Laser optoacoustic imaging system for detection of breast cancer.

We designed, fabricated and tested the laser optoacoustic imaging system for breast cancer detection (LOIS-64), which fuses optical and acoustic imaging techniques in one modality by utilizing pulsed optical illumination and ultrawide-band ultrasonic detection of resulting optoacoustic (OA) signals. The system was designed to image a single breast slice in craniocaudal or mediolateral projection with an arc-shaped array of 64 ultrawide-band acoustic transducers. The system resolution on breast phantoms was at least 0.5 mm. The single-channel sensitivity of 1.66 mVPa was estimated to be sufficient for single-pulse imaging of 6 to 11 mm tumors through the whole imaging slice of the breast. The implemented signal processing using the wavelet transform allowed significant reduction of the low-frequency (LF) acoustic noise, allowed localization of the optoacoustic signals from tumors, and enhanced the contrast and sharpened the boundaries of the optoacoustic images of the tumors. During the preliminary clinical studies on 27 patients, the LOIS-64 was able to visualize 18 out of 20 malignant lesions suspected from mammography and ultrasound images and confirmed by the biopsy performed after the optoacoustic tomography (OAT) procedure.

Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit.

Breast density (the percentage of fibroglandular tissue in the breast) has been suggested to be a useful surrogate marker for breast cancer risk. It is conventionally measured using screen-film mammographic images by a labor-intensive histogram segmentation method (HSM). We have adapted and modified the HSM for measuring breast density from raw digital mammograms acquired by full-field digital mammography. Multiple regression model analyses showed that many of the instrument parameters for acquiring the screening mammograms (e.g. breast compression thickness, radiological thickness, radiation dose, compression force, etc) and image pixel intensity statistics of the imaged breasts were strong predictors of the observed threshold values (model R(2) = 0.93) and %-density (R(2) = 0.84). The intra-class correlation coefficient of the %-density for duplicate images was estimated to be 0.80, using the regression model-derived threshold values, and 0.94 if estimated directly from the parameter estimates of the %-density prediction regression model. Therefore, with additional research, these mathematical models could be used to compute breast density objectively, automatically bypassing the HSM step, and could greatly facilitate breast cancer research studies.

Comparison of breast tissue measurements using magnetic resonance imaging, digital mammography and a mathematical algorithm.

Women with mostly mammographically dense fibroglandular tissue (breast density, BD) have a four- to six-fold increased risk for breast cancer compared to women with little BD. BD is most frequently estimated from two-dimensional (2D) views of mammograms by a histogram segmentation approach (HSM) and more recently by a mathematical algorithm consisting of mammographic imaging parameters (MATH). Two non-invasive clinical magnetic resonance imaging (MRI) protocols: 3D gradient-echo (3DGRE) and short tau inversion recovery (STIR) were modified for 3D volumetric reconstruction of the breast for measuring fatty and fibroglandular tissue volumes by a Gaussian-distribution curve-fitting algorithm. Replicate breast exams (N = 2 to 7 replicates in six women) by 3DGRE and STIR were highly reproducible for all tissue-volume estimates (coefficients of variation <5%). Reliability studies compared measurements from four methods, 3DGRE, STIR, HSM, and MATH (N = 95 women) by linear regression and intra-class correlation (ICC) analyses. Rsqr, regression slopes, and ICC, respectively, were (1) 0.76-0.86, 0.8-1.1, and 0.87-0.92 for %-gland tissue, (2) 0.72-0.82, 0.64-0.96, and 0.77-0.91, for glandular volume, (3) 0.87-0.98, 0.94-1.07, and 0.89-0.99, for fat volume, and (4) 0.89-0.98, 0.94-1.00, and 0.89-0.98, for total breast volume. For all values estimated, the correlation was stronger for comparisons between the two MRI than between each MRI versus mammography, and between each MRI versus MATH data than between each MRI versus HSM data. All ICC values were >0.75 indicating that all four methods were reliable for measuring BD and that the mathematical algorithm and the two complimentary non-invasive MRI protocols could objectively and reliably estimate different types of breast tissues.

Bilateral extensive ductitis obliterans manifested by bloody nipple discharge in a patient with long-term diabetes mellitus.

Ductitis obliterans or mastitis obliterans is a rare late manifestation of mammary ductal ectasia. We describe a long-term diabetic patient who presented with bilateral bloody nipple discharge and poorly defined nodularities around the nipple of both breasts. The ductography showed multiple segments of irregular ductal narrowing and intraluminal filling defects in both breasts. The bilateral resection of the sub-areolar portion of the breast showed exuberant fibrous obliteration of the large- and medium-sized ducts by granulation tissue associated with few histiocytes. Ductal dilatation and intraductal accumulation of histiocytes was also present. This represents a late and florid form of mammary ductal ectasia. Differential diagnostic considerations including fibrocystic changes, diabetic sclerosing lymphocytic lobulitis, idiopathic granulomatous lobular mastitis, and periductal mastitis (Zuska disease) are discussed. Accurate diagnosis can help avoid or limit radical surgeries in this group of patients.