Dynamic contrast-enhanced MRI in the differentiation of breast tumors: user-defined versus semi-automated region-of-interest analysis

Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm)

OBJECTIVES

To determine if whole-lesion histogram analysis on dynamic contrast-enhanced (DCE) parametric maps help to improve the diagnostic accuracy of small suspicious breast lesions (≤ 1 cm).

METHODS

This retrospective study included 99 female patients with 114 lesions (40 malignant and 74 benign lesions) suspicious on magnetic resonance imaging (MRI).Two radiologists reviewed all lesions and descripted the morphologic and kinetic characteristics according to BI-RADS by consensus. Whole lesions were segmented on DCE parametric maps (washin and washout), and quantitative histogram features were extracted. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. Diagnostic performance was assessed and compared with that of qualitative BI-RADS assessment and quantitative histogram analysis by ROC analysis.

RESULTS

For malignancy defined as a washout or plateau pattern, the qualitative kinetic pattern showed a significant difference between the two groups (p = 0.023), yielding an AUC of 0.603 (95% confidence interval [CI]: 0.507, 0.694). The mean and median of washout were independent quantitative predictors of malignancy (p = 0.002, 0.010), achieving an AUC of 0.796 (95% CI: 0. 709, 0.865). The AUC of the quantitative model was better than that of the qualitative model (p < 0.001).

CONCLUSIONS

Compared with the qualitative BI-RADS assessment, quantitative whole-lesion histogram analysis on DCE parametric maps was better to discriminate between small benign and malignant breast lesions (≤ 1 cm) initially defined as suspicious on DCE-MRI.

KEY POINTS

• For malignancy defined as a washout or plateau, the kinetic pattern may provide information to diagnose small breast cancer. • The mean and median of washout map were significantly lower for small malignant breast lesions than for benign lesions. • Quantitative histogram analysis on MRI parametric maps improves diagnostic accuracy for small breast cancer, which may obviate unnecessary biopsy.

Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

Purpose

To evaluate the potential of the texture features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intratumoral subregions to distinguish benign from malignant breast tumors.

Materials and Methods

A total of 299 patients with pathologically verified breast tumors who underwent breast DCE-MRI examination were enrolled in this study, including 124 benign cases and 175 malignant cases. The whole tumor area was semi-automatically segmented on the basis of subtraction images of DCE-MRI in Matlab 2018b. According to the time to peak of the contrast agent, the whole tumor area was partitioned into three subregions: early, moderate, and late. A total of 467 texture features were extracted from the whole tumor area and the three subregions, respectively. Patients were divided into training (n = 209) and validation (n = 90) cohorts by different MRI scanners. The least absolute shrinkage and selection operator (LASSO) method was used to select the optimal feature subset in the training cohort. The Kolmogorov-Smirnov test was first performed on texture features selected by LASSO to test whether the samples followed a normal distribution. Two machine learning methods, decision tree (DT) and support vector machine (SVM), were used to establish classification models with a 10-fold cross-validation method. The performance of the classification models was evaluated with receiver operating characteristic (ROC) curves.

Results

In the training cohort, the areas under the ROC curve (AUCs) for the DT_Whole model and SVM_Whole model were 0.744 and 0.806, respectively. In contrast, the AUCs of the DT_Early model (P = 0.004), DT_Late model (P = 0.015), SVM_Early model (P = 0.002), and SVM_Late model (P = 0.002) were significantly higher: 0.863 (95% CI, 0.808-0.906), 0.860 (95% CI, 0.806-0.904), 0.934 (95% CI, 0.891-0.963), and 0.921 (95% CI, 0.876-0.954), respectively. The SVM_Early model and SVM_Late model achieved better performance than the DT_Early model and DT_Late model (P = 0.003, 0.034, 0.008, and 0.026, respectively). In the validation cohort, the AUCs for the DT_Whole model and SVM_Whole model were 0.670 and 0.708, respectively. In comparison, the AUCs of the DT_Early model (P = 0.006), DT_Late model (P = 0.043), SVM_Early model (P = 0.001), and SVM_Late model (P = 0.007) were significantly higher: 0.839 (95% CI, 0.747-0.908), 0.784 (95% CI, 0.601-0.798), 0.890 (95% CI, 0.806-0.946), and 0.865 (95% CI, 0.777-0.928), respectively.

Conclusion

The texture features from intratumoral subregions of breast DCE-MRI showed potential in identifying benign and malignant breast tumors.

Hypoxia and perfusion in breast cancer: simultaneous assessment using PET/MR imaging

OBJECTIVES

Hypoxia is associated with poor prognosis and treatment resistance in breast cancer. However, the temporally variant nature of hypoxia can complicate interpretation of imaging findings. We explored the relationship between hypoxia and vascular function in breast tumours through combined 18F-fluoromisonidazole (18 F-FMISO) PET/MRI, with simultaneous assessment circumventing the effect of temporal variation in hypoxia and perfusion.

METHODS

Women with histologically confirmed, primary breast cancer underwent a simultaneous 18F-FMISO-PET/MR examination. Tumour hypoxia was assessed using influx rate constant Ki and hypoxic fractions (%HF), while parameters of vascular function (Ktrans, kep, ve, vp) and cellularity (ADC) were derived from dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI, respectively. Additional correlates included histological subtype, grade and size. Relationships between imaging variables were assessed using Pearson correlation (r).

RESULTS

Twenty-nine women with 32 lesions were assessed. Hypoxic fractions > 1% were observed in 6/32 (19%) cancers, while 18/32 (56%) tumours showed a %HF of zero. The presence of hypoxia in lesions was independent of histological subtype or grade. Mean tumour Ktrans correlated negatively with Ki (r = - 0.38, p = 0.04) and %HF (r = - 0.33, p = 0.04), though parametric maps exhibited intratumoural heterogeneity with hypoxic regions colocalising with both hypo- and hyperperfused areas. No correlation was observed between ADC and DCE-MRI or PET parameters. %HF correlated positively with lesion size (r = 0.63, p = 0.001).

CONCLUSION

Hypoxia measured by 18F-FMISO-PET correlated negatively with Ktrans from DCE-MRI, supporting the hypothesis of perfusion-driven hypoxia in breast cancer. Intratumoural hypoxia-perfusion relationships were heterogeneous, suggesting that combined assessment may be needed for disease characterisation, which could be achieved using simultaneous multimodality imaging.

KEY POINTS

• At the tumour level, hypoxia measured by 18F-FMISO-PET was negatively correlated with perfusion measured by DCE-MRI, which supports the hypothesis of perfusion-driven hypoxia in breast cancer. • No associations were observed between 18F-FMISO-PET parameters and tumour histology or grade, but tumour hypoxic fractions increased with lesion size. • Intratumoural hypoxia-perfusion relationships were heterogeneous, suggesting that the combined hypoxia-perfusion status of tumours may need to be considered for disease characterisation, which can be achieved via simultaneous multimodality imaging as reported here.

Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE-MRI in classifying benign from malignant lesions

BACKGROUND AND OBJECTIVES

To investigate the performance of texture analysis based on enhancement kinetic parametric maps derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in discriminating benign from malignant tumors.

METHODS

A total of 192 cases confirmed by histopathology were retrospectively selected from our Picture Archiving and Communication System, including 93 benign and 99 malignant tumors. Lesion areas were delineated semi-automatically, and six kinetic parametric maps reflecting the perfusion information were generated, namely the maximum slope of increase (MSI), slope of signal intensity (SI_slope ), initial percentage of peak enhancement (E_initial ), percentage of peak enhancement (E_peak ), early signal enhancement ratio (ESER), and second enhancement percentage (SEP) maps. A total of 286 texture features were extracted from those quantitative parametric maps. The Student t test or Mann-Whitney U test was used to select features that were statistically significantly different between the benign and malignant groups. A support vector machine was employed with a leave-one-out cross-validation method to establish the classification model. Classification performance was evaluated according to the receiver operating characteristic (ROC) theory.

RESULTS

The areas under ROC curves (AUCs) indicating the diagnostic performance were 0.925 for MSI, 0.854 for SI_slope , 0.756 for E_initial , 0.923 for E_peak , 0.871 for ESER and 0.881 for SEP. Significant differences in AUCs were found between E_initial vs MSI, E_initial vs E_peak and E_initial vs SEP (P < .05). There were no significant differences in other pairwise comparisons.

CONCLUSION

Texture analysis of the kinetic parametric maps derived from breast DCE-MRI can contribute to the discrimination between malignant and benign lesions. It can be considered as a supplementary tool for breast diagnosis.

MRI to predict nipple-areola complex (NAC) involvement: An automatic method to compute the 3D distance between the NAC and tumor

OBJECTIVES

To assess the role in predicting nipple-areola complex (NAC) involvement of a newly developed automatic method which computes the 3D tumor-NAC distance.

PATIENTS AND METHODS

Ninety-nine patients scheduled to nipple sparing mastectomy (NSM) underwent magnetic resonance (MR) examination at 1.5 T, including sagittal T2w and dynamic contrast enhanced (DCE)-MR imaging. An automatic method was developed to segment the NAC and the tumor and to compute the 3D distance between them. The automatic measurement was compared with manual axial and sagittal 2D measurements. NAC involvement was defined by the presence of invasive ductal or lobular carcinoma and/or ductal carcinoma in situ or ductal intraepithelial neoplasia (DIN1c - DIN3).

RESULTS

Tumor-NAC distance was computed on 95/99 patients (25 NAC+), as three tumors were not correctly segmented (sensitivity = 97%), and 1 NAC was not detected (sensitivity = 99%). The automatic 3D distance reached the highest area under the receiver operating characteristic (ROC) curve (0.830) with respect to the manual axial (0.676), sagittal (0.664), and minimum distances (0.664). At the best cut-off point of 21 mm, the 3D distance obtained sensitivity = 72%, specificity = 80%, positive predictive value = 56%, and negative predictive value = 89%.

CONCLUSIONS

This method could provide a reproducible biomarker to preoperatively select breast cancer patients candidates to NSM, thus helping surgical planning and intraoperative management of patients.

A computer-aided diagnosis (CAD) scheme for pretreatment prediction of pathological response to neoadjuvant therapy using dynamic contrast-enhanced MRI texture features

OBJECTIVE

To assess whether a computer-aided, diagnosis (CAD) system can predict pathological Complete Response (pCR) to neoadjuvant chemotherapy (NAC) prior to treatment using texture features.

METHODS

Response to treatment of 44 patients was defined according to the histopatology of resected tumour and extracted axillary nodes in two ways: (a) pCR+ (Smith's Grade = 5) vs pCR- (Smith's Grade < 5); (b) pCRN+ (pCR+ and absence of residual lymph node metastases) vs pCRN - . A CAD system was developed to: (i) segment the breasts; (ii) register the DCE-MRI sequence; (iii) detect the lesion and (iv) extract 27 3D texture features. The role of individual texture features, multiparametric models and Bayesian classifiers in predicting patients' response to NAC were evaluated.

RESULTS

A cross-validated Bayesian classifier fed with 6 features was able to predict pCR with a specificity of 72% and a sensitivity of 67%. Conversely, 2 features were used by the Bayesian classifier to predict pCRN, obtaining a sensitivity of 69% and a specificity of 61%.

CONCLUSION

A CAD scheme, that extracts texture features from an automatically segmented 3D mask of the tumour, could predict pathological response to NAC. Additional research should be performed to validate these promising results on a larger cohort of patients and using different classification strategies. Advances in knowledge: This is the first study assessing the role of an automatic CAD system in predicting the pathological response to NAC before treatment. Fully automatic methods represent the backbone of standardized analysis and may help in timely managing patients candidate to NAC.

Effect of Radiofrequency Transmit Field Correction on Quantitative Dynamic Contrast-enhanced MR Imaging of the Breast at 3.0 T

PURPOSE

To investigate the effects of radiofrequency transmit field (B1(+)) correction on (a) the measured T1 relaxation times of normal breast tissue and malignant lesions and (b) the pharmacokinetically derived parameters of malignant breast lesions at 3 T.

MATERIALS AND METHODS

Ethics approval and informed consent were obtained. Between May 2013 and January 2014, 30 women (median age, 58 years; range, 32-83 years) with invasive ductal carcinoma of at least 10 mm were recruited to undergo dynamic contrast material-enhanced magnetic resonance (MR) imaging before surgery. B1(+) and T1 mapping sequences were performed to determine the effect of B1(+) correction on the native tissue relaxation time (T10) of fat, parenchyma, and malignant lesions in both breasts. Pharmacokinetic parameters were calculated before and after correction for B1(+) variations. Results were correlated with histologic grade by using the Kruskal-Wallis test.

RESULTS

Measurements showed a mean 37% flip angle difference between the right and left breast, which resulted in a 61% T10 difference in fat and a 41.5% difference in parenchyma between the two breasts. The T1 of lesions in the right breast increased by 58%, whereas that of lesions in the left breast decreased by 30% after B1(+) correction. The whole-tumor transendothelial permeability across the vascular compartment(K(trans)) of lesions in the right breast decreased by 41%, and that of lesions in the left breast increased by 46% after correction. A systematic increase in K(trans) was observed, with significant differences found across the histologic grades (P < .001). The effect size of B1(+) correction on K(trans) calculation was large for lesions in the right breast and moderate for lesions in the left breast (Cohen effect size, d = 0.86 and d = 0.59, respectively).

CONCLUSION

B1(+) correction demonstrates a substantial effect on the results of quantitative dynamic contrast-enhanced analysis of breast tissue at 3 T, which propagates into the pharmacokinetic analysis of tumors that is dependent on whether the tumor is located in the right or left breast.

Texture analysis on MR images helps predicting non-response to NAC in breast cancer

BACKGROUND

To assess the performance of a predictive model of non-response to neoadjuvant chemotherapy (NAC) in patients with breast cancer based on texture, kinetic, and BI-RADS parameters measured from dynamic MRI.

METHODS

Sixty-nine patients with invasive ductal carcinoma of the breast who underwent pre-treatment MRI were studied. Morphological parameters and biological markers were measured. Pathological complete response was defined as the absence of invasive and in situ cancer in breast and nodes. Pathological non-responders, partial and complete responders were identified. Dynamic imaging was performed at 1.5 T with a 3D axial T1W GRE fat-suppressed sequence. Visual texture, kinetic and BI-RADS parameters were measured in each lesion. ROC analysis and leave-one-out cross-validation were used to assess the performance of individual parameters, then the performance of multi-parametric models in predicting non-response to NAC.

RESULTS

A model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity. BI-RADS mass/non-mass enhancement, biological markers and histological grade did not contribute significantly to the prediction.

CONCLUSION

Pre-NAC texture and kinetic parameters help predicting non-benefit to NAC. Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model.

Antivascular ultrasound therapy: magnetic resonance imaging validation and activation of the immune response in murine melanoma

OBJECTIVES

The purpose of this study was to investigate the treatment effects of antivascular ultrasound (US) with dynamic contrast-enhanced magnetic resonance imaging (MRI), contrast-enhanced sonography, and histopathologic analysis in a murine melanoma model.

METHODS

Subcutaneous K1735 murine melanoma tumors were grown in syngeneic C3H/HeN mice. Quantitative tumor perfusion characteristics were measured before antivascular US treatment with both dynamic contrast-enhanced MRI and high-resolution contrast-enhanced sonography. Tumors were subsequently treated with 1 or 3 minutes of continuous low-intensity US after intravenous administration of a US contrast agent. Treatment effects were assessed by quantitative dynamic contrast-enhanced MRI, contrast-enhanced sonography, histopathologic analysis, and immunohistochemistry.

RESULTS

Low-intensity antivascular US treatment resulted in approximately a doubling and tripling of the time to peak enhancement on dynamic contrast-enhanced MRI in the 1- and 3-minute treatment groups, respectively, along with a significant decrease in contrast wash-out (P < .01). There was a potent reduction in tumor perfusion on contrast-enhanced sonography, with approximately 40% and 70% reductions in the tumor area perfused as assessed by contrast-enhanced sonography after 1 (P < .05) and 3 (P < .01) minutes of antivascular US. The pathologic and histologic changes spatially correlated with the regions of diminished perfusion seen on contrast-enhanced sonography and dynamic contrast-enhanced MRI. Antivascular US therapy resulted in a significant increase in the number of hypoxia-inducible factor 1A(+) cells, indicating tumor hypoxia (P < .01), and of CD45(+)/CD3(+) cells in tumors after treatment, in keeping with increased T-cell infiltration (P < .01).

CONCLUSIONS

Antivascular US treatment effects extend beyond direct cytotoxicity from hemorrhagic necrosis to include ischemia-mediated cytotoxicity, enhanced small molecule retention, and intratumoral immune activation.

Evaluation of femoral perfusion in a rabbit model of steroid-induced osteonecrosis by dynamic contrast-enhanced MRI with a high magnetic field MRI system

PURPOSE

To evaluate perfusion during the early phase after steroid administration in vivo using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with a high magnetic field MRI system. The main pathogenesis of steroid-induced osteonecrosis is considered to be ischemia.

MATERIALS AND METHODS

A single dose of methylprednisolone (MPSL) was injected into nine rabbits. DCE-MRI was performed for these rabbits before MPSL administration and 1, 5, 10, and 14 days after administration. Time-signal intensity curves were created for each femur based on the signal intensity to evaluate perfusion. Enhancement ratio (ER), initial slope (IS), and area under the curve (AUC) were calculated and the value before MPSL administration and the minimal value after administration were compared statistically.

RESULTS

ER, IS, and AUC values after MPSL administration significantly decreased (P < 0.05, P < 0.01, and P < 0.01, respectively). All of them decreased by the 5th day in 56% of the femora and by the 14th day in 83%, and some femora even showed a decrease from the 1st day.

CONCLUSION

In this study, decreased perfusion in the femora after steroid administration was proven. Additionally, we could show that it occurred from the early days after steroid administration.

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.

Differentiation of breast cancer from fibroadenoma with dual-echo dynamic contrast-enhanced MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) of the breast is a routinely used imaging method which is highly sensitive for detecting breast malignancy. Specificity, though, remains suboptimal. Dynamic susceptibility contrast magnetic resonance imaging (DSC MRI), an alternative dynamic contrast imaging technique, evaluates perfusion-related parameters unique from DCE MRI. Previous work has shown that the combination of DSC MRI with DCE MRI can improve diagnostic specificity, though an additional administration of intravenous contrast is required. Dual-echo MRI can measure both T₁W DCE MRI and T₂*W DSC MRI parameters with a single contrast bolus, but has not been previously implemented in breast imaging. We have developed a dual-echo gradient-echo sequence to perform such simultaneous measurements in the breast, and use it to calculate the semi-quantitative T₁W and T₂*W related parameters such as peak enhancement ratio, time of maximal enhancement, regional blood flow, and regional blood volume in 20 malignant lesions and 10 benign fibroadenomas in 38 patients. Imaging parameters were compared to surgical or biopsy obtained tissue samples. Receiver operating characteristic (ROC) curves and area under the ROC curves were calculated for each parameter and combination of parameters. The time of maximal enhancement derived from DCE MRI had a 90% sensitivity and 69% specificity for predicting malignancy. When combined with DSC MRI derived regional blood flow and volume parameters, sensitivity remained unchanged at 90% but specificity increased to 80%. In conclusion, we show that dual-echo MRI with a single administration of contrast agent can simultaneously measure both T₁W and T₂*W related perfusion and kinetic parameters in the breast and the combination of DCE MRI and DSC MRI parameters improves the diagnostic performance of breast MRI to differentiate breast cancer from benign fibroadenomas.

Performance of a fully automatic lesion detection system for breast DCE-MRI

PURPOSE

To describe and test a new fully automatic lesion detection system for breast DCE-MRI.

MATERIALS AND METHODS

Studies were collected from two institutions adopting different DCE-MRI sequences, one with and the other one without fat-saturation. The detection pipeline consists of (i) breast segmentation, to identify breast size and location; (ii) registration, to correct for patient movements; (iii) lesion detection, to extract contrast-enhanced regions using a new normalization technique based on the contrast-uptake of mammary vessels; (iv) false positive (FP) reduction, to exclude contrast-enhanced regions other than lesions. Detection rate (number of system-detected malignant and benign lesions over the total number of lesions) and sensitivity (system-detected malignant lesions over the total number of malignant lesions) were assessed. The number of FPs was also assessed.

RESULTS

Forty-eight studies with 12 benign and 53 malignant lesions were evaluated. Median lesion diameter was 6 mm (range, 5-15 mm) for benign and 26 mm (range, 5-75 mm) for malignant lesions. Detection rate was 58/65 (89%; 95% confidence interval [CI] 79%-95%) and sensitivity was 52/53 (98%; 95% CI 90%-99%). Mammary median FPs per breast was 4 (1st-3rd quartiles 3-7.25).

CONCLUSION

The system showed promising results on MR datasets obtained from different scanners producing fat-sat or non-fat-sat images with variable temporal and spatial resolution and could potentially be used for early diagnosis and staging of breast cancer to reduce reading time and to improve lesion detection. Further evaluation is needed before it may be used in clinical practice.

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.

Quantifying tumor vascular heterogeneity with dynamic contrast-enhanced magnetic resonance imaging: a review

Tumor microvasculature possesses a high degree of heterogeneity in its structure and function. These features have been demonstrated to be important for disease diagnosis, response assessment, and treatment planning. The exploratory efforts of quantifying tumor vascular heterogeneity with DCE-MRI have led to promising results in a number of studies. However, the methodological implementation in those studies has been highly variable, leading to multiple challenges in data quality and comparability. This paper reviews several heterogeneity quantification methods, with an emphasis on their applications on DCE-MRI pharmacokinetic parametric maps. Important methodological and technological issues in experimental design, data acquisition, and analysis are also discussed, with the current opportunities and efforts for standardization highlighted.

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.

Multilevel analysis of spatiotemporal association features for differentiation of tumor enhancement patterns in breast DCE-MRI

PURPOSE

Analyzing spatiotemporal enhancement patterns is an important task for the differential diagnosis of breast tumors in dynamic contrast-enhanced MRI (DCE-MRI), and yet remains challenging because of complexities in analyzing the time-series of three-dimensional image data. The authors propose a novel approach to breast MRI computer-aided diagnosis (CAD) using a multilevel analysis of spatiotemporal association features for tumor enhancement patterns in DCE-MRI.

METHODS

A database of 171 cases consisting of 111 malignant and 60 benign tumors was used. Time-series contrast-enhanced MR images were obtained from two different types of MR scanners and protocols. The images were first registered for motion compensation, and then tumor regions were segmented using a fuzzy c-means clustering-based method. Spatiotemporal associations of tumor enhancement patterns were analyzed at three levels: Mapping of pixelwise kinetic features within a tumor, extraction of spatial association features from kinetic feature maps, and extraction of kinetic association features at the spatial feature level. A total of 84 initial features were extracted. Predictable values of these features were evaluated with an area under the ROC curve, and were compared between the spatiotemporal association features and a subset of simple form features which do not reflect spatiotemporal association. Several optimized feature sets were identified among the spatiotemporal association feature group or among the simple feature group based on a feature ranking criterion using a support vector machine based recursive feature elimination algorithm. A least-squares support vector machine (LS-SVM) classifier was used for tumor differentiation and the performances were evaluated using a leave-one-out testing.

RESULTS

Predictable values of the extracted single features ranged in 0.52-0.75. By applying multilevel analysis strategy, the spatiotemporal association features became more informative in predicting tumor malignancy, which was shown by a statistical testing in ten spatiotemporal association features. By using a LS-SVM classifier with the optimized second and third level feature set, the CAD scheme showed Az of 0.88 in classification of malignant and benign tumors. When this performance was compared to the same LS-SVM classifier with simple form features which do not reflect spatiotemporal association, there was a statistically significant difference (0.88 vs 0.79, p <0.05), suggesting that the multilevel analysis strategy yields a significant performance improvement.

CONCLUSIONS

The results suggest that the multilevel analysis strategy characterizes the complex tumor enhancement patterns effectively with the spatiotemporal association features, which in turn leads to an improved tumor differentiation. The proposed CAD scheme has a potential for improving diagnostic performance in breast DCE-MRI.

Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis

The current study investigates the feasibility of using texture analysis to quantify the heterogeneity of lesion enhancement kinetics in order to discriminate malignant from benign breast lesions. A total of 82 biopsy-proven breast lesions (51 malignant, 31 benign), originating from 74 women subjected to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were analysed. Pixel-wise analysis of DCE-MRI lesion data was performed to generate initial enhancement, post-initial enhancement and signal enhancement ratio (SER) parametric maps; these maps were subsequently subjected to co-occurrence matrix texture analysis. The discriminating ability of texture features extracted from each parametric map was investigated using a least-squares minimum distance classifier and further compared with the discriminating ability of the same texture features extracted from the first post-contrast frame. Selected texture features extracted from the SER map achieved an area under receiver operating characteristic curve of 0.922 +/- 0.029, a performance similar to post-initial enhancement map features (0.906 +/- 0.032) and statistically significantly higher than for initial enhancement map (0.767 +/- 0.053) and first post-contrast frame (0.756 +/- 0.060) features. Quantifying the heterogeneity of parametric maps that reflect lesion washout properties could contribute to the computer-aided diagnosis of breast lesions in DCE-MRI.

DCE-MRI parameters have potential to predict response of locally advanced breast cancer patients to neoadjuvant chemotherapy and hyperthermia: a pilot study

Combined therapies represent a staple of modern medicine. For women treated with neoadjuvant chemotherapy (NA ChT) for locally advanced breast cancer (LABC), early determination of whether the patient will fail to respond can enable the use of alternative, more beneficial therapies. This is even more desirable when the combined therapy includes hyperthermia (HT), an efficient way to improve drug delivery, however, more costly and time consuming. There is data showing that this goal can be achieved using magnetic resonance imaging (MRI) with contrast agent (CA) enhancement. This work for the first time proposes combining the information extracted from pre-treatment MR imaging into a morpho-physiological tumour score (MPTS) with the hypothesis that this score will increase the prognostic efficacy, compared to each of its MR-derived components: morphological (derived from the shape of the tumour enhancement) and physiological (derived from the CA enhancement variance dynamics parameters). The MPTS was correlated with response as determined by both pathologic residual tumour and MRI imaging, and was shown to have potential to predict response. The MPTS was extracted from pre-treatment MRI parameters, so independent of the combined therapy used.

PURPOSE

To use a novel morpho-physiological tumour score (MPTS) generated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict response to treatment.

MATERIALS AND METHODS

A protocol was designed to acquire DCE-MRI images of 20 locally advanced breast cancer (LABC) patients treated with neoadjuvant chemotherapy (NA ChT) and hyperthermia (HT). Imaging was done over 30 min following bolus injection of gadopentetate-based contrast agent. Parametric maps were generated by fitting the signal intensity to a double exponential curve and were used to derive a morphological characterisation of the lesions. Enhancement-variance dynamics parameters, wash-in and wash-out parameters (WiP, WoP), were extracted. The morphological characterisation and the WiP and WoP were combined into a MPTS with the intent of achieving better prognostic efficacy. The MPTS was correlated with response to NA therapy as determined by pathological residual tumour and MRI imaging.

RESULTS

The contrast agent in all tumours typically peaked in the first 1-4 min. The tumours' WiP and WoP varied considerably. The MPTS was highly correlated with whether the patients had a pathological response. This scoring system has a specificity of 78% and a sensitivity of 91% for predicting response to NA chemotherapy. The kappa was 0.69 with a 95% confidence interval of [0.38, 1] and a p-value of 0.002.

CONCLUSIONS

This pilot study shows that the MPTS derived using pre-treatment MRI images has the potential to predict response to NA ChT and HT in LABC patients. Further prospective studies are needed to confirm the validity of these results.

Typical atypical findings on dynamic MRI of the breast

Dynamic contrast enhanced magnetic resonance imaging (DCE MRI) of the breast has become an important tool to detect and characterize breast disease. The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS(®)) provides a standardized vocabulary for describing the morphologic features and contrast kinetics of breast lesions. However, some lesions may show morphologic and dynamic MR features not consistent with their histologic nature resulting in incorrect categorization as malignant or benign. Another cause of diagnostic problems is artifacts. Thus correct interpretation of dynamic MRI of the breast demands knowledge of the most common pitfalls encountered in clinical practice. A pictorial overview of these is presented, with particular reference to the differentiation of malignant tumors from benign lesions.

Dynamic contrast-enhanced MRI in the diagnosis and management of breast cancer

Dynamic contrast-enhanced MRI (DCE-MRI) is an evolving tool for determining breast disease, which benefits from the move to imaging at 3 T. It has major capabilities for the diagnosis, detection and monitoring of malignancy. It benefits from being non-invasive and three-dimensional, allowing visualisation of the extent of disease and its angiogenic properties, visualisation of lesion heterogeneity, detection of changes in angiogenic properties before morphological alterations, and the potential to predict the overall response either before the start of therapy or early during treatment. In addition, DCE-MRI is emerging as a powerful tool for screening high-risk patients and for detecting high-grade ductal carcinoma in situ. However, there are also a number of limitations, including the overlap in enhancement patterns between malignant and benign disease, the failure to resolve microscopic disease particularly in the neoadjuvant setting, and the inconsistent predictive value of the enhancement pattern for clinical outcome. Careful consideration should be given to the technical requirements of individual examinations and the need for automation of post-processing techniques to appropriately handle the growing volume of data acquired. Research continues, focusing on the use of higher field strengths with improved spatial and temporal resolution data, improving understanding of the mechanism of contrast enhancement at the cellular level, and developing macromolecular and targeted contrast agents.

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.

Comparison of tumor histology to dynamic contrast enhanced magnetic resonance imaging-based physiological estimates

PURPOSE

The purpose of this study was to compare histologically determined cellularity and extracellular space to dynamic contrast-enhanced magnetic resonance imaging (DCE MRI)-based maps of a two-compartment model's parameters describing tumor contrast agent extravasation, specifically tumor extravascular extracellular space (EES) volume fraction (ve), tumor plasma volume fraction (vp) and volume-normalized contrast agent transfer rate between tumor plasma and interstitium (KTRANS/VT).

MATERIALS AND METHODS

Obtained ve, vp and KTRANS/VT maps were estimated from gadolinium diethylenetriamine penta-acetic acid DCE T1-weighted gradient-echo images at resolutions of 469, 938 and 2500 microm. These parameter maps were compared at each resolution to histologically determined tumor type, and the high-resolution 469-microm maps were compared with automated cell counting using Otsu's method and a color-thresholding method for estimated intracellular (Vintracellular) and extracellular (Vextracellular) space fractions.

RESULTS

The top five KTRANS/VT values obtained from each tumor at 469 and 938 microm resolutions are significantly different from those obtained at 2500 microm (P<.0001) and from one another (P=.0014). Using these top five KTRANS/VT values and the corresponding tumor EES volume fractions ve, we can statistically differentiate invasive ductal carcinomas from noninvasive papillary carcinomas for the 469- and 938-microm resolutions (P=.0017 and P=.0047, respectively), but not for the 2500-microm resolution (P=.9008). The color-thresholding method demonstrated that ve measured by DCE MRI is statistically similar to histologically determined EES. The Vextracellular obtained from the color-thresholding method was statistically similar to the ve measured with DCE MRI for the top 10 KTRANS/VT values (P>.05). DCE MRI-based KTRANS/VT estimates are not statistically correlated with histologically determined cellularity.

CONCLUSION

DCE MRI estimates of tumor physiology are a limited representation of tumor histological features. Extracellular spaces measured by both DCE MRI and microscopic analysis are statistically similar. Tumor typing by DCE MRI is spatial resolution dependent, as lower resolutions average out contributions to voxel-based estimates of KTRANS/VT. Thus, an appropriate resolution window is essential for DCE MRI tumor diagnosis. Within this resolution window, the top KTRANS/VT values with corresponding ve are diagnostic for the tumor types analyzed in this study.

Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI

A novel automated computerized scheme has been developed for determining a likelihood measure of malignancy for cancer suspicious regions in the prostate based on dynamic contrast-enhanced magnetic resonance imaging (MRI) (DCE-MRI) images. Our database consisted of 34 consecutive patients with histologically proven adenocarcinoma in the peripheral zone of the prostate. Both carcinoma and non-malignant tissue were annotated in consensus on MR images by a radiologist and a researcher using whole mount step-section histopathology as standard of reference. The annotations were used as regions of interest (ROIs). A feature set comprising pharmacokinetic parameters and a T1 estimate was extracted from the ROIs to train a support vector machine as classifier. The output of the classifier was used as a measure of likelihood of malignancy. Diagnostic performance of the scheme was evaluated using the area under the ROC curve. The diagnostic accuracy obtained for differentiating prostate cancer from non-malignant disorders in the peripheral zone was 0.83 (0.75-0.92). This suggests that it is feasible to develop a computer aided diagnosis system capable of characterizing prostate cancer in the peripheral zone based on DCE-MRI.

Quantitative assessment of colorectal cancer tumor vascular parameters by using perfusion CT: influence of tumor region of interest

PURPOSE

To prospectively determine whether position and size of tumor region of interest (ROI) influence estimates of colorectal cancer vascular parameters at computed tomography (CT).

MATERIALS AND METHODS

After institutional review board approval and informed consent, 25 men and 22 women (mean age, 65.8 years) with colorectal adenocarcinoma underwent 65-second CT perfusion study. Blood volume, blood flow, and permeability-surface area product were determined for 40- or 120-mm(2) circular ROIs placed at the tumor edge and center and around (outlining) visible tumor. ROI analysis was repeated by two observers in different subsets of patients to assess intra- and interobserver variation. Measurements were compared by using analysis of variance; a difference with P = .002 was significant.

RESULTS

Blood volume, blood flow, and permeability-surface area product measurements were substantially higher at the edge than at the center for both 40- and 120-mm(2) ROIs. For 40-mm(2) ROI, means of the three measurements were 6.9 mL/100 g (standard deviation [SD], 1.4), 108.7 mL/100 g per minute (SD, 39.2), and 16.9 mL/100 g per minute (SD, 4.2), respectively, at the edge versus 5.1 mL/100 g (SD, 1.5), 56.3 mL/100 g per minute (SD, 33.1), and 13.9 mL/100 g per minute (SD, 4.6), respectively, at the center. For 120-mm(2) ROI, means of the three measurements were 6.6 mL/100 g (SD, 1.3), 96.7 mL/100 g per minute (SD, 42.5), and 16.3 mL/100 g per minute (SD, 5.6), respectively, at the edge versus 5.1 mL/100 g (SD, 1.4), 58.3 mL/100 g per minute (SD, 32.5), and 13.4 mL/100 g per minute (SD, 4.3) at the center (P < .0001). Measurements varied substantially depending on the ROI size; values for the ROI for outlined tumor were intermediate between those at the tumor edge and center. Inter- and intraobserver agreement was poor for both 40- and 120-mm(2) ROIs.

CONCLUSION

Position and size of tumor ROI and observer variation substantially influence ultimate perfusion values. ROI for outlined entire tumor is more reliable for perfusion measurements and more appropriate clinically than use of arbitrarily determined smaller ROIs.

Contrast-enhanced magnetic resonance imaging of the breast: the value of pharmacokinetic parameters derived from fast dynamic imaging during initial enhancement in classifying lesions

The value of pharmacokinetic parameters derived from fast dynamic imaging during initial enhancement in characterizing breast lesions on magnetic resonance imaging (MRI) was evaluated. Sixty-eight malignant and 34 benign lesions were included. In the scanning protocol, high temporal resolution imaging was combined with high spatial resolution imaging. The high temporal resolution images were recorded every 4.1 s during initial enhancement (fast dynamic analysis). The high spatial resolution images were recorded at a temporal resolution of 86 s (slow dynamic analysis). In the fast dynamic evaluation pharmacokinetic parameters (K^trans, V_e and k_ep) were evaluated. In the slow dynamic analysis, each lesion was scored according to the BI-RADS classification. Two readers evaluated all data prospectively. ROC and multivariate analysis were performed. The slow dynamic analysis resulted in an AUC of 0.85 and 0.83, respectively. The fast dynamic analysis resulted in an AUC of 0.83 in both readers. The combination of both the slow and fast dynamic analyses resulted in a significant improvement of diagnostic performance with an AUC of 0.93 and 0.90 (P = 0.02). The increased diagnostic performance found when combining both methods demonstrates the additional value of our method in further improving the diagnostic performance of breast MRI.

Contrast kinetics of the malignant breast tumour—Border versus centre enhancement on dynamic midfield MRI

PURPOSE

To quantify the border versus centre enhancement of malignant breast tumours on dynamic magnetic resonance mammography.

MATERIALS AND METHODS

Fifty-two women diagnosed with primary breast cancer underwent dynamic magnetic resonance mammography (Omniscan 0.2 mmol/kg bodyweight) on a midfield scanner (0.6 T), prior to surgery. The following five variables were recorded from the border and centre regions of the tumours: Early Enhancement, Time to Peak, Wash-in rate, Wash-out rate and Area under Curve. Information on histology type, oestrogen and progesterone receptor status was collected. Statistical analysis was performed in SAS 9.1 as paired samples t-tests.

RESULTS

Fifty of 52 malignant tumours displayed a faster Early Enhancement in the border region compared to the centre (p<0.0001). Significant differences between the border and centre values were found for Time to Peak, Wash-in rate, Wash-out rate and Area under Curve. Hormone receptor positive tumours displayed an over-all highly significant difference between border and centre enhancement, whereas no significant differences for any of the five variables were recorded in neither oestrogen nor progesterone hormone receptor negative tumours.

CONCLUSION

The border/centre enhancement difference in malignant breast tumours is easily visualized on midfield dynamic magnetic resonance mammography. The dynamic behaviour is significantly correlated to histological features and receptor status of the tumours.

Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching

A novel fully automated system is introduced to facilitate lesion detection in dynamic contrast-enhanced, magnetic resonance mammography (DCE-MRM). The system extracts breast regions from pre-contrast images using a cellular neural network, generates normalized maximum intensity-time ratio (nMITR) maps and performs 3D template matching with three layers of 12x12 cells to detect lesions. A breast is considered to be properly segmented when relative overlap >0.85 and misclassification rate <0.10. Sensitivity, false-positive rate per slice and per lesion are used to assess detection performance. The system was tested with a dataset of 2064 breast MR images (344slicesx6 acquisitions over time) from 19 women containing 39 marked lesions. Ninety-seven percent of the breasts were segmented properly and all the lesions were detected correctly (detection sensitivity=100%), however, there were some false-positive detections (31%/lesion, 10%/slice).

Assessment of three different software systems in the evaluation of dynamic MRI of the breast

OBJECTIVE

The aim was to compare the diagnostic performance and handling of dynamic contrast-enhanced MRI of the breast with two commercial software solutions ("CADstream" and "3TP") and one self-developed software system ("Mammatool").

MATERIALS AND METHODS

Identical data sets of dynamic breast MRI from 21 patients were evaluated retrospectively with all three software systems. The exams were classified according to the BI-RADS classification. The number of lesions in the parametric mapping was compared to histology or follow-up of more than 2 years. In addition, 25 quality criteria were judged by 3 independent investigators with a score from 0 to 5. Statistical analysis was performed to document the quality ranking of the different software systems.

RESULTS

There were 9 invasive carcinomas, one pure DCIS, one papilloma, one radial scar, three histologically proven changes due to mastopathy, one adenosis and two fibroadenomas. Additionally two patients with enhancing parenchyma followed with MRI for more than 3 years and one scar after breast conserving therapy were included. All malignant lesions were classified as BI-RADS 4 or 5 using all software systems and showed significant enhancement in the parametric mapping. "CADstream" showed the best score on subjective quality criteria. "3TP" showed the lowest number of false-positive results. "Mammatool" produced the lowest number of benign tissues indicated with parametric overlay.

CONCLUSION

All three software programs tested were adequate for sensitive and efficient assessment of dynamic MRI of the breast. Improvements in specificity may be achievable.

Inflammatory Breast Cancer: Dynamic Contrast-enhanced MR in Patients Receiving Bevacizumab—Initial Experience

PURPOSE

To retrospectively compare three dynamic contrast material-enhanced magnetic resonance (MR) imaging (dynamic MR imaging) analytic methods to determine the parameter or combination of parameters most strongly associated with changes in tumor microvasculature during treatment with bevacizumab alone and bevacizumab plus chemotherapy in patients with inflammatory or locally advanced breast cancer.

MATERIALS AND METHODS

This study was conducted in accordance with the institutional review board of the National Cancer Institute and was compliant with the Privacy Act of 1974. Informed consent was obtained from all patients. Patients with inflammatory or locally advanced breast cancer were treated with one cycle of bevacizumab alone (cycle 1) followed by six cycles of combination bevacizumab and chemotherapy (cycles 2-7). Serial dynamic MR images were obtained, and the kinetic parameters measured by using three dynamic analytic MR methods (heuristic, Brix, and general kinetic models) and two region-of-interest strategies were compared by using two-sided statistical tests. A P value of .01 was required for significance.

RESULTS

In 19 patients, with use of a whole-tumor region of interest, the authors observed a significant decrease in the median values of three parameters measured from baseline to cycle 1: forward transfer rate constant (Ktrans) (-34% relative change, P=.003), backflow compartmental rate constant extravascular and extracellular to plasma (Kep) (-15% relative change, P<.001), and integrated area under the gadolinium concentration curve (IAUGC) at 180 seconds (-23% relative change, P=.009). A trend toward differences in the heuristic slope of the washout curve between responders and nonresponders to therapy was observed after cycle 1 (bevacizumab alone, P=.02). The median relative change in slope of the wash-in curve from baseline to cycle 4 was significantly different between responders and nonresponders (P=.009).

CONCLUSION

The dynamic contrast-enhanced MR parameters Ktrans, Kep, and IAUGC at 180 seconds appear to have the strongest association with early physiologic response to bevacizumab. Clinical trial registration no. NCT00016549

Computer assistance for MR based diagnosis of breast cancer: present and future challenges

MR based methods have gained an important role for the clinical detection and diagnosis of breast cancer. Dynamic contrast-enhanced MRI of the breast has become a robust and successful method, especially for diagnosis of high-risk cases due to its higher sensitivity compared to X-ray mammography. The application of MR based imaging methods depends on various automated image processing routines. The combination of techniques for preprocessing, quantification and visualization of datasets is necessary to achieve fast and solid assessment of valuable parameters for diagnosis. In this paper, different aspects such as registration methods for the reduction of motion artifacts, segmentation issues, as well as morphologic and dynamic lesion analysis will be reviewed with a focus on breast MRI, MR spectroscopy and MR guided biopsies of the breast, their implications and technical challenges from a computer assistance point of view.

Computerized Classification Method for Differentiating Between Benign and Malignant Lesions on Breast MR Images

Contrast-enhanced breast MRI has been shown to have very high sensitivity in the detection of breast cancers. A new computerized classification method for differentiating between benign and malignant lesions on breast MRIs was developed. This method was based on temporal feature analysis. We experimented with a set of thresholds of the contrast uptake and washout speed to automatically determine suspicious malignant areas. An angiogenesis map was generated to indicate suspicious malignant areas by color. The results obtained from the retrospective analysis on 64 malignant and 29 benign breast lesions showed that our method achieved 90.5% (57/63) sensitivity in detecting malignant lesions, and it correctly classified 55% (16/29) benign lesions as benign. The study results demonstrated the effectiveness of this temporal feature analysis method for the detection of malignant lesions and its performance in delineating malignant lesions from benign lesions.

Improved lesion detection in MR mammography: three-dimensional segmentation, moving voxel sampling, and normalized maximum intensity-time ratio entropy

RATIONALE AND OBJECTIVES

The objective of this work was to develop a quantitative method for improving lesion detection in dynamic contrast-enhanced magnetic resonance mammography (DCEMRM). For this purpose, we segmented and analyzed suspicious regions according to their contrast enhancement dynamics, generated a normalized maximum intensity-time ratio (nMITR) projection, and explored it to extract important features, to improve accuracy and reproducibility of detection.

MATERIALS AND METHODS

A novel automated method is introduced to segment and analyze lesions in three dimensions. It consists of four consecutive stages: volume of interest selection, nMITR projection generation using a voxel sampling method based on a moving 3 x 3 mask, three-dimensional lesion segmentation, and feature extraction. The nMITR projection of the detected lesion is used to extract six features: mean, maximum, standard deviation, kurtosis, skewness, and entropy, and their diagnostic significance is studied in detail. High-resolution MR images of 52 breast masses from 46 women are analyzed using the technique developed.

RESULTS

Entropy, standard deviation, and the maximum and mean value features were found to have high significance (P < 0.001) and diagnostic accuracy (0.86-0.97). The kurtosis and skewness were not significant. Automated analysis of DCEMRM using nMITR was shown to be feasible.

CONCLUSION

The lesion detection method described is efficient and leads to improved, accurate, reproducible diagnoses. It is reliable in terms of observer variability and may allow for a better standardization of clinical evaluations. The findings demonstrate the usefulness of nMITR based features; nMITR-entropy shows the best performance for quantitative diagnosis.

Spatial and temporal resolution effects on dynamic contrast-enhanced magnetic resonance mammography

We tested the hypothesis that partial volume effects due to poor in-plane resolution and/or low temporal resolution used in clinical dynamic contrast-enhanced magnetic resonance imaging results in erroneous diagnostic information based on inaccurate estimates of tumor contrast agent extravasation and tested whether reduced encoding techniques can correct for dynamic data volume averaging. Image spatial resolution was reduced from 469 x 469 microm2 to those reported below by selecting a subset of k-space data. We then compared the top five K(trans)/V(T) "hot spots" obtained from the original data set, 469 x 469-microm in-plane spatial resolution and an 18-s temporal resolution processed by fast Fourier transform (FFT), with values obtained from data sets having in-plane spatial resolutions of 938 x 938, 1875 x 1875 and 2500 x 2500 microm2 and a temporal resolution of 18 s, or data sets with temporal resolutions of 36, 54 and 72 and a spatial resolution of 469 x 469 microm2, and found them to statistically differ from the parent data sets. We then tested four different post processing methods for improving the spatial resolution without sacrificing temporal resolution: zero-filled FFT, keyhole, reduced-encoding imaging by generalized-series reconstruction (RIGR) and two-reference RIGR (TRIGR). The top five values of K(trans)/V(T) obtained from data sets, the in-plane spatial resolutions of which were improved to 469 x 469 microm2 by zero-filling FFT, Keyhole and RIGR, statistically differed from those obtained from the original 469 x 469 microm2 FFT parent image data set. Only the 938 x 938 and 1875 x 1875 microm2 data sets reconstructed to 469 x 469 microm2 with TRIGR reconstruction method yielded values of the top five K(trans)/V(T) hot spots statistically the same as the original parent data set, 469 x 469 microm2 in-plane spatial and 18-s temporal-resolution FFT. That is, partial volume effects from data sets of different in-plane spatial resolution resulted in statistically different values of the top five K(trans)/V(T) hot spots relative to a high spatial and temporal resolution data set, and TRIGR reconstruction of these low resolution data sets to high resolution images provided statistically similar values with a savings in temporal resolution of 2 to 4 times.

Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast is being used increasingly in the detection and diagnosis of breast cancer as a complementary modality to mammography and sonography. Although the potential diagnostic value of kinetic curves in DCE-MRI is established, the method for generating kinetic curves is not standardized. The inherent reason that curve identification is needed is that the uptake of contrast agent in a breast lesion is often heterogeneous, especially in malignant lesions. It is accepted that manual region of interest selection in 4D breast magnetic resonance (MR) images to generate the kinetic curve is a time-consuming process and suffers from significant inter- and intraobserver variability. We investigated and developed a fuzzy c-means (FCM) clustering-based technique for automatically identifying characteristic kinetic curves from breast lesions in DCE-MRI of the breast. Dynamic contrast-enhanced MR images were obtained using a T1-weighted 3D spoiled gradient echo sequence with Gd-DTPA dose of 0.2 mmol/kg and temporal resolution of 69 s. FCM clustering was applied to automatically partition the signal-time curves in a segmented 3D breast lesion into a number of classes (i.e., prototypic curves). The prototypic curve with the highest initial enhancement was selected as the representative characteristic kinetic curve (CKC) of the lesion. Four features were then extracted from each characteristic kinetic curve to depict the maximum contrast enhancement, time to peak, uptake rate, and washout rate of the lesion kinetics. The performance of the kinetic features in the task of distinguishing between benign and malignant lesions was assessed by receiver operating characteristic analysis. With a database of 121 breast lesions (77 malignant and 44 benign cases), the classification performance of the FCM-identified CKCs was found to be better than that from the curves obtained by averaging over the entire lesion and similar to kinetic curves generated from regions drawn within the lesion by a radiologist experienced in breast MRI.

Breast lesion analysis of shape technique: semiautomated vs. manual morphological description

PURPOSE

To investigate the efficacy of an automated method of shape measurement for improving the discrimination of benign and malignant breast lesions.

MATERIALS AND METHODS

A total of 47 breast lesions (32 malignant and 15 benign) were examined using a 1.5 Tesla system. Regions of interest (ROIs) were manually drawn and extracted from high-resolution, fat-suppressed, postcontrast images, or were extracted with the use of a semiautomated computer algorithm. Shape parameters (i.e., complexity, convexity, circularity, and degree of elongation) were determined to assess whether they could be used to discriminate breast lesions.

RESULTS

Convexity differed significantly between the benign and malignant groups for both ROI methods. In addition, the semiautomated method demonstrated significantly different values of complexity.

CONCLUSION

This work demonstrates the usefulness of several shape descriptors for characterizing breast lesions, and shows that the automated method of analysis improves the discrimination and standardization of data.

Influence of post-treatment delay on the evaluation of the response to focused ultrasound surgery of breast cancer by dynamic contrast enhanced MRI

The assessment of the effectiveness of MRI-guided focused ultrasound surgery (MRIgFUS) of breast carcinomas can be performed by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters which monitor the presence of residual tumour. The aim of this study was to evaluate the effect of the post-treatment delay on this assessment. DCE-MRI data were acquired immediately and 3-14 days after MRIgFUS treatment of 26 tumours (<7 days, n = 6; = or > ge;7 days, n = 20). The percentage of residual tumour was determined histologically on the resected mass and correlated with two DCE-MRI parameters: increase in signal intensity (ISI) and positive enhancement integral (PEI). No correlation could be found between DCE-MRI data acquired immediately after treatment and the percentage of residual tumour. Good correlation coefficients were found for data acquired several days after treatment (ISI, r = 0.749; PEI, r = 0.778). However, they were higher when the post-treatment time interval was 7 days or more (ISI, r = 0.962; PEI, r = 0.934). These results suggest that a post-treatment delay of 7 days is necessary for the accurate assessment of the presence of residual tumour by DCE-MRI parameters.

Treatment planning challenges in breast irradiation: the ideal and the practical

Radiotherapy has recently undergone some interesting developments, with the introduction of new technology and techniques in many departments. Arguably, with this comes an increase in the expectation of its capability. The treatment site that continues to represent most of the workload in our departments is breast. We should consider how relevant these contemporary changes are in the treatment of breast cancer. In this paper, we review some of the challenges in planning breast treatments and how they may be addressed with contemporary radiotherapy techniques.

Inter- and intraobserver variability in the evaluation of dynamic breast cancer MRI

PURPOSE

To quantify variations within and between observers ascribable to manual region of interest (ROI) placement in patients with breast cancer undergoing dynamic MRI.

MATERIALS AND METHODS

Expert and nonexpert observers independently outlined tumor ROIs on 30 dynamic T(1)-weighted (T(1)W) MRI scans on five occasions over two months. Lesion size (number of pixels) and kinetic parameter estimates, including the transfer constant (K(trans)), were calculated for each ROI placement. Inter- and intraobserver variability was assessed with respect to the interval between drawings, lesion morphology, and observer experience.

RESULTS

For the nonexpert, the variability reduced with decreasing time intervals between ROI drawings (the coefficient of variance (wCV) values at two months, two weeks, one day, and same-day time intervals were respectively 11.6%, 10.7%, 4.8%, and 2.6% for lesion size, and 8.9%, 9.7%, 6.7%, and 3.2% for K(trans)). For the expert observer, the variability was smaller overall and more constant, but improved for same-day ROI placements (region size wCV: 7.5%, 6.2%, 7.1%, and 3.7%; K(trans) wCV: 5.4%, 5.3%, 5.6%, and 4.5%).

CONCLUSION

Significant observer variability in manual ROI placement occurs in dynamic MRI of breast cancer. For serial patient studies, ROI placements should be outlined at the same sitting to minimize observer error.

Variability in the description of morphologic and contrast enhancement characteristics of breast lesions on magnetic resonance imaging

RATIONALE AND OBJECTIVE

The objective of this study was to evaluate the interobserver variability in reporting descriptive kinetic and morphologic enhancement features at breast magnetic resonance imaging.

MATERIALS AND METHODS

Four observers evaluated 103 lesions, 49 malignant and 54 benign, proven by histopathology. They used standardized terminology with the following characteristics: "early enhancement kinetics" and "late enhancement kinetics" in curves from both reader-determined and preset regions of interest (ROIs), "enhancement pattern," "shape," "margin," "internal enhancement," and a final assessment score. Agreement was calculated using the kappa statistic. Differences in agreement were calculated using Fisher exact test.

RESULTS

kappa was 0.27 for both early and late enhancement; preset ROIs improved kappa to 0.47 and 0.67, respectively (odds ratios, 1.7 and 4.5). kappa was 0.45 for pattern, 0.42 for shape, 0.26 for margin, 0.25 for internal enhancement, and 0.28 for final assessment.

CONCLUSIONS

There was considerable variability in the use of most generally accepted terms. The preparation of ROIs was a major source of variability in the interpretation of enhancement curves.

The choice of region of interest measures in contrast-enhanced magnetic resonance image characterization of experimental breast tumors

OBJECTIVES

The objectives of this study were to determine if magnetic resonance (MR) estimates of quantitative tissue microvascular characteristics from regions of interest (ROI) limited to the tumor periphery provided a better correlation with tumor histologic grade than ROI defined for the whole tumor in cross-section.

METHODS

A metaanalysis was based on 98 quantitative MR image breast tumor characterizations acquired in 3 separate experimental studies using identical methods for tumor induction and contrast enhancement.

RESULTS

The endothelial transfer coefficient (K) of albumin (Gd-DTPA)30 from the tumor periphery correlated (r = 0.784) significantly more strongly (P < 0.001) with the pathologic tumor grade than K derived from the whole tumor (r = 0.604). K estimates, either from the tumor periphery or from the whole tumor, correlated significantly more strongly with histologic grade (P < 0.01) than MR image estimates of fractional plasma volume (fPV) from either tumor periphery (r = 0.368) or whole tumor (r = 0.323).

CONCLUSIONS

K estimates from the tumor periphery were the best of these measurable MR image microvascular characteristics for predicting the histologic grade.

Region-of-interest-based analysis of clustered BOLD MRI data in experimental arthritis

RATIONALE AND OBJECTIVES

BOLD MRI provides functional information based on minimal changes. Problems inherent in data processing of the very low signal-to-noise-ratio of BOLD experiments have created obstacles for validation of certain techniques using standard strength-field MR scanners. Measures of diagnostic accuracy of clustered data are directly related to the reading parameters used to define regions-of-interest (ROIs). Our primary aim was to determine the combination of ROI-related reading parameters that provides highest accuracy for discrimination of presence or absence of arthritis in acute and subacute stages of the disease using paired comparisons of BOLD MRI data.

MATERIALS AND METHODS

Six male New Zealand white rabbits were injected with albumin into one knee and saline into the contralateral knee, 3 animals had albumin injected into only one of the knees, 2 had saline injected into one of the knees, and 3 animals were not injected. The rabbits' knees underwent BOLD MRI on days 1 and 28 after induction of arthritis, except for the knees of 3 animals (albumin- vs saline-injected knees, n = 2 animals; saline- vs noninjected knees, n = 1 animal) that died before expected and had only the first MRI examination done. Percentage of activated voxels and differences in on-and-off signal intensities were the BOLD MRI methods applied. Data were analyzed using anatomic-driven small ROI, voxel-chaser-driven small ROI and anatomic-driven large ROI techniques.

RESULTS

Diagnostic areas-under-the curve (AUCs) were obtained only for acute arthritis and only when percentage of activated voxels was used. Low threshold, positive voxel activations and small ROIs generated the largest AUCs (AUC +/- SE, .911 +/- .092, P = .014) using either anatomic-driven or voxel-chaser-driven techniques. A sensitivity analysis confirmed the importance of threshold as a parameter for analysis.

CONCLUSION

Low threshold, positive voxel activations and small ROIs constituted the set of reading parameters that provided the most accurate BOLD MRI results.

The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations

Vascular and angiogenic processes provide an important target for novel cancer therapeutics. Dynamic contrast-enhanced magnetic resonance imaging is being used increasingly to noninvasively monitor the action of these therapeutics in early-stage clinical trials. This publication reports the outcome of a workshop that considered the methodology and design of magnetic resonance studies, recommending how this new tool might best be used.

Role of dynamic contrast enhanced MRI in monitoring early response of locally advanced breast cancer to neoadjuvant chemotherapy

Neoadjuvant chemotherapy has become the standard treatment for patients with locally advanced breast cancer; however a technique that can accurately differentiate responders from non-responders at an early time point during treatment has still to be identified. The purpose of this work was to evaluate the ability of pharmacokinetically modelled dynamic contrast-enhanced MRI data to predict and monitor response of patients diagnosed with locally advanced breast cancer to neoadjuvant chemotherapy, at an early time point during treatment. Sixty-eight patients with histology proven breast cancer underwent MRI examination prior to treatment, early during treatment and following the final cycle of chemotherapy. A two compartment pharmacokinetic model provided the kinetic parameters transfer constant (Ktrans), rate constant (Kep) and extracellular extravascular space (Ve) for a region of interest encompassing the whole lesion (ROIwhole) and a 3x3 pixel 'hot-spot' showing the greatest mean maximum percentage enhancement from within that region (ROIhs). Following treatment 48 patients were classified as responders and 20 as non-responders based on total tumour volume reduction. Tumour volume changes between the pre-treatment and early treatment time points demonstrated differences between responders and non-responders with percentage change revealing the most significant result (p<0.001). Analysis based on ROIhs provided more statistically significant differences between responders and non-responders then ROIwhole analysis. ROIhs analysis demonstrated differences between responders and non-responders both prior to and early during treatment. A highly significant reduction in both Ktrans and Kep (p<0.001) was noted for responders between the pre-treatment and early treatment time points, while Ve significantly increased during the same time period for non-responders (p<0.001). Quantification of dynamic contrast enhancement parameters provides a potential means for differentiating responders from non-responders early during their treatment, thereby allowing a prompt change in treatment if necessary.

Dynamic Breast Magnetic Resonance Imaging

OBJECTIVE

To assess the effect of changing the region of interest (ROI) on early enhancement (EE) in dynamic breast magnetic resonance (MR) imaging.

METHODS

We evaluated retrospectively 102 breast lesions: 54 lesions (33 malignancies and 21 benignancies) studied with 2D and 48 lesions (30 and 18, respectively) with 3D gradient-echo dynamic technique (contrast dose 0.1 mmol/kg). Each lesion was postprocessed using 3 different regions of interest (ROIs): small circular ROI on maximal enhancement (SCR); large circular ROI within the lesion (LCR); and irregular ROI by manual contouring (IRR). EE was classified as benign (< or = 50%), uncertain (51-89%), or malignant (> or = 90%).

RESULTS

With 2D, the uncertain EEs were 17% for both SCR and LCR, 33% for IRR (P = 0.008); with 3D, the uncertain EEs were 4%, 15%, and 13%, respectively (SCR versus LCR, P = 0.063). More uncertain EEs were obtained with 2D (17-33%) than with 3D (4-15%), significantly for SCR (P = 0.043) and IRR (P = 0.013). Considering uncertain EEs as positive, sensitivity was 100% for SCR, 91% for LCR, and 82% for IRR (SCR versus IRR, P = 0.031) with 2D, 100%, 97%, and 87%, respectively, with 3D technique, without significant differences; specificity ranged from 71% to 90% with 2D and 61% to 83% with 3D, without significant differences.

CONCLUSION

The type of ROI influences the EE in dynamic breast MR. Using 3D technique with small ROI located on the area of maximal enhancement gives the best results in terms of certainty of the level of EE together with top levels of sensitivity.

Differentiation of benign and malignant sub-1 cm breast lesions using dynamic contrast enhanced MRI

The purpose of this work is to assess the additional benefit of MRI-based morphology and quantification of contrast enhancement in the differential diagnosis of sub-1cm breast lesions. Forty-three women with suspected breast cancer were examined using X-ray mammography, ultrasound mammography, and MRI. Dynamic contrast imaging was performed and relative enhancement at various time-points was calculated. The dynamic data was also processed using a two-compartment pharmacokinetic model. Radiological interpretation of high-resolution post-contrast images revealed a similar accuracy (69%) compared to X-ray mammography (69%) and ultrasound mammography (67%). The best individual parameter calculated from the dynamic images was found to be the exchange rate constant which revealed a diagnostic accuracy of 0.74 +/- 0.08. When information from the post-contrast images and dynamic data was combined in a logistic regression model a diagnostic accuracy of 0.92 +/- 0.03 was achieved. In conclusion, MR imaging of small breast lesions is feasible and the incorporation of quantitative MR derived parameters is beneficial.

Correlation of dynamic contrast enhancement MRI parameters with microvessel density and VEGF for assessment of angiogenesis in breast cancer

PURPOSE

To investigate the association between parameters obtained from dynamic contrast enhanced MRI (DCE-MRI) of breast cancer using different analysis approaches, as well as their correlation with angiogenesis biomarkers (vascular endothelial growth factor and vessel density).

MATERIALS AND METHODS

DCE-MRI results were obtained from 105 patients with breast cancer (108 lesions). Three analysis methods were applied: 1) whole tumor analysis, 2) regional hot-spot analysis, and 3) intratumor pixel-by-pixel analysis. Early enhancement intensities and fitted pharmacokinetic parameters were studied. Paraffin blocks of 71 surgically resected specimens were analyzed by immunohistochemical staining to measure microvessel counts (with CD31) and vascular endothelial growth factor (VEGF) expression levels.

RESULTS

MRI parameters obtained from the three analysis methods showed significant correlations (P < 0.0001), but a substantial dispersion from the linear regression line was noted (r = 0.72-0.97). The entire region of interest (ROI) vs. pixel population analyses had a significantly higher association compared to the entire ROI vs. hot-spot analyses. Cancer specimens with high VEGF expression had significantly higher CD31 microvessel densities than did specimens with low VEGF levels (P < 0.005). No significant association was found between MRI parameters obtained from the three analysis strategies and IHC based measurements of angiogenesis.

CONCLUSION

A consistent analysis strategy was important in the DCE-MRI study. In this series, none of these strategies yielded results for MRI based quantitation of tumor vascularity that were associated with IHC based measurements. Therefore, different analyses could not account for the lack of association.