Characterization of Portal Vein Thrombosis (Neoplastic Versus Bland) on CT Images Using Software-Based Texture Analysis and Thrombus Density (Hounsfield Units)

Canellas, Rodrigo; Mehrkhani, Farhad; Patiño, Manuel; Kambadakone, Avinash; Sahani, Dushyant V.

doi:10.2214/ajr.15.15928

Cited by 51 publications

(29 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although there was no statistically difference in kurtosis and entropy, the values of them gradually increased as tumor malignant degree increased (from pNET to PDAC). This variation trend was in accordance with previous researches . Hence, this study could provide reference value for further large sample research and contribute to the development of CTTA.…”

Section: Discussionsupporting

confidence: 91%

See 1 more Smart Citation

Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole‐tumor CT texture analysis as quantitative biomarkers

Liang

et al. 2018

Cancer Medicine

View full text Add to dashboard Cite

BackgroundTo explore the application value of computed tomography (CT) texture analysis in differentiating atypical pancreatic neuroendocrine tumors (pNET) from pancreatic ductal adenocarcinomas (PDAC).Materials and methodsThis single‐center retrospective study was approved by local institutional review board, and the requirement for informed consent was waived. We retrospectively analyzed 127 patients with 50 PDACs and 77 pNETs in pathology database between January 2012 and May 2017.These patients successfully finished preoperative contrast‐enhanced CT test. Texture parameters (mean, median, 5th, 10th, 25th, 75th, 90th percentiles, skewness, kurtosis and entropy) were extracted from portal images and compared between PDAC and 77 pNET groups using proper statistical method. The optimal parameters for differentiating PDACs and atypical pNETs were gained through receiver operating characteristic (ROC) curves.ResultsOn the basis of arterial enhancement, 52 pNETs (67%, 52/77) were typical hypervascular and 25 pNETs (32%, 25/77) were atypical hypovascular. Compared with PDACs, atypical pNETs had statistically higher mean, median, 5th, 10th, and 25th percentiles (P = 0.006, 0.024, 0.000, 0.001, 0.021, respectively) and statistically lower skewness (P = 0.017). However, there were no difference for 75th, 90th percentiles, kurtosis and entropy between these two tumors (P = 0.232, 0.415, 0.143, 0.291, respectively). For differentiating PDACs and atypical pNETs, 5th percentile and 5th+skewness were optimal parameters for alone and combined diagnosis, respectively.ConclusionVolumetric CT texture features, especially combined diagnosis of 5th+skewness can be used as a quantitative tool to distinguish atypical pNETs from PDACs.

show abstract

Section: Discussionsupporting

confidence: 91%

“…This variation trend was in accordance with previous researches. 26,27 Hence, this study could provide reference value for further large sample research and contribute to the development of CTTA. This study had several limitations.…”

Section: T a B L E 5 Comparison Of Area Undermentioning

confidence: 86%

Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole‐tumor CT texture analysis as quantitative biomarkers

Liang

et al. 2018

Cancer Medicine

View full text Add to dashboard Cite

show abstract

“…Contrary to our initial concerns that GA-MRI may have a theoretical disadvantage in detecting PVTT owing to the decreased contrast between the portal vein and liver parenchyma arising from the early hepatocyte uptake of GA, as well as its lower gadolinium content [22,24,25], our study results demonstrated that GA-MRI better detected PVTT than CECT in HCC patients meeting the Milan criteria (p = 0.039 for both reviewers). This is in concordance with previous studies which reported a sensitivity of 81-93% using GA-MRI for the detection of PVTT [25] and of 48-86% using CECT [26,27,36]. This result may mainly be attributed to the better depiction of common imaging findings, such as continuity with the tumor in the adjacent liver parenchyma, and the additional contribution of diffusion-and T2-weighted imaging, which are not available with CECT.…”

Section: Discussionsupporting

confidence: 92%

How to Best Detect Portal Vein Tumor Thrombosis in Patients with Hepatocellular Carcinoma Meeting the Milan Criteria: Gadoxetic Acid-Enhanced MRI versus Contrast-Enhanced CT

et al. 2020

View full text Add to dashboard Cite

Introduction: The detection of portal vein tumor thrombosis (PVTT) in patients with hepatocellular carcinoma (HCC) meeting the Milan criteria is of critical importance as PVTT is known to be a contraindication to transplantation and an indicator of a dismal prognosis. Objective: To determine which modality may best detect PVTT, we compared the diagnostic performance of gadoxetic acid-enhanced MRI (GA-MRI) and contrast-enhanced CT (CECT) in HCC patients meeting the Milan criteria. Methods: We retrospectively enrolled 310 patients with HCCs meeting the Milan criteria who underwent both GA-MRI and CECT between June 2007 and May 2017. Among them, 44 patients were demonstrated to have PVTT while 266 patients had no PVTT. Two radiologists then assessed GA-MRI and CECT images for the presence of PVTT on a 5-point scale as well as vessel expansion, continuity with tumors, and enhancement on both modalities, as well as T2 hyperintensity and diffusion restriction on GA-MRI. The McNemar test was used to compare sensitivity and specificity of GA-MRI and CECT for the detection of PVTT, and Fisher's exact test was used to compare their imaging features.

show abstract

“…Liver.-Another important imaging task is differentiating benign from malignant portal vein thrombus. Canellas et al (25) studied 117 patients with portal vein thrombus. They found that texture features including MPP and entropy had AUCs of 0.97 and 0.93, respectively, and 0.99 when combined, in differentiating bland (benign) thrombus from malignant (tumor) thrombus.…”

Section: Lesion Characterizationmentioning

confidence: 99%

CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges

et al. 2017

View full text Add to dashboard Cite

This review discusses potential oncologic and nononcologic applications of CT texture analysis ( CTTA CT texture analysis ), an emerging area of "radiomics" that extracts, analyzes, and interprets quantitative imaging features. CTTA CT texture analysis allows objective assessment of lesion and organ heterogeneity beyond what is possible with subjective visual interpretation and may reflect information about the tissue microenvironment. CTTA CT texture analysis has shown promise in lesion characterization, such as differentiating benign from malignant or more biologically aggressive lesions. Pretreatment CT texture features are associated with histopathologic correlates such as tumor grade, tumor cellular processes such as hypoxia or angiogenesis, and genetic features such as KRAS or epidermal growth factor receptor (EGFR) mutation status. In addition, and likely as a result, these CT texture features have been linked to prognosis and clinical outcomes in some tumor types. CTTA CT texture analysis has also been used to assess response to therapy, with decreases in tumor heterogeneity generally associated with pathologic response and improved outcomes. A variety of nononcologic applications of CTTA CT texture analysis are emerging, particularly quantifying fibrosis in the liver and lung. Although CTTA CT texture analysis seems to be a promising imaging biomarker, there is marked variability in methods, parameters reported, and strength of associations with biologic correlates. Before CTTA CT texture analysis can be considered for widespread clinical implementation, standardization of tumor segmentation and measurement techniques, image filtration and postprocessing techniques, and methods for mathematically handling multiple tumors and time points is needed, in addition to identification of key texture parameters among hundreds of potential candidates, continued investigation and external validation of histopathologic correlates, and structured reporting of findings. RSNA, 2017.

show abstract

Characterization of Portal Vein Thrombosis (Neoplastic Versus Bland) on CT Images Using Software-Based Texture Analysis and Thrombus Density (Hounsfield Units)

Abstract: CT texture analysis and CT attenuation values allow reliable differentiation between neoplastic and bland thrombi on a single portal venous phase CT examination.

Cited by 51 publications

References 32 publications

Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole‐tumor CT texture analysis as quantitative biomarkers

Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole‐tumor CT texture analysis as quantitative biomarkers

How to Best Detect Portal Vein Tumor Thrombosis in Patients with Hepatocellular Carcinoma Meeting the Milan Criteria: Gadoxetic Acid-Enhanced MRI versus Contrast-Enhanced CT

CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges

Contact Info

Product

Resources

About