The effect of hepatic steatosis on liver volume determined by proton density fat fraction and deep learning-measured liver volume.

OBJECTIVES: We aimed to evaluate the effect of hepatic steatosis (HS) on liver volume and to develop a formula to estimate lean liver volume correcting the HS effect. METHODS: This retrospective study included healthy adult liver donors who underwent gadoxetic acid-enhanced MRI and proton density fat fraction (PDFF) measurement from 2015 to 2019. The degree of HS was graded at 5% PDFF intervals from grade 0 (no HS; PDFF < 5.5%). Liver volume was measured with hepatobiliary phase MRI using deep learning algorithm, and standard liver volume (SLV) was calculated as the reference lean liver volume. The association between liver volume and SLV ratio with PDFF grades was evaluated using Spearman's correlation (ρ). The effect of PDFF grades on liver volume was evaluated using the multivariable linear regression model. RESULTS: The study population included 1038 donors (mean age, 31 ± 9 years; 689 men). Mean liver volume to SLV ratio increased according to PDFF grades (ρ = 0.234, p < 0.001). The multivariable analysis indicated that SLV (ß = 1.004, p < 0.001) and PDFF grade*SLV (ß = 0.044, p < 0.001) independently affected liver volume, suggesting a 4.4% increase in liver volume per one-point increment in the PDFF grade. PDFF-adjusted lean liver volume was estimated using the formula, liver volume/[1.004 + 0.044 × PDFF grade]. The mean estimated lean liver volume to SLV ratio approximated to one for all PDFF grades, with no significant association with PDFF grades (p = 0.851). CONCLUSION: HS increases liver volume. The formula to estimate lean liver volume may be useful to adjust for the effect of HS on liver volume. KEY POINTS: • Hepatic steatosis increases liver volume. • The presented formula to estimate lean liver volume using MRI-measured proton density fat fraction and liver volume may be useful to adjust for the effect of hepatic steatosis on measured liver volume.

Development and validation of an ensemble artificial intelligence model for comprehensive imaging quality check to classify body parts and contrast enhancement.

BACKGROUND: Despite the dramatic increase in the use of medical imaging in various therapeutic fields of clinical trials, the first step of image quality check (image QC), which aims to check whether images are uploaded appropriately according to the predefined rules, is still performed manually by image analysts, which requires a lot of manpower and time. METHODS: In this retrospective study, 1669 computed tomography (CT) images with five specific anatomical locations were collected from Asan Medical Center and Kangdong Sacred Heart Hospital. To generate the ground truth, two radiologists reviewed the anatomical locations and presence of contrast enhancement using the collected data. The individual deep learning model is developed through InceptionResNetv2 and transfer learning, and we propose Image Quality Check-Net (Image QC-Net), an ensemble AI model that utilizes it. To evaluate their clinical effectiveness, the overall accuracy and time spent on image quality check of a conventional model and ImageQC-net were compared. RESULTS: ImageQC-net body part classification showed excellent performance in both internal (precision, 100%; recall, 100% accuracy, 100%) and external verification sets (precision, 99.8%; recovery rate, 99.8%, accuracy, 99.8%). In addition, contrast enhancement classification performance achieved 100% precision, recall, and accuracy in the internal verification set and achieved (precision, 100%; recall, 100%; accuracy 100%) in the external dataset. In the case of clinical effects, the reduction of time by checking the quality of artificial intelligence (AI) support by analysts 1 and 2 (49.7% and 48.3%, respectively) was statistically significant (p < 0.001). CONCLUSIONS: Comprehensive AI techniques to identify body parts and contrast enhancement on CT images are highly accurate and can significantly reduce the time spent on image quality checks.

A reliable and robust method for the upper thigh muscle quantification on computed tomography: toward a quantitative biomarker for sarcopenia.

BACKGROUND: We aimed to evaluate the feasibility of the upper thigh level as a landmark to measure muscle area for sarcopenia assessment on computed tomography (CT). METHODS: In the 116 healthy subjects who performed CT scans covering from mid-abdomen to feet, the skeletal muscle area in the upper thigh level at the inferior tip of ischial tuberosity (SMAUT), the mid-thigh level (SMAMT), and L3 inferior endplate level (SMAL3) were measured by two independent readers. Pearson correlation coefficients between SMAUT, SMAMT, and SMAL3 were calculated. Inter-reader agreement between the two readers were evaluated using intraclass correlation coefficient (ICC) and Bland-Altman plots with 95% limit of agreement (LOA). RESULTS: In readers 1 and 2, very high positive correlations were observed between SMAUT and SMAMT (r = 0.91 and 0.92, respectively) and between SMAUT and SMAL3 (r = 0.90 and 0.91, respectively), while high positive correlation were observed between SMAMT and SMAL3 (r = 0.87 and 0.87, respectively). Based on ICC values, the inter-reader agreement was the best in the SMAUT (0.999), followed by the SMAL3 (0.990) and SMAMT (0.956). The 95% LOAs in the Bland-Altman plots indicated that the inter-reader agreement of the SMAUT (- 0.462 to 1.513) was the best, followed by the SMAL3 (- 9.949 to 7.636) and SMAMT (- 12.105 to 14.605). CONCLUSION: Muscle area measurement at the upper thigh level correlates well with those with the mid-thigh and L3 inferior endpoint level and shows the highest inter-reader agreement. Thus, the upper thigh level might be an excellent landmark enabling SMAUT as a reliable and robust biomarker for muscle area measurement for sarcopenia assessment.

Intermuscular fat density as a novel prognostic factor in breast cancer patients treated with adjuvant chemotherapy.

BACKGROUND: Body composition, including sarcopenia and fat parameters, has received much attention as a prognostic factor in breast cancer. METHODS: A total of 479 breast cancer patients who underwent surgery and received adjuvant chemotherapy were enrolled in this study. Body composition, including the index and density of skeletal muscle, visceral fat, subcutaneous fat, and intermuscular fat calculated by CT scan, was used as a prognostic factor. The endpoints were breast cancer-specific survival (BCSS) and overall survival (OS). RESULTS: The number of patients with stages I, II, and III was 146 (30.5%), 237 (49.5%), and 96 (20%), respectively. Sarcopenia and muscle density were not significant prognostic factors for BCSS and OS. A high visceral fat index (VFI) was an independent prognostic factor for BCSS (HR, 2.55; 95% CI 1.10-5.95, p = 0.03) and OS (HR 2.55, 95% CI 1.26-5.16, p = 0.01). In addition, high intermuscular fat density (IMFD) was also a significant prognostic factor for BCSS (HR, 2.95; 95% CI 1.34-6.46, p = 0.007) and OS (HR 2.28, 95% CI 1.22-4.26, p = 0.01) in multivariate analysis. CONCLUSION: VFI and IMFD were significant prognostic factors for BCSS and OS in breast cancer patients treated with adjuvant chemotherapy.

Population-based and Personalized Reference Intervals for Liver and Spleen Volumes in Healthy Individuals and Those with Viral Hepatitis.

Background Reference intervals guiding volumetric assessment of the liver and spleen have yet to be established. Purpose To establish population-based and personalized reference intervals for liver volume, spleen volume, and liver-to-spleen volume ratio (LSVR). Materials and Methods This retrospective study consecutively included healthy adult liver donors from 2001 to 2013 (reference group) and from 2014 to 2016 (healthy validation group) and patients with viral hepatitis from 2007 to 2017. Liver volume, spleen volume, and LSVR were measured with CT by using a deep learning algorithm. In the reference group, the reference intervals for the volume indexes were determined by using the population-based (ranges encompassing the central 95% of donors) and personalized (quantile regression modeling of the 2.5th and 97.5th percentiles as a function of age, sex, height, and weight) approaches. The validity of the reference intervals was evaluated in the healthy validation group and the viral hepatitis group. Results The reference and healthy validation groups had 2989 donors (mean age ± standard deviation, 30 years ± 9; 1828 men) and 472 donors (mean age, 30 years ± 9; 334 men), respectively. The viral hepatitis group had 158 patients (mean age, 48 years ± 12; 95 men). The population-based reference intervals were 824.5-1700.0 cm3 for liver volume, 81.1-322.0 cm3 for spleen volume, and 3.96-13.78 for LSVR. Formulae and a web calculator (https://i-pacs.com/calculators) were presented to calculate the personalized reference intervals. In the healthy validation group, both the population-based and personalized reference intervals were used to classify the volume indexes of 94%-96% of the donors as falling within the reference interval. In the viral hepatitis group, when compared with the population-based reference intervals, the personalized reference intervals helped identify more patients with volume indexes outside the reference interval (liver volume, 21.5% [34 of 158] vs 13.3% [21 of 158], P = .01; spleen volume, 29.1% [46 of 158] vs 22.2% [35 of 158], P = .01; LSVR, 35.4% [56 of 158] vs 26.6% [42 of 158], P < .001). Conclusion Reference intervals derived from a deep learning approach in healthy adults may enable evidence-based assessments of liver and spleen volume in clinical practice. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Ringl in this issue.

An index based on deep learning-measured spleen volume on CT for the assessment of high-risk varix in B-viral compensated cirrhosis.

OBJECTIVES: Deep learning enables an automated liver and spleen volume measurements on CT. The purpose of this study was to develop an index combining liver and spleen volumes and clinical factors for detecting high-risk varices in B-viral compensated cirrhosis. METHODS: This retrospective study included 419 patients with B-viral compensated cirrhosis who underwent endoscopy and CT from 2007 to 2008 (derivation cohort, n = 239) and from 2009 to 2010 (validation cohort, n = 180). The liver and spleen volumes were measured on CT images using a deep learning algorithm. Multivariable logistic regression analysis of the derivation cohort developed an index to detect endoscopically confirmed high-risk varix. The cumulative 5-year risk of varix bleeding was evaluated with patients stratified by their index values. RESULTS: The index of spleen volume-to-platelet ratio was devised from the derivation cohort. In the validation cohort, the cutoff index value for balanced sensitivity and specificity (> 3.78) resulted in the sensitivity of 69.4% and the specificity of 78.5% for detecting high-risk varix, and the cutoff index value for high sensitivity (> 1.63) detected all high-risk varices. The index stratified all patients into the low (index value ≤ 1.63; n = 118), intermediate (n = 162), and high (index value > 3.78; n = 139) risk groups with cumulative 5-year incidences of varix bleeding of 0%, 1.0%, and 12.0%, respectively (p < .001). CONCLUSION: The spleen volume-to-platelet ratio obtained using deep learning-based CT analysis is useful to detect high-risk varices and to assess the risk of varix bleeding. KEY POINTS: • The criterion of spleen volume to platelet > 1.63 detected all high-risk varices in the validation cohort, while the absence of visible varix did not exclude all high-risk varices. • Visual varix grade ≥ 2 detected high-risk varix with a high specificity (96.5-100%). • Combining spleen volume-to-platelet ratio ≤ 1.63 and visual varix grade of 0 identified low-risk patients who had no high-risk varix and varix bleeding on 5-year follow-up.

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.

CDISC-compliant clinical trial imaging management system with automatic verification and data Transformation: Focusing on tumor response assessment data in clinical trials.

OBJECTIVE: Major issues in imaging data management of tumor response assessment in clinical trials include high human errors in data input and unstandardized data structures, warranting a new breakthrough IT solution. Thus, we aim to develop a Clinical Data Interchange Standards Consortium (CDISC)-compliant clinical trial imaging management system (CTIMS) with automatic verification and transformation modules for implementing the CDISC Study Data Tabulation Model (SDTM) in the tumor response assessment dataset of clinical trials. MATERIALS AND METHODS: In accordance with various CDISC standards guides and Response Evaluation Criteria in Solid Tumors (RECIST) guidelines, the overall system architecture of CDISC-compliant CTIMS was designed. Modules for standard-compliant electronic case report form (eCRF) to verify data conformance and transform into SDTM data format were developed by experts in diverse fields such as medical informatics, medical, and clinical trial. External validation of the CDISC-compliant CTIMS was performed by comparing it with our previous CTIMS based on real-world data and CDISC validation rules by Pinnacle 21 Community Software. RESULTS: The architecture of CDISC-compliant CTIMS included the standard-compliant eCRF module of RECIST, the automatic verification module of the input data, and the SDTM transformation module from the eCRF input data to the SDTM datasets based on CDISC Define-XML. This new system was incorporated into our previous CTIMS. External validation demonstrated that all 176 human input errors occurred in the previous CTIMS filtered by a new system yielding zero error and CDISC-compliant dataset. The verified eCRF input data were automatically transformed into the SDTM dataset, which satisfied the CDISC validation rules by Pinnacle 21 Community Software. CONCLUSIONS: To assure data consistency and high quality of the tumor response assessment data, our new CTIMS can minimize human input error by using standard-compliant eCRF with an automatic verification module and automatically transform the datasets into CDISC SDTM format.

Histogram analysis of arterial spin labeling perfusion data to determine the human papillomavirus status of oropharyngeal squamous cell carcinomas.

PURPOSE: To evaluate the correlation between histogram parameters derived from pseudo-continuous arterial spin labeling (PCASL) and human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC). METHODS: This study included a total of 58 patients (HPV-positive: n = 45; -negative: n = 13) from a prospective cohort of consecutive patients aged ≥ 18 years, who were newly diagnosed with oropharyngeal squamous cell carcinoma. All patients were required to have undergone pre-treatment MRI with PCASL to measure regional perfusion. The region of interest was drawn by two radiologists, encompassing the entire tumor volume on all corresponding slices. Differences in the histogram parameters derived from tumor blood flow (TBF) in ASL were assessed for HPV-positive and -negative patients. Receiver operating characteristic curve analysis was performed to determine the best differentiating parameters, and a leave-one-out cross-validation was used. RESULTS: Patients with HPV-positive OPSCC showed a significantly lower overall standard deviation and 95th percentile value of tumor blood flow (P < .007). The standard deviation of TBF was the single best predictive parameter. Leave-one-out cross-validation tests revealed that the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.745, 75.9%, 75.6%, and 76.9%, respectively. CONCLUSION: PCASL revealed differences in perfusion parameters according to HPV status in patients with OPSCC, reflecting their distinct histopathology.

Assessment of liver fibrosis severity using computed tomography-based liver and spleen volumetric indices in patients with chronic liver disease.

OBJECTIVES: To evaluate whether the liver and spleen volumetric indices, measured on portal venous phase CT images, could be used to assess liver fibrosis severity in chronic liver disease. METHODS: From 2007 to 2017, 558 patients (mean age 48.7 ± 13.1 years; 284 men and 274 women) with chronic liver disease (n = 513) or healthy liver (n = 45) were retrospectively enrolled. The liver volume (sVolL) and spleen volume (sVolS), normalized to body surface area and liver-to-spleen volume ratio (VolL/VolS), were measured on CT images using a deep learning algorithm. The correlation between the volumetric indices and the pathologic liver fibrosis stages combined with the presence of decompensation (F0, F1, F2, F3, F4C [compensated cirrhosis], and F4D [decompensated cirrhosis]) were assessed using Spearman's correlation coefficient. The performance of the volumetric indices in the diagnosis of advanced fibrosis, cirrhosis, and decompensated cirrhosis were evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: The sVolS (ρ = 0.47-0.73; p < .001) and VolL/VolS (ρ = -0.77-- 0.48; p < .001) showed significant correlation with liver fibrosis stage in all etiological subgroups (i.e., viral hepatitis, alcoholic and non-alcoholic fatty liver, and autoimmune diseases), while the significant correlation of sVolL was noted only in the viral hepatitis subgroup (ρ = - 0.55; p < .001). To diagnose advanced fibrosis, cirrhosis, and decompensated cirrhosis, the VolL/VolS (AUC 0.82-0.88) and sVolS (AUC 0.82-0.87) significantly outperformed the sVolL (AUC 0.63-0.72; p < .001). CONCLUSION: The VolL/VolS and sVolS may be used for assessing liver fibrosis severity in chronic liver disease. KEY POINTS: • Volumetric indices of liver and spleen measured on computed tomography images may allow liver fibrosis severity to be assessed in patients with chronic liver disease.

Radiomics Analysis of Gadoxetic Acid-enhanced MRI for Staging Liver Fibrosis.

Purpose To develop and validate a radiomics-based model for staging liver fibrosis by using gadoxetic acid-enhanced hepatobiliary phase MRI. Materials and Methods In this retrospective study, 436 patients (mean age, 51 years; age range, 18-86 years; 319 men [mean age, 51 years; age range, 18-86 years]; 117 women [mean age, 50 years; age range, 18-79 years]) with pathologic analysis-proven liver fibrosis who underwent gadoxetic acid-enhanced MRI from June 2015 to December 2016 were randomized in a three-to-one ratio into development (n = 329) and test (n = 107) cohorts, respectively. In the development cohort, a model was developed to calculate radiomics fibrosis index (RFI) by using logistic regression with elastic net regularization to differentiate stage F3-F4 from stage F0-F2. Optimal RFI cutoffs to diagnose clinically significant fibrosis (stage F2-F4), advanced fibrosis (stage F3-F4), and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. In the test cohort, the diagnostic performance of RFI was compared with that of normalized liver enhancement, aspartate transaminase-to-platelet ratio index (APRI), and fibrosis-4 index by using the Obuchowski index. Results In the test cohort, RFI (Obuchowski index, 0.86) significantly outperformed normalized liver enhancement (Obuchowski index, 0.77; P < .03), APRI (Obuchowski index, 0.60; P < .001), and fibrosis-4 index (Obuchowski index, 0.62; P < .001) for staging liver fibrosis. By using the cutoffs, RFI had sensitivities and specificities as follows: 81% (95% confidence interval: 71%, 89%) and 78% (95% confidence interval: 63%, 89%) for diagnosing stage F2-F4, respectively; 79% (95% confidence interval: 67%, 88%) and 82% (95% confidence interval: 69%, 91%), respectively, for diagnosing stage F3-F4; and 92% (95% confidence interval: 79%, 98%) and 75% (95% confidence interval: 62%, 83%), respectively, for diagnosing stage F4. Conclusion Radiomics analysis of gadoxetic acid-enhanced hepatobiliary phase images allows for accurate diagnosis of liver fibrosis. © RSNA, 2018 Online supplemental material is available for this article.

CT indices for the diagnosis of hepatic steatosis using non-enhanced CT images: development and validation of diagnostic cut-off values in a large cohort with pathological reference standard.

OBJECTIVES: To compare the performances of CT indices for diagnosing hepatic steatosis (HS) and to determine and validate the CT index cut-off values. METHODS: Three indices were measured on non-enhanced CT images of 4413 living liver donor candidates (2939 men, 1474 women; mean age, 31.4 years): hepatic attenuation (CTL), hepatic attenuation minus splenic attenuation (CTL-S), and hepatic attenuation divided by splenic attenuation (CTL/S). The performances of these CT indices in diagnosing HS, relative to pathologic diagnosis, were compared in the development cohort of 3312 subjects by receiver operating characteristic (ROC) analysis. The cut-off values for diagnosing HS > 33% in the development cohort were determined at 95% specificity and 95% sensitivity using bootstrap ROC analysis, and the diagnostic performance of these cut-off values was validated in the test cohort of 1101 subjects. RESULTS: CTL-S showed the highest performance for diagnosing HS ≥ 5% and HS > 33% (areas under the curve (AUCs) = 0.737 and 0.926, respectively), followed by CTL/S (AUCs = 0.732 and 0.925, respectively) and CTL (AUCs = 0.707 and 0.880, respectively). For CT scans using 120 kVp, the CTL-S cut-off values for highly specific (i.e., - 2.1) and highly sensitive (i.e., 7.6) diagnosis of HS > 33% resulted in a specificity of 96.4% with a sensitivity of 64.0% and a sensitivity of 97.3% with a specificity of 54.9%, respectively, in the test cohort. CONCLUSION: CT indices using liver and spleen attenuations have higher performance for diagnosing HS than indices using liver attenuation alone. The CTL-S cut-off values in this study may have utility for diagnosing HS in clinical practice and research. KEY POINTS: • CT indices based on both liver attenuation and spleen attenuation (CTL-Sand CTL/S) have higher diagnostic performance than CTLbased on liver attenuation alone in diagnosing HS using various CT techniques. • The CT index cut-off values determined in this study can be utilized for reliable diagnosis or to rule out subjects with moderate to severe HS in clinical practice and research, including the selection of living liver donors and the development of cohorts with HS or healthy controls.

Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent-enhanced CT Images in the Liver.

Purpose To develop and validate a deep learning system (DLS) for staging liver fibrosis by using CT images in the liver. Materials and Methods DLS for CT-based staging of liver fibrosis was created by using a development data set that included portal venous phase CT images in 7461 patients with pathologically confirmed liver fibrosis. The diagnostic performance of the DLS was evaluated in separate test data sets for 891 patients. The influence of patient characteristics and CT techniques on the staging accuracy of the DLS was evaluated by logistic regression analysis. In a subset of 421 patients, the diagnostic performance of the DLS was compared with that of the radiologist's assessment, aminotransferase-to-platelet ratio index (APRI), and fibrosis-4 index by using the area under the receiver operating characteristic curve (AUROC) and Obuchowski index. Results In the test data sets, the DLS had a staging accuracy of 79.4% (707 of 891) and an AUROC of 0.96, 0.97, and 0.95 for diagnosing significant fibrosis (F2-4), advanced fibrosis (F3-4), and cirrhosis (F4), respectively. At multivariable analysis, only pathologic fibrosis stage significantly affected the staging accuracy of the DLS (P = .016 and .013 for F1 and F2, respectively, compared with F4), whereas etiology of liver disease and CT technique did not. The DLS (Obuchowski index, 0.94) outperformed the radiologist's interpretation, APRI, and fibrosis-4 index (Obuchowski index range, 0.71-0.81; P ˂ .001) for staging liver fibrosis. Conclusion The deep learning system allows for accurate staging of liver fibrosis by using CT images. © RSNA, 2018 Online supplemental material is available for this article.

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

PURPOSE: 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). MATERIALS AND METHODS: In all, 84 pathologically confirmed pancreatic tumors (60 PDACs, 15 NETs, 9 SPTs), 20 pancreatitis (13 AcPs, 7 AIPs), and 30 NP subjects underwent IVIM diffusion-weighted imaging using 10 b-values (0-900 sec/mm2 ) at 1.5T. The ADC, pure molecular diffusion coefficient (Dslow ), perfusion fraction (f), and perfusion-related diffusion coefficient (Dfast ) were calculated and compared using a Kruskal-Wallis test and post-hoc Dunn procedure. Receiver operating characteristic (ROC) analysis was performed to assess diagnostic performance. RESULTS: The f and Dfast of the PDAC were significantly lower than that of the NP (f = 0.10 vs. 0.24; Dfast = 42.21 vs. 71.74 × 10-3 mm2 /sec; P < 0.05). In ROC analysis, f showed the best diagnostic performance (area-under-the-curve, 0.919) among all parameters in differentiating PDAC from NP (P ≤ 0.001). The f values of AcP (0.11) and AIP (0.13) and the Dfast values of SPT (20.48 × 10-3 mm2 /sec) and AcP (24.49 × 10-3 mm2 /sec) were significantly lower compared with NP (f = 0.24; Dfast = 71.74 × 10-3 mm2 /sec; P < 0.05). For NET, the f (0.21) was significantly higher than that of PDAC (0.10, P < 0.01). CONCLUSION: Perfusion-related parameters f and Dfast are more helpful in characterizing pancreatic diseases than ADC or Dslow . The PDCA, SPT, AcP, and AIP were characterized by reduced f and Dfast values compared with normal pancreas. The f value might help in differentiating between PDAC and NET. LEVEL OF EVIDENCE: 3 J. Magn. Reson. Imaging 2017;45:260-269.

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

PURPOSE: 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. MATERIALS AND METHODS: Diffusion-weighted (DW) imaging data were analyzed using IVIM-based full-fitting (simultaneous fit of all parameters) and segmented-fitting (step-by-step fit of each parameter), each with and without the constraint function, to estimate the molecular diffusion coefficient (Dslow ), perfusion fraction (f), and flow-related diffusion coefficient (Dfast ). Computational simulations were performed at variable signal-to-noise ratios to evaluate the relative error (RE) and coefficient of variation (CV) of the estimated IVIM parameters. DW imaging of the abdomen was performed twice at 1.5 Tesla using nine b-values (0-900 s/mm2 ) in 12 health volunteers (6 men and 6 women; mean age: 30 years). The measurement repeatability of IVIM parameters in the liver and the pancreas was evaluated using the within-subject coefficient of variation (w CV). RESULTS: In simulations, full-fitting without the constraint function yielded the largest RE (P < 0.001 for Dslow and f; P ≤ 0.044 for Dfast ) and CV (P ≤ 0.033 for Dslow and f; P ≤ 0.473 for Dfast ) for IVIM parameters among all four algorithms. In volunteer imaging, full-fitting without the constraint function also resulted in the poorest repeatability for Dslow (w CV, 17.12%-65.45%) and f (w CV, 19.35%-42.84%) in the liver and pancreas, while the other algorithms had similar repeatability values (w CV, 4.05%-11.99% for Dslow and 9.65%-18.66% for f). Measurement repeatability of Dfast (w CV, 29.52%-85.01%) was the poorest among the IVIM parameters. CONCLUSION: For accurate and reliable measurement of IVIM parameters, segmented fitting or full-fitting with the constraint function should be used for IVIM-based analysis of DW imaging. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;45:1637-1647.

Intravoxel incoherent motion diffusion-weighted imaging for characterizing focal hepatic lesions: Correlation with lesion enhancement.

PURPOSE: To evaluate the value of intravoxel incoherent motion (IVIM) parameters for characterizing focal hepatic lesions, and to assess the correlation between IVIM parameters and arterial nodule enhancement. MATERIALS AND METHODS: We retrospectively evaluated 161 lesions (91 hepatocellular carcinomas [HCCs], 27 intrahepatic cholangiocarcinomas [IHCCs], 20 hemangiomas, 9 combined hepatocellular-cholangiocarcinomas, 9 metastases, and 5 other tumors) in 161 patients (105 men and 56 women; mean age, 56.4 years). Diffusion-weighted imaging was performed using nine b-values (0-900 s/mm2 ) at 1.5T. Apparent diffusion coefficient (ADC), molecular diffusion coefficient (Dslow ), perfusion fraction (f), and perfusion-related diffusion coefficient (Dfast ) were compared among the hepatic lesions using analysis of variance (ANOVA). Receiver-operating-characteristic analysis was performed to assess diagnostic performance. The enhancement fraction (EF) and the relative enhancement (RE) of the hepatic lesions on arterial phase gadoxetic acid-enhanced images were correlated with the IVIM parameters using Spearman's test. RESULTS: For the differentiation of hemangiomas from malignant tumors, Dslow showed the largest area under the curve (0.933) among all parameters. Although ADC did not show any difference among malignant lesions (P ≥ 0.28), HCCs showed a significantly lower Dslow than IHCC (P < 0.001) and a higher f than did IHCC (P < 0.001) and metastasis (P = 0.027); f had a significant positive correlation with EF (r = 0.420, P < 0.001) and RE (r = 0.264, P = 0.001). CONCLUSION: IVIM parameters are more helpful in characterizing malignant hepatic lesions than ADC; f may reflect the extent and degree of hepatic nodule enhancement in the arterial phase, and may allow for differentiation of HCC from IHCC and metastasis. LEVEL OF EVIDENCE: 3 J. MAGN. RESON. IMAGING 2017;45:1589-1598.

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.

Difference in the intratumoral distributions of extracellular-fluid and intravascular MR contrast agents in glioblastoma growth.

Contrast enhancement by an extracellular-fluid contrast agent (CA) (Gd-DOTA) depends primarily on the blood-brain-barrier permeability (bp), and transverse-relaxation change caused by intravascular T2 CA (superparamagnetic iron oxide nanoparticles, SPIONs) is closely associated with the blood volume (BV). Pharmacokinetic (PK) vascular characterization based on single-CA-using dynamic contrast-enhanced MRI (DCE-MRI) has shown significant measurement variation according to the molecular size of the CA. Based on this recognition, this study used a dual injection of Gd-DOTA and SPIONs for tracing the changes of bp and BV in C6 glioma growth (Days 1 and 7 after the tumor volume reached 2 mL). bp was quantified according to the non-PK parameters of Gd-DOTA-using DCE-MRI (wash-in rate, maximum enhancement ratio and initial area under the enhancement curve (IAUC)). BV was estimated by SPION-induced ΔR2 * and ΔR2 . With validated measurement reliability of all the parameters (coefficients of variation ≤10%), dual-contrast MRI demonstrated a different region-oriented distribution between Gd-DOTA and SPIONs within a tumor as follows: (a) the BV increased stepwise from the tumor center to the periphery; (b) the tumor periphery maintained the augmented BV to support continuous tumor expansion from Day 1 to Day 7; (c) the internal tumor area underwent significant vascular shrinkage (i.e. decreased ΔR2 and ΔR2 ) as the tumor increased in size; (d) the tumor center showed greater bp-indicating parameters, i.e. wash-in rate, maximum enhancement ratio and IAUC, than the periphery on both Days 1 and 7 and (e) the tumor center showed a greater increase of bp than the tumor periphery in tumor growth, as suggested to support tumor viability when there is insufficient blood supply. In the MRI-histologic correlation, a prominent BV increase in the tumor periphery seen in MRI was verified with increased fluorescein isothiocyanate-dextran signals and up-regulated immunoreactivity of CD31-VEGFR. In conclusion, the spatiotemporal alterations of BV and bp in glioblastoma growth, i.e. augmented BV in the tumor periphery and increased bp in the center, can be sufficiently evaluated by MRI with dual injection of extracellular-fluid Gd chelates and intravascular SPION.

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.

Simultaneous evaluation of vascular morphology, blood volume and transvascular permeability using SPION-based, dual-contrast MRI: imaging optimization and feasibility test.

Exploiting ultrashort-T(E) (UTE) MRI, T1-weighted positive contrast can be obtained from superparamagnetic iron oxide nanoparticles (SPIONs), which are widely used as a robust T2-weighted, negative contrast agent on conventional MR images. Our study was designed (a) to optimize the dual-contrast MRI method using SPIONs and (b) to validate the feasibility of simultaneously evaluating the vascular morphology, blood volume and transvascular permeability using the dual-contrast effect of SPIONs. All studies were conducted using 3 T MRI. According to numerical simulation, 0.15 mM was the optimal blood SPION concentration for visualizing the positive contrast effect using UTE MRI (T(E) = 0.09 ms), and a flip angle of 40° could provide sufficient SPION-induced enhancement and acceptable measurement noise for UTE MR angiography. A pharmacokinetic study showed that this concentration can be steadily maintained from 30 to 360 min after the injection of 29 mg/kg of SPIONs. An in vivo study using these settings displayed image quality and CNR of SPION-enhanced UTE MR angiography (image quality score 3.5; CNR 146) comparable to those of the conventional, Gd-enhanced method (image quality score 3.8; CNR 148) (p > 0.05). Using dual-contrast MR images obtained from SPION-enhanced UTE and conventional spin- and gradient-echo methods, the transvascular permeability (water exchange index 1.76-1.77), cerebral blood volume (2.58-2.60%) and vessel caliber index (3.06-3.10) could be consistently quantified (coefficient of variation less than 9.6%; Bland-Altman 95% limits of agreement 0.886-1.111) and were similar to the literature values. Therefore, using the optimized setting of combined SPION-based MRI techniques, the vascular morphology, blood volume and transvascular permeability can be comprehensively evaluated during a single session of MR examination.