The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: a comparison with radiologists.

PURPOSE: To compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions. METHODS: This retrospective study included patients with endometrial cancer or non-cancerous lesions who underwent MRI between 2015 and 2020. In Experiment 1, single and combined image sets of several sequences from 204 patients with cancer and 184 patients with non-cancerous lesions were used to train CNNs. Subsequently, testing was performed using 97 images from 51 patients with cancer and 46 patients with non-cancerous lesions. The test image sets were independently interpreted by three blinded radiologists. Experiment 2 investigated whether the addition of different types of images for training using the single image sets improved the diagnostic performance of CNNs. RESULTS: The AUC of the CNNs pertaining to the single and combined image sets were 0.88-0.95 and 0.87-0.93, respectively, indicating non-inferior diagnostic performance than the radiologists. The AUC of the CNNs trained with the addition of other types of single images to the single image sets was 0.88-0.95. CONCLUSION: CNNs demonstrated high diagnostic performance for the diagnosis of endometrial cancer using MRI. Although there were no significant differences, adding other types of images improved the diagnostic performance for some single image sets.

Differentiation of carcinosarcoma from endometrial carcinoma on magnetic resonance imaging using deep learning.

Purpose: To verify whether deep learning can be used to differentiate between carcinosarcomas (CSs) and endometrial carcinomas (ECs) using several magnetic resonance imaging (MRI) sequences. Material and methods: This retrospective study included 52 patients with CS and 279 patients with EC. A deep-learning model that uses convolutional neural networks (CNN) was trained with 572 T2-weighted images (T2WI) from 42 patients, 488 apparent diffusion coefficient of water maps from 33 patients, and 539 fat-saturated contrast-enhanced T1-weighted images from 40 patients with CS, as well as 1612 images from 223 patients with EC for each sequence. These were tested with 9-10 images of 9-10 patients with CS and 56 images of 56 patients with EC for each sequence, respectively. Three experienced radiologists independently interpreted these test images. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) for each sequence were compared between the CNN models and the radiologists. Results: The CNN model of each sequence had sensitivity 0.89-0.93, specificity 0.44-0.70, accuracy 0.83-0.89, and AUC 0.80-0.94. It also showed an equivalent or better diagnostic performance than the 3 readers (sensitivity 0.43-0.91, specificity 0.30-0.78, accuracy 0.45-0.88, and AUC 0.49-0.92). The CNN model displayed the highest diagnostic performance on T2WI (sensitivity 0.93, specificity 0.70, accuracy 0.89, and AUC 0.94). Conclusions: Deep learning provided diagnostic performance comparable to or better than experienced radiologists when distinguishing between CS and EC on MRI.

Endoleak detection after endovascular aneurysm repair using unenhanced MRI with flow suppression technique: Feasibility study in comparison with contrast-enhanced CT.

OBJECTIVES: To evaluate the feasibility of unenhanced motion-sensitized-driven equilibrium (MSDE)-prepared balanced turbo field echo (BTFE) sequences for detecting endoleaks after endovascular aneurysm repair (EVAR). METHODS: Forty-six patients treated with EVAR for aortic and/or iliac arterial aneurysms underwent contrast-enhanced CT and MSDE-prepared BTFE sequences with and without flow suppression. Two independent observers reviewed these sequences and their subtraction images and assigned confidence levels for detecting endoleaks. Relative contrast values were calculated by dividing signal intensities by those of paraspinal muscles. CT provided the reference standard. RESULTS: CT showed types I and II endoleaks in one and ten patients, respectively. Areas under receiver operating characteristic curves were 0.92 and 0.97 for observers 1 and 2, respectively. Sensitivity, specificity, accuracy, positive predictive value and negative predictive value of both observers were 91 (10/11), 91(32/35), 91 (42/46), 77 (10/13) and 97 % (32/33), respectively. Relative contrast values of endoleaks and flowing blood significantly decreased by flow suppression on MSDE-prepared BTFE images (P = 0.002 and P < 0.0001 respectively), and were significantly higher than those of the excluded aneurysms on subtraction images (P = 0.003 and P = 0.001, respectively). CONCLUSIONS: Unenhanced MSDE-prepared BTFE sequences are feasible for detecting endoleaks. KEY POINTS: • Flow suppression significantly reduces endoleak signals on MSDE-prepared BTFE images. • Subtraction images of MSDE-prepared BTFE sequences ± flow suppression demonstrate endoleaks. • MSDE-prepared BTFE sequences indicate high diagnostic values (>90 %) except PPV (77 %). • MSDE-prepared BTFE sequences need further refinement to reduce false positives. • Endoleaks can be detected without contrast injection using MSDE-prepared BTFE sequences.

Organ Atrophy Induced by Sorafenib and Sunitinib - Quantitative Computed Tomography (CT) Evaluation of the Pancreas, Thyroid Gland and Spleen.

BACKGROUND: To evaluate organ atrophy induced by sorafenib and sunitinib, we retrospectively reviewed the CT scans of renal cell carcinoma (RCC) patients receiving molecular targeted therapy (MTT) using sorafenib or sunitinib, and performed volumetric analysis of the pancreas, thyroid gland, and spleen. MATERIAL/METHODS: Thirteen RCC patients receiving MTT were assigned as the evaluation cases (MTT group), while thirteen additional RCC patients not receiving MTT were retrieved as the Control group. We evaluated the baseline and follow-up CT studies. The volume of the three organs estimated by CT volumetry was compared between the baseline and follow-up CTs. The atrophic ratio of the organ volume in the follow-up CT to that in the baseline CT was calculated, and compared between the MTT and Control groups. RESULTS: All measured organs in the MTT group showed statistically significant volume loss, while no significant change was observed in the Control group. Mean atrophic ratio in the MTT group was 0.74, 0.58, and 0.82 for the pancreas, thyroid and spleen, respectively. The differences in atrophic ratios between both groups were all statistically significant (P<0.05). CONCLUSIONS: Single-agent sorafenib or sunitinib therapy induced statistically significant atrophy in the pancreas, thyroid, and spleen.

The Utility of Apparent Water Diffusion Coefficient Maps for Evaluating the Presence of Myometrial Invasion in Patients with Endometrial Cancer.

PURPOSE: To assess the utility of apparent diffusion coefficient maps (ADC) for diagnosing myometrial invasion (MI) in endometrial cancer (EC). METHODS: This retrospective study included 164 patients (mean age, 56 years; range, 25-89 years) who underwent preoperative MRI for EC with <1/2 MI or no MI between April 2016 and July 2023. Five sequences were evaluated: T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), ADC, dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI), and contrast-enhanced T1WI (CE-T1WI). Three experienced radiologists independently assessed the sequences for MI. For ADC, MI was determined if the endometrial-myometrial junction-tumor boundary had disappeared. Additionally, the assessment of MI was performed using the combination of T2WI, DWI, and ADC, as well as T2WI, DCE-T1WI, and CE-T1WI. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) for the presence of MI were calculated and compared between the sequences and combinations. Inter-reader agreement was assessed using kappa (κ) statistics. RESULTS: The sensitivity of ADC was significantly higher than T2WI (P < 0.001) and DCE-T1WI (P = 0.018) for one reader and significantly higher than CE-T1WI (P = 0.045 and 0.043) for two readers. The specificity of ADC was significantly lower than T2WI (P = 0.015 and < 0.001) and CE-T1WI (P = 0.031 and 0.01) for two readers and significantly lower than DCE-T1WI (P = 0.031) for one reader. The AUC of ADC was significantly higher than T2WI (P = 0.048) and DCE-T1WI (P = 0.049) for one reader. The combination including ADC showed higher positive predictive value for all three readers compared to any sequence or combination including contrast enhancement. Additionally, ADC demonstrated the highest agreement rates. CONCLUSION: ADC had high sensitivity for MI and the highest agreement rate among all sequences. Thus, this sequence, combined with other sequences, can be crucial for a comprehensive evaluation of MI.

Diagnostic imaging analysis to differentiate struma ovarii from mucinous carcinomas, encompassing T2*-based imaging, diffusion-weighted imaging, and dynamic contrast-enhanced imaging.

PURPOSE: To clarify the differences between struma ovarii (SO) and mucinous carcinomas (MC) on CT and MRI, including T2*-based images, diffusion-weighted images (DWI), and time-intensity curve (TIC) patterns, which have not been previously reported. METHODS: We retrospectively compared the presence of low intensity on T2-weighted and T2*-based images, high intensity on T1-weighted images, hyperattenuation on non-contrast CT, TIC pattern, T2 ratio, T1 ratio, CT value, and apparent diffusion coefficient (ADC) value in 15 patients with SO and 27 patients with MC. RESULTS: SO exhibited a significantly higher frequency of low intensity on T2-weighted and T2*-based images, and hyperattenuation on non-contrast CT than MC (P < 0.001, <0.001, and 0.006, respectively). The T2 ratios and CT attenuation of the locules were also significantly different (P < 0.001, and 0.006, respectively). In SO, sites of low intensity on T2-weighted and T2*-based images and sites of hyperattenuation on CT images always coincided. Regarding the TIC pattern, most SO showed a high-risk pattern, with a significant difference (P = 0.003). The ADC values of SO were significantly lower, and only one case of SO showed high signal intensity on DWI. CONCLUSION: SO were more frequently with low intensity on T2-weighted and T2*-based images, and hyperattenuation on non-contrast CT, and showed high-risk TIC patterns without diffusion restriction. ADVANCES IN KNOWLEDGE: SO shows a high-risk TIC pattern, but can be specifically diagnosed in combination with the lack of diffusion restriction and loculi with marked hypointensity on T2-weighted and T2*-based images consistent with hyperattenuation on non-contrast CT.

Comparison of Benign, Borderline, and Malignant Ovarian Seromucinous Neoplasms on MR Imaging.

PURPOSE: This study aimed to compare MRI findings among benign, borderline, and malignant ovarian seromucinous neoplasms. METHODS: We retrospectively analyzed MRI data from 24 patients with ovarian seromucinous neoplasms-seven benign, thirteen borderline, and six malignant. The parameters evaluated included age, tumour size, morphology, number, height, apparent diffusion coefficient (ADC) values, T2 ratios, time-intensity curve (TIC) descriptors, and TIC patterns of the mural nodules. Additionally, we examined the T2 and T1 ratios of the cyst contents, tumour markers, and the presence of endometriosis. We used statistical tests, including the Kruskal-Wallis and Fisher-Freeman-Halton exact tests, to compare these parameters among the three aforementioned groups. RESULTS: The cases showed papillary architecture with internal branching in 57% of benign, 92% of borderline, and 17% of malignant cases. Three or fewer mural nodules were seen in 57% of benign, 8% of borderline, and 17% of malignant cases. Compared to benign and borderline tumours, mural nodules of malignant neoplasms had significantly increased height (P = 0.015 and 0.011, respectively), lower means ADC values (P = 0.003 and 0.035, respectively). The mural nodules in malignant cases also demonstrated significantly lower T2 ratios than those in the benign cases (P = 0.045). Most neoplasms displayed an intermediate-risk TIC pattern, including 80% benign, 83% borderline, and 60% malignant neoplasms, and no significant differences were observed. CONCLUSION: Most benign and borderline tumours exhibited a papillary architecture with an internal branching pattern, whereas this feature was less common in malignant neoplasms. Additionally, benign tumours had fewer mural nodules compared to borderline tumours. Malignant neoplasms were characterized by mural nodules with increased height and lower ADC values than those in benign and borderline tumours. Interestingly, all three groups predominantly exhibited an intermediate-risk TIC pattern, emphasizing the complexity of diagnosing seromucinous neoplasms using MRI.

Imaging approaches for the diagnosis of genetic diseases affecting the female reproductive organs and beyond.

This review aims to provide an overview of neoplastic lesions associated with genetic diseases affecting the female reproductive organs. It seeks to enhance our understanding of the radiological aspects in diagnosing genetic diseases including hereditary breast and ovarian cancer syndromes, Lynch syndrome, Peutz-Jeghers syndrome, nevoid basal cell carcinoma syndrome, and Swyer syndrome, and explores the patterns and mechanisms of inheritance that require elucidation. Additionally, we discuss the imaging characteristics of lesions occurring in other regions due to the same genetic diseases.

Imaging findings in inflammatory disease of the genital organs.

This review focuses on inflammatory diseases of female and male genital organs and discusses their epidemiology, pathogenesis, clinical presentation, and imaging findings. The female section covers pelvic inflammatory disease (PID) primarily caused by sexually transmitted infections (STIs) that affect the uterus, fallopian tubes, and ovaries. Unusual causes such as actinomycosis and tuberculosis have also been explored. The male section delves into infections affecting the vas deferens, epididymis, testes, prostate, and seminal vesicles. Uncommon causes such as tuberculosis, and Zinner syndrome have also been discussed. In addition, this review highlights other conditions that mimic male genital tract infections such as vasculitis, IgG4-related diseases, and sarcoidosis. Accurate diagnosis and appropriate management of these inflammatory diseases are essential for preventing serious complications and infertility. Imaging modalities such as ultrasound, magnetic resonance imaging, and computed tomography play a crucial role in diagnosis. Understanding the diverse etiologies and imaging findings is vital for the effective management of inflammatory diseases of the genital organs.

Diagnostic Imaging Performance of Dual-Energy Computed Tomography Compared with Conventional Computed Tomography and Magnetic Resonance Imaging for Uterine Cervical Cancer.

Objective This article evaluates the ability of low-energy (40 keV) virtual monoenergetic images (VMIs) in the local diagnosis of cervical cancer compared with that of conventional computed tomography (C-CT) and magnetic resonance imaging (MRI), using clinicopathologic staging as a reference. Methods This prospective study included 33 patients with pathologically confirmed cervical cancer who underwent dual-energy CT and MRI between 2021 and 2022. The contrast-to-noise ratio (CNR) of the tumor-to-myometrium was compared between C-CT and VMI. Additionally, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for each local diagnostic parameter were compared between C-CT, VMI, and MRI. Interradiologist agreement was also assessed. Results The mean CNR was significantly higher on VMI ( p = 0.002). No significant difference in AUC was found between C-CT and VMI for all local diagnostic parameters, and the specificity of VMI was often significantly less than that of MRI. For parametrial invasion, mean sensitivity, specificity, and AUC for C-CT, VMI, and MRI were 0.81, 0.99, 0.93; 0.64, 0.35, 0.79; and 0.73, 0.67, 0.86, respectively, and MRI had significantly higher specificity and AUC than that of VMI ( p = 0.013 and 0.008, respectively). Interradiologist agreement was higher for VMI than C-CT and for MRI than VMI. Conclusion The CNR of VMI was significantly higher than C-CT and interradiologist agreement was better than with C-CT; however, the overall diagnostic performance of VMI did not significantly differ from C-CT and was inferior to MRI. VMI was characterized by low specificity, which should be understood and used for reading.

Differences in the position of endometriosis-associated and non-associated ovarian cancer relative to the uterus.

BACKGROUND: Preoperative assessment of the histological type of ovarian cancer is essential to determine the appropriate treatment strategy. Tumor location may be helpful in this regard. The purpose of this study was to compare the position of endometriosis-associated (EAOCs) and non-associated (non-EAOCs) ovarian cancer relative to the uterus using MRI. METHODS: This retrospective study included patients with pathologically confirmed malignant epithelial ovarian tumors who underwent MRI at our hospital between January 2015 and January 2023. T2-weighted images of the sagittal and axial sections of the long axis of the uterine body were used for the analysis. Three blinded experienced radiologists independently interpreted the images and assessed whether the ovarian tumor was attached to the uterus, and the angle between the uterus and the tumor was measured. The presence of attachment and the measured angles were compared for each histology. In addition, the angles between EAOCs, including endometrioid carcinomas (ECs) and clear cell carcinomas (CCCs), were compared with non-EAOCs. RESULTS: In total, 184 women (mean age, 56 years; age range, 20-91 years) were evaluated. High-grade serous carcinomas (HGSCs) were significantly smaller than the others and had significantly less uterine attachment than CCCs (p < 0.01 for all readers). According to the mean of the measured angles, CCCs were positioned significantly more posteriorly than HGSCs and mucinous carcinomas (p < 0.02), and EAOCs were positioned significantly more posteriorly to the uterus than non-EAOCs (p < 0.01). CONCLUSION: HGSCs are often not attached to the uterus, and EAOCs are positioned more posteriorly to the uterus than non-EAOCs. CRITICAL RELEVANCE STATEMENT: High-grade serous carcinomas were often not attached to the uterus, and endometriosis-associated ovarian cancers were positioned more posteriorly to the uterus than non-endometriosis-associated ovarian cancers. KEY POINTS: • The position of the ovarian tumor can be determined using MRI. • High-grade serous carcinomas had less attachment to the uterus. • Endometriosis-associated cancers were positioned more posteriorly to the uterus. • The location of ovarian tumors is helpful in estimating histology.

Comparing Characteristics of Pelvic High-grade Serous Carcinomas with and without Breast Cancer Gene Variants on MR Imaging.

PURPOSE: To compare MRI findings of high-grade serous carcinoma (HGSC) with and without breast cancer (BRCA) gene variants to explore the feasibility of MRI as a genetic predictor. METHODS: We retrospectively reviewed MRI data from 16 patients with BRCA variant-positive (11 patients of BRCA1 and 5 patients of BRCA2 variant-positive) and 32 patients with BRCA variant-negative HGSCs and evaluated tumor size, appearance, nature of solid components, apparent diffusion coefficient (ADC) value, time-intensity curve, several dynamic contrast-enhanced curve descriptors, and nature of peritoneal metastasis. Age, primary site, tumor stage, bilaterality, presence of lymph node metastasis, presence of peritoneal metastasis, and tumor markers were also compared between the groups with the Mann-Whitney U and chi-square tests. RESULTS: The mean tumor size of BRCA variant-positive HGSCs was 9.6 cm, and that of variant-negative HGSCs was 6.8 cm, with no significant difference (P = 0.241). No significant difference was found between BRCA variant-positive and negative HGSCs in other evaluated factors, except for age (mean age, 53 years old; range, 32-78 years old for BRCA variant-positive and mean age, 61 years old; range, 44-80 years old for BRCA variant-negative, P = 0.033). Comparing BRCA1 variant-positive and BRCA2 variant-positive HGSCs, BRCA1 variant-positive HGSCs were larger (P = 0.040), had greater Max enhancement (P = 0.013), Area under the curve (P = 0.013), and CA125 (P = 0.038), and had a higher frequency of lymph node metastasis (P = 0.049), with significance. CONCLUSION: There was no significant difference in the MRI findings between patients with HGSCs with and without BRCA variants. Although studied in small numbers, BRCA1 variant-positive HGSCs were larger and more enhanced than BRCA2 variant-positive HGSCs with higher CA125 and more frequent lymph node metastases, and may represent more aggressive features.

Effectiveness of temporal subtraction computed tomography images using deep learning in detecting vertebral bone metastases.

PURPOSE: To assess the clinical effectiveness of temporal subtraction computed tomography (TS CT) using deep learning to improve vertebral bone metastasis detection. METHOD: This retrospective study used TS CT comprising bony landmark detection, bone segmentation with a multi-atlas-based method, and spatial registration of two images by a log-domain diffeomorphic Demons algorithm. Paired current and past CT images of 50 patients without vertebral metastasis, recorded during June 2011-September 2016, were included as training data. A deep learning-based method estimated registration errors and suppressed false positives. Thereafter, paired CT images of 40 cancer patients with newly developed vertebral metastases and 40 control patients without vertebral metastases were evaluated. Six board-certified radiologists and five radiology residents independently interpreted 80 paired CT images with and without TS CT. RESULTS: Records of 40 patients in the metastasis group (median age: 64.5 years; 20 males) and 40 patients in the control group (median age: 64.0 years; 20 males) were evaluated. With TS CT, the overall figure of merit (FOM) of the board-certified radiologist and resident groups improved from 0.848 to 0.876 (p = 0.01) and from 0.752 to 0.799 (p = 0.02), respectively. The sub-analysis focusing on attenuation changes in lesions revealed that the FOM of osteoblastic lesions significantly improved in both the board-certified radiologist and resident groups using TS CT. The sub-analysis focusing on lesion location showed that the FOM of the resident group significantly improved in the vertebral arch (p = 0.04). CONCLUSIONS: TS CT was effective in detecting bone metastasis by both board-certified radiologists and radiology residents.

Diagnosing Ovarian Cancer on MRI: A Preliminary Study Comparing Deep Learning and Radiologist Assessments.

BACKGROUND: This study aimed to compare deep learning with radiologists' assessments for diagnosing ovarian carcinoma using MRI. METHODS: This retrospective study included 194 patients with pathologically confirmed ovarian carcinomas or borderline tumors and 271 patients with non-malignant lesions who underwent MRI between January 2015 and December 2020. T2WI, DWI, ADC map, and fat-saturated contrast-enhanced T1WI were used for the analysis. A deep learning model based on a convolutional neural network (CNN) was trained using 1798 images from 146 patients with malignant tumors and 1865 images from 219 patients with non-malignant lesions for each sequence, and we tested with 48 and 52 images of patients with malignant and non-malignant lesions, respectively. The sensitivity, specificity, accuracy, and AUC were compared between the CNN and interpretations of three experienced radiologists. RESULTS: The CNN of each sequence had a sensitivity of 0.77-0.85, specificity of 0.77-0.92, accuracy of 0.81-0.87, and an AUC of 0.83-0.89, and it achieved a diagnostic performance equivalent to the radiologists. The CNN showed the highest diagnostic performance on the ADC map among all sequences (specificity = 0.85; sensitivity = 0.77; accuracy = 0.81; AUC = 0.89). CONCLUSION: The CNNs provided a diagnostic performance that was non-inferior to the radiologists for diagnosing ovarian carcinomas on MRI.

Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists.

PURPOSE: To compare deep learning with radiologists when diagnosing uterine cervical cancer on a single T2-weighted image. METHODS: This study included 418 patients (age range, 21-91 years; mean, 50.2 years) who underwent magnetic resonance imaging (MRI) between June 2013 and May 2020. We included 177 patients with pathologically confirmed cervical cancer and 241 non-cancer patients. Sagittal T2-weighted images were used for analysis. A deep learning model using convolutional neural networks (DCNN), called Xception architecture, was trained with 50 epochs using 488 images from 117 cancer patients and 509 images from 181 non-cancer patients. It was tested with 60 images for 60 cancer and 60 non-cancer patients. Three blinded experienced radiologists also interpreted these 120 images independently. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were compared between the DCNN model and radiologists. RESULTS: The DCNN model and the radiologists had a sensitivity of 0.883 and 0.783-0.867, a specificity of 0.933 and 0.917-0.950, and an accuracy of 0.908 and 0.867-0.892, respectively. The DCNN model had an equal to, or better, diagnostic performance than the radiologists (AUC = 0.932, and p for accuracy = 0.272-0.62). CONCLUSION: Deep learning provided diagnostic performance equivalent to experienced radiologists when diagnosing cervical cancer on a single T2-weighted image.

Ovarian and non-ovarian teratomas: a wide spectrum of features.

Teratoma is a germ cell tumor (GCT) derived from stem cells of the early embryo and the germ line. Teratoma is the most common neoplasm of the ovaries and is usually diagnosed easily using imagings by detecting fat components. However, there are various histopathological types and the imaging findings differ according to the type. Teratoma usually occurs in the gonads or in the midline due to migration of primordial germ cells during development. The clinical course of teratomas depends on the age of the patient, histological type, and anatomical site. Sometimes teratomas show unusual manifestations, such as mature teratoma without demonstrable fat components, torsion, rupture, growing teratoma syndrome, anti-N-methyl-D-aspartate receptor encephalitis, and autoimmune hemolytic anemia. For all of these reasons, teratomas demonstrate a wide spectrum of imaging features and radiologists should be familiar with these variabilities. The present article aims to introduce a model encompassing types of GCTs based on their developmental potential, and to review several histopathological types in various anatomical sites and unusual manifestations of teratomas, with representative imaging findings.

Impaired lesion detectability on gadoxetic acid-enhanced MR imaging in indocyanine green excretory defect: case series of three patients.

Indocyanine green (ICG) excretory defect is characterized by an ICG retention rate of more than 50% at 15 min without any other abnormal liver functions. The incidence of ICG excretory defect is 0.007% in the Japanese population. Due to its rarity, the imaging characteristics associated with ICG excretory defect remain unclear. Herein, we present three cases of ICG excretory defect, which showed impaired lesion detectability on gadoxetic acid-enhanced MR imaging (EOB-MRI). In the hepatobiliary phase (HBP) of EOB-MRI, diminished enhancement of the liver parenchyma, prolonged intravascular enhancement, and attenuated gadoxetic acid excretion to the bile duct were observed. Our study also investigated the expression level of organic anion transporting polypeptide (OATP) 1B3 and OATP1B1/1B3, which is related to the uptake of ICG and gadoxetic acid into hepatocytes. All cases showed decreased expression of OATP1B3, which was assumed to be characteristic of ICG excretory defect. The present study indicates that, when patients with ICG excretory defect are evaluated using EOB-MRI, attention should be paid to the impaired lesion detectability in the HBP due to the attenuated gadoxetic acid uptake into the liver parenchyma.

Thoracic abnormal air collections in patients in the intensive care unit: radiograph findings correlated with CT.

An abnormal collection of air in the thorax is one of the most common life-threatening events that occurs in the intensive care unit. Patient management differs depending on the location of the air collection; therefore, detecting abnormal air collection and identifying its exact location on supine chest radiographs is essential for early treatment and positive patient outcomes. Thoracic abnormal air collects in multiple thoracic spaces, including the pleural cavity, chest wall, mediastinum, pericardium, and lung. Pneumothorax in the supine position shows different radiographic findings depending on the location. Many conditions, such as skin folds, interlobar fissure, bullae in the apices, and air collection in the intrathoracic extrapleural space, mimic pneumothorax on radiographs. Additionally, pneumopericardium may resemble pneumomediastinum and needs to be differentiated. Further, some conditions such as inferior pulmonary ligament air collection versus a pneumatocele or pneumothorax in the posteromedial space require a differential diagnosis based on radiographs. Computed tomography (CT) is required to localize the air and delineate potential etiologies when a diagnosis by radiography is difficult. The purposes of this article are to review the anatomy of the potential spaces in the chest where abnormal air can collect, explain characteristic radiographic findings of the abnormal air collection in supine patients with illustrations and correlated CT images, and describe the distinguishing features of conditions that require a differential diagnosis. Since management differs based on the location of the air collection, radiologists should try to accurately detect and identify the location of air collection on supine radiographs.

High prevalence of intrapelvic parasitic arteries in patients with placenta accreta spectrum: A case-control study using unenhanced magnetic resonance angiography.

PURPOSE: To compare the prevalence of enlarged ovarian and intrapelvic parasitic arteries to the gravid uterus between cases of placenta accreta spectrum (PAS) and those with normal placentation using unenhanced magnetic resonance (MR) angiography. METHODS: Unenhanced time-of-flight MR angiography was performed in 12 consecutive women with PAS (mean age, 34 years; range, 23-42 years) and 24 women with normal placentation (mean age, 31 years; range, 24-42 years) in their third trimester and reviewed by two independent observers. The consensus reading served as the reference standard. Findings of pelvic arteriography performed at cesarean hysterectomy were reviewed in all cases of PAS. The prevalence of enlarged ovarian and intrapelvic parasitic arteries was compared using Fisher's exact test. The interobserver agreement was assessed with Kappa statistics. RESULTS: The prevalence of enlarged ovarian arteries was not significantly different between cases of PAS and normal placentation (17% [4/24 pelvic sides] vs. 4% [2/48 pelvic sides], P = .091). The prevalence of intrapelvic parasitic arteries was significantly higher in cases of PAS than in those with normal placentation (67% [16/24 pelvic sides] vs. 0% [0/48 pelvic sides], P < .0001). On a patient-by-patient basis, the intrapelvic parasitic artery was frequently present in women with PAS (92% [11/12 patients]). The Kappa values were 0.915 and 0.852 for detecting enlarged ovarian and intrapelvic parasitic arteries, respectively, indicating excellent interobserver agreement. CONCLUSIONS: The development of intrapelvic parasitic arteries was an anomalous phenomenon observed on unenhanced MR angiography in the majority of women with PAS but was not observed in those with normal placentation.

Clinical and MRI Characteristics of Uterine Cervical Adenocarcinoma: Its Variants and Mimics.

Adenocarcinoma currently accounts for 10-25% of all uterine cervical carcinomas and has a variety of histopathological subtypes. Among them, mucinous carcinoma gastric type is not associated with high-risk human papillomavirus (HPV) infection and a poor prognosis, while villoglandular carcinoma has an association with high-risk HPV infection and a good prognosis. They show relatively characteristic imaging findings which can be suggested by magnetic resonance imaging (MRI), though the former is sometimes difficult to be distinguished from lobular endocervical glandular hyperplasia. Various kinds of other tumors including squamous cell carcinoma should be also differentiated on MRI, while it is currently difficult to distinguish them on MRI, and HPV screening and pathological confirmation are usually necessary for definite diagnosis and further patient management.