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BackgroundCurrent online prognostic prediction models for breast cancer, such as Adjuvant! Online and PREDICT, are based on specific populations. They have been well validated and widely used in the United States and Western Europe; however, several validation attempts in non-European countries have revealed suboptimal predictions. ObjectiveWe aimed to develop an advanced breast cancer prognosis model for disease progression, cancer-specific mortality, and all-cause mortality by integrating tumor, demographic, and treatment characteristics from a large breast cancer cohort in China. MethodsThis study was approved by the Clinical Test and Biomedical Ethics Committee of West China Hospital, Sichuan University on May 17, 2012. Data collection for this project was started in May 2017 and ended in March 2019. Data on 5293 women diagnosed with stage I to III invasive breast cancer between 2000 and 2013 were collected. Disease progression, cancer-specific mortality, all-cause mortality, and the likelihood of disease progression or death within a 5-year period were predicted. Extreme gradient boosting was used to develop the prediction model. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUROC), and the model was calibrated and compared with PREDICT. ResultsThe training, test, and validation sets comprised 3276 (499 progressions, 202 breast cancer-specific deaths, and 261 all-cause deaths within 5-year follow-up), 1405 (211 progressions, 94 breast cancer-specific deaths, and 129 all-cause deaths), and 612 (109 progressions, 33 breast cancer-specific deaths, and 37 all-cause deaths) women, respectively. The AUROC values for disease progression, cancer-specific mortality, and all-cause mortality were 0.76, 0.88, and 0.82 for training set; 0.79, 0.80, and 0.83 for the test set; and 0.79, 0.84, and 0.88 for the validation set, respectively. Calibration analysis demonstrated good agreement between predicted and observed events within 5 years. Comparable AUROC and calibration results were confirmed in different age, residence status, and receptor status subgroups. Compared with PREDICT, our model showed similar AUROC and improved calibration values. ConclusionsOur prognostic model exhibits high discrimination and good calibration. It may facilitate prognosis prediction and clinical decision making for patients with breast cancer in China.

Background Women with mental illness experience an increased risk of cervical cancer. The excess risk is partly due to low participation in cervical screening; however, it remains unknown whether it is also attributable to an increased risk of infection with human papillomavirus (HPV). We aimed to examine whether women with mental illness had an increased infection rate of HPV compared to women without mental illness. Methods and findings Using a cohort design, we analyzed all 337,116 women aged 30 to 64 and living in Stockholm, who had a negative test result of 14 high-risk HPV subtypes in HPV-based screening, during August 2014 to December 2019. We defined women as exposed to mental illness if they had a specialist diagnosis of mental disorder or had a filled prescription of psychotropic medication. We identified incident infection of any high-risk HPV during follow-up and fitted multivariable Cox models to estimate hazard ratios (HR) with 95% confidence intervals (CI) for HPV infection. A total of 3,263 women were tested positive for high-risk HPV during follow-up (median: 2.21 years; range: 0 to 5.42 years). The absolute infection rate of HPV was higher among women with a specialist diagnosis of mental disorder (HR = 1.45; 95% CI [1.34, 1.57]; p < 0.001) or a filled prescription of psychotropic medication (HR = 1.67; 95% CI [1.55, 1.79]; p < 0.001), compared to women without such. The increment in absolute infection rate was noted for depression, anxiety, stress-related disorder, substance-related disorder, and ADHD, and for use of antidepressants, anxiolytics, sedatives, and hypnotics, and was consistent across age groups. The main limitations included selection of the female population in Stockholm as they must have at least 1 negative test result of HPV, and relatively short follow-up as HPV-based screening was only introduced in 2014 in Stockholm. Conclusions Mental illness is associated with an increased infection rate of high-risk HPV in women. Our findings motivate refined approaches to facilitate the WHO elimination agenda of cervical cancer among these marginalized women worldwide., Author(s): Eva Herweijer 1, Kejia Hu 1,*, Jiangrong Wang 2, Donghao Lu 1, Pär Sparén 3, Hans-Olov Adami 3,4, Unnur Valdimarsdóttir 1,5,6, Karin Sundström 2, Fang Fang 1,* Introduction Cervical [...]

Deep brain stimulation(DBS) has become an effective intervention for advanced Parkinson’s disease(PD), but the exact mechanism of DBS is still unclear. In this review, we discuss the history of DBS, the anatomy and internal architecture of the basal ganglia (BG), the abnormal pathological changes of the BG in PD, and how computational models can help understand and advance DBS. We also describe two types of models: mathematical theoretical models and clinical predictive models. Mathematical theoretical models simulate neurons or neural networks of BG to shed light on the mechanistic principle underlying DBS, while clinical predictive models focus more on patients’ outcomes, helping to adapt treatment plans for each patient and advance novel electrode designs. Finally, we provide insights and an outlook on future technologies. [ABSTRACT FROM AUTHOR]