Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions.
Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions. Biomed Res Int. 2020;2020:4671349 Authors: Dhahri H, Rahmany I, Mahmood A, Al Maghayreh E, Elkilani W Abstract Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative breast lesion diagnosis based on machine-learning algorithms. We used Tabu search to select the most significant features. The evaluation of the feature is based on the dependency degree of each attribute in the rough set. The categorization of reduced features was built using five machine-learning algorithms. The proposed models were applied to the BIDMC-MGH and Wisconsin Diagnostic Breast Cancer datasets. The performance measures of the used models were evaluated owing to five criteria. The top performing models were AdaBoost and logistic regression. Comparisons with others works prove the efficiency of the proposed method for superior diagnosis of breast cancer against the reviewed classification techniques. PMID: 32258124 [PubMed - in process]
According to a study published in theInternational Journal of Cancer, light pollution at night time may increase breast cancer risk by 10% in post-menopausal women.inews
Publication date: 2 June 2020Source: Cell Reports, Volume 31, Issue 9Author(s): Lisa M. Becker, Joyce T. O’Connell, Annie P. Vo, Margo P. Cain, Desiree Tampe, Lauren Bizarro, Hikaru Sugimoto, Anna K. McGow, John M. Asara, Sara Lovisa, Kathleen M. McAndrews, Rafal Zielinski, Philip L. Lorenzi, Michael Zeisberg, Sughra Raza, Valerie S. LeBleu, Raghu Kalluri
ABSTRACT Objective To assess the outcome of an educational nutritional intervention in the quality of diet of women with breast cancer in adjuvant treatment. Methods Women with breast cancer and admitted for surgical treatment were divided in an intervention group (n=18) and a comparison group (n=78), and participated in a nonrandomized clinical trial. Participants were assessed before and after the treatment and/or intervention. A food frequency questionnaire was applied and the quality of diet was calculated using the Brazilian Healthy Eating Index Revised. The educational nutritional intervention lasted 12 months and wa...
DISCUSSION: Our results clearly demonstrate that the tECM is able to shift the gene expression pattern of murine mammary epithelial cells towards that of carcinoma, indicating a role in breast cancer initiation. These data underlines that the acellular component of the tumor (ECM) can lead to a transformation of mammary gland tissue cells. These data show for the first time that the interaction of normal breast tissue cells with tumor ECM leads to an exchange of information and a consecutive overexpression of tumor-specific genes. PMID: 31796667 [PubMed - indexed for MEDLINE]
Conclusions: In this population-based study, the detection of breast cancer gradually increased with GLP-1 RA weight loss categories, particularly among those achieving>10% weight loss. These results are consistent with the hypothesis that substantial weight loss with GLP-1 RAs may improve detection of breast cancer among obese patients with type 2 diabetes.
Authors: Gomes I, de Almeida BP, Dâmaso S, Mansinho A, Correia I, Henriques S, Cruz-Duarte R, Vilhais G, Félix P, Alves P, Corredeira P, Barbosa-Morais NL, Costa L, Casimiro S Abstract The role of RANKL-RANK pathway in progesterone-driven mammary carcinogenesis and triple negative breast cancer tumorigenesis has been well characterized. However, and despite evidences of the existence of RANK-positive hormone receptor (HR)-positive breast tumors, the implication of RANK expression in HR-positive breast cancers has not been addressed before. Here, we report that RANK pathway affects the expression of cel...