Breast Cancer Diagnosis and Treatment Wait Times in Specialized Diagnostic Units Compared with Usual Care: A Population-Based Study

Background：Breast assessment sites (bass) were developed to provide expedited and coordinated care for patients being evaluated for breast cancer (bca) in Ontario. We compared the diagnostic and treatment intervals for patients diagnosed at a bas and for those diagnosed through a usual care (uc) route. Methods: This population-based, cross-sectional study of patients diagnosed with bca in Ontario during 2007–2015 used linked administrative data. “Diagnostic interval” was the time from the earliest cancer-related health care encounter before diagnosis to diagnosis; “treatment interval” was the time from diagnosis to treatment. Diagnosis at a (bas was determined from the patient’s biopsy and mammography institutions. Interval lengths for the (bas and uc groups were compared using multivariable quantile regression, stratified by detection method. Results: The diagnostic interval was shorter for patients who were (bas-diagnosed than for those who were uc-diagnosed, with adjusted median differences of −4.0 days [95% confidence interval (ci): −3.2 days to −4.9 days] for symptomatic patients and −5.4 days (95% ci: −4.7 days to −6.1 days) for screen-detected patients. That association was modified by stage at diagnosis, with larger differences in patients with early-stage cancers. In contrast, the treatment interval was longer in patients who were (bas-diagnosed than in those who were uc-diagnosed, with adjusted median differences of 4.2 days (95% ci: 3.8 days to 4.7 days) for symptomatic patients and 4.2 days (95% ci: 3.7 days to 4.8 days) for screen-detected patients. Conclusions: Diagnosis of bca through a (bas was associated with a shorter diagnostic interval, but a longer treatment interval. Although efficiencies in the diagnostic interval might help to reduce distress experienced by patients, the longer treatment intervals for patients who are (bas-diagnosed remain a cause for concern

Learning Genetic Regulatory Network Connectivity from Time Series Data

Abstract. Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This paper proposes an efficient method to generate the genetic regulatory network inferred from time series data. Our method first encodes the data into levels. Next, it determines the set of potential parents for each gene based upon the probability of the gene’s expression increasing. After a subset of potential parents are selected, it determines if any genes in this set may have a combined effect. Finally, the potential sets of parents are competed against each other to determine the final set of parents. The result is a directed graph representation of the genetic network’s repression and activation connections. Our results on synthetic data generated from models for several genetic networks with tight feedback are promising.