Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in dual-energy CT of the abdomen: a phantom and clinical study.

OBJECTIVES: To investigate the effect of deep learning image reconstruction (DLIR) on the accuracy of iodine quantification and image quality of dual-energy CT (DECT) compared to that of other reconstruction algorithms in a phantom experiment and an abdominal clinical study. METHODS: An elliptical phantom with five different iodine concentrations (1-12 mgI/mL) was imaged five times with fast-kilovoltage-switching DECT for three target volume CT dose indexes. All images were reconstructed using filtered back-projection, iterative reconstruction (two levels), and DLIR algorithms. Measured and nominal iodine concentrations were compared among the algorithms. Contrast-enhanced CT of the abdomen with the same scanner was acquired in clinical patients. In arterial and portal venous phase images, iodine concentration, image noise, and coefficients of variation for four locations were retrospectively compared among the algorithms. One-way repeated-measures analyses of variance were used to evaluate differences in the iodine concentrations, standard deviations, coefficients of variation, and percentages of error among the algorithms. RESULTS: In the phantom study, the measured iodine concentrations were equivalent among the algorithms: within ± 8% of the nominal values, with root-mean-square deviations of 0.08-0.36 mgI/mL, regardless of radiation dose. In the clinical study (50 patients; 35 men; mean age, 68 ± 11 years), iodine concentrations were equivalent among the algorithms for each location (all p > .99). Image noise and coefficients of variation were lower with DLIR than with the other algorithms (all p < .01). CONCLUSIONS: The DLIR algorithm reduced image noise and variability of iodine concentration values compared with other reconstruction algorithms in the fast-kilovoltage-switching dual-energy CT. KEY POINTS: • In the phantom study, standard deviations and coefficients of variation in iodine quantification were lower on images with the deep learning image reconstruction algorithm than on those with other algorithms. • In the clinical study, iodine concentrations of measurement location in the upper abdomen were consistent across four reconstruction algorithms, while image noise and variability of iodine concentrations were lower on images with the deep learning image reconstruction algorithm.

Development and Validation of Multiparametric MRI-based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer.

Background Stratifying high-risk histopathologic features in endometrial carcinoma is important for treatment planning. Radiomics analysis at preoperative MRI holds potential to identify high-risk phenotypes. Purpose To evaluate the performance of multiparametric MRI three-dimensional radiomics-based machine learning models for differentiating low- from high-risk histopathologic markers-deep myometrial invasion (MI), lymphovascular space invasion (LVSI), and high-grade status-and advanced-stage endometrial carcinoma. Materials and Methods This dual-center retrospective study included women with histologically proven endometrial carcinoma who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. Exclusion criteria were tumor diameter less than 1 cm, missing MRI sequences or histopathology reports, neoadjuvant therapy, and malignant neoplasms other than endometrial carcinoma. Three-dimensional radiomics features were extracted after tumor segmentation at MRI (T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI). Predictive features were selected in the training set with use of random forest (RF) models for each end point, and trained RF models were applied to the external test set. Five board-certified radiologists conducted MRI-based staging and deep MI assessment in the training set. Areas under the receiver operating characteristic curve (AUCs) were reported with balanced accuracies, and radiologists' readings were compared with radiomics with use of McNemar tests. Results In total, 157 women were included: 94 at the first institution (training set; mean age, 66 years ± 11 [SD]) and 63 at the second institution (test set; 67 years ± 12). RF models dichotomizing deep MI, LVSI, high grade, and International Federation of Gynecology and Obstetrics (FIGO) stage led to AUCs of 0.81 (95% CI: 0.68, 0.88), 0.80 (95% CI: 0.67, 0.93), 0.74 (95% CI: 0.61, 0.86), and 0.84 (95% CI: 0.72, 0.92), respectively, in the test set. In the training set, radiomics provided increased performance compared with radiologists' readings for identifying deep MI (balanced accuracy, 86% vs 79%; P = .03), while no evidence of a difference was observed in performance for advanced FIGO stage (80% vs 78%; P = .27). Conclusion Three-dimensional radiomics can stratify patients by using preoperative MRI according to high-risk histopathologic end points in endometrial carcinoma and provide nonsignificantly different or higher performance than radiologists in identifying advanced stage and deep myometrial invasion, respectively. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kido and Nishio in this issue.

Abbreviated Gadoxetic Acid-Enhanced MRI for the Detection of Liver Metastases in Patients With Potentially Resectable Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Gadoxetic acid-enhanced magnetic resonance imaging (MRI) is useful in detecting liver metastases from pancreatic ductal adenocarcinoma (PDAC). However, the long examination time limits its utility in the initial workup of patients with PDAC. PURPOSE: To evaluate the incremental value of an abbreviated gadoxetic acid-enhanced MRI for the detection of liver metastases in patients with PDAC. STUDY TYPE: Retrospective. POPULATION: Patients (N = 130) with potentially resectable PDAC (women, 58 [44.6%]). FIELD STRENGTH/SEQUENCE: 1.5 T and 3 T; gradient dual-echo T1-weighted (in-phase and opposed-phase), fat-suppressed fast spin-echo T2-weighted, single-shot echo-planar diffusion-weighted, and three-dimensional fat-suppressed T1-weighted gradient-echo dynamic contrast-enhanced and hepatobiliary phase sequences, as well as contrast-enhanced computed tomography (CECT). ASSESSMENT: Three radiologists independently reviewed three different image sets to detect liver metastases: set 1, CECT alone; set 2, CECT and abbreviated MRI comprising fat-suppressed T2-weighted, diffusion-weighted, and hepatobiliary phase images; and set 3, CECT and standard gadoxetic acid-enhanced MRI. STATISTICAL TESTS: Figure of merit (FOM) was compared using the jackknife alternative free-response receiver operating characteristics, and other per-lesion and per-patient diagnostic parameters for each image set were compared using McNemar's and Fisher's test. P < 0.05 was considered statistically significant. RESULTS: A total of 43 liver metastases were identified in 13 patients. Reader-averaged FOM to detect liver metastases were significantly higher for sets 2 (0.884) and 3 (0.886) than for set 1 (0.609), while they were comparable between sets 2 and 3 (P = 0.96). The mean per-patient sensitivities, negative predictive values, and accuracies were significantly higher for sets 2 and 3 than for set 1, while those between sets 2 and 3 were not significantly different (not applicable, P > 0.99, and P > 0.99, respectively). DATA CONCLUSION: Gadoxetic acid-enhanced MRI combined with CECT had higher diagnostic performance than CECT alone for the detection of liver metastases in patients with PDAC. The incremental values were comparable for the abbreviated MRI and standard MRI. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Estimation of pancreatic fibrosis and prediction of postoperative pancreatic fistula using extracellular volume fraction in multiphasic contrast-enhanced CT.

OBJECTIVE: To investigate the diagnostic performance of the extracellular volume (ECV) fraction in multiphasic contrast-enhanced computed tomography (CE-CT) for estimating histologic pancreatic fibrosis and predicting postoperative pancreatic fistula (POPF). METHODS: Eighty-five patients (49 men; mean age, 69 years) who underwent multiphasic CE-CT followed by pancreaticoduodenectomy with pancreaticojejunal anastomosis between January 2012 and December 2018 were retrospectively included. The ECV fraction was calculated from absolute enhancements of the pancreas and aorta between the precontrast and equilibrium-phase images, followed by comparisons among histologic pancreatic fibrosis grades (F0‒F3). The diagnostic performance of the ECV fraction in advanced fibrosis (F2‒F3) was evaluated using receiver operating characteristic curve analysis. Multivariate logistic regression analysis was used to evaluate the associations of the risk of POPF development with patient characteristics, histologic findings, and CT imaging parameters. RESULTS: The mean ECV fraction of the pancreas was 34.4% ± 9.5, with an excellent intrareader agreement of 0.811 and a moderate positive correlation with pancreatic fibrosis (r = 0.476; p < 0.001). The mean ECV fraction in advanced fibrosis was significantly higher than that in no/mild fibrosis (44.4% ± 10.8 vs. 31.7% ± 6.7; p < 0.001), and the area under the receiver operating characteristic curve for the diagnosis of advanced fibrosis was 0.837. Twenty-two patients (25.9%) developed clinically relevant POPF. Multivariate logistic regression analysis demonstrated that the ECV fraction was a significant predictor of POPF. CONCLUSIONS: The ECV fraction can offer quantitative information for assessing pancreatic fibrosis and POPF after pancreaticojejunal anastomosis. KEY POINTS: • There was a moderate positive correlation of the extracellular volume (ECV) fraction of the pancreas in contrast-enhanced CT with the histologic grade of pancreatic fibrosis (r = 0.476; p < 0.001). • The ECV fraction was higher in advanced fibrosis (F2‒F3) than in no/mild fibrosis (F0‒F1) (p < 0.001), with an AUC of 0.837 for detecting advanced fibrosis. • The ECV fraction was an independent risk factor for predicting subclinical (odds ratio, 0.81) and clinical (odds ratio, 0.80) postoperative pancreatic fistula.

Diffusion-weighted imaging in prostate cancer.

Diffusion-weighted imaging (DWI), a key component in multiparametric MRI (mpMRI), is useful for tumor detection and localization in clinically significant prostate cancer (csPCa). The Prostate Imaging Reporting and Data System versions 2 and 2.1 (PI-RADS v2 and PI-RADS v2.1) emphasize the role of DWI in determining PIRADS Assessment Category in each of the transition and peripheral zones. In addition, several recent studies have demonstrated comparable performance of abbreviated biparametric MRI (bpMRI), which incorporates only T2-weighted imaging and DWI, compared with mpMRI with dynamic contrast-enhanced MRI. Therefore, further optimization of DWI is essential to achieve clinical application of bpMRI for efficient detection of csPC in patients with elevated PSA levels. Although DWI acquisition is routinely performed using single-shot echo-planar imaging, this method suffers from such as susceptibility artifact and anatomic distortion, which remain to be solved. In this review article, we will outline existing problems in standard DWI using the single-shot echo-planar imaging sequence; discuss solutions that employ newly developed imaging techniques, state-of-the-art technologies, and sequences in DWI; and evaluate the current status of quantitative DWI for assessment of tumor aggressiveness in PC.

VI-RADS: Multiinstitutional Multireader Diagnostic Accuracy and Interobserver Agreement Study.

BACKGROUND. The Vesical Imaging Reporting and Data System (VI-RADS), based on multiparametric MRI (mpMRI), was developed to provide accurate information for the diagnosis of muscle-invasive bladder cancer (MIBC). OBJECTIVE. The purpose of our study was to evaluate the interobserver agreement and diagnostic performance of VI-RADS among readers with different levels of experience. METHODS. This retrospective study included 91 consecutive patients who underwent mpMRI before transurethral resection of bladder tumor (TURBT) from July 2010 through August 2018. After attending a training session, seven radiologists (five radiologists experienced in bladder MRI and two inexperienced radiologists) reviewed and scored all MRI examinations according to VI-RADS. The interobserver agreement was assessed by kappa statistics. ROC analysis was used to evaluate the diagnostic performance for MIBC. AUCs were estimated. RESULTS. Among 91 patients (72 men and 19 women; mean age ± SD, 73.2 ± 10.2 years), 48 (52.7%) had MIBC and 43 (47.3%) had non-muscle-invasive bladder cancer. Sixty-eight patients were treated with TURBT, and 23 were treated with radical cystectomy. Interobserver agreement was moderate to substantial (κ = 0.60-0.80) among the experienced readers, substantial (κ = 0.67) between the two inexperienced readers, and moderate to substantial (κ = 0.55-0.75) between the experienced and inexperienced readers. The pooled AUC was 0.88 (range, 0.82-0.91) for experienced readers and 0.84 (range, 0.83-0.85) for inexperienced readers, and 0.87 for all readers. Using a VI-RADS score of 4 or greater as the cutoff value for MIBC, the pooled sensitivity and specificity were 74.1% (range, 66.0-80.9%) and 94.1% (range, 88.6-97.7%) for experienced readers and 63.9% (range, 59.6-68.1%) and 86.4% (range, 84.1-88.6%) for inexperienced readers. Using a VI-RADS score of 3 or greater as the cutoff value, the pooled sensitivity and specificity were 83.4% (range, 80.9-85.1%) and 77.3% (range, 61.4-88.6%) for experienced readers and 82.0% (range, 80.9-83.0%) and 73.9% (range, 72.7-75.0%) for inexperienced readers. CONCLUSION. We observed moderate to substantial interobserver agreement and a pooled AUC of 0.87 among radiologists of different levels of expertise using VI-RADS. CLINICAL IMPACT. VI-RADS could help determine the depth and range of excision in TURBT, decreasing the risk of complications and enhancing the accuracy of pathologic diagnosis.

Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT.

Background Deep learning may help to improve computer-aided detection of volume (CADv) measurement of pulmonary nodules at chest CT. Purpose To determine the efficacy of a deep learning method for improving CADv for measuring the solid and ground-glass opacity (GGO) volumes of a nodule, doubling time (DT), and the change in volume at chest CT. Materials and Methods From January 2014 to December 2016, patients with pulmonary nodules at CT were retrospectively reviewed. CADv without and with a convolutional neural network (CNN) automatically determined total nodule volume change per day and DT. Area under the curves (AUCs) on a per-nodule basis and diagnostic accuracy on a per-patient basis were compared among all indexes from CADv with and without CNN for differentiating benign from malignant nodules. Results The CNN training set was 294 nodules in 217 patients, the validation set was 41 nodules in 32 validation patients, and the test set was 290 nodules in 188 patients. A total of 170 patients had 290 nodules (mean size ± standard deviation, 11 mm ± 5; range, 4-29 mm) diagnosed as 132 malignant nodules and 158 benign nodules. There were 132 solid nodules (46%), 106 part-solid nodules (36%), and 52 ground-glass nodules (18%). The test set results showed that the diagnostic performance of the CNN with CADv for total nodule volume change per day was larger than DT of CADv with CNN (AUC, 0.94 [95% confidence interval {CI}: 0.90, 0.96] vs 0.67 [95% CI: 0.60, 0.74]; P < .001) and CADv without CNN (total nodule volume change per day: AUC, 0.69 [95% CI: 0.62, 0.75]; P < .001; DT: AUC, 0.58 [95% CI: 0.51, 0.65]; P < .001). The accuracy of total nodule volume change per day of CADv with CNN was significantly higher than that of CADv without CNN (P < .001) and DT of both methods (P < .001). Conclusion Convolutional neural network is useful for improving accuracy of computer-aided detection of volume measurement and nodule differentiation capability at CT for patients with pulmonary nodules. © RSNA, 2020 Online supplemental material is available for this article.

Diagnostic performance of zero-TE lung MR imaging in FDG PET/MRI for pulmonary malignancies.

OBJECTIVES: This study aimed to evaluate the diagnostic performance of the lung zero-echo time (ZTE) sequence in FDG PET/MRI for detection and differentiation of lung lesions in oncologic patients in comparison with conventional two-point Dixon-based MR imaging. METHODS: In this single-institution retrospective study approved by the institutional review board, 209 patients with malignancies (97 men and 112 women; age range, 17-89 years; mean age, 66.5 ± 12.9 years) underwent 18F-FDG PET/MRI between August 2017 and August 2018, with diagnostic Dixon and ZTE under respiratory gating acquired simultaneously with PET. Image analysis was performed for PET/Dixon and PET/ZTE fused images by two readers to assess the detectability and differentiation of lung lesions. The reference standard was pathological findings and/or the data from a chest CT. The detection and differentiation abilities were evaluated for all lesions and subgroups divided by lesion size and maximum standardized uptake value (SUVmax). RESULTS: Based on the reference standard, 227 lung lesions were identified in 113 patients. The detectability of PET/ZTE was significantly better than that of PET/Dixon for overall lesions, lesions with a SUVmax less than 3.0 and lesions smaller than 4 mm (p < 0.01). The diagnostic performance of PET/ZTE was significantly better than that of PET/Dixon for overall lesions and lesions smaller than 4 mm (p < 0.01). CONCLUSIONS: ZTE can improve diagnostic performance in the detection and differentiation of both FDG-avid and non-FDG-avid lung lesions smaller than 4 mm in size, yielding a promising tool to enhance the utility of FDG PET/MRI in oncology patients with lung lesions. KEY POINTS: • The detection rate of PET/ZTE for lesions with a SUVmax of less than 1.0 was significantly better than that of PET/Dixon. • The performance for differentiation of PET/ZTE for lesions that were even smaller than 4 mm in size were significantly better than that of PET/Dixon. • Inter-rater agreement of PET/ZTE for the differentiation of lesions less than 4 mm in size was substantial and better than that of PET/Dixon.

Magnetic resonance imaging of intraocular optic nerve disorders: review article.

The optic nerve is morphologically classified as a peripheral nerve, but histologically it shares characteristics with the central nerves. Diseases that affect vision and the optic nerve are many and varied: optic neuritis, demyelination (multiple sclerosis, acute disseminated encephalomyelitis, neuromyelitis optica spectrum disorder, and myelin oligodendrocyte glycoprotein antibody-related disorders), drugs, collagen disease, vasculitis, infection, trauma, vascular abnormalities, tumours, and non-tumoural masses. In this review, we summarise the magnetic resonance imaging findings for various pathological conditions that cause deterioration in visual acuity.

Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning.

OBJECTIVES: This study was conducted in order to evaluate a novel risk stratification model using dual-energy CT (DECT) texture analysis of head and neck squamous cell carcinoma (HNSCC) with machine learning to (1) predict associated cervical lymphadenopathy and (2) compare the accuracy of spectral versus single-energy (65 keV) texture evaluation for endpoint prediction. METHODS: Eighty-seven patients with HNSCC were evaluated. Texture feature extraction was performed on virtual monochromatic images (VMIs) at 65 keV alone or different sets of multi-energy VMIs ranging from 40 to 140 keV, in addition to iodine material decomposition maps and other clinical information. Random forests (RF) models were constructed for outcome prediction with internal cross-validation in addition to the use of separate randomly selected training (70%) and testing (30%) sets. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined for predicting positive versus negative nodal status in the neck. RESULTS: Depending on the model used and subset of patients evaluated, an accuracy, sensitivity, specificity, PPV, and NPV of up to 88, 100, 67, 83, and 100%, respectively, could be achieved using multi-energy texture analysis. Texture evaluation of VMIs at 65 keV alone or in combination with only iodine maps had a much lower accuracy. CONCLUSIONS: Multi-energy DECT texture analysis of HNSCC is superior to texture analysis of 65 keV VMIs and iodine maps alone and can be used to predict cervical nodal metastases with relatively high accuracy, providing information not currently available by expert evaluation of the primary tumor alone. KEY POINTS: • Texture features of HNSCC tumor are predictive of nodal status. • Multi-energy texture analysis is superior to analysis of datasets at a single energy. • Dual-energy CT texture analysis with machine learning can enhance noninvasive diagnostic tumor evaluation.

Features from Computerized Texture Analysis of Breast Cancers at Pretreatment MR Imaging Are Associated with Response to Neoadjuvant Chemotherapy.

Purpose To evaluate whether features from texture analysis of breast cancers were associated with pathologic complete response (pCR) after neoadjuvant chemotherapy and to explore the association between texture features and tumor subtypes at pretreatment magnetic resonance (MR) imaging. Materials and Methods Institutional review board approval was obtained. This retrospective study included 85 patients with 85 breast cancers who underwent breast MR imaging before neoadjuvant chemotherapy between April 10, 2008, and March 12, 2015. Two-dimensional texture analysis was performed by using software at T2-weighted MR imaging and contrast material-enhanced T1-weighted MR imaging. Quantitative parameters were compared between patients with pCR and those with non-pCR and between patients with triple-negative breast cancer and those with non-triple-negative cancer. Multiple logistic regression analysis was used to determine independent parameters. Results Eighteen tumors (22%) were triple-negative breast cancers. pCR was achieved in 30 of the 85 tumors (35%). At univariate analysis, mean pixel intensity with spatial scaling factor (SSF) of 2 and 4 on T2-weighted images and kurtosis on contrast-enhanced T1-weighted images showed a significant difference between triple-negative breast cancer and non-triple-negative breast cancer (P = .009, .003, and .001, respectively). Kurtosis (SSF, 2) on T2-weighted images showed a significant difference between pCR and non-pCR (P = .015). At multiple logistic regression, kurtosis on T2-weighted images was independently associated with pCR in non-triple-negative breast cancer (P = .033). A multivariate model incorporating T2-weighted and contrast-enhanced T1-weighted kurtosis showed good performance for the identification of triple-negative breast cancer (area under the receiver operating characteristic curve, 0.834). Conclusion At pretreatment MR imaging, kurtosis appears to be associated with pCR to neoadjuvant chemotherapy in non-triple-negative breast cancer and may be a promising biomarker for the identification of triple-negative breast cancer. © RSNA, 2017.

Endometrial Carcinoma: MR Imaging-based Texture Model for Preoperative Risk Stratification-A Preliminary Analysis.

Purpose To evaluate the associations among mathematical modeling with the use of magnetic resonance (MR) imaging-based texture features and deep myometrial invasion (DMI), lymphovascular space invasion (LVSI), and histologic high-grade endometrial carcinoma. Materials and Methods Institutional review board approval was obtained for this retrospective study. This study included 137 women with endometrial carcinomas measuring greater than 1 cm in maximal diameter who underwent 1.5-T MR imaging before hysterectomy between January 2011 and December 2015. Texture analysis was performed with commercial research software with manual delineation of a region of interest around the tumor on MR images (T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced images and apparent diffusion coefficient maps). Areas under the receiver operating characteristic curve and diagnostic performance of random forest models determined by using a subset of the most relevant texture features were estimated and compared with those of independent and blinded visual assessments by three subspecialty radiologists. Results A total of 180 texture features were extracted and ultimately limited to 11 features for DMI, 12 for LVSI, and 16 for high-grade tumor for random forest modeling. With random forest models, areas under the receiver operating characteristic curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were estimated at 0.84, 79.3%, 82.3%, 81.0%, 76.7%, and 84.4% for DMI; 0.80, 80.9%, 72.5%, 76.6%, 74.3%, and 79.4% for LVSI; and 0.83, 81.0%, 76.8%, 78.1%, 60.7%, and 90.1% for high-grade tumor, respectively. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of visual assessment for DMI were 84.5%, 82.3%, 83.2%, 77.7%, and 87.8% (reader 3). Conclusion The mathematical models that incorporated MR imaging-based texture features were associated with the presence of DMI, LVSI, and high-grade tumor and achieved equivalent accuracy to that of subspecialty radiologists for assessment of DMI in endometrial cancers larger than 1 cm. However, these preliminary results must be interpreted with caution until they are validated with an independent data set, because the small sample size relative to the number of features extracted may have resulted in overfitting of the models. © RSNA, 2017 Online supplemental material is available for this article.

Present and future roles of FDG-PET/CT imaging in the management of gastrointestinal cancer: an update.

Positron emission tomography/computed tomography (PET/CT) integrated with 2-[18F]fluoro-2-deoxy-D-glucose (FDG) is a useful tool for acquisition of both glucose metabolism and anatomic imaging data, as only a single device and one diagnostic session is required, thus opening a new field in clinical oncologic imaging. FDG-PET/CT has been successfully used for initial staging, restaging, assessment of early treatment response, evaluation of metastatic disease response, and prognostication of intestinal cancer as well as various malignant tumors. We reviewed the current status and role of FDG-PET/CT for management of patients with esophageal cancer, gastric cancer, and colorectal cancer, with focus on both its usefulness and limitations.

Chemical Exchange Saturation Transfer MR Imaging: Preliminary Results for Differentiation of Malignant and Benign Thoracic Lesions.

PURPOSE: To prospectively evaluate the capability of amide proton transfer-weighted chemical exchange saturation transfer magnetic resonance (MR) imaging for characterization of thoracic lesions. MATERIALS AND METHODS: The institutional review board approved this study, and written informed consent was obtained from 21 patients (13 men and eight women; mean age, 72 years) prior to enrollment. Each patient underwent chemical exchange saturation transfer MR imaging by using respiratory-synchronized half-Fourier fast spin-echo imaging after a series of magnetization transfer pulses. Next, a magnetization transfer ratio asymmetry at 3.5 ppm map was computationally generated. Pathology examinations resulted in a diagnosis of 13 malignant and eight benign thoracic lesions. The malignant lesions were further diagnosed as being nine lung cancers, comprising six adenocarcinomas, three squamous cell carcinomas, and four other thoracic malignancies. The Student t test was used to evaluate the capability of magnetization transfer ratio asymmetry (at 3.5 ppm), as assessed by means of region of interest measurements, for differentiating benign and malignant lesions, lung cancers and other thoracic lesions, and adenocarcinomas and squamous cell carcinomas. RESULTS: Magnetization transfer ratio asymmetry (at 3.5 ppm) was significantly higher for malignant tumors (mean ± standard deviation, 3.56% ± 3.01) than for benign lesions (0.33% ± 0.38, P = .008). It was also significantly higher for other thoracic malignancies (6.71% ± 3.46) than for lung cancer (2.16% ± 1.41, P = .005) and for adenocarcinoma (2.88% ± 1.13) than for squamous cell carcinoma (0.71% ± 0.17, P = .02). CONCLUSION: Amide proton transfer-weighted chemical exchange saturation transfer MR imaging allows characterization of thoracic lesions.

Evaluation of interobserver variability and diagnostic performance of developed MRI-based radiological scoring system for invasive placenta previa.

PURPOSE: To evaluate the interobserver variability and diagnostic performance of a developed magnetic resonance imaging (MRI)-based scoring system for invasive placenta previa. MATERIALS AND METHODS: Prenatal MR images of 70 women were retrospectively evaluated, 18 of whom were diagnosed with invasive placenta. The six MR features (dark band on T2 -weighted images, intraplacental abnormal vascularity, placental bulge, heterogeneous placenta, myometrial thinning, and placental protrusion sign) were scored on 5-point Likert scale separately, and the cumulative radiological score (CRS) was defined as the sum of each score. Two more experienced radiologists (readers A and B) and two less experienced residents (readers C and D) calculated the CRS. Interobserver variability was assessed by measuring the intraclass correlation coefficient. Diagnostic performance was evaluated by means of receiver operating characteristic (ROC) analysis. RESULTS: Interobserver variability for CRS was excellent for the more experienced radiologists (0.85), and good for all readers (0.72) and the less experienced residents (0.66). The area under the ROC curve (Az) and accuracy (Acc) for CRS were significantly higher or equivalent to those of other MR features for all readers (Az and Acc for reader A; CRS, 0.92, 91.4%; intraplacental T2 dark band, 0.83, P = 0.009, 81.4%, P = 0.03; intraplacental abnormal vascularity, 0.9, P = 0.3, 90.0%, P = 1.00; placental bulge, 0.81, P = 0.0008, 80.0%, P = 0.02; heterogeneous placenta, 0.85, P = 0.11, 74.3%, P = 0.002; myometrial thinning, 0.84, P = 0.06, 60.0%, P < 0.0001; placental protrusion sign, 0.81, P = 0.01, 81.4%, P = 0.26). CONCLUSION: This developed MRI-based scoring system demonstrated excellent or good interobserver variability, and good diagnostic performance for invasive placenta previa. J. Magn. Reson. Imaging 2016;44:573-583.

Triexponential function analysis of diffusion-weighted MRI for diagnosing prostate cancer.

BACKGROUND: To evaluate more detailed information noninvasively through on diffusion and perfusion in prostate cancer (PCa) using triexponential analysis of diffusion-weighted imaging (DWI). METHODS: Sixty-three prostate cancer patients underwent preoperative 3.0 Tesla MRI including eight b-values DWI. Triexponential analysis was performed to obtain three diffusion coefficients (Dp , Df , Ds ), as well as fractions (Fp , Ff , Fs ). Each diffusion parameter for cancerous lesions and normal tissues was compared and the relationship between diffusion parameters and Gleason score (GS) was assessed. K(trans) , Ve , and the ratios of intracellular components measured in histopathological specimens were compared with diffusion parameters. RESULTS: Dp was significantly greater for cancerous lesions than normal peripheral zone (PZ) (P < 0.001), whereas Dp in transition zone (TZ) showed no significant difference (P = 0.74, 95% confidence interval (CI) = -4.69-6.48). Ds was significantly smaller for each cancerous lesions in PZ and TZ (P < 0.001, respectively). There was no significant difference in Df between cancerous lesions and normal tissues in PZ and TZ (P = 0.07, 95% CI = -0.29-0.12 and P = 0.53, 95% CI = -3.51-2.29, respectively). D obtained with biexponential analysis were significantly smaller in cancerous lesions than in normal tissue in PZ and TZ (P < 0.001 for both), while D* in PZ and TZ showed no significant difference (P = 0.14, 95% CI = -1.60-0.24 and P = 0.31, 95% CI = -3.43-1.16, respectively). Dp in PZ and TZ showed significant correlation with K(trans) (R = 0.85, P < 0.001; R = 0.81, P < 0.001, respectively), while D(*) in PZ obtained with biexponential analysis showed no such correlation (P = 0.08, 95% CI = -0.14-0.30). Fs was significantly correlated with intracellular space fraction evaluated in histopathological specimens in PZ and TZ cancer (R = 0.41, P < 0.05; R = 0.59, P < 0.001, respectively). Ff and Fs correlated significantly with GS in PZ and TZ cancer (PZ: R = -0.44, P < 0.05; R = 0.37, P < 0.05, TZ: R = -0.59, P < 0.05; R = 0.57, P < 0.05, respectively). CONCLUSION: Triexponential analysis is a noninvasive approach that can provide more detailed information regarding diffusion and perfusion of PCa than biexponential analysis.

Multiparametric magnetic resonance imaging: Current role in prostate cancer management.

Digital rectal examination, serum prostate-specific antigen screening and transrectal ultrasound-guided biopsy are conventionally used as screening, diagnostic and surveillance tools for prostate cancer. However, they have limited sensitivity and specificity. In recent years, the role of multiparametric magnetic resonance imaging has steadily grown, and is now part of the standard clinical management in many institutions. In multiparametric magnetic resonance imaging, the morphological assessment of T2-weighted imaging is correlated with diffusion-weighted imaging, dynamic contrast-enhanced imaging perfusion and/or magnetic resonance spectroscopic imaging. Multiparametric magnetic resonance imaging is currently regarded as the most sensitive and specific imaging technique for the evaluation of prostate cancer, including detection, staging, localization and aggressiveness evaluation. This article presents an overview of multiparametric magnetic resonance imaging, and discusses the current role of multiparametric magnetic resonance imaging in the different fields of prostate cancer management.

Preoperative risk stratification using metabolic parameters of (18)F-FDG PET/CT in patients with endometrial cancer.

PURPOSE: To evaluate the usefulness of metabolic parameters obtained by (18)F-FDG PET/CT for preoperative stratification of high-risk and low-risk endometrial carcinomas. METHODS: Preoperative (18)F-FDG PET/CT was performed in 56 women with endometrial cancer. Maximum standardized uptake values (SUVmax), metabolic tumour volume (MTV) and total lesion glycolysis (TLG) of primary tumours were compared with clinicopathological features of surgical specimens. Diagnostic performance in terms of differentiation of low-risk disease (endometrioid histology, histological grade 1 or 2, invasion of less than half of the myometrium, and FIGO stage I) from high-risk disease was assessed. RESULTS: MTV and TLG were significantly higher in patients with higher histological grade (p = 0.0026 and p = 0.034), larger tumour size (p = 0.002 and p = 0.0017), lymphovascular space involvement (LVSI; p = 0.012 and p = 0.0051), myometrial invasion (p = 0.027 and p = 0.031), cervical stromal invasion (p = 0.023 and p = 0.014), ovarian metastasis (p = 0.00022 and p = 0.00034), lymph node metastasis (p < 0.0001 and p < 0.0001), and higher FIGO stage (p = 0.0011 and p = 0.00048). SUVmax was significantly higher in patients with larger tumour size (p = 0.0025), LVSI (p = 0.00023) and myometrial invasion (p < 0.0001). The areas under the ROC curves (AUCs) for distinguishing high-risk from low-risk carcinoma were 0.625, 0.829 and 0.797 for SUVmax, MTV and TLG, respectively. AUCs for both MTV and TLG were significantly larger than that for SUVmax (p = 0.0049 and p = 0.021). The optimal TLG cut-off value of 70.2, determined by ROC analysis, was found to have 72.0% sensitivity and 74.2% specificity for risk stratification. CONCLUSION: MTV and TLG of primary endometrial cancer show better correlations with clinicopathological features and are more useful for differentiating high-risk from low-risk carcinoma than SUVmax.

Novel MRI finding for diagnosis of invasive placenta praevia: evaluation of findings for 65 patients using clinical and histopathological correlations.

OBJECTIVE: To review established magnetic resonance (MR) criteria and describe a new MR finding for the diagnosis of invasive placenta praevia. METHODS: A retrospective review of prenatal MRI examinations of 65 patients (median age: 35 years) who underwent MR for the screening of invasive placenta praevia. All MRIs were performed on a 1.5-T unit, including axial, coronal and sagittal T2-weighted half-Fourier single-shot turbo spin echo imaging. Fifteen patients were diagnosed with invasive placenta praevia. Two experienced radiologists reviewed the MR images and evaluated a total of six MRI features of the placenta, including our novel finding of the placental protrusion into the internal os (placental protrusion sign). Inter-rater reliability was assessed by using kappa statistics. Features with a kappa statistic >0.40 were evaluated using Fisher's two-sided exact test for comparison of their capabilities for placental invasion assessment. RESULTS: Interobserver reliability was moderate or better for the intraplacental T2 dark band, intraplacental abnormal vascularity, uterine bulging, heterogeneous placenta and placental protrusion sign. Fisher's two-sided exact test results showed all these features were significantly associated with invasive placenta praevia. CONCLUSION: The novel MRI finding of a placental protrusion sign is a useful addition to the established MRI findings for the diagnosis of invasive placenta praevia. KEY POINTS: • Prenatal diagnosis for an invasive placenta is essential for perinatal planning. • Magnetic resonance imaging provides useful information for the diagnosis of invasive placenta. • The placental protrusion sign is a useful novel MRI finding for predicting invasive placenta.

Pictorial review of the diagnosis of muscle-invasive bladder cancer using vesical imaging-reporting and data system (VI-RADS).

The Vesical Imaging-Reporting and Data System (VI-RADS) is a standard magnetic resonance imaging (MRI) and diagnostic method for muscle-invasive bladder cancer that was published in 2018. Several studies have demonstrated that VI-RADS has high diagnostic power and reproducibility. However, reading VI-RADS requires a certain amount of expertise, and radiologists need to be aware of the various pitfalls. MRI of the bladder includes T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCEI). T2WI is excellent for understanding anatomy. DWI and DCEI show high contrast between the tumor and normal anatomical structures and are suitable for staging local tumors. Bladder tumors are classified into five categories according to their size and morphology and their positional relationship to the bladder wall based on the VI-RADS diagnostic criteria. If the T2WI, DWI, and DCEI categories are the same, the category is the VI-RADS category. If the categories do not match, the DWI category is the VI-RADS category. If image quality of DWI is not evaluable, the DCEI category is the final category. In many cases, DWI is dominant, but this does not mean that T2WI and DCEI can be omitted from the reading of the bladder. In this educational review, typical and atypical teaching cases are demonstrated, and how to resolve misdiagnosis and the limitations of VI-RADS are discussed. The most important aspect of VI-RADS reading is to practice multiparametric reading with a solid understanding of the characteristics and role of each sequence and an awareness of the various pitfalls.