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1.  Computational Pathology to Discriminate Benign from Malignant Intraductal Proliferations of the Breast 
PLoS ONE  2014;9(12):e114885.
The categorization of intraductal proliferative lesions of the breast based on routine light microscopic examination of histopathologic sections is in many cases challenging, even for experienced pathologists. The development of computational tools to aid pathologists in the characterization of these lesions would have great diagnostic and clinical value. As a first step to address this issue, we evaluated the ability of computational image analysis to accurately classify DCIS and UDH and to stratify nuclear grade within DCIS. Using 116 breast biopsies diagnosed as DCIS or UDH from the Massachusetts General Hospital (MGH), we developed a computational method to extract 392 features corresponding to the mean and standard deviation in nuclear size and shape, intensity, and texture across 8 color channels. We used L1-regularized logistic regression to build classification models to discriminate DCIS from UDH. The top-performing model contained 22 active features and achieved an AUC of 0.95 in cross-validation on the MGH data-set. We applied this model to an external validation set of 51 breast biopsies diagnosed as DCIS or UDH from the Beth Israel Deaconess Medical Center, and the model achieved an AUC of 0.86. The top-performing model contained active features from all color-spaces and from the three classes of features (morphology, intensity, and texture), suggesting the value of each for prediction. We built models to stratify grade within DCIS and obtained strong performance for stratifying low nuclear grade vs. high nuclear grade DCIS (AUC = 0.98 in cross-validation) with only moderate performance for discriminating low nuclear grade vs. intermediate nuclear grade and intermediate nuclear grade vs. high nuclear grade DCIS (AUC = 0.83 and 0.69, respectively). These data show that computational pathology models can robustly discriminate benign from malignant intraductal proliferative lesions of the breast and may aid pathologists in the diagnosis and classification of these lesions.
PMCID: PMC4260962  PMID: 25490766
2.  Subtypes of Mild Cognitive Impairment in Older Postmenopausal Women: The Women’s Health Initiative Memory Study 
Mild cognitive impairment (MCI) is a transitional state between normal cognitive functioning and dementia. A proposed MCI typology1 classifies individuals by the type and extent of cognitive impairment, yet few studies have characterized or compared these subtypes. 447 women 65 years of age and older from the Women’s Health Initiative Memory Study2 were classified into the four MCI subgroups and a ‘no impairment’ group and compared on clinical, sociodemographic, and health variables.
82.1% of participants had a cognitive deficit in at least one domain with most (74.3%) having deficits in multiple cognitive domains. Only 4.3% had an isolated memory deficit, while 21.3% had an isolated non-memory deficit. Of the 112 women who met all MCI criteria examined, the most common subtype was amnestic multi-domain MCI (42.8%) followed by non-amnestic multiple domain MCI (26.7%), non-amnestic single domain (24.1%) and amnestic single domain MCI (6.3%). Subtypes were similar with respect to education, health status, smoking, depression and pre- and on-study use of hormone therapy.
Despite the attention it receives in the literature amnestic MCI is the least common type highlighting the importance of identifying and characterizing other non-amnestic and multi-domain subtypes. Further research is needed on the epidemiology of MCI subtypes, clinical and biological differences between them and rates for conversion to dementia.
PMCID: PMC2929315  PMID: 20473134
MCI; women; WHIMS; postmenopausal; cognition; dementia; hormone therapy
3.  An extensive phenotypic characterization of the hTNFα transgenic mice 
BMC Physiology  2007;7:13.
Tumor necrosis factor alpha (TNFα) is implicated in a wide variety of pathological and physiological processes, including chronic inflammatory conditions, coronary artery disease, diabetes, obesity, and cachexia. Transgenic mice expressing human TNFα (hTNFα) have previously been described as a model for progressive rheumatoid arthritis. In this report, we describe extensive characterization of an hTNFα transgenic mouse line.
In addition to arthritis, these hTNFα transgenic mice demonstrated major alterations in body composition, metabolic rate, leptin levels, response to a high-fat diet, bone mineral density and content, impaired fertility and male sexual function. Many phenotypes displayed an earlier onset and a higher degree of severity in males, pointing towards a significant degree of sexual dimorphism in response to deregulated expression of TNFα.
These results highlight the potential usefulness of this transgenic model as a resource for studying the progressive effects of constitutively expressed low levels of circulating TNFα, a condition mimicking that observed in a number of human pathological conditions.
PMCID: PMC2222242  PMID: 18070349

Results 1-3 (3)