ARE NUCLEAR TEXTURE FEATURES A SUITABLE TOOL FOR PREDICTING NON-ORGAN-CONFINED PROSTATE CANCER?

1999 ◽  
Vol 162 (1) ◽  
pp. 258-262 ◽  
Author(s):  
T. MAIRINGER ◽  
G. MIKUZ ◽  
A. GSCHWENDTNER
Keyword(s):  
Prostate Cancer ◽  
Texture Features ◽  
Suitable Tool ◽  
Nuclear Texture

scholarly journals Aquaglyceroporin-3’s Expression and Cellular Localization Is Differentially Modulated by Hypoxia in Prostate Cancer Cell Lines

Cells ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 838
Author(s):  
Andreia de Almeida ◽  
Dimitris Parthimos ◽  
Holly Dew ◽  
Oliver Smart ◽  
Marie Wiltshire ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cell Lines ◽  
Crucial Role ◽  
Cellular Localization ◽  
Texture Features ◽  
Stress Factors ◽  
Adaptation To Hypoxia ◽  
Machine Learning Classification ◽  
Hypoxia Exposure ◽  
Pc3 Cells

Aquaporins are required by cells to enable fast adaptation to volume and osmotic changes, as well as microenvironmental metabolic stimuli. Aquaglyceroporins play a crucial role in supplying cancer cells with glycerol for metabolic needs. Here, we show that AQP3 is differentially expressed in cells of a prostate cancer panel. AQP3 is located at the cell membrane and cytoplasm of LNCaP cell while being exclusively expressed in the cytoplasm of Du145 and PC3 cells. LNCaP cells show enhanced hypoxia growth; Du145 and PC3 cells display stress factors, indicating a crucial role for AQP3 at the plasma membrane in adaptation to hypoxia. Hypoxia, both acute and chronic affected AQP3′s cellular localization. These outcomes were validated using a machine learning classification approach of the three cell lines and of the six normoxic or hypoxic conditions. Classifiers trained on morphological features derived from cytoskeletal and nuclear labeling alongside corresponding texture features could uniquely identify each individual cell line and the corresponding hypoxia exposure. Cytoskeletal features were 70–90% accurate, while nuclear features allowed for 55–70% accuracy. Cellular texture features (73.9% accuracy) were a stronger predictor of the hypoxic load than the AQP3 distribution (60.3%).

scholarly journals Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaofu Huang ◽  
Ming Chen ◽  
Peizhong Liu ◽  
Yongzhao Du
Keyword(s):  
Prostate Cancer ◽  
Early Detection ◽  
Markov Random Fields ◽  
Transrectal Ultrasound ◽  
Ultrasound Image ◽  
Texture Features ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Malignant Tumours ◽  
Data Set

Prostate cancer is one of the most common cancers in men. Early detection of prostate cancer is the key to successful treatment. Ultrasound imaging is one of the most suitable methods for the early detection of prostate cancer. Although ultrasound images can show cancer lesions, subjective interpretation is not accurate. Therefore, this paper proposes a transrectal ultrasound image analysis method, aiming at characterizing prostate tissue through image processing to evaluate the possibility of malignant tumours. Firstly, the input image is preprocessed by optical density conversion. Then, local binarization and Gaussian Markov random fields are used to extract texture features, and the linear combination is performed. Finally, the fused texture features are provided to SVM classifier for classification. The method has been applied to data set of 342 transrectal ultrasound images obtained from hospitals with an accuracy of 70.93%, sensitivity of 70.00%, and specificity of 71.74%. The experimental results show that it is possible to distinguish cancerous tissues from noncancerous tissues to some extent.

scholarly journals Association analysis between quantitative MRI features and hypoxia-related genetic profiles in prostate cancer: a pilot study

2019 ◽  
Vol 92 (1104) ◽  
pp. 20190373 ◽  
Author(s):  
Yu Sun ◽  
Scott Williams ◽  
David Byrne ◽  
Simon Keam ◽  
Hayley M. Reynolds ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Pilot Study ◽  
Glucose Transporter ◽  
Pearson Correlation ◽  
Texture Features ◽  
Quantitative Mri ◽  
Imaging Features ◽  
Selective Treatment ◽  
Mri Features ◽  
Genetic Profiles

Objective: To investigate the association between multiparametric MRI (mpMRI) imaging features and hypoxia-related genetic profiles in prostate cancer. Methods: In vivo mpMRI was acquired from six patients prior to radical prostatectomy. Sequences included T2 weighted (T2W) imaging, diffusion-weighted imaging, dynamic contrast enhanced MRI and blood oxygen-level dependent imaging. Imaging data were co-registered with histology using three-dimensional deformable registration methods. Texture features were extracted from T2W images and parametric maps from functional MRI. Full transcriptome genetic profiles were obtained using next generation sequencing from the prostate specimens. Pearson correlation coefficients were calculated between mpMRI data and hypoxia-related gene expression levels. Results were validated using glucose transporter one immunohistochemistry (IHC). Results: Correlation analysis identified 34 candidate imaging features (six from the mpMRI data and 28 from T2W texture features). The IHC validation showed that 16 out of the 28 T2W texture features achieved weak but significant correlations (p < 0.05). Conclusions: Weak associations between mpMRI features and hypoxia gene expressions were found. This indicates the potential use of MRI in assessing hypoxia status in prostate cancer. Further validation is required due to the low correlation levels. Advances in knowledge: This is a pilot study using radiogenomics approaches to address hypoxia within the prostate, which provides an opportunity for hypoxia-guided selective treatment techniques.

scholarly journals Quantitative nuclear texture features analysis confirms WHO classification 2004 for lung carcinomas

2006 ◽  
Vol 19 (3) ◽  
pp. 453-459 ◽  
Author(s):  
Katharina Schmid ◽  
Nina Angerstein ◽  
Silvana Geleff ◽  
Andreas Gschwendtner
Keyword(s):  
Who Classification ◽  
Texture Features ◽  
Lung Carcinomas ◽  
Nuclear Texture
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