scholarly journals Retrospective CT/MRI Texture Analysis of Rapidly Progressive Hepatocellular Carcinoma

2020 ◽  
Vol 10 (3) ◽  
pp. 136
Author(s):  
Charissa Kim ◽  
Natasha Cigarroa ◽  
Venkateswar Surabhi ◽  
Balaji Ganeshan ◽  
Anil K. Pillai
Keyword(s):  
Hepatocellular Carcinoma ◽  
Texture Analysis ◽  
Visual Analysis ◽  
Intensity Variation ◽  
Texture Features ◽  
Composite Score ◽  
Imaging Features ◽  
Coarse Texture ◽  
Significant Difference ◽  
Magnetic Resonance Imaging Mri

Rapidly progressive hepatocellular carcinoma (RPHCC) is a subset of hepatocellular carcinoma that demonstrates accelerated growth, and the radiographic features of RPHCC versus non-RPHCC have not been determined. The purpose of this retrospective study was to use baseline radiologic features and texture analysis for the accurate detection of RPHCC and subsequent improvement of clinical outcomes. We conducted a qualitative visual analysis and texture analysis, which selectively extracted and enhanced imaging features of different sizes and intensity variation including mean gray-level intensity (mean), standard deviation (SD), entropy, mean of the positive pixels (MPP), skewness, and kurtosis at each spatial scaling factor (SSF) value of RPHCC and non-RPHCC tumors in a computed tomography (CT) cohort of n = 11 RPHCC and n = 11 non-RPHCC and a magnetic resonance imaging (MRI) cohort of n = 13 RPHCC and n = 10 non-RPHCC. There was a statistically significant difference across visual CT irregular margins p = 0.030 and CT texture features in SSF between RPHCC and non-RPHCC for SSF-6, coarse-texture scale, mean p = 0.023, SD p = 0.053, MPP p = 0.023. A composite score of mean SSF-6 binarized + SD SSF-6 binarized + MPP SSF-6 binarized + irregular margins was significantly different between RPHCC and non-RPHCC (p = 0.001). A composite score ≥3 identified RPHCC with a sensitivity of 81.8% and specificity of 81.8% (AUC = 0.884, p = 0.002). CT coarse-texture-scale features in combination with visually detected irregular margins were able to statistically differentiate between RPHCC and non-RPHCC. By developing an image-based, non-invasive diagnostic criterion, we created a composite score that can identify RPHCC patients at their early stages when they are still eligible for transplantation, improving the clinical course of patient care.

scholarly journals Magnetic resonance imaging of the cirrhotic liver: diagnosis of hepatocellular carcinoma and evaluation of response to treatment – Part 2

2017 ◽  
Vol 50 (2) ◽  
pp. 115-125 ◽  
Author(s):  
Miguel Ramalho ◽  
António P. Matos ◽  
Mamdoh AlObaidy ◽  
Fernanda Velloni ◽  
Ersan Altun ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Hepatocellular Carcinoma ◽  
Magnetic Resonance ◽  
Diagnostic Algorithm ◽  
Response To Treatment ◽  
Locoregional Therapy ◽  
Liver Imaging ◽  
Imaging Features ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Abstract In the second part of this review, we will describe the ancillary imaging features of hepatocellular carcinoma (HCC) that can be seen on standard magnetic resonance imaging (MRI) protocol, and on novel and emerging protocols such as diffusion weighted imaging and utilization of hepatocyte-specific/hepatobiliary contrast agent. We will also describe the morphologic sub-types of HCC, and give a simplified non-invasive diagnostic algorithm for HCC, followed by a brief description of the liver imaging reporting and data system (LI-RADS), and MRI assessment of tumor response following locoregional therapy.

scholarly journals Clinical manifestations and imaging features of white matter demyelination in older patients

2020 ◽  
Vol 48 (11) ◽  
pp. 030006052096680
Author(s):  
Jian Li ◽  
Yi-Ming Zhao
Keyword(s):  
White Matter ◽  
Clinical Diagnosis ◽  
Older Patients ◽  
Demyelinating Disease ◽  
Clinical Manifestations ◽  
Imaging Features ◽  
Significant Difference ◽  
Clinical Diagnoses ◽  
Chief Complaints ◽  
Magnetic Resonance Imaging Mri

Objective To investigate the clinical manifestations and imaging features of older patients with white matter demyelination diagnosed by magnetic resonance imaging (MRI). Methods Ninety-six patients with leukoaraiosis diagnosed by MRI were divided by their clinical diagnoses into a demyelinating group (40 cases) and a non-demyelinating group (56 cases). The imaging and clinical features of the patients in the two groups were analyzed. Results Compared with the non-demyelinating group, there were significantly more women in the demyelinating group than men. There was no significant difference in age between the two groups. Of the 37 cases who had an imaging report of “white matter demyelination and multiple sclerosis,” 36 cases had a clinical diagnosis in accordance with white matter demyelination (97.3%). Of the 59 cases who had an imaging report of “white matter demyelination”, only four cases had a clinical diagnosis in accordance with demyelination (6.8%). Conclusion In older patients with headaches, vertigo, other head symptoms, and unilateral numbness as the chief complaints, a clinical diagnosis of demyelinating disease is very unlikely when the imaging report states white matter demyelination only.

CT-based radiomic analysis of hepatocellular carcinoma patients to predict key genomic information.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15623-e15623 ◽  
Author(s):  
Derek L West ◽  
Aikaterini Kotrotsou ◽  
Andrew Scott Niekamp ◽  
Tagwa Idris ◽  
Dunia Giniebra Camejo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Texture Features ◽  
Ct Images ◽  
The Cancer Genome Atlas ◽  
Tumor Segmentation ◽  
Full Potential ◽  
Imaging Features ◽  
Treatment Naïve

e15623 Background: The utilization of computed tomography (CT) has virtually replaced the need for tissue diagnosis in hepatocellular carcinoma (HCC). Imaging features (e.g. size, shape and vascularity) have been associated with patient survival. However, the full potential of CT in HCC diagnosis may not be reached, as high-throughput computing allows for extraction of quantitative features that are not part of radiologists’ lexicon. The purpose of this study was to investigate the ability of radiomic analysis to successfully identify specific doxorubicin chemoresistant genes on CT images of treatment-naïve hepatocellular carcinoma (HCC). Methods: We identified 27 treatment-naïve patients with a single HCC tumor from The Cancer Genome Atlas (TCGA) whom had gene expression profiles. Baseline CT images were obtained from The Cancer Imaging Archive (TCIA). 3D Slicer software was used for manual tumor segmentation and final segmented images were reviewed by a board-certified radiologist. Following tumor segmentation, texture analysis was performed on MATLAB environment. A total of 310 rotation invariant texture features, which measure tumor heterogeneity, were obtained (first-order histogram and grey level co-occurrence matrix). The mRMR method was used to select the most relevant radiomic features. ROC analysis and LOOCV were used to assess the performance of five specific genes known to confer doxorubicin chemoresistance (TP53, TOP2A, CTNNB1, CDKN2A and AKT1). Results: Radiomic analysis identified TP53 (AUC = 86.61%, Specificity = 92.31%, Sensitivity = 92.9%), TOP2A (AUC = 78.0%, Specificity = 69%, Sensitivity = 85.7%), CTNNB1 (AUC = 86.8%, Specificity = 92.3%, Sensitivity = 85.7%), CDKN2A (AUC = 76.9%, Specificity = 76.9%, Sensitivity = 78.6%) and AKT1 (AUC = 72.5%, Specificity = 69.2%, Sensitivity = 85.7%) in treatment-naïve HCC CT studies. Conclusions: The identification of specific genes that confer chemoresistance to doxorubicin can be reliably ascertained via the use of radiomic analysis. This study may help tailor future treatment paradigms via the ability to categorize HCC tumors on genetic level and identify tumors which may not have a favorable response to doxorubicin based therapies.

The role of MRI-based texture analysis to predict the severity of brain injury in neonates with perinatal asphyxia

2021 ◽  
Author(s):  
Fatma Ceren Sarioglu ◽  
Orkun Sarioglu ◽  
Handan Guleryuz ◽  
Burak Deliloglu ◽  
Funda Tuzun ◽  
...  
Keyword(s):  
Basal Ganglia ◽  
Texture Analysis ◽  
Diagnostic Yield ◽  
Perinatal Asphyxia ◽  
Characteristic Curve ◽  
Internal Capsule ◽  
Texture Features ◽  
Posterior Limb ◽  
Apparent Diffusion ◽  
Magnetic Resonance Imaging Mri

Objectives: To evaluate the efficacy of the magnetic resonance imaging (MRI)-based texture analysis (TA) of the basal ganglia and thalami to distinguish moderate-to-severe hypoxic-ischemic encephalopathy (HIE) from mild HIE in neonates. Methods: This study included 68 neonates (15 with mild, 20 with moderate-to-severe HIE, and 33 control) were born at 37 gestational weeks or later and underwent MRI in first 10 days after birth. The basal ganglia and thalami were delineated for TA on the apparent diffusion coefficient (ADC) maps, T1-, and T2-weighted images. The basal ganglia, thalami, and the posterior limb of the internal capsule (PLIC) were also evaluated visually on diffusion-weighted imaging and T1-weighted sequence. Receiver operating characteristic curve and logistic regression analyses were used. Results: Totally 56 texture features for the basal ganglia and 46 features for the thalami were significantly different between the HIE groups on the ADC maps, T1-, and T2-weighted sequences. Using a Histogram_entropy log-10 value as >1.8 from the basal ganglia on the ADC maps (p < 0.001; OR, 266) and the absence of hyperintensity of the PLIC on T1-weighted images (p = 0.012; OR, 17.11) were found as independent predictors for moderate-to-severe HIE. Using only a Histogram_entropy log-10 value had an equal diagnostic yield when compared to its combination with other texture features and imaging findings. Conclusion: The Histogram_entropy log-10 value can be used as an indicator to differentiate from moderate-to-severe to mild HIE. Advances in knowledge: MRI-based TA may provide quantitative findings to indicate different stages in neonates with perinatal asphyxia.

scholarly journals Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Hai-Yan Chen ◽  
Xue-Ying Deng ◽  
Yao Pan ◽  
Jie-Yu Chen ◽  
Yun-Ying Liu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Texture Analysis ◽  
Validation Cohort ◽  
Texture Features ◽  
Imaging Features ◽  
Cystic Neoplasms ◽  
Radiological Features ◽  
Model Combining ◽  
Enhanced Ct ◽  
Ct Texture Analysis

ObjectiveTo establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs).Materials and MethodsFifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity.ResultsFollowing multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively.ConclusionThis study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.

scholarly journals CT Texture analysis and CT scores for characterization of fluid collections

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hans-Jonas Meyer ◽  
Benedikt Schnarkowski ◽  
Jakob Leonhardi ◽  
Matthias Mehdorn ◽  
Sebastian Ebel ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Texture Features ◽  
Imaging Features ◽  
Conventional Imaging ◽  
Imaging Findings ◽  
Ct Guided ◽  
Septic Focus ◽  
Fluid Collections ◽  
Better Than

Abstract Background Texture analysis derived from Computed tomography (CT) might be able to better characterize fluid collections undergoing CT-guided percutaneous drainage treatment. The present study tested, whether texture analysis can reflect microbiology results in fluid collections suspicious for septic focus. Methods Overall, 320 patients with 402 fluid collections were included into this retrospective study. All fluid collections underwent CT-guided drainage treatment and were microbiologically evaluated. Clinically, serologically parameters and conventional imaging findings as well as textures features were included into the analysis. A new CT score was calculated based upon imaging features alone. Established CT scores were used as a reference standard. Results The present score achieved a sensitivity of 0.78, a specificity of 0.69, area under curve (AUC 0.82). The present score and the score by Gnannt et al. (AUC 0.81) were both statistically better than the score by Radosa et al. (AUC 0.75). Several texture features were statistically significant between infected fluid collections and sterile fluid collections, but these features were not significantly better compared with conventional imaging findings. Conclusions Texture analysis is not superior to conventional imaging findings for characterizing fluid collections. A novel score was calculated based upon imaging parameters alone with similar diagnostic accuracy compared to established scores using imaging and clinical features.

scholarly journals Combined Texture Analysis of MR images and Glypican-3 to Identify the Differentiated Degree of Hepatocellular Carcinoma: a retrospective study

2020 ◽  
Author(s):  
Mengmeng Feng ◽  
Mengchao Zhang ◽  
Yuanqing Liu ◽  
Nan Jiang ◽  
Qian Meng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Retrospective Study ◽  
Texture Analysis ◽  
Statistical Significance ◽  
Area Under The Curve ◽  
Texture Features ◽  
Mr Images ◽  
Clinical Value ◽  
Enhanced Mri ◽  
Well Differentiated

Abstract BACKGROUND To explore the clinical value of texture analysis of MR images (multiphase Gd-EOB-DTPA-enhanced MRI and T2 weighted imaging (T2WI) and the glypican-3 (GPC-3) to identify the differentiated degree of hepatocellular carcinoma (HCC). METHOD In this retrospective study, 104 participants were enrolled (GPC-3 data obtained in 51 participants). Each participant performed preoperative Gd-EOB-DTPA-enhanced MR scanning. Texture analysis was calculated by MaZda and then using the B11 program for data analysis and classification. The performance of texture features and GPC-3 in identifying the differentiated degree of HCC was assessed by receiver operating characteristic (ROC) analysis. RESULTS There were no statistically significances for the expression of GPC-3 between poorly-, well- and moderately-differentiated HCC. The area under the curve (AUC) of the combined texture features between poorly- and well-differentiated HCC, poorly- and moderately-differentiated HCC, moderately- and well-differentiated HCC was 0.812, 0.879 and 0.808 respectively. With GPC-3 combined, the AUC was increased to 0.868, while accuracy was decreased, in poorly- verse well-differentiated HCC, and the AUC and accuracy were the same as those without GPC-3 combined in poorly- verse moderately-differentiated HCC. Although the AUC was increased to 0.818 with GPC-3 combined in moderately- verse well-differentiated HCC, there were no statistical significance for the value change (p>0.05). CONCLUSION S Texture analysis of Gd-EOB-DTPA-enhanced MRI and T2WI are valuable in identifying the differentiated degree of HCC. There is no significant effect of GPC-3 in identifying the differentiated degree of HCC, suggesting the promising value of texture analysis of MR images in the precise presurgical diagnosis of HCC.

scholarly journals Diagnosis of Non-Hepatocellular Carcinoma Malignancies in Patients With Risks for Hepatocellular Carcinoma: CEUS LI-RADS Versus CT/MRI LI-RADS

2021 ◽  
Vol 11 ◽  
Author(s):  
Yi-Xin Hu ◽  
Jing-Xian Shen ◽  
Jing Han ◽  
Si-Yue Mao ◽  
Ru-Shuang Mao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Imaging ◽  
Imaging Features ◽  
Hepatocelluar Carcinoma ◽  
Undifferentiated Sarcoma ◽  
Primary Hepatic Malignancies ◽  
Contrast Enhanced ◽  
Significant Difference ◽  
Kappa Value ◽  
Hepatic Malignancies

ObjectiveData regarding direct comparison of contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) and Computed Tomography/Magnetic Resonance Imaging (CT/MR) LI-RADS in diagnosis of non-hepatocelluar carcinoma (non-HCC) malignancies remain limited. Our study aimed to compare the diagnostic performance of the CEUS LI-RADS version 2017 and CT/MRI LI-RADS v2018 for diagnosing non-HCC malignancies in patients with risks for HCC.Materials and MethodsIn this retrospective study, 94 liver nodules pathologically-confirmed as non-HCC malignancies in 92 patients at risks for HCC from January 2009 to December 2018 were enrolled. The imaging features and the LI-RADS categories on corresponding CEUS and CT/MRI within 1 month were retrospectively analyzed according to the ACR CEUS LI-RADS v2017 and ACR CT/MRI LI-RADS v2018 by two radiologists in consensus for each algorithm. The sensitivity of LR-M category, inter-reader agreement and inter-modality agreement was compared between these two standardized algorithms.ResultsNinety-four nodules in 92 patients (mean age, 54 years ± 10 [standard deviation] with 65 men [54 years ± 11] and 27 women [54 years ± 8]), including 56 intrahepatic cholangiocarcinomas, 34 combined hepatocellular cholangiocarcinomas, two adenosquamous carcinomas of the liver, one primary hepatic neuroendocrine carcinoma and one hepatic undifferentiated sarcoma were included. On CEUS, numbers of lesions classified as LR-3, LR-4, LR-5 and LR-M were 0, 1, 10 and 83, and on CT/MRI, the corresponding numbers were 3, 0, 14 and 77. There was no significant difference in the sensitivity of LR-M between these two standardized algorithms (88.3% of CEUS vs 81.9% of CT/MRI, p = 0.210). Seventy-seven lesions (81.9%) were classified as the same LI-RADS categories by both standardized algorithms (five for LR-5 and 72 for LR-M, kappa value = 0.307). In the subgroup analysis for ICC and CHC, no significant differences were found in the sensitivity of LR-M category between these two standardized algorithms (for ICC, 94.6% of CEUS vs 89.3% of CT/MRI, p = 0.375; for CHC, 76.5% of CEUS vs 70.6% of CT/MRI, p = 0. 649).ConclusionCEUS LI-RADS v2017 and CT/MRI LI-RADS v2018 showed similar value for diagnosing non-HCC primary hepatic malignancies in patients with risks.

A Novel Texture Analysis Method Based on Reverse Biorthogonal Wavelet and Co-Occurrence Matrix Applied for Classification of Hepatocellular Carcinoma and Hepatic Hemangioma

2018 ◽  
Vol 8 (9) ◽  
pp. 1835-1843 ◽  
Author(s):  
Jia-Jun Qiu ◽  
Yue Wu ◽  
Bei Hui ◽  
Jia Chen ◽  
Lin Ji ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Texture Analysis ◽  
Texture Features ◽  
Ct Images ◽  
Hepatic Hemangioma ◽  
Gray Level ◽  
Biorthogonal Wavelet ◽  
Analysis Method ◽  
Enhanced Ct ◽  
Occurrence Matrix

Purpose: To explore the feasibility of classifying hepatocellular carcinoma (HCC) and hepatic hemangioma (HEM) using texture features of non-enhanced computed tomography (CT) images, especially to investigate the effectiveness of a novel texture analysis method based on the combination of wavelet and co-occurrence matrix. Methods: 269 patients were retrospectively analyzed, including 129 HCCs and 140 HEMs. We cropped tumor regions of interest (ROIs) on non-enhanced CT images, and then used four texture analysis methods to extract quantitative data of the ROIs: gray-level histogram (GLH), gray-level co-occurrence matrix (GLCM), reverse biorthogonal wavelet transform (RBWT), and reverse biorthogonal wavelet co-occurrence matrix (RBCM). The RBCM was a novel method proposed in this study that combined wavelet transform and co-occurrence matrix. It discretized wavelet coefficient matrices based on the statistical characteristics of the training set. Thus, four sets of texture features were obtained. We then conducted classification studies using support vector machine on each set of texture features. 10-fold cross training and testing were used in the classifications, and their results were evaluated and compared. In addition, we tested the significant differences in the texture features of the RBCM method and explored the possible relationships between textures and lesion types. Results: The RBCM method achieved the best classification performance: its average accuracy was 82.14%; its average AUC (area under the receiver operating characteristic curve) was 0.8423. In addition, using the methods of GLH, GLCM, and RBWT, their average accuracies were 75.81%, 78.79%, and 78.8%, respectively. Conclusions: It indicates that the developed texture analysis methods are rewarding for computer-aided diagnosis of HCC and HEM based on non-enhanced CT images. Furthermore, the distinguishing ability of the proposed RBCM method is more pronounced.

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