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Thermoacoustic Computed Tomography of the Breast
Published in Lihong V. Wang, Photoacoustic Imaging and Spectroscopy, 2017
We were able to visualize most cysts seen on ultrasound quite well. Fibroadenomas were not visualized except in the case of a fibroadenoma associated with atypical hyperplasia. However, only four of seven cancers were visualized. We attribute the lack of visualization of these three cancers to inadequate image quality, technical factors, such as motion, and an inadequate field of view for the TCT scan. Future improvements to the TCT scanner are expected to overcome these technical difficulties.
BCRAM: A Social-Network-Inspired Breast Cancer Risk Assessment Model
Published in Huansheng Ning, Liming Chen, Ata Ullah, Xiong Luo, Cyber-Enabled Intelligence, 2019
Ali Li, Rui Wang, Liyuan Liu, Lei Xu, Fei Wang, Fei Chang, Lixiang Yu, Yujuan Xiang, Fei Zhou, Zhigang Yu
We firstly give an introduction to the compared models. The Gail model uses age, age at menarche, age at first parturition, number of previous biopsies and number of first-degree relatives with breast cancer to assess breast cancer risk. The Gail model has been available for nearly 30 years and has been modified and applied to real patients. The model has reduced the mortality of breast cancer in the United States. In the medical field, the duration of use of the Gail model is long, and it is a classic model that has wide application. Many models have been put forward since the Gail model. Here, we select a modified version of the Gail model [13] and the Tyrer-Cuzick model for comparison with our model. The Tyrer-Cuzick model has been certified to have better assessment ability [42]. Compared with the Gail model, the modified model added three modifiable risk factors (alcohol consumption, leisure physical activity and body mass index). The analysis and data give perspective on the potential reductions in absolute breast cancer risk from preventative strategies based on lifestyle changes. The relative risk model (Rrm) uses the following rrfs: age at menarche, number of previous breast biopsies, number of first-degree female relatives with breast cancer, age at first live birth (Age1st), body mass index for women aged 50 years and older, body mass index for women younger than 50 years in age, alcohol consumption in three categories (never, current and former for women who stopped drinking at least 1 year before the interview), occupational physical activity at ages 30–39 years, leisure-time physical activity at ages 30–39 years, education level and age at interview. The Tyrer-Cuzick model incorporates the BRCA genes, a low penetrance gene and personal risk factors. The personal risk factors include age at menarche, age at menopause, age at first parturition, height, BMI, atypical hyperplasia and lobular carcinoma in situ. The last model is one we have put forward in earlier work [9,10]. Here, the model is marked as the Liu-Yu model.
Breast Cancer and Machine Learning
Published in K. Gayathri Devi, Mamata Rath, Nguyen Thi Dieu Linh, Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches, 2020
Atapaka Thrilok Gayathri, Samuel Theodore Deepa
Factors that can be considered to be the reason for high chances of breast cancer can be: Feminine. According to research, women are more commonly affected when compared to men.Age Factor. Increasing lifespan of an individual is one factor.A previous medical history of breast ailments. Breast conditions such as LCIS or atypical hyperplasia of the breast, can be another reason.A chance of occurrence in another breast. Breast cancer is the kind of disease that spreads, so there is a chance of occurrence in the other side, too.Earlier generations of family members who had breast cancer. Hereditary is considered to be one of the factors for breast cancer.Inheritance of genes from family members considered as risk. Genes that are inherited from parent to child are another risk factor. The most commonly known genes that are inherited are BRCA1 and BRCA2. These genes increased chances of breast cancer and other cancers.Radiation treatment. If any such kind of treatments to the chest were undergone at a young age, they can be considered as a risk factor.Diet factor. Obesity increases the chances of disease.Childhood onset of menstruation. If a woman began her periods before the age of 12, she has greater chances of breast cancer.Menopause. Menopause increases the chances of breast cancer.Giving birth to a child at an older age. When a woman delivers a child after the age of 30 she is more at risk for breast cancer.Less chances of pregnancy. Women who have less chances of pregnancy have a greater chances of breast cancer.Postmenopausal hormone therapy treatment. A woman who is under the treatment of hormone therapy with the combination of estrogens and progesterone to overcome the problems of periods increases her risk of developing breast cancer.Alcohol intake. Alcohol intake increases the chances of breast cancer [3].
Methyl-tert-butyl ether (MTBE): integration of rat and mouse carcinogenicity data with mode of action and human and rodent bioassay dosimetry and toxicokinetics indicates MTBE is not a plausible human carcinogen
Published in Journal of Toxicology and Environmental Health, Part B, 2022
James S. Bus, B. Bhaskar Gollapudi, Gordon C. Hard
In 2012, Melnick et al. (2012) relied on an analysis of 60 National Toxicology Program (NTP) studies to challenge the conclusion that the exacerbated CPN was a MOA contributing to chemically induced rat kidney toxicity and tumorigenicity. However, a critical shortcoming of that analysis was that it did not incorporate the key importance of the CPN histopathological grading system in examining the relationship of this constellation of lesions to kidney tumor outcomes. In order to reveal an association between exacerbated CPN and renal tubule tumor occurrence, a more sensitive grading system than the conventional grade 0–4 scale (based upon % kidney affected by CPN) used for nephropathy in the NTP chronic studies is required. The negative results of Melnick et al. (2012) substantiate this point. Hard, Betz, and Seely (2012) found that using an extended grading system of 0–8 grades based on lesion progression, and particularly where grade 8 was end-stage advanced CPN, is associated with an elevated incidence of renal tubule adenoma and its precursor, atypical hyperplasia (ATH). The kidneys of male and female control F344 rats (2391 rats in total) from 24 chronic studies in the NTP Archives were re-examined histopathologically and checked for ATH, renal tubule tumors, and severity grade of CPN using the 0–8 scale. In 1236 male control rats, there were 43 ATH and 26 adenomas (no carcinomas) and all but two of these lesions (which were both grade 6) occurred in rats with grade 7 or 8 CPN. The incidence of ATH in male rats with grades 7 and 8 CPN was 7.2%, and with adenoma, 4.3%. Although the incidence in rats with advanced grades of CPN and incidence of these lesions was lower in the 1155 control female rats (as expected due to the known gender difference for CPN occurrence in rats), the distribution of ATH and adenoma showed a similar pattern of distribution according to severity grades of CPN as in the male rats, namely a 2.8% incidence of ATH and 1.4% incidence of adenoma in rats with grades 7 and 8 CPN. There was one carcinoma at grade 5 CPN in the female rats, but this lesion was not associated with CPN-affected tissue and was considered to be a true spontaneous tumor.