Brandt, Kathy R.

Abstract Purpose:Quantify in vivo biomechanical tissue properties in various breast densities and in normal risk and high risk women using Magnetic Resonance Imaging (MRI)/MRE and examine the association between breast biomechanical properties and cancer risk.Methods: Patients with normal risk or high risk of breast cancer underent 3.0 T breast MR imaging and elastography. Breast parenchymal enhancement (BPE), density (from most recent mammogram), stiffness, elasticity, and viscosity were recorded. Within each breast density group (non-dense versus dense), stiffness, elasticity, and viscosity were compared across risk groups (normal versus high). A multivariable logistic regression model was used to evaluate whether the MRE parameters (separately for stiffness, elasticity, and viscosity) predicted risk status after controlling for clinical factors.Results: 50 normal risk and 86 high risk patients were included. Risk groups were similar on age, density, and menopausal status. Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high risk patients (N = 55) compared to normal risk patients (N = 34; all p < 0.001). Stiffness remained a significant predictor of risk status (OR=4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, BPE, age, and menopausal status. Similar results were seen for elasticity and viscosity.Conclusion: A structurally-based, quantitative biomarker of tissue stiffness obtained from MRE is associated with differences in breast cancer risk in dense breasts. Tissue stiffness could provide a novel prognostic marker to help identify high risk women with dense breasts who would benefit from increased surveillance and/or risk reduction measures.

10541 Background: Biomechanical tissue properties may vary in the breasts of patients at elevated risk for breast cancer. We aim to quantify in vivo biomechanical tissue properties in various breast densities and in both normal risk and high risk women using Magnetic Resonance Imaging (MRI)/MRE and examine the association of biomechanical properties of the breast with cancer risk. Methods: In this IRB–approved prospective single-institution study, we recruited two groups of women differing by breast cancer risk to undergo a 3.0 T dynamic contrast enhanced MRI/MRE of the breast. Low-average risk women were defined as having no personal or significant family history of breast cancer, no prior high risk breast biopsies and a negative mammography within 12 months. High-risk breast cancer patients were recruited from those patients who underwent standard of care breast MR. Within each breast density group (non-dense versus dense), two-sample t-tests were used to compare breast stiffness, elasticity, and viscosity across risk groups (low-average vs high). Results: There were 50 low-average risk and 86 high-risk patients recruited to the study. The risk groups were similar on age (mean age = 55.6 and 53.6 years), density (68% vs. 64% dense breasts) and menopausal status (66.0% vs. 69.8%). Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high risk patients ( N = 55) compared to low-average risk patients ( N = 34; all p < 0.001). In the multivariate logistic regression model, breast stiffness remained a significant predictor of risk status (OR=4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, MRI BPE, age, and menopausal status. Similar results were seen for breast elasticity (OR=4.88, 95% CI [2.08, 11.43]) and viscosity (OR=11.49, 95% CI [1.15, 114.89]). Conclusions: Structurally-based, quantitative biomarker of tissue stiffness obtained from global 3D breast MRE is associated with differences in breast cancer risk in dense breasts. As such, tissue stiffness could provide a novel prognostic marker to help identify the subset of high-risk women with dense breasts who would benefit from increased surveillance.[Table: see text]

Abstract Background: In an adaptive randomized trial, when new treatment combinations are being tested, it is important to be able to identify patients who are progressing on treatment so that they can be changed to a different therapeutic regimen. We know that even within the molecularly high risk patients in I-SPY 2, there is considerable variation in biology. In this study, we will present results of using MRI-calculated functional tumor volume (FTV) to identify tumor progression for each breast cancer subtype. Methods: Patients (n=990) enrolled in the I-SPY 2 TRIAL who were randomized to the graduated experimental drug arms or controls from 2010 to 2016 were analyzed. Four MRI exams were performed for each patient: pre-NAC (T0), after 3 weeks of NAC (T1), between regimens (T2), and post-NAC (T3). Functional tumor volume (FTV) was calculated at each exam by summing voxels meeting enhancement thresholds. Tumor progression at T1, T2 or T3 was identified by a positive FTV change relative to T0. Visual inspection was used to exclude false progression due to strong background parenchymal enhancement post-contrast, prominent vessels, motion, or insufficient image quality. pCR was defined as no invasive disease in the breast and lymph nodes. Negative predictive value for pCR was defined as:NPV=number of true non-pCRs / number of patients with MRI assessed tumor progressions, where “true non-pCRs” referred to patients who were non-pCRs at surgery and were assessed as progressors by MRI. The analysis was performed in the full cohort and in sub-cohorts defined by HR and HER2 statuses. Results: Out of 990 patients, 878 had pCR outcome data (pCR or non-pCR, pCR rate = 35%). Total and non-pCR numbers for each subtype, number of patients with tumor progression assessed by MRI at T1, T2, and T3, and NPVs, are shown in Table 1. In the full cohort, the NPV increased consistently over treatment, from T1 (NPV=83%) to T2 (93%), and to T3 (100%). The HER2+ cancer subtypes showed fewer MRI-assessed tumor progressions than HER2- subtypes: e.g. 10/209 (5%) vs. 108/669 (16%) at T1. NPV was 100% for HER2+ subtypes at T1 and T2 except for a single misclassification of a HR- tumor at T1. Only 6 tumor progressors, all HER2- were identified at T3, and all were confirmed at surgery as non-pCRs (NPV=100%). For HR+/HER2-, the NPV increased slightly from 89% at T1 to 91% at T2, while triple negative subtype had a more substantial increase, from 78% to 92%. Conclusions: Our study showed strong association between tumor progressors assessed by MRI with true non-pCRs after NAC. For HER2+ tumors, although MRI progressors are rare, they strongly indicate non-pCR at all treatment time points, while HER2- subtypes show more accurate results later in treatment. We are evaluating MRI change at 6 weeks to determine if that time point is sufficient to predict progressors. Table 1 MRI assessed tumor progression at different treatment time pointN/non-pCRs/%non-pCRMRI assessed tumor progressionT1 (after 3 weeks)T2 (inter-regimen)T3 (post-NAC)NNPV (%)NNPV (%)NNPV (%)Full cohort878/572/65%11883.14192.76100%HR+/HER2-344/280/81%4588.91190.93100%HR+/HER2+134/85/63%610021000N/AHR-/HER2+75/23/31%47521000N/Atriple negative325/184/57%6377.82692.33100% Citation Format: Wen Li, Natsuko Onishi, David C Newitt, Jessica Gibbs, Lisa J Wilmes, Ella F Jones, Bonnie N Joe, Laura S Sit, Christina Yau, A. Jo Chien, Elissa Price, Kathy S Albain, Theresa Kuritza, Kevin Morley, Judy C Boughey, Kathy Brandt, Sadia Choudhery, Amy S Clark, Mark Rosen, Elizabeth S McDonald, Anthony D Elias, Dulcy Wolverton, Kelly Fountain, David M Euhus, Heather S Han, Bethany Niell, Jennifer Drukteinis, Julie E Lang, Janice Lu, Jane L Meisel, Zaha Mitri, Rita Nanda, Donald W Northfelt, Tara Sanft, Erica Stringer-Reasor, Rebecca K Viscusi, Anne M Wallace, Douglas Yee, Rachel Yung, Smita M Asare, Michelle E Melisko, Jane Perlmutter, Hope S Rugo, Richard Schwab, W. Fraser Symmans, Laura J van't Veer, Donald A Berry, Angela DeMichele, Hiroyuki Abe, Deepa Sheth, Kirsten K Edmiston, Erin D Ellis, Richard Ha, Ralph Wynn, Erin P Crane, Charlotte Dillis, Michael Nelson, An Church, Claudine Isaacs, Qamar J Khan, Karen Y Oh, Neda Jafarian, Dae Hee Bang, Christiane Mullins, Stefanie Woodard, Kathryn W Zamora, Haydee Ojeda-Fornier, Pulin Sheth, Linda Hovanessian-Larsen, Mohammad Eghtedari, Michael Spektor, Marina Giurescu, Mary S Newell, Michael A Cohen, Elise Berman, Constance Lehman, William Smith, Kim Fitzpatrick, Marisa H Borders, Wei Yang, Basak Dogan, Sally Goudreau, Thelma Brown, Laura J Esserman, Nola M Hylton. Breast cancer subtype specific association of pCR with MRI assessed tumor volume progression during NAC in the I-SPY 2 trial [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD9-04.

Abstract Background: Strong background parenchymal enhancement (BPE) may cause overestimation in tumor volume measured from dynamic contrast-enhanced (DCE) MRI, which may adversely affect the ability of MR tumor volume to predict treatment outcome for patients undergoing neoadjuvant chemotherapy (NAC). Specifically, an overestimation of tumor volume can result in misclassification of patients with complete pathologic response (pCR) as non-responders, leading to less confidence in MRI prediction. As well, overestimation of extent of disease might lead to more aggressive surgical therapy than necessary. This study investigated whether high BPE in the contralateral breast influences the predictive performance of MRI-measured functional tumor volume (FTV) for patients with locally advanced breast cancer undergoing NAC. Methods: patients (n=990) enrolled in the I-SPY 2 TRIAL who were randomized to the graduated experimental drug arms or controls from 2010 to 2016 were analyzed. Each patient had 4 MRI exams: pre-NAC (T0), after 3 weeks of NAC (T1), between NAC regimens (T2), and post-NAC (T3). FTV was calculated at each MRI exam by summing voxels meeting enhancement thresholds. Background parenchymal enhancement (BPE) in the contralateral breast was calculated automatically as mean percentage enhancement on the early (nominal 150sec post-contrast) image in the fibroglandular tissue segmented from 5 continuous axial slices centered in the inferior-to-superior stack. For each treatment time point, patients having both FTV and BPE measurements were included in the analysis. The area under the ROC curve (AUC) was estimated as the association between FTV and pCR at T1, T2, and T3. The analysis was conducted in the full patient cohort and in sub-cohorts defined by hormone receptor (HR) and HER2 status. In each patient cohort, a cut-off BPE value was selected to classify patients with high vs. low BPE by testing AUCs estimated with low-BPE patients reached maximum when the cut-off value varied from median to maximum in steps of 10%. Results: Out of 990 patients, 878 had pCR outcome data (pCR or non-pCR, pCR rate = 35%). Table 1 shows the number of patients, pCR rate, and AUC of FTV for predicting pCR using all patients available vs. a subset patients with low BPE (&lt; BPE cut-off). In the full cohort, AUC increased slightly across all time points after patients with high BPE were removed. In the HR+/HER2- subtype, AUC increased at T1 after removal of cases with high BPE (0.65 vs. 0.71). For HR-/HER2+, AUC increased substantially after removal of high BPE cases (0.65 to 0.86 at T1, 0.71 to 0.87 at T2, and 0.71 to 0.89 at T3), with greater improvement at the early time point (T1) compared to later time points (T2 and T3). Only a slight improvement in the AUC was observed in the HR+/HER2+ and HR-/HER2- subtypes across all time points. Conclusions: High background parenchymal enhancement adversely affected the predictive performance of functional tumor volume measured by DCE-MRI, at early treatment time point for HR+/HER2- and across all time points for HR-/HER2+ cancer subtype. The adverse effect might be offset using subtype-optimized enhancement threshold in calculating functional tumor volume. Table 1 Effect of BPE on the prediction of pCR using FTV at various treatment time pointsT1T2T3npCR rateAUCBPE cut-offnpCR rateAUCBPE cut-offnpCR rateAUCBPE cut-offFullAll64734%0.662762334%0.701761134%0.6925Subset45334%0.6831133%0.7230534%0.72HR+/HER2-All26218%0.651924918%0.718225518%0.7519Subset13118%0.7124818%0.7120419%0.76HR+/HER2+All10636%0.642110538%0.62269634%0.7120Subset5332%0.668438%0.665740%0.73HR-/HER2+All5175%0.65204774%0.71204973%0.7116Subset3073%0.862871%0.872475%0.89HR-/HER2-All22842%0.682822243%0.751821143%0.6916Subset15940%0.7111137%0.7810540%0.75 Citation Format: Wen Li, Natsuko Onishi, David C Newitt, Roy Harnish, Ella F Jones, Lisa J Wilmes, Jessica Gibbs, Elissa Price, Bonnie N Joe, A. Jo Chien, Donald A Berry, Judy C Boughey, Kathy S Albain, Amy S Clark, Kirsten K Edmiston, Anthony D Elias, Erin D Ellis, David M Euhus, Heather S Han, Claudine Isaacs, Qamar J Khan, Julie E Lang, Janice Lu, Jane L Meisel, Zaha Mitri, Rita Nanda, Donald W Northfelt, Tara Sanft, Erica Stringer-Reasor, Rebecca K Viscusi, Anne M Wallace, Douglas Yee, Rachel Yung, Michelle E Melisko, Jane Perlmutter, Hope S Rugo, Richard Schwab, W. Fraser Symmans, Laura J van't Veer, Christina Yau, Smita M Asare, Angela DeMichele, Sally Goudreau, Hiroyuki Abe, Deepa Sheth, Dulcy Wolverton, Kelly Fountain, Richard Ha, Ralph Wynn, Erin P Crane, Charlotte Dillis, Theresa Kuritza, Kevin Morley, Michael Nelson, An Church, Bethany Niell, Jennifer Drukteinis, Karen Y Oh, Neda Jafarian, Kathy Brandt, Sadia Choudhery, Dae Hee Bang, Christiane Mullins, Stefanie Woodard, Kathryn W Zamora, Haydee Ojeda-Fornier, Mohammad Eghedari, Pulin Sheth, Linda Hovanessian-Larsen, Mark Rosen, Elizabeth S McDonald, Michael Spektor, Marina Giurescu, Mary S Newell, Michael A Cohen, Elise Berman, Constance Lehman, William Smith, Kim Fitzpatrick, Marisa H Borders, Wei Yang, Basak Dogan, Laura J Esserman, Nola M Hylton. The effect of background parenchymal enhancement on the predictive performance of functional tumor volume measured in MRI [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-02-01.

Objectives: The purpose of this study is to compare diagnostic outcomes of digital breast tomosynthesis screen-detected masses worked up with mammography first with those evaluated with diagnostic ultrasound initially. Methods: All masses recalled from screening digital breast tomosynthesis between July 1, 2017 and December 31, 2017 that were sent either to diagnostic mammography or ultrasound were compared. Size, shape, margins, visibility on ultrasound, diagnostic assessment and pathology of all masses along with breast density were evaluated. Results: 102/212 digital breast tomosynthesis screen-detected masses were worked up with diagnostic mammography initially and 110/212 were worked up with ultrasound directly. There was no significant difference in ultrasound visibility of masses sent to diagnostic mammography first with those sent to ultrasound first (p = 0.42). 4 (4%) masses sent to mammogram first and 2 (2%) masses sent to ultrasound first were not visualized. There was a significant difference in size between masses that were visualized under ultrasound versus those that were not (p = 0.01), when masses in both groups were assessed cumulatively. Conclusions: 98% of digital breast tomosynthesis screen-detected masses sent to ultrasound directly were adequately assessed without diagnostic mammography. Advances in knowledge: There is potential for avoiding a diagnostic mammogram for evaluation of majority of digital breast tomosynthesis screen-detected masses.

AbstractMammographic percent density (PD) is a strong risk factor for breast cancer, but there has been relatively little systematic evaluation of other features in mammographic images that might additionally predict breast cancer risk. We evaluated the association of a large number of image texture features with risk of breast cancer using a clinic-based case-control study of digitized film mammograms, all with screening mammograms before breast cancer diagnosis. The sample was split into training (123 cases and 258 controls) and validation (123 cases and 264 controls) data sets. Age-adjusted and body mass index (BMI)–adjusted odds ratios (OR) per SD change in the feature, 95% confidence intervals, and the area under the receiver operator characteristic curve (AUC) were obtained using logistic regression. A bootstrap approach was used to identify the strongest features in the training data set, and results for features that validated in the second half of the sample were reported using the full data set. The mean age at mammography was 64.0 ± 10.2 years, and the mean time from mammography to breast cancer was 3.7 ± 1.0 (range, 2.0-5.9 years). PD was associated with breast cancer risk (OR, 1.49; 95% confidence interval, 1.25-1.78). The strongest features that validated from each of several classes (Markovian, run length, Laws, wavelet, and Fourier) showed similar ORs as PD and predicted breast cancer at a similar magnitude (AUC = 0.58-0.60) as PD (AUC = 0.58). All of these features were automatically calculated (unlike PD) and measure texture at a coarse scale. These features were moderately correlated with PD (r = 0.39-0.76), and after adjustment for PD, each of the features attenuated only slightly and retained statistical significance. However, simultaneous inclusion of these features in a model with PD did not significantly improve the ability to predict breast cancer. (Cancer Epidemiol Biomarkers Prev 2009;18(3):837–45)

Purpose To compare magnetic resonance imaging (MRI) and computed tomography (CT) with each other and to International Federation of Gynecology and Obstetrics (FIGO) clinical staging in the pretreatment evaluation of early invasive cervical cancer, using surgicopathologic findings as the reference standard. Patients and Methods This prospective multicenter clinical study was conducted by the American College of Radiology Imaging Network and the Gynecologic Oncology Group from March 2000 to November 2002; 25 United States health centers enrolled 208 consecutive patients with biopsy-confirmed cervical cancer of FIGO stage ≥ IB who were scheduled for surgery based on clinical assessment. Patients underwent FIGO clinical staging, helical CT, and MRI. Surgicopathologic findings constituted the reference standard for statistical analysis. Results Complete data were available for 172 patients; surgicopathologic findings were consistent with FIGO stages IA to IIA in 76% and stage ≥ IIB in 21%. For the detection of advanced stage (≥ IIB), sensitivity was poor for FIGO clinical staging (29%), CT (42%), and MRI (53%); specificity was 99% for FIGO clinical staging, 82% for CT, and 74% for MRI; and negative predictive value was 84% for FIGO clinical staging, 84% for CT, and 85% for MRI. MRI (area under the receiver operating characteristic curve [AUC], 0.88) was significantly better than CT (AUC, 0.73) for detecting cervical tumors (P = .014). For 85% of patients, FIGO clinical staging forms were submitted after MRI and/or CT was performed. Conclusion CT and MRI performed similarly; both had lower staging accuracy than in prior single-institution studies. Accuracy of FIGO clinical staging was higher than previously reported. The temporal data suggest that FIGO clinical staging was influenced by CT and MRI findings.

Abstract Background: Adult mammographic percent density is one of the strongest known risk factors for breast cancer. In utero exposure to high levels of endogenous estrogens (or other pregnancy hormones) has been hypothesized to increase breast cancer risk in later life. We examined the hypothesis that those factors associated with higher levels of estrogen during pregnancy or shortly after birth are associated with higher mammographic breast density in adulthood. Methods: We analyzed data on 1,893 women from 360 families in the Minnesota Breast Cancer Family Study who had screening mammograms, risk factor data, over age 40, and no history of breast cancer. Prenatal and perinatal risk factor data were ascertained using a mailed questionnaire. Mammographic percent density and dense area were estimated from the mediolateral oblique view using Cumulus, a computer-assisted thresholding program. Linear mixed effects models incorporating familial correlation were used to assess the association of risk factors with percent density, adjusting for age, weight, and other breast cancer risk factors, all at time of mammography. Results: The mean age at mammography was 60.4 years (range, 40-91 years), and 76% were postmenopausal. Among postmenopausal women, there was a positive association of birthweight with percent density (P trend &lt;0.01), with an adjusted mean percent density of 17.1% for &lt;2.95 kg versus 21.0% for ≥3.75 kg. There were suggestive positive associations with gestational age (mean percent density of 16.7% for preterm birth, 20.2% for term birth, and 23.0% for late birth; P trend = 0.07), maternal eclampsia/preeclampsia (mean percent density of 19.9% for no and 14.6% for yes; P = 0.16), and being breast-fed as an infant (mean percent density of 18.2% for never and 20.0% for ever; P = 0.08). There was no association of percent density with maternal age, birth order, maternal use of alcohol or cigarettes, or neonatal jaundice. Except for being breast-fed, these associations showed similar but attenuated trends among premenopausal women, although none were statistically significant. The results for dense area paralleled the percent density results. The associations of gestational age and being breast-fed as an infant with percent density attenuated when included in the same model as birthweight. Conclusions: Birthweight was positively associated with mammographic breast density and dense area among postmenopausal women and more weakly among premenopausal women, suggesting that it may be a marker of this early life exposure. These results offer some support to the hypothesis that pregnancy estrogens or other pregnancy changes may play a role in breast cancer etiology, and suggest that these factors may act in part through long-term effects on breast density.