to develop a clinical-CT index predicting pathologically proven NAFLD. The diagnostic performance was evaluated by analyzing the areas under the receiver operating characteristic curve (AUC). The cutoffs for the clinical-CT index were determined for 90% sensitivity and 90% specificity in the development cohort, and their diagnostic performance was evaluated in the test cohort.RESULTS: The clinical-CT index included CT(L-S), body mass index, and aspartate transaminase and triglyceride concentrations. In the test cohort, the clinical-CT index (AUC, 0.81) outperformed CT(L-S) (0.74; p < 0.001) and clinical indices (0.73–0.75; p < 0.001) in diagnosing NAFLD. A cutoff of ≥ 46 had a sensitivity of 89% and a specificity of 41%, whereas a cutoff of ≥ 56.5 had a sensitivity of 57% and a specificity of 89%.CONCLUSION: The clinical-CT index is more accurate than CT(L-S) and clinical indices alone for the diagnosis of NAFLD and may be clinically useful in screening for NAFLD.
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Recently, sarcopenia has garnered renewed interest. Sarcopenia is a disease characterized by decreased skeletal muscle mass and strength/function, which can impair the quality of life and increase physical disability, adverse metabolic effects, and mortality. Imaging tools for evaluating and diagnosing sarcopenia have developed rapidly. Radiologists should be aware of sarcopenia and its clinical implications. We review current knowledge about sarcopenia, its pathophysiological impact, and advantages and disadvantages of methods for evaluation of sarcopenia focusing on body composition imaging modalities such as whole-body dual-energy X-ray absorptiometry, CT, and MRI. Controversial issues are discussed, including the lack of consensus and standardization of the disease definition, imaging modality, measurement methods, and diagnostic cutoff points.
Subject(s)
Absorptiometry, Photon , Body Composition , Consensus , Magnetic Resonance Imaging , Mortality , Muscle, Skeletal , Quality of Life , SarcopeniaABSTRACT
OBJECTIVE: To evaluate the frequency and adequacy of statistical analyses in a general radiology journal when reporting a reliability analysis for a diagnostic test. MATERIALS AND METHODS: Sixty-three studies of diagnostic test accuracy (DTA) and 36 studies reporting reliability analyses published in the Korean Journal of Radiology between 2012 and 2016 were analyzed. Studies were judged using the methodological guidelines of the Radiological Society of North America-Quantitative Imaging Biomarkers Alliance (RSNA-QIBA), and COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) initiative. DTA studies were evaluated by nine editorial board members of the journal. Reliability studies were evaluated by study reviewers experienced with reliability analysis. RESULTS: Thirty-one (49.2%) of the 63 DTA studies did not include a reliability analysis when deemed necessary. Among the 36 reliability studies, proper statistical methods were used in all (5/5) studies dealing with dichotomous/nominal data, 46.7% (7/15) of studies dealing with ordinal data, and 95.2% (20/21) of studies dealing with continuous data. Statistical methods were described in sufficient detail regarding weighted kappa in 28.6% (2/7) of studies and regarding the model and assumptions of intraclass correlation coefficient in 35.3% (6/17) and 29.4% (5/17) of studies, respectively. Reliability parameters were used as if they were agreement parameters in 23.1% (3/13) of studies. Reproducibility and repeatability were used incorrectly in 20% (3/15) of studies. CONCLUSION: Greater attention to the importance of reporting reliability, thorough description of the related statistical methods, efforts not to neglect agreement parameters, and better use of relevant terminology is necessary.
Subject(s)
Biomarkers , Diagnostic Tests, Routine , MethodsABSTRACT
OBJECTIVE: To simulate the B₁-inhomogeneity-induced variation of pharmacokinetic parameters on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: B₁-inhomogeneity-induced flip angle (FA) variation was estimated in a phantom study. Monte Carlo simulation was performed to assess the FA-deviation-induced measurement error of the pre-contrast R₁, contrast-enhancement ratio, Gd-concentration, and two-compartment pharmacokinetic parameters (K(trans), v(e), and v(p)). RESULTS: B₁-inhomogeneity resulted in −23–5% fluctuations (95% confidence interval [CI] of % error) of FA. The 95% CIs of FA-dependent % errors in the gray matter and blood were as follows: −16.7–61.8% and −16.7–61.8% for the pre-contrast R₁, −1.0–0.3% and −5.2–1.3% for the contrast-enhancement ratio, and −14.2–58.1% and −14.1–57.8% for the Gd-concentration, respectively. These resulted in −43.1–48.4% error for K(trans), −32.3–48.6% error for the v(e), and −43.2–48.6% error for v(p). The pre-contrast R₁ was more vulnerable to FA error than the contrast-enhancement ratio, and was therefore a significant cause of the Gd-concentration error. For example, a −10% FA error led to a 23.6% deviation in the pre-contrast R₁, −0.4% in the contrast-enhancement ratio, and 23.6% in the Gd-concentration. In a simulated condition with a 3% FA error in a target lesion and a −10% FA error in a feeding vessel, the % errors of the pharmacokinetic parameters were −23.7% for K(trans), −23.7% for v(e), and −23.7% for v(p). CONCLUSION: Even a small degree of B₁-inhomogeneity can cause a significant error in the measurement of pharmacokinetic parameters on DCE-MRI, while the vulnerability of the pre-contrast R₁ calculations to FA deviations is a significant cause of the miscalculation.
Subject(s)
Brain , Gray Matter , Magnetic Resonance Imaging , Monte Carlo Method , Phantoms, ImagingABSTRACT
OBJECTIVE: To investigate the correlation between perfusion- and diffusion-related parameters from intravoxel incoherent motion (IVIM) and those from dynamic contrast-enhanced MR imaging (DCE-MRI) and diffusion-weighted imaging in tumors and normal muscles of the head and neck. MATERIALS AND METHODS: We retrospectively enrolled 20 consecutive patients with head and neck tumors with MR imaging performed using a 3T MR scanner. Tissue diffusivity (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) were derived from bi-exponential fitting of IVIM data obtained with 14 different b-values in three orthogonal directions. We investigated the correlation between D, f, and D* and model-free parameters from the DCE-MRI (wash-in, Tmax, Emax, initial AUC60, whole AUC) and the apparent diffusion coefficient (ADC) value in the tumor and normal masseter muscle using a whole volume-of-interest approach. Pearson's correlation test was used for statistical analysis. RESULTS: No correlation was found between f or D* and any of the parameters from the DCE-MRI in all patients or in patients with squamous cell carcinoma (p > 0.05). The ADC was significantly correlated with D values in the tumors (p 0.05, respectively). CONCLUSION: Intravoxel incoherent motion shows no significant correlation with model-free perfusion parameters derived from the DCE-MRI but is feasible for the analysis of diffusivity in both tumors and normal muscles of the head and neck.
Subject(s)
Humans , Carcinoma, Squamous Cell , Diffusion , Head , Magnetic Resonance Imaging , Masseter Muscle , Muscles , Neck , Perfusion , Retrospective StudiesABSTRACT
PURPOSE: Diffusion-weighted MR images (DWI) obtained after endovascular treatment of cerebral aneurysms frequently show multiple high-signal intensity (HSI) dots. The purpose of this study was to see whether we could reduce their incidence after embolization of unruptured cerebral aneurysms by modification of our coiling technique, which involves the deliberate aspiration of the microcatheter lumen right after delivery of each detachable coil into the aneurysm sac. MATERIALS AND METHODS: From January 2011 to June 2011, all 71 patients with unruptured cerebral aneurysms were treated using various endovascular methods. During the earlier period, 37 patients were treated using our conventional embolization technique (conventional period). Then 34 patients were treated with a modified coiling technique (modified period). DWI was obtained on the following day. We compared the occurrence of any DWI HSI lesions and the presence of the symptomatic lesions during the two time periods. RESULTS: The incidence of the DWI HSI lesions differed significantly at 89.2% (33/37) during the conventional period and 26.5% (9/34) during the modified period (p < 0.0001). The incidence of symptomatic lesions differed between the two periods (29.7% during the conventional period vs. 2.9% during the modified period, p < 0.003). CONCLUSION: Aspiration of the inner content of the microcatheter right after detachable coil delivery was helpful for the reduction of the incidence of microembolisms after endovascular coil embolization for the treatment of unruptured cerebral aneurysms.
Subject(s)
Humans , Aneurysm , Embolization, Therapeutic , Incidence , Intracranial AneurysmABSTRACT
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.