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Big Data in Prostate Cancer
Published in Ayman El-Baz, Jasjit S. Suri, Big Data in Multimodal Medical Imaging, 2019
Islam Reda, Ashraf Khalil, Mohammed Ghazal, Ahmed Shalaby, Mohammed Elmogy, Ahmed Aboelfetouh, Ali Mahmoud, Mohamed Abou El-Ghar, Ayman El-Baz
Although many researchers have investigated the different causes of prostate cancer, only an indistinct list of risk factors has been recognized. Those risk factors include, for example, the family history, genetic factors, race, and body mass index (BMI) [4]. The likelihood to develop prostate cancer for a man with a first degree relative who suffers from prostate cancer was found to be twice as high as the likelihood to develop prostate cancer for a man with no relatives affected [5,6]. According to the research conducted by Agalliu et al. [7] on 979 cases of prostate cancer, men who suffer from protein-truncating mutations BRCA2 genes have been associated with high Gleason score prostate cancer. It was shown that the race of a person has an effect on the probability of developing prostate cancer. African Americans have a 1.6 times higher chance of developing prostate cancer than European Americans [8]. Rodriguez et al. [9] investigated the association between BMI and weight change and the incidence of prostate cancer. They found that there was a positive correlation between BMI and the risk of aggressive prostate cancer.
Assessment of Quercetin Isolated from Enicostemma Littorale Against Few Cancer Targets: An in Silico Approach
Published in A. K. Haghi, Ana Cristina Faria Ribeiro, Lionello Pogliani, Devrim Balköse, Francisco Torrens, Omari V. Mukbaniani, Applied Chemistry and Chemical Engineering, 2017
Prostate cancer is a form of cancer that develops in the prostate gland of the male reproductive system. Most prostate cancers are slow growing; however, there are cases of aggressive prostate cancers. The cancer cells may metastasize (spread) from the prostate to other parts of the body, particularly the bones and lymph nodes. Prostate cancer may cause pain, difficulty in urinating, problems during sexual intercourse or erectile dysfunction. Other symptoms can potentially develop during later stages of the disease.
Developing a Decision on the Type of Prostate Cancer Using FAHP
Published in Ali Emrouznejad, William Ho, Fuzzy Analytic Hierarchy Process, 2017
All types of prostate cancer typically share almost common symptoms. The most common symptom of prostate cancer is difficulty in urinating. The prostate is located underneath the bladder and surrounds a portion of the urethra, a small tube that carries urine from the bladder out of your body. When the prostate becomes enlarged because of cancerous cell growth, the urethra can be pinched, which can lead to urinary problems. It can be difficult to begin urinating, even if the bladder is full. Very often patients with prostate cancer experience a weak flow of urine when urinating. The weak flow of urine may prevent patients from fully emptying the bladder. Certain men with prostate cancer experience sensations of burning or pain while urinating that can be accompanied by blood in the urine (hematuria). They also experience difficulty in starting the urine stream (hesitancy) and urinate excessively at night (nocturia).
Locating and sizing tumor nodules in human prostate using instrumented probing – computational framework and experimental validation
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Antonio Candito, Daniel W. Good, Javier Palacio-Torralba, Steven Hammer, Olufemi Johnson, S. Alan McNeill, Robert L. Reuben, Yuhang Chen
Detection of tumor nodules is the key to early cancer diagnosis. For prostate cancer (PCa), first indications are usually a raised level of prostate-specific antigen (PSA) in the blood, supplemented by digital rectal examination (DRE), which involves palpation of the accessible surface of the prostate through the rectum. There is a continuing clinical need to improve both sensitivity and specificity of simple early screening methods, to better stratify risk for further diagnostic steps such as magnetic resonance imaging (MRI), transrectal ultrasound (TRUS) and biopsy. Recent research has seen increasing interest in instrumenting prostate gland palpation, either for the purpose of enhancing DRE (Kim et al. 2014; Scanlan et al. 2015; Palacio-Torralba et al. 2016) or minimally invasive robot-assisted surgery (Li et al. 2017). The data acquired from such methods are often in the format of force feedback when probing the prostate, either quasi-statically or dynamically, and integrating such strategy of data analysis into DRE procedures has shown promise for improving the effectiveness of early screening for PCa (Hammer et al. 2017). This has been based on observations that cancerous tissue has a higher elastic modulus (Krouskop et al. 1998; Phipps et al. 2005; Carson et al. 2011) than, and different viscoelastic behaviors (Palacio-Torralba et al. 2015; Baghban and Mojra 2018; Yang et al. 2018) from, its healthy counterpart in many tissue types including prostate, breast and pancreas, thus making it possible to detect the tumor nodules based on mechanical measurement.
Automatic pathology of prostate cancer in whole mount slides incorporating individual gland classification
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2019
Sabrina Rashid, Guy Nir, Ladan Fazli, Alexander H. Boag, D. Robert Siemens, S. Larry Goldenberg, Purang Abolmaesumi, Septimiu E. Salcudean
Prostate cancer (PCa) is one of the most frequently diagnosed cancers and ranks high among the total cancer related deaths of men worldwide stat. The usual PCa screening process involves a prostate specific antigen test and/or a digital rectal examination. Anomalies in these tests lead clinicians to conduct prostate biopsy. Examination of the microscopic biopsy specimens by pathologists is required for confirming the diagnosis of malignancy and guiding the treatment (Zhu et al. 2006). In case of localised cancers, surgeons often perform radical prostatectomy (RP) on patients, i.e. surgical removal of the entire prostate. The histopathology slices obtained from the cross section of these ex vivo prostates are termed as whole mount (WM) slides. A typical prostate WM slide can be seen in Figure 1. The black contour is the coarse annotation marked by the pathologist on the slide before digitisation.
Fuzzy model-based sparse clustering with multivariate t-mixtures
Published in Applied Artificial Intelligence, 2023
Wajid Ali, Miin-Shen Yang, Mehboob Ali, Saif Ud-Din
(Prostate cancer Saifi, 2018)) Prostate cancer is second major common type of cancer and fifth leading cause of death among men worldwide and occurs over the age of 70 years (Bray et al., 2018).This kind of cancer starts, when cells in the prostate gland start to grow out of control. The most leading countries in this domain are Australia, America, New Zealand, Norway, Sweden and Ireland (Bray et al., 2018). Here we consider a real prostate cancer data set consists of 100 patients of prostate cancer having eight features namely; radius, texture, perimeter, area, smoothness, compactness, symmetry, fractal dimension and one categorical parameter diagnosis results (benign tumors = 38 and malignant tumors = 68). When FG- Lasso algorithm is applied on the prostate cancer data set, this identified that when we are increasing the value of up to 60, we observed the features like radius, texture, perimeter, and area as irrelevant features. After removing these irrelevant features, we obtain (AR = 0.617) from FG-Lasso, (AR = 0.517) from F-MB-N, and (AR = 0.635) using FCML-T after taking the average of 30 different initializations. When the proposed F-MT-Lasso algorithm is applied to the prostate cancer data set, it has been noticed that 4th feature “area” became irrelevant feature against = 78, and consequently has been discarded. Hence we found that after discardng it, we get better average accuracy rate (AR = 0.807) with 30 different initializations. The comprision of each average accuracy rate, are shown is Table 11 and graphical comparisons are shown in Figure 8. This shows that, the proposed F-MT-Lasso algorithm is more significant and effective for relevant feature selection of the prostate cancer data set.