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Nativity is not often considered in the study of health disparities. We conducted a cross-sectional, parent-reported survey of demographics, socioeconomic characteristics, healthcare access, and health conditions in New York City schoolchildren (n = 9029). US-born children with US-born parents (US/US) had higher socioeconomic status, better access to healthcare, and reported higher rates of disease diagnoses compared to US-born children with immigrant parents and to immigrant children. Dental cavities were the only condition in which US/US children reported lower prevalence. US/US children had the best healthcare access, most favorable parent-reported health status and highest rate of satisfaction with healthcare. The magnitude of racial/ethnic disparities varied based on nativity of the children being compared. Factors such as the healthy immigrant effect and differential diagnosis rates may explain the results. In conclusion, nativity influences disease burdens and should be considered in health disparities studies.

Microcalcifications are a feature of diagnostic significance on a mammogram and a target for stereotactic breast needle biopsy. Here, we report development of a Raman spectroscopy technique to simultaneously identify microcalcification status and diagnose the underlying breast lesion, in real-time, during stereotactic core needle biopsy procedures. Raman spectra were obtained ex vivo from 146 tissue sites from fresh stereotactic breast needle biopsy tissue cores from 33 patients, including 50 normal tissue sites, 77 lesions with microcalcifications, and 19 lesions without microcalcifications, using a compact clinical system. The Raman spectra were modeled on the basis of the breast tissue components, and a support vector machine framework was used to develop a single-step diagnostic algorithm to distinguish normal tissue, fibrocystic change (FCC), fibroadenoma, and breast cancer, in the absence and presence of microcalcifications. This algorithm was subjected to leave-one-site-out cross-validation, yielding a positive predictive value, negative predictive value, sensitivity, and specificity of 100%, 95.6%, 62.5%, and 100% for diagnosis of breast cancer (with or without microcalcifications) and an overall accuracy of 82.2% for classification into specific categories of normal tissue, FCC, fibroadenoma, or breast cancer (with and without microcalcifications). Notably, the majority of breast cancers diagnosed are ductal carcinoma in situ (DCIS), the most common lesion associated with microcalcifications, which could not be diagnosed using previous Raman algorithm(s). Our study shows the potential of Raman spectroscopy to concomitantly detect microcalcifications and diagnose associated lesions, including DCIS, and thus provide real-time feedback to radiologists during such biopsy procedures, reducing nondiagnostic and false-negative biopsies. Cancer Res; 73(11); 3206–15. ©2013 AACR.

OBJECTIVES: We used reflectance and fluorescence spectroscopy to noninvasively and quantitatively distinguish benign from dysplastic/malignant oral lesions. We designed diagnostic algorithms to account for differences in the spectral properties among anatomic sites (gingiva, buccal mucosa, etc). METHODS: In vivo reflectance and fluorescence spectra were collected from 71 patients with oral lesions. The tissue was then biopsied and the specimen evaluated by histopathology. Quantitative parameters related to tissue morphology and biochemistry were extracted from the spectra. Diagnostic algorithms specific for combinations of sites with similar spectral properties were developed. RESULTS: Discrimination of benign from dysplastic/malignant lesions was most successful when algorithms were designed for individual sites (area under the receiver operator characteristic curve [ROC-AUC],0.75 for the lateral surface of the tongue) and was least accurate when all sites were combined (ROC-AUC, 0.60). The combination of sites with similar spectral properties (floor of mouth and lateral surface of the tongue) yielded an ROC-AUC of 0.71. CONCLUSIONS: Accurate spectroscopic detection of oral disease must account for spectral variations among anatomic sites. Anatomy-based algorithms for single sites or combinations of sites demonstrated good diagnostic performance in distinguishing benign lesions from dysplastic/malignant lesions and consistently performed better than algorithms developed for all sites combined., National Institutes of Health (U.S) (R0I-CA097966), National Institutes of Health (U.S) (P4I-RR02594- 21)

Microcalcifications are an early mammographic sign of breast cancer and frequent target for stereotactic biopsy. Despite their indisputable value, microcalcifications, particularly of the type II variety that are comprised of calcium hydroxyapatite deposits, remain one of the least understood disease markers. Here we employed Raman spectroscopy to elucidate the relationship between pathogenicity of breast lesions in fresh biopsy cores and composition of type II microcalcifications. Using a chemometric model of chemical-morphological constituents, acquired Raman spectra were translated to characterize chemical makeup of the lesions. We find that increase in carbonate intercalation in the hydroxyapatite lattice can be reliably employed to differentiate benign from malignant lesions, with algorithms based only on carbonate and cytoplasmic protein content exhibiting excellent negative predictive value (93–98%). Our findings highlight the importance of calcium carbonate, an underrated constituent of microcalcifications, as a spectroscopic marker in breast pathology evaluation and pave the way for improved biopsy guidance., National Institute for Biomedical Imaging and Bioengineering (U.S.) (9P41EB015871-27A1), Johns Hopkins University. Whiting School of Engineering, National Cancer Institute (U.S.) (R01-CA140288)

Background: Timeliness of care may contribute to racial disparities in breast cancer mortality. African American women experience greater treatment delay than White women in most, but not all studies. Understanding these disparities is challenging as many studies lack patient-reported data and use administrative data sources that collect limited types of information. We used interview and medical record data from the Carolina Breast Cancer Study (CBCS) to identify determinants of delay and assess whether disparities exist between White and African American women (n = 601). Methods: The CBCS is a population-based study of North Carolina women. We investigated the association of demographic and socioeconomic characteristics, healthcare access, clinical factors, and measures of emotional and functional well-being with treatment delay. The association of race and selected characteristics with delays of more than 30 days was assessed using logistic regression. Results: Household size, losing a job due to one's diagnosis, and immediate reconstruction were associated with delay in the overall population and among White women. Immediate reconstruction and treatment type were associated with delay among African American women. Racial disparities in treatment delay were not evident in the overall population. In the adjusted models, African American women experienced greater delay than White women for younger age groups: OR, 3.34; 95% confidence interval (CI), 1.07–10.38 for ages 20 to 39 years, and OR, 3.40; 95% CI, 1.76–6.54 for ages 40 to 49 years. Conclusions: Determinants of treatment delay vary by race. Racial disparities in treatment delay exist among women younger than 50 years. Impact: Specific populations need to be targeted when identifying and addressing determinants of treatment delay. Cancer Epidemiol Biomarkers Prev; 22(7); 1227–38. ©2013 AACR.

Microcalcifications are an early mammographic sign of breast cancer and a target for stereotactic breast needle biopsy. Here, we develop and compare different approaches for developing Raman classification algorithms to diagnose invasive and in situ breast cancer, fibrocystic change and fibroadenoma that can be associated with microcalcifications. In this study, Raman spectra were acquired from tissue cores obtained from fresh breast biopsies and analyzed using a constituent-based breast model. Diagnostic algorithms based on the breast model fit coefficients were devised using logistic regression, C4.5 decision tree classification, k-nearest neighbor (k -NN) and support vector machine (SVM) analysis, and subjected to leave-one-out cross validation. The best performing algorithm was based on SVM analysis (with radial basis function), which yielded a positive predictive value of 100% and negative predictive value of 96% for cancer diagnosis. Importantly, these results demonstrate that Raman spectroscopy provides adequate diagnostic information for lesion discrimination even in the presence of microcalcifications, which to the best of our knowledge has not been previously reported.

We report a fully quantitative spectroscopy imaging instrument for wide area detection of early cancer (dysplasia). This instrument provides quantitative maps of tissue biochemistry and morphology, making it a potentially powerful surveillance tool for objective early cancer detection. We describe the design, construction, calibration, and first clinical application of this new system. We demonstrate its accuracy using physical tissue models. We validate its diagnostic ability on a resected colon adenoma, and demonstrate feasibility of in vivo imaging in the oral cavity.

It has long been speculated that underlying variations in tissue anatomy affect in vivo spectroscopic measurements. We inves- tigate the effects of cervical anatomy on reflectance and fluorescence spectroscopy to guide the development of a diagnostic algorithm for identifying high-grade squamous intraepithelial lesions HSILs free of the confounding effects of anatomy. We use spectroscopy in both contact probe and imaging modes to study patients undergoing either colposcopy or treatment for HSIL. Physical models of light propaga- tion in tissue are used to extract parameters related to tissue morphol- ogy and biochemistry. Our results show that the transformation zone, the area in which the vast majority of HSILs are found, is spectroscopi- cally distinct from the adjacent squamous mucosa, and that these anatomical differences can directly influence spectroscopic diagnostic parameters. Specifically, we demonstrate that performance of diag- nostic algorithms for identifying HSILs is artificially enhanced when clinically normal squamous sites are included in the statistical analy- sis of the spectroscopic data. We conclude that underlying differences in tissue anatomy can have a confounding effect on diagnostic spec- troscopic parameters and that the common practice of including clini- cally normal squamous sites in cervical spectroscopy results in artifi- cially improved performance in distinguishing HSILs from clinically suspicious non-HSILs. © 2009 Society of Photo-Optical Instrumentation Engineers. DOI: 10.1117/1.3194142