Dataset Information

Intraindividual Comparison between Gadoxetate-Enhanced Magnetic Resonance Imaging and Dynamic Computed Tomography for Characterizing Focal Hepatic Lesions: A Multicenter, Multireader Study.

ABSTRACT: OBJECTIVE:To compare the diagnostic accuracy of dynamic computed tomography (CT) and gadoxetate-enhanced magnetic resonance imaging (MRI) for characterization of hepatic lesions by using the Liver Imaging Reporting and Data System (LI-RADS) in a multicenter, off-site evaluation. MATERIALS AND METHODS:In this retrospective multicenter study, we evaluated 231 hepatic lesions (114 hepatocellular carcinomas [HCCs], 58 non-HCC malignancies, and 59 benign lesions) confirmed histologically in 217 patients with chronic liver disease who underwent both gadoxetate-enhanced MRI and dynamic CT at one of five tertiary hospitals. Four radiologists at different institutes independently reviewed all MR images first and the CT images 4 weeks later. They evaluated the major and ancillary imaging features and categorized each hepatic lesion according to the LI-RADS v2014. Diagnostic performance was calculated and compared using generalized estimating equations. RESULTS:MRI showed higher sensitivity and accuracy than CT for diagnosing hepatic malignancies; the pooled sensitivities, specificities, and accuracies for categorizing LR-5/5V/M were 59.0% vs. 72.4% (CT vs. MRI; p < 0.001), 83.5% vs. 83.9% (p = 0.906), and 65.3% vs. 75.3% (p < 0.001), respectively. CT and MRI showed comparable capabilities for differentiating between HCC and other malignancies, with pooled accuracies of 79.9% and 82.4% for categorizing LR-M, respectively (p = 0.139). CONCLUSION:Gadoxetate-enhanced MRI showed superior accuracy for categorizing LR-5/5V/M in hepatic malignancies in comparison with dynamic CT. Both modalities had comparable accuracies for distinguishing other malignancies from HCC.

SUBMITTER: An C

PROVIDER: S-EPMC6923212 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Intraindividual Comparison between Gadoxetate-Enhanced Magnetic Resonance Imaging and Dynamic Computed Tomography for Characterizing Focal Hepatic Lesions: A Multicenter, Multireader Study.

An Chansik C Lee Chang Hee CH Byun Jae Ho JH Lee Min Hee MH Jeong Woo Kyoung WK Choi Sang Hyun SH Kim Do Young DY Lim Young Suk YS Kim Young Seok YS Kim Ji Hoon JH Choi Moon Seok MS Kim Myeong Jin MJ

Korean journal of radiology 20191201 12

<h4>Objective</h4>To compare the diagnostic accuracy of dynamic computed tomography (CT) and gadoxetate-enhanced magnetic resonance imaging (MRI) for characterization of hepatic lesions by using the Liver Imaging Reporting and Data System (LI-RADS) in a multicenter, off-site evaluation.<h4>Materials and methods</h4>In this retrospective multicenter study, we evaluated 231 hepatic lesions (114 hepatocellular carcinomas [HCCs], 58 non-HCC malignancies, and 59 benign lesions) confirmed histological ...[more]

PMID: 31854149

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Project description:Purpose: The purpose of this study was to investigate the feasibility of Single-Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) image-based radiomics in differentiating bone metastases from benign bone lesions in patients with tumors. Methods: A total of 192 lesions from 132 patients (134 in the training group, 58 in the validation group) diagnosed with vertebral bone metastases or benign bone lesions were enrolled. All images were evaluated and diagnosed independently by two physicians with more than 20 years of diagnostic experience for qualitative classification, the images were imported into MaZda software in Bitmap (BMP) format for feature extraction. All radiomics features were selected by least absolute shrinkage and selection operator (LASSO) regression and 10-fold cross-validation algorithms after the process of normalization and correlation analysis. Based on these selected features, two models were established: The CT model and SPECT model (radiomics features were derived from CT and SPECT images, respectively). In addition, a combination model (ComModel) combined CT and SPECT features was developed in order to better evaluate the predictive performance of radiomics models. Subsequently, the diagnostic performance between each model was separately evaluated by a confusion matrix. Results: There were 12, 13, and 18 features contained within the CT, SPECT, and ComModel, respectively. The constructed radiomics models based on SPECT/CT images to discriminate between bone metastases and benign bone lesions not only had high diagnostic efficacy in the training group (AUC of 0.894, 0.914, 0.951 for CT model, SPECT model, and ComModel, respectively), but also performed well in the validation group (AUC; 0.844, 0.871, 0.926). The AUC value of the human experts was 0.849 and 0.839 in the training and validation groups, respectively. Furthermore, both SPECT model and ComModel show higher classification performance than human experts in the training group (P = 0.021 and P = 0.001, respectively) and the validation group (P = 0.037 and P = 0.007, respectively). All models showed better diagnostic accuracy than human experts in the training group and the validation group. Conclusion: Radiomics derived from SPECT/CT images could effectively discriminate between bone metastases and benign bone lesions. This technique may be a new non-invasive way to help prevent unnecessary delays in diagnosis and a potential contribution in disease staging and treatment planning.

Project description:BackgroundWe aimed to prospectively compare the diagnostic performance of gadoxetic acid-enhanced MRI (EOB-MRI) and contrast-enhanced Computed Tomography (CECT) for hepatocellular carcinoma (HCC) detection and liver transplant (LT) eligibility assessment in cirrhotic patients with explant histopathology correlation.MethodsIn this prospective, single-institution ethics-approved study, 101 cirrhotic patients were enrolled consecutively from the pre-LT clinic with written informed consent. Patients underwent CECT and EOB-MRI alternately every 3 months until LT or study exclusion. Two blinded radiologists independently scored hepatic lesions on CECT and EOB-MRI utilizing the liver imaging reporting and data system (LI-RADS) version 2018. Liver explant histopathology was the reference standard. Pre-LT eligibility accuracies with EOB-MRI and CECT as per Milan criteria (MC) were assessed in reference to post-LT explant histopathology. Lesion-level and patient-level statistical analyses were performed.ResultsSixty patients (49 men; age 33-72 years) underwent LT successfully. One hundred four non-treated HCC and 42 viable HCC in previously treated HCC were identified at explant histopathology. For LR-4/5 category lesions, EOB-MRI had a higher pooled sensitivity (86.7% versus 75.3%, p < 0.001) but lower specificity (84.6% versus 100%, p < 0.001) compared to CECT. EOB-MRI had a sensitivity twice that of CECT (65.9% versus 32.2%, p < 0.001) when all HCC identified at explant histopathology were included in the analysis instead of imaging visible lesions only. Disregarding the hepatobiliary phase resulted in a significant drop in EOB-MRI performance (86.7 to 72.8%, p < 0.001). EOB-MRI had significantly lower pooled sensitivity and specificity versus CECT in the LR5 category with lesion size < 2 cm (50% versus 79%, p = 0.002 and 88.9% versus 100%, p = 0.002). EOB-MRI had higher sensitivity (84.8% versus 75%, p < 0.037) compared to CECT for detecting < 2 cm viable HCC in treated lesions. Accuracies of LT eligibility assessment were comparable between EOB-MRI (90-91.7%, p = 0.156) and CECT (90-95%, p = 0.158).ConclusionEOB-MRI had superior sensitivity for HCC detection; however, with lower specificity compared to CECT in LR4/5 category lesions while it was inferior to CECT in the LR5 category under 2 cm. The accuracy for LT eligibility assessment based on MC was not significantly different between EOB-MRI and CECT.Trial registrationClinicalTrials.gov Identifier: NCT03342677 , Registered: November 17, 2017.

Project description:OBJECTIVE:To test the feasibility of non-contrast magnetic resonance imaging (MRI) in a sequential imaging study for characterizing computed tomography (CT)-detected arterial-enhancing nodules that do not washout in patients at risk of hepatocellular carcinoma (HCC). MATERIALS AND METHODS:In this retrospective study, 134 patients (mean age ± standard deviation, 56.8 ± 10.0 years) with 151 arterial enhancing-only nodules measuring up to 2 cm during multiphasic CT that were subsequently evaluated using gadoxetic acid-enhanced MRI in treatment-naïve at-risk patients from three tertiary referral centers were included. Tentative diagnostic criteria for HCC and hepatic malignancy were defined as the presence of one of eight MRI features favoring HCC in combinations of the following sequences: T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), T1-weighted dual gradient-echo in-phase and out-of-phase imaging (Dual-GRE), and hepatobiliary phase imaging (HBP). Typical hemangiomas and arterioportal shunts were excluded from the analysis. Diagnostic performance for HCC and hepatic malignancy was calculated and compared between the abbreviated MRI and full-sequence gadoxetic acid-enhanced MRI. RESULTS:Of 151 nodules (mean size, 1.2 cm) 68 HCCs and 83 non-HCC benignities and malignancies were included. The combination of T2WI, DWI, and Dual-GRE showed per-lesion sensitivity, specificity, and accuracy of 88.2%, 90.4%, and 89.4%, respectively, comparable to those of full-sequence MRI. Applying the same sequence combination to diagnose hepatic malignancy had per-lesion sensitivity, specificity, and accuracy of 86.8%, 97.3%, and 92.1%. In nodules < 1 cm, adding HBP increased sensitivity by up to 13% without compromising the specificity or accuracy. CONCLUSION:The non-contrast MRI protocol comprising T2WI, DWI, and Dual-GRE showed reasonable and comparable performance to full-sequence MRI for discriminating HCC and primary liver malignancies in CT-detected indeterminate arterial enhancing-only nodules in at-risk patients, and can be potentially used for sequential imaging in place of a full-sequence MRI. In nodules < 1 cm, HBP may still be needed to preserve sensitivity.

Project description:PurposeTo demonstrate the clinical translation of optical molecular imaging (OMI) for the localization of focal hepatic lesions during percutaneous hepatic interventions.Materials and methodsInstitutional review board approval was obtained for this prospective, single-center, HIPAA-compliant trial. Patients who were suspected of having hepatocellular carcinoma or liver metastases from colorectal cancer and were scheduled for percutaneous liver biopsy or thermal ablation were eligible for this study. Patients (n = 5) received 0.5 mg per kilogram of body weight of indocyanine green (ICG) intravenously 24 hours prior to their scheduled procedure in this study. Intraprocedurally, a handheld device composed of an endoscope that fits coaxially through a standard 17-gauge introducer needle was advanced into the liver, and real-time measurements of ICG fluorescence were obtained. A point-of-care fluorescence imaging system was used to image ICG fluorescence in biopsy samples. Target-to-background ratios (TBRs) were calculated by dividing the mean fluorescence intensity in the lesion by the mean fluorescence intensity in the adjacent liver parenchyma. The reference standard for determination of proper needle positioning in patients undergoing biopsy was final pathologic analysis of biopsy specimens or follow-up imaging.ResultsIntraprocedural OMI was successfully performed in six lesions (two lesions in patient 3) in five patients. The median size of the targeted lesions was 16 mm (range, 10-21 mm). Four of five biopsies (80%) yielded an accurate pathologic diagnosis, and one biopsy specimen showed benign liver parenchyma; both ablated lesions showed no residual disease 1 month after the procedure. The median overall added procedure time to perform OMI was 2 minutes. ICG was found to localize with TBRs greater than 2.0 (median, 7.9; range, 2.4-13.4) in all target lesions. No trial-related adverse events were reported.ConclusionThe clinical translation of OMI to percutaneous hepatic interventions was demonstrated.

Project description:BackgroundLesion segmentation is a critical step in medical image analysis, and methods to identify pathology without time-intensive manual labeling of data are of utmost importance during a pandemic and in resource-constrained healthcare settings. Here, we describe a method for fully automated segmentation and quantification of pathological COVID-19 lung tissue on chest Computed Tomography (CT) scans without the need for manually segmented training data.MethodsWe trained a cycle-consistent generative adversarial network (CycleGAN) to convert images of COVID-19 scans into their generated healthy equivalents. Subtraction of the generated healthy images from their corresponding original CT scans yielded maps of pathological tissue, without background lung parenchyma, fissures, airways, or vessels. We then used these maps to construct three-dimensional lesion segmentations. Using a validation dataset, Dice scores were computed for our lesion segmentations and other published segmentation networks using ground truth segmentations reviewed by radiologists.ResultsThe COVID-to-Healthy generator eliminated high Hounsfield unit (HU) voxels within pulmonary lesions and replaced them with lower HU voxels. The generator did not distort normal anatomy such as vessels, airways, or fissures. The generated healthy images had higher gas content (2.45 ± 0.93 vs 3.01 ± 0.84 L, P < 0.001) and lower tissue density (1.27 ± 0.40 vs 0.73 ± 0.29 Kg, P < 0.001) than their corresponding original COVID-19 images, and they were not significantly different from those of the healthy images (P < 0.001). Using the validation dataset, lesion segmentations scored an average Dice score of 55.9, comparable to other weakly supervised networks that do require manual segmentations.ConclusionOur CycleGAN model successfully segmented pulmonary lesions in mild and severe COVID-19 cases. Our model's performance was comparable to other published models; however, our model is unique in its ability to segment lesions without the need for manual segmentations.

Project description:PurposeThe novel coronavirus COVID-19, which spread globally in late December 2019, is a global health crisis. Chest computed tomography (CT) has played a pivotal role in providing useful information for clinicians to detect COVID-19. However, segmenting COVID-19-infected regions from chest CT results is challenging. Therefore, it is desirable to develop an efficient tool for automated segmentation of COVID-19 lesions using chest CT. Hence, we aimed to propose 2D deep-learning algorithms to automatically segment COVID-19-infected regions from chest CT slices and evaluate their performance.Material and methodsHerein, 3 known deep learning networks: U-Net, U-Net++, and Res-Unet, were trained from scratch for automated segmenting of COVID-19 lesions using chest CT images. The dataset consists of 20 labelled COVID-19 chest CT volumes. A total of 2112 images were used. The dataset was split into 80% for training and validation and 20% for testing the proposed models. Segmentation performance was assessed using Dice similarity coefficient, average symmetric surface distance (ASSD), mean absolute error (MAE), sensitivity, specificity, and precision.ResultsAll proposed models achieved good performance for COVID-19 lesion segmentation. Compared with Res-Unet, the U-Net and U-Net++ models provided better results, with a mean Dice value of 85.0%. Compared with all models, U-Net gained the highest segmentation performance, with 86.0% sensitivity and 2.22 mm ASSD. The U-Net model obtained 1%, 2%, and 0.66 mm improvement over the Res-Unet model in the Dice, sensitivity, and ASSD, respectively. Compared with Res-Unet, U-Net++ achieved 1%, 2%, 0.1 mm, and 0.23 mm improvement in the Dice, sensitivity, ASSD, and MAE, respectively.ConclusionsOur data indicated that the proposed models achieve an average Dice value greater than 84.0%. Two-dimensional deep learning models were able to accurately segment COVID-19 lesions from chest CT images, assisting the radiologists in faster screening and quantification of the lesion regions for further treatment. Nevertheless, further studies will be required to evaluate the clinical performance and robustness of the proposed models for COVID-19 semantic segmentation.

Dataset Information

Intraindividual Comparison between Gadoxetate-Enhanced Magnetic Resonance Imaging and Dynamic Computed Tomography for Characterizing Focal Hepatic Lesions: A Multicenter, Multireader Study.

Publications

Intraindividual Comparison between Gadoxetate-Enhanced Magnetic Resonance Imaging and Dynamic Computed Tomography for Characterizing Focal Hepatic Lesions: A Multicenter, Multireader Study.

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