Predictive role of T2WI and ADC-derived texture parameters in differentiating Gleason score 3 + 4 and 4 + 3 prostate cancer

2021 ◽  
pp. 1-9
Author(s):  
Zhen Kang ◽  
Anhui Xu ◽  
Liang Wang
Keyword(s):  
Prostate Cancer ◽  
Gleason Score ◽  
Area Under The Curve ◽  
Texture Feature ◽  
Texture Features ◽  
Feature Reduction ◽  
Image Texture ◽  
Roc Curve Analysis ◽  
Texture Parameters ◽  
Predictive Role

BACKGROUND: Since Gleason score (GS) 4 + 3 prostate cancer (PCa) has the worse prognosis than GS 3 + 4 PCa, differentiating these two types of PCa is of clinical significance. OBJECTIVE: To assess the predictive roles of using T2WI and ADC-derived image texture parameters in differentiating GS 3 + 4 from GS 4 + 3 PCa. METHODS: Forty-eight PCa patients of GS 3 + 4 and 37 patients of GS 4 + 3 are retrieved and randomly divided into training (60%) and testing (40%) sets. Axial image showing the maximum tumor size is selected in the T2WI and ADC maps for further image texture feature analysis. Three hundred texture features are computed from each region of interest (ROI) using MaZda software. Feature reduction is implemented to obtain 30 optimal features, which are then used to generate the most discriminative features (MDF). Receiver operating characteristic (ROC) curve analysis is performed on MDF values in the training sets to achieve cutoff values for determining the correct rates of discrimination between two Gleason patterns in the testing sets. RESULTS: ROC analysis on T2WI and ADC-derived MDF values in the training set (n = 51) results in a mean area under the curve (AUC) of 0.953±0.025 (with sensitivity 92.74±6.15 and specificity 89.7±6.9), and 0.985±0.013 (with sensitivity 96.36±4.46 and specificity 97.26±2.58), respectively. Using the corresponding MDF cutoffs, 95.3% (ranges from 76.5% to 100%) and 94.1% (ranged from 76.5% to 100%) of test cases (n = 34) are correctly discriminated using T2WI and ADC-derived MDF values, respectively. CONCLUSIONS: The study demonstrates that using T2WI and ADC-derived image texture parameters has a potentially predictive role in differentiating GS 3 + 4 and GS 4 + 3 PCa.

scholarly journals Research on college gymnastics teaching model based on multimedia image and image texture feature analysis

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ying Wu ◽  
Jikun Liu
Keyword(s):  
Human Body ◽  
Rapid Development ◽  
Texture Feature ◽  
Texture Features ◽  
Dynamics Simulation ◽  
Image Texture ◽  
Whole Body ◽  
Database Modeling ◽  
Rigid Model ◽  
The Subject

AbstractWith the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.

scholarly journals APTIMA PCA3 Molecular Urine Test: Development of a Method to Aid in the Diagnosis of Prostate Cancer

2006 ◽  
Vol 52 (6) ◽  
pp. 1089-1095 ◽  
Author(s):  
Jack Groskopf ◽  
Sheila MJ Aubin ◽  
Ina Lim Deras ◽  
Amy Blase ◽  
Sharon Bodrug ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Biopsy ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Prostate Cancer Risk ◽  
Area Under The Curve ◽  
Roc Curve Analysis ◽  
Cancer Risk Factors ◽  
Specimen Processing ◽  
Whole Urine

Abstract Background: Prostate cancer gene 3 (PCA3) encodes a prostate-specific mRNA that has shown promise as a prostate cancer diagnostic tool. This report describes the characterization of a prototype quantitative PCA3-based test for whole urine. Methods: Whole-urine specimens were collected after digital rectal examination from 3 groups: men scheduled for prostate biopsy (n = 70), healthy men (<45 years of age with no known prostate cancer risk factors; n = 52), and men who had undergone radical prostatectomy (n = 21). PCA3 and prostate-specific antigen (PSA) mRNAs were isolated, amplified, and quantified by use of Gen-Probe DTS400® Systems. Prostate biopsy results were correlated with the PCA3/PSA mRNA ratio, and PSA mRNA concentrations were used to normalize PCA3 signals and confirm the yield of prostate-specific RNA. Assay precision, specimen stability, and mRNA yield were also evaluated. Results: The specimen informative rate (fraction of specimens yielding sufficient RNA for analysis) was 98.2%. In this clinical research study, ROC curve analysis of prebiopsy specimens yielded an area under the curve of 0.746; sensitivity was 69% and specificity 79%. Serum PSA assay specificity was 28% for this same group. PCA3 and PSA mRNAs were undetectable in postprostatectomy specimens except for one man with recurrent prostate cancer. Assay interrun CVs were ≤12%. Both mRNAs were stable in processed urine up to 5 days at 4 °C and after 5 freeze–thaw cycles. Conclusion: The APTIMA® PCA3 assay combines simple specimen processing with precise assays and existing instruments and could add specificity to the current algorithm for prostate cancer diagnosis.

scholarly journals Cotton stubble detection based on wavelet decomposition and texture features

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Yukun Yang ◽  
Jing Nie ◽  
Za Kan ◽  
Shuo Yang ◽  
Hangxing Zhao ◽  
...  
Keyword(s):  
Navigation System ◽  
Wavelet Decomposition ◽  
Back Propagation ◽  
Texture Feature ◽  
Texture Features ◽  
Back Propagation Neural Network ◽  
Visual Navigation ◽  
Image Texture ◽  
Original Image ◽  
Residual Film

Abstract Background At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system. Methods Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and Support Vector Machine in order to classify the sample images. Finally, the possibility of improving the classification accuracy using the texture features extracted from the wavelet decomposition coefficients, is discussed. Results The experiment proves that the GLCM texture feature of the original image has the best performance under the Back Propagation Neural Network classifier. As for the different wavelet bases, the vertical coefficient texture feature of coif3 wavelet decomposition, combined with the texture feature of the original image, is the feature having the best classification effect. Compared with the original image texture features, the classification accuracy is increased by 3.8%, the sensitivity is increased by 4.8%, and the specificity is increased by 1.2%. Conclusions The algorithm can complete the task of stubble detection in different locations, different periods and abnormal driving conditions, which shows that the wavelet coefficient texture feature combined with the original image texture feature is a useful fusion feature for detecting stubble and can provide a reference for different crop stubble detection.

Prediction of texture in different beef cuts applying image analysis technique

2018 ◽  
Vol 120 (8) ◽  
pp. 1929-1940
Author(s):  
Facundo Pieniazek ◽  
Agustina Roa Andino ◽  
Valeria Messina
Keyword(s):  
Image Analysis ◽  
Linear Trend ◽  
Texture Features ◽  
Image Texture ◽  
Content Type ◽  
Analysis Techniques ◽  
Texture Parameters ◽  
Freeze Dried ◽  
Instrumental Texture ◽  
Image Analysis Techniques

Purpose Measuring texture parameters are time consuming and expensive; it is necessary to develop an efficient and rapid method to evaluate them. Image analysis can be a useful tool. The purpose of this paper is to predict texture parameters in different beef cuts applying image analysis techniques. Design/methodology/approach Samples were analyzed by scanning electron microscopy. Texture parameters were analyzed by instrumental, image analysis techniques and by Warner–Bratzler shear force. Findings Significant differences (p<0.05) were obtained for image and instrumental texture features. Higher amount of porous were observed in freeze dried samples of beef cuts from Gluteus Medius and semintendinosus muscles. A linear trend with a linear correlation was applied for instrumental and image texture. High correlations were found between image and instrumental texture features. Instrumental parameters showed a positive correlation with image texture feature. Originality/value This research suggests that the addition of image texture features improves the accuracy to predict texture parameter. The prediction of quality parameters can be performed easily with a computer by recognizing attributes within an image.

Application of texture analysis based on T2-weighted magnetic resonance images in discriminating Gleason scores of prostate cancer

2020 ◽  
Vol 28 (6) ◽  
pp. 1207-1218
Author(s):  
Ruigen Pan ◽  
Xueli Yang ◽  
Zhenyu Shu ◽  
Yifeng Gu ◽  
Lihua Weng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Texture Analysis ◽  
Gleason Score ◽  
Texture Features ◽  
Roc Curves ◽  
Magnetic Resonance Images ◽  
Diagnostic Efficiency ◽  
Energy Value ◽  
Tumor Region

OBJECTIVE: To investigate the value of texture analysis in magnetic resonance images for the evaluation of Gleason scores (GS) of prostate cancer. METHODS: Sixty-six prostate cancer patients are retrospective enrolled, which are divided into five groups namely, GS = 6, 3 + 4, 4 + 3, 8 and 9–10 according to postoperative pathological results. Extraction and analysis of texture features in T2-weighted MR imaging defined tumor region based on pathological specimen after operation are performed by texture software OmniKinetics. The values of texture are analyzed by single factor analysis of variance (ANOVA), and Spearman correlation analysis is used to study the correlation between the value of texture and Gleason classification. Receiver operating characteristic (ROC) curve is then used to assess the ability of applying texture parameters to predict Gleason score of prostate cancer. RESULTS: Entropy value increases and energy value decreases as the elevation of Gleason score, both with statistical difference among five groups (F = 10.826, F = 2.796, P < 0.05). Energy value of group GS = 6 is significantly higher than that of groups GS = 8 and 9–10 (P < 0.005), which is similar between three groups (GS = 3 + 4, 8 and 9–10). The entropy and energy values correlate with GS (r = 0.767, r = –0.692, P < 0.05). Areas under ROC curves (AUC) of combination of entropy and energy are greater than that of using energy alone between groups GS = 6 and ≥7. Analogously, AUC of combination of entropy and energy are significantly higher than that of using entropy alone between groups GS≤3 + 4 and ≥4 + 3, as well as between groups GS≤4 + 3 and ≥8. CONCLUSION: Texture analysis on T2-weighted images of prostate cancer can evaluate Gleason score, especially using the combination of entropy and energy rendering better diagnostic efficiency.

Content-Based Image Classification and Retrieval

2011 ◽  
pp. 68-83 ◽  
Author(s):  
Jafar M. Ali
Keyword(s):  
Image Retrieval ◽  
Image Classification ◽  
Data Storage ◽  
Rough Set ◽  
Texture Features ◽  
Feature Reduction ◽  
Image Texture ◽  
Content Based Image Retrieval ◽  
Attribute Vector ◽  
Real Value

Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. Thus, it is necessary to develop appropriate information systems to efficiently manage these datasets. Image classification and retrieval is one of the most important services that must be supported by such systems. The most common approach used is content-based image retrieval (CBIR) systems. This paper presents a new application of rough sets to feature reduction, classification, and retrieval for image databases in the framework of content-based image retrieval systems. The suggested approach combines image texture features with color features to form a powerful discriminating feature vector for each image. Texture features are extracted, represented, and normalized in an attribute vector, followed by a generation of rough set dependency rules from the real value attribute vector. The rough set reduction technique is applied to find all reducts with the minimal subset of attributes associated with a class label for classification.

Prostate-Specific Antigen Kinetics During Follow-Up Are an Unreliable Trigger for Intervention in a Prostate Cancer Surveillance Program

2010 ◽  
Vol 28 (17) ◽  
pp. 2810-2816 ◽  
Author(s):  
Ashley E. Ross ◽  
Stacy Loeb ◽  
Patricia Landis ◽  
Alan W. Partin ◽  
Jonathan I. Epstein ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Active Surveillance ◽  
Gleason Score ◽  
Specific Antigen ◽  
Area Under The Curve ◽  
Cancer Surveillance ◽  
Surveillance Program

Purpose To assess the predictive ability of prostate-specific antigen (PSA) velocity (PSAV) and doubling time (PSADT) for biopsy progression and adverse pathology at prostatectomy among men with low-risk prostate cancer enrolled on an active-surveillance program. Methods We evaluated 290 men who met criteria for active surveillance (ie, PSA density < 0.15 ng/mL/cm3 and Gleason score ≤ 6 with no pattern ≥ 4, involving ≤ 2 cores with cancer, and ≤ 50% involvement of any core by cancer) with two or more serial PSA measurements after diagnosis from 1994 to 2008. Follow-up included twice-yearly digital rectal exam and PSA measurements and yearly surveillance biopsy. Treatment was recommended for biopsy progression (ie, Gleason score ≥ 7, or > 2 positive cores, or > 50% core involvement). Sensitivity and specificity of postdiagnostic PSAV and PSADT were explored by using receiver operating characteristic (ROC) analysis. Results Overall, 188 (65%) men remained on active surveillance, and 102 (35%) developed biopsy progression at a median follow-up of 2.9 years. PSADT was not significantly associated with subsequent adverse biopsy findings (P = .83), and PSAV was marginally significant (P = .06). No PSAV or PSADT cut point had both high sensitivity and specificity (area under the curve, 0.61 and 0.59, respectively) for biopsy progression. In those who eventually underwent radical prostatectomy, PSAV (P = .79) and PSADT (P = .87) were not associated with the presence of unfavorable surgical pathology. Conclusion Postdiagnostic PSA kinetics do not reliably predict adverse pathology and should not be used to replace annual surveillance biopsy for monitoring men on active surveillance.

TEXTURE ANALYSIS USING LOCAL TEXTURE PATTERNS: A FUZZY LOGIC APPROACH

2011 ◽  
Vol 25 (05) ◽  
pp. 741-762 ◽  
Author(s):  
E. M. SRINIVASAN ◽  
K. RAMAR ◽  
A. SURULIANDI
Keyword(s):  
Texture Analysis ◽  
Texture Classification ◽  
Texture Feature ◽  
Texture Features ◽  
Vital Role ◽  
Image Texture ◽  
Feature Extraction Method ◽  
Local Texture ◽  
Texture Model ◽  
Fuzzy Logic Approach

Texture analysis plays a vital role in image processing. The prospect of texture based image analysis depends on the texture features and the texture model. This paper presents a new texture feature extraction method 'Fuzzy Local Texture Patterns (FLTP)' and 'Fuzzy Pattern Spectrum (FPS)', suitable for texture analysis. The local image texture is described by FLTP and the global image texture is described by FPS. The proposed method is tested with texture classification, texture segmentation and texture edge detection. The results show that the proposed method provides a very good and robust performance for texture analysis.

scholarly journals Texture Analysis Based on Pi-Rads 4/5-Scored MRI Images Combined with Machine Learning to Distinguish Benign Lesions from Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Qi Zhou ◽  
Huming Yin ◽  
Xiaojie Ang ◽  
Yu Li ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Texture Feature ◽  
Texture Features ◽  
Classification Model ◽  
Clinical Indicators ◽  
Model Based ◽  
Feature Parameters ◽  
Auc Value ◽  
Malignant Prostate

Abstract Background: To extract the texture features of Apparent Diffusion Coefficient (ADC) images in Mp-MRI and build a machine learning model based on radiomics texture analysis to determine its ability to distinguish benign from prostate cancer (PCa) lesions using PI-RADS 4/5 score.Materials and methods: First, use ImageJ software to obtain texture feature parameters based on ADC images; use R language to standardize texture feature parameters, and use Lasso regression to reduce the dimensionality of multiple feature parameters; then, use the feature parameters after dimensionality reduction to construct image-based groups. Learn R-Logistic, R-SVM, R-AdaBoost to identify the machine learning classification model of prostate benign and malignant nodules. Secondly, the clinical indicators of the patients were statistically analyzed, and the three clinical indicators with the largest AUC values were selected to establish a classification model based on clinical indicators of benign and malignant prostate nodules. Finally, compare the performance of the model based on radiomics texture features and clinical indicators to identify benign and malignant prostate nodules in PI-RADS 4/5.Results: The experimental results show that the AUC of the R-Logistic model test set is 0.838, which is higher than the R-SVM and R-AdaBoost classification models. At this time, the corresponding R-Logistic classification model formula is: Y_radiomics=9.396-7.464*median ADC-0.584 *kurtosis+0.627*skewness+0.576*MRI lesions volume; analysis of clinical indicators shows that the 3 indicators with the highest discrimination efficiency are PSA, Fib, LDL-C, and the corresponding C-Logistic classification model formula is: Y_clinical =-2.608 +0.324*PSA-3.045*Fib+4.147*LDL-C, the AUC value of the model training set is 0.860, which is smaller than the training set R-Logistic classification model AUC value of 0.936.Conclusion: The machine learning classifier model is established based on the texture features of radiomics. It has a good classification performance in identifying benign and malignant nodules of the prostate in PI-RADS 4/5. This has certain potential and clinical value for patients with prostate cancer to adopt different treatment methods and prognosis.

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