Benign breast disease increases breast cancer risk independent of underlying familial risk profile: Findings from a Prospective Family Study Cohort

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) (p_interaction = 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.     

Preoperative Risk Stratification of Adnexal Masses in the Pediatric and Adolescent Population: Evaluating the Decision Tree System

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.     

Intelligence artificielle et radiothérapie : quelles bases et quelles perspectives ?

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.     

健康状态辨识模型算法的探讨

健康状态辨识模型算法是对中医辨证思维的数学建模与实现。模型算法在求解表征参数对状态要素贡献度的基础上,还必须遵循中医理论体系和思维规律,充分考虑表征参数的表达和分类的差异、隐性参数与证候真假、状态要素的兼杂与缓急等问题。文章在总结分析了"中医专家系统"和原有辨证模型利弊的基础上,提出了中医健康状态辨识模型算法研究的基本框架。本领域的探索将有助于推动中医健康理论创新,同时也为健康状态辨识工具的研发提供方法学平台。     

中草药复方治疗艾滋病的配伍分析与计算

在中国,中草药被广泛应用于艾滋病的治疗及研究中,临床研究表明,中医药治疗可明显改善艾滋病患者的临床症状,提高免疫功能及生活质量,减轻抗病毒药物的毒副作用.本研究以28篇发表于中文期刊的中草药治疗艾滋病的临床研究为对象,设计了用以进行方剂配伍关系分析的树形分析算法,对治疗艾滋病中草药的配伍进行分析.分析结果显示,由于对发...     

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.     

手指解剖结构的三维建模及临床意义

目的:应用计算机辅助模拟技术(CAD)建立手指解剖结构的三维模型.在计算机上实现手指解剖结构的三维演示.方法:根据手部解剖图谱及相关资料,应用3ds max的放样、多边形等算法建立三维模型.结果:应用上述方法成功地建立了手指解剖结构及指固有动脉逆行岛状瓣的三维模型.该模型可在电脑屏幕上自由旋转,隐藏不感兴趣结构.结论:应用三维建模软件进行手指解剖结构的三维演示是可行的,它有助于人们更好的理解和记忆解剖结构,进行手指手术设计.     

Consistency of the blind source separation computed with five common algorithms for magnetoencephalogram background activity

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.