Hon J. Yu

Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI.

Breast density has been established as an independent risk factor associated with the development of breast cancer. It is known that an increase of mammographic density is associated with an increased cancer risk. Since a mammogram is a projection image, different body position, level of compression, and the x-ray intensity may lead to a large variability in the density measurement. Breast MRI provides strong soft tissue contrast between fibroglandular and fatty tissues, and three-dimensional coverage of the entire breast, thus making it suitable for density analysis. To develop the MRI-based method, the first task is to achieve consistency in segmentation of the breast region from the body. The method included an initial segmentation based on body landmarks of each individual woman, followed by fuzzy C-mean (FCM) classification to exclude air and lung tissue, B-spline curve fitting to exclude chest wall muscle, and dynamic searching to exclude skin. Then, within the segmented breast, the adaptive FCM was used for simultaneous bias field correction and fibroglandular tissue segmentation. The intraoperator and interoperator reproducibility was evaluated using 11 selected cases covering a broad spectrum of breast densities with different parenchymal patterns. The average standard deviation for breast volume and percent density measurements was in the range of 3%-4% among three trials of one operator or among three different operators. The body position dependence was also investigated by performing scans of two healthy volunteers, each at five different positions, and found the variation in the range of 3%-4%. These initial results suggest that the technique based on three-dimensional MRI can achieve reasonable consistency to be applied in longitudinal follow-up studies to detect small changes. It may also provide a reliable method for evaluating the change of breast density for risk management of women, or for evaluating the benefits/risks when considering hormonal replacement therapy or chemoprevention.

Predicting Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer by Using MR Imaging and Quantitative 1H MR Spectroscopy

PURPOSE To compare changes in the concentration of choline-containing compounds (tCho) and in tumor size at follow-up after neoadjuvant chemotherapy (NAC) between patients who achieved pathologic complete response (pCR) and those who did not (non-pCR). MATERIALS AND METHODS This study was approved by the institutional review board and was compliant with HIPAA; each patient gave informed consent. Thirty-five patients (mean age, 48 years +/- 11 [standard deviation]; range, 29-75 years) with breast cancer were included. Treatment included doxorubicin and cyclophosphamide followed by a taxane-based regimen. Changes in tCho and tumor size in pCR versus non-pCR groups were compared by using the two-way Mann-Whitney nonparametric test. Receiver operating characteristic (ROC) analysis was performed to differentiate between them and the area under the ROC curve (AUC) was compared. RESULTS In the pCR group, the tCho level change was greater compared with change in tumor size (P = .003 at first follow-up, P = .01 at second follow-up), but they were not significantly different in the non-pCR group. Changes in tumor size and tCho level at the first follow-up study were not significantly different between the pCR and non-pCR groups but reached significance at the second follow-up. In ROC analysis, the magnetic resonance (MR) imaging and MR spectroscopic parameters had AUCs of 0.65-0.68 at first follow-up; at second follow-up, AUC for change in tumor size was 0.9, AUC for change in tCho was 0.73. CONCLUSION Patients who show greater reduction in tCho compared with changes in tumor size are more likely to achieve pCR. The change in tumor size halfway through therapy was the most accurate predictor of pCR.

Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement

PurposeTo investigate methods developed for the characterisation of the morphology and enhancement kinetic features of both mass and non-mass lesions, and to determine their diagnostic performance to differentiate between malignant and benign lesions that present as mass versus non-mass types. MethodsQuantitative analysis of morphological features and enhancement kinetic parameters of breast lesions were used to differentiate among four groups of lesions: 88 malignant (43 mass, 45 non-mass) and 28 benign (19 mass, 9 non-mass). The enhancement kinetics was measured and analysed to obtain transfer constant (Ktrans) and rate constant (kep). For each mass eight shape/margin parameters and 10 enhancement texture features were obtained. For the lesions presenting as nonmass-like enhancement, only the texture parameters were obtained. An artificial neural network (ANN) was used to build the diagnostic model.ResultsFor lesions presenting as mass, the four selected morphological features could reach an area under the ROC curve (AUC) of 0.87 in differentiating between malignant and benign lesions. The kinetic parameter (kep) analysed from the hot spot of the tumour reached a comparable AUC of 0.88. The combined morphological and kinetic features improved the AUC to 0.93, with a sensitivity of 0.97 and a specificity of 0.80. For lesions presenting as non-mass-like enhancement, four texture features were selected by the ANN and achieved an AUC of 0.76. The kinetic parameter kep from the hot spot only achieved an AUC of 0.59, with a low added diagnostic value. ConclusionThe results suggest that the quantitative diagnostic features can be used for developing automated breast CAD (computer-aided diagnosis) for mass lesions to achieve a high diagnostic performance, but more advanced algorithms are needed for diagnosis of lesions presenting as non-mass-like enhancement.

MRI measurements of tumor size and pharmacokinetic parameters as early predictors of response in breast cancer patients undergoing Neoadjuvant anthracycline chemotherapy

To investigate the value of using changes in three parameters (tumor size, transfer constant (Ktrans), and rate constant (kep)) obtained after the first treatment‐cycle in predicting the final clinical response after two to four cycles of neoadjuvant anthracycline and cyclophosphamide (AC) chemotherapy.

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.

Detection of choline signal in human breast lesions with chemical-shift imaging.

To investigate the application of MR spectroscopy using chemical‐shift imaging (CSI) for characterizing human breast lesions at 1.5T, and to evaluate the diagnostic performance using ROC (receiver operating characteristics) analysis.

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

Diagnostic performance of magnetic resonance imaging for assessing tumor response in patients with HER2-negative breast cancer receiving neoadjuvant chemotherapy is associated with molecular biomarker profile.

BACKGROUND This study aimed to evaluate the influence of hormone receptor (HR) and Ki-67 proliferation markers in predicting the accuracy of magnetic resonance imaging (MRI) for measuring residual tumor size in patients with HER2-negative (HER2(-)) breast cancer receiving neoadjuvant chemotherapy (NAC). PATIENTS AND METHODS Fifty-four women were studied. Patients received AC (doxorubicin (Adriamycin)/cyclophosphamide) and/or taxane-based regimens. The accuracy of MR-determined clinical complete response (CCR) was compared to pathological complete response (pCR). The size of detectable residual tumor on MRI was correlated with pathologically diagnosed tumor size using the Pearson correlation. RESULTS MRI correctly diagnosed 16 of the 17 cases of pCR. There were 8 false-negative diagnoses: 7 HR(+) and 1 HR(-). The overall sensitivity, specificity, and accuracy of MRI were 78%, 94%, and 83%, respectively. The positive predictive value was 97% and the negative predictive value was 67%. For MRI vs. pathologically determined tumor size correlation, HR(-) cancers showed a higher correlation (R = 0.79) than did HR(+) cancers (R = 0.58). A worse MRI/pathology size discrepancy was found in HR(+) cancer than in HR(-)cancer (1.6 ± 2.8 cm vs. 0.56 ± 0.9 cm; P = .05). Tumors with low Ki-67 proliferation (< 40%) showed a larger size discrepancy than did those with high Ki-67 proliferation (≥ 40%) (1.2 ± 2.0 cm vs. 0.4 ± 0.8 cm; P = .05). CONCLUSIONS The results showed that the diagnostic performance of MRI for patients with breast cancer undergoing NAC is associated with a molecular biomarker profile. Among HER2(-)tumors, the accuracy of MRI was worse in HR(+)cancers than in HR(-)cancers and was also worse in low-proliferation tumors than in high-proliferation tumors. These findings may help in surgical planning.

Can dynamic contrast-enhanced MRI (DCE-MRI) predict tumor recurrence and lymph node status in patients with breast cancer?

The lymph node status is regarded as one of the most important prognostic factors for the overall survival and disease-free survival of patients with breast cancer. While morphological features and contrast enhancement kinetics of breast cancer shown on dynamic contrast enhanced MRI (DCE-MRI) have been correlated with tumor histological type, grade, and biomarkers [1-4], there were only a few studies reporting the association with nodal status, also results were controversial [5-7]. In this study we investigated the MR imaging features of the primary tumor between patients who had early recurrence vs. those who remained cancerfree, and also between node positive and negative patients. We analyzed 62 patients (30-83 years old, median 58) with histologically confirmed breast cancer who were enrolled into a breast MRI study during years 2000-2003. A telephone survey was conducted in 2006 to follow-up all patients regarding their disease status. Of the 62 patients, 6 had confirmed cancer recurrence in the previously treated breast. Three had positive nodes (sentinel and/or axillary) at the time of first cancer diagnosis, and 3 had negative nodes. Of the 56 patients who were cancer-free, 28 had positive node and the other 28 had negative nodes. The MRI features of all 62 patients were retrospectively reviewed, and compared between the 6 with early recurrence vs. those who were cancer-free. Breast MRI was performed on a 1.5T MR scanner. The protocol included pre-contrast images and dynamic contrast enhanced imaging. The characteristics of primary tumor were analyzed. The longest and perpendicular dimension of the tumor size was measured on contrast-enhanced MRI, and then converted to 1-D size. The morphological appearances were characterized using features described in BI-RADS MRI lexicon [8], separated into mass lesions and non-mass like enhancements. The following enhancement kinetic parameters were analyzed: the enhancement percentage at 1-min (E1), 2-min (E2), 7-min (E3), and the washout slope between 7-min and 2-min. Furthermore pharmacokinetic parameters, including transfer constant (K trans ) and exchange rate constant (kep), were also analyzed with the Tofts 2-compartmental model [9]. The comparison of lesion morphology, size, and enhancement kinetic parameters in 3 groups is summarized in Table 1. LN (+) group has more irregular mass lesion (19/28, 68%) compared to LN (−) group (12/28, 43%), fewer round mass (4/28, 14% vs. 10/28, 36%), and more nonmass like lesions (3/28 vs. 0/28). The tumor size in the LN(+) group (0.7 - 4.0 cm, mean 1.8 cm) is bigger compared to that in the LN(−) group (0.5 - 3.0 cm, mean 1.5 cm), but not

Pharmacokinetic Parameters Analyzed from MR Contrast Enhancement Kinetics of Multiple Malignant and Benign Breast Lesions Detected in the Same Patients

Ninety-nine patients with confirmed breast cancer were reviewed to identify patients who had two confirmed malignant lesions of identical pathology (Group-1, N=17), and patients who had one malignant lesion and the second benign lesion (Group-2, N=8). Contrast enhancement kinetics from every lesion was measured and analyzed using three different models to obtain fitting parameters related to up-slope, enhancement amplitude, and wash-out, including Model-1: modified Tofts model (vp, Ktrans, kep), Model-2: standard Tofts model (Ktrans, kep), and Model-3: a 3-parameter heuristic model (Tc, A, C). By analyzing lesions from same patients, the differences in whole body hemodynamics thus the blood kinetics could be controlled. Two questions were addressed in this study: i) What is the association between pharmacokinetic parameters analyzed from multiple cancers of identical pathology in same patients?; and ii) What is the difference between secondary malignant lesions and secondary benign lesions with reference to the primary cancer? All three models could fit the enhancement kinetics satisfactorily. Regardless of the analysis model the parameter obtained from the primary cancer and the secondary cancer showed significant correlations. In comparison between Group-1 and Group-2 subjects, the wash-out parameter kep in Models-1 and 2 could significantly differentiate benign from malignant lesions, but not the magnitude parameters, Ktrans in Model-2 or the parameter A in Model-3. If analyzed using appropriate models the early up-slope parameters, vp in Model-1 and Tc in Model-3, might be able to distinguish between benign and malignant lesions. When more data are available a reference database can be established with the method described in this study, and from which to determine the likelihood of malignancy for each incidental lesion found in preoperative MRI, with reference to the primary cancer.