scholarly journals Ultrasonic tissue characterization-assessment of prostate tissue malignancy in vivo using a conventional classifier based tissue classification approach and elastographic imaging

2002 ◽  
Author(s):  
A. Lorenz ◽  
A. Pesavento ◽  
U. Scheipers ◽  
H. Ermert ◽  
M. Garcia-Schurmann ◽  
...  
Keyword(s):  
Tissue Characterization ◽  
Prostate Tissue ◽  
Tissue Classification ◽  
Classification Approach

scholarly journals High-Fat Diet Induced Gut Microbiota Alterations Associating With Ghrelin/Jak2/Stat3 Up-Regulation to Promote Benign Prostatic Hyperplasia Development

2021 ◽  
Vol 9 ◽  
Author(s):  
Meng Gu ◽  
Chong Liu ◽  
TianYe Yang ◽  
Ming Zhan ◽  
Zhikang Cai ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Benign Prostatic Hyperplasia ◽  
Gut Microbiota ◽  
High Fat Diet ◽  
Prostatic Hyperplasia ◽  
Prostate Tissue ◽  
High Fat ◽  
Ghrelin Receptor

The role of high-fat diet (HFD) induced gut microbiota alteration and Ghrelin as well as their correlation in benign prostatic hyperplasia (BPH) were explored in our study. The gut microbiota was analyzed by 16s rRNA sequencing. Ghrelin levels in serum, along with Ghrelin and Ghrelin receptor in prostate tissue of mice and patients with BPH were measured. The effect of Ghrelin on cell proliferation, apoptosis, and induction of BPH in mice was explored. Our results indicated that BPH mice have the highest ratio of Firmicutes and Bacteroidetes induced by HFD, as well as Ghrelin level in serum and prostate tissue was significantly increased compared with control. Elevated Ghrelin content in the serum and prostate tissue of BPH patients was also observed. Ghrelin promotes cell proliferation while inhibiting cell apoptosis of prostate cells. The effect of Ghrelin on enlargement of the prostate was found almost equivalent to that of testosterone propionate (TP) which may be attenuated by Ghrelin receptor antagonist YIL-781. Ghrelin could up-regulate Jak2/pJak2/Stat3/pStat3 expression in vitro and in vivo. Our results suggested that Gut microbiota may associate with Ghrelin which plays an important role in activation of Jak2/Stat3 in BPH development. Gut microbiota and Ghrelin might be pathogenic factors for BPH and could be used as a target for mediation.

Impact of Intramural Thrombus in Coronary Arteries on the Accuracy of Tissue Characterization by In Vivo Intravascular Ultrasound Radiofrequency Data Analysis

2008 ◽  
Vol 101 (8) ◽  
pp. 1079-1083 ◽  
Author(s):  
Kenya Nasu ◽  
Etsuo Tsuchikane ◽  
Osamu Katoh ◽  
D. Geoffrey Vince ◽  
Pauliina M. Margolis ◽  
...  
Keyword(s):  
Data Analysis ◽  
Intravascular Ultrasound ◽  
Coronary Arteries ◽  
Tissue Characterization

scholarly journals Multiclass Classification of Hepatic Anomalies with Dielectric Properties: From Phantom Materials to Rat Hepatic Tissues

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 530 ◽  
Author(s):  
Tuba Yilmaz
Keyword(s):  
Logistic Regression ◽  
Dielectric Property ◽  
Measurement Error ◽  
Tissue Characterization ◽  
Linear Models ◽  
Hepatic Tissue ◽  
Data Set ◽  
Property Data

Open-ended coaxial probes can be used as tissue characterization devices. However, the technique suffers from a high error rate. To improve this technology, there is a need to decrease the measurement error which is reported to be more than 30% for an in vivo measurement setting. This work investigates the machine learning (ML) algorithms’ ability to decrease the measurement error of open-ended coaxial probe techniques to enable tissue characterization devices. To explore the potential of this technique as a tissue characterization device, performances of multiclass ML algorithms on collected in vivo rat hepatic tissue and phantom dielectric property data were evaluated. Phantoms were used for investigating the potential of proliferating the data set due to difficulty of in vivo data collection from tissues. The dielectric property measurements were collected from 16 rats with hepatic anomalies, 8 rats with healthy hepatic tissues, and in house phantoms. Three ML algorithms, k-nearest neighbors (kNN), logistic regression (LR), and random forests (RF) were used to classify the collected data. The best performance for the classification of hepatic tissues was obtained with 76% accuracy using the LR algorithm. The LR algorithm performed classification with over 98% accuracy within the phantom data and the model generalized to in vivo dielectric property data with 48% accuracy. These findings indicate first, linear models, such as logistic regression, perform better on dielectric property data sets. Second, ML models fitted to the data collected from phantom materials can partly generalize to in vivo dielectric property data due to the discrepancy between dielectric property variability.

scholarly journals Numerical simulation modeling of the irreversible electroporation treatment zone for focal therapy of prostate cancer, correlation with whole-mount pathology and T2-weighted MRI sequences

2019 ◽  
Vol 11 ◽  
pp. 175628721985230 ◽  
Author(s):  
Matthijs J. Scheltema ◽  
Tim J. O’Brien ◽  
Willemien van den Bos ◽  
Daniel M. de Bruin ◽  
Rafael V. Davalos ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Irreversible Electroporation ◽  
Prostate Tissue ◽  
Human Prostate ◽  
Ablation Zone ◽  
Electrical Field ◽  
Whole Mount ◽  
Human Prostate Tissue ◽  
Mri Sequences

Background: At present, it is not possible to predict the ablation zone volume following irreversible electroporation (IRE) for prostate cancer (PCa). This study aimed to determine the necessary electrical field threshold to ablate human prostate tissue in vivo with IRE. Methods: In this prospective multicenter trial, patients with localized PCa were treated with IRE 4 weeks before their scheduled radical prostatectomy. In 13 patients, numerical models of the electrical field were generated and compared with the ablation zone volume on whole-mount pathology and T2-weighted magnetic resonance imaging (MRI) sequences. Volume-generating software was used to calculate the ablation zone volumes on histology and MRI. The electric field threshold to ablate prostate tissue was determined for each patient. Results: A total of 13 patients were included for histological and simulation analysis. The median electrical field threshold was 550 V/cm (interquartile range 383–750 V/cm) for the software-generated histology volumes. The median electrical field threshold was 500 V/cm (interquartile range 386–580 V/cm) when the ablation zone volumes were used from the follow-up MRI. Conclusions: The electrical field threshold to ablate human prostate tissue in vivo was determined using whole-mount pathology and MRI. These thresholds may be used to develop treatment planning or monitoring software for IRE prostate ablation; however, further optimization of simulation methods are required to decrease the variance that was observed between patients.

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