scholarly journals CT Findings of Primary Renal Angiosarcoma

2021 ◽  
pp. 212-216
Author(s):  
Hisashi Komoto ◽  
Kazuhiro Kitajima ◽  
Yusuke Kawanaka ◽  
Nahomi Yoshimura ◽  
Ryo Kunimoto ◽  
...  
Keyword(s):  
Left Kidney ◽  
Ct Images ◽  
Flank Pain ◽  
Renal Tumors ◽  
Left Flank ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Aggressive Tumors ◽  
Left Flank Pain

Primary angiosarcomas of the kidney are very rare but highly aggressive tumors showing poor prognosis. We present a case of primary renal angiosarcoma occurring in a 60-year-old man with left flank pain. CT images depicted a huge exophytic mass (14 cm in diameter) in the left kidney, exhibiting central extensive hemorrhage or necrosis without contrast enhancement. The mass showed centripetal peripheral nodular enhancement on dynamic contrast-enhanced CT images. We suggest its inclusion in the differential diagnosis of cases of hemorrhagic renal tumors with prominent vasculature.

scholarly journals Multi-factorial considerations for intra-thoracic lymph node evaluations of healthy cats on computed tomographic images

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Ninlawan Thammasiri ◽  
Chutimon Thanaboonnipat ◽  
Nan Choisunirachon ◽  
Damri Darawiroj
Keyword(s):  
Lymph Node ◽  
Lymph Nodes ◽  
Ct Images ◽  
Slice Thickness ◽  
Maximum Width ◽  
Post Contrast ◽  
Computed Tomographic ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

Abstract Background It is difficult to examine mild to moderate feline intra-thoracic lymphadenopathy via and thoracic radiography. Despite previous information from computed tomographic (CT) images of intra-thoracic lymph nodes, some factors from animals and CT setting were less elucidated. Therefore, this study aimed to investigate the effect of internal factors from animals and external factors from the CT procedure on the feasibility to detect the intra-thoracic lymph nodes. Twenty-four, client-owned, clinically healthy cats were categorized into three groups according to age. They underwent pre- and post-contrast enhanced CT for whole thorax followed by inter-group evaluation and comparison of sternal, cranial mediastinal, and tracheobronchial lymph nodes. Results Post contrast-enhanced CT appearances revealed that intra-thoracic lymph nodes of kittens were invisible, whereas the sternal, cranial mediastinal, and tracheobronchial nodes of cats aged over 7 months old were detected (6/24, 9/24 and 7/24, respectively). Maximum width of these lymph nodes were 3.93 ± 0.74 mm, 4.02 ± 0.65 mm, and 3.51 ± 0.62 mm, respectively. By age, lymph node sizes of these cats were not significantly different. Transverse lymph node width of males was larger than that of females (P = 0.0425). Besides, the detection score of lymph nodes was affected by slice thickness (P < 0.01) and lymph node width (P = 0.0049). Furthermore, an irregular, soft tissue structure, possibly the thymus, was detected in all juvenile cats and three mature cats. Conclusions Despite additional information on intra-thoracic lymph nodes in CT images, which can be used to investigate lymphatic-related abnormalities, age, sex, and slice thickness of CT images must be also considered.

scholarly journals Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong Zhu ◽  
Yingfan Mao ◽  
Jun Chen ◽  
Yudong Qiu ◽  
Yue Guan ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Ct Images ◽  
Textural Features ◽  
Mutation Status ◽  
Portal Venous Phase ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Venous Phase ◽  
Ct Texture Analysis

AbstractTo explore the value of contrast-enhanced CT texture analysis in predicting isocitrate dehydrogenase (IDH) mutation status of intrahepatic cholangiocarcinomas (ICCs). Institutional review board approved this study. Contrast-enhanced CT images of 138 ICC patients (21 with IDH mutation and 117 without IDH mutation) were retrospectively reviewed. Texture analysis was performed for each lesion and compared between ICCs with and without IDH mutation. All textural features in each phase and combinations of textural features (p < 0.05) by Mann–Whitney U tests were separately used to train multiple support vector machine (SVM) classifiers. The classification generalizability and performance were evaluated using a tenfold cross-validation scheme. Among plain, arterial phase (AP), portal venous phase (VP), equilibrium phase (EP) and Sig classifiers, VP classifier showed the highest accuracy of 0.863 (sensitivity, 0.727; specificity, 0.885), with a mean area under the receiver operating characteristic curve of 0.813 in predicting IDH mutation in validation cohort. Texture features of CT images in portal venous phase could predict IDH mutation status of ICCs with SVM classifier preoperatively.

scholarly journals Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model

2021 ◽  
Author(s):  
Yunchao Yin ◽  
Derya Yakar ◽  
Rudi A. J. O. Dierckx ◽  
Kim B. Mouridsen ◽  
Thomas C. Kwee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Liver Fibrosis ◽  
Liver Parenchyma ◽  
Ct Images ◽  
Fibrosis Stage ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Liver Surface ◽  
Fibrosis Staging

Abstract Objectives Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation of the diagnostic decisions made by deep learning. Methods The liver fibrosis staging network (LFS network) was developed at contrast-enhanced CT images in the portal venous phase in 252 patients with histologically proven liver fibrosis stage. To give a visual explanation of the diagnostic decisions made by the LFS network, Gradient-weighted Class Activation Mapping (Grad-cam) was used to produce location maps indicating where the LFS network focuses on when predicting liver fibrosis stage. Results The LFS network had areas under the receiver operating characteristic curve of 0.92, 0.89, and 0.88 for staging significant fibrosis (F2–F4), advanced fibrosis (F3–F4), and cirrhosis (F4), respectively, on the test set. The location maps indicated that the LFS network had more focus on the liver surface in patients without liver fibrosis (F0), while it focused more on the parenchyma of the liver and spleen in case of cirrhosis (F4). Conclusions Deep learning methods are able to exploit CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage. Therefore, we suggest using the entire upper abdomen on CT images when developing deep learning–based liver fibrosis staging algorithms. Key Points • Deep learning algorithms can stage liver fibrosis using contrast-enhanced CT images, but the algorithm is still used as a black box and lacks transparency. • Location maps produced by Gradient-weighted Class Activation Mapping can indicate the focus of the liver fibrosis staging network. • Deep learning methods use CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage.

scholarly journals Clinical application of contrast-enhanced ultrasound after percutaneous renal tumor ablation

2019 ◽  
Vol 92 (1103) ◽  
pp. 20190183 ◽  
Author(s):  
Fang Guo ◽  
Bing Hu ◽  
Lei Chen ◽  
Jia Li
Keyword(s):  
Local Recurrence ◽  
Tumor Ablation ◽  
Renal Tumors ◽  
Ablation Zone ◽  
Recurrent Tumors ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Complete Tumor

Objective: To evaluate the effectiveness of contrast-enhanced ultrasound (CEUS) in detecting incomplete ablation and local recurrence of renal tumors after percutaneous radiofrequency ablation (RFA). Methods: 31 patients were included for RFA treatment and underwent CEUS examination after RFA, ablation zone and contrast distribution in the ablation area were observed, CEUS images were compared with enhanced CT/MRI images to determine the residual tumors and local recurrence of renal tumors. Results: The average maximum diameters of the tumor and the ablation zone after the first RFA were 32.3 ± 14.7 mm and 35.9 ± 12.2 mm, respectively. A higher rate of complete tumor ablation was achieved if the ablation zone was larger than the primary tumor (p = 0.026). Within 1 month after RFA, contrast-enhanced CT/MRI examinations demonstrated incomplete ablation in 9 of 31 patients (29.0%), while CEUS revealed incomplete ablation in 8 of 31 patients (25.8%). The sensitivity, specificity, positive predictive value and negative predictive value of CEUS in evaluating complete ablation of renal tumors were 88.9%, 100%, 100%, 95.7%, respectively. During the follow-up period, local recurrence was reported in 2 (7.4%) of the 27 patients with complete tumor ablation. Tumor recurrence signs in the two patients were identified by both CEUS and contrast-enhanced CT/MRI. Therefore, both the sensitivity and specificity of CEUS for the evaluation of tumor recurrence were 100%. Conclusion: After percutaneous RFA of renal tumors, the effectiveness of CEUS in the follow-up assessment of residual and recurrent tumors is basically the same as that of contrast-enhanced CT/MRI. Advances in knowledge: In this study, we evaluated the effectiveness of CEUS in the follow-up assessment of residual and recurrent tumors after RFA is basically the same as that of contrast-enhanced CT/MRI. Combining multiple follow-up methods may improve the detection rate of residual or recurrent tumors.

scholarly journals Model-Based Pancreas Segmentation in Portal Venous Phase Contrast-Enhanced CT Images

2013 ◽  
Vol 26 (6) ◽  
pp. 1082-1090 ◽  
Author(s):  
Matthias Hammon ◽  
Alexander Cavallaro ◽  
Marius Erdt ◽  
Peter Dankerl ◽  
Matthias Kirschner ◽  
...  
Keyword(s):  
Phase Contrast ◽  
Ct Images ◽  
Portal Venous Phase ◽  
Model Based ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Venous Phase ◽  
Pancreas Segmentation
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