Mammographic breast density and serum phytoestrogen levels.

Some forms of estrogen are associated with breast cancer risk as well as with mammographic density (MD), a strong marker of breast cancer risk. Whether phytoestrogen intake affects breast density, however, remains unclear. We evaluated the association between serum levels of phytoestrogens and MD in postmenopausal women. We enrolled 269 women, ages 55-70 yr, who received a screening mammogram and had no history of postmenopausal hormone use. Subjects completed a survey on diet and factors related to MD and provided a blood sample for analysis of 3 phytoestrogens: genistein, daidzein, and coumestrol. We examined whether mean percent MD was related to serum level of phytoestrogens, adjusting for age and body mass index. Genistein and daidzein levels correlated with self-reported soy consumption. Mean percent MD did not differ across women with different phytoestrogen levels. For example, women with nondetectable genistein levels had mean density of 11.0% [95% confidence intervals (CI) = 9.9-12.4], compared to 10.5% (95% CI = 8.0-13.7) and 11.2% (95% CI = 8.7-14.6) for < and ≥ median detectable levels, respectively. In a population with relatively low soy intake, serum phytoestrogens were not associated with mammographic density. Additional studies are needed to determine effects of higher levels, particularly given patterns of increasing phytoestrogen intake.

Utility of 6-month follow-up imaging after a concordant benign breast biopsy result.

PURPOSE: To determine the utility of 6-month follow-up imaging after benign concordant image-guided percutaneous breast biopsy results. MATERIALS AND METHODS: The institutional review board approved this retrospective, HIPAA-compliant study; informed consent was waived. Findings from consecutive stereotactic and ultrasonographically guided core breast biopsies performed from 2001 to 2005 were analyzed and included lesions with benign pathologic findings without atypia found to be concordant with imaging at a consensus conference. Rebiopsy recommendation rates and positive predictive values (PPVs) for detecting malignancy at each follow-up interval were measured and compared by using a two-tailed Fisher exact test. RESULTS: In 2244 biopsies, lesions in 1465 were benign, concordant, and not excised. In 1057 of 1465 (72.2%) biopsies with imaging follow-up (average, 26.4 months; range, 4.0-49.9 months), recommended rebiopsy rates were 0.8% (four of 526), 0.5% (three of 588), and 1.0% (eight of 802) at 6-month, 12-month, and long-term follow-up intervals, respectively. When the initial follow-up did not occur until 12 months, the recommended rebiopsy rate was 0.9% (three of 322), compared with 0.8% (four of 526) at 6 months (P > .99), and no malignancies were found in either group. One malignancy was detected at the long-term follow-up interval (PPV for excision recommended, 12% [one of eight]; PPV for excision performed, 20% [one of five]). CONCLUSION: Because rebiopsy recommendation rates and PPVs did not differ in the 6- and 12-month groups, a 6-month follow-up imaging examination, in the context of a formal concordancy consensus conference, may not contribute to improved breast cancer diagnosis. SUPPLEMENTAL MATERIAL: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.10091824/-/DC1.

Feasibility of near-infrared diffuse optical spectroscopy on patients undergoing imageguided core-needle biopsy.

We describe a side-firing fiber optic sensor based on near-infrared spectroscopy for guiding core needle biopsy diagnosis of breast cancer. The sensor is composed of three side firing optical fibers (two source fibers and one detection fiber), providing two source-detector separations. The entire assembly is inserted into a core biopsy needle, allowing for sampling to occur at the biopsy site. A multi-wavelength frequency-domain near-infrared instrument is used to collect diffuse reflectance in the breast tissue through an aperture on the biopsy needle before the tissue is removed for histology. Preliminary in vivo measurements performed on 10 normal or benign breast tissues from 5 women undergoing stereo- or ultrasound-guided core needle biopsy show the ability of the system to determine tissue optical properties and constituent concentrations, which are correlated with breast tissue composition derived from histopathology.

Impact of axillary ultrasound and core needle biopsy on the utility of intraoperative frozen section analysis and treatment decision making in women with invasive breast cancer.

BACKGROUND: Our objective was to evaluate the impact of preoperative axillary ultrasound and core needle biopsy (CNB) on breast cancer treatment decision making. A secondary aim was to evaluate the impact on the utility of intraoperative sentinel lymph node (SLN) frozen section. METHODS: A review of 84 patients with clinically negative axilla who underwent axillary ultrasound was performed. Sensitivity, specificity, and positive/negative predictive value for axillary ultrasound with CNB was calculated. RESULTS: Thirty-one (37%) had suspicious nodes. Of 27 amenable to CNB, 12 (14%) were malignant, changing treatment plans. The sensitivity of ultrasound and CNB was 54% and specificity 100%; the positive and negative predictive values were 100% and 80%, respectively. In 41 patients with normal ultrasounds who underwent SLN frozen section, 10 (24%) were positive. CONCLUSIONS: Preoperative axillary ultrasound impacts treatment decision making in 14%. With a sensitivity of 54%, it is a useful adjunct to, but not replacement for, SLN biopsy. Frozen section remains of utility even after a negative axillary ultrasound.

Axial-shear strain imaging for differentiating benign and malignant breast masses.

Axial strain imaging has been utilized for the characterization of breast masses for over a decade; however, another important feature namely the shear strain distribution around breast masses has only recently been used. In this article, we examine the feasibility of utilizing in vivo axial-shear strain imaging for differentiating benign from malignant breast masses. Radio-frequency data was acquired using a VFX 13-5 linear array transducer on 41 patients using a Siemens SONOLINE Antares real-time clinical scanner at the University of Wisconsin Breast Cancer Center. Free-hand palpation using deformations of up to 10% was utilized to generate axial strain and axial-shear strain images using a two-dimensional cross-correlation algorithm from the radio-frequency data loops. Axial-shear strain areas normalized to the lesion size, applied strain and lesion strain contrast was utilized as a feature for differentiating benign from malignant masses. The normalized axial-shear strain area feature estimated on eight patients with malignant tumors and 33 patients with fibroadenomas was utilized to demonstrate its potential for lesion differentiation. Biopsy results were considered the diagnostic standard for comparison. Our results indicate that the normalized axial-shear strain area is significantly larger for malignant tumors compared with benign masses such as fibroadenomas. Axial-shear strain pixel values greater than a specified threshold, including only those with correlation coefficient values greater than 0.75, were overlaid on the corresponding B-mode image to aid in diagnosis. A scatter plot of the normalized area feature demonstrates the feasibility of developing a linear classifier to differentiate benign from malignant masses. The area under the receiver operator characteristic curve utilizing the normalized axial-shear strain area feature was 0.996, demonstrating the potential of this feature to noninvasively differentiate between benign and malignant breast masses.

Fluorescence spectroscopy: an adjunct diagnostic tool to image-guided core needle biopsy of the breast.

We explored the use of a fiber-optic probe for in vivo fluorescence spectroscopy of breast tissues during percutaneous image-guided breast biopsy. A total of 121 biopsy samples with accompanying histological diagnosis were obtained clinically and investigated in this study. The tissue spectra were analyzed using partial least-squares analysis and represented using a set of principal components (PCs) with dramatically reduced data dimension. For nonmalignant tissue samples, a set of PCs that account for the largest amount of variance in the spectra displayed correlation with the percent tissue composition. For all tissue samples, a set of PCs was identified using a Wilcoxon rank-sum test as showing statistically significant differences between: 1) malignant and fibrous/benign; 2) malignant and adipose; and 3) malignant and nonmalignant breast samples. These PCs were used to distinguish malignant from other nonmalignant tissue types using a binary classification scheme based on both linear and nonlinear support vector machine (SVM) and logistic regression (LR). For the sample set investigated in this study, the SVM classifier provided a cross-validated sensitivity and specificity of up to 81% and 87%, respectively, for discrimination between malignant and fibrous/benign samples, and up to 81% and 81%, respectively, for discriminating between malignant and adipose samples. Classification based on LR was used to generate receiver operator curves with an area under the curve (AUC) of 0.87 for discriminating malignant versus fibrous/benign tissues, and an AUC of 0.84 for discriminating malignant from adipose tissue samples. This study demonstrates the feasibility of performing fluorescence spectroscopy during clinical core needle breast biopsy, and the potential of this technique for identifying breast malignancy in vivo.

Unresected Breast Cancer: Evolution of Imaging Findings Following Cryoablation.

Cryoablation has been used to treat both benign and malignant breast tumors. In all but one published case, cryoablation in breast cancer has been followed by post-procedural tumor resection. We present a case of an 85-year-old woman with two nonpalpable breast cancers treated with cryoablation with 18 months of mammographic, ultrasound and histologic follow-up.

Use of microcalcification descriptors in BI-RADS 4th edition to stratify risk of malignancy.

PURPOSE: To retrospectively evaluate whether microcalcification descriptors and the categorization of microcalcification descriptors in the Breast Imaging Reporting and Data System (BI-RADS) 4th edition help stratify the risk of malignancy, by using biopsy and clinical follow-up as reference standards. MATERIALS AND METHODS: The institutional review board approved this HIPAA-compliant study and waived informed consent. The study included 115 women (age range, 26-82 years; mean age, 55.8 years +/- 10.5 [standard deviation]) who consecutively underwent image-guided biopsy of microcalcifications between November 2001 and October 2002. Screening and diagnostic mammograms (including magnification views) obtained before biopsy were analyzed in a blinded manner by a subspecialty-trained breast imager who recorded BI-RADS descriptors on a checklist. The proportion of malignant cases was used as the outcome variable to evaluate the ability of the descriptors to help capture the risk of malignancy. Fisher exact test was used to calculate the difference among the individual descriptors and descriptor categories. RESULTS: The positive predictive value of biopsy for malignancy was 21.7%. Each calcification morphologic descriptor was able to help stratify the probability of malignancy as follows: coarse heterogeneous, one (7%) of 14; amorphous, four (13%) of 30; fine pleomorphic, 10 (29%) of 34; and fine linear, 10 (53%) of 19. Fisher exact test results revealed a significant difference among these descriptor categories (P = .005). Significant differences among the risks suggested by microcalcification distribution descriptors (P = .004) and between that of stability descriptors (P = .001) were found. CONCLUSION: The microcalcification descriptors and categories in BI-RADS 4th edition help predict the risk of malignancy for suspicious microcalcifications.

Differentiating benign from malignant solid breast masses with US strain imaging.

PURPOSE: To prospectively evaluate the sensitivity and specificity of ultrasonographic (US) strain imaging for distinguishing between benign and malignant solid breast masses, with biopsy results as the reference standard. MATERIALS AND METHODS: The study was institutional review board approved and HIPAA compliant. Informed consent was obtained from all participating patients. US strain imaging of 403 breast masses was performed. The 50 malignant and 48 benign lesions (in patients aged 19-83 years; mean age, 49 years +/- 17 [standard deviation]) with the highest quality were selected for the reader study. Three observers blinded to the pathologic outcomes first described the B-mode image findings by using US Breast Imaging Reporting and Data System descriptors and derived a probability of malignancy. They then updated the probability by assessing strain images. Receiver operating characteristic (ROC) curves were constructed by using these probabilities. Areas under the ROC curve, sensitivities, and specificities were calculated and compared. Interobserver variability and the correlation between automated and subjective image quality assessment were analyzed. RESULTS: The average area under the ROC curve for all three readers after US strain imaging (0.903) was greater than that after B-mode US alone (0.876, P = .014). With use of a 2% probability of malignancy threshold, strain imaging-as compared with B-mode US alone-had improved average specificity (0.257 vs 0.132, P < .001) and high sensitivity (0.993 vs 0.987, P > .99). Significant interobserver variability was observed (P < .001). The ability to assess strain image quality appeared to correlate with the highest observer performance. CONCLUSION: US strain imaging can facilitate improved classification of benign and malignant breast masses. However, interobserver variability and image quality influence observer performance.

Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience.

PURPOSE: To retrospectively determine whether a Bayesian network (BN) computer model can accurately predict the probability of breast cancer on the basis of risk factors and mammographic appearance of microcalcifications, to improve the positive predictive value (PPV) of biopsy, with pathologic examination and follow-up as reference standards. MATERIALS AND METHODS: The institutional review board approved this HIPAA-compliant study; informed consent was not required. Results of 111 consecutive image-guided breast biopsies performed for microcalcifications deemed suspicious by radiologists were analyzed. Mammograms obtained before biopsy were analyzed in a blinded manner by a breast imager who recorded Breast Imaging Reporting and Data System (BI-RADS) descriptors and provided a probability of malignancy. The BN uses probabilistic relationships between breast disease and mammography findings to estimate the risk of malignancy. Probability estimates from the radiologist and the BN were used to create receiver operating characteristic (ROC) curves, and area under the ROC curve (A(z)) values were compared. PPV of biopsy was also evaluated on the basis of these probability estimates. RESULTS: The BN and the radiologist achieved A(z) values of 0.919 and 0.916, respectively, which were not significantly different. If the 34 patients estimated by the BN to have less than a 10% probability of malignancy had not undergone biopsy, the PPV of biopsy would have increased from 21.6% to 31.2% without missing a breast cancer (P < .001). At this level, the radiologist's probability estimation improved the PPV to 30.0% (P < .001). CONCLUSION: A probabilistic model that includes BI-RADS descriptors for microcalcifications can distinguish between benign and malignant abnormalities at mammography as well as a breast imaging specialist can and may be able to improve the PPV of image-guided breast biopsy.

The use of batch reading to improve the performance of screening mammography.

OBJECTIVE: The objective of our study was to prove that batch reading of screening mammograms can reduce recall rates without sacrificing cancer detection. MATERIALS AND METHODS: We analyzed recall rate, cancer detection, minimal cancer detection, detection of low-stage cancer, and tumor size from consecutive screening mammography examinations from October 2001 to July 2003. The initial 7,984 mammograms were interpreted in the midst of a busy breast imaging practice. Although these studies were not read online, the interpretations were often interrupted for telephone calls, procedures, and diagnostic mammograms. The remaining 1,538 studies were interpreted after the institution of dedicated uninterrupted batch reading. RESULTS: Recall rates were 20.1% before and 16.2% after the introduction of batch reading (p < 0.001). Cancer detection rates were not significantly different: 5.6 cancers were detected per 1,000 examinations without and 7.2 were detected per 1,000 with batch reading. Prognostic factors for breast cancers diagnosed between these groups also were not significantly different. Of the screening-detected cancers diagnosed before batch reading, minimal cancers comprised 67% and low-stage cancers accounted for 76%. Of the cancers diagnosed using batch reading, 73% were minimal and 91% were low stage. The mean size of cancers, 11.7 mm without batch reading and 9.1 mm with batch reading, also showed no statistically significant difference. CONCLUSION: Our experience shows that batch reading can significantly reduce screening mammography recall rates without affecting the cancer detection rate or the proportion of cancers diagnosed with favorable prognostic indicators.