Women with atypical ductal hyperplasia (ADH), atypical lobular hyperplasia (ALH), lobular carcinoma in situ (LCIS), and severe ADH are at increased risk of breast cancer, but a systematic quantification of this risk and the efficacy of chemoprevention in the clinical setting is still lacking. The objective of this study is to evaluate a woman's risk of breast cancer based on atypia type and to determine the effect of chemoprevention in decreasing this risk. Review of 76,333 breast pathology reports from three institutions within Partners Healthcare System, Boston, from 1987 to 2010 using natural language processing was carried out. This approach identified 2,938 women diagnosed with atypical breast lesions. The main outcome of this study is breast cancer occurrence. Of the 2,938 patients with atypical breast lesions, 1,658 were documented to have received no chemoprevention, and 184/1,658 (11.1 %) developed breast cancer at a mean follow-up of 68 months. Estimated 10-year cancer risks were 17.3 % with ADH, 20.7 % with ALH, 23.7 % with LCIS, and 26.0 % with severe ADH. In a subset of patients treated from 1999 on (the chemoprevention era), those who received no chemoprevention had an estimated 10-year breast cancer risk of 21.3 %, whereas those treated with chemoprevention had a 10-year risk of 7.5 % (p < 0.001). Chemoprevention use significantly reduced breast cancer risk for all atypia types (p < 0.05). The risk of breast cancer with atypical breast lesions is substantial. Physicians should counsel patients with ADH, ALH, LCIS, and severe ADH about the benefit of chemoprevention in decreasing their breast cancer risk.

Radviz is a radial visualization with dimensions assigned to points called dimensional anchors (DAs) placed on the circumference of a circle. Records are assigned locations within the circle as a function of its relative attraction to each of the DAs. The DAs can be moved either interactively or algorithmically to reveal different meaningful patterns in the dataset. In this paper we describe Vectorized Radviz (VRV) which extends the number of dimensions through data flattening. We show how VRV increases the power of Radviz through these extra dimensions by enhancing the flexibility in the layout of the DAs. We apply VRV to the problem of analyzing the results of multiple clusterings of the same data set, called multiple cluster sets or cluster ensembles. We show how features of VRV help discern patterns across the multiple cluster sets. We use the Iris data set to explain VRV and a newt gene microarray data set used in studying limb regeneration to show its utility. We then discuss further applications of VRV.

Despite advances in identifying genetic markers of high risk patients and the availability of genetic testing, it remains challenging to efficiently identify women who are at hereditary risk and to manage their care appropriately. HughesRiskApps, an open-source family history collection, risk assessment, and Clinical Decision Support (CDS) software package, was developed to address the shortcomings in our ability to identify and treat the high risk population. This system is designed for use in primary care clinics, breast centers, and cancer risk clinics to collect family history and risk information and provide the necessary CDS to increase quality of care and efficiency. This paper reports on the first implementation of HughesRiskApps in the community hospital setting. HughesRiskApps was implemented at the Newton-Wellesley Hospital. Between April 1, 2007 and March 31, 2008, 32,966 analyses were performed on 25,763 individuals. Within this population, 915 (3.6%) individuals were found to be eligible for risk assessment and possible genetic testing based on the 10% risk of mutation threshold. During the first year of implementation, physicians and patients have fully accepted the system, and 3.6% of patients assessed have been referred to risk assessment and consideration of genetic testing. These early results indicate that the number of patients identified for risk assessment has increased dramatically and that the care of these patients is more efficient and likely more effective.

Objective:The opportunity to integrate clinical decision support systems into clinical practice is limited due to the lack of structured, machine readable data in the current format of the electronic health record. Natural language processing has been designed to convert free text into machine readable data. The aim of the current study was to ascertain the feasibility of using natural language processing to extract clinical information from >76,000 breast pathology reports.Approach and Procedure:Breast pathology reports from three institutions were analyzed using natural language processing software (Clearforest, Waltham, MA) to extract information on a variety of pathologic diagnoses of interest. Data tables were created from the extracted information according to date of surgery, side of surgery, and medical record number. The variety of ways in which each diagnosis could be represented was recorded, as a means of demonstrating the complexity of machine interpretation of free text.Results:There was widespread variation in how pathologists reported common pathologic diagnoses. We report, for example, 124 ways of saying invasive ductal carcinoma and 95 ways of saying invasive lobular carcinoma. There were >4000 ways of saying invasive ductal carcinoma was not present. Natural language processor sensitivity and specificity were 99.1% and 96.5% when compared to expert human coders.Conclusion:We have demonstrated how a large body of free text medical information such as seen in breast pathology reports, can be converted to a machine readable format using natural language processing, and described the inherent complexities of the task.

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