Observer variability in the interpretation of contrast enhanced MRI of the breast

Identification of breast lesion through integrated study of gorilla troops optimization and rotation-based learning from MRI images

Breast cancer has emerged as the most life-threatening disease among women around the world. Early detection and treatment of breast cancer are thought to reduce the need for surgery and boost the survival rate. The Magnetic Resonance Imaging (MRI) segmentation techniques for breast cancer diagnosis are investigated in this article. Kapur's entropy-based multilevel thresholding is used in this study to determine optimal values for breast DCE-MRI lesion segmentation using Gorilla Troops Optimization (GTO). An improved GTO, is developed by incorporating Rotational opposition based-learning (RBL) into GTO called (GTORBL) and applied it to the same problem. The proposed approaches are tested on 20 patients' T2 Weighted Sagittal (T2 WS) DCE-MRI 100 slices. The proposed approaches are compared with Tunicate Swarm Algorithm (TSA), Particle Swarm Optimization (PSO), Arithmetic Optimization Algorithm (AOA), Slime Mould Algorithm (SMA), Multi-verse Optimization (MVO), Hidden Markov Random Field (HMRF), Improved Markov Random Field (IMRF), and Conventional Markov Random Field (CMRF). The Dice Similarity Coefficient (DSC), sensitivity, and accuracy of the proposed GTO-based approach is achieved [Formula: see text], [Formula: see text], and [Formula: see text] respectively. Another proposed GTORBL-based segmentation method achieves accuracy values of [Formula: see text] , sensitivity of [Formula: see text] , and DSC of [Formula: see text]. The one-way ANOVA test followed by Tukey HSD and Wilcoxon Signed Rank Test are used to examine the results. Furthermore, Multi-Criteria Decision Making is used to evaluate overall performance focused on sensitivity, accuracy, false-positive rate, precision, specificity, [Formula: see text]-score, Geometric-Mean, and DSC. According to both quantitative and qualitative findings, the proposed strategies outperform other compared methodologies.

A review on advances in 18F-FDG PET/CT radiomics standardisation and application in lung disease management

Radiomics analysis quantifies the interpolation of multiple and invisible molecular features present in diagnostic and therapeutic images. Implementation of 18-fluorine-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) radiomics captures various disorders in non-invasive and high-throughput manner. ¹⁸F-FDG PET/CT accurately identifies the metabolic and anatomical changes during cancer progression. Therefore, the application of ¹⁸F-FDG PET/CT in the field of oncology is well established. Clinical application of ¹⁸F-FDG PET/CT radiomics in lung infection and inflammation is also an emerging field. Combination of bioinformatics approaches or textual analysis allows radiomics to extract additional information to predict cell biology at the micro-level. However, radiomics texture analysis is affected by several factors associated with image acquisition and processing. At present, researchers are working on mitigating these interrupters and developing standardised workflow for texture biomarker establishment. This review article focuses on the application of ¹⁸F-FDG PET/CT in detecting lung diseases specifically on cancer, infection and inflammation. An overview of different approaches and challenges encountered on standardisation of ¹⁸F-FDG PET/CT technique has also been highlighted. The review article provides insights about radiomics standardisation and application of ¹⁸F-FDG PET/CT in lung disease management.

Artificial Intelligence Applied to Breast MRI for Improved Diagnosis

Background Recognition of salient MRI morphologic and kinetic features of various malignant tumor subtypes and benign diseases, either visually or with artificial intelligence (AI), allows radiologists to improve diagnoses that may improve patient treatment. Purpose To evaluate whether the diagnostic performance of radiologists in the differentiation of cancer from noncancer at dynamic contrast material-enhanced (DCE) breast MRI is improved when using an AI system compared with conventionally available software. Materials and Methods In a retrospective clinical reader study, images from breast DCE MRI examinations were interpreted by 19 breast imaging radiologists from eight academic and 11 private practices. Readers interpreted each examination twice. In the "first read," they were provided with conventionally available computer-aided evaluation software, including kinetic maps. In the "second read," they were also provided with AI analytics through computer-aided diagnosis software. Reader diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis, with the area under the ROC curve (AUC) as a figure of merit in the task of distinguishing between malignant and benign lesions. The primary study end point was the difference in AUC between the first-read and the second-read conditions. Results One hundred eleven women (mean age, 52 years ± 13 [standard deviation]) were evaluated with a total of 111 breast DCE MRI examinations (54 malignant and 57 nonmalignant lesions). The average AUC of all readers improved from 0.71 to 0.76 (P = .04) when using the AI system. The average sensitivity improved when Breast Imaging Reporting and Data System (BI-RADS) category 3 was used as the cut point (from 90% to 94%; 95% confidence interval [CI] for the change: 0.8%, 7.4%) but not when using BI-RADS category 4a (from 80% to 85%; 95% CI: -0.9%, 11%). The average specificity showed no difference when using either BI-RADS category 4a or category 3 as the cut point (52% and 52% [95% CI: -7.3%, 6.0%], and from 29% to 28% [95% CI: -6.4%, 4.3%], respectively). Conclusion Use of an artificial intelligence system improves radiologists' performance in the task of differentiating benign and malignant MRI breast lesions. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Krupinski in this issue.

Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence

The emergence of digital pathology has opened new horizons for histopathology. Artificial intelligence (AI) algorithms are able to operate on digitized slides to assist pathologists with different tasks. Whereas AI-involving classification and segmentation methods have obvious benefits for image analysis, image search represents a fundamental shift in computational pathology. Matching the pathology of new patients with already diagnosed and curated cases offers pathologists a new approach to improve diagnostic accuracy through visual inspection of similar cases and computational majority vote for consensus building. In this study, we report the results from searching the largest public repository (The Cancer Genome Atlas, TCGA) of whole-slide images from almost 11,000 patients. We successfully indexed and searched almost 30,000 high-resolution digitized slides constituting 16 terabytes of data comprised of 20 million 1000 × 1000 pixels image patches. The TCGA image database covers 25 anatomic sites and contains 32 cancer subtypes. High-performance storage and GPU power were employed for experimentation. The results were assessed with conservative "majority voting" to build consensus for subtype diagnosis through vertical search and demonstrated high accuracy values for both frozen section slides (e.g., bladder urothelial carcinoma 93%, kidney renal clear cell carcinoma 97%, and ovarian serous cystadenocarcinoma 99%) and permanent histopathology slides (e.g., prostate adenocarcinoma 98%, skin cutaneous melanoma 99%, and thymoma 100%). The key finding of this validation study was that computational consensus appears to be possible for rendering diagnoses if a sufficiently large number of searchable cases are available for each cancer subtype.

Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution

Background

As artificial intelligence methods for the diagnosis of disease advance, we aimed to evaluate machine learning in the predictive task of distinguishing between malignant and benign breast lesions on an independent clinical magnetic resonance imaging (MRI) dataset within a single institution for subsequent use as a computer aid for radiologists.

Methods

Computer analysis was conducted on consecutive dynamic contrast-enhanced MRI (DCE-MRI) studies from 1483 breast cancer and 496 benign patients who underwent MRI examinations between February 2015 and October 2017; with the age ranges of the cancer and benign patients being 19 to 77 and 16 to 76 years old, respectively. Cases were separated into a training dataset (years 2015 & 2016; 1444 cases) and an independent testing dataset (year 2017; 535 cases) based solely on MRI examination date. After radiologist indication of the lesion, the computer automatically segmented and extracted radiomic features, which were subsequently merged with a support-vector machine (SVM) to yield a lesion signature. Area under the receiving operating characteristic (ROC) curve (AUC) with 95% confidence intervals (CI) served as the primary figure of merit in the statistical evaluation for this clinical classification task.

Results

In the task of distinguishing malignant and benign breast lesions DCE-MRI, the trained predictive model yielded an AUC value of 0.89 (95% CI: 0.858, 0.922) on the independent image set. AUC values of 0.88 (95% CI: 0.845, 0.926) and 0.90 (95% CI: 0.837, 0.940) were obtained for mass lesions only and non-mass lesions only, respectively. Compared with actual clinical management decisions, the predictive model achieved 99.5% sensitivity with 9.6% fewer recommended biopsies.

Conclusion

On an independent, consecutive clinical dataset within a single institution, a trained machine learning system yielded promising performance in distinguishing between malignant and benign breast lesions.

Applications and limitations of radiomics

Radiomics is an emerging field in quantitative imaging that uses advanced imaging features to objectively and quantitatively describe tumour phenotypes. Radiomic features have recently drawn considerable interest due to its potential predictive power for treatment outcomes and cancer genetics, which may have important applications in personalized medicine. In this technical review, we describe applications and challenges of the radiomic field. We will review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.

Automatic ROI construction for analyzing time-signal intensity curve in dynamic contrast-enhanced MR imaging of the breast

Our purpose in this study was to construct a 3-dimensional (3D) region of interest (ROI) for analyzing the time-signal intensity curve (TIC) semi-automatically in dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging of the breast. DCE-MR breast imaging datasets were acquired by a 3.0-Tesla MR system with the use of a 3D fast gradient echo sequence. The essential idea in the new method was to analyze each pixel and to construct an ROI made up of pixels with similar TICs. First, an analyst selected a starting point in the contrast media-enhanced tumor. Second, we calculated Pearson's correlation coefficients (CCs) between the TIC in the starting coordinate selected by the analyst and the TIC in the other coordinates. Third, ROI pixels were selected if their CC threshold satisfied a level of coefficient variation of the ROI determined by prior research performed in our institution. We made a retrospective review of patients who underwent breast DCE-MR examination for pre-operative diagnosis. To confirm the feasibility of the resulting 3D-ROI from TIC analysis, we compared Fischer's score obtained from 3D-ROI by applying a new method to a score obtained from a manually selected 2-dimensional (2D) ROI which was used during routine clinical examination. The Fischer's scores obtained from both the automatically selected 3D-ROI and the manually selected 2D-ROI showed almost equivalent results. Thus, we considered that the new method was comparable to the conventional method. Furthermore, the new method has the potential to be used for evaluation of the extent of tumors.

Breast imaging reporting and data system (BI-RADS) lexicon for breast MRI: interobserver variability in the description and assignment of BI-RADS category

PURPOSE

To retrospectively evaluate interobserver variability between breast radiologists when describing abnormal enhancement on breast MR examinations and assigning a BI-RADS category using the Breast Imaging Reporting and Data System (BI-RADS) terminology.

MATERIALS AND METHODS

Five breast radiologists blinded to patients' medical history and pathologic results retrospectively and independently reviewed 257 abnormal areas of enhancement on breast MRI performed in 173 women. Each radiologist described the focal enhancement using BI-RADS terminology and assigned a final BI-RADS category. Krippendorff's α coefficient of agreement was used to asses interobserver variability.

RESULTS

All radiologists agreed on the morphology of enhancement in 183/257 (71%) lesions, yielding a substantial agreement (Krippendorff's α=0.71). Moderate agreement was obtained for mass descriptors - shape, margins and internal enhancement - (α=0.55, 0.51 and 0.45 respectively) and NME (non-mass enhancement) descriptors - distribution and internal enhancement - (α=0.54 and 0.43). Overall substantial agreement was obtained for BI-RADS category assignment (α=0.71). It was however only moderate (α=0.38) for NME compared to mass (α=0.80).

CONCLUSION

Our study shows good agreement in describing mass and NME on a breast MR examination but a better agreement in predicting malignancy for mass than NME.

Intraindividual, randomized comparison of the macrocyclic contrast agents gadobutrol and gadoterate meglumine in breast magnetic resonance imaging

OBJECTIVES

To compare intraindividually two macrocyclic contrast agents - gadobutrol and gadoterate meglumine (Gd-DOTA) - for dynamic and quantitative assessment of relative enhancement (RE) in benign and malignant breast lesions.

METHODS

This was an ethically approved, prospective, single-centre, randomized, crossover study in 52 women with suspected breast lesions referred for magnetic resonance imaging (MRI). Each patient underwent one examination with gadobutrol and one with Gd-DOTA (0.1 mmol/kg BW) on a 1.5 T system 1 - 7 days apart. Dynamic, T1-weighted, 3D gradient echo sequences were acquired under identical conditions. Quantitative evaluation with at least three regions of interest (ROI) per lesion was performed. Primary endpoint was RE during the initial postcontrast phase after the first and second dynamic acquisition, and peak RE. All lesions were histologically proven; differences between the examinations were evaluated.

RESULTS

Forty-five patients with a total of 11 benign and 34 malignant lesions were assessed. Mean RE was significantly higher for gadobutrol than Gd-DOTA (p < 0.0001). Gadobutrol showed significantly less washout (64.4 %) than Gd-DOTA (75.4 %) in malignant lesions (p = 0.048) CONCLUSIONS: Gadobutrol has higher RE values compared with Gd-DOTA, whereas Gd-DOTA shows more marked washout in malignant lesions. This might improve the detection of breast lesions and influence the specificity of breast MRI-imaging.

The added diagnostic value of dynamic contrast-enhanced MRI at 3.0 T in nonpalpable breast lesions

Objective

To investigate the added diagnostic value of 3.0 Tesla breast MRI over conventional breast imaging in the diagnosis of in situ and invasive breast cancer and to explore the role of routine versus expert reading.

Materials and Methods

We evaluated MRI scans of patients with nonpalpable BI-RADS 3–5 lesions who underwent dynamic contrast-enhanced 3.0 Tesla breast MRI. Initially, MRI scans were read by radiologists in a routine clinical setting. All histologically confirmed index lesions were re-evaluated by two dedicated breast radiologists. Sensitivity and specificity for the three MRI readings were determined, and the diagnostic value of breast MRI in addition to conventional imaging was assessed. Interobserver reliability between the three readings was evaluated.

Results

MRI examinations of 207 patients were analyzed. Seventy-eight of 207 (37.7%) patients had a malignant lesion, of which 33 (42.3%) patients had pure DCIS and 45 (57.7%) invasive breast cancer. Sensitivity of breast MRI was 66.7% during routine, and 89.3% and 94.7% during expert reading. Specificity was 77.5% in the routine setting, and 61.0% and 33.3% during expert reading. In the routine setting, MRI provided additional diagnostic information over clinical information and conventional imaging, as the Area Under the ROC Curve increased from 0.76 to 0.81. Expert MRI reading was associated with a stronger improvement of the AUC to 0.87. Interobserver reliability between the three MRI readings was fair and moderate.

Conclusions

3.0 T breast MRI of nonpalpable breast lesions is of added diagnostic value for the diagnosis of in situ and invasive breast cancer.

Prognostic role of MRI enhancement features in patients with breast cancer: value of adjacent vessel sign and increased ipsilateral whole-breast vascularity

OBJECTIVE

The purpose of this study was to compare adjacent vessel sign, increased ipsilateral whole-breast vascularity, and various MRI features as described in the American College of Radiology BI-RADS MRI lexicon with histopathologic predictors in patients with unilateral breast cancer.

MATERIALS AND METHODS

We retrospectively evaluated breast MRI examinations of 249 patients with histologically confirmed breast cancer. In addition to the BI-RADS MRI lexicon, the adjacent vessel sign and increased ipsilateral whole-breast vascularity of the cancer-bearing breast were evaluated by two independent observers. MRI features were then correlated with histopathologic prognostic factors.

RESULTS

The adjacent vessel sign was significantly (p=0.023 to p<0.001) associated with tumor size, lymph node metastasis, distant metastasis, nuclear grade, and expression of estrogen and progesterone receptors. Increased ipsilateral whole-breast vascularity was significantly associated with all histopathologic predictors (p=0.017 to p<0.001). In multivariate analysis, the significant and independent predictors were a spiculated margin and rim enhancement for negative estrogen and progesterone receptors, a kinetic curve type for higher histologic grade, and an increased ipsilateral whole-breast vascularity for larger tumor size, lymph node metastasis, distant metastasis, higher nuclear grade, and higher histologic grade.

CONCLUSION

In conjunction with the standard BI-RADS MRI lexicon, the adjacent vessel sign and increased ipsilateral whole-breast vascularity may serve as additional predictors of a poor prognosis.

Computerized segmentation and characterization of breast lesions in dynamic contrast-enhanced MR images using fuzzy c-means clustering and snake algorithm

This paper presents a novel two-step approach that incorporates fuzzy c-means (FCMs) clustering and gradient vector flow (GVF) snake algorithm for lesions contour segmentation on breast magnetic resonance imaging (BMRI). Manual delineation of the lesions by expert MR radiologists was taken as a reference standard in evaluating the computerized segmentation approach. The proposed algorithm was also compared with the FCMs clustering based method. With a database of 60 mass-like lesions (22 benign and 38 malignant cases), the proposed method demonstrated sufficiently good segmentation performance. The morphological and texture features were extracted and used to classify the benign and malignant lesions based on the proposed computerized segmentation contour and radiologists' delineation, respectively. Features extracted by the computerized characterization method were employed to differentiate the lesions with an area under the receiver-operating characteristic curve (AUC) of 0.968, in comparison with an AUC of 0.914 based on the features extracted from radiologists' delineation. The proposed method in current study can assist radiologists to delineate and characterize BMRI lesion, such as quantifying morphological and texture features and improving the objectivity and efficiency of BMRI interpretation with a certain clinical value.

High-spatial-resolution 3-T breast MRI of nonmasslike enhancement lesions: an analysis of their features as significant predictors of malignancy

OBJECTIVE

The purpose of this study was to analyze the features of nonmasslike enhancement detected on 3-T MRI and to determine which of these features are significant predictors of malignancy.

MATERIALS AND METHODS

Retrospective review was performed of 124 consecutive patients with nonmasslike enhancement detected on 3-T MRI after biopsy or surgery. We described nonmasslike enhancement using the descriptors in the BI-RADS MRI lexicon. In addition to the BI-RADS descriptors, whether clustered ring enhancement was present and whether surrounding high signal intensity (SI) was present on T2-weighted imaging were assessed.

RESULTS

Cancer was identified in 85 lesions (69%). Of these lesions, ductal carcinoma in situ (DCIS) was found in 41 (48%) and invasive cancer in 44 (52%). The features found to be significant predictors of malignancy were segmental (p = 0.001), focal (p = 0.006), dendritic (p = 0.017), and clustered ring enhancement (p = 0.026) and surrounding high SI on T2-weighted imaging (p < 0.0001). The features found to be significant predictors of invasive cancer were dendritic enhancement (p < 0.0001) and surrounding high SI on T2-weighted imaging (p < 0.0001). There were no significant predictive features for DCIS. Homogeneous enhancement was found to be a significant predictor of benignancy (p = 0.001). Kinetic patterns were not significant predictors of malignancy. Nonmasslike enhancement of 1 cm or larger was more often malignant than lesions smaller than 1 cm (p < 0.0001). In multivariate analysis, a lesion size of 1 cm or larger was found to be the only significant predictor of malignancy for nonmasslike enhancement.

CONCLUSION

Segmental, focal, dendritic, and clustered ring enhancement; surrounding high SI on T2-weighted imaging; and a lesion size of 1 cm or larger can act as predictors of malignancy for nonmasslike enhancement detected on 3-T MRI, but kinetic characteristics cannot.

Quantifying heterogeneity in human tumours using MRI and PET

Most tumours, even those of the same histological type and grade, demonstrate considerable biological heterogeneity. Variations in genomic subtype, growth factor expression and local microenvironmental factors can result in regional variations within individual tumours. For example, localised variations in tumour cell proliferation, cell death, metabolic activity and vascular structure will be accompanied by variations in oxygenation status, pH and drug delivery that may directly affect therapeutic response. Documenting and quantifying regional heterogeneity within the tumour requires histological or imaging techniques. There is increasing evidence that quantitative imaging biomarkers can be used in vivo to provide important, reproducible and repeatable estimates of tumoural heterogeneity. In this article we review the imaging methods available to provide appropriate biomarkers of tumour structure and function. We also discuss the significant technical issues involved in the quantitative estimation of heterogeneity and the range of descriptive metrics that can be derived. Finally, we have reviewed the existing clinical evidence that heterogeneity metrics provide additional useful information in drug discovery and development and in clinical practice.

Computerized three-class classification of MRI-based prognostic markers for breast cancer

The purpose of this study is to investigate whether computerized analysis using three-class Bayesian artificial neural network (BANN) feature selection and classification can characterize tumor grades (grade 1, grade 2 and grade 3) of breast lesions for prognostic classification on DCE-MRI. A database of 26 IDC grade 1 lesions, 86 IDC grade 2 lesions and 58 IDC grade 3 lesions was collected. The computer automatically segmented the lesions, and kinetic and morphological lesion features were automatically extracted. The discrimination tasks-grade 1 versus grade 3, grade 2 versus grade 3, and grade 1 versus grade 2 lesions-were investigated. Step-wise feature selection was conducted by three-class BANNs. Classification was performed with three-class BANNs using leave-one-lesion-out cross-validation to yield computer-estimated probabilities of being grade 3 lesion, grade 2 lesion and grade 1 lesion. Two-class ROC analysis was used to evaluate the performances. We achieved AUC values of 0.80 ± 0.05, 0.78 ± 0.05 and 0.62 ± 0.05 for grade 1 versus grade 3, grade 1 versus grade 2, and grade 2 versus grade 3, respectively. This study shows the potential for (1) applying three-class BANN feature selection and classification to CADx and (2) expanding the role of DCE-MRI CADx from diagnostic to prognostic classification in distinguishing tumor grades.

Interpretation of positron emission mammography and MRI by experienced breast imaging radiologists: performance and observer reproducibility

OBJECTIVE

In preparation for a multicenter trial of positron emission mammography (PEM) and MRI in women with newly diagnosed cancer, the two purposes of this study were to validate training of breast imagers in standardized interpretation of PEM and to validate performance of the same specialists interpreting MRI.

MATERIALS AND METHODS

A 2-hour didactic module was developed to train Mammography Quality Standards Act-qualified radiologist observers to interpret PEM images, consisting of a sample feature analysis lexicon analogous to BI-RADS and 12 sample cases. Observers were then asked to review separate interpretive skills tasks for PEM (49 breasts, 20 [41%] of which were malignant) and MRI (32 breasts, 11 [34%] of which were malignant), describe findings, and give assessments analogous to BI-RADS (category 1, 2, 3, 4A, 4B, 4C, or 5). Demographic experience variables were collected for 36 observers from 15 sites. Performance against histopathologic truth was determined, and interobserver agreement for classifying features and final assessments was evaluated using kappa statistics.

RESULTS

Across 36 observers, mean sensitivity, specificity, and area under the curve (AUC) for PEM were 96% (range, 75-100%), 84% (range, 66-97%), and 0.95 (range, 0.82-1.0), respectively. Mean sensitivity, specificity, and AUC for the MRI task were 82% (range, 45-100%), 67% (range, 38-91%), and 0.80 (range, 0.48-0.96), respectively. Interobserver agreement for PEM findings ranged from moderate to substantial, with kappa values of 0.57 for lesion type and 0.63 for final assessments.

CONCLUSION

With minimal training, experienced breast imagers showed high performance in interpreting PEM images. Performance in MRI interpretation by the same observers validated expected 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.

Combined use of T2-weighted MRI and T1-weighted dynamic contrast-enhanced MRI in the automated analysis of breast lesions

A multiparametric computer-aided diagnosis scheme that combines information from T1-weighted dynamic contrast-enhanced (DCE)-MRI and T2-weighted MRI was investigated using a database of 110 malignant and 86 benign breast lesions. Automatic lesion segmentation was performed, and three categories of lesion features (geometric, T1-weighted DCE, and T2-weighted) were automatically extracted. Stepwise feature selection was performed considering only geometric features, only T1-weighted DCE features, only T2-weighted features, and all features. Features were merged with Bayesian artificial neural networks, and diagnostic performance was evaluated by ROC analysis. With leave-one-lesion-out cross-validation, an area under the ROC curve value of 0.77±0.03 was achieved with T2-weighted-only features, indicating high diagnostic value of information in T2-weighted images. Area under the ROC curve values of 0.79±0.03 and 0.80 ± 0.03 were obtained for geometric-only features and T1-weighted DCE-only features, respectively. When all features were considered, an area under the ROC curve value of 0.85±0.03 was achieved. We observed P values of 0.006, 0.023, and 0.0014 between the geometric-only, T1-weighted DCE-only, and T2-weighted-only features and all features conditions, respectively. When ranked, the P values satisfied the Holm-Bonferroni multiple-comparison test; thus, the improvement of multiparametric computer-aided diagnosis was statistically significant. A computer-aided diagnosis scheme that combines information from T1-weighted DCE and T2-weighted MRI may be advantageous over conventional T1-weighted DCE-MRI computer-aided diagnosis.

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.

MRI features of intraductal papilloma of the breast: sheep in wolf's clothing?

BACKGROUND

Intraductal papillomas often present as small, smooth masses, dilated ducts or microcalcifications at mammography and as smooth, hypoechoic masses at sonography. At magnetic resonance imaging (MRI), intraductal papillomas often present as small smooth masses, however, often with strong enhancement with type 2 or 3 time intensity curves. The result of the MR analysis is therefore not infrequently inconclusive in order to characterize the mass as benign or malignant.

PURPOSE

To characterize the appearance of intraductal papillomas of the breast at MRI, and determine whether the application of diagnostic rules described in literature could contribute to correctly classifying the lesions as benign.

MATERIAL AND METHODS

Twenty patients with histologically proven intraductal papillomas were included. Two radiologists independently reviewed the MR images of the breast. The BI-RADS(®) nomenclature was used to describe morphology and contrast-enhancement kinetics. Interobserver agreement in the interpretation of the MR images by the two investigators was performed. Kappa coefficient was calculated as index for the level of agreement. Subsequently, three sets of diagnostic rules, including the Göttinger score described by Fischer and the interpretation flowcharts according to Kinkel and to Tozaki were applied to characterize whether a biopsy should be recommended or not.

RESULTS

All papillomas presented as masses on dynamic contrast-enhanced MRI. Only five papillomas showed a round, oval, or lobulated shape combined with smooth margins and continuous rise of the time intensity curve. Using the Göttingen score, biopsy would be recommended in 16 patients. Based on the interpretation flowcharts of Kinkel and of Tozaki, an additional 13 and 10 papillomas, respectively, were correctly classified as benign. Dilated ducts were visible in 10 patients. The interobserver agreement was good or excellent for all included variables.

CONCLUSION

Including systematic analysis of breast MRI to the diagnostic protocol and interpreting the images according to predetermined diagnostic rules, most solitary intraductal papillomas of the breast may be correctly characterized as benign.

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.

[Quantitative parametric analysis of contrast-enhanced lesions in dynamic MR mammography]

PURPOSE

The aim of the study was an evaluation of the quantitative parametric analysis of contrast-enhanced lesions in dynamic MR mammography.

MATERIAL AND METHODS

In 137 patients, 183 contrast-enhanced lesions were identified in dynamic MR mammography. In 82 lesions histopathology was performed and in 101 lesions follow-up MR mammography was carried out. The contrast kinetics of lesions was analyzed quantitatively, on a pixel-by-pixel basis. The initial signal enhancement was coded by color intensity (bright, medium, dark), the post-initial signal enhancement was coded by color hue (blue, green, red). ROC analysis and logistic regression were performed.

RESULTS

Malignant lesions showed a significantly higher number of bright red, medium red and dark red, bright green and medium green pixels than benign lesions. Benign lesions showed a significantly higher number of bright blue, medium blue and dark blue pixels than malignant lesions. The highest areas under the ROC curves (AUC) were found for medium red (AUC = 0.782) and medium green pixels (AUC = 0.733). A regression model with medium red and medium green pixels allows diagnosis of malignant lesions with a sensitivity of 60.7% and a specificity of 83.6%.

CONCLUSIONS

The quantification of contrast-enhanced lesions allows objective analysis of the signal intensities in malignant and benign lesions. Therefore, this method might increase the specificity of MR mammography. Further developments are necessary before this method can be used for routine analysis of contrast-enhancing lesions in MR mammography.

Quantification of viable tumor microvascular characteristics by multispectral analysis

Tumor heterogeneity complicates the quantification of tumor microvascular characteristics assessed by dynamic contrast-enhanced MRI (DCE-MRI). To address this issue a novel approach was developed that combines DCE-MRI with diffusion-based multispectral (MS) analysis to quantify the microvascular characteristics of specific tumor tissue populations. Diffusion-based MS segmentation (feature space: apparent diffusion coefficient, T(2) and proton density) was performed to identify tumor tissue populations and the DCE-MRI characteristics were determined for each tissue class. The ability of this MS DCE-MRI technique to detect microvascular changes due to treatment with an antibody (G6-31) to vascular endothelial growth factor-A (VEGF) was evaluated in a tumor xenograft mouse model. Anti-VEGF treatment resulted in a significant reduction in K(trans) for the MS viable tumor tissue class (-0.0034 +/- 0.0022 min(-1), P < 0.01) at 24 hr posttreatment that differ significantly from the change observed in the control group (0.0002 +/- 0.0025 min(-1)). Viable tumor K(trans) for the anti-VEGF group was also reduced 62% relative to the pretreatment values (P < 0.01). Necrotic tissue classes were found to add only noise to DCE-MRI estimates. This approach provides a means to measure physiological parameters within the viable tumor and address the issue of tumor heterogeneity that complicates DCE-MRI analysis.

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.

Automated analysis of contrast enhancement in breast MRI lesions using mean shift clustering for ROI selection

PURPOSE

To evaluate a new method for automated determination of a region of interest (ROI) for the analysis of contrast enhancement in breast MRI.

MATERIALS AND METHODS

Mean shift multidimensional clustering (MS-MDC) was employed to divide 92 lesions into several spatially contiguous clusters each, based on multiple enhancement parameters. The ROIs were defined as the clusters with the highest probability of malignancy. The performance of enhancement analysis within these ROIs was estimated using the area under the receiver operator characteristic curve (AUC), and compared against a radiologist's final assessment and a classifier using histogram analysis (HA). For HA, the first, second, and third quartiles were evaluated.

RESULTS

MS-MDC resulted in AUC = 0.88 with a 95% confidence interval (CI) of 0.81-0.95. The AUC for the radiologist's assessment was 0.93 (95%CI = 0.87-0.97). Best HA performance was found using the first quartile, with AUC = 0.79 (95%CI = 0.69-0.88). There was no significant difference between MS-MDC and the radiologist (P = 0.40). The improvement of MS-MDC over HA was significant (P = 0.018).

CONCLUSION

Mean shift clustering followed by automated selection of the most suspicious cluster resulted in accurate ROIs in breast MRI lesions.

Morphologic blooming in breast MRI as a characterization of margin for discriminating benign from malignant lesions

RATIONALE AND OBJECTIVES

Develop a fully automated, objective method for evaluating morphology on breast magnetic resonance (MR) images and evaluate effectiveness of the new morphologic method for detecting breast cancers.

MATERIALS AND METHODS

We present a new automated method (morphologic blooming) for identifying and classifying breast lesions on MR that measures margin sharpness, a characteristic related to blooming, defined as rapid enhancement, with a border that is initially sharp but becomes unsharp after 7 minutes. Independent training sets (98 biopsy-proven lesions) and testing sets (179 breasts, 127 patients, acquired at five institutions) were used. Morphologic blooming was evaluated as a stand-alone feature and as an adjunct to kinetics using free-response receiver operating characteristic and sensitivity analysis. Dependence of false-positive (FP) rates on acquisition times and pathologies of contralateral breasts were evaluated.

RESULTS

Sensitivity of morphologic blooming was 80% with 2.46 FP per noncancerous breast: FPs did not vary significantly by acquisition times. FPs varied significantly by pathologies of contralateral breasts (cancerous contralateral: 4.29 FP/breast; noncancerous contralateral: 0.48 FP/breast; P < .0001). Evaluation of 45 cancers showed suspicious morphologies on 10/15 (67%) cancers with benign-like kinetics and suspicious kinetics on 5/10 (50%) cancers with benign-like morphologies.

CONCLUSION

We present a new, fully automated method of identifying and classifying margin sharpness of breast lesions on MR that can be used to direct radiologists' attention to lesions with suspicious morphologies. Morphologic blooming may have important utility for assisting radiologists in identifying cancers with benign-like kinetics and discriminating normal tissues that exhibit cancer-like enhancement curves and for improving the performance of computer-aided detection systems.

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.

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.

MR Quantification of the Washout Changes in Breast Tumors Under Preoperative Chemotherapy: Feasibility and Preliminary Results

OBJECTIVE

The objective of our study was to describe and determine the feasibility of an MR washout quantification method in patients with breast cancer under preoperative chemotherapy.

MATERIALS AND METHODS

Nineteen patients with breast T2 or T3 tumors were enrolled in a previous study for tumor response evaluation during chemotherapy based on dynamic contrast-enhanced MRI. We retrospectively used the dynamic acquisition data to produce parametric images representing the washout pattern. Two radiologists unaware of the final pathologic results measured the volume of pixels exhibiting washout within the tumor before chemotherapy (volume 1), after two courses of chemotherapy (volume 2), and before surgery after four courses of chemotherapy (volume 3). The interobserver variability and intraobserver variability were calculated to evaluate the reproducibility of our method with the Pearson's correlation coefficient and the concordance correlation coefficient. We correlated the washout changes by means of a Student's t test and noted the histopathologic final outcome.

RESULTS

A washout pattern was present in all patients on the initial MR study. The quantification method of the washout changes was reproducible with good interobserver agreement (r = 0.85, p < 10(-5)) and an excellent intraobserver agreement (r = 0.94, p < 10(-5)). A significant decrease of the washout volume was observed after two courses of chemotherapy (p = 0.004), whereas no significant modification was observed between two and four courses of chemotherapy (p = 0.52).

CONCLUSION

Quantification of the washout variation in breast tumor based on the use of parametric images is feasible and reproducible. It may add information to the evaluation of tumor response to preoperative therapy.

Decision Making in Medicine and Health Care

Decision making is central to health policy and medical practice. Because health outcomes are probabilistic, most decisions are made under conditions of uncertainty. This review considers two classes of decisions in health care: decisions made by providers on behalf of patients, and shared decisions between patients and providers. Considerable evidence suggests wide regional variation exists in services received by patients. Evidence-based guidelines that incorporate quality of life and patient preferences may help address this problem. Systematic cost-effectiveness analysis can be used to improve resource allocation decisions. Shared medical decision making seeks to engage patients and providers in a collaborative process to choose clinical options that reflect patient preferences. Although some evidence indicates patients want an active role in making decisions, other evidence suggests that some patients prefer a passive role. Decision aids hold promise for improving individual decisions, but there are still few systematic evaluations of these aids. Several directions for future research are offered.

Interactive detection and visualization of breast lesions from dynamic contrast enhanced MRI volumes

Mammography is currently regarded as the most effective and widely used method for early detection of breast cancer, but recently its sensitivity in certain high risk cases has been less than desired. The use of Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has gained considerable attention in the past 10 years, especially for high risk cases, for smaller multi-focal lesions, or very sparsely distributed lesions. In this work, we present an interactive visualization system to identify, process, visualize and quantify lesions from DCE-MRI volumes. Our approach has the following key features: (1) we determine a confidence measure for each voxel, representing the probability that the voxel is part of the tumor, using a rough goodness-of-fit for the shape of the intensity-time curves, (2) our system takes advantage of low-cost, readily available 3D texture mapping hardware to produce both 2D and 3D visualizations of the segmented MRI volume in near real-time, enabling improved spatial perception of the tumor location, shape, size, distribution, and other characteristics useful in staging and treatment courses, and (3) our system permits interactive manipulation of the signal-time curves, adapts to different tumor types and morphology, thus making it a powerful tool for radiologists/physicians to rapidly assess probable malignant volumes. We illustrate the application of our system with four case studies: invasive ductal cancer, benign fibroadenoma, ductal carcinoma in situ and lobular carcinoma.

Can breast MRI help in the management of women with breast cancer treated by neoadjuvant chemotherapy?

Contrast-enhanced (CE) MRI was used to monitor breast cancer response to neoadjuvant chemotherapy. Patients underwent CE MRI before and after therapy, together with conventional assessment methods (CAM). CE MRI was carried out at 1.5 T in the coronal plain with 3D sequences before and after bolus injection. An expert panel determined chemotherapy response using both CE MRI and CAM. Histopathological response in the surgical specimen was then used to determine the sensitivity and specificity of CE MRI and CAM. In total, 67 patients with 69 breast cancers were studied (mean age of 46 years). Tumour characteristics showed a high-risk tumour population: median size 49 mm: histopathological grade 3 (55%): oestrogen receptor (ER) negative (48%). Histopathological response was as follows: – complete pathological response (pCR) 17%; partial response (pPR) 68%; no response (NR) 15%. Sensitivity of CAM for pCR or pPR was 98% (CI 91–100%) and specificity was 50% (CI 19–81%). CE MRI sensitivity was 100% (CI 94–100%), and specificity was 80% (CI 44–97%). The absolute agreement between assessment methods and histopathology was marginally higher for CE MRI than CAM (81 vs 68%; P=0.09). In 71%, CE MRI increased diagnostic knowledge, although in 20% it was judged confusing or incorrect. The 2nd MRI study significantly increased diagnostic confidence, and in 19% could have changed the treatment plan. CE MRI persistently underestimated minimal residual disease. In conclusion, CE MRI of breast cancer proved more reliable for predicting histopathological response to neoadjuvant chemotherapy than conventional assessment methods.

Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography

The aim of this study was to evaluate the capability of improved artificial neural networks (ANN) and additional novel training methods in distinguishing between benign and malignant breast lesions in contrast-enhanced magnetic resonance-mammography (MRM). A total of 604 histologically proven cases of contrast-enhanced lesions of the female breast at MRI were analyzed. Morphological, dynamic and clinical parameters were collected and stored in a database. The data set was divided into several groups using random or experimental methods [Training & Testing (T&T) algorithm] to train and test different ANNs. An additional novel computer program for input variable selection was applied. Sensitivity and specificity were calculated and compared with a statistical method and an expert radiologist. After optimization of the distribution of cases among the training and testing sets by the T & T algorithm and the reduction of input variables by the Input Selection procedure a highly sophisticated ANN achieved a sensitivity of 93.6% and a specificity of 91.9% in predicting malignancy of lesions within an independent prediction sample set. The best statistical method reached a sensitivity of 90.5% and a specificity of 68.9%. An expert radiologist performed better than the statistical method but worse than the ANN (sensitivity 92.1%, specificity 85.6%). Features extracted out of dynamic contrast-enhanced MRM and additional clinical data can be successfully analyzed by advanced ANNs. The quality of the resulting network strongly depends on the training methods, which are improved by the use of novel training tools. The best results of an improved ANN outperform expert radiologists.

MR imaging-guided focused ultrasound surgery of breast cancer: correlation of dynamic contrast-enhanced MRI with histopathologic findings

PURPOSE

To assess the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters to monitor residual tumor following non-invasive MRI-guided focused ultrasound surgery (MRIgFUS) of breast tumors.

METHODS

DCE-MRI data were acquired before and after the MRIgFUS treatment of small breast tumors (d < 3.5 cm) for 17 patients. The lesion was surgically resected and the presence of residual tumor was determined by histopathological analysis. The percentage of residual tumor was correlated with three DCE-MRI parameters measured at the maximally enhancing site of each tumor: increase in signal intensity (ISI), maximum difference function (MDF) and positive enhancement integral (PEI).

RESULTS

A good correlation was found between the ISI (r = 0.897), MDF (r = 0.789) and PEI (r = 0.859) parameters and the percentage of residual viable tumor determined by histopathology. A receiver operator characteristic curve analysis yielded a cutoff value for ISI at 20% with a sensitivity of 77% and a specificity of 100%.

CONCLUSION

These results suggest that parameters from DCE-MRI data could provide a reliable non-invasive method for assessing residual tumor following MRIgFUS treatment of breast tumors.

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.

Gadobenate dimeglumine-enhanced MRI of the breast: analysis of dose response and comparison with gadopentetate dimeglumine

OBJECTIVE

The purpose of this study was to evaluate the clinical efficacy and dose response relationship of three doses of gadobenate dimeglumine for MRI of the breast and to compare the results with those obtained after a dose of 0.1 mmol/kg of body weight of gadopentetate dimeglumine. SUBJECTS AND METHODS. Gadobenate dimeglumine at 0.05, 0.1, or 0.2 mmol/kg of body weight or gadopentetate dimeglumine at 0.1 mmol/kg of body weight was administered by IV bolus injection to 189 patients with known or suspected breast cancer. Coronal three-dimensional T1-weighted gradient-echo images were acquired before and at 0, 2, 4, 6, and 8 min after the administration of the dose. Images were evaluated for lesion presence, location, size, morphology, enhancement pattern, conspicuity, and type. Lesion signal intensity-time curves were acquired, and lesion matching with on-site final diagnosis was performed. A determination of global lesion detection from unenhanced to contrast-enhanced and combined images was performed, and evaluations were made of the diagnostic accuracy for lesion detection and characterization. A full safety evaluation was conducted.

RESULTS

Significant dose-related increases in global lesion detection were noted for patients who received gadobenate dimeglumine (p < 0.04, all evaluations). The sensitivity for detection was comparable for 0.1 and 0.2 mmol/kg of gadobenate dimeglumine, and specificity was highest with the 0.1 mmol/kg dose. Higher detection scores and higher sensitivity values for lesion characterization were found for 0.1 mmol/kg of gadobenate dimeglumine compared with 0.1 mmol/kg of gadopentetate dimeglumine, although more variable specificity values were obtained. No differences in safety were observed, and no serious adverse events were reported.

CONCLUSION

Gadobenate dimeglumine is a capable diagnostic agent for MRI of the breast. Although preliminary, our results suggest that 0.1 mmol/kg of gadobenate dimeglumine may offer advantages over doses of 0.05 and 0.2 mmol/kg of gadobenate dimeglumine and 0.1 mmol/kg of gadopentetate dimeglumine for breast lesion detection and characterization.

The significance of quality of life in health care

This paper compares a traditional biomedical model with an outcomes model for evaluating health care. The traditional model emphasizes diagnosis and disease-specific outcomes. In contrast, the outcomes model emphasizes life expectancy and health-related quality of life. Although the models are similar, they lead to different conclusions with regard to some interventions. For some conditions, diagnosis and treatment may reduce the impact of a particular disease without extending life expectancy or improving quality of life. Older individuals with multiple co-morbidities may not benefit from treatments for a particular disease if competing health problems threaten life or reduce quality of life. In preventive medicine, diagnosis of disease is made more difficult because of ambiguity, uncertainty, lead-time bias, and length bias. In some circumstances, successful diagnosis and treatment may actually reduce life expectancy or overall life quality. Example applications of the outcomes model from clinical policy analysis, individual decision making and shared decision-making are offered. The outcomes model has received little attention in dental health care but may have parallels to applications in other areas of medicine.

Dynamic-enhanced MRI predicts metastatic potential of invasive ductal breast cancer

BACKGROUND

Dynamic magnetic resonance imaging (MRI) has improved the detection of breast malignancies. The method is based on estimating the velocity of contrast enhancement taking into account increased angiogenesis in tumor. Microvessel density correlates with breast carcinoma metastasis. Thus, we hypothesized that contrast enhancement on MRI correlates with metastasis in breast cancer patients. The present study attempts to clarify the quantitative assessment of dynamic data, and examines the correlation between MRI enhancement and breast carcinoma metastasis.

METHODS

The subjects consisted of 31 patients with invasive ductal breast cancer. Twenty patients were disease free for five years (group A), and eleven patients suffered from metastatic disease at distant sites concurrently or postoperatively (group B). Dynamic MRI was performed preoperatively using a 1.5T system in all cases. Using the dynamic data, the signal intensity (SI)ratio and SI index were determined and analyzed retrospectively taking into account the presence of distant metastases.

RESULTS

The values of the SI ratio were 2.2+/-0.7 in group A and 2.3+/-0.4 in group B, respectively, with no significant difference seen between the groups. The SI index value was significantly higher in group B (28.5+/-32.8) than in group A (10.3+/-5.5, p<0.05).

CONCLUSIONS

The current series suggests that the SI index could distinguish patients with high risk of distant metastasis from disease free patients, preoperatively. If a suitable borderline value were established, the quantitative dynamic parameter determined by MRI may be useful for predicting the prognosis of breast cancer patients.

What is the recall rate of breast MRI when used for screening asymptomatic women at high risk?

Breast screening acceptability is dependent on sensitivity and recall rate. We aimed to establish the recall rate for MRI and mammography, separately and together, when screening a cohort of women at high genetic risk. Women aged 35-49 years in the MARIBS study form the cohort. We analysed the recall rate, the number of extra tests and their effectiveness. Wilcoxon Rank test was used to estimate the effect of age and logistic regression with robust variance the effect of mammographic density on recall rates. The first 726 screening studies took place in 415 women. Following 86 of these recall occurred, comprising 140 additional investigations. 28 of the cases were resolved without further MRI, and 18 women had more than 2 additional tests. Neither age nor mammographic density was associated with recall. MRI had a recall of rate of 10.19%, and mammography 4.00%. The two techniques largely recalled different cases and 10 cases only (11.62% of those recalled) were abnormal by both tests. The two together had a recall rate of 11.85%. Recall rates varied widely between centres of the study. Breast MRI in asymptomatic high-risk women age 35-49 years largely recalls different women from mammography. The combined figure of approximately 12% may be acceptable for screening and will be useful for planning similar studies.