Combination of CEUS and MRI for the diagnosis of periampullary space-occupying lesions: a retrospective analysis

Chen, Xinpei; Liu, Jiang; Zhou, Jing; Zhou, Pengcheng; Shu, Jiwu; Xu, Lulu; Li, Bo; Su, Song

doi:10.1186/s12880-019-0376-7

Cited by 6 publications

(6 citation statements)

References 29 publications

(36 reference statements)

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“…A study has reported that the specificity of MRI was only 78.26%, while the accuracy was 89.89% in the diagnosis of PAC (5). Similarly, our previous study also found that MRI had only 87% accuracy in detecting PAC (8). For the disease in PA region, misdiagnose will lead to many adverse factors for the follow-up treatment of patients (8,9).…”

Section: Introductionmentioning

confidence: 58%

“…Similarly, our previous study also found that MRI had only 87% accuracy in detecting PAC (8). For the disease in PA region, misdiagnose will lead to many adverse factors for the follow-up treatment of patients (8,9). Therefore, it is necessary to further improve the preoperative diagnostic accuracy of the diseases in this special region.…”

Section: Introductionmentioning

confidence: 85%

“…(3) Tumour parenchyma on T2WI was equally or slightly stronger signal; (4) DWI showed high signal intensity; (5) the mass was mild or moderate enhancement after contrast and (6) when MRCP was performed, the bile duct suddenly terminated asymmetrically and expanded proportionally (double-duct signs may occur when the lesion obstructed the ducts (8).…”

Section: Mri Imaging Analysismentioning

confidence: 99%

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Identifying Periampullary Regions in MRI Images Using Deep Learning

et al. 2021

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BackgroundDevelopment and validation of a deep learning method to automatically segment the peri-ampullary (PA) region in magnetic resonance imaging (MRI) images.MethodsA group of patients with or without periampullary carcinoma (PAC) was included. The PA regions were manually annotated in MRI images by experts. Patients were randomly divided into one training set, one validation set, and one test set. Deep learning methods were developed to automatically segment the PA region in MRI images. The segmentation performance of the methods was compared in the validation set. The model with the highest intersection over union (IoU) was evaluated in the test set.ResultsThe deep learning algorithm achieved optimal accuracies in the segmentation of the PA regions in both T1 and T2 MRI images. The value of the IoU was 0.68, 0.68, and 0.64 for T1, T2, and combination of T1 and T2 images, respectively.ConclusionsDeep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the PA region in MRI images. This automated non-invasive method helps clinicians to identify and locate the PA region using preoperative MRI scanning.

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Section: Introductionmentioning

confidence: 58%

Section: Introductionmentioning

confidence: 85%

Section: Mri Imaging Analysismentioning

confidence: 99%

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Identifying Periampullary Regions in MRI Images Using Deep Learning

et al. 2021

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“…However, the sonographic appearance of intrahepatic space-occupying lesions under the background of fatty liver is atypical, and conventional ultrasound examinations cannot display the microvessels in the lesions. At the same time, due to the interference of fatty liver, the original blood flow signal in the lesion is weakened or even disappeared, resulting in decreased accuracy of diagnosis of intrahepatic spaceoccupying lesions under the background of fatty liver, thereby leading to missed diagnoses and misdiagnoses (9,10). At present, CEUS is the main clinical imaging method for evaluating liver blood flow and diagnosing focal liver lesions.…”

Section: Discussionmentioning

confidence: 99%

Contrast-enhanced ultrasound and its differential diagnosis in 21 patients with intrahepatic space-occupying lesions under the background of fatty liver

Luo

Yue

et al. 2021

Ann Palliat Med

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Background: The sonographic appearance in intrahepatic space-occupying lesions under the background of fatty liver is atypical. This study aimed to explore the value of contrast-enhanced ultrasonography (CEUS) in the differential diagnosis of intrahepatic space-occupying lesions under the background of fatty liver. Methods: Twenty-one patients with intrahepatic space-occupying lesions under the background of fatty liver who were treated in our hospital from January 2017 to September 2020 and met the inclusion/ exclusion criteria were enrolled in this study. The clinical and imaging data, size, location, shape, and other characteristics of the lesions were analyzed. The accuracy of conventional ultrasound and contrast-enhanced ultrasound in diagnosing intrahepatic space-occupying lesions under the background of fatty liver were compared. Results: The 21 patients had moderate to severe fatty liver, including 11 cases with malignant lesions (52.38%) and 10 cases with benign lesions (47.62%). The accuracy of CEUS (90.48%) was significantly higher than that of conventional ultrasound (61.90%) in diagnosing intrahepatic space-occupying lesions under the background of fatty liver. A total of 33 lesions were detected in 21 patients, which were hypoechoic. Liver abscesses mainly showed ring-shaped enhancement in the arterial phase and low enhancement in the portal phase, with abscess necrosis in the center and no significant enhancement. Liver cyst lesions showed no significant enhancement in the arterial, portal, and delayed phases. The three stages of focal fat loss were synchronized and simultaneously enhanced with surrounding fatty liver tissues. The area under the diagnostic curve of conventional ultrasound and CEUS was 0.668 and 0.809, respectively, and the area under the curve of CEUS detection was the largest (P<0.05). Conclusions: CEUS can effectively improve the diagnostic accuracy of intrahepatic space-occupying lesions under the background of fatty liver, and is an effective means to differentially diagnose intrahepatic space-occupying lesions under the background of fatty liver.

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“…But its value is still unsatisfying in the diagnosis in periampullary lesions. Previous studies have reported that the specificity was 75.68-100%, sensitivity was 60-100%, and accuracy was 91.17-92.92% in the diagnosis of periampullary space-occupying lesions [11][12][13][14]. Then, MRCP examination requires a magnetic field, which is contradicted for patients with metal implanted material, such as cardiac pacemaker, metal stent, etc.…”

Section: Introductionmentioning

confidence: 99%

Role of endoscopic ultrasound in the evaluation of unexplained extrahepatic bile duct dilatation

Chen

Wang

Dai

et al. 2022

European Journal of Gastroenterology &Amp; Hepatology

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Objective This study was performed to assess the diagnostic performance of endoscopic ultrasonography (EUS) in patients with extrahepatic bile duct (EBD) dilatation and develop a novel model incorporating EUS-based signature with clinical parameters for distinguishing the malignant dilation of EBD. Methods The EUS data and clinical parameters of the patients were collected and analyzed retrospectively. First, we evaluated the diagnostic performance of EUS in detecting the cause of EBD dilatation. Then, we performed univariate and multivariate binary logistic regression analyses based on clinical and EUS features. Finally, a nomogram was established to aid in distinguishing between malignant dilation and noncalculous benign dilatation of EBD in patients.Results A total of 184 patients were enrolled. For the diagnosis of malignant dilation, EUS achieved an accuracy of 90.76%, sensitivity of 85.96%, and specificity of 92.91%. For the diagnosis of calculous dilation, EUS achieved an accuracy of 100%, sensitivity of 100%, and specificity of 100%. For the diagnosis of noncalculous benign dilatation, EUS achieved an accuracy of 90.76%, sensitivity of 90.90%, and specificity of 90.58%. Multivariable logistic regression analyses indicated that abnormal liver function test, elevated tumor markers, and EUS findings were the well-diagnostic factors of malignant EBD dilation. The nomogram established by these factors showed good calibration and discrimination. Conclusion EUS is a useful examinational modality in the work-up of EBD dilatation. In combination with abnormal liver function test and elevated tumor markers, EUS may provide additional information for the detection of malignant dilation of EBD and should be further investigated.

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Combination of CEUS and MRI for the diagnosis of periampullary space-occupying lesions: a retrospective analysis

Cited by 6 publications

References 29 publications

Identifying Periampullary Regions in MRI Images Using Deep Learning

Identifying Periampullary Regions in MRI Images Using Deep Learning

Contrast-enhanced ultrasound and its differential diagnosis in 21 patients with intrahepatic space-occupying lesions under the background of fatty liver

Role of endoscopic ultrasound in the evaluation of unexplained extrahepatic bile duct dilatation

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