• Journal of Biomedical Engineering Research
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• v.44 no.1
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• pp.25-32
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• 2023

Recognizing the size and location of prostate cancer is critical for prostate cancer diagnosis, treatment, and predicting prognosis. This paper proposes a model to classify the tumor region and normal tissue with cross-sectional visual images of prostatectomy tissue. We used specimen images of 44 prostate cancer patients who received prostatectomy at Gachon University Gil Hospital. A total of 289 prostate slice images consist of 200 slices including tumor region and 89 slices not including tumor region. Images were divided based on the presence or absence of tumor, and a total of 93 features from each slice image were extracted using Radiomics: 18 first order, 24 GLCM, 16 GLRLM, 16 GLSZM, 5 NGTDM, and 14 GLDM. We compared feature selection techniques such as LASSO, ANOVA, SFS, Ridge and RF, LR, SVM classifiers for the model's high performances. We evaluated the model's performance with AUC of the ROC curve. The results showed that the combination of feature selection techniques LASSO, Ridge, and classifier RF could be best with an AUC of 0.99±0.005.

• Journal of Digital Contents Society
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• v.16 no.5
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• pp.717-725
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• 2015

Prostate images have been used in the diagnosis of prostate using TRUS images being relatively cheap. Ultrasound images are recorded with 3 dimension and one diagnostic exam is made with a number of the images. A doctor can see 2 dimensional images on the monitor sequentially and 3 dimensional ones to diagnose a disease. To display the images, 2-d images are used with raw 2-d ones, but 3-d images need to be segmented by the prostates and their backgrounds to be seen from different angles and with cut images of inner side. Especially on detecting the boundary, the ones in the middle of all images are easy to find the boundary but the base and apex of the images are hard to do it since there are lots of uncertain boundary. So, in this paper we propose the method that applies an average shape model and detects the boundary, and shows its superiority compared to the existing methods with experiments.

• Journal of Life Science
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• v.32 no.7
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• pp.532-541
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• 2022

Obesity induced by high-fat diet (HFD) is verified as a strong risk factor and negative prognostic factor for prostate cancer in several genetically engineered mice although it was not examined in the normal mice. To investigate whether HFD-induced obesity can affect the development and progression of cancer in the prostate of normal mice, alterations in the weight and histological structure of the prostate as well as the expression of cancer-related proteins were analyzed in obese C57BL/6N mice fed with 60% HFD for 16 weeks. First, HFD-induced obesity, including an increase in organ weight, body weight, fat accumulation, and serum lipid profile, was successfully induced in C57BL/6N mice after HFD treatment. The total weight of the prostate significantly increased HFD-induced obesity in the model mice compared with the control group. Among the four lobes of the prostate, the weight of the ventral prostate (VP) and anterior prostate (AP) were higher in HFD-induced obesity model mice than in the control group, although the weights of the lateral prostate (DLP) and seminal vesicle (SV) were constantly maintained. In addition, the incidences of hyperplasia and non-hodgkin's lymphoma (NHL) in the histological structure were remarkably increased in HFD-induced obesity model mice, while the epithelial thickness was higher in the same group. A significant increase in the phosphorylation levels of key proteins in the AKT (protein kinase B) signaling pathway was detected in HFD-induced obesity model mice. Therefore, these results suggest that HFD-induced obesity can promote hyperplasia and NHL in the prostates of C57BL/6N mice through the activation of the AKT signaling pathway.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

• Journal of Computational Design and Engineering
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• v.1 no.4
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• pp.272-288
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• 2014

This paper presents a two-step, semi-automated method for reconstructing a three-dimensional (3D) shape of the prostate from a 3D transrectal ultrasound (TRUS) image. While the method has been developed for prostate ultrasound imaging, it can potentially be applicable to any other organ of the body and other imaging modalities. The proposed method takes as input a 3D TRUS image and generates a watertight 3D surface model of the prostate. In the first step, the system lets the user visualize and navigate through the input volumetric image by displaying cross sectional views oriented in arbitrary directions. The user then draws partial/full contours on selected cross sectional views. In the second step, the method automatically generates a watertight 3D surface of the prostate by fitting a deformable spherical template to the set of user-specified contours. Since the method allows the user to select the best cross-sectional directions and draw only clearly recognizable partial or full contours, the user can avoid time-consuming and inaccurate guesswork on where prostate contours are located. By avoiding the usage of noisy, incomprehensible portions of the TRUS image, the proposed method yields more accurate prostate shapes than conventional methods that demand complete cross-sectional contours selected manually, or automatically using an image processing tool. Our experiments confirmed that a 3D watertight surface of the prostate can be generated within five minutes even from a volumetric image with a high level of speckles and shadow noises.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1937-1943
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• 2013

Altered expression or function of manganese superoxide dismutase (MnSOD) has been shown to be associated with cancer risk but assessment of gene polymorphisms has resulted in inconclusive data. Here a search of published data was made and 22 studies were recruited, covering 20,025 case and control subjects, for meta-analyses of the association of MnSOD polymorphisms with the risk of prostate, esophageal, and lung cancers. The data on 12 studies of prostate cancer (including 4,182 cases and 6,885 controls) showed a statistically significant association with the risk of development in co-dominant models and dominant models, but not in the recessive model. Subgroup analysis showed there was no statistically significant association of MnSOD polymorphisms with aggressive or nonaggressive prostate cancer in different genetic models. In addition, the data on four studies of esophageal cancer containing 620 cases and 909 controls showed a statistically significant association between MnSOD polymorphisms and risk in all comparison models. In contrast, the data on six studies of lung cancer with 3,375 cases and 4,050 controls showed that MnSOD polymorphisms were significantly associated with the decreased risk of lung cancer in the homozygote and dominant models, but not the heterozygote model. A subgroup analysis of the combination of MnSOD polymorphisms with tobacco smokers did not show any significant association with lung cancer risk, histological type, or clinical stage of lung cancer. The data from the current study indicated that the Ala allele MnSOD polymorphism is associated with increased risk of prostate and esophageal cancers, but with decreased risk of lung cancer. The underlying molecular mechanisms warrant further investigation.

• Journal of Biomedical Engineering Research
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• v.23 no.6
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• pp.467-475
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• 2002

Hyperthermia using transrectal thermal probes has been used for a noninvasive treatment of prostate diseases. However it is known that heating the rectal wall at excessively high temperature can lead to destruction of the rectal mucous membrane. and it is difficult to maintain an optimum temperature over the entire prostate. Thus, a more accurate understanding of the heat transfer mechanism between prostate and hyperthermia system is needed Numerical analysis was performed to investigate how the cold/warm stimulations on the prostate surface affect the temperature distribution in the prostate model. The general purpose software "FLUENT" was used for obtaining a finite volume solution to the unsteady conduction equation and to calculate the time-varying temperature in the prostate. Effects of the warm/cold stimulations and the stimulation frequency on the temperature distribution were simulated. and we visualized how hyperthermia affected the inside of the prostate. It was found that the effect of hyperthermia by using a typical heating method is limited due to the low thermal conductivity of the prostate. Consecutive repetitions of warm and cold stimulations were considered to provide the thermal irritations inside a prostate. The effects of temperature difference and duration of warm/cold stimulations were investigated, and basic data for the optimum period and effective patterns of stimulations were obtained. A simplified bioheat equation was also solved to describe effects of the blood flow on the blood-tissue heat transfer. The effect of blood flow was not dominant compared to that of warm/cold stimulations. These results might be used as data for design of prostate treating probe, prostatic therapy and thermal stimulation effects on the prostate.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1486-1495
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• 2020

The analysis of digital microscopy images plays a vital role in computer-aided diagnosis (CAD) and prognosis. The main purpose of this paper is to develop a machine learning technique to predict the histological grades in prostate biopsy. To perform a multiclass classification, an AI-based deep learning algorithm, a multichannel convolutional neural network (MCCNN) was developed by connecting layers with artificial neurons inspired by the human brain system. The histological grades that were used for the analysis are benign, grade 3, grade 4, and grade 5. The proposed approach aims to classify multiple patterns of images extracted from the whole slide image (WSI) of a prostate biopsy based on the Gleason grading system. The Multichannel Convolution Neural Network (MCCNN) model takes three input channels (Red, Green, and Blue) to extract the computational features from each channel and concatenate them for multiclass classification. Stain normalization was carried out for each histological grade to standardize the intensity and contrast level in the image. The proposed model has been trained, validated, and tested with the histopathological images and has achieved an average accuracy of 96.4%, 94.6%, and 95.1%, respectively.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.12
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• pp.6245-6250
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• 2012

Background: With increase in life expectancy, adoption of newer lifestyles and screening using prostate specific antigen (PSA), the incidence of prostate cancer is on rise. Globally prostate cancer is the second most frequently diagnosed cancer and sixth leading cause of cancer death in men. The present communication makes an attempt to analyze the time trends in incidence for different age groups of the Indian population reported in different Indian registries using relative difference and regression approaches. Materials and Methods: The data published in Cancer Incidence in Five Continents for various Indian registries for different periods and/or publications by the individual registries served as the source materials. Trends were estimated by computing the mean annual percentage change (MAPC) in the incidence rates using the relative difference between two time periods (latest and oldest) and also by estimation of annual percentage change (EAPC) by the Poisson regression model. Results: Age adjusted incidence rates (AAR) of prostate cancer for the period 2005-2008 ranged from 0.8 (Manipur state excluding Imphal west) to 10.9 (Delhi) per $10^5$ person-years. Age specific incidence rates (ASIR) increased in all PBCRs especially after 55 years showing a peak incidence at +65 years clearly indicating that prostate cancer is a cancer of the elderly. MAPC in crude incidence rate(CR) ranged from 0.14 (Ahmedabad) to 8.6 (Chennai). Chennai also recorded the highest MAPC of 5.66 in ASIR in the age group of 65+. Estimated annual percentage change (EAPC) in the AAR ranged from 0.8 to 5.8 among the three registries. Increase in trend was seen in the 55-64 year age group cohort in many registries and in the 35-44 age group in Metropolitan cities such as Delhi and Mumbai. Conclusions: Several Indian registries have revealed an increasing trend in the incidence of prostate cancer and the mean annual percentage change has ranged from 0.14-8.6.

• Bulletin of the Korean Chemical Society
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• v.33 no.1
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• pp.111-114
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• 2012

Androgen receptor (AR) is ligand-inducible nuclear hormone receptor which has been focused on key molecular target in growth and progression of prostate cancer. We synthesized a series of 4-aryl 2-substituted aniline-thiazole analogs and evaluated their anti-cancer activity in AR-dependent human prostate cancer LNCap cells. Among them, the compound 6 inhibited the tumor growth in LNCap-inoculated xenograft model.