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排序方式: 共有473条查询结果,搜索用时 15 毫秒
91.
Nur Zeinomar Kelly-Anne Phillips Mary B. Daly Roger L. Milne Gillian S. Dite Robert J. MacInnis Yuyan Liao Rebecca D. Kehm Julia A. Knight Melissa C. Southey Wendy K. Chung Graham G. Giles Sue-Anne McLachlan Michael L. Friedlander Prue C. Weideman Gord Glendon Stephanie Nesci kConFab Investigators Irene L. Andrulis Saundra S. Buys Esther M. John John L. Hopper Mary Beth Terry 《International journal of cancer. Journal international du cancer》2019,145(2):370-379
Benign breast disease (BBD) is an established breast cancer (BC) risk factor, but it is unclear whether the magnitude of the association applies to women at familial or genetic risk. This information is needed to improve BC risk assessment in clinical settings. Using the Prospective Family Study Cohort, we used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of BBD with BC risk. We also examined whether the association with BBD differed by underlying familial risk profile (FRP), calculated using absolute risk estimates from the Breast Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model. During 176,756 person-years of follow-up (median: 10.9 years, maximum: 23.7) of 17,154 women unaffected with BC at baseline, we observed 968 incident cases of BC. A total of 4,704 (27%) women reported a history of BBD diagnosis at baseline. A history of BBD was associated with a greater risk of BC: HR = 1.31 (95% CI: 1.14–1.50), and did not differ by underlying FRP, with HRs of 1.35 (95% CI: 1.11–1.65), 1.26 (95% CI: 1.00–1.60), and 1.40 (95% CI: 1.01–1.93), for categories of full-lifetime BOADICEA score <20%, 20 to <35%, ≥35%, respectively. There was no difference in the association for women with BRCA1 mutations (HR: 1.64; 95% CI: 1.04–2.58), women with BRCA2 mutations (HR: 1.34; 95% CI: 0.78–2.3) or for women without a known BRCA1 or BRCA2 mutation (HR: 1.31; 95% CI: 1.13–1.53) (pinteraction = 0.95). Women with a history of BBD have an increased risk of BC that is independent of, and multiplies, their underlying familial and genetic risk. 相似文献
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Hanna R. Goldberg Sari Kives Lisa Allen Oscar M. Navarro Christopher Z. Lam 《Journal of pediatric and adolescent gynecology》2019,32(6):633-638
Study ObjectiveTo evaluate the diagnostic performance of the Decision Tree System (DTS) rules 2 and 3 for surgically managed adnexal masses in the North American population and to compare it with the risk stratification criteria used at The Hospital for Sick Children (≥8 cm and complex/solid).DesignA retrospective cohort study of patients who presented with adnexal masses and were surgically treated between April 2011 and March 2016.SettingThe Hospital for Sick Children (Toronto, Ontario, Canada).ParticipantsPatients 1-18 years of age with adnexal masses who underwent surgical treatment.Interventions and Main Outcome MeasuresMain outcome measures included diagnostic performance (preoperative sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV] for malignancy) of the DTS rules 2 and 3 and ≥8 cm and complex/solid criteria.ResultsThe malignancy rate was 10.4%. The DTS rules 2 and 3 had a sensitivity of 84% (95% confidence interval [CI], 79-90), specificity of 77% (95% CI, 71-83), PPV of 30% (95% CI, 17-42), and NPV of 98% (95% CI, 94-100). The 8 cm or larger and complex/solid criteria had a sensitivity of 89% (95% CI, 85-94), specificity of 71% (95% CI, 64-77), PPV of 27% (95% CI, 16-38), and NPV of 98% (95% CI, 96-100).ConclusionOur study showed that DTS rules 2 and 3 had similar diagnostic performance as the 8 cm or larger and complex/solid criteria in the same population, with a very high NPV and a low PPV. Future prospective investigations should be conducted to further assess how DTS components can be incorporated into future algorithms for the management of adnexal masses in the pediatric population. 相似文献
93.
《Cancer radiothérapie》2019,23(8):913-916
Artificial intelligence is a highly polysemic term. In computer science, with the objective of being able to solve totally new problems in new contexts, artificial intelligence includes connectionism (neural networks) for learning and logics for reasoning. Artificial intelligence algorithms mimic tasks normally requiring human intelligence, like deduction, induction, and abduction. All apply to radiation oncology. Combined with radiomics, neural networks have obtained good results in image classification, natural language processing, phenotyping based on electronic health records, and adaptive radiation therapy. General adversial networks have been tested to generate synthetic data. Logics based systems have been developed for providing formal domain ontologies, supporting clinical decision and checking consistency of the systems. Artificial intelligence must integrate both deep learning and logic approaches to perform complex tasks and go beyond the so-called narrow artificial intelligence that is tailored to perform some highly specialized task. Combined together with mechanistic models, artificial intelligence has the potential to provide new tools such as digital twins for precision oncology. 相似文献
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Optimization of nonlinear dose- and concentration-response models utilizing evolutionary computation
An essential part of toxicity and chemical screening is assessing the concentrated related effects of a test article. Most often this concentration-response is a nonlinear, necessitating sophisticated regression methodologies. The parameters derived from curve fitting are essential in determining a test article's potency (EC(50)) and efficacy (E(max)) and variations in model fit may lead to different conclusions about an article's performance and safety. Previous approaches have leveraged advanced statistical and mathematical techniques to implement nonlinear least squares (NLS) for obtaining the parameters defining such a curve. These approaches, while mathematically rigorous, suffer from initial value sensitivity, computational intensity, and rely on complex and intricate computational and numerical techniques. However if there is a known mathematical model that can reliably predict the data, then nonlinear regression may be equally viewed as parameter optimization. In this context, one may utilize proven techniques from machine learning, such as evolutionary algorithms, which are robust, powerful, and require far less computational framework to optimize the defining parameters. In the current study we present a new method that uses such techniques, Evolutionary Algorithm Dose Response Modeling (EADRM), and demonstrate its effectiveness compared to more conventional methods on both real and simulated data. 相似文献
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Blind source separation (BSS) is widely used to analyse brain recordings like the magnetoencephalogram (MEG). However, few studies have compared different BSS decompositions of real brain data. Those comparisons were usually limited to specific applications. Therefore, we aimed at studying the consistency (i.e., similarity) of the decompositions estimated for real MEGs from 26 subjects using five widely used BSS algorithms (AMUSE, SOBI, JADE, extended-Infomax and FastICA) for five epoch lengths (10 s, 20 s, 40 s, 60 s and 90 s). A statistical criterion based on Factor Analysis was applied to calculate the number of components into which each epoch would be decomposed. Then, the BSS techniques were applied. The results indicate that the pair of algorithms ‘AMUSE–SOBI’, followed by ‘JADE–FastICA’, provided the most similar separations. On the other hand, the most dissimilar outcomes were computed with ‘AMUSE–JADE’ and ‘SOBI–JADE’. The BSS decompositions were more similar for longer epochs. Furthermore, additional analyses of synthetic signals supported the results of the real MEGs. Thus, when selecting BSS algorithms to explore brain signals, the techniques offering the most different decompositions, such as AMUSE and JADE, may be preferred to obtain complementary, or at least different, perspectives of the underlying components. 相似文献
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