Case 5.6 | ScienceGate

66-year-old woman with nausea, vomiting, and abdominal pain after a recent fundoplication. Abdominal CT revealed a right hepatic lobe mass Coronal SSFSE (Figure 1.2.1) and axial fat-suppressed FSE T2-weighted (Figure 1.2.2) images demonstrate a lobulated mass with high signal intensity in the right hepatic lobe. Axial arterial phase, portal venous phase, and coronal oblique reformatted equilibrium phase postgadolinium 3D SPGR images (...

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Acute-onset abdominal pain in a woman in her 30s

Heart ◽

10.1136/heartjnl-2018-313691 ◽

2018 ◽

Vol 105 (4) ◽

pp. 275-322 ◽

Cited By ~ 1

Author(s):

Rory O’Donohoe ◽

Samantha Fitzsimmons ◽

Timothy J C Bryant

Keyword(s):

Abdominal Pain ◽

Hepatocellular Adenoma ◽

Sudden Onset ◽

Acute Onset ◽

Arterial Phase ◽

List Type ◽

Portal Venous Phase ◽

Venous Phase ◽

Axial Ct ◽

Multiphase Ct

Clinical introductionA woman in her 30s presented to the emergency department with sudden-onset abdominal pain with hypotension and tachycardia. She gave a history of congenital heart disease for which she had previously undergone multiple operations. On examination she demonstrated right upper quadrant tenderness. She underwent an urgent multiphase CT (figure 1A–C).Figure 1(A) Arterial phase coronal CT. (B) Arterial phase axial CT. (C) Portal venous phase axial CT.QuestionWhat is the underlying liver pathology?Hepatocellular adenomaCholangiocarcinomaHepatocellular carcinomaFocal nodular hyperplasiaHepatoblastoma

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Hepatic parenchymal perfusion abnormalities after pancreaticobiliary surgery

Acta Radiologica ◽

10.1080/02841859809172200 ◽

1998 ◽

Vol 39 (3) ◽

pp. 304-308

Author(s):

K. Mitsuzaki ◽

Y. Yamashita ◽

I. Ogata ◽

T. Nishiharu ◽

J. Urata ◽

...

Keyword(s):

Malignant Tumors ◽

Equilibrium Phase ◽

Contrast Material ◽

Helical Ct ◽

Arterial Phase ◽

Perfusion Abnormality ◽

Portal Venous Phase ◽

Before And After ◽

Venous Phase ◽

One Year

Purpose: To evaluate perfusion abnormalities of the liver after pancreaticobiliary surgery Material and Methods: We retrospectively evaluated 128 patients with pancreaticobiliary malignant tumors who had been examined both before and after surgery by means of helical CT of the liver. An infusion of 3 ml/s of 60% nonionic contrast material was followed by helical CT of the liver in a sequential arterial phase, portal venous phase, and equilibrium phase Results: of 128 patients, we followed 97. In 21 patients (22%) we found 47 lesions with perfusion abnormalities that were detected 1–33 months (mean 6.6 months) after the operation. All patients were asymptomatic. The shape of each perfusion abnormality was characterized as geographic (n=23, 47%), wedgeshaped (n=21, 45%), or round (n=3, 83.8%). The abnormalities were seen in the arterial phase in 46 lesions (98%), in the portal venous phase in 18 lesions (38%), and in the equilibrium phase in 1 lesion (0.2%). In all lesions, the size either decreased spontaneously, or it remained unchanged for more than one year Conclusion: Perfusion abnormalities of the liver may occur in patients who undergo pancreaticobiliary surgery. This findings should not be confused with hypervascular metastases

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Case 5.16

Mayo Clinic Body MRI Case Review ◽

10.1093/med/9780199915705.003.0144 ◽

2014 ◽

pp. 272-273

Author(s):

Christine U. Lee ◽

James F. Glockner

Keyword(s):

Polycythemia Vera ◽

Signal Intensity ◽

Arterial Phase ◽

Endoscopic Drainage ◽

Blood Transfusions ◽

Splenic Hilum ◽

Portal Venous Phase ◽

History Of ◽

Venous Phase ◽

Multiple Blood

68-year-old man with a history of polycythemia vera and a recent episode of pancreatitis, which required endoscopic drainage of a pancreatic pseudocyst with a cystogastrostomy tube Coronal SSFSE (Figure 5.16.1) and axial fat-suppressed FSE T2-weighted (Figure 5.16.2) images show splenomegaly with a cyst in the posterior spleen. Note also the decreased signal intensity in the liver and spleen due to hemosiderosis from multiple blood transfusions. A round structure in the splenic hilum is bright on T2-weighted images and is surrounded by a small amount of fluid containing a fluid-fluid level. Axial arterial phase and portal venous phase postgadolinium 3D SPGR images (...

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Case 9.7

Mayo Clinic Body MRI Case Review ◽

10.1093/med/9780199915705.003.0229 ◽

2014 ◽

pp. 433-434

Author(s):

Christine U. Lee ◽

James F. Glockner

Keyword(s):

Abdominal Pain ◽

Arterial Phase ◽

Hepatic Mass ◽

History Of

44-year-old woman with a 2-year history of abdominal pain and bloating; CT identified a hepatic mass that was indeterminate but thought to most likely be a hemangioma, as well as a mass in the second portion of the duodenum Axial arterial phase postgadolinium 3D SPGR images (...

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Quantitative Analysis of Enhanced Computed Tomography in Differentiating Cystitis Glandularis and Bladder Cancer

BioMed Research International ◽

10.1155/2020/4930621 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7

Author(s):

Hui Hua ◽

Yuanxiang Gao ◽

Jizheng Lin ◽

Feng Hou ◽

Jun wei Wang ◽

...

Keyword(s):

Bladder Cancer ◽

Quantitative Analysis ◽

Sensitivity And Specificity ◽

Arterial Phase ◽

Relative Enhancement ◽

Enhanced Ct ◽

Venous Phase ◽

Enhanced Computed Tomography ◽

Quantitative Indicators ◽

Delayed Phase

Objective. This study was performed to assess the value of quantitative analysis of enhanced computed tomography (CT) values in the differential diagnosis of bladder cancer and cystitis glandularis (CG). Methods. Eighty patients with bladder masses (39 with CG and 41 with bladder cancer) who underwent enhanced CT were retrospectively reviewed. The CT enhancement values of the lesion and normal bladder wall in the arterial phase, venous phase, and delayed phase were measured. The relative enhancement CT values (relative enhancement CT value=enhancement CT value of lesion−enhancement CT value of normal bladder) in the arterial phase, venous phase, and delayed phase were also calculated. The pathological results were used as the gold standard, and the area under the curve (AUC), sensitivity, and specificity were calculated for the six groups of quantitative indicators (enhanced CT values and relative enhanced CT values of CG and bladder cancer in the arterial, venous, and delayed phases). We performed the leave-group-out cross-validation method to validate the accuracy, AUC, sensitivity, and specificity. The differences in accuracy, AUC, sensitivity, and specificity among the six groups of quantitative indicators were compared by the t-test. Results. In a combined analysis of the AUC, sensitivity, and specificity performance, the best indicator was the arterial-phase relative enhancement CT value with a cut-off of 25.85 HU (AUC, 0.966; sensitivity, 95.1%; specificity, 92.3%). We used the 100-times leave-group-out cross-validation method to validate the accuracy, AUC, sensitivity, and specificity. Arterial-phase relative enhancement CT values showed the highest AUC and accuracy among the six groups, with statistical significance (P<0.05). Conclusion. Quantitative analysis of enhanced CT is of great clinical value in the differential diagnosis of CG and bladder cancer.

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Helical CT for Detecting Focal Liver Lesions in Patients with Breast Carcinoma: Comparison of Noncontrast Phase, Hepatic Arterial Phase, and Portal Venous Phase

Journal of Computer Assisted Tomography ◽

10.1097/00004728-199703000-00012 ◽

1997 ◽

Vol 21 (2) ◽

pp. 229-235 ◽

Cited By ~ 23

Author(s):

M. Gena Frederick ◽

Erik K. Paulson ◽

Rendon C. Nelson

Keyword(s):

Breast Carcinoma ◽

Helical Ct ◽

Arterial Phase ◽

Focal Liver Lesions ◽

Liver Lesions ◽

Portal Venous Phase ◽

Hepatic Arterial Phase ◽

Venous Phase

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Hepatic arterial phase and portal venous phase computed tomography for dose calculation of stereotactic body radiation therapy plans in liver cancer: a dosimetric comparison study

Radiation Oncology ◽

10.1186/1748-717x-8-264 ◽

2013 ◽

Vol 8 (1) ◽

pp. 264 ◽

Cited By ~ 2

Author(s):

Jianghong Xiao ◽

Yan Li ◽

Qingfeng Jiang ◽

Lan Sun ◽

Fraser Henderson Jr ◽

...

Keyword(s):

Computed Tomography ◽

Radiation Therapy ◽

Liver Cancer ◽

Stereotactic Body Radiation Therapy ◽

Dose Calculation ◽

Arterial Phase ◽

Comparison Study ◽

Portal Venous Phase ◽

Hepatic Arterial Phase ◽

Venous Phase

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CT differentiation of gastric ectopic pancreas from gastric stromal tumors

10.21203/rs.3.rs-22218/v1 ◽

2020 ◽

Author(s):

Jian Wang ◽

Chang LIU ◽

Fang Yang ◽

Wenming Zhang ◽

Weiqun Ao ◽

...

Keyword(s):

Univariate Analysis ◽

Ectopic Pancreas ◽

Arterial Phase ◽

Portal Venous Phase ◽

Test Efficiency ◽

Attenuation Value ◽

Ct Attenuation ◽

Venous Phase ◽

Ct Attenuation Value ◽

Ct Features

Abstract BackgroundGastric ectopic pancreas (GEPs) is a rare developmental anomaly which is difficult to differentiate it from submucosal tumor such as gastrointestinal stromal tumor (GIST) by imaging methods. So we retrospectively investigated the CT features of them to help us make the correct diagnosis.Materials and MethodsThis study enrolled 17 GEPs and 119 GSTs, which were proven pathologically. We assessed clinical and CT features to identify significant differential features of GEPs from GSTs using univariate and multivariate analyses.ResultsIn univariate analysis, among all clinicoradiologic features, features of age, symptom, tumor marker, location, contour, blurred serosa or fat-line of peritumor, necrosis, calcification, CT attenuation value of unenhancement phase/arterial phase/portal venous phase (CTu/CTa/CTp), the CT attenuation value of arterial phase/portal venous phase minus that of unenhanced phase (DEAP/DEPP), long diameter (LD), short diameter (SD) were considered statistically significant for the differentiation of them. And the multivariate analysis revealed that location, blurred serosa or fat-line of peritumor, necrosis and DEPP were independent factors affecting the identification of them.What's more, ROC analysis showed that the test efficiency of CTp was perfect(AUC= 0.900).ConclusionLocation, blurred serosa or fat-line of peritumor, necrosis and DEPP are useful CT differentiators of GEPs from GSTs. In addition, the test efficiency of CTp in differentiating them was perfect (AUC=0.900).

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Advanced Deep Learning Approach to Automatically Segment Malignant Tumors and Ablation Zone in the Liver With Contrast-Enhanced CT

Frontiers in Oncology ◽

10.3389/fonc.2021.669437 ◽

2021 ◽

Vol 11 ◽

Author(s):

Kan He ◽

Xiaoming Liu ◽

Rahil Shahzad ◽

Robert Reimer ◽

Frank Thiele ◽

...

Keyword(s):

Deep Learning ◽

Liver Tumors ◽

Treatment Success ◽

Detection Sensitivity ◽

Arterial Phase ◽

Liver Segmentation ◽

Portal Venous Phase ◽

Venous Phase ◽

Pre Treatment ◽

Multi Phase

ObjectiveLiver cancer is one of the most commonly diagnosed cancer, and energy-based tumor ablation is a widely accepted treatment. Automatic and robust segmentation of liver tumors and ablation zones would facilitate the evaluation of treatment success. The purpose of this study was to develop and evaluate an automatic deep learning based method for (1) segmentation of liver and liver tumors in both arterial and portal venous phase for pre-treatment CT, and (2) segmentation of liver and ablation zones in both arterial and portal venous phase for after ablation treatment.Materials and Methods252 CT images from 63 patients undergoing liver tumor ablation at a large University Hospital were retrospectively included; each patient had pre-treatment and post-treatment multi-phase CT images. 3D voxel-wise manual segmentation of the liver, tumors and ablation region by the radiologist provided reference standard. Deep learning models for liver and lesion segmentation were initially trained on the public Liver Tumor Segmentation Challenge (LiTS) dataset to obtain base models. Then, transfer learning was applied to adapt the base models on the clinical training-set, to obtain tumor and ablation segmentation models both for arterial and portal venous phase images. For modeling, 2D residual-attention Unet (RA-Unet) was employed for liver segmentation and a multi-scale patch-based 3D RA-Unet for tumor and ablation segmentation.ResultsOn the independent test-set, the proposed method achieved a dice similarity coefficient (DSC) of 0.96 and 0.95 for liver segmentation on arterial and portal venous phase, respectively. For liver tumors, the model on arterial phase achieved detection sensitivity of 71%, DSC of 0.64, and on portal venous phase sensitivity of 82%, DSC of 0.73. For liver tumors >0.5cm3 performance improved to sensitivity 79%, DSC 0.65 on arterial phase and, sensitivity 86%, DSC 0.72 on portal venous phase. For ablation zone, the model on arterial phase achieved detection sensitivity of 90%, DSC of 0.83, and on portal venous phase sensitivity of 90%, DSC of 0.89.ConclusionThe proposed deep learning approach can provide automated segmentation of liver tumors and ablation zones on multi-phase (arterial and portal venous) and multi-time-point (before and after treatment) CT enabling quantitative evaluation of treatment success.

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