Magnetic resonance imaging reveals functional diversity of the vasculature in benign and malignant breast lesions

Contrasts Between Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced MR in Diagnosing Malignancies of Breast Nonmass Enhancement Lesions Based on Morphologic Assessment

BACKGROUND

Nonmass enhancement (NME) breast lesions are considered to be the leading cause of unnecessary biopsies. Diffusion-weighted imaging (DWI) or dynamic contrast-enhanced (DCE) sequences are typically used to differentiate between benign and malignant NMEs. It is important to know which one is more effective and reliable.

PURPOSE

To compare the diagnostic performance of DCE curves and DWI in discriminating benign and malignant NME lesions on the basis of morphologic characteristics assessment on contrast-enhanced (CE)-MRI images.

STUDY TYPE

Retrospective.

SUBJECTS

A total of 180 patients with 184 lesions in the training cohort and 75 patients with 77 lesions in the validation cohort with pathological results.

FIELD STRENGTH/SEQUENCE

A 3.0 T/multi-b-value DWI (b values = 0, 50, 1000, and 2000 sec/mm2 ) and time-resolved angiography with stochastic trajectories and volume-interpolated breath-hold examination (TWIST-VIBE) sequence.

ASSESSMENT

In the training cohort, a diagnostic model for morphology based on the distribution and internal enhancement characteristics was first constructed. The apparent diffusion coefficient (ADC) model (ADC + morphology) and the time-intensity curves (TIC) model (TIC + morphology) were then established using binary logistic regression with pathological results as the reference standard. Both models were compared for sensitivity, specificity, and area under the curve (AUC) in the training and the validation cohort.

STATISTICAL TESTS

Receiver operating characteristic (ROC) curve analysis and two-sample t-tests/Mann-Whitney U-test/Chi-square test were performed. P < 0.05 was considered statistically significant.

RESULTS

For the TIC/ADC model in the training cohort, sensitivities were 0.924/0.814, specificities were 0.615/0.615, and AUCs were 0.811 (95%, 0.727, 0.894)/0.769 (95%, 0.681, 0.856). The AUC of the TIC-ADC combined model was significantly higher than ADC model alone, while comparable with the TIC model (P = 0.494). In the validation cohort, the AUCs of TIC/ADC model were 0.799/0.635.

DATA CONCLUSION

Based on the morphologic analyses, the performance of the TIC model was found to be superior than the ADC model for differentiating between benign and malignant NME lesions.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 2.

Assessment of Suspected Breast Lesions in Early-Stage Triple-Negative Breast Cancer during Follow-Up after Breast-Conserving Surgery Using Multiparametric MRI

Background

The local recurrence rate of triple-negative breast cancer (TNBC) can be as high as 12%.The standard treatment for early-stage TNBC is breast-conserving surgery (BCS), followed by postoperative radiotherapy with or without chemotherapy. However, detection of the local recurrence of the disease after radiotherapy is a major issue.

Objective

The aim of this study was at investigating the role of dynamic and functional magnetic resonance imaging (MRI) during follow-up after BCS and radiotherapy with/without chemotherapy to differentiate between locoregional recurrence and postoperative fibrosis. Patients and Methods. This prospective study was conducted at the oncology, radiology, and pathology departments, Tanta University. It involved 50 patients with early-stage TNBC who were treated with BCS, followed by radiotherapy with/without chemotherapy. The suspected lesions were evaluated during the follow-up period by sonomammography. All patients were subjected to MRI, including conventional sequences, diffusion-weighted imaging (DWI), and dynamic postcontrast study.

Results

Ten cases were confirmed as recurrent malignant lesions. After contrast administration, they all exhibited irregular T1 hypodense lesions of variable morphology with diffusion restriction and positive enhancement. Eight cases displayed a type III curve, while two showed a type II curve. Histopathological assessment was consistent with the MRI findings in all eight cases. The combination of the data produced by DWI-MRI and dynamic contrast-enhanced (DCE) MRI resulted in 100%sensitivity, 92.5% specificity, 90.9% positive predictive value, 100% negative predictive value, and 98% accuracy.

Conclusion

Combination of DWI-MRI and DCE-MRI could have high diagnostic value for evaluating postoperative changes in patients with TNBC after BCS, followed by radiotherapy with/without chemotherapy. Trial Registrations. No trial to be registered.

MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions

Background

Differential diagnosis between benign and malignant breast lesions is of crucial importance relating to follow-up treatment. Recent development in texture analysis and machine learning may lead to a new solution to this problem.

Method

This current study enrolled a total number of 265 patients (benign breast lesions:malignant breast lesions = 71:194) diagnosed in our hospital and received magnetic resonance imaging between January 2014 and August 2017. Patients were randomly divided into the training group and validation group (4:1), and two radiologists extracted their texture features from the contrast-enhanced T1-weighted images. We performed five different feature selection methods including Distance correlation, Gradient Boosting Decision Tree (GBDT), least absolute shrinkage and selection operator (LASSO), random forest (RF), eXtreme gradient boosting (Xgboost) and five independent classification models were built based on Linear discriminant analysis (LDA) algorithm.

Results

All five models showed promising results to discriminate malignant breast lesions from benign breast lesions, and the areas under the curve (AUCs) of receiver operating characteristic (ROC) were all above 0.830 in both training and validation groups. The model with a better discriminating ability was the combination of LDA + gradient boosting decision tree (GBDT). The sensitivity, specificity, AUC, and accuracy in the training group were 0.814, 0.883, 0.922, and 0.868, respectively; LDA + random forest (RF) also suggests promising results with the AUC of 0.906 in the training group.

Conclusion

The evidence of this study, while preliminary, suggested that a combination of MRI texture analysis and LDA algorithm could discriminate benign breast lesions from malignant breast lesions. Further multicenter researches in this field would be of great help in the validation of the result.

Can we see what is invisible? The role of MRI in the evaluation and management of patients with pathological nipple discharge

Introduction

The aim of this study was to determine the ability of MRI to identify and assess the extent of disease in patients with pathological nipple discharge (PND) with an occult malignancy not evident on standard pre-operative evaluation with mammography and ultrasound.

Methods

Patients presenting to the breast unit of Imperial College Healthcare NHS Trust between December 2009 and December 2018 with PND and normal imaging were enrolled in the study. Pre-operative bilateral breast MRI was performed in all patients as part of our protocol and all patients were offered diagnostic microdochectomy.

Results

A total of 82 patients fulfilled our selection criteria and were enrolled in our study. The presence of an intraductal papilloma (IDP) was identified as the cause of PND in 38 patients (46.3%), 14 patients had duct ectasia (DE-17%) and 5 patients had both an IDP and DE. Other benign causes were identified in 11 patients (13.4%). Despite normal mammography and ultrasound a malignancy was identified in 14 patients (17%). Eleven patients had DCIS (13.4%), two had invasive lobular carcinoma and one patient had an invasive ductal carcinoma. The sensitivity of MRI in detecting an occult malignancy was 85.71% and the specificity was 98.53%. The positive predictive value was 92.31% and the negative predictive value was 97.1%.

Conclusions

Although a negative MRI does not exclude the presence of an occult malignancy the high sensitivity and specificity of this diagnostic modality can guide the surgeon and alter the management of patients with PND.

Feasibility study of dual parametric 2D histogram analysis of breast lesions with dynamic contrast-enhanced and diffusion-weighted MRI

Background

This study aimed to investigate the diagnostic value of a dual-parametric 2D histogram classification method for breast lesions.

Methods

This study included 116 patients with 72 malignant and 44 benign breast lesions who underwent CAIPIRINHA-Dixon-TWIST-VIBE dynamic contrast-enhanced (CDT-VIBE DCE) and readout-segmented diffusion-weighted magnetic resonance examination. The volume of interest (VOI), which encompassed the entire lesion, was segmented from the last phase of DCE images. For each VOI, a 1D histogram analysis (mean, median, 10th percentile, 90th percentile, kurtosis and skewness) was performed on apparent diffusion coefficient (ADC) and volume transfer constant (Ktrans) maps; a 2D histogram image (Ktrans-ADC) was generated from the pixelwise aligned maps, and its kurtosis and skewness were calculated. Each parameter was correlated with pathological results using the Mann–Whitney test and receiver operating characteristic curve analysis.

Results

For the Ktrans histogram, the area under the curve (AUC) of the mean, median, 90th percentile and kurtosis had statistically diagnostic values (mean: 0.760; median: 0.661; 90th percentile: 0.781; and kurtosis: 0.620). For the ADC histogram, the AUC of the mean, median, 10th percentile, skewness and kurtosis had statistically diagnostic values (mean: 0.661; median: 0.677; 10th percentile: 0.656; skewness: 0.664; and kurtosis: 0.620). For the 2D Ktrans-ADC histogram, the skewness and kurtosis had statistically higher diagnostic values (skewness: 0.831, kurtosis: 0.828) than those of the 1D histogram (all P < 0.05).

Conclusions

The dual-parametric 2D histogram analysis revealed better diagnostic accuracy for breast lesions than single parametric histogram analysis of either Ktrans or ADC maps.

Not all false positive diagnoses are equal: On the prognostic implications of false-positive diagnoses made in breast MRI versus in mammography / digital tomosynthesis screening

Background

Breast magnetic resonance imaging (MRI) has been reported to frequently result in false-positive diagnoses, limiting its positive predictive value (PPV). However, for PPV calculation, all nonmalignant tissue changes are equally considered false-positive, although the respective prognostic importance, and thus patient management implications, of different pathologies may well differ. We investigated the pathology of false-positive diagnoses made by MRI compared with radiographic (digital mammography/tomosynthesis [DM/DBT]) screening.

Methods

We conducted an institutional review board-approved prospective analysis of 710 consecutive asymptomatic women at average risk for breast cancer who underwent vacuum biopsy with or without surgical biopsy for screen-detected DM/DBT (n = 344) or MRI (n = 366) findings. We compared the frequency of false-positive biopsies (given by PPV3), as well as the types of nonmalignant tissue changes that caused the respective false-positive biopsies. In an order of increasing relative risk of subsequent breast cancer, pathologies of false-positive biopsies were categorized as nonproliferative, simple proliferative, complex proliferative, or atypical proliferative (including lobular carcinoma in situ/lobular intraepithelial neoplasia). The Mann-Whitney U test was used to compare distributions.

Results

Histology yielded nonmalignant tissue in 202 of 366 biopsies done for positive MRI studies and 195 of 344 biopsies for positive DM/DBT studies, respectively, yielding a similar PPV3 percentages of 44.8% (164 of 202) and 43.3% (149 of 202) for both methods. However, the distribution of tissue types that caused false-positive diagnoses differed significantly (p < 0.0001). On the basis of MRI, high-risk atypical proliferative changes (40.1%; 81 of 202) were most common, followed by complex proliferative changes (23.8%; 48 of 202). In DM/DBT, low-risk, nonproliferative changes were the dominant reason for false-positive diagnoses (49.7%; 97 of 195), followed by simple proliferative changes (25.2%; 51 of 195). Low-risk nonproliferative changes resulted in false-positive diagnoses based on MRI as infrequently as did high-risk atypical proliferative changes based on DM/DBT (18.8% [38 of 202] vs. 18.0% [35 of 195]). The likelihood of a false-positive diagnosis including atypias was twice as high in women undergoing biopsy for MRI findings (81 of 202; 40%) as for those with DM/DBT findings (35 of 195; 18%).

Conclusions

The prognostic importance, and thus the clinical implications, of false-positive diagnoses made on the basis of breast MRI vs. radiographic screening differed significantly, with a reversed prevalence of high- and low-risk lesions. This should be taken into account when discussing the rate of false-positive diagnoses (i.e., PPV levels of MRI vs. radiographic screening). Current benchmarks that rate the utility of breast cancer screening programs (i.e., cancer detection rates and PPVs) do not reflect these substantial biological differences and the different prognostic implications.

Abbreviated breast magnetic resonance protocol: Value of high-resolution temporal dynamic sequence to improve lesion characterization

PURPOSE

To evaluate the added value of ULTRAFAST-MR sequence to an abbreviated FAST protocol in comparison with FULL protocol to distinguish benign from malignant lesions in a population of women, regardless of breast MR imaging indication.

MATERIALS AND METHODS

From March 10th to September 22th, 2014, we retrospectively included a total of 70 consecutive patients with 106 histologically proven lesions (58 malignant and 48 benign) who underwent breast MR imaging for preoperative breast staging (n=38), high-risk screening (n=7), problem solving (n=18), and nipple discharge (n=4) with 12 time resolved imaging of contrast kinetics (TRICKS) acquisitions during contrast inflow interleaved in a regular high-resolution dynamic MRI protocol (FULL protocol). Two readers scored MR exams as either positive or negative and described significant lesions according to Bi-RADS lexicon with a TRICKS images (ULTRAFAST), an abbreviated protocol (FAST) and all images (FULL protocol). Sensitivity, specificity, positive and negative predictive values, and accuracy were calculated for each protocol and compared with McNemar's test.

RESULTS

For all readers, the combined FAST-ULTRAFAST protocol significantly improved the reading with a specificity of 83.3% and 70.8% in comparison with FAST protocol or FULL protocol, respectively, without change in sensitivity. By adding ULTRAFAST protocol to FAST protocol, readers 1 and 2 were able to correctly change the diagnosis in 22.9% (11/48) and 10.4% (5/48) of benign lesions, without missing any malignancy, respectively. Both interpretation and image acquisition times for combined FAST-ULTRAFAST protocol and FAST protocol were shorter compared to FULL protocol (p<0.001).

CONCLUSION

Compared to FULL protocol, adding ULTRAFAST to FAST protocol improves specificity, mainly in correctly reclassifying benign masses and reducing interpretation and acquisition time, without decreasing sensitivity.

Separation of benign and malignant breast lesions using dynamic contrast enhanced MRI in a biopsy cohort

PURPOSE

To assess the diagnostic utility of contrast kinetic analysis for breast lesions and background parenchyma of women undergoing MRI-guided biopsies, for whom standard clinical analysis had failed to separate benign and malignant lesions.

MATERIALS AND METHODS

This study included 115 women who had indeterminate lesions based on routine diagnostic breast MRI exams and underwent an MRI (3 Tesla) -guided biopsy of one or more lesions suspicious for breast cancer. Breast dynamic contrast-enhanced (DCE)-MRI was performed using a radial stack-of-stars three-dimensional spoiled gradient echo pulse sequence and modified k-space weighted image contrast image reconstruction. Contrast kinetic model analysis was conducted to characterize the contrast enhancement patterns measured in lesions and background parenchyma (BP). The transfer rate (K^trans ), interstitial volume fraction (v_e ), and vascular volume fraction (v_p ) estimated from the lesion and BP were used to separate malignant from benign lesions.

RESULTS

The patients with malignant lesions had significantly (P < 0.05) higher median lesion-K^trans (0.081 min^-1 ), higher median BP-K^trans (0.032 min^-1 ), and BP-v_p (0.020) than those without malignant lesions (0.056 min^-1 , 0.017 min^-1 and 0.012, respectively). The area under the receiver operating characteristic curve (AUC) of the BP-K^trans (0.687) was highest among the single parameters and higher than that of the lesion-K^trans (0.664). The combined logistic regression model of lesion-K^trans , lesion-v_e , BP-K^trans , BP-v_e , and BP-v_p had the highest AUC of 0.730.

CONCLUSION

Our results suggest that the contrast kinetic analysis of DCE-MRI data can be used to differentiate the malignant lesions from the benign and high-risk lesions among the indeterminate breast lesions recommended for MRI-guided biopsy exams.

LEVEL OF EVIDENCE

3 J. MAGN. RESON. IMAGING 2017;45:1385-1393.

Mathematical Models of Contrast Transport Kinetics for Cancer Diagnostic Imaging: A Review

Angiogenesis plays a fundamental role in cancer growth and the formation of metastasis. Novel cancer therapies aimed at inhibiting angiogenic processes and/or disrupting angiogenic tumor vasculature are currently being developed and clinically tested. The need for earlier and improved cancer diagnosis, and for early evaluation and monitoring of therapeutic response to angiogenic treatment, have led to the development of several imaging methods for in vivo noninvasive assessment of angiogenesis. The combination of dynamic contrast-enhanced imaging with mathematical modeling of the contrast agent kinetics enables quantitative assessment of the structural and functional changes in the microvasculature that are associated with tumor angiogenesis. In this paper, we review quantitative imaging of angiogenesis with dynamic contrast-enhanced magnetic resonance imaging, computed tomography, and ultrasound.

Noninvasive strategies for breast cancer early detection

Breast cancer screening and presurgical diagnosis are currently based on mammography, ultrasound and more sensitive imaging technologies; however, noninvasive biomarkers represent both a challenge and an opportunity for early detection of cancer. An extensive number of potential breast cancer biomarkers have been discovered by microarray hybridization or sequencing of circulating DNA, noncoding RNA and blood cell RNA; multiplex analysis of immune-related molecules and mass spectrometry-based approaches for high-throughput detection of protein, endogenous peptides, circulating and volatile metabolites. However, their medical relevance and their translation to clinics remain to be exploited. Once they will be fully validated, cancer biomarkers, used in combination with the current and emerging imaging technologies, represent an avenue to a personalized breast cancer diagnosis.

Dual Energy Spectral CT Imaging for Colorectal Cancer Grading: A Preliminary Study

Objectives

To assess the diagnostic value of dual energy spectral CT imaging for colorectal cancer grading using the quantitative iodine density measurements in both arterial phase (AP) and venous phase (VP).

Methods

81 colorectal cancer patients were divided into two groups based on their pathological findings: a low grade group including well (n = 13) and moderately differentiated cancer (n = 24), and a high grade group including poorly differentiated (n = 42) and signet ring cell cancer (n = 2). Iodine density (ID) in the lesions was derived from the iodine-based material decomposition (MD) image and normalized to that in the psoas muscle to obtain normalized iodine density (NID). The difference in ID and NID between AP and VP was calculated.

Results

The ID and NID values of the low grade cancer group were, 14.65±3.38mg/mL and 1.70±0.33 in AP, and 21.90±3.11mg/mL and 2.05± 0.32 in VP, respectively. The ID and NID values for the high grade cancer group were 20.63±3.72mg/mL and 2.95±0.72 in AP, and 26.27±3.10mg/mL and 3.51±1.12 in VP, respectively. There was significant difference for ID and NID between the low grade and high grade cancer groups in both AP and VP (all p<0.001). ROC analysis indicated that NID of 1.92 in AP provided 70.3% sensitivity and 97.7% specificity in differentiating low grade cancer from high grade cancer.

Conclusions

The quantitative measurement of iodine density in AP and VP can provide useful information to differentiate low grade colorectal cancer from high grade colorectal cancer with NID in AP providing the greatest diagnostic value.

Models and methods for analyzing DCE-MRI: a review

PURPOSE

To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches.

METHODS

DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases.

RESULTS

Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.

CONCLUSIONS

Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

Quantitative discrimination between invasive ductal carcinomas and benign lesions based on semi-automatic analysis of time intensity curves from breast dynamic contrast enhanced MRI

Background

Traditional subjective method for the analysis of time-intensity curves (TICs) from breast dynamic contrast enhanced MRI (DCE-MRI) presented a low specificity. Hence, a semi-automatic quantitative method was proposed and evaluated for distinguishing invasive ductal carcinomas from benign lesions.

Materials and methods

In the traditional method, the lesion was extracted by placing a region of interest (ROI) manually. The mean curve of the TICs from the ROI was subjectively classified as one of three patterns. Only one quantitative parameter, the mean value of maximum slope of increase (MSI), was provided. In the new method, the lesion was identified semi-automatically, and the mean curve was classified quantitatively. Some additional parameters, the signal intensity slope (SI_slope), initial percentage of enhancement (E_initial), percentage of peak enhancement (E_peak), early signal enhancement ratio (ESER), and second enhancement percentage (SEP) were derived from the mean curves as well as the lesion areas. Wilcoxon’s test and receiver operating characteristic (ROC) analyses were performed, and P < 0.05 was considered significant.

Results

According to the TIC classification results, the accuracies were 59.16% for the traditional manual method and 76.05% for the new method (P < 0.05). For the mean MSI values from the manual method, the accuracy was 63.35%. For the mean TICs derived from the semi-automatic method, the accuracies were 77.47% for SI_slope, 65.24% for MSI, 58.45% for E_initial, 66.20% for E_peak, 71.83% for ESER, and 54.93% for SEP, respectively. For the lesion regions identified by the semi-automatic method, the accuracies were 73.24%, 72.54%, 58.45%, 62.68%, 64.09%, and 55.64%, respectively.

Conclusion

Compared with traditional subjective method, the semi-automatic quantitative method proposed in this study showed a higher performance, and should be used as a supplementary tool to aid radiologist's subjective interpretation.

Perspectives in molecular imaging through translational research, human medicine, and veterinary medicine

The concept of molecular imaging has taken off over the past 15 years to the point of the renaming of the Society of Nuclear Medicine (Society of Nuclear Medicine and Molecular Imaging) and Journals (European Journal of Nuclear Medicine and Molecular Imaging) and offering of medical fellowships specific to this area of study. Molecular imaging has always been at the core of functional imaging related to nuclear medicine. Even before the phrase molecular imaging came into vogue, radionuclides and radiopharmaceuticals were developed that targeted select physiological processes, proteins, receptor analogs, antibody-antigen interactions, metabolites and specific metabolic pathways. In addition, with the advent of genomic imaging, targeted genomic therapy, and theranostics, a number of novel radiopharmaceuticals for the detection and therapy of specific tumor types based on unique biological and cellular properties of the tumor itself have been realized. However, molecular imaging and therapeutics as well as the concept of theranostics are yet to be fully realized. The purpose of this review article is to present an overview of the translational approaches to targeted molecular imaging with application to some naturally occurring animal models of human disease.

Dynamic contrast-enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer

The aim of this study was to investigate the potential of texture analysis, applied to dynamic contrast-enhanced MRI (DCE-MRI), to predict the clinical and pathological response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) before NAC is started. Fifty-eight patients with LABC were classified on the basis of their clinical response according to the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines after four cycles of NAC, and according to their pathological response after surgery. T1 -weighted DCE-MRI with a temporal resolution of 1 min was acquired on a 3-T Siemens Trio scanner using a dedicated four-channel breast coil before the onset of treatment. Each lesion was segmented semi-automatically using the 2-min post-contrast subtracted image. Sixteen texture features were obtained at each non-subtracted post-contrast time point using a gray level co-occurrence matrix. Appropriate statistical analyses were performed and false discovery rate-based q values were reported to correct for multiple comparisons. Statistically significant results were found at 1-3 min post-contrast for various texture features for the prediction of both the clinical and pathological response. In particular, eight texture features were found to be statistically significant at 2 min post-contrast, the most significant feature yielding an area under the curve (AUC) of 0.77 for response prediction for stable disease versus complete responders after four cycles of NAC. In addition, four texture features were found to be significant at the same time point, with an AUC of 0.69 for response prediction using the most significant feature for classification based on the pathological response. Our results suggest that texture analysis could provide clinicians with additional information to increase the accuracy of prediction of an individual response before NAC is started.

Is there any correlation between model-based perfusion parameters and model-free parameters of time-signal intensity curve on dynamic contrast enhanced MRI in breast cancer patients?

OBJECTIVE

To find out any correlation between dynamic contrast-enhanced (DCE) model-based parameters and model-free parameters, and evaluate correlations between perfusion parameters with histologic prognostic factors.

METHODS

Model-based parameters (Ktrans, Kep and Ve) of 102 invasive ductal carcinomas were obtained using DCE-MRI and post-processing software. Correlations between model-based and model-free parameters and between perfusion parameters and histologic prognostic factors were analysed.

RESULTS

Mean Kep was significantly higher in cancers showing initial rapid enhancement (P = 0.002) and a delayed washout pattern (P = 0.001). Ve was significantly lower in cancers showing a delayed washout pattern (P = 0.015). Kep significantly correlated with time to peak enhancement (TTP) (ρ = -0.33, P < 0.001) and washout slope (ρ = 0.39, P = 0.002). Ve was significantly correlated with TTP (ρ = 0.33, P = 0.002). Mean Kep was higher in tumours with high nuclear grade (P = 0.017). Mean Ve was lower in tumours with high histologic grade (P = 0.005) and in tumours with negative oestrogen receptor status (P = 0.047). TTP was shorter in tumours with negative oestrogen receptor status (P = 0.037).

CONCLUSIONS

We could acquire general information about the tumour vascular physiology, interstitial space volume and pathologic prognostic factors by analyzing time-signal intensity curve without a complicated acquisition process for the model-based parameters.

KEY POINTS

• Kep mainly affected the initial and delayed curve pattern in time-signal intensity curve. • There is significant correlation between model-based and model-free parameters. • We acquired information about tumour vascular physiology, interstitial space volume and prognostic factors.

Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis

Three-dimensional (3-D) dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) consists of a large number of images in different enhancement phases which are used to identify and characterize breast lesions. The purpose of this study was to develop a computer-assisted algorithm for tumor segmentation and characterization using both kinetic information and morphological features of 3-D breast DCE-MRI. An integrated color map created by intersecting kinetic and area under the curve (AUC) color maps was used to detect potential breast lesions, followed by the application of a region growing algorithm to segment the tumor. Modified fuzzy c-means clustering was used to identify the most representative kinetic curve of the whole segmented tumor, which was then characterized by using conventional curve analysis or pharmacokinetic model. The 3-D morphological features including shape features (compactness, margin, and ellipsoid fitting) and texture features (based on the grey level co-occurrence matrix) of the segmented tumor were obtained to characterize the lesion. One hundred and thirty-two biopsy-proven lesions (63 benign and 69 malignant) were used to evaluate the performance of the proposed computer-aided system for breast MRI. Five combined features including rate constant (kep), volume of plasma (vp), energy (G1), entropy (G2), and compactness (C1), had the best performance with an accuracy of 91.67% (121/132), sensitivity of 91.30% (63/69), specificity of 92.06% (58/63), and Az value of 0.9427. Combining the kinetic and morphological features of 3-D breast MRI is a potentially useful and robust algorithm when attempting to differentiate benign and malignant lesions.

Invasive breast carcinoma: influence of prognosis and patient-related factors on kinetic MR imaging characteristics

PURPOSE

To retrospectively compare the kinetic magnetic resonance (MR) imaging characteristics of invasive breast carcinomas with both prognostic tumoral and patient-related parameters.

MATERIALS AND METHODS

This HIPAA-compliant retrospective study was approved by the institutional review board, and informed consent was waived. From January 2008 to January 2011, 273 consecutive women (mean age, 55 years; range, 23-83 years) with invasive breast cancers who had undergone MR imaging were selected. The kinetic curves were retrospectively classified according to the Breast Imaging Reporting and Data System lexicon. Initial enhancement and maximal enhancement percentages, time to peak enhancement, and the signal enhancement ratio were calculated for each lesion. Kinetic characteristics were compared according to tumoral parameters (size, pathologic type, grade, hormone receptor status, and c-erbB-2 status) and patient parameters (menopausal status, personal history of breast carcinoma) by means of univariate and then multivariate analysis by using false-discovery-rate statistics.

RESULTS

Lesions in menopausal patients exhibited less suspicious quantitative and qualitative characteristics than lesions in nonmenopausal patients. There was an independent association between the kinetic characteristics and menopausal status, with an odds ratio of 2.94 for the lack of rapid initial contrast material uptake and of 2.38 for the lack of washout in menopausal patients as compared with nonmenopausal patients. The odds ratio was 4.00 for not having rapid initial contrast material uptake in patients with a personal history of ipsilateral breast cancer.

CONCLUSION

Kinetic data in invasive breast cancer are associated with the patient's menopausal status, with a typical kinetic pattern of malignancy being less common in menopausal patients.

Role of quantitative pharmacokinetic parameter (transfer constant: K(trans)) in the characterization of breast lesions on MRI

Background:

The semi-quantitative analysis of the time–intensity curves in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a limited specificity due to overlapping enhancement patterns after gadolinium administration. With the advances in technology and faster sequences, imaging of the entire breast can be done in a few seconds, which allows measuring the transit of contrast (transfer constant: K^trans) through the vascular bed at capillary level that reflects quantitative measure of porosity/permeability of tumor vessels.

Aim:

Our study aims to evaluate the pharmacokinetic parameter K^trans for enhancing breast lesions and correlate it with histopathology, and assess accuracy, sensitivity, and specificity of this parameter in discriminating benign and malignant breast lesions.

Materials and Methods:

One hundred and fifty-one women with 216 histologically proved enhancing breast lesions underwent high temporal resolution DCE-MRI for the early dynamic analysis for calculation of pharmacokinetic parameters (K^trans) using standard two compartment model. The calculated values of K^trans were correlated with histopathology to calculate the sensitivity, specificity, and accuracy.

Results:

Receiver operating characteristic (ROC) curve analysis revealed a mean K^trans value of 0.56, which reliably distinguished benign and malignant breast lesions with a sensitivity of 91.1% and specificity of 90.3% with an overall accuracy of 89.3%. The area under curve (AUC) was 0.907.

Conclusion:

K^trans is a reliable quantitative parameter for characterizing benign and malignant lesions in routine DCE-MRI of breasts.

Optimization of time-to-peak analysis for differentiating malignant and benign breast lesions with dynamic contrast-enhanced MRI

RATIONALE AND OBJECTIVES

The aim of this study was to investigate the feasibility of applying measures sensitive to time-to-peak (T(peak)) heterogeneity as indicators for malignancy on breast dynamic contrast-enhanced magnetic resonance imaging.

MATERIALS AND METHODS

The study included 39 benign and 97 malignant breast lesions from 103 patients. Lesions were automatically segmented by k-means clustering. Voxel-by-voxel T(peak) values were extracted using an empirical model. The pth percentile values (p = 10, 20…) of the T(peak) distribution within each lesion and the fractional and absolute hot spot volumes were determined, where the hot spot volume is the volume of tissue with T(peak) less than a threshold value. Using the area under the receiver-operating characteristic curve (AUC), these measures were tested as indicators for differentiating fibroadenomas from invasive lesions and from ductal carcinoma in situ, as well as for differentiating nonfibroadenoma benign lesions from these malignant lesions. Region of interest-based T(peak) measurements were also tested. Finally, the relationship between hot spot volume and lesion volume was investigated.

RESULTS

For differentiating fibroadenomas from malignant lesions, AUC values increased with decreasing values of p. At the optimal threshold (3 minutes), the hot spot volume provided high diagnostic performance (AUC ≥0.96 ± 0.02 for absolute hot spot volume). However, for differentiating nonfibroadenoma benign lesions from malignant lesions, AUC values were low. A significant correlation between absolute hot spot volume and lesion volume was found for malignant lesions and nonfibroadenoma benign lesions.

CONCLUSION

Quantitative analysis of the T(peak) distribution can be optimized for diagnostic performance, providing indicators sensitive to intralesion T(peak) heterogeneity.

Multi-parametric assessment of the anti-angiogenic effects of liposomal glucocorticoids

Inflammation plays a prominent role in tumor growth. Anti-inflammatory drugs have therefore been proposed as anti-cancer therapeutics. In this study, we determined the anti-angiogenic activity of a single dose of liposomal prednisolone phosphate (PLP-L), by monitoring tumor vascular function and viability over a period of one week. C57BL/6 mice were inoculated subcutaneously with B16F10 melanoma cells. Six animals were PLP-L-treated and six served as control. Tumor tissue and vascular function were probed using MRI before and at three timepoints after treatment. DCE-MRI was used to determine K(trans), v(e), time-to-peak, initial slope and the fraction of non-enhancing pixels, complemented with immunohistochemistry. The apparent diffusion coefficient (ADC), T(2) and tumor size were assessed with MRI as well. PLP-L treatment resulted in smaller tumors and caused a significant drop in K(trans) 48 h post-treatment, which was maintained until one week after drug administration. However, this effect was not sufficient to significantly distinguish treated from non-treated animals. The therapy did not affect tumor tissue viability but did prevent the ADC decrease observed in the control group. No evidence for PLP-L-induced tumor vessel normalization was found on histology. Treatment with PLP-L altered tumor vascular function. This effect did not fully explain the tumor growth inhibition, suggesting a broader spectrum of PLP-L activities.

Magnetic resonance imaging: a potential tool in assessing the addition of hyperthermia to neoadjuvant therapy in patients with locally advanced breast cancer

The poor overall survival for patients with locally advanced breast cancers has led over the past decade to the introduction of numerous neoadjuvant combined therapy regimens to down-stage the disease before surgery. At the same time, more evidence suggests the need for treatment individualisation with a wide variety of new targets for cancer therapeutics and also multi modality therapies. In this context, early determination of whether the patient will fail to respond can enable the use of alternative therapies that can be more beneficial. The purpose of this review is to examine the potential role of magnetic resonance imaging (MRI) in early prediction of treatment response and prognosis of overall survival in locally advanced breast cancer patients enrolled on multi modality therapy trials that include hyperthermia. The material is organised with a review of dynamic contrast (DCE)-MRI and diffusion weighted (DW)-MRI for characterisation of phenomenological parameters of tumour physiology and their potential role in estimating therapy response. Most of the work published in this field has focused on responses to neoadjuvant chemotherapy regimens alone, so the emphasis will be there, however the available data that involves the addition of hyperthermia to the regimen will be discussed The review will also include future directions that include the potential use of MRI imaging techniques in establishing the role of hyperthermia alone in modifying breast tumour microenvironment, together with specific challenges related to performing such studies.

Characterizing early contrast uptake of ductal carcinoma in situ with high temporal resolution dynamic contrast-enhanced MRI of the breast: a pilot study

Improvements in the reliable diagnosis of preinvasive ductal carcinoma in situ (DCIS) by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are needed. In this study, we present a new characterization of early contrast kinetics of DCIS using high temporal resolution (HiT) DCE-MRI and compare it with other breast lesions and normal parenchyma. Forty patients with mammographic calcifications suspicious for DCIS were selected for HiT imaging using T(1)-weighted DCE-MRI with ∼7 s temporal resolution for 90 s post-contrast injection. Pixel-based and whole-lesion kinetic curves were fit to an empirical mathematical model (EMM) and several secondary kinetic parameters derived. Using the EMM parameterized and fitted concentration time curve for subsequent analysis allowed for calculation of kinetic parameters that were less susceptible to fluctuations due to noise. The parameters' initial area under the curve (iAUC) and contrast concentration at 1 min (C(1 min)) provided the highest diagnostic accuracy in the task of distinguishing pathologically proven DCIS from normal tissue. There was a trend for DCIS lesions with solid architectural pattern to exhibit a negative slope at 1 min (i.e. increased washout rate) compared to those with a cribriform pattern (p < 0.04). This pilot study demonstrates the feasibility of quantitative analysis of early contrast kinetics at high temporal resolution and points to the potential for such an analysis to improve the characterization of DCIS.

Temporal analysis of tumor heterogeneity and volume for cervical cancer treatment outcome prediction: preliminary evaluation

In this paper, we present a method of quantifying the heterogeneity of cervical cancer tumors for use in radiation treatment outcome prediction. Features based on the distribution of masked wavelet decomposition coefficients in the tumor region of interest (ROI) of temporal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) studies were used along with the imaged tumor volume to assess the response of the tumors to treatment. The wavelet decomposition combined with ROI masking was used to extract local intensity variations in the tumor. The developed method was tested on a data set consisting of 23 patients with advanced cervical cancer who underwent radiation therapy; 18 of these patients had local control of the tumor, and five had local recurrence. Each patient participated in two DCE-MRI studies: one prior to treatment and another early into treatment (2-4 weeks). An outcome of local control or local recurrence of the tumor was assigned to each patient based on a posttherapy follow-up at least 2 years after the end of treatment. Three different supervised classifiers were trained on combinational subsets of the full wavelet and volume feature set. The best-performing linear discriminant analysis (LDA) and support vector machine (SVM) classifiers each had mean prediction accuracies of 95.7%, with the LDA classifier being more sensitive (100% vs. 80%) and the SVM classifier being more specific (100% vs. 94.4%) in those cases. The K-nearest neighbor classifier performed the best out of all three classifiers, having multiple feature sets that were used to achieve 100% prediction accuracy. The use of distribution measures of the masked wavelet coefficients as features resulted in much better predictive performance than those of previous approaches based on tumor intensity values and their distributions or tumor volume alone.

Prospective multicenter cohort study to refine management recommendations for women at elevated familial risk of breast cancer: the EVA trial

PURPOSE

We investigated the respective contribution (in terms of cancer yield and stage at diagnosis) of clinical breast examination (CBE), mammography, ultrasound, and quality-assured breast magnetic resonance imaging (MRI), used alone or in different combination, for screening women at elevated risk for breast cancer.

METHODS

Prospective multicenter observational cohort study. Six hundred eighty-seven asymptomatic women at elevated familial risk (> or = 20% lifetime) underwent 1,679 annual screening rounds consisting of CBE, mammography, ultrasound, and MRI, read independently and in different combinations. In a subgroup of 371 women, additional half-yearly ultrasound and CBE was performed more than 869 screening rounds. Mean and median follow-up was 29.18 and 29.09 months.

RESULTS

Twenty-seven women were diagnosed with breast cancer: 11 ductal carcinoma in situ (41%) and 16 invasive cancers (59%). Three (11%) of 27 were node positive. All cancers were detected during annual screening; no interval cancer occurred; no cancer was identified during half-yearly ultrasound. The cancer yield of ultrasound (6.0 of 1,000) and mammography (5.4 of 1,000) was equivalent; it increased nonsignificantly (7.7 of 1,000) if both methods were combined. Cancer yield achieved by MRI alone (14.9 of 1,000) was significantly higher; it was not significantly improved by adding mammography (MRI plus mammography: 16.0 of 1,000) and did not change by adding ultrasound (MRI plus ultrasound: 14.9 of 1,000). Positive predictive value was 39% for mammography, 36% for ultrasound, and 48% for MRI.

CONCLUSION

In women at elevated familial risk, quality-assured MRI screening shifts the distribution of screen-detected breast cancers toward the preinvasive stage. In women undergoing quality-assured MRI annually, neither mammography, nor annual or half-yearly ultrasound or CBE will add to the cancer yield achieved by MRI alone.

Principal component analysis of breast DCE-MRI adjusted with a model-based method

PURPOSE

To investigate a fast, objective, and standardized method for analyzing breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) applying principal component analysis (PCA) adjusted with a model-based method.

MATERIALS AND METHODS

3D gradient-echo DCE breast images of 31 malignant and 38 benign lesions, recorded on a 1.5T scanner, were retrospectively analyzed by PCA and by the model-based three-timepoints (3TP) method.

RESULTS

Intensity-scaled (IS) and enhancement-scaled (ES) datasets were reduced by PCA yielding a first IS-eigenvector that captured the signal variation between fat and fibroglandular tissue; two IS-eigenvectors and the two first ES-eigenvectors captured contrast-enhanced changes, whereas the remaining eigenvectors captured predominantly noise changes. Rotation of the two contrast-related eigenvectors led to a high congruence between the projection coefficients and the 3TP parameters. The ES-eigenvectors and the rotation angle were highly reproducible across malignant lesions, enabling calculation of a general rotated eigenvector base. Receiver operating characteristic (ROC) curve analysis of the projection coefficients of the two eigenvectors indicated high sensitivity of the first rotated eigenvector to detect lesions (area under the curve [AUC] > 0.97) and of the second rotated eigenvector to differentiate malignancy from benignancy (AUC = 0.87).

CONCLUSION

PCA adjusted with a model-based method provided a fast and objective computer-aided diagnostic tool for breast DCE-MRI.

Ductal carcinoma in situ: X-ray fluorescence microscopy and dynamic contrast-enhanced MR imaging reveals gadolinium uptake within neoplastic mammary ducts in a murine model

PURPOSE

To combine dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging with x-ray fluorescence microscopy (XFM) of mammary gland tissue samples from mice to identify the spatial distribution of gadolinium after intravenous injection.

MATERIALS AND METHODS

C3(1) Sv-40 large T antigen transgenic mice (n = 23) were studied with institutional animal care and use committee approval. Twelve mice underwent DCE MR imaging after injection of gadodiamide, and gadolinium concentration-time curves were fit to a two-compartment pharmacokinetic model with the following parameters: transfer constant (K(trans)) and volume of extravascular extracellular space per unit volume of tissue (v(e)). Eleven mice received gadodiamide before XFM. These mice were sacrificed 2 minutes after injection, and frozen slices containing ducts distended with murine ductal carcinoma in situ (DCIS) were prepared for XFM. One mouse received saline and served as the control animal. Elemental gadolinium concentrations were measured in and around the ducts with DCIS. Hematoxylin-eosin-stained slices of mammary tissues were obtained after DCE MR imaging and XFM.

RESULTS

Ducts containing DCIS were unambiguously identified on MR images. DCE MR imaging revealed gadolinium uptake along the length of ducts with DCIS, with an average K(trans) of 0.21 min(-1) +/- 0.14 (standard deviation) and an average v(e) of 0.40 +/- 0.16. XFM revealed gadolinium uptake inside ducts with DCIS, with an average concentration of 0.475 mmol/L +/- 0.380; the corresponding value for DCE MR imaging was 0.30 mmol/L +/- 0.13.

CONCLUSION

These results provide insight into the physiologic basis of contrast enhancement of DCIS lesions on DCE MR images: Gadolinium penetrates and collects inside neoplastic ducts.

Kinetic curves of malignant lesions are not consistent across MRI systems: need for improved standardization of breast dynamic contrast-enhanced MRI acquisition

OBJECTIVE

The purpose of this study was to compare MRI kinetic curve data acquired with three systems in the evaluation of malignant lesions of the breast.

MATERIALS AND METHODS

The cases of 601 patients with 682 breast lesions (185 benign, 497 malignant) were selected for review. The malignant lesions were classified as ductal carcinoma in situ (DCIS), invasive ductal carcinoma (IDC), and other. The dynamic MRI protocol consisted of one unenhanced and three to seven contrast-enhanced images acquired with one of three imaging protocols and systems. An experienced radiologist analyzed the shapes of the kinetic curves according to the BI-RADS lexicon. Several quantitative kinetic parameters were calculated, and the kinetic parameters of malignant lesions were compared across the three systems.

RESULTS

Imaging protocol and system 1 were used to image 304 malignant lesions (185 IDC, 62 DCIS); imaging protocol and system 2, 107 lesions (72 IDC, 21 DCIS); and imaging protocol and system 3, 86 lesions (64 IDC, 17 DCIS). Compared with those visualized with imaging protocols and systems 1 and 2, IDC lesions visualized with imaging protocol and system 3 had significantly less initial enhancement, longer time to peak enhancement, and a slower washout rate (p < 0.004). Only 47% of IDC lesions imaged with imaging protocol and system 3 exhibited washout type curves, compared with 75% and 74% of those imaged with imaging protocols and systems 2 and 1, respectively. The diagnostic accuracy of kinetic analysis was lowest for imaging protocol and system 3, but the difference was not statistically significant.

CONCLUSION

The kinetic curve data on malignant lesions acquired with one system showed significantly lower initial contrast uptake and a different curve shape in comparison with data acquired with the other two systems. Differences in k-space sampling, T1 weighting, and magnetization transfer effects may be explanations for the difference.

Adjunctive diagnostic value of MRI in the breast radial scar

PURPOSE

We sought to identify breast magnetic resonance imaging (MRI) criteria capable of influencing the differential diagnosis between radial scars related to benign proliferative disease and those associated with breast cancer with a view to proposing breast MRI as a promising and cost-effective modality to be carried out between mammography and surgical biopsy.

MATERIALS AND METHODS

From 1998 to June 2006, we studied 20 patients with a focal architectural distortion on mammography. All patients underwent contrast-enhanced breast MRI with a T1 Philips Gyroscan scanner and the acquisition of T1-weighted fast field echo, echo planar imaging (FFE EPI) axial dynamic sequences with a slice thickness of 3 mm. During postprocessing, subtracted images were assessed for morphological features, pattern of contrast enhancement, time-intensity curve and lesion enhancement rate.

RESULTS

Breast MRI depicted 27 lesions between 7 mm and 30 mm in size. Fifteen of the 27 breast lesions showed benign features, eight showed malignant features and four were classified as suspicious. Pathological examination confirmed the benignity of all 15 lesions showing benign MRI features and revealed the benign nature of the four lesions classified as suspicious.

CONCLUSIONS

Evaluation of breast MRI showed that enhancement rate and time-intensity curve were useful only in the differential diagnosis between benign and malignant breast lesions. Our experience confirmed that breast MRI has very high sensitivity and, in particular, a negative predictive value of 100%. Breast MRI could thus be considered a useful diagnostic tool that can guide the choice between follow-up or surgical excision of radial scars.

The additional value of three time point color coding in dynamic contrast-enhanced MRI of the breast for inexperienced and experienced readers

PURPOSE

To evaluate the additional value of the color coding of dynamic data using the 3TP method in the evaluation of contrast-enhanced breast MRI for readers with different levels of experience.

MATERIALS AND METHODS

A total of 52 lesions were included in this study, 25 malignant and 27 benign. All lesions were evaluated by four readers on two different workstations for the evaluation of dynamic breast MRI; one displaying the subtracted images and relative enhancement versus time curves and one displaying the subtracted images together with the 3TP color coding projected onto pre-contrast T1 images. Readers with different levels of experience were used. The diagnostic performance of both workstations was evaluated using ROC curve analyses. Interobserver variations were evaluated using kappa statistics.

RESULTS

All lesions were detected by all four readers on both workstations. The diagnostic performance found in the inexperienced readers improved significantly when using the 3TP evaluations (p=0.04 and p=0.03). No significant difference was found for the more experienced readers (p=0.94 and p=0.54). The level of agreement between the readers improved significantly when using the 3TP evaluation method (p=0.01).

CONCLUSION

Even though the 3TP color coding did not improve the diagnostic performance of the more experienced readers, this study clearly shows its value for inexperienced readers. The use of 3TP color coding is therefore recommended for inexperienced readers.

Magnetic resonance imaging of ductal carcinoma in situ: what is its clinical application? A review

BACKGROUND

After breast-conserving surgery of ductal carcinoma in situ (DCIS) of the breast or invasive breast carcinoma with an extensive intraductal component, tumor-positive surgical margins are frequently found. Therefore, the extent of the intraductal disease needs to be accurately determined preoperatively.

METHODS

Data for this review were identified by search of PubMed. Reference lists of selected articles were cross-searched for additional literature.

RESULTS

DCIS is accurately detected with magnetic resonance imaging (MRI), but the typical malignant features are inconsistently seen and most often in high-grade DCIS or in DCIS with a small invasive component. The histopathologic extent of DCIS is more accurately demonstrated with MRI. However, overestimation due to benign proliferative lesions does frequently occur. An improved depiction of DCIS could lead to improved preoperative staging. Conversely, the identification of more extensive disease on MRI could give rise to unnecessary interventions. Therefore, MRI should be used carefully and preferable in specialized and experienced centers.

CONCLUSION

[corrected] To date, there is no evidence that the use of MRI improves outcomes (ie, decreases recurrence rates) in patients with DCIS.

Model-based and model-free parametric analysis of breast dynamic-contrast-enhanced MRI

A wide range of dynamic-contrast-enhanced (DCE) sequences and protocols, image processing methods, and interpretation criteria have been developed and evaluated over the last 20 years. In particular, attempts have been made to better understand the origin of the contrast observed in breast lesions using physiological models that take into account the vascular and tissue-specific features that influence tracer perfusion. In addition, model-free algorithms to decompose enhancement patterns in order to segment and classify different breast tissue types have been developed. This review includes a description of the mechanism of contrast enhancement by gadolinium-based contrast agents, followed by the current status of the physiological models used to analyze breast DCE-MRI and related critical issues. We further describe more recent unsupervised and supervised methods that use a range of different common algorithms. The model-based and model-free methods strive to achieve scientific accuracy and high clinical performance--both important goals yet to be reached.

Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS) in Breast Cancer

Breast cancer is a major health problem in women and early detection is of prime importance. Breast magnetic resonance imaging (MRI) provides both physical and physiologic tissue features that are useful in discriminating malignant from benign lesions. Contrast enhanced MRI is valuable for diagnosis of small tumors in dense breast and the structural and kinetic parameters improved the specificity of diagnosing benign from malignant lesions. It is a complimentary modality for preoperative staging, to follow response to therapy, to detect recurrences and for screening high risk women. Diffusion, perfusion and MR elastography have been applied to breast lesion characterization and show promise.

In-vivo MR spectroscopy (MRS) is a valuable method to obtain the biochemical status of normal and diseased tissues. Malignant tissues contain high concentration of choline containing compounds that can be used as a biochemical marker. MRS helps to increase the specificity of MRI in lesions larger than 1cm and to monitor the tumor response. Various MR techniques show promise primarily as adjunct to the existing standard detection techniques, and its acceptability as a screening method will increase if specificity can be improved. This review presents the progress made in different MRI and MRS techniques in beast cancer management.

DCEMRI of breast lesions: is kinetic analysis equally effective for both mass and nonmass-like enhancement?

To perform a pilot study investigating whether the sensitivity and specificity of kinetic parameters can be improved by considering mass and nonmass breast lesions separately. The contrast media uptake and washout kinetics in benign and malignant breast lesions were analyzed using an empirical mathematical model (EMM), and model parameters were compared in lesions with mass-like and nonmass-like enhancement characteristics. 34 benign and 78 malignant breast lesions were selected for review. Dynamic MR protocol: 1 pre and 5 postcontrast images acquired in the coronal plane using a 3D T1-weighted SPGR with 68 s timing resolution. An experienced radiologist classified the type of enhancement as mass, nonmass, or focus, according to the BI-RADS lexicon. The kinetic curve obtained from a radiologist-drawn region within the lesion was analyzed quantitatively using a three parameter EMM. Several kinetic parameters were then derived from the EMM parameters: the initial slope (Slope(ini)), curvature at the peak (kappa(peak)), time to peak (T(peak)), initial area under the curve at 30 s (iAUC30), and the signal enhancement ratio (SER). The BI-RADS classification of the lesions yielded: 70 mass lesions, 38 nonmass, 4 focus. For mass lesions, the contrast uptake rate (alpha), contrast washout rate (beta), iAUC30, SER, Slope(ini), T(peak) and kappa(peak) differed substantially between benign and malignant lesions, and after correcting for multiple tests of significance SER and T(peak) demonstrated significance (p < 0.007). For nonmass lesions, we did not find statistically significant differences in any of the parameters for benign vs. malignant lesions (p > 0.5). Kinetic parameters could distinguish benign and malignant mass lesions effectively, but were not quite as useful in discriminating benign from malignant nonmass lesions. If the results of this pilot study are validated in a larger trial, we expect that to maximize diagnostic utility, it will be better to classify lesion morphology as mass or nonmass-like enhancement prior to kinetic analysis.

Estrogen receptor and breast MR imaging features: a correlation study

PURPOSE

To compare the MRI features between estrogen receptor (ER) positive and negative breast cancers.

MATERIALS AND METHODS

Breast MRI of 90 consecutive patients confirmed with invasive ductal carcinoma (IDC), 51 ER positive and 39 ER negative, were analyzed. The tumor morphology and dynamic contrast-enhanced (DCE) kinetics were evaluated based on the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) MRI lexicon and compared. Enlarged axillary lymph nodes on MRI and choline (Cho) detection using MR spectroscopy (MRS) were also analyzed and compared. For patients receiving axillary node dissection the pathological nodal status was also compared.

RESULTS

ER negative breast cancer had bigger tumors compared to ER positive cancer (3.6 +/- 2.0 cm vs. 1.8 +/- 1.3 cm, P < 0.00005). ER negative cancer was more likely to exhibit nonmass type enhancements compared to ER positive cancer (P < 0.005). Enlarged axillary lymph nodes were more frequently identified on MRI in ER negative compared to ER positive patients (P < 0.05). After excluding patients undergoing neoadjuvant chemotherapy, auxiliary lymph node status did not show significant difference between ER positive and ER negative cancer on MRI and pathology. ER negative cancer was more likely to show the malignant type enhancement kinetics (P = 0.15), rim enhancement (P = 0.15), and Cho detection on MRS (P = 0.23) compared to ER positive cancer, but it did not reach a level of statistical significance.

CONCLUSION

ER negative breast cancer was more aggressive, with larger tumor size, more non-mass-type enhancement lesions, and a higher percentage showing enlarged axillary nodes on MRI. These features might be related to its poorer cellular differentiation and/or a higher angiogenesis.

Invasive breast cancer: predicting disease recurrence by using high-spatial-resolution signal enhancement ratio imaging

PURPOSE

To retrospectively evaluate high-spatial-resolution signal enhancement ratio (SER) imaging for the prediction of disease recurrence in patients with breast cancer who underwent preoperative magnetic resonance (MR) imaging.

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board and was HIPAA compliant; informed consent was waived. From 1995 to 2002, gadolinium-enhanced MR imaging data were acquired with a three time point high-resolution method in women undergoing neoadjuvant therapy for invasive breast cancers. Forty-eight women (mean age, 49.1 years; range, 29.7-72.4 years) were divided into recurrence-free or recurrence groups. Volume measurements were tabulated for SER values between set ranges; cutoff criteria were defined to predict disease recurrence after surgery. Wilcoxon rank sum tests and the multivariate Cox proportional hazards regression model were used for evaluation.

RESULTS

Breast tumor volume calculated from the number of voxels with SER values above a threshold corresponding to the upper limit of mean redistribution rate constant in benign tumors (0.88 minutes(-1)) and the volume of cancerous breast tissue infiltrating into the parenchyma were important predictors of disease recurrence. Seventy-five percent of patients with recurrence and 100% of deceased patients were identified as being at high risk for recurrence. Thirty percent of patients with recurrence and 67% of deceased patients were identified as having high risk before chemotherapy. No patients in the recurrence-free group were misidentified as likely to have recurrence. All three prechemotherapy parameters (total tumor volume, tumor volumes with high and low SER) and the postchemotherapy tumor volume with high SER were significantly different between the two groups. The multivariate Cox proportional hazards regression showed that, of the three prechemotherapy covariates, only the low SER and high SER tumor volumes (P = .017 and .049, respectively) were significant and independent predictors of tumor recurrence. Tumor volume with high SER was the only significant postchemotherapy covariate predictor (P = .038).

CONCLUSION

High-spatial-resolution SER imaging may improve prediction for patients at high risk for disease recurrence and death.

Differentiation between benign and malignant breast lesions detected by bilateral dynamic contrast-enhanced MRI: a sensitivity and specificity study

The purpose of this study was to apply an empirical mathematical model (EMM) to kinetic data acquired under a clinical protocol to determine if the sensitivity and specificity can be improved compared with qualitative BI-RADS descriptors of kinetics. 3D DCE-MRI data from 100 patients with 34 benign and 79 malignant lesions were selected for review under an Institutional Review Board (IRB)-approved protocol. The sensitivity and specificity of the delayed phase classification were 91% and 18%, respectively. The EMM was able to accurately fit these curves. There was a statistically significant difference between benign and malignant lesions for several model parameters: the uptake rate, initial slope, signal enhancement ratio, and curvature at the peak enhancement (at most P=0.04). These results demonstrated that EMM analysis provided at least the diagnostic accuracy of the kinetic classifiers described in the BI-RADS lexicon, and offered a few key advantages. It can be used to standardize data from institutions with different dynamic protocols and can provide a more objective classification with continuous variables so that thresholds can be set to achieve desired sensitivity and specificity. This suggests that the EMM may be useful for analysis of routine clinical data.

Quantitative 2- and 3-dimensional analysis of pharmacokinetic model-derived variables for breast lesions in dynamic, contrast-enhanced MR mammography

PURPOSE

2- and 3-dimensional evaluation of quantitative pharmacokinetic parameters derived from the Tofts model modeling dynamic contrast enhancement of lesions in MR mammography.

MATERIALS AND METHODS

In 95 patients, MR mammography revealed 127 suspicious lesions. The initial rate of enhancement was coded by color intensity, the post-initial enhancement change is coded by color hue. 2D and 3D analysis of distribution of color hue and intensity, vascular permeability and extracellular volume were performed.

RESULTS

In 2D, malignant lesions showed significant higher number of bright red, medium red, dark red, bright green, medium green, dark green and bright blue pixels than benign lesions. In 3D, statistical significant differences between malignant and benign lesions was found for all this parameters. Vascular permeability was significant higher in malignant lesions than in benign lesions. Regression model using the 3D data found that the best discriminator between malignant and benign lesions was combined number of voxels and medium green pixels, with a sensitivity of 79.4% and a specificity of 83.1%.

CONCLUSIONS

Quantitative analysis of pharmacokinetic variables of contrast kinetics showed significant differences between malignant and benign lesions. 3D analysis showed superior diagnostic differentiation between malignant and benign lesions than 2D analysis. The parametric analysis using a pharmacokinetic model allows objective analysis of contrast enhancement in breast lesions.