Muqing Lin

Breast Cancer: Evaluation of Response to Neoadjuvant Chemotherapy with 3.0-T MR Imaging

PURPOSE To assess how the molecular biomarker status of a breast cancer, including human epidermal growth factor receptor 2 (HER2), hormone receptors, and the proliferation marker Ki-67 status, affects the diagnosis at 3.0-T magnetic resonance (MR) imaging. MATERIALS AND METHODS This study was approved by the institutional review board and was HIPAA compliant. Fifty patients (age range, 28-82 years; mean age, 49 years) receiving neoadjuvant chemotherapy were monitored with 3.0-T MR imaging. The longest dimension of the residual cancer was measured at MR imaging and correlated with pathologic findings. Patients were further divided into subgroups on the basis of HER2, hormone receptor, and Ki-67 status. Pathologic complete response (pCR) was defined as when there were no residual invasive cancer cells. The Pearson correlation was used to correlate MR imaging-determined and pathologic tumor size, and the unpaired t test was used to compare MR imaging-pathologic size discrepancies. RESULTS Of the 50 women, 14 achieved pCR. There were seven false-negative diagnoses at MR imaging. The overall sensitivity, specificity, and accuracy for diagnosing invasive residual disease at MR imaging were 81%, 93%, and 84%, respectively. The mean MR imaging-pathologic size discrepancy was 0.5 cm ± 0.9 (standard deviation) for HER2-positive cancer and 2.3 cm ± 3.5 for HER2-negative cancer (P = .009). In the HER2-negative group, the size discrepancy was smaller for hormone receptor-negative than for hormone receptor-positive cancers (1.0 cm ± 1.1 vs 3.0 cm ± 4.0, P = .04). The size discrepancy was smaller in patients with 40% or greater Ki-67 expression (0.8 cm ± 1.1) than in patients with 10% or less Ki-67 expression (3.9 cm ± 5.1, P = .06). CONCLUSION The diagnostic accuracy of breast MR imaging is better in more aggressive than in less aggressive cancers. When MR imaging is used for surgical planning, caution should be taken with HER2-negative hormone receptor-positive cancers.

A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI

PURPOSE Quantitative breast density is known as a strong risk factor associated with the development of breast cancer. Measurement of breast density based on three-dimensional breast MRI may provide very useful information. One important step for quantitative analysis of breast density on MRI is the correction of field inhomogeneity to allow an accurate segmentation of the fibroglandular tissue (dense tissue). A new bias field correction method by combining the nonparametric nonuniformity normalization (N3) algorithm and fuzzy-C-means (FCM)-based inhomogeneity correction algorithm is developed in this work. METHODS The analysis is performed on non-fat-sat T1-weighted images acquired using a 1.5 T MRI scanner. A total of 60 breasts from 30 healthy volunteers was analyzed. N3 is known as a robust correction method, but it cannot correct a strong bias field on a large area. FCM-based algorithm can correct the bias field on a large area, but it may change the tissue contrast and affect the segmentation quality. The proposed algorithm applies N3 first, followed by FCM, and then the generated bias field is smoothed using Gaussian kernal and B-spline surface fitting to minimize the problem of mistakenly changed tissue contrast. The segmentation results based on the N3+FCM corrected images were compared to the N3 and FCM alone corrected images and another method, coherent local intensity clustering (CLIC), corrected images. The segmentation quality based on different correction methods were evaluated by a radiologist and ranked. RESULTS The authors demonstrated that the iterative N3+FCM correction method brightens the signal intensity of fatty tissues and that separates the histogram peaks between the fibroglandular and fatty tissues to allow an accurate segmentation between them. In the first reading session, the radiologist found (N3+FCM > N3 > FCM) ranking in 17 breasts, (N3+FCM > N3 = FCM) ranking in 7 breasts, (N3+FCM = N3 > FCM) in 32 breasts, (N3+FCM = N3 = FCM) in 2 breasts, and (N3 > N3+FCM > FCM) in 2 breasts. The results of the second reading session were similar. The performance in each pairwise Wilcoxon signed-rank test is significant, showing N3+FCM superior to both N3 and FCM, and N3 superior to FCM. The performance of the new N3+FCM algorithm was comparable to that of CLIC, showing equivalent quality in 57/60 breasts. CONCLUSIONS Choosing an appropriate bias field correction method is a very important preprocessing step to allow an accurate segmentation of fibroglandular tissues based on breast MRI for quantitative measurement of breast density. The proposed algorithm combining N3+FCM and CLIC both yield satisfactory results.

Clinical characteristics and biomarkers of breast cancer associated with choline concentration measured by 1H MRS

This study investigated the association between the total choline (tCho) concentration and the clinical characteristics and biomarker status of breast cancer. Sixty‐two patients with breast cancer, 1.5 cm or larger in size on MR images, were studied. The tCho concentration was correlated with the MRI features, contrast enhancement kinetics, clinical variables and biomarkers. Pairwise two‐tailed Spearmans nonparametric test was used for statistical analysis. The tCho concentration was higher in high‐grade than moderate‐/low‐grade tumors (p = 0.04) and in tumors with higher Ktrans and kep (p < 0.001 for both). The association of tCho concentration with age (p = 0.05) and triple negative biomarker (p = 0.09) approached significance. tCho was not detected in 17 patients, including 15 with invasive ductal cancer and two with infiltrating lobular cancer. Fifteen of the 17 patients had moderate‐ to low‐grade cancers, and 11 had human epidermal growth factor‐2‐negative cancer, suggesting that these two factors might lead to false‐negative choline. Higher tCho concentration in high‐grade tumors and tumors with higher Ktrans and kep indicates that choline is associated with cell proliferation and tumor angiogenesis. The higher choline level in younger women may be caused by their more aggressive tumor type. The results presented here may aid in the better interpretation of 1H MRS for the diagnosis of breast lesions. Copyright

Template-based automatic breast segmentation on MRI by excluding the chest region.

PURPOSE Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method. METHODS Nonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary of the pectoral muscle) were identified for performing the initial V-shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subjects image space to obtain a subject-specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to mark voxels that were wrongly included or excluded for error analysis. RESULTS The breast volumes measured by the proposed algorithm were very close to the radiologists corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% ± 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% ± 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% ± 1.03%. The total error by adding the inclusion and exclusion errors ranged from 0.16% to 11.8%, with a mean of 2.89% ± 2.55%. CONCLUSIONS The automatic chest template-based breast MRI segmentation method worked well for cases with different body and breast shapes and different density patterns. Compared to the radiologist-established truth, the mean difference in segmented breast volume was approximately 1%, and the total error by considering the additive inclusion and exclusion errors was approximately 3%. This method may provide a reliable tool for MRI-based segmentation of breast density.

Decrease in Breast Density in the Contralateral Normal Breast of Patients Receiving Neoadjuvant Chemotherapy: MR Imaging Evaluation

PURPOSE To investigate the change of breast density with quantitative magnetic resonance (MR) imaging in the contralateral normal breast of patients receiving neoadjuvant chemotherapy. MATERIALS AND METHODS This study was approved by the institutional review board and was HIPAA compliant. Informed consent was obtained. Fifty-four patients with breast cancer (mean age, 47 years; age range, 30-74 years) treated with NAC protocol and enrolled in a breast MR imaging research study were studied. The density in the contralateral normal breast was analyzed by using an MR imaging-based segmentation method. The effect of chemotherapy on the change of density following the doxorubicin and cyclophosphamide (AC) and the AC and taxane regimen was evaluated. The dependence on age was investigated by using a multivariate regression model. RESULTS In patients who underwent both AC and taxane follow-up, the mean percentage of change from the individuals baseline density was -10% (95% confidence interval: -12.8%, -7.2%) after AC and -12.7% (95% confidence interval: -16%, -9.4%) after AC and taxane. In patients who underwent both follow-up studies after one to two and four cycles of AC, the mean percentage of change was -9.4% (95% confidence interval: -13.5%, -5.3%) after one to two cycles of AC and -14.7% (95% confidence interval: -20.6%, -8.7%) after four cycles of AC. The percentage reduction of density was significantly dependent on age. Patients younger than 40 years had a greater reduction after chemotherapy than patients older than 55 years (P = .01). CONCLUSION By using three-dimensional MR imaging, patients receiving chemotherapy showed reduction of breast density, and the effects were significant after initial treatment with one to two cycles of the AC regimen.

Background parenchymal enhancement in the contralateral normal breast of patients undergoing neoadjuvant chemotherapy measured by DCE-MRI.

The purpose of this study was to analyze background parenchymal enhancement (BPE) in the contralateral normal breast of cancer patients during the course of neoadjuvant chemotherapy (NAC). Forty-five subjects were analyzed. Each patient had three MRIs, one baseline (B/L) and two follow-up (F/U) studies. The fibroglandular tissue in the contralateral normal breast was segmented using a computer-assisted algorithm. Based on the segmented fibroglandular tissue, BPE was calculated. BPE measured in baseline (B/L) and follow-up (F/U) MR studies were compared. The baseline BPE was also correlated with age and compared between pre/peri-menopausal (<55 years old) and post-menopausal women (≥55 years old). The pre-treatment BPE measured in B/L MRI was significantly higher in women <55 years old than in women ≥55 years old (20.1%±7.4% vs. 12.1%±5.1%, p≤0.01). A trend of negative correlation between BPE and age was noted (r=-0.29). In women <55years old, BPE at F/U-1 (18.8%±6.9%) was decreased compared to B/L, and was further decreased in F/U-2 (13.3%±5.7%) which was significant compared to B/L and F/U-1. In women ≥55 years old, no significant difference was noted in any paired comparison among B/L, F/U-1 and F/U-2 MRI. A higher baseline BPE was associated with a greater reduction of BPE in F/U-2 MRI (r=0.73). Our study showed that younger women tended to have higher BPE than older women. BPE was significantly decreased in F/U-2 MRI after NAC in women <55 years old. The reduction in BPE was most likely due to the ovarian ablation induced by chemotherapeutic agents.

Menstrual Cycle–related Fluctuations in Breast Density Measured by Using Three-dimensional MR Imaging

PURPOSE To investigate the fluctuation of fibroglandular tissue volume (FV) and percentage of breast density (PD) during the menstrual cycle and compare with postmenopausal women by using three-dimensional magnetic resonance (MR)-based segmentation methods. MATERIALS AND METHODS This study was approved by the Institutional Review Board and was HIPAA compliant. Written informed consent was obtained. Thirty healthy female subjects, 24 premenopausal and six postmenopausal, were recruited. All subjects underwent MR imaging examination each week for 4 consecutive weeks. The breast volume (BV), FV, and PD were measured by two operators to evaluate interoperator variation. The fluctuation of each parameter measured over the course of the four examinations was evaluated on the basis of the coefficient of variation (CV). RESULTS The results from two operators showed a high Pearson correlation for BV (R(2) = 0.99), FV (R(2) = 0.98), and PD (R(2) = 0.96). The interoperator variation was 3% for BV and around 5%-6% for FV and PD. In the respective premenopausal and postmenopausal groups, the mean CV was 5.0% and 5.6% for BV, 7.6% and 4.2% for FV, and 7.1% and 6.0% for PD. The difference between premenopausal and postmenopausal groups was not significant (all P values > .05). CONCLUSION The fluctuation of breast density measured at MR imaging during a menstrual cycle was around 7%. The results may help the design and interpretation of future studies by using the change of breast density as a surrogate marker to evaluate the efficacy of hormone-modifying drugs for cancer treatment or cancer prevention.

Characterization of Pure Ductal Carcinoma In Situ on Dynamic Contrast-Enhanced MR Imaging: Do Nonhigh Grade and High Grade Show Different Imaging Features?

To characterize imaging features of pure DCIS on dynamic contrast-enhanced MR imaging (DCE-MRI), 31 consecutive patients (37-81 years old, mean 56), including 2 Grade I, 16 Grade II, and 13 Grade III, were studied. MR images were reviewed retrospectively and the morphological appearances and kinetic features of breast lesions were categorized according to the ACR BI-RADS breast MRI lexicon. DCE-MRI was a sensitive imaging modality in detecting pure DCIS. MR imaging showed enhancing lesions in 29/31 (94%) cases. Pure DCIS appeared as mass type or non-mass lesions on MRI with nearly equal frequency. The 29 MR detected lesions include 15 mass lesions (52%), and 14 lesions showing non-mass-like lesions (48%). For the mass lesions, the most frequent presentations were irregular shape (50%), irregular margin (50%) and heterogeneous enhancement (67%). For the non-mass-like lesions, the clumped internal enhancement pattern was the dominate feature, seen in 9/14 cases (64%). Regarding enhancement kinetic curve, 21/29 (78%) lesions showed suspicious malignant type kinetics. No significant difference was found in morphology (P > .05), tumor size (P = 0.21), and kinetic characteristics (P = .38) between non-high grade (I+II) and high-grade (III) pure DCIS.

Computational simulation of breast compression based on segmented breast and fibroglandular tissues on magnetic resonance images

This study presents a finite element-based computational model to simulate the three-dimensional deformation of a breast and fibroglandular tissues under compression. The simulation was based on 3D MR images of the breast, and craniocaudal and mediolateral oblique compression, as used in mammography, was applied. The geometry of the whole breast and the segmented fibroglandular tissues within the breast were reconstructed using triangular meshes by using the Avizo 6.0 software package. Due to the large deformation in breast compression, a finite element model was used to simulate the nonlinear elastic tissue deformation under compression, using the MSC.Marc software package. The model was tested in four cases. The results showed a higher displacement along the compression direction compared to the other two directions. The compressed breast thickness in these four cases at a compression ratio of 60% was in the range of 5-7 cm, which is a typical range of thickness in mammography. The projection of the fibroglandular tissue mesh at a compression ratio of 60% was compared to the corresponding mammograms of two women, and they demonstrated spatially matched distributions. However, since the compression was based on magnetic resonance imaging (MRI), which has much coarser spatial resolution than the in-plane resolution of mammography, this method is unlikely to generate a synthetic mammogram close to the clinical quality. Whether this model may be used to understand the technical factors that may impact the variations in breast density needs further investigation. Since this method can be applied to simulate compression of the breast at different views and different compression levels, another possible application is to provide a tool for comparing breast images acquired using different imaging modalities--such as MRI, mammography, whole breast ultrasound and molecular imaging--that are performed using different body positions and under different compression conditions.

Differentiation of myeloma and metastatic cancer in the spine using dynamic contrast-enhanced MRI

Spinal myeloma and metastatic cancer cause similar symptoms and show similar imaging presentations, thus making them difficult to differentiate. In this study, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was performed to differentiate between 9 myelomas and 22 metastatic cancers that present as focal lesions in the spine. The characteristic DCE parameters, including the peak signal enhancement percentage (SE%), the steepest wash-in SE% during the ascending phase and the wash-out SE%, were calculated by normalizing to the precontrast signal intensity. The two-compartmental pharmacokinetic model was used to obtain K(trans) and kep. All nine myelomas showed the wash-out DCE pattern. Of the 22 metastatic cancers, 12 showed wash-out, 7 showed plateau, and 3 showed persistent enhancing patterns. The fraction of cases that showed the wash-out pattern was significantly higher in the myeloma group than the metastatic cancer group (9/9=100% vs. 12/22=55%, P=.03). Compared to the metastatic cancer group, the myeloma group had a higher peak SE% (226%±72% vs. 165%±60%, P=.044), a higher steepest wash-in SE% (169%±51% vs. 111%±41%, P=.01), a higher K(trans) (0.114±0.036 vs. 0.077±0.0281/min, P=.016) and a higher kep (0.88±0.26 vs. 0.49±0.23 1/min, P=.002). The receiver operating characteristic analysis to differentiate between these two groups showed that the area under the curve was 0.798 for K(trans), 0.864 for kep and 0.919 for combined K(trans) and kep. These results show that DCE-MRI may provide additional information for making differential diagnosis to aid in choosing the optimal subsequent procedures or treatments for spinal lesions.