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Thermal Therapy Applications of Electromagnetic Energy
Published in Ben Greenebaum, Frank Barnes, Biological and Medical Aspects of Electromagnetic Fields, 2018
P.R. Stauffer, D.B. Rodrigues, D. Haemmerich, C.-K. Chou
Surgical resection is the only curative treatment for liver cancer but only 10–20% of patients with liver tumors can be treated by surgery [210]. For this reason, minimally invasive RFA and MWA have become widely used cancer therapies for the liver [211,212]. Since tumor RFA was introduced more than 20 years ago [213], there has been extensive work on liver tumor RFA [214,215], and only more recently with MWA [216]. Studies of over 3000 RFA-treated patients have shown the efficacy of percutaneous RFA for small (<3 cm) primary liver tumors. Complete local response averages 70–75% in tumors between 3 and 5 cm, but drops to <25% in larger tumors. With successful ablation, 5-year survival rates of 51–76% have been reported [211]. This pattern of excellent local control for small primary liver tumors with significant long-term nonlocal recurrence is also true in RFA of hepatic metastases. Livraghi et al. concluded that RF ablation is a relatively low-risk procedure for focal liver tumor treatment based on results with 3,554 lesions [217]. However, to date there are no randomized prospective controlled studies comparing liver RFA to standard surgical resection in any population.
Hepatoprotective Marine Phytochemicals
Published in Se-Kwon Kim, Marine Biochemistry, 2023
BR Annapoorna, S Vasudevan, K Sindhu, V Vani, V Nivya, VP Venkateish, P Madan Kumar
The epidemiology of liver cancer is influenced by some of the key risk factors involving chronic infection with hepatitis B virus (HBV) or hepatitis C virus (HCV), aflatoxin-contaminated foods, heavy alcohol intake, excess body weight, type 2 diabetes, and smoking (Mohamed-Alaa-Eldeen H. Mohamed 2018). The major risk factors vary from region to region. In most high-risk HCC countries (China, the Republic of Korea, sub-Saharan Africa), the key determinants are chronic HBV infection, aflatoxin exposure, or both, whereas, in other countries (Japan, Italy, Egypt), HCV infection is likely the predominant cause. In Mongolia, HBV and HCV and coinfections of HBV carriers with HCV or hepatitis delta viruses, as well as alcohol consumption, all contribute to the high burden (Chimed et al. 2017). Although risk factors tend to vary substantially by geographic region, major risk factors for cholangiocarcinoma include liver flukes (e.g., in the northeastern region of Thailand, where Opisthorchis viverrini is endemic; Prueksapanich et al. 2018) metabolic conditions (including obesity, diabetes, and nonalcoholic fatty liver disease), excess alcohol consumption, and HBV or HCV infection (Welzel et al. 2007; Petrick et al. 2020). HBV infection and HCV infection account for 56% and 20% of liver cancer deaths worldwide, respectively (Donato et al. 2001). The risk of liver cancer due to aflatoxin B1 consumption is higher in India. However, in comparison to the high-aflatoxin-incidence countries such as China and Taiwan, the aflatoxin level among Indians are relatively low. In conclusion, HBV is the major risk factor for HCC in India followed by HCV (Asim et al. 2013).
Histopathological Cancer Detection Using CNN
Published in Meenu Gupta, Rachna Jain, Arun Solanki, Fadi Al-Turjman, Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective, 2021
Soham Taneja, Rishika Garg, Preeti Nagrath, Bhawna Gupta
Hepatic (Liver) Cancer – This cancer begins in the cells of the liver. Its symptoms also tend to be physical, and it usually requires complex procedures as treatment. Like lung cancer, this cancer also spreads to other organs fast due to the proximity, decreasing the chances of survival. Therefore, like all other cancers, early detection is key. We went through some of the research work done with respect to this cancer [20] and expect our method to fare well.
Risk of death from liver cancer in relation to long-term exposure to fine particulate air pollution in Taiwan
Published in Journal of Toxicology and Environmental Health, Part A, 2023
Shang-Shyue Tsai, Chun-Ta Hsu, ChunYuh Yang
Exposure to concentrated levels of ambient PM2.5 were found to induce a nonalcoholic steatohepatitis-like phenotype as well as liver fibrosis (Sui et al. 2022; Zheng et al. 2013). Nonalcoholic steatohepatitis is an increasingly important cause of liver cancer especially when there is also fibrosis present (Li et al. 2018; Wong, Nguyen, and Lim 2016). In rats, intragastric exposure to diesel exhaust particles was noted to induce oxidative stress associated with DNA damage, bulky DNA adducts formation, induction of apoptosis and the upregulation of hepatic DNA repair (Danielsen et al. 2008; Dybdahl et al. 2003). In humans, long-term exposure to ambient air pollution has been associated with upregulation of liver damage biomarkers such as ALT activity (Kim et al. 2015; Markevych et al. 2013; Pan et al. 2016). ALT and other biomarkers of liver function and inflammation such as C-reactive protein (CRP) and interleukin-6 (IL-6) were reported to predict occurrence of liver cancer risk (Aleksandrova et al. 2014; Stepien et al. 2016). Therefore, exposure to ambient PM2.5 may conceivably contribute to development of liver cancer.
A review of medical image detection for cancers in digestive system based on artificial intelligence
Published in Expert Review of Medical Devices, 2019
Jiangchang Xu, Mengjie Jing, Shiming Wang, Cuiping Yang, Xiaojun Chen
Liver cancer detection is mainly based on the detection of the presence of cancers from CT/MRI/US image. Machine learning methods are used for early-stage detection. Gatos et al. [14] combined wavelet transformation with Markov random field for lesion segmentation. SVM is used to classify features to detect liver cancers on US images, the highest accuracy of which being 90.3%. With the development of CNN, many experts used it for cancer detection. Hoogi et al. [80] and Todoroki et al. [81] used CNN to detect cancers on liver CT images automatically. Ben-Cohen et al. [82] combined FCN and superpixel sparse classification to detect liver CT images automatically. The true positive rate was 94.6% in threefold cross validation. Ben-Cohen et al. [83] also proposed a method combining FCN with Generative Adversarial Network (GAN) for the detection of liver cancers in PET images, and the average false-positive rate per case dropped from 2.9 to 2.1, down 28%.
Ontology-Based decision tree model for prediction of fatty liver diseases
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Seyed Yashar Banihashem, Saman Shishehchi
Non-alcoholic fatty liver disease (NAFLD) is characterized by fatty change of hepatocytes in patients with no history of overdrinking alcohol (Chalasani et al. 2012; Nikkhajoei et al. 2016). Some studies reveal that 15% to 50% of liver cancer incidence rates are caused by NAFLD rather than hepatitis B, hepatitis C, or alcohol consumption in the developed countries (Duan et al. 2014). So, early detection of NAFLD can prevent the disease conditions to worsen and avoid morbidity and mortality. As a consequence of the modern lifestyle and the increasing senior population, fatty liver and related diseases have been rising (Nikkhajoei et al. 2016). In addition to diagnostic expenses and medical costs, this disease causes loss of productivity for patients and reduces health-related quality of life. Nowadays, detection application helps doctors to discover diseases easier and make the treatment faster. A large volume of data in the healthcare industry makes data collection and data interpreter difficult. Data mining (Han et al. 2011) is a more applicable field of computer science with various algorithms to analyse big data sets in many industries, including the healthcare industry (dos Santos et al. 2019). Machine learning (ML) algorithms (Jordan and Mitchell 2015) are data-mining tools that refer to various techniques based on models for classification and the prediction of new data. Data mining algorithms are different and must be chosen correctly based on the problems. The algorithms are using for two main reasons, clustering and classification. By referring to the past researches (Öztürk et al. 2006; Kotsiantis et al. 2008; Phyu 2009) show that, It is possible to use both clustering and classification methods for estimating systems. In our study we used decision tree to classify patient or non-patient.