Breast MR imaging: current indications and advanced imaging techniques

Development and validation of a four-dimensional registration technique for DCE breast MRI

BACKGROUND

Patient motion can degrade image quality of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) due to subtraction artifacts. By objectively and subjectively assessing the impact of principal component analysis (PCA)-based registration on pretreatment DCE-MRIs of breast cancer patients, we aim to validate four-dimensional registration for DCE breast MRI.

RESULTS

After applying a four-dimensional, PCA-based registration algorithm to 154 pretreatment DCE-MRIs of histopathologically well-described breast cancer patients, we quantitatively determined image quality in unregistered and registered images. For subjective assessment, we ranked motion severity in a clinical reading setting according to four motion categories (0: no motion, 1: mild motion, 2: moderate motion, 3: severe motion with nondiagnostic image quality). The median of images with either moderate or severe motion (median category 2, IQR 0) was reassigned to motion category 1 (IQR 0) after registration. Motion category and motion reduction by registration were correlated (Spearman's rho: 0.83, p < 0.001). For objective assessment, we performed perfusion model fitting using the extended Tofts model and calculated its volume transfer coefficient K^trans as surrogate parameter for motion artifacts. Mean K^trans decreased from 0.103 (± 0.077) before registration to 0.097 (± 0.070) after registration (p < 0.001). Uncertainty in perfusion quantification was reduced by 7.4% after registration (± 15.5, p < 0.001).

CONCLUSIONS

Four-dimensional, PCA-based image registration improves image quality of breast DCE-MRI by correcting for motion artifacts in subtraction images and reduces uncertainty in quantitative perfusion modeling. The improvement is most pronounced when moderate-to-severe motion artifacts are present.

Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1

Breast cancer is one of the most pervasive forms of cancer and its inherent intra- and inter-tumor heterogeneity contributes towards its poor prognosis. Multiple studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of having consistency in: a) data quality, b) quality of expert annotation of pathology, and c) availability of baseline results from computational algorithms. To address these limitations, here we propose the enhancement of the I-SPY1 data collection, with uniformly curated data, tumor annotations, and quantitative imaging features. Specifically, the proposed dataset includes a) uniformly processed scans that are harmonized to match intensity and spatial characteristics, facilitating immediate use in computational studies, b) computationally-generated and manually-revised expert annotations of tumor regions, as well as c) a comprehensive set of quantitative imaging (also known as radiomic) features corresponding to the tumor regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models

RATIONALE AND OBJECTIVES

We aimed to investigate the value of magnetic resonance image (MRI)-based radiomics in predicting Ki-67 expression of breast cancer.

METHODS

In this retrospective study, 159 lesions from 154 patients were included. Radiomic features were extracted from contrast-enhanced T1-weighted MRI (C+MRI) and apparent diffusion coefficient (ADC) maps, with open-source software. Dimension reduction was done with reliability analysis, collinearity analysis, and feature selection. Two different Ki-67 expression cut-off values (14% vs 20%) were studied as reference standard for the classifications. Input for the models were radiomic features from individual MRI sequences or their combination. Classifications were performed using a generalized linear model.

RESULTS

Considering Ki-67 cut-off value of 14%, training and testing AUC values were 0.785 (standard deviation [SD], 0.193) and 0.849 for ADC; 0.696 (SD, 0.150) and 0.695 for C+MRI; 0.755 (SD, 0.171) and 0.635 for the combination of both sequences, respectively. Regarding Ki-67 cut-off value of 20%, training and testing AUC values were 0.744 (SD, 0.197) and 0.617 for ADC; 0.629 (SD, 0.251) and 0.741 for C+MRI; 0.761 (SD, 0.207) and 0.618 for the combination of both sequences, respectively.

CONCLUSION

ADC map-based selected radiomic features coupled with generalized linear modeling might be a promising non-invasive method to determine the Ki-67 expression level of breast cancer.

Intratumoral and Peritumoral Analysis of Mammography, Tomosynthesis, and Multiparametric MRI for Predicting Ki-67 Level in Breast Cancer: a Radiomics-Based Study

PURPOSE

To noninvasively evaluate the use of intratumoral and peritumoral regions from full-field digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced (DCE), and diffusion-weighted (DW) magnetic resonance imaging (MRI) images separately and combined to predict the Ki-67 level based on radiomics.

PROCEDURES

A total of 209 patients with pathologically confirmed breast cancer were consecutively enrolled from September 2017 to March 2021, who underwent DM, DBT, DCE-MRI, and DW MRI scans. Radiomics features were calculated from intratumoral and peritumoral regions in each modality and selected with the least absolute shrinkage and selection operator (LASSO) regression. Radiomics signatures (RSs) were built based on intratumoral, peritumoral, and combined intra- and peritumoral regions. The prediction performance of the RSs was evaluated using the area under the receiver operating characteristic curve (AUC), specificity, and sensitivity as comparison metrics. A nomogram was constructed by integrating the multi-model RS and important clinical predictors and assessed by calibration and decision curve analysis.

RESULTS

The combined intra- and peritumoral RSs improved the AUC compared with intra- or peritumoral RSs in each modality. The DCE plus DW MRI yielded higher AUC and specificity but lower sensitivity compared with the DM plus DBT. The nomogram incorporating the multi-model RS, age, and lymph node metastasis status achieved the best prediction performance in the training (AUC, nomogram vs. fusion RS vs. clinical model, 0.922 vs. 0.917 vs. 0.672) and validation (AUCs, nomogram vs. fusion RS vs. clinical model, 0.866 vs. 0.838 vs. 0.661) cohorts. DCA analysis confirmed the potential clinical utility of the nomogram.

CONCLUSIONS

Peritumoral regions can provide complementary information to intratumoral regions in mammography and MRI for the prediction of Ki-67 levels. The MRI performed better than mammography in terms of AUC and specificity but weaker in sensitivity. The nomogram has a predictive advantage over each modality and could be a potential tool for predicting Ki-67 levels in breast cancer.

Radiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer

Objective: To explore whether the pretreatment dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) and radiomics signatures were associated with pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer. Method: A retrospective review of 70 patients with breast invasive carcinomas proved by biopsy between June 2017 and October 2020 (26 patients were pathological complete response, and 44 patients were non-pathological complete response). Within the pre-contrast and five post-contrast dynamic series, a total of 1037 quantitative imaging features were extracted from in each phase. Additionally, the Δfeatures (the difference between the features before and after the comparison) were used for subsequent analysis. The least absolute shrinkage and selection operator (LASSO) regression method was used to select features related to pCR, and then use these features to train multiple machine learning classifiers to predict the probability of pCR for a given patient. The area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the predictive performances of the radiomics model for each of the five phases of time points. Result: Among the five phases, each individual phase performed with AUCs ranging from 0.845 to 0.919 in predicting pCR. The best single phases performance was given by the 3rd phase (AUC = 0.919, sensitivity 0.885, specificity 0.864). 5 of the features have significant differences between pCR and non-pCR groups in each phase, most features reach their maximum or minimum in the 2nd or 3rd phase. Conclusion: The radiomic features extracted from each phase of pre-treatment DCE-MRI possess discriminatory power to predict tumor response.

Diagnostic performance of mammography and magnetic resonance imaging for evaluating mammographically visible breast masses

OBJECTIVE

We compared the diagnostic values of mammography and magnetic resonance imaging (MRI) for evaluating breast masses.

METHODS

We retrospectively analyzed mammography, MRI, and histopathological data for 377 patients with breast masses on mammography, including 73 benign and 304 malignant masses.

RESULTS

The sensitivities and negative predictive values (NPVs) were significantly higher for MRI compared with mammography for detecting breast cancer (98.4% vs. 89.8% and 87.8% vs. 46.6%, respectively). The specificity and positive predictive values (PPV) were similar for both techniques. Compared with mammography alone, mammography plus MRI improved the specificity (67.1% vs. 37.0%) and PPV (91.8% vs. 85.6%), but there was no significant difference in sensitivity or NPV. Compared with MRI alone, the combination significantly improved the specificity (67.1% vs. 49.3%), but the sensitivity (88.5% vs. 98.4%) and NPV (58.3% vs. 87.8%) were reduced, and the PPV was similar in both groups. There was no significant difference between mammography and MRI in terms of sensitivity or specificity among 81 patients with breast masses with calcification.

CONCLUSION

Breast MRI improved the sensitivity and NPV for breast cancer detection. Combining MRI and mammography improved the specificity and PPV, but MRI offered no advantage in patients with breast masses with calcification.

Screening Breast MRI and the Science of Premenopausal Background Parenchymal Enhancement

The significance of background parenchymal enhancement (BPE) on screening and diagnostic breast MRI continues to be elucidated. Background parenchymal enhancement was initially deemed probably benign and followed or thought of as an artifact degrading the accuracy of breast cancer detection on breast MRI examinations. Subsequent research has focused on understanding the role of BPE regarding screening breast MRI. Today, there is growing evidence that a myriad of factors affect BPE, which in turn may influence patient outcomes. Additionally, BPE could represent an important risk factor for the future development of breast cancer. This article aims to describe the most up-to-date research on BPE as it relates to screening breast MRI in premenopausal women.

A T1-based correction method for proton resonance frequency shift thermometry in breast tissue

PURPOSE

Develop and evaluate the effectiveness of a T1-based correction method for errors in proton resonant frequency shift thermometry due to non-local field effects caused by heating in fatty breast tissues.

METHODS

Computational models of human breast tissue were created by segmenting MRI data from a healthy human volunteer. MR-guided focused ultrasound (MRgFUS) heating and MR thermometry measurements were simulated in several locations in the heterogeneous segmented breast models. A T1-based correction method for PRF thermometry errors was applied and the maximum positive and negative errors and the root mean squared error (RMSE) in a region around each heating location was evaluated with and without correction. The method uses T1 measurements to estimate the temperature change in fatty tissues and correct for their influence. Experimental data from a heating study in cadaver breast tissue were analyzed, and the expected PRFS error computed.

RESULTS

The simulated MR thermometry had maximum single voxel errors ranging between 10% and 18% when no correction was applied. Applying the correction led to a considerable improvement, lowering the maximum error range to 2%-5%. The 5th to 95th percentile interval of the temperature error distribution was also lowered with correction, from approximately 3.5 to 1°C. This correction worked even when T1 times were uniformly raised or lowered by 5%-10%. The experimental data showed predicted errors of 15%.

CONCLUSIONS

This simulation study demonstrates that the T1-based correction method reduces MR thermometry errors due to non-local effects from heating in fatty tissues, potentially improving the accuracy of thermometry measurements during MRgFUS treatments. The presented correction method is reliant on having a patient-specific 3D model of the breast, and may be limited by the accuracy of the fat temperatures which in turn may be limited by noise or bias present in the T1 measurements.

Abbreviated Breast MRI for Screening High-risk Women: Comparison with the Full Clinical Protocol

OBJECTIVE

To compare cancer detection rate (CDR), patient recall, and interpretation time of a full protocol MRI (fpMRI) to an abbreviated MRI protocol (abMRI) in high-risk women.

METHODS

This retrospective study was approved by the IRB. All sequential high-risk screening MRI examinations performed between January 1, 2013, and December 31, 2016, were included. Breast radiologists reviewed patient history, prior images, and abMRI images and recorded their interpretation. Time for interpretation reflected review of the MRI study but not dictation or report generation. Following a minimum 30-day washout period, radiologists interpreted the fpMRI, with interpretation and timing recorded. Data collected included CDR, interpretation time, and patient recall rate. Statistical analyses utilized were Cohen's kappa coefficient, Student's t-test, and McNemar's test.

RESULTS

Included were 334 MRI examinations of 286 women. Interpretation time was 60.7 seconds for the abMRI compared to 99.4 seconds for the fpMRI, with an average difference of 38.7 ± 5.4 seconds per patient (P < 0.0001). Recall rates were comparable: the abMRI recall rate was 82/334 (24.6%) and the fpMRI 81/334 (24.3%). All five cancers included were detected by both protocols with equal recall rate. However, there were more recommendations for biopsy with the fpMRI, although this difference was not statistically significant.

CONCLUSION

The abMRI demonstrated comparable CDR to fpMRI, with shortened interpretation time and similar recall rates. Implementing an abMRI to screen high-risk women reduces imaging and interpretation time, thereby improving cost-effectiveness and the patient experience without reduction in cancer detection.

A Breast-Specific MR Guided Focused Ultrasound Platform and Treatment Protocol: First-in-Human Technical Evaluation

OBJECTIVE

This paper presents and evaluates a breast-specific magnetic resonance guided focused ultrasound (MRgFUS) system. A first-in-human evaluation demonstrates the novel hardware, a sophisticated tumor targeting algorithm and a volumetric magnetic resonance imaging (MRI) protocol.

METHODS

At the time of submission, N = 10 patients with non-palpable T0 stage breast cancer have been treated with the breast MRgFUS system. The described tumor targeting algorithm is evaluated both with a phantom test and in vivo during the breast MRgFUS treatments. Treatments were planned and monitored using volumetric MR-acoustic radiation force imaging (MR-ARFI) and temperature imaging (MRTI).

RESULTS

Successful technical treatments were achieved in 80 % of the patients. All patients underwent the treatment with no sedation and 60 % of participants had analgesic support. The total MR treatment time ranged from 73 to 114 minutes. Mean error between desired and achieved targeting in a phantom was 2.9 ±1.8 mm while 6.2 ±1.9 mm was achieved in patient studies, assessed either with MRTI or MR-ARFI measurements. MRTI and MR-ARFI were successful in 60 % and 70 % of patients, respectively.

CONCLUSION

The targeting accuracy allows the accurate placement of the focal spot using electronic steering capabilities of the transducer. The use of both volumetric MRTI and MR-ARFI provides complementary treatment planning and monitoring information during the treatment, allowing the treatment of all breast anatomies, including homogeneously fatty breasts.

Intravenous Line Phase-Wrap Artifact at Bilateral Axial 3-T Breast MRI: Identification, Analysis, and Solution

Purpose

To understand and remove the source of a phase-wrap artifact produced by residual contrast agent in the intravenous line during acquisition of bilateral axial 3-T dynamic contrast material-enhanced (DCE) breast MRI.

Materials and Methods

A two-part study involved a phantom experiment, followed by an institutional review board approved clinical intervention, to evaluate the phase-wrap artifact at MRI. A phantom model evaluated artifact production by using an intravenous line filled with fluids with varying concentrations of gadolinium-based contrast agent (0, 0.4, 0.8, 1.2, 1.6, and 2 mmol/mL) and by positioning the simulated intravenous line within several fields of view (FOV) at 3-T MRI in breast coils. Next, a clinical assessment was performed with a total of 400 patients (control group:interventional group, 200:200) to determine the effect of taping the intravenous line to the patients' backs. Breast MR images were assessed blindly for the presence of the artifact. Software was used for statistical analysis with a P value of less than .05 considered a significant difference.

Results

In the phantom model, the artifact was produced only with a 0.4 mmol/mL gadolinium concentration and when the tubing was either close to the edge or within a FOV of 350-450 mm. In the clinical experiment, the artifact was more prevalent in the retrospective control group than in the prospective intervention group (52.5% [105 of 200] vs 22% [44 of 200]; P < .005).

Conclusion

The presence of phase-wrap artifacts can be reduced by moving the contrast agent intravenous line out of the FOV during acquisition by taping it to a patient's back during bilateral axial 3-T DCE breast MRI.Keywords: Breast, MR-Imaging, Phantom Studies© RSNA, 2020.

Differentiating solid breast masses: comparison of the diagnostic efficacy of shear wave elastography and magnetic resonance imaging

OBJECTIVES

Shear wave elastography (SWE) quantitatively determines the nature of the breast lesions. Few previous studies have compared the diagnostic value of this modality with other imaging techniques. The present study aimed to compare the diagnostic value of SWE with that of magnetic resonance imaging (MRI) in detecting the nature of the breast masses.

METHODS

In this cross-sectional study, 80 patients with breast lumps who had Breast Imaging Reporting and Data System (BI-RADS) score of three or higher based on mammography and/or screening ultrasonography, underwent 3D SWE and MRI. The lesions were classified according to MRI BI-RADS scoring; Mean elasticity (Emean) and elasticity ratio (Eratio) for each lesion were also determined by SWE. The results of these two modalities were compared with histopathologic diagnosis as the gold standard method; diagnostic value and diagnostic agreement were then calculated.

RESULTS

Of the masses, 46.2% were histopathologically proven to be malignant. The Emean for benign and malignant masses was 34.04 ± 19.51 kPa and 161.92 ± 58.14 kPa, respectively. Both modalities had diagnostic agreement with histopathologic results (p<0.001). Kappa coefficient was 0.87 for SWE and 0.42 for MRI. The sensitivity of both methods was 94.59% (95% CI: 81.81-99.34), while the specificity and accuracy were 48.84% [95% CI: 33.31-64.54] and 70.0% [95% CI: 58.72-79.74] for MRI, and 93.02% [95% CI: 80.94-98.54] and 93.75% [95% CI: 86.01-97.94] for SWE.

CONCLUSIONS

SWE has better diagnostic value in terms of determining the nature of the breast masses. SWE can increase the diagnostic function of differentiating benign masses from malignant ones.

Multiparametric deep learning tissue signatures for a radiological biomarker of breast cancer: Preliminary results

PURPOSE

Deep learning is emerging in radiology due to the increased computational capabilities available to reading rooms. These computational developments have the ability to mimic the radiologist and may allow for more accurate tissue characterization of normal and pathological lesion tissue to assist radiologists in defining different diseases. We introduce a novel tissue signature model based on tissue characteristics in breast tissue from multiparametric magnetic resonance imaging (mpMRI). The breast tissue signatures are used as inputs in a stacked sparse autoencoder (SSAE) multiparametric deep learning (MPDL) network for segmentation of breast mpMRI.

METHODS

We constructed the MPDL network from SSAE with 5 layers with 10 nodes at each layer. A total cohort of 195 breast cancer subjects were used for training and testing of the MPDL network. The cohort consisted of a training dataset of 145 subjects and an independent validation set of 50 subjects. After segmentation, we used a combined SAE-support vector machine (SAE-SVM) learning method for classification. Dice similarity (DS) metrics were calculated between the segmented MPDL and dynamic contrast enhancement (DCE) MRI-defined lesions. Sensitivity, specificity, and area under the curve (AUC) metrics were used to classify benign from malignant lesions.

RESULTS

The MPDL segmentation resulted in a high DS of 0.87 ± 0.05 for malignant lesions and 0.84 ± 0.07 for benign lesions. The MPDL had excellent sensitivity and specificity of 86% and 86% with positive predictive and negative predictive values of 92% and 73%, respectively, and an AUC of 0.90.

CONCLUSIONS

Using a new tissue signature model as inputs into the MPDL algorithm, we have successfully validated MPDL in a large cohort of subjects and achieved results similar to radiologists.

Imaging Phenotypes of Breast Cancer Heterogeneity in Preoperative Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) Scans Predict 10-Year Recurrence

PURPOSE

Identifying imaging phenotypes and understanding their relationship with prognostic markers and patient outcomes can allow for a noninvasive assessment of cancer. The purpose of this study was to identify and validate intrinsic imaging phenotypes of breast cancer heterogeneity in preoperative breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) scans and evaluate their prognostic performance in predicting 10 years recurrence.

EXPERIMENTAL DESIGN

Pretreatment DCE-MRI scans of 95 women with primary invasive breast cancer with at least 10 years of follow-up from a clinical trial at our institution (2002-2006) were retrospectively analyzed. For each woman, a signal enhancement ratio (SER) map was generated for the entire segmented primary lesion volume from which 60 radiomic features of texture and morphology were extracted. Intrinsic phenotypes of tumor heterogeneity were identified via unsupervised hierarchical clustering of the extracted features. An independent sample of 163 women diagnosed with primary invasive breast cancer (2002-2006), publicly available via The Cancer Imaging Archive, was used to validate phenotype reproducibility.

RESULTS

Three significant phenotypes of low, medium, and high heterogeneity were identified in the discovery cohort and reproduced in the validation cohort (P < 0.01). Kaplan-Meier curves showed statistically significant differences (P < 0.05) in recurrence-free survival (RFS) across phenotypes. Radiomic phenotypes demonstrated added prognostic value (c = 0.73) predicting RFS.

CONCLUSIONS

Intrinsic imaging phenotypes of breast cancer tumor heterogeneity at primary diagnosis can predict 10-year recurrence. The independent and additional prognostic value of imaging heterogeneity phenotypes suggests that radiomic phenotypes can provide a noninvasive characterization of tumor heterogeneity to augment personalized prognosis and treatment.

Role of texture analysis in breast MRI as a cancer biomarker: A review

Breast cancer is a known heterogeneous disease. Current clinically utilized histopathologic biomarkers may undersample tumor heterogeneity, resulting in higher rates of misdiagnosis for breast cancer. MRI can provide a whole-tumor sampling of disease burden and is widely utilized in clinical care. Texture analysis can provide a localized description of breast cancer, with particular emphasis on quantifying breast lesion heterogeneity. The object of this review is to provide an overview of texture analysis applications towards breast cancer diagnosis, prognosis, and treatment response evaluation and review the role of image-based texture features as noninvasive prognostic and predictive biomarkers. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:927-938.

Application of BI-RADS Descriptors in Contrast-Enhanced Dual-Energy Mammography: Comparison with MRI

BACKGROUND

Contrast-enhanced (CE) magnetic resonance imaging (MRI) BI-RADS descriptors are used in the evaluation of contrast-enhanced dual-energy mammography (CEDEM) images of mass lesions and are assumed to be applicable.

PATIENTS AND METHODS

Patients with suspicious mass lesions on mammography (BI-RADS 4 or 5) were included. CEDEM examinations were performed using a modified prototype unit. CE-MRI was performed using a high temporal and high spatial resolution imaging protocol. 2 blinded breast radiologists evaluated all images using criteria related to contrast enhancement intensity and morphology according to the BI-RADS lexicon (5th edition) in 2 sessions. Histopathology was used as the standard of reference.

RESULTS

11 patients with 5 benign and 6 malignant index lesions were included. Enhancement characteristics were similar in the malignant cases. Enhancement of the benign lesions was moderate on CEDEM and strong on MRI. Discrepancies in the BI-RADS descriptors did not influence the final BI-RADS score. Overall, the BI-RADS assessment was almost identical in all cases. 1 malignant lesion was rated BI-RADS 4 with CEDEM and BI-RADS 5 with MRI, and 1 benign was rated BI-RADS 2 and BI-RADS 1, respectively.

CONCLUSION

MRI BI-RADS descriptors of contrast-enhancing lesions can be applied for the morphologic analysis of mass lesions on CEDEM.

DCE-MRI Background Parenchymal Enhancement Quantified from an Early versus Delayed Post-contrast Sequence: Association with Breast Cancer Presence

We investigated automated quantitative measures of background parenchymal enhancement (BPE) derived from an early versus delayed post-contrast sequence in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for association with breast cancer presence in a case-control study. DCE-MRIs were retrospectively analyzed for 51 cancer cases and 51 controls with biopsy-proven benign lesions, matched by age and year-of-MRI. BPE was quantified using fully-automated validated computer algorithms, separately from three sequential DCE-MRI post-contrast-subtracted sequences (SUB1, SUB2, and SUB3). The association of BPE computed from the three SUBs and other known factors with breast cancer were assessed in terms of odds ratio (OR) and area under the receiver operating characteristic curve (AUC). The OR of breast cancer for the percentage BPE measure (BPE%) quantified from SUB1 was 3.5 (95% Confidence Interval: 1.3, 9.8; p = 0.015) for 20% increments. Slightly lower and statistically significant ORs were also obtained for BPE quantified from SUB2 and SUB3. There was no significant difference (p > 0.2) in AUC for BPE quantified from the three post-contrast sequences and their combination. Our study showed that quantitative measures of BPE are associated with breast cancer presence and the association was similar across three breast DCE-MRI post-contrast sequences.

Clinical PET-MR Imaging in Breast Cancer and Lung Cancer

Hybrid imaging systems have dramatically improved thoracic oncology patient care over the past 2 decades. PET-MR imaging systems have the potential to further improve imaging of thoracic neoplasms, resulting in diagnostic and therapeutic advantages compared with current MR imaging and PET-computed tomography systems. Increasing soft tissue contrast and lesion sensitivity, improved image registration, reduced radiation exposure, and improved patient convenience are immediate clinical advantages. Multiparametric quantitative imaging capabilities of PET-MR imaging have the potential to improve understanding of the molecular mechanisms of cancer and treatment effects, potentially guiding improvements in diagnosis and therapy.

Breast MRI contrast enhancement kinetics of normal parenchyma correlate with presence of breast cancer

Background

We investigated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contrast enhancement kinetic variables quantified from normal breast parenchyma for association with presence of breast cancer, in a case-control study.

Methods

Under a Health Insurance Portability and Accountability Act compliant and Institutional Review Board-approved protocol, DCE-MRI scans of the contralateral breasts of 51 patients with cancer and 51 controls (matched by age and year of MRI) with biopsy-proven benign lesions were retrospectively analyzed. Applying fully automated computer algorithms on pre-contrast and multiple post-contrast MR sequences, two contrast enhancement kinetic variables, wash-in slope and signal enhancement ratio, were quantified from normal parenchyma of the contralateral breasts of both patients with cancer and controls. Conditional logistic regression was employed to assess association between these two measures and presence of breast cancer, with adjustment for other imaging factors including mammographic breast density and MRI background parenchymal enhancement (BPE). The area under the receiver operating characteristic curve (AUC) was used to assess the ability of the kinetic measures to distinguish patients with cancer from controls.

Results

When both kinetic measures were included in conditional logistic regression analysis, the odds ratio for breast cancer was 1.7 (95 % CI 1.1, 2.8; p = 0.017) for wash-in slope variance and 3.5 (95 % CI 1.2, 9.9; p = 0.019) for signal enhancement ratio volume, respectively. These odds ratios were similar on respective univariate analysis, and remained significant after adjustment for menopausal status, family history, and mammographic density. While percent BPE was associated with an odds ratio of 3.1 (95 % CI 1.2, 7.9; p = 0.018), in multivariable analysis of the three measures, percent BPE was non-significant (p = 0.897) and the two kinetics measures remained significant. For the differentiation of patients with cancer and controls, the unadjusted AUC was 0.71 using a combination of the two measures, which significantly (p = 0.005) outperformed either measure alone (AUC = 0.65 for wash-in slope variance and 0.63 for signal enhancement ratio volume).

Conclusions

Kinetic measures of wash-in slope and signal enhancement ratio quantified from normal parenchyma in DCE-MRI are jointly associated with presence of breast cancer, even after adjustment for mammographic density and BPE.

Dose-response effects of aerobic exercise on estrogen among women at high risk for breast cancer: a randomized controlled trial

Medical and surgical interventions for elevated breast cancer risk (e.g., BRCA1/2 mutation, family history) focus on reducing estrogen exposure. Women at elevated risk may be interested in less aggressive approaches to risk reduction. For example, exercise might reduce estrogen, yet has fewer serious side effects and less negative impact than surgery or hormonal medications. Randomized controlled trial. Increased risk defined by risk prediction models or BRCA mutation status. Eligibility: Age 18-50, eumenorrheic, non-smokers, and body mass index (BMI) between 21 and 50 kg/m(2). 139 were randomized. Treadmill exercise: 150 or 300 min/week, five menstrual cycles. Control group maintained exercise <75 min/week.

PRIMARY OUTCOME

Area under curve (AUC) for urinary estrogen. Secondary measures: urinary progesterone, quantitative digitized breast dynamic contrast-enhanced magnetic resonance imaging background parenchymal enhancement. Mean age 34 years, mean BMI 26.8 kg/m(2). A linear dose-response relationship was observed such that every 100 min of exercise is associated with 3.6 % lower follicular phase estrogen AUC (linear trend test, p = 0.03). No changes in luteal phase estrogen or progesterone levels. There was also a dose-response effect noted: for every 100 min of exercise, there was a 9.7 % decrease in background parenchymal enhancement as measured by imaging (linear trend test, p = 0.009). Linear dose-response effect observed to reduce follicular phase estrogen exposure measured via urine and hormone sensitive breast tissue as measured by imaging. Future research should explore maintenance of effects and extent to which findings are repeatable in lower risk women. Given the high benefit to risk ratio, clinicians can inform young women at increased risk that exercise may blunt estrogen exposure while considering whether to try other preventive therapies.

Breast magnetic resonance imaging performance: safety, techniques, and updates on diffusion-weighted imaging and magnetic resonance spectroscopy

Dynamic contrast-enhanced breast magnetic resonance imaging (MRI) is a well-established, highly sensitive technique for the detection and evaluation of breast cancer. Optimal performance of breast MRI continues to evolve. This article addresses breast MRI applications, covers emerging breast MRI safety concerns; outlines the technical aspects of breast MRI, including equipment and protocols at 3 T and 1.5 T; and describes current promising areas of research including diffusion-weighted imaging and magnetic resonance spectroscopy.

Breast DCE-MRI Kinetic Heterogeneity Tumor Markers: Preliminary Associations With Neoadjuvant Chemotherapy Response

The ability to predict response to neoadjuvant chemotherapy for women diagnosed with breast cancer, either before or early on in treatment, is critical to judicious patient selection and tailoring the treatment regimen. In this paper, we investigate the role of contrast agent kinetic heterogeneity features derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting treatment response. We propose a set of kinetic statistic descriptors and present preliminary results showing the discriminatory capacity of the proposed descriptors for predicting complete and non-complete responders as assessed from pre-treatment imaging exams. The study population consisted of 15 participants: 8 complete responders and 7 non-complete responders. Using the proposed kinetic features, we trained a leave-one-out logistic regression classifier that performs with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.84 under the ROC. We compare the predictive value of our features against commonly used MRI features including kinetics of the characteristic kinetic curve (CKC), maximum peak enhancement (MPE), hotspot signal enhancement ratio (SER), and longest tumor diameter that give lower AUCs of 0.71, 0.66, 0.64, and 0.54, respectively. Our proposed kinetic statistics thus outperform the conventional kinetic descriptors as well as the classifier using a combination of all the conventional descriptors (i.e., CKC, MPE, SER, and longest diameter), which gives an AUC of 0.74. These findings suggest that heterogeneity-based DCE-MRI kinetic statistics could serve as potential imaging biomarkers for tumor characterization and could be used to improve candidate patient selection even before the start of the neoadjuvant treatment.

Quantitative assessment of background parenchymal enhancement in breast MRI predicts response to risk-reducing salpingo-oophorectomy: preliminary evaluation in a cohort of BRCA1/2 mutation carriers

Introduction

We present a fully automated method for deriving quantitative measures of background parenchymal enhancement (BPE) from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and perform a preliminary evaluation of these measures to assess the effect of risk-reducing salpingo-oophorectomy (RRSO) in a cohort of breast cancer susceptibility gene 1/2 (BRCA1/2) mutation carriers.

Methods

Breast DCE-MRI data from 50 BRCA1/2 carriers were retrospectively analyzed in compliance with the Health Insurance Portability and Accountability Act and with institutional review board approval. Both the absolute (| |) and relative (%) measures of BPE and fibroglandular tissue (FGT) were computed from the MRI scans acquired before and after RRSO. These pre-RRSO and post-RRSO measures were compared using paired Student’s t test. The area under the curve (AUC) of the receiver operating characteristic (ROC) was used to evaluate the performance of relative changes in the BPE and FGT measures in predicting breast cancer that developed in these women after the RRSO surgery.

Results

For the 44 women who did not develop breast cancer after RRSO, the absolute volume of BPE and FGT had a significant decrease (P < 0.05) post-RRSO, whereas for the 6 women who developed breast cancer, there were no significant changes in these measures. Higher values in all BPE and FGT measures were also observed post-RRSO for the women who developed breast cancer, compared with women who did not. Relative changes in BPE percentage were most predictive of women who developed breast cancer after RRSO (P < 0.05), whereas combining BPE percentage and |FGT| yielded an AUC of 0.80, higher than BPE percentage (AUC = 0.78) or |FGT| (AUC = 0.66) alone (both P > 0.02).

Conclusions

Quantitative measures of BPE and FGT are different before and after RRSO, and their relative changes are associated with prediction of developing breast cancer, potentially indicative of women who are more susceptible to develop breast cancer after RRSO in BRCA1/2 mutation carriers.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-015-0577-0) contains supplementary material, which is available to authorized users.

Role of Multidetector Computed Tomography in Evaluating Incidentally Detected Breast Lesions

Aims and Background

Computed tomography (CT) does not represent the primary method for the evaluation of breast lesions; however, it can detect breast abnormalities, even when performed for other reasons related to thoracic structures. The aim of this study is to evaluate the potential benefits of 320-row multidetector CT (MDCT) in evaluating and differentiating incidentally detected breast lesions by using vessel probe and 3D analysis software with net enhancement value.

Methods and Study design

Sixty-two breast lesions in 46 patients who underwent 320-row chest CT examination were retrospectively evaluated. CT scans were assessed searching for the presence, location, number, morphological features, and density of breast nodules. Net enhancement was calculated by subtracting precontrast density from the density obtained by postcontrast values. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of CT were calculated for morphological features and net enhancement.

Results

Thirty of 62 lesions were found to be malignant at histological examination and 32 were found to be benign. When morphological features were considered, the sensitivity, specificity, accuracy, PPV, and NPV of CT were 87%, 100%, 88%, 100%, and 50%, respectively. Based on net enhancement, CT reached a sensitivity, specificity, accuracy, PPV, and NPV of 100%, 94%, 97%, 94%, and 100%, respectively.

Conclusions

MDCT allows to recognize and characterize breast lesions based on morphological features. Net enhancement can be proposed as an additional accurate feature of CT.

Emerging modalities in breast cancer imaging

Breast cancer continues to be the most frequently diagnosed malignancy and the second leading cause of death caused by cancer in women in the United States. Although each of the emerging imaging techniques discussed in this article has advantages compared with standard mammography, they are not perfect, and each has inherent limitations. To date, none have been studied by large randomized clinical trials to match the proven benefits of screening mammography; namely the reduction of mortality caused by breast cancer by nearly 30%.

Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images

PURPOSE

Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Computerized analysis is increasingly used to quantify breast MRI features in applications such as computer-aided lesion detection and fibroglandular tissue estimation for breast cancer risk assessment. Automated segmentation of the whole-breast as an organ from the other parts imaged is an important step in aiding lesion localization and fibroglandular tissue quantification. For this task, identifying the chest wall line (CWL) is most challenging due to image contrast variations, intensity discontinuity, and bias field.

METHODS

In this work, the authors develop and validate a fully automated image processing algorithm for accurate delineation of the CWL in sagittal breast MRI. The CWL detection is based on an integrated scheme of edge extraction and CWL candidate evaluation. The edge extraction consists of applying edge-enhancing filters and an edge linking algorithm. Increased accuracy is achieved by the synergistic use of multiple image inputs for edge extraction, where multiple CWL candidates are evaluated by the dynamic time warping algorithm coupled with the construction of a CWL reference. Their method is quantitatively validated by a dataset of 60 3D bilateral sagittal breast MRI scans (in total 3360 2D MR slices) that span the full American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) breast density range. Agreement with manual segmentation obtained by an experienced breast imaging radiologist is assessed by both volumetric and boundary-based metrics, including four quantitative measures.

RESULTS

In terms of breast volume agreement with manual segmentation, the overlay percentage expressed by the Dice's similarity coefficient is 95.0% and the difference percentage is 10.1%. More specifically, for the segmentation accuracy of the CWL boundary, the CWL overlay percentage is 92.7% and averaged deviation distance is 2.3 mm. Their method requires ≈ 4.5 min for segmenting each 3D breast MRI scan (56 slices) in comparison to ≈ 35 min required for manual segmentation. Further analysis indicates that the segmentation performance of their method is relatively stable across the different BI-RADS density categories and breast volume, and also robust with respect to a varying range of the major parameters of the algorithm.

CONCLUSIONS

Their fully automated method achieves high segmentation accuracy in a time-efficient manner. It could support large scale quantitative breast MRI analysis and holds the potential to become integrated into the clinical workflow for breast cancer clinical applications in the future.

[Perception of healthcare professionals on the Breast Cancer Screening Programme in Barcelona]

OBJECTIVE

A good communication plan is vital for optimal results in any screening programme. The objective of this study was to assess the knowledge, involvement and opinion of health professionals regarding the Breast Cancer Screening Programme in Barcelona in 2008.

MATERIAL AND METHODS

A cross-sectional study using an anonymous and self-administered questionnaire. The study population (N = 960) were health professionals from Primary Health-care (PH), Programs for Sexual and Reproductive Health (PSRH), and Community Pharmacies (CP). The dependent variables were: knowledge of the Programme, professional involvement and opinion of the Programme. The independent variables were: sex, age, qualifications, employment status, and health team. A descriptive and bivariate analysis was performed. Using multivariate logistic regression models adjusted for age, an Odds Ratios (OR) were obtained along with the 95% confidence intervals (CI 95%).

RESULTS

PSRH professionals know the target population better; 80.2% versus 26.1% PH, and 14.0% CP, respectively. Professional involvement was related to the health care team (ORCP/PH: 0.32, CI 95%: 0.22-0.43) being observed more in PH. The opinion on the Programme in reducing breast cancer mortality was similar in the three teams (61.6% PH, 59.3% PSRH, and 56.5% CP).

CONCLUSIONS

Healthcare professionals are unaware of some aspects of Programme, such as age range or periodicity. There is great professional involvement and belief that the Programme has helped disseminate information and knowledge on the early detection of breast cancer.

Imaging of Her2-targeted magnetic nanoparticles for breast cancer detection: comparison of SQUID-detected magnetic relaxometry and MRI

Both magnetic relaxometry and magnetic resonance imaging (MRI) can be used to detect and locate targeted magnetic nanoparticles, noninvasively and without ionizing radiation. Magnetic relaxometry offers advantages in terms of its specificity (only nanoparticles are detected) and the linear dependence of the relaxometry signal on the number of nanoparticles present. In this study, detection of single-core iron oxide nanoparticles by superconducting quantum interference device (SQUID)-detected magnetic relaxometry and standard 4.7 T MRI are compared. The nanoparticles were conjugated to a Her2 monoclonal antibody and targeted to Her2-expressing MCF7/Her2-18 (breast cancer cells); binding of the nanoparticles to the cells was assessed by magnetic relaxometry and iron assay. The same nanoparticle-labeled cells, serially diluted, were used to assess the detection limits and MR relaxivities. The detection limit of magnetic relaxometry was 125 000 nanoparticle-labeled cells at 3 cm from the SQUID sensors. T(2)-weighted MRI yielded a detection limit of 15 600 cells in a 150 µl volume, with r(1) = 1.1 mm(-1) s(-1) and r(2) = 166 mm(-1) s(-1). Her2-targeted nanoparticles were directly injected into xenograft MCF7/Her2-18 tumors in nude mice, and magnetic relaxometry imaging and 4.7 T MRI were performed, enabling direct comparison of the two techniques. Co-registration of relaxometry images and MRI of mice resulted in good agreement. A method for obtaining accurate quantification of microgram quantities of iron in the tumors and liver by relaxometry was also demonstrated. These results demonstrate the potential of SQUID-detected magnetic relaxometry imaging for the specific detection of breast cancer and the monitoring of magnetic nanoparticle-based therapies.