Background: Studies of 3D cell cultures have shown that mammary epithelial cell growth and morphogenesis is regulated by extracellular matrix (ECM) stiffness, linking ECM stiffening to malignant transformation. Tumors are consistently stiffer than normal adjacent tissue, and matrix stiffening is caused by ECM cross-linking and increased deposition of collagen. Some evidence suggests that collagen orientation at tumor boundaries can promote tumor metastasis. Measuring the stiffness of the tumor boundaries and adjacent stromal tissue may give additional information 1) about tumor microenvironment and 2) to guide treatment. Diffusion-weighted imaging (DWI) MRI measures the mobility of water in tissue and may be sensitive to this phenomenon. Material and Methods: MRI data was collected on patients with locally advance breast cancer enrolled in an IRB-approved study at UCSF and signed informed consent. In addition to a standard dynamic contrast enhanced (DCE) MRI, a high-resolution diffusion-weighted image (HR DWI) was acquired with an echo planar imaging sequence and the following parameters: TR/TE=4000/64.8 ms, b=0,600, FOV=70×140mm, matrix=28×64, and voxel size=0.55×0.55×4.0mm. Apparent diffusion coefficient (ADC) maps were created. HR DWI images were segmented into tumor and non-enhancing, surrounding stromal tissue. A proximity mapping method was used to measure ADC values at the inner edge of tumor and at increasing distances from the tumor boundary on HR DWI. The mean was calculated for the voxels in 1 mm increments, starting at 5 mm into the tumor (−5 mm) and ending at 2.5 cm away from the tumor (25 mm). Results: The average of the changes per 1 mm shell was largest for the transition of the tumor boundary (Table 1). In Table 1, the −5 to 0 mm, 0 to 5 mm, and 5 to 25 mm columns represent inside the tumor, tumor boundary, and outside the tumor, respectively. In general, ADC values were consistently lower inside the tumor than outside. The greatest changes per 1 mm shell was seen in the transition from inside to outside tumor, although the values varied among tumor types. Each of the three cases analyzed had different patterns of ADC values. Discussion: These preliminary studies show that water mobility measurements change at the tumor boundary, with different patterns observed among individual patients. We are further investigating the influence of density and tumor margin morphology on these ADC measurements. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P2-09-12.
Diffusion weighted imaging (DWI) has shown potential for separating malignant from benign lesions on breast MRI, theoretically improving the specificity of the breast MRI exam. However, standard DWI is hampered by multiple factors in the breast which limit utility in clinical practice. A readout segmented diffusion technique (RESOLVE)(1) permits the use of extremely short echo spacing and resamples uncorrectable data using a real-time navigator, reducing image distortion and potentially providing more accurate apparent diffusion coefficient (ADC) within lesions. In order to study the potentially utility of this method against standard diffusion, we compared ADC values from retrospectively-identified BI-RADS 4/5 (suspicious) lesions with both standard single shot-spin echo EPI (ss-EPI) and RESOLVE diffusion at 3 Tesla over a 10-month period. All patients subsequently went to image-directed biopsy. The imaging parameters were as follows: TR/TE=7500-10000/60 ms (ss-EPI) and 8000–12000/64 ms (RESOLVE), averages=8 (ss-EPI) and 1 (RESOLVE), readout segmentation factor 5 with echo spacing of 0.3 ms (RESOLVE), slices = 47–50, b-values 0, 800, resolution = 1.8×1.8×2.4 mm3, imaging times of approximately 5 minutes for both techniques. Freehand ROI's were drawn on each suspicious lesion based on b=800 maps in close reference to the post-contrast T1 series by a board-certified radiologist who was blinded to final pathology and sequence. Similar ROI's were drawn in normal tissue as a control. For each lesion, mean ADC values and signal intensities at b=800 were recorded. ADC values were averaged by technique and pathologic outcome (benign or malignant). Signal intensity was normalized by dividing mean signal intensity of the lesion ROI by that of the control. Significance was determined using Wilcoxon rank-sum test for ADC, and paired t-test for signal intensity measures. Of the final cohort of 38 lesions in 31 patients, 9 were malignant and the remainder were benign. Two lesions were no longer present at breast MRI biopsy, and hence deemed benign. The lesion-to-background signal intensity on diffusion was higher (p = 0.05) on RESOLVE (1.9±0.1) compared to standard diffusion (1.7±0.1). Statistically significant differences between benign and malignant lesion were observed for mean ADC obtained by both methods (Table 1; p < 0.001). Between sequences, there was excellent agreement between RESOLVE and standard EPI for control values obtained from normal tissue, and for mean ADC values of benign lesions. Among malignant lesions, however, there was a statistically significant decrease in mean ADC values measured by RESOLVE (p = 0.05), which effectively widened the separation between benign and malignant lesions. Our results suggest improved separation of benign from malignant lesions by RESOLVE compared to standard diffusion, as well as increased lesion-to-background contrast, suggesting that this diffusion method has particular promise as an adjunct to dynamic-contrast-enhanced breast MRI. (1) Porter DA, et al MRM 2009; 62:468–75. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P4-01-10.
Purpose: To determine the agreement between expert readers on mammographic findings and calcification patterns. Materials and Methods: Ten academic radiologists from 5 centers reviewed 250 de-identified mammographic cases without prior exams which were previously assessed as BI-RADS 4 or 5 with subsequent pathologic diagnosis by percutaneous or surgical biopsy. For benign cases diagnosed by percutaneous biopsy, 1 year of benign or negative imaging follow-up was required. Using standardized forms, each radiologist assessed the presence of any suspicious mammographic findings (microcalcifications, asymmetry (1-vew), focal asymmetry (2-view), architectural distortion), and the morphology (none, round/punctate, amorphous, coarse heterogeneous, fine pleomorphic, fine linear branching) and distribution (none, diffuse, regional, grouped, linear, segmental) of any identified microcalcifications. Agreement between radiologists for presence/absence of findings, morphology, and distribution of calcifications was determined by calculating the Kappa (k) coefficient with 95% confidence interval (95% CI). The kappa coefficient proposed strength of agreement is ≤0 = poor, .01-.20 = slight, .21-.40 = fair, .41-.60 = moderate, .61-.80 = substantial, and .81-1 = almost perfect, as established by Landis and Koch.1 Results: Of the 250 lesions, 156 (62%) were benign and 94 (38%) malignant. Agreement among the 10 expert readers was strongest for recognizing the presence/absence of calcifications (k = 0.82, 95% CI: 0.80-84), “almost perfect”). There was substantial agreement among the readers for the identification of a mass (k = 0.67, 95% CI: 0.66-69), whereas agreement was fair for the presence of a focal (2-view) asymmetry (k = 0.21, 95% CI: 0.1900.23) or architectural distortion (k = 0.28, 95%CI: 0.26-0.30). Agreement for asymmetries (1-view) was slight (k = 0.09, 95%CI: 0.08-0.11). Among the 6 categories of microcalcification distribution and morphology, reader agreement was moderate (distribution k = 0.60, 95%CI:0.59-0.61; morphology k = 0.51, 95%CI: 0.50-0.52). Conclusion: When asked to characterize suspicious mammographic findings, this sampling of 10 expert academic breast imagers across 5 centers revealed varying strength of agreement for different findings, ranging from slight to almost perfect. Strongest agreement (“almost perfect”) was found for identifying the presence or absence of microcalcifications, although agreement drops to moderate when readers are asked to specify microcalcification morphology and distribution. 1 Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics.1977;33:159-174. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P2-01-06.
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