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Background Supplemental screening with MRI has proved beneficial in women with extremely dense breasts. Most MRI examinations show normal anatomic and physiologic variation that may not require radiologic review. Thus, ways to triage these normal MRI examinations to reduce radiologist workload are needed. Purpose To determine the feasibility of an automated triaging method using deep learning (DL) to dismiss the highest number of MRI examinations without lesions while still identifying malignant disease. Materials and Methods This secondary analysis of data from the Dense Tissue and Early Breast Neoplasm Screening, or DENSE, trial evaluated breast MRI examinations from the first screening round performed in eight hospitals between December 2011 and January 2016. A DL model was developed to differentiate between breasts with lesions and breasts without lesions. The model was trained to dismiss breasts with normal phenotypical variation and to triage lesions (Breast Imaging Reporting and Data System [BI-RADS] categories 2-5) using eightfold internal-external validation. The model was trained on data from seven hospitals and tested on data from the eighth hospital, alternating such that each hospital was used once as an external test set. Performance was assessed using receiver operating characteristic analysis. At 100% sensitivity for malignant disease, the fraction of examinations dismissed from radiologic review was estimated. Results A total of 4581 MRI examinations of extremely dense breasts from 4581women (mean age, 54.3 years; interquartile range, 51.5-59.8 years) were included. Of the 9162 breasts, 838 had at least one lesion (BI-RADS category 2-5, of which 77 were malignant) and 8324 had no lesions. At 100% sensitivity for malignant lesions, the DL model considered 90.7% (95% CI: 86.7, 94.7) of the MRI examinations with lesions to be nonnormal and triaged them to radiologic review. The DL model dismissed 39.7% (95% CI: 30.0, 49.4) of the MRI examinations without lesions. The DL model had an average area under the receiver operating characteristic curve of 0.83 (95% CI: 0.80, 0.85) in the differentiation between normal breast MRI examinations and MRI examinations with lesions. Conclusion Automated analysis of breast MRI examinations in women with dense breasts dismissed nearly 40% of MRI scans without lesions while not missing any cancers. ClinicalTrials.gov: NCT01315015 © RSNA, 2021

Background: While several methods have been proposed for automated assessment of breast-cancer response to neoadjuvant chemotherapy on breast MRI, limited information is available about their performance across multiple institutions. Purpose: To assess the value and robustness of deep learning-derived volumes of locally advanced breast cancer (LABC) on MRI to infer the presence of residual disease after neoadjuvant chemotherapy. Study Type: Retrospective. Subjects: Training cohort: 102 consecutive female patients with LABC scheduled for neoadjuvant chemotherapy (NAC) from a single institution (age: 25–73 years). Independent testing cohort: 55 consecutive female patients with LABC from four institutions (age: 25–72 years). Field Strength/Sequence: Training cohort: single vendor 1.5 T or 3.0 T. Testing cohort: multivendor 3.0 T. Gradient echo dynamic contrast-enhanced sequences. Assessment: A convolutional neural network (nnU-Net) was trained to segment LABC. Based on resulting tumor volumes, an extremely randomized tree model was trained to assess residual cancer burden (RCB)-0/I vs. RCB-II/III. An independent model was developed using functional tumor volume (FTV). Models were tested on an independent testing cohort and response assessment performance and robustness across multiple institutions were assessed. Statistical Tests: The receiver operating characteristic (ROC) was used to calculate the area under the ROC curve (AUC). DeLong's method was used to compare AUCs. Correlations were calculated using Pearson's method. P values

BackgroundSeveral single-center studies found that high contralateral parenchymal enhancement (CPE) at breast MRI was associated with improved long-term survival in patients with estrogen receptor (ER)–positive and human epidermal growth factor receptor 2 (HER2)–negative breast cancer. Due to varying sample sizes, population characteristics, and follow-up times, consensus of the association is currently lacking.PurposeTo confirm whether CPE is associated with long-term survival in a large multicenter retrospective cohort, and to investigate if CPE is associated with endocrine therapy effectiveness.Materials and MethodsThis multicenter observational cohort included women with unilateral ER-positive HER2-negative breast cancer (tumor size ≤50 mm and ≤three positive lymph nodes) who underwent MRI from January 2005 to December 2010. Overall survival (OS), recurrence-free survival (RFS), and distant RFS (DRFS) were assessed. Kaplan-Meier analysis was performed to investigate differences in absolute risk after 10 years, stratified according to CPE tertile. Multivariable Cox proportional hazards regression analysis was performed to investigate whether CPE was associated with prognosis and endocrine therapy effectiveness.ResultsOverall, 1432 women (median age, 54 years [IQR, 47–63 years]) were included from 10 centers. Differences in absolute OS after 10 years were stratified according to CPE tertile as follows: 88.5% (95% CI: 88.1, 89.1) in tertile 1, 85.8% (95% CI: 85.2, 86.3) in tertile 2, and 85.9% (95% CI: 85.4, 86.4) in tertile 3. CPE was independently associated with OS, with a hazard ratio (HR) of 1.17 (95% CI: 1.0, 1.36; P = .047), but was not associated with RFS (HR, 1.11; P = .16) or DRFS (HR, 1.11; P = .19). The effect of endocrine therapy on survival could not be accurately assessed; therefore, the association between endocrine therapy efficacy and CPE could not reliably be estimated.ConclusionHigh contralateral parenchymal enhancement was associated with a marginally decreased overall survival in patients with estrogen receptor–positive and human epidermal growth factor receptor 2–negative breast cancer, but was not associated with recurrence-free survival (RFS) or distant RFS.

Objectives Incidental MR-detected breast lesions (ie, additional lesions to the index cancer) pose challenges in the preoperative workup of patients with early breast cancer. We pursue computer-assisted triaging of magnetic resonance imaging (MRI)-guided breast biopsy of additional lesions at high specificity. Materials and methods We investigated 316 consecutive female patients (aged 26 to 76 years; mean, 54 years) with early breast cancer who received preoperative multiparametric breast MRI between 2013 and 2016. In total, 82 (26%) of 316 patients had additional breast lesions on MRI. These 82 patients had 101 additional lesions in total, 51 were benign and 50 were malignant. We collected 4 clinical features and 46 MRI radiomic features from T1-weighted dynamic contrast-enhanced imaging, high-temporal-resolution dynamic contrast-enhanced imaging, T2-weighted imaging, and diffusion-weighted imaging. A multiparametric computer-aided diagnosis (CAD) model using 10-fold cross-validated ridge regression was constructed. The sensitivities were calculated at operating points corresponding to 98%, 95%, and 90% specificity. The model calibration performance was evaluated by calibration plot analysis and goodness-of-fit tests. The model was tested in an independent testing cohort of 187 consecutive patients from 2017 and 2018 (aged 35 to 76 years; mean, 59 years). In this testing cohort, 45 (24%) of 187 patients had 55 additional breast lesions in total, 23 were benign and 32 were malignant. Results The multiparametric CAD model correctly identified 48% of the malignant additional lesions with a specificity of 98%. At specificity 95% and 90%, the sensitivity was 62% and 72%, respectively. Calibration plot analysis and goodness-of-fit tests indicated that the model was well fitted.In the independent testing cohort, the specificity was 96% and the sensitivity 44% at the 98% specificity operating point of the training set. At operating points 95% and 90%, the specificity was 83% at 69% sensitivity and the specificity was 78% at 81% sensitivity, respectively. Conclusions The multiparametric CAD model showed potential to identify malignant disease extension with near-perfect specificity in approximately half the population of preoperative patients originally indicated for a breast biopsy. In the other half, patients would still proceed to MRI-guided biopsy to confirm absence of malignant disease. These findings demonstrate the potential to triage MRI-guided breast biopsy.

Background: Differences in imaging parameters influence computer-extracted parenchymal enhancement measures from breast MRI. Purpose: To investigate the effect of differences in dynamic contrast-enhanced MRI acquisition parameter settings on quantitative parenchymal enhancement of the breast, and to evaluate harmonization of contrast-enhancement values with respect to flip angle and repetition time. Study Type: Retrospective. Phantom/Populations: We modeled parenchymal enhancement using simulations, a phantom, and two cohorts (N = 398 and N = 302) from independent cancer centers. Sequence Field/Strength: 1.5T dynamic contrast-enhanced T1-weighted spoiled gradient echo MRI. Vendors: Philips, Siemens, General Electric Medical Systems. Assessment: We assessed harmonization of parenchymal enhancement in simulations and phantom by varying the MR parameters that influence the amount of T1-weighting: flip angle (8°–25°) and repetition time (4–12 msec). We calculated the median and interquartile range (IQR) of the enhancement values before and after harmonization. In vivo, we assessed overlap of quantitative parenchymal enhancement in the cohorts before and after harmonization using kernel density estimations. Cohort 1 was scanned with flip angle 20° and repetition time 8 msec; cohort 2 with flip angle 10° and repetition time 6 msec. Statistical Tests: Paired Wilcoxon signed-rank-test of bootstrapped kernel density estimations. Results: Before harmonization, simulated enhancement values had a median (IQR) of 0.46 (0.34–0.49). After harmonization, the IQR was reduced: median (IQR): 0.44 (0.44–0.45). In the phantom, the IQR also decreased, median (IQR): 0.96 (0.59–1.22) before harmonization, 0.96 (0.91–1.02) after harmonization. Harmonization yielded significantly (P < 0.001) better overlap in parenchymal enhancement between the cohorts: median (IQR) was 0.46 (0.37–0.58) for cohort 1 vs. 0.37 (0.30–0.44) for cohort 2 before harmonization (57% overlap); and 0.35 (0.28–0.43) vs.0.37 (0.30–0.44) after harmonization (85% overlap). Data Conclusion: The proposed practical harmonization method enables an accurate comparison between patients scanned with differences in imaging parameters. Level of Evidence: 3. Technical Efficacy Stage: 4.

Objectives To investigate whether contralateral parenchymal enhancement (CPE) on MRI during neoadjuvant endocrine therapy (NET) is associated with the preoperative endocrine prognostic index (PEPI) of ER+/HER2− breast cancer. Methods This retrospective observational cohort study included 40 unilateral ER+/HER2− breast cancer patients treated with NET. Patients received NET for 6 to 9 months with MRI response monitoring after 3 and/or 6 months. PEPI was used as endpoint. PEPI is based on surgery-derived pathology (pT- and pN-stage, Ki67, and ER-status) and stratifies patients in three groups with distinct prognoses. Mixed effects and ROC analysis were performed to investigate whether CPE was associated with PEPI and to assess discriminatory ability. Results The median patient age was 61 (interquartile interval: 52, 69). Twelve patients had PEPI-1 (good prognosis), 15 PEPI-2 (intermediate), and 13 PEPI-3 (poor). High pretreatment CPE was associated with PEPI-3: pretreatment CPE was 39.4% higher on average (95% CI = 1.3, 91.9%; p = .047) compared with PEPI-1. CPE decreased after 3 months in PEPI-2 and PEPI-3. The average reduction was 24.4% (95% CI = 2.6, 41.3%; p = .032) in PEPI-2 and 29.2% (95% CI = 7.8, 45.6%; p = .011) in PEPI-3 compared with baseline. Change in CPE was predictive of PEPI-1 vs PEPI-2+3 (AUC = 0.77; 95% CI = 0.57, 0.96). Conclusions CPE during NET is associated with PEPI-group in ER+/HER2− breast cancer: a high pretreatment CPE and a decrease in CPE during NET were associated with a poor prognosis after NET on the basis of PEPI. Key Points • Change in contralateral breast parenchymal enhancement on MRI during neoadjuvant endocrine therapy distinguished between patients with a good and intermediate/poor prognosis at final pathology. • Patients with a poor prognosis at final pathology showed higher baseline parenchymal enhancement on average compared to patients with a good prognosis. • Patients with an intermediate/poor prognosis at final pathology showed a higher average reduction in parenchymal enhancement after 3 months of neoadjuvant endocrine therapy.

IntroductionThe response to neoadjuvant chemotherapy (NAC) in breast cancer has important prognostic implications. Dynamic prediction of tumour regression by NAC may allow for adaption of the treatment plan before completion, or even before the start of treatment. Such predictions may help prevent overtreatment and related toxicity and correct for undertreatment with ineffective regimens. Current imaging methods are not able to fully predict the efficacy of NAC. To successfully improve response prediction, tumour biology and heterogeneity as well as treatment-induced changes have to be considered. In the LIMA study, multiparametric MRI will be combined with liquid biopsies. In addition to conventional clinical and pathological information, these methods may give complementary information at multiple time points during treatment.AimTo combine multiparametric MRI and liquid biopsies in patients with breast cancer to predict residual cancer burden (RCB) after NAC, in adjunct to standard clinico-pathological information. Predictions will be made before the start of NAC, approximately halfway during treatment and after completion of NAC.MethodsIn this multicentre prospective observational study we aim to enrol 100 patients. Multiparametric MRI will be performed prior to NAC, approximately halfway and after completion of NAC. Liquid biopsies will be obtained immediately prior to every cycle of chemotherapy and after completion of NAC. The primary endpoint is RCB in the surgical resection specimen following NAC. Collected data will primarily be analysed using multivariable techniques such as penalised regression techniques.Ethics and disseminationMedical Research Ethics Committee Utrecht has approved this study (NL67308.041.19). Informed consent will be obtained from each participant. All data are anonymised before publication. The findings of this study will be submitted to international peer-reviewed journals.Trial registration numberNCT04223492.

Background The response to neoadjuvant chemotherapy (NAC) in early stage breast cancer has important prognostic implications1-3. Early, dynamic prediction of response allows for adaption of the treatment plan before completion, or even before the start of treatment. This strategy can help prevent overtreatment and related toxicity and correct for undertreatment with an ineffective regimen. We hypothesize that accurate dynamic response prediction may be reached by combining multi-parametric MRI with liquid biopsies prior to, during and after NAC, in addition to conventional clinical and pathological information. Magnetic resonance imaging (MRI) is non-invasive and is typically used for response evaluation in current clinical practice. It shows the size and perfusion of the tumor as they change during treatment. However, tumor size on MRI has limited predictive value for response to therapy4. Multi-parametric MRI uses different imaging protocols in one session to measure more functional items than perfusion alone, addressing different aspects of tumor biology, and possibly improving predictive value. With this improvement, imaging still only visualizes macroscopic disease. Therefore, in the LIMA study, MRI will be combined with liquid biopsies containing circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), which have both shown prognostic and predictive values in early stage breast cancer5-7. Since the ctDNA may originate from cells in every part of the tumor, it may capture tumor heterogeneity. Liquid biopsies are minimally invasive and provide insight into microscopic tumor load and the tumor’s genetic picture. There is an abundance of clinical studies investigating the predictive value of liquid biopsies and MRI. On breast cancer however, no studies have been published that combine these two modalities. Aim show proof of concept for combining multi-parametric MRI with liquid biopsies in addition to conventional clinical and pathologic information, to accurately predict response to NAC at different time points. Trial design A multicenter prospective observational cohort study. Multi-parametric MRI will be performed prior to NAC, halfway and after completion of NAC. Liquid biopsies will be obtained before start of treatment, every 2 weeks during treatment and after completion of NAC. Statistical methods The primary endpoint is Residual Cancer Burden (RCB) in the surgical resection specimen after NAC. Collected data will be analyzed with efficient statistical methods dedicated to sparse datasets primarily using penalized regression techniques. Eligibility criteria Non-pregnant, non-lactating women ≥18 years of age with non-metastatic, histologically proven invasive breast cancer who are planned to be treated with NAC (and in case of a HER2-positive tumor: addition of trastuzumab and/or pertuzumab) who have: - no luminal A breast cancer - no inflammatory breast cancer - no other active malignant disease in the past 5 years (excluding squamous cell or basal cell carcinoma of the skin) -no contra-indications for MRI or gadolinium-based contrast agents Target accrual 100 patients (present: 0) References 1von Minckwitz 2012 J Clin Oncol; 2Cortazar 2014 Lancet; 3Symmans 2007 J Clin Oncol; 4Gu 2017 Clin Breast Cancer; 5Bidard 2018 J Natl Cancer Inst; 6Garcia-Murillas 2015 Sci Transl Med 7Magbanua 2018 SABCS p. Abstract No. PD2-01 Acknowledgements Funding from the European Union Horizon 2020 research and innovation program under grant agreement no. 755333 (LIMA). In collaboration with Philips, INSERM, ICM, Agena Bioscience, ANGLE, Stilla Technologies, DiaDx and ALS Automated Lab Solutions. Citation Format: Liselore Maria Janssen, Britt Berendine Maria Suelmann, Markus Hendrik Adriaan Janse, Sjoerd Geert Elias, Paul J van Diest, Wouter Bernard Veldhuis, Elsken van der Wall, Kenneth George Antonius Gilhuijs. Combining MRI and liquid biopsies for prediction of response to neoadjuvant chemotherapy in early stage breast cancer: The LIMA study [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 OT1-03-02.

Purpose: DWI is a promising modality in breast MRI, but its clinical acceptance is slow. Analysis of DWI is hampered by geometric distortion artifacts, which are caused by off-resonant spins in combination with the low phase-encoding bandwidth of the EPI sequence used. Existing correction methods assume smooth off-resonance fields, which we show to be invalid in the human breast, where high discontinuities arise at tissue interfaces. Methods: We developed a distortion correction method that incorporates high-resolution off-resonance maps to better solve for severe distortions at tissue interfaces. The method was evaluated quantitatively both ex vivo in a porcine tissue phantom and in vivo in 5 healthy volunteers. The added value of high-resolution off-resonance maps was tested using a Wilcoxon signed rank test comparing the quantitative results obtained with a low-resolution off-resonance map with those obtained with a high-resolution map. Results: Distortion correction using low-resolution off-resonance maps corrected most of the distortions, as expected. Still, all quantitative comparison metrics showed increased conformity between the corrected EPI images and a high-bandwidth reference scan for both the ex vivo and in vivo experiments. All metrics showed a significant improvement when a high-resolution off-resonance map was used (P < 0.05), in particular at tissue boundaries. Conclusion: The use of off-resonance maps of a resolution higher than EPI scans significantly improves upon existing distortion correction techniques, specifically by superior correction at glandular tissue boundaries.

Incidental MR-detected breast lesions (ie, additional lesions to the index cancer) pose challenges in the preoperative workup of patients with early breast cancer. We pursue computer-assisted triaging of magnetic resonance imaging (MRI)-guided breast biopsy of additional lesions at high specificity.We investigated 316 consecutive female patients (aged 26 to 76 years; mean, 54 years) with early breast cancer who received preoperative multiparametric breast MRI between 2013 and 2016. In total, 82 (26%) of 316 patients had additional breast lesions on MRI. These 82 patients had 101 additional lesions in total, 51 were benign and 50 were malignant. We collected 4 clinical features and 46 MRI radiomic features from T1-weighted dynamic contrast-enhanced imaging, high-temporal-resolution dynamic contrast-enhanced imaging, T2-weighted imaging, and diffusion-weighted imaging. A multiparametric computer-aided diagnosis (CAD) model using 10-fold cross-validated ridge regression was constructed. The sensitivities were calculated at operating points corresponding to 98%, 95%, and 90% specificity. The model calibration performance was evaluated by calibration plot analysis and goodness-of-fit tests. The model was tested in an independent testing cohort of 187 consecutive patients from 2017 and 2018 (aged 35 to 76 years; mean, 59 years). In this testing cohort, 45 (24%) of 187 patients had 55 additional breast lesions in total, 23 were benign and 32 were malignant.The multiparametric CAD model correctly identified 48% of the malignant additional lesions with a specificity of 98%. At specificity 95% and 90%, the sensitivity was 62% and 72%, respectively. Calibration plot analysis and goodness-of-fit tests indicated that the model was well fitted.In the independent testing cohort, the specificity was 96% and the sensitivity 44% at the 98% specificity operating point of the training set. At operating points 95% and 90%, the specificity was 83% at 69% sensitivity and the specificity was 78% at 81% sensitivity, respectively.The multiparametric CAD model showed potential to identify malignant disease extension with near-perfect specificity in approximately half the population of preoperative patients originally indicated for a breast biopsy. In the other half, patients would still proceed to MRI-guided biopsy to confirm absence of malignant disease. These findings demonstrate the potential to triage MRI-guided breast biopsy.

Purpose To assess whether contralateral parenchymal enhancement (CPE) on MRI is associated with gene expression pathways in ER+/HER2-breast cancer, and if so, whether such pathways are related to survival. Methods Preoperative breast MRIs were analyzed of early ER+/HER2-breast cancer patients eligible for breast-conserving surgery included in a prospective observational cohort study (MARGINS). The contralateral parenchyma was segmented and CPE was calculated as the average of the top-10% delayed enhancement. Total tumor RNA sequencing was performed and gene set enrichment analysis was used to reveal gene expression pathways associated with CPE (N = 226) and related to overall survival (OS) and invasive disease-free survival (IDFS) in multivariable survival analysis. The latter was also done for the METABRIC cohort (N = 1355). Results CPE was most strongly correlated with proteasome pathways (normalized enrichment statistic = 2.04, false discovery rate = .11). Patients with high CPE showed lower tumor proteasome gene expression. Proteasome gene expression had a hazard ratio (HR) of 1.40 (95% CI = 0.89, 2.16; P = .143) for OS in the MARGINS cohort and 1.53 (95% CI = 1.08, 2.14; P = .017) for IDFS, in METABRIC proteasome gene expression had an HR of 1.09 (95% CI = 1.01, 1.18; P = .020) for OS and 1.10 (95% CI = 1.02, 1.18; P = .012) for IDFS. Conclusion CPE was negatively correlated with tumor proteasome gene expression in early ER+/HER2-breast cancer patients. Low tumor proteasome gene expression was associated with improved survival in the METABRIC data., Highlights • Contralateral parenchymal enhancement on MRI was associated with tumor proteasome gene expression in ER+/HER2-breast cancer. • A high contralateral parenchymal enhancement was associated with a low proteasome gene expression in the breast cancer. • Low proteasome tumor gene expression was associated with improved survival in an independent patient cohort.

To purpose of this paper was to assess the feasibility of volumetric breast density estimations on MRI without segmentations accompanied with an explainability step. A total of 615 patients with breast cancer were included for volumetric breast density estimation. A 3-dimensional regression convolutional neural network (CNN) was used to estimate the volumetric breast density. Patients were split in training (N = 400), validation (N = 50), and hold-out test set (N = 165). Hyperparameters were optimized using Neural Network Intelligence and augmentations consisted of translations and rotations. The estimated densities were evaluated to the ground truth using Spearman’s correlation and Bland–Altman plots. The output of the CNN was visually analyzed using SHapley Additive exPlanations (SHAP). Spearman’s correlation between estimated and ground truth density was ρ = 0.81 (N = 165, P

Purpose: Inhomogeneous excitation at ultrahigh field strengths (7T and above) compromises the reliability of quantified dynamic contrast-enhanced breast MRI. This can hamper the introduction of ultrahigh field MRI into the clinic. Compensation for this non-uniformity effect can consist of both hardware improvements and post-acquisition corrections. This paper investigated the correctable radiofrequency transmit ((Formula presented.)) range post-acquisition in both simulations and patient data for 7T MRI. Methods: Simulations were conducted to determine the minimum (Formula presented.) level at which corrections were still beneficial because of noise amplification. Two correction strategies leading to differences in noise amplification were tested. The effect of the corrections on a 7T patient data set (N = 38) with a wide range of (Formula presented.) levels was investigated in terms of time-intensity curve types as well as washin, washout and peak enhancement values. Results: In simulations assuming a common amount of T 1 saturation, the lowest (Formula presented.) level at which the SNR of the corrected images was at least that of the original precontrast image was 43% of the nominal angle. After correction, time-intensity curve types changed in 24% of included patients, and the distribution of curve types corresponded better to the distribution found in literature. Additionally, the overlap between the distributions of washin, washout, and peak enhancement values for grade 1 and grade 2 tumors was slightly reduced. Conclusion: Although the correctable range varies with the amount of T 1 saturation, post-acquisition correction for inhomogeneous excitation was feasible down to (Formula presented.) levels of 43% of the nominal angle in vivo.

Objectives To reduce the number of false-positive diagnoses in the screening of women with extremely dense breasts using magnetic resonance imaging (MRI), we aimed to predict which BI-RADS 3 and BI-RADS 4 lesions are benign. For this purpose, we use computer-aided diagnosis (CAD) based on multiparametric assessment. Materials and methods Consecutive data were used from the first screening round of the DENSE (Dense Tissue and Early Breast Neoplasm Screening) trial. In this trial, asymptomatic women with a negative screening mammography and extremely dense breasts were screened using multiparametric MRI. In total, 4783 women, aged 50 to 75 years, enrolled and were screened in 8 participating hospitals between December 2011 and January 2016. In total, 525 lesions in 454 women were given a BI-RADS 3 (n = 202), 4 (n = 304), or 5 score (n = 19). Of these lesions, 444 were benign and 81 were malignant on histologic examination.The MRI protocol consisted of 5 different MRI sequences: T1-weighted imaging without fat suppression, diffusion-weighted imaging, T1-weighted contrast-enhanced images at high spatial resolution, T1-weighted contrast-enhanced images at high temporal resolution, and T2-weighted imaging. A machine-learning method was developed to predict, without deterioration of sensitivity, which of the BI-RADS 3- and BI-RADS 4-scored lesions are actually benign and could be prevented from being recalled. BI-RADS 5 lesions were only used for training, because the gain in preventing false-positive diagnoses is expected to be low in this group. The CAD consists of 2 stages: feature extraction and lesion classification. Two groups of features were extracted: the first based on all multiparametric sequences, the second based only on sequences that are typically used in abbreviated MRI protocols. In the first group, 49 features were used as candidate predictors: 46 were automatically calculated from the MRI scans, supplemented with 3 clinical features (age, body mass index, and BI-RADS score). In the second group, 36 image features and the same 3 clinical features were used. Each group was considered separately in a machine-learning model to differentiate between benign and malignant lesions. We developed a Ridge regression model using 10-fold cross validation. Performance of the models was analyzed using an accuracy measure curve and receiver-operating characteristic analysis. Results Of the total number of BI-RADS 3 and BI-RADS 4 lesions referred to additional MRI or biopsy, 425/487 (87.3%) were false-positive. The full multiparametric model classified 176 (41.5%) and the abbreviated-protocol model classified 111 (26.2%) of the 425 false-positive BI-RADS 3- and BI-RADS 4-scored lesions as benign without missing a malignant lesion.If the full multiparametric CAD had been used to aid in referral, recall for biopsy or repeat MRI could have been reduced from 425/487 (87.3%) to 311/487 (63.9%) lesions. For the abbreviated protocol, it could have been 376/487 (77.2%). Conclusions Dedicated multiparametric CAD of breast MRI for BI-RADS 3 and 4 lesions in screening of women with extremely dense breasts has the potential to reduce false-positive diagnoses and consequently to reduce the number of biopsies without missing cancers.

Background Differences in imaging parameters influence computer‐extracted parenchymal enhancement measures from breast MRI. Purpose To investigate the effect of differences in dynamic contrast‐enhanced MRI acquisition parameter settings on quantitative parenchymal enhancement of the breast, and to evaluate harmonization of contrast‐enhancement values with respect to flip angle and repetition time. Study Type Retrospective. Phantom/Populations We modeled parenchymal enhancement using simulations, a phantom, and two cohorts (N = 398 and N = 302) from independent cancer centers. Sequence Field/Strength 1.5T dynamic contrast‐enhanced T1‐weighted spoiled gradient echo MRI. Vendors: Philips, Siemens, General Electric Medical Systems. Assessment We assessed harmonization of parenchymal enhancement in simulations and phantom by varying the MR parameters that influence the amount of T1‐weighting: flip angle (8°–25°) and repetition time (4–12 msec). We calculated the median and interquartile range (IQR) of the enhancement values before and after harmonization. In vivo, we assessed overlap of quantitative parenchymal enhancement in the cohorts before and after harmonization using kernel density estimations. Cohort 1 was scanned with flip angle 20° and repetition time 8 msec; cohort 2 with flip angle 10° and repetition time 6 msec. Statistical Tests Paired Wilcoxon signed‐rank‐test of bootstrapped kernel density estimations. Results Before harmonization, simulated enhancement values had a median (IQR) of 0.46 (0.34–0.49). After harmonization, the IQR was reduced: median (IQR): 0.44 (0.44–0.45). In the phantom, the IQR also decreased, median (IQR): 0.96 (0.59–1.22) before harmonization, 0.96 (0.91–1.02) after harmonization. Harmonization yielded significantly (P

Breast density, which is the ratio between fibroglandular tissue (FGT) and total breast volume, can be assessed qualitatively by radiologists and quantitatively by computer algorithms. These algorithms often rely on segmentation of breast and FGT volume. In this study, we propose a method to directly assess breast density on MRI, and provide interpretations of these assessments. We assessed breast density in 506 patients with breast cancer using a regression convolutional neural network (CNN). The input for the CNN were slices of breast MRI of 128 x 128 voxels, and the output was a continuous density value between 0 (fatty breast) and 1 (dense breast). We used 350 patients to train the CNN, 75 for validation, and 81 for independent testing. We investigated why the CNN came to its predicted density using Deep SHapley Additive exPlanations (SHAP). The density predicted by the CNN on the testing set was significantly correlated with the ground truth densities (N = 81 patients, Spearman's rho = 0.86, P < 0.001). When inspecting what the CNN based its predictions on, we found that voxels in FGT commonly had positive SHAP-values, voxels in fatty tissue commonly had negative SHAP-values, and voxels in non-breast tissue commonly had SHAP-values near zero. This means that the prediction of density is based on the structures we expect it to be based on, namely FGT and fatty tissue. To conclude, we presented an interpretable deep learning regression method for breast density estimation on MRI with promising results., This paper has been published as: Van der Velden, B.H.M., Ragusi, M.A.A., Janse, M.H.A., Loo, C.E., Gilhuijs, K.G.A. "Interpretable deep learning regression for breast density estimation on MRI." Medical Imaging 2020: Computer-Aided Diagnosis. Vol. 11314. International Society for Optics and Photonics, 2020

Background Better understanding of the molecular biology associated with MRI phenotypes may aid in the diagnosis and treatment of breast cancer. Purpose To discover the associations between MRI phenotypes of breast cancer and their underlying molecular biology derived from gene expression data. Materials and Methods This is a secondary analysis of the Multimodality Analysis and Radiologic Guidance in Breast-Conserving Therapy, or MARGINS, study. MARGINS included patients eligible for breast-conserving therapy between November 2000 and December 2008 for preoperative breast MRI. Tumor RNA was collected for sequencing from surgical specimen. Twenty-one computer-generated MRI features of tumors were condensed into seven MRI factors related to tumor size, shape, initial enhancement, late enhancement, smoothness of enhancement, sharpness, and sharpness variation. These factors were associated with gene expression levels from RNA sequencing by using gene set enrichment analysis. Statistical significance of these associations was evaluated by using a sample permutation test and the false discovery rate. Results Gene expression and MRI data were obtained for 295 patients (mean age, 56 years ± 10.3 [standard deviation]). Larger and more irregular tumors showed increased expression of cell cycle and DNA damage checkpoint genes (false discovery rate0.25; normalized enrichment statistic [NES], 2.15). Enhancement and sharpness of the tumor margin were associated with expression of ribosomal proteins (false discovery rate0.25; NES, 1.95). Smoothness of enhancement, tumor size, and tumor shape were associated with expression of genes involved in the extracellular matrix (false discovery rate0.25; NES, 2.25). Conclusion Breast cancer MRI phenotypes were related to their underlying molecular biology revealed by using RNA sequencing. The association between enhancements and sharpness of the tumor margin with the ribosome suggests that these MRI features may be imaging biomarkers for drugs targeting the ribosome. © RSNA, 2020

Current unsupervised deep learning-based image registration methods are trained with mean squares or nor- malized cross correlation as a similarity metric. These metrics are suitable for registration of images where a linear relation between image intensities exists. When such a relation is absent knowledge from conventional image registration literature suggests the use of mutual information. In this work we investigate whether mutual information can be used as a loss for unsupervised deep learning image registration by evaluating it on two datasets: breast dynamic contrast-enhanced MR and cardiac MR images. The results show that training with mutual information as a loss gives on par performance compared with conventional image registration in contrast enhanced images, and the results show that it is generally applicable since it has on par performance compared with normalized cross correlation in single-modality registration.

Purpose To retrospectively explore the relation between parenchymal enhancement of the healthy contralateral breast on dynamic contrast-enhanced magnetic resonance imaging (MRI) and genomic tests for estrogen receptor (ER)-pathway activity in patients with ER-positive/HER2-negative cancer. Methods A subset of 227 consecutively included patients with unilateral invasive ER-positive/HER2-negative breast cancer underwent dynamic contrast-enhanced MRI prior to breast-conserving therapy between 2000 and 2008. Perfusion of the parenchyma in the healthy breast was assessed using a previously reported measure of contralateral parenchymal enhancement (CPE), consisting of the mean of the top-10% late enhancement. ER-pathway activity was assessed from the surgical resection specimen by the previously reported sensitivity to endocrine therapy (SET)-index and ER-factor. The SET-index is a genetic test to estimate survival benefit from endocrine therapy, consisting of genes related to the ESR1 gene. The ER-factor examines other factors as well including protein expression. The relation between CPE and ER-pathway activity was modeled using linear regression. Results Patients had a median age of 59 years. CPE was not significantly associated with the SET-index (R-squared = 0.005) nor the ER-factor (R-squared = 0.0002). The only variable significantly different between low and high CPE was age at diagnosis (P Conclusions Contralateral parenchymal enhancement on dynamic contrast-enhanced MRI was not associated with tumor-derived estrogen receptor pathway activity.

To assess whether contralateral parenchymal enhancement reproduces as an independent biomarker for patient survival in an independent patient cohort from a different cancer institution. This is a HIPAA-compliant IRB approved retrospective study. Patients with ER-positive/HER2-negative operable invasive ductal carcinoma and preoperative dynamic contrast-enhanced MRI were consecutively included between 2005 and 2009. The parenchyma of the breast contralateral to known cancer was segmented automatically on MRI and contralateral parenchymal enhancement (CPE) was calculated. CPE was split into tertiles and tested for association with invasive disease-free survival (IDFS) and overall survival (OS). Propensity score analysis with inverse probability weighting (IPW) was used to adjust CPE for patient and tumour characteristics as well as systemic therapy. Three hundred and two patients were included. The median age at diagnosis was 48 years (interquartile range, 42-57). Median follow-up was 88 months (interquartile range, 76-102); 15/302 (5%) patients died and 37/302 (13%) had a recurrence or died. In context of multivariable analysis, IPW-adjusted CPE was associated with IDFS [hazard ratio (HR) = 0.27, 95% confidence interval (CI) = 0.05-0.68, p = 0.004] and OS (HR = 0.22, 95% CI = 0.00-0.83, p = 0.032). Contralateral parenchymal enhancement on pre-treatment dynamic contrast-enhanced MRI as an independent biomarker of survival in patients with ER-positive/HER2-negative breast cancer has been upheld in this study. These findings are a promising next step towards a practical and inexpensive test for risk stratification of ER-positive/HER2-negative breast cancer. • High parenchymal-enhancement in the disease-free contralateral breast reproduces as biomarker for survival. • This is in patients with ER-positive/HER2-negative breast cancer from an independent cancer centre. • This is independent of patient and pathology parameters and systemic therapy.

PURPOSE: In breast-conserving surgery (BCS), the cancer is sometimes incompletely excised, leading to reduced patient survival. To pursue complete excisions, radioactive seed localization (RSL) may be used to insert an iodine-125 seed into the tumor. The seed is used as a marker for the location of the tumor during surgery. RSL does not, however, show the extent of the tumor. Based on RSL, we pursue to visualize the seed location together with the tumor extent from diagnostic images. METHODS: A system with two gamma camera heads and two parallel-hole collimators was recently proposed to triangulate the location of an iodine-125 seed during BCS. In the present study, this system was extended with a range camera to visualize a sphere centered on the seed in relation to the breast. This sphere contains the entire tumor and thus defines the target volume for BCS. Physical experiments with acrylic block phantoms (thickness ranging from 3.5 to 6.5 cm) were performed to assess the absolute bias and the precision with which this sphere can be visualized. RESULTS: When a 6.5 cm thick phantom was used, along the horizontal plane, the target volume was visualized with an absolute bias and a precision of 2.1 mm and 0.8 mm, respectively. Along the vertical axis (i.e., Z-axis), these values were 4.2 mm and 2.8 mm, respectively. CONCLUSIONS: The proposed system visualizes the target volume with an absolute bias that may be acceptable for BCS. This article is protected by copyright. All rights reserved.

We present a radiomics model to discriminate between patients at low risk and those at high risk of treatment failure at long-term follow-up based on eigentumors: principal components computed from volumes encompassing tumors in washin and washout images of pre-treatment dynamic contrast-enhanced (DCE-) MR images. Eigentumors were computed from the images of 563 patients from the MARGINS study. Subsequently, a least absolute shrinkage selection operator (LASSO) selected candidates from the components that contained 90% of the variance of the data. The model for prediction of survival after treatment (median follow-up time 86 months) was based on logistic regression. Receiver operating characteristic (ROC) analysis was applied and area-under-the-curve (AUC) values were computed as measures of training and cross-validated performances. The discriminating potential of the model was confirmed using Kaplan-Meier survival curves and log-rank tests. From the 322 principal components that explained 90% of the variance of the data, the LASSO selected 28 components. The ROC curves of the model yielded AUC values of 0.88, 0.77 and 0.73, for the training, leave-one-out cross-validated and bootstrapped performances, respectively. The bootstrapped Kaplan-Meier survival curves confirmed significant separation for all tumors (P < 0.0001). Survival analysis on immunohistochemical subgroups shows significant separation for the estrogen-receptor (ER) subtype tumors (P < 0.0001) and the triple-negative (TN) subtype tumors (P=0.0039), but not for tumors of the HER2 subtype (P=0.41). The results of this retrospective study show the potential of early-stage pre-treatment eigentumors for use in prediction of treatment failure of breast cancer.&#13.

PURPOSE: Segmentation of the chest wall, is an important component of methods for automated analysis of breast magnetic resonance imaging (MRI). Methods reported to date show promising results but have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Knowledge-based methods (KBMs) as well as methods based on deep learning have been proposed, but a systematic comparison of these approaches within one cohort of images is currently lacking. Therefore, we developed a KBM and a deep learning method for segmentation of the chest wall in MRI of dense breasts and compared their performances. METHODS: Two automated methods were developed, an optimized KBM incorporating heuristics aimed at shape, location, and gradient features, and a deep learning-based method (DLM) using a dilated convolution neural network. A data set of 115 T1-weighted MR images was randomly selected from MR images of women with extremely dense breasts (ACR BI-RADS category 4) participating in a screening trial of women (mean age 56.6 yr, range 49.5-75.2 yr) with dense breasts. Manual segmentations of the chest wall, acquired under supervision of an experienced breast radiologist, were available for all data sets. Both methods were optimized using the same randomly selected 36 MRI data sets from a total of 115 data sets. Each MR data set consisted of 179 transversal images with voxel size 0.64 mm 3 × 0.64 mm 3 × 1.00 mm 3 . In the remaining 79 data sets, the results of both segmentation methods were qualitatively evaluated. A radiologist reviewed the segmentation results of both methods in all transversal images (n = 14 141) and determined whether the result would impact the ability to accurately determine the volume of fibroglandular and fatty tissue and whether segmentations masked breast regions that might harbor lesions. When no relevant deviation was detected, the result was considered successful. In addition, all segmentations were quantitatively assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD), 95th percentile of the Hausdorff distance (HD95), false positive fraction (FPF), and false negative fraction (FNF) metrics. RESULTS: According to the radiologist's evaluation, the DLM had a significantly higher success rate than the KBM (81.6% vs 78.4%, P < 0.01). The success rate was further improved to 92.1% by combining both methods. Similarly, the DLM had significantly lower values for FNF (0.003 ± 0.003 vs 0.009 ± 0.011, P < 0.01) and HD95 (2.58 ± 1.78 mm vs 3.37 ± 2.11, P < 0.01). However, the KBM resulted in a significantly lower FPF than the DLM (0.018 ± 0.009 vs 0.030 ± 0.009, P < 0.01).There was no significant difference between the KBM and DLM in terms of DSC (0.982 ± 0.006 vs 0.984 ± 0.008, P = 0.08) or HD (24.14 ± 20.69 mm vs 12.81 ± 27.28 mm, P = 0.05). CONCLUSION: Both optimized knowledge-based and DLM showed good results to segment the pectoral muscle in women with dense breasts. Qualitatively assessed, the DLM was the most robust method. A quantitative comparison, however, did not indicate a preference for one method over the other.

Purpose: The aim of the study was to compare simple SUVmax and SUVpeak based segmentation methods for calculating the lung tumour volume, compared to a pathology ground truth.Methods: Thirty patients diagnosed with early stage Non-Small Cell lung cancer (NSCLC) underwent surgical resection in the Netherlands between 2006 and 2008. FDG PET-CT scans for these patients were acquired within a median of 20 days before surgery. The tumour volume for each percentage SUVmax and SUVpeak threshold, with and without background correction, was calculated for each patient. The percentage threshold that provided the tumour volume that corresponded best with the pathology volume was considered to be the optimal threshold. The optimal thresholds were plotted as a function of tumour volume using a power law function and cross validated using the leave-one-out technique.Results: The mean optimal percentage threshold was 50% +/- 10% and 62% +/- 15% for the SUVmax and SUVpeak without background correction respectively and 47% +/- 10% and 60 +/- 15% for the SUVmax and SUVpeak with background correction respectively. The optimal threshold curves could be fitted well with power law function. After cross validation the correlation between the effective tumour diameter in pathology and autosegmentation was 0.900 and 0.905 for the SUVmax and SUVpeak without background correction respectively and 0.913 and 0.908 for the SUVmax and SUVpeak with background correction respectively.Conclusion: No benefit was shown on clinical data for the SUVpeak based segmentation method over a SUVmax based one. Both methods can be used to determine the tumour volumes in resected NSCLC tumours. (C) 2018 Elsevier B.V. All rights reserved.

In the treatment of breast cancer, breast-conserving surgery (BCS) may result in incomplete excision of the tumor. In order to improve the outcome of this surgery, iodine-125 seeds may be inserted in the tumor under mammographic or ultrasonic guidance prior to surgery. These seeds provide intraoperative information about the location of the tumor. A system of two gamma camera heads and two parallel-hole collimators was recently proposed to record projection images from which the location of a single radioactive seed can be derived. In the present study, computer simulations were used to evaluate the influence of two standard and 95 non-standard collimators on the accuracy and precision with which this location can be derived. Simulation results indicate that this location can be derived with accuracies ± precisions of 5.2 mm ± 1.8 mm and 5.6 mm ± 2.1 mm if the system is equipped with standard collimators. Simulation results also indicate that this location can be derived with an accuracy ± precision of 4.9 mm ± 1.7 mm if the system is equipped with non-standard collimators (each of these collimators had a resolution of 5 cm at a collimator-source distance of 75 cm). These results were observed when the signal from the radioactive seed with an activity of 7.4 MBq was recorded at a collimator-source distance of 75 cm with an imaging time of 2.5 s (this seed was positioned within a 6.5 cm thick block phantom). In conclusion, these results indicate that the accuracy with which the 3D location of a single radioactive seed can be established during BCS by this gamma camera system cannot be significantly improved if non-standard collimators are used instead of standard collimators.

Objectives: Ductal carcinoma in situ (DCIS) is a risk factor for incomplete resection of breast cancer. Especially, extensive DCIS (E-DCIS) or extensive intraductal component often results in positive resection margins. Detecting DCIS around breast cancer before treatment may therefore alter surgery. The purpose of this study was to develop a prediction model for E-DCIS around early-stage invasive breast cancer, using clinicohistopathological and dynamic contrast-enhanced magnetic resonance imaging (MRI) features. Materials and Methods: Dynamic contrast-enhanced MRI and local excision were performed in 322 patients with 326 ductal carcinomas. Tumors were segmented from dynamic contrast-enhanced MRI, followed by 3-dimensional extension of the margins with 10 mm. Amount of fibroglandular tissue (FGT) and enhancement features in these extended margins were automatically extracted from the MRI scans. Clinicohistopathological features were also obtained. Principal component analysis and multivariable logistic regression were used to develop a prediction model for E-DCIS. Discrimination and calibration were assessed, and bootstrapping was applied for internal validation. Results: Extensive DCIS occurred in 48 (14.7%) of 326 tumors. Incomplete resection occurred in 56.3% of these E-DCIS-positive versus 9.0% of E-DCIS-negative tumors (P < 0.001). Five components with eigenvalue exceeding 1 were identified; 2 were significantly associated with E-DCIS. The first, positively associated, component expressed early and overall enhancement in the 10-mm tissue margin surrounding the MRI-visible tumor. The second, positively associated, component expressed human epidermal growth factor receptor 2 and amount of FGT around the MRI-visible tumor. The area under the curve value was 0.79 (0.76 after bootstrapping). Conclusions: Human epidermal growth factor receptor 2 status, early and overall enhancement in the 10-mm margin around the MRI-visible tumor, and amount of FGT in the 10 mm around the MRI-visible tumor were associated with E-DCIS.

Dynamic contrast‐enhanced MRI is the workhorse of breast MRI, where the diagnosis of lesions is largely based on the enhancement curve shape. However, this curve shape is biased by RF transmit (B1⁺) field inhomogeneities. B1⁺ field information is required in order to correct these. The use of a generic, coil‐specific B1⁺ template is proposed and tested. Finite‐difference time‐domain simulations for B1⁺ were performed for healthy female volunteers with a wide range of breast anatomies. A generic B1⁺ template was constructed by averaging simulations based on four volunteers. Three‐dimensional B1⁺ maps were acquired in 15 other volunteers. Root mean square error (RMSE) metrics were calculated between individual simulations and the template, and between individual measurements and the template. The agreement between the proposed template approach and a B1⁺ mapping method was compared against the agreement between acquisition and reacquisition using the same mapping protocol. RMSE values (% of nominal flip angle) comparing individual simulations with the template were in the range 2.00‐4.01%, with mean 2.68%. RMSE values comparing individual measurements with the template were in the range8.1‐16%, with mean 11.7%. The agreement between the proposed template approach and a B1⁺ mapping method was only slightly worse than the agreement between two consecutive acquisitions using the same mapping protocol in one volunteer: the range of agreement increased from ±16% of the nominal angle for repeated measurement to ±22% for the B1⁺ template. With local RF transmit coils, intersubject differences in B1⁺ fields of the breast are comparable to the accuracy of B1⁺ mapping methods, even at 7 T. Consequently, a single generic B1⁺ template suits subjects over a wide range of breast anatomies, eliminating the need for a time‐consuming B1⁺ mapping protocol.

There is growing interest in minimally invasive breast cancer therapy. Eligibility of patients is, however, dependent on several factors related to the tumor and treatment technology. The aim of this study is to assess the proportion of patients eligible for minimally invasive breast cancer therapy for different safety and treatment margins based on breast tumor location. Patients with invasive ductal cancer were selected from the MARGINS cohort. Semiautomatic segmentation of tumor, skin, and pectoral muscle was performed in Magnetic Resonance images. Shortest distances of tumors to critical organs (ie, skin and pectoral muscle) were calculated. Proportions of eligible patients were determined for different safety and treatment margins. Three-hundred-forty-eight patients with 351 tumors were included. If a 10 mm safety margin to skin and pectoral muscle is required without treatment margin, 72.3% of patients would be eligible for minimally invasive treatment. This proportion decreases to 45.9% for an additional treatment margin of 5 mm. Shortest distances between tumors and critical organs are larger in older patients and in patients with less aggressive tumor subtypes. If a 10 mm safety margin to skin and pectoral muscle is required, more than two-thirds of patients would be eligible for minimally invasive breast cancer therapy.

Dynamic contrast‐enhanced MRI is the workhorse of breast MRI, where the diagnosis of lesions is largely based on the enhancement curve shape. However, this curve shape is biased by RF transmit (B 1 +) field inhomogeneities. B 1 + field information is required in order to correct these. The use of a generic, coil‐specific B 1 + template is proposed and tested. Finite‐difference time‐domain simulations for B 1 + were performed for healthy female volunteers with a wide range of breast anatomies. A generic B 1 + template was constructed by averaging simulations based on four volunteers. Three‐dimensional B 1 + maps were acquired in 15 other volunteers. Root mean square error (RMSE) metrics were calculated between individual simulations and the template, and between individual measurements and the template. The agreement between the proposed template approach and a B 1 + mapping method was compared against the agreement between acquisition and reacquisition using the same mapping protocol. RMSE values (% of nominal flip angle) comparing individual simulations with the template were in the range 2.00‐4.01%, with mean 2.68%. RMSE values comparing individual measurements with the template were in the range8.1‐16%, with mean 11.7%. The agreement between the proposed template approach and a B 1 + mapping method was only slightly worse than the agreement between two consecutive acquisitions using the same mapping protocol in one volunteer: the range of agreement increased from ±16% of the nominal angle for repeated measurement to ±22% for the B 1 + template. With local RF transmit coils, intersubject differences in B 1 + fields of the breast are comparable to the accuracy of B 1 + mapping methods, even at 7 T. Consequently, a single generic B 1 + template suits subjects over a wide range of breast anatomies, eliminating the need for a time‐consuming B 1 + mapping protocol.

Purpose: To determine whether markers of healthy breast stroma are able to select a subgroup of patients at low risk of death or metastasis from patients considered at high risk according to routine markers of the tumor. Experimental Design: Patients with ER+/HER2− breast cancer were consecutively included for retrospective analysis. The contralateral parenchyma was segmented automatically on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), where upon the average of the top-10% late enhancement was calculated. This contralateral parenchymal enhancement (CPE) was analyzed with respect to routine prognostic models and molecular assays (Nottingham Prognostic Index, Dutch clinical chemotherapy-selection guidelines, 70-gene signature, and 21-gene recurrence score). CPE was split in tertiles and tested for overall and distant disease-free survival. CPE was adjusted for patient and tumor characteristics, as well as systemic therapy, using inverse probability weighting (IPW). Subanalyses were performed in patients at high risk according to prognostic models and molecular assays. Results: Four-hundred-and-fifteen patients were included, constituting the same group in which the association between CPE and survival was discovered. Median follow-up was 85 months, 34/415(8%) patients succumbed. After IPW-adjustment for patient and tumor characteristics, patients with high CPE had significantly better overall survival than those with low CPE in groups at high risk according to the Nottingham Prognostic Index [HR (95% CI): 0.08 (0.00–0.40), P < 0.001]; Dutch clinical guidelines [HR (95% CI): 0.22 (0.00–0.81), P = 0.021]; and 21-gene recurrence score [HR (95% CI): 0.14 (0.00–0.84), P = 0.030]. One group showed a trend [70-gene signature: HR (95% CI): 0.25 (0.00–1.02), P = 0.054]. Conclusions: In patients at high risk based on the tumor, subgroups at relatively low risk were identified using pretreatment enhancement of the stroma on breast DCE-MRI. Clin Cancer Res; 23(21); 6505–15. ©2017 AACR.

Purpose To explore guidelines on the use of MRI and PET/CT monitoring primary tumor response to neoadjuvant chemotherapy (NAC), taking breast cancer subtype into account. Materials and methods In this prospective cohort study, 188 women were included with stages II and III breast cancer. MRI and 18F-FDG-PET/CT were acquired before and during NAC. Baseline pathology was assessed from tumor biopsy. Tumors were stratified into HER2-positive, ER-positive/HER2-negative (ER-positive), and ER-negative/PR-negative/HER2-negative (triple-negative) subtypes, and treated according to subtype. Primary endpoint was pathological complete response (pCRmic) defined as no or only small numbers of scattered invasive tumor cells. We evaluated imaging scenarios using MRI only, PET/CT only, and combinations. Results pCRmic was found in 35/46 (76.1%) of HER2-positive, 11/87 (12.6%) of ER-positive, and 31/55 (56.4%) of triple-negative tumors. For HER2-positive tumors, MRI yielded the strongest predictor (AUC: 0.735; sensitivity 36.2%), outperforming PET/CT (AUC: 0.543; p = 0.04), and with comparable results to combined imaging (AUC: 0.708; p = 0.213). In ER-positive tumors, the combination of MRI and PET/CT was slightly superior (AUC: 0.818; sensitivity 55.8%) over MRI alone (AUC: 0.742; p = 0.117) and PET/CT alone (AUC: 0.791). However, even though relatively large numbers of ER-positive tumor patients were included, no significant differences were yet found. For triple-negative tumors, MRI (AUC: 0.855; sensitivity 45.4%), PET/CT (AUC: 0.844; p = 0.220) and combined imaging (AUC: 0.868; p = 0.213) yielded comparable results. Conclusions For HER2-positive tumors, MRI shows significant advantage over PET/CT. For triple-negative tumors, comparable results were seen for MRI, PET/CT and combined imaging. For ER-positive tumors, combining MRI with PET/CT may result in optimal response monitoring, although not yet significantly.

Background 18F-FDG PET/CT can monitor metabolic activity in early breast cancer during neoadjuvant systemic therapy (NST), but it is unknown if the metabolic breast and axillary response differ. We evaluated the correlation between metabolic breast and axillary response at various time points during NST. Furthermore, we analysed if the combined metabolic response improves pathologic complete response (pCR) prediction compared to using the metabolic breast response alone. Methods 18F-FDG PET/CT was performed at baseline (PET1), 2–3 weeks (PET2), and 6–8 weeks (PET3) of NST in patients with triple-negative (TN) and HER2-positive node-positive breast cancer. SUVmax and ∆SUVmax were determined separately for breast and axilla. Spearman’s correlation coefficients (r) between both localisations were calculated. The accuracy of pCR total (ypT0/is,ypN0) prediction using the metabolic response in breast, axilla or both was examined using logistic regression analysis. Results Hundred-five patients were included: 45 TN and 60 HER2-positive tumours. The metabolic response in breast and axilla correlated moderately in TN tumours (r = 0.57) using ∆SUVmax between PET1-PET3 and poorly in HER2-positive tumours (r = 0.49) using SUVmax at PET2. In TN tumours, metabolic breast response predicted pCR well without improvement after adding axillary response (c-index 0.82 versus 0.85, p = 0.63). In HER2-positive tumours, metabolic breast response predicted pCR poorly with improvement after adding axillary response (c-index 0.64 versus 0.72, p = 0.06). Conclusions 18F-FDG PET/CT response during NST differs between breast and axilla. In TN tumours, pCR total prediction can be made independent of metabolic axillary response. In HER2-positive tumours, axillary response may improve pCR total prediction. These findings may help guide PET/CT-response-based changes during NST. Trial registration NTR NTR1797. Registered 29 May 2009, retrospectively registered. Electronic supplementary material The online version of this article (doi:10.1186/s40644-017-0117-5) contains supplementary material, which is available to authorized users.