scholarly journals The Potential Value of Texture Analysis Based on Dynamic Contrast-Enhanced MR Images in the Grading of Breast Phyllode Tumors

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoguang Li ◽  
Hong Guo ◽  
Chao Cong ◽  
Huan Liu ◽  
Chunlai Zhang ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Roc Curve ◽  
Breast Imaging ◽  
Statistical Significance ◽  
Texture Features ◽  
Imaging Findings ◽  
Mr Images ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Occurrence Matrix

PurposeTo explore the value of texture analysis (TA) based on dynamic contrast-enhanced MR (DCE-MR) images in the differential diagnosis of benign phyllode tumors (BPTs) and borderline/malignant phyllode tumors (BMPTs).MethodsA total of 47 patients with histologically proven phyllode tumors (PTs) from November 2012 to March 2020, including 26 benign BPTs and 21 BMPTs, were enrolled in this retrospective study. The whole-tumor texture features based on DCE-MR images were calculated, and conventional imaging findings were evaluated according to the Breast Imaging Reporting and Data System (BI-RADS). The differences in the texture features and imaging findings between BPTs and BMPTs were compared; the variates with statistical significance were entered into logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of models from image-based analysis, TA, and the combination of these two approaches.ResultsRegarding texture features, three features of the histogram, two features of the gray-level co-occurrence matrix (GLCM), and three features of the run-length matrix (RLM) showed significant differences between the two groups (all p < 0.05). Regarding imaging findings, however, only cystic wall morphology showed significant differences between the two groups (p = 0.014). The areas under the ROC curve (AUCs) of image-based analysis, TA, and the combination of these two approaches were 0.687 (95% CI, 0.518–0.825, p = 0.014), 0.886 (95% CI, 0.760–0.960, p < 0.0001), and 0.894 (95% CI, 0.754–0.970, p < 0.0001), respectively.ConclusionTA based on DCE-MR images has potential in differentiating BPTs and BMPTs.

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 Differentiation of Cartilage Repair Techniques Using Texture Analysis from T2 Maps

Cartilage ◽  
2021 ◽  
pp. 194760352110296
Author(s):  
Vladimir Juras ◽  
Pavol Szomolanyi ◽  
Veronika Janáčová ◽  
Alexandra Kirner ◽  
Peter Angele ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Cartilage Repair ◽  
Femoral Condyle ◽  
Statistical Significance ◽  
Texture Feature ◽  
Texture Features ◽  
Linear Regression Models ◽  
Operating Characteristics ◽  
Repair Tissue ◽  
Occurrence Matrix

Objective The aim of this study was to investigate texture features from T2 maps as a marker for distinguishing the maturation of repair tissue after 2 different cartilage repair procedures. Design Seventy-nine patients, after either microfracture (MFX) or matrix-associated chondrocyte transplantation (MACT), were examined on a 3-T magnetic resonance (MR) scanner with morphological and quantitative (T2 mapping) MR sequences 2 years after surgery. Twenty-one texture features from a gray-level co-occurrence matrix (GLCM) were extracted. The texture feature difference between 2 repair types was assessed individually for the femoral condyle and trochlea/anterior condyle using linear regression models. The stability and reproducibility of texture features for focal cartilage were calculated using intra-observer variability and area under curve from receiver operating characteristics. Results There was no statistical significance found between MFX and MACT for T2 values ( P = 0.96). There was, however, found a statistical significance between MFX and MACT in femoral condyle in GLCM features autocorrelation ( P < 0.001), sum of squares ( P = 0.023), sum average ( P = 0.005), sum variance ( P = 0.0048), and sum entropy ( P = 0.05); and in anterior condyle/trochlea homogeneity ( P = 0.02) and dissimilarity ( P < 0.001). Conclusion Texture analysis using GLCM provides a useful extension to T2 mapping for the characterization of cartilage repair tissue by increasing its sensitivity to tissue structure. Some texture features were able to distinguish between repair tissue after different cartilage repair procedures, as repair tissue texture (and hence, probably collagen organization) 24 months after MACT more closely resembled healthy cartilage than did MFX repair tissue.

scholarly journals Apparent Diffusion Coefficient in Invasive Ductal Breast Carcinoma: Correlation with Detailed Histologic Features and the Enhancement Ratio on Dynamic Contrast-Enhanced MR Images

2010 ◽  
Vol 2010 ◽  
pp. 1-6 ◽  
Author(s):  
Roka Namoto Matsubayashi ◽  
Teruhiko Fujii ◽  
Kotaro Yasumori ◽  
Toru Muranaka ◽  
Seiya Momosaki
Keyword(s):  
Diffusion Coefficient ◽  
Growth Patterns ◽  
Breast Carcinomas ◽  
Mr Images ◽  
Cellular Density ◽  
Dynamic Contrast Enhanced ◽  
Diffusion Weighted Images ◽  
Architectural Features ◽  
Contrast Enhanced ◽  
Adc Values

Purpose. To investigate the correlation of Apperent Diffusion Coefficient (ADC) values in invasive ductal breast carcinomas with detailed histologic features and enhancement ratios on dynamic contrast-enhanced MRI.Methods and Materials. Dynamic MR images and diffusion-weighted images (DWIs) of invasive ductal breast carcinomas were reviewed in 25 (26 lesions) women. In each patient, DWI, T2WI, T1WI, and dynamic images were obtained. The ADC values of the 26 carcinomas were calculated with b-factors of 0 and 1000 s/ using echoplanar DWI. Correlations of the ADC values were examined on dynamic MRI with enhancement ratios (early to delayed phase: E/D ratio) and detailed histologic findings for each lesion, including cellular density, the size of cancer nests, and architectural features of the stroma (broad, narrow, and delicate) between cancer nests.Results. The mean ADC was  /sec. Cellular density was significantly correlated with ADC values () and E/D ratios (). The ADC values were also significantly correlated to features of the stroma (broad to narrow, ).Conclusion. The findings suggest that DWIs reflect the growth patterns of carcinomas, including cellular density and architectural features of the stroma, and E/D ratios may also be closely correlated to cellular density.

scholarly journals Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sihua Niu ◽  
Jianhua Huang ◽  
Jia Li ◽  
Xueling Liu ◽  
Dan Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Breast Imaging ◽  
Texture Features ◽  
Matrix Analysis ◽  
Width Ratio ◽  
Malignant Group ◽  
Malignant Lesions ◽  
Occurrence Matrix ◽  
Benign Group

Abstract Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

scholarly journals FORMING THE STACK OF TEXTURE FEATURES FOR LIVER ULTRASOUND IMAGES CLASSIFICATION

2020 ◽  
Vol 3 (4) ◽  
pp. 240-251
Author(s):  
Dmitro Yuriiovych Hrishko ◽  
Ievgen Arnoldovich Nastenko ◽  
Maksym Oleksandrovych Honcharuk ◽  
Volodymyr Anatoliyovich Pavlov
Keyword(s):  
Random Forest ◽  
Texture Analysis ◽  
Binary Classification ◽  
Texture Features ◽  
Normal Liver ◽  
Group Method ◽  
Ultrasound Images ◽  
Liver Ultrasound ◽  
Random Forest Models ◽  
Occurrence Matrix

This article discusses the use of texture analysis methods to obtain informative features that describe the texture of liver ultrasound images. In total, 317 liver ultrasound images were analyzed, which were provided by the Institute of Nuclear Medicine and Radiation Diagnostics of NAMS of Ukraine. The images were taken by three different sensors (convex, linear, and linear sensor in increased signal level mode). Both images of patients with a normal liver condition and patients with specific liver disease (there were diseases such as: autoimmune hepatitis, Wilson's disease, hepatitis B and C, steatosis, and cirrhosis) were present in the database. Texture analysis was used for “Feature Construction”, which resulted in more than a hundred different informative features that made up a common stack. Among them, there are such features as: three authors’ patented features derived from the grey level co-occurrence matrix; features, obtained with the help of spatial sweep method (working by the principle of group method of data handling), which was applied to ultrasound images; statistical features, calculated on the images, brought to one scale with the help of differential horizontal and vertical matrices, which are proposed by the authors; greyscale pairs ensembles (found using the genetic algorithm), which identify liver pathology on images, transformed with the help of horizontal and vertical differentiations, in the best possible way. The resulting trait stack was used to solve the problem of binary classification (“norma-pathology”) of ultrasound liver images. A Machine Learning method, namely “Random Forest”, was used for this purpose. Before the classification, in order to obtain objective results, the total samples were divided into training (70 %), testing (20 %), and examining (10 %). The result was the best three Random Forest models separately for each sensor, which gave the following recognition rates: 93.4 % for the convex sensor, 92.9 % for the linear sensor, and 92 % for the reinforced linear sensor

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