The Experts below are selected from a list of 8838 Experts worldwide ranked by ideXlab platform
Lei Xu - One of the best experts on this subject based on the ideXlab platform.
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BCRAM: A Social-Network-Inspired Breast Cancer Risk Assessment Model
IEEE Transactions on Industrial Informatics, 2019Co-Authors: Ali Li, Lei Xu, Rui Wang, Fei Wang, Fei Chang, Lixiang Yu, Yujuan Xiang, Fei Zhou, Zhigang YuAbstract:The pathogenesis of breast Cancer is not the same in all countries and regions; therefore, some existing breast Cancer Risk Assessment models are not well adapted to all countries and regions, including China. This paper puts forward a new model named BCRAM (a social-network-inspired breast Cancer Risk Assessment model) that depends on epidemiological factors, which is more adaptive to the populous country like China than those models based on gene. The model utilizes the similarities among epidemiological factors to construct a breast Cancer high-Risk group, the members of which have high similarity with breast Cancer patients. Then, three tests based on real data are used to determine the Assessment value of BCRAM. The AUC of BCRAM is 0.785, which is larger than that of the classic Gail model, a modified Gail model, the Tyrer-Cuzick model, and the Liu-Yu model for Chinese women. F-Measure value is 0.696, which is the largest among those of all models. Moreover, follow-up data are used to demonstrate that the model can give early warning to a high proportion of patients discovered to have breast Cancer in the future. Therefore, the model is meaningful for the prevention and control of breast Cancer. And the unique design of the method for selecting Risk factors related to breast Cancer results in our model having good generality, and it can be generalized to other countries and regions.
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Shrink: A Breast Cancer Risk Assessment Model Based on Medical Social Network
2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017Co-Authors: Ali Li, Rui Wang, Lei XuAbstract:Breast Cancer Risk Assessment model can assess whether a people is at a high Risk of developing breast Cancer disease or not and confirm a breast Cancer high-Risk group. Because the etiology of breast Cancer disease is different in different country and region, the existing Risk Assessment model is only adaptive to certain countries and regions. And the parameters of these models are fixed, so these models have poor generality. Aiming at these problems, the paper puts forward a new breast Cancer Risk Assessment model named as Shrink. Using the idea of social network, Shrink constructs a medical social network to show the similarity among people, and uses group division algorithm to divide the network into breast Cancer high-Risk group and low-Risk group. The parameters of this model can be set according to the needs of the breast census, and these parameters can be directly acquired through questionnaire, therefore Shrink has good generality. Moreover, under the uncertain classification standard, Shrink adopts a new classification method to discover breast Cancer high-Risk group. Based on the real data from questionnaires, we make experiments in Matlab, and obtain the evaluation index of the model. The experiment proves that the model itself has good evaluation result and is better than classic Gail model.
Ali Li - One of the best experts on this subject based on the ideXlab platform.
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BCRAM: A Social-Network-Inspired Breast Cancer Risk Assessment Model
IEEE Transactions on Industrial Informatics, 2019Co-Authors: Ali Li, Lei Xu, Rui Wang, Fei Wang, Fei Chang, Lixiang Yu, Yujuan Xiang, Fei Zhou, Zhigang YuAbstract:The pathogenesis of breast Cancer is not the same in all countries and regions; therefore, some existing breast Cancer Risk Assessment models are not well adapted to all countries and regions, including China. This paper puts forward a new model named BCRAM (a social-network-inspired breast Cancer Risk Assessment model) that depends on epidemiological factors, which is more adaptive to the populous country like China than those models based on gene. The model utilizes the similarities among epidemiological factors to construct a breast Cancer high-Risk group, the members of which have high similarity with breast Cancer patients. Then, three tests based on real data are used to determine the Assessment value of BCRAM. The AUC of BCRAM is 0.785, which is larger than that of the classic Gail model, a modified Gail model, the Tyrer-Cuzick model, and the Liu-Yu model for Chinese women. F-Measure value is 0.696, which is the largest among those of all models. Moreover, follow-up data are used to demonstrate that the model can give early warning to a high proportion of patients discovered to have breast Cancer in the future. Therefore, the model is meaningful for the prevention and control of breast Cancer. And the unique design of the method for selecting Risk factors related to breast Cancer results in our model having good generality, and it can be generalized to other countries and regions.
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Shrink: A Breast Cancer Risk Assessment Model Based on Medical Social Network
2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017Co-Authors: Ali Li, Rui Wang, Lei XuAbstract:Breast Cancer Risk Assessment model can assess whether a people is at a high Risk of developing breast Cancer disease or not and confirm a breast Cancer high-Risk group. Because the etiology of breast Cancer disease is different in different country and region, the existing Risk Assessment model is only adaptive to certain countries and regions. And the parameters of these models are fixed, so these models have poor generality. Aiming at these problems, the paper puts forward a new breast Cancer Risk Assessment model named as Shrink. Using the idea of social network, Shrink constructs a medical social network to show the similarity among people, and uses group division algorithm to divide the network into breast Cancer high-Risk group and low-Risk group. The parameters of this model can be set according to the needs of the breast census, and these parameters can be directly acquired through questionnaire, therefore Shrink has good generality. Moreover, under the uncertain classification standard, Shrink adopts a new classification method to discover breast Cancer high-Risk group. Based on the real data from questionnaires, we make experiments in Matlab, and obtain the evaluation index of the model. The experiment proves that the model itself has good evaluation result and is better than classic Gail model.
Rui Wang - One of the best experts on this subject based on the ideXlab platform.
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BCRAM: A Social-Network-Inspired Breast Cancer Risk Assessment Model
IEEE Transactions on Industrial Informatics, 2019Co-Authors: Ali Li, Lei Xu, Rui Wang, Fei Wang, Fei Chang, Lixiang Yu, Yujuan Xiang, Fei Zhou, Zhigang YuAbstract:The pathogenesis of breast Cancer is not the same in all countries and regions; therefore, some existing breast Cancer Risk Assessment models are not well adapted to all countries and regions, including China. This paper puts forward a new model named BCRAM (a social-network-inspired breast Cancer Risk Assessment model) that depends on epidemiological factors, which is more adaptive to the populous country like China than those models based on gene. The model utilizes the similarities among epidemiological factors to construct a breast Cancer high-Risk group, the members of which have high similarity with breast Cancer patients. Then, three tests based on real data are used to determine the Assessment value of BCRAM. The AUC of BCRAM is 0.785, which is larger than that of the classic Gail model, a modified Gail model, the Tyrer-Cuzick model, and the Liu-Yu model for Chinese women. F-Measure value is 0.696, which is the largest among those of all models. Moreover, follow-up data are used to demonstrate that the model can give early warning to a high proportion of patients discovered to have breast Cancer in the future. Therefore, the model is meaningful for the prevention and control of breast Cancer. And the unique design of the method for selecting Risk factors related to breast Cancer results in our model having good generality, and it can be generalized to other countries and regions.
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Shrink: A Breast Cancer Risk Assessment Model Based on Medical Social Network
2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017Co-Authors: Ali Li, Rui Wang, Lei XuAbstract:Breast Cancer Risk Assessment model can assess whether a people is at a high Risk of developing breast Cancer disease or not and confirm a breast Cancer high-Risk group. Because the etiology of breast Cancer disease is different in different country and region, the existing Risk Assessment model is only adaptive to certain countries and regions. And the parameters of these models are fixed, so these models have poor generality. Aiming at these problems, the paper puts forward a new breast Cancer Risk Assessment model named as Shrink. Using the idea of social network, Shrink constructs a medical social network to show the similarity among people, and uses group division algorithm to divide the network into breast Cancer high-Risk group and low-Risk group. The parameters of this model can be set according to the needs of the breast census, and these parameters can be directly acquired through questionnaire, therefore Shrink has good generality. Moreover, under the uncertain classification standard, Shrink adopts a new classification method to discover breast Cancer high-Risk group. Based on the real data from questionnaires, we make experiments in Matlab, and obtain the evaluation index of the model. The experiment proves that the model itself has good evaluation result and is better than classic Gail model.
Julie O. Culver - One of the best experts on this subject based on the ideXlab platform.
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Essential Elements of Genetic Cancer Risk Assessment, Counseling, and Testing: Updated Recommendations of the National Society of Genetic Counselors
Journal of Genetic Counseling, 2012Co-Authors: Bronson D. Riley, Julie O. Culver, June A. Peters, Sherry Campbell Grumet, Cécile Skrzynia, Leigha A. Senter, Josephine W. Costalas, Faith Callif-daley, Katherine S. Hunt, Rebecca S. NagyAbstract:Updated from their original publication in 2004, these Cancer genetic counseling recommendations describe the medical, psychosocial, and ethical ramifications of counseling at-Risk individuals through genetic Cancer Risk Assessment with or without genetic testing. They were developed by members of the Practice Issues Subcommittee of the National Society of Genetic Counselors Familial Cancer Risk Counseling Special Interest Group. The information contained in this document is derived from extensive review of the current literature on Cancer genetic Risk Assessment and counseling as well as the personal expertise of genetic counselors specializing in Cancer genetics. The recommendations are intended to provide information about the process of genetic counseling and Risk Assessment for hereditary Cancer disorders rather than specific information about individual syndromes. Essential components include the intake, Cancer Risk Assessment, genetic testing for an inherited Cancer syndrome, informed consent, disclosure of genetic test results, and psychosocial Assessment. These recommendations should not be construed as dictating an exclusive course of management, nor does use of such recommendations guarantee a particular outcome. These recommendations do not displace a health care provider’s professional judgment based on the clinical circumstances of a client.
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Genetics, genomics, and Cancer Risk Assessment: State of the Art and Future Directions in the Era of Personalized Medicine
CA A Cancer Journal for Clinicians, 2011Co-Authors: Kathleen R Blazer, Deborah J Macdonald, Julie O. Culver, Kenneth OffitAbstract:Scientific and technologic advances are revolutionizing our approach to genetic Cancer Risk Assessment, Cancer screen- ing and prevention, and targeted therapy, fulfilling the promise of personalized medicine. In this monograph, we review the evolution of scientific discovery in Cancer genetics and genomics, and describe current approaches, benefits, and barriers to the translation of this information to the practice of preventive medicine. Summaries of known hereditary Cancer syndromes and highly penetrant genes are provided and contrasted with recently discovered genomic variants associated with modest increases in Cancer Risk. We describe the scope of knowledge, tools, and expertise required for the translation of complex genetic and genomic test information into clinical practice. The challenges of genomic coun- seling include the need for genetics and genomics professional education and multidisciplinary team training, the need for evidence-based information regarding the clinical utility of testing for genomic variants, the potential dangers posed by premature marketing of first-generation genomic profiles, and the need for new clinical models to improve access to and responsible communication of complex disease Risk information. We conclude that given the experiences and les- sons learned in the genetics era, the multidisciplinary model of genetic Cancer Risk Assessment and management will serve as a solid foundation to support the integration of personalized genomic information into the practice of Cancer C medicine. CA
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Genetic Cancer Risk Assessment and Counseling: Recommendations of the National Society of Genetic Counselors
Journal of Genetic Counseling, 2004Co-Authors: Angela Trepanier, Julie O. Culver, Mary Ahrens, Wendy Mckinnon, June Peters, Jill Stopfer, Sherry Campbell Grumet, Susan Manley, Ronald Acton, Joy Larsen-haidleAbstract:These Cancer genetic counseling recommendations describe the medical, psychosocial, and ethical ramifications of identifying at-Risk individuals through Cancer Risk Assessment with or without genetic testing. They were developed by members of the Practice Issues Subcommittee of the National Society of Genetic Counselors Cancer Genetic Counseling Special Interest Group. The information contained in this document is derived from extensivereview of the current literature on Cancer genetic Risk Assessment and counseling as well as the personal expertise of genetic counselors specializing in Cancer genetics. The recommendations are intended to provid information about the process of genetic counseling and Risk Assessment for hereditary Cancer disorders rather than specific information about individual syndromes. Key components include the intake (medical and family histories), psychosocial Assessment (Assessment of Risk perception), Cancer Risk Assessment (determination and communication of Risk), molecular testing for hereditary Cancer syndromes (regulations, informed consent, and counseling process), and follow-up considerations. These recommendations should not be construed as dictating an exclusive course of management, nor does use of such recommendations guarantee a particular outcome. These recommendations do not displace a health care provider's professional judgment based on the clinical circumstances of a client.
Zhigang Yu - One of the best experts on this subject based on the ideXlab platform.
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BCRAM: A Social-Network-Inspired Breast Cancer Risk Assessment Model
IEEE Transactions on Industrial Informatics, 2019Co-Authors: Ali Li, Lei Xu, Rui Wang, Fei Wang, Fei Chang, Lixiang Yu, Yujuan Xiang, Fei Zhou, Zhigang YuAbstract:The pathogenesis of breast Cancer is not the same in all countries and regions; therefore, some existing breast Cancer Risk Assessment models are not well adapted to all countries and regions, including China. This paper puts forward a new model named BCRAM (a social-network-inspired breast Cancer Risk Assessment model) that depends on epidemiological factors, which is more adaptive to the populous country like China than those models based on gene. The model utilizes the similarities among epidemiological factors to construct a breast Cancer high-Risk group, the members of which have high similarity with breast Cancer patients. Then, three tests based on real data are used to determine the Assessment value of BCRAM. The AUC of BCRAM is 0.785, which is larger than that of the classic Gail model, a modified Gail model, the Tyrer-Cuzick model, and the Liu-Yu model for Chinese women. F-Measure value is 0.696, which is the largest among those of all models. Moreover, follow-up data are used to demonstrate that the model can give early warning to a high proportion of patients discovered to have breast Cancer in the future. Therefore, the model is meaningful for the prevention and control of breast Cancer. And the unique design of the method for selecting Risk factors related to breast Cancer results in our model having good generality, and it can be generalized to other countries and regions.
