Shonket Ray

Contrast-enhanced Dedicated Breast CT: Initial Clinical Experience

PURPOSE To quantify contrast material enhancement of breast lesions scanned with dedicated breast computed tomography (CT) and to compare their conspicuity with that at unenhanced breast CT and mammography. MATERIALS AND METHODS Approval of the institutional review board and the Radiation Use Committee and written informed consent were obtained for this HIPAA-compliant study. Between September 2006 and April 2009, 46 women (mean age, 53.2 years; age range, 35-72 years) with Breast Imaging Reporting and Data System category 4 or 5 lesions underwent unenhanced breast CT and contrast material-enhanced breast CT before biopsy. Two radiologists independently scored lesion conspicuity for contrast-enhanced breast CT versus mammography and for contrast-enhanced breast CT versus unenhanced breast CT. Mean lesion voxel intensity was measured in Hounsfield units and normalized to adipose tissue intensity on manually segmented images obtained before and after administration of contrast material. Regression models focused on conspicuity and quantified enhancement were used to estimate the effect of pathologic diagnosis (benign vs malignant), lesion type (mass vs calcifications), breast density, and interradiologist variability. RESULTS Fifty-four lesions (25 benign, 29 malignant) in 46 subjects were analyzed. Malignant lesions were seen significantly better at contrast-enhanced breast CT than at unenhanced breast CT (P < .001) or mammography (P < .001). Malignant calcifications (malignant lesions manifested mammographically as microcalcifications only, n = 7) were seen better at contrast-enhanced breast CT than at unenhanced breast CT (P < .001) and were seen similarly at contrast-enhanced breast CT and mammography. Malignant lesions enhanced 55.9 HU +/- 4.0 (standard error), whereas benign lesions enhanced 17.6 HU +/- 6.1 (P < .001). Ductal carcinoma in situ (n = 5) enhanced a mean of 59.6 HU +/- 2.8. Receiver operating characteristic curve analysis of lesion enhancement yielded an area under the receiver operating characteristic curve of 0.876. CONCLUSION Conspicuity of malignant breast lesions, including ductal carcinoma in situ, is significantly improved at contrast-enhanced breast CT. Quantifying lesion enhancement may aid in the detection and diagnosis of breast cancer.

Volume assessment accuracy in computed tomography: a phantom study.

There is a broad push in the cancer imaging community to eventually replace linear tumor measurements with three‐dimensional evaluation of tumor volume. To evaluate the potential accuracy of volume measurement in tumors by CT, a gelatin phantom consisting of 55 polymethylmethacrylate (PMMA) spheres spanning diameters from 1.6 mm to 25.4 mm was fabricated and scanned using thin slice (0.625 mm) CT (GE LightSpeed 16). Nine different reconstruction combinations of field of view dimension (FOV=20,30,40 cm) and CT kernel (standard, lung, bone) were analyzed. Contiguous thin‐slice images were averaged to produce CT images with greater thicknesses (1.25, 2.50, 5.0 mm). Simple grayscale thresholding techniques were used to segment the PMMA spheres from the gelatin background, where a total of 1800 spherical volumes were evaluated across the permutations studied. The geometric simplicity of the phantom established upper limits on measurement accuracy. In general, smaller slice thickness and larger sphere diameters produced more accurate volume assessment than larger slice thickness and smaller sphere diameter. The measured volumes were smaller than the actual volumes by a common factor depending on slice thickness; overall, 0.625 mm slices produced on average 18%, 1.25 mm slices produced 22%, 2.5 mm CT slices produced 29%, and 5.0 mm slices produced 39% underestimates of volume (mm3). Field of view did not have a significant effect on volume accuracy. Reconstruction algorithm significantly affected volume accuracy (p<0.0001), with the lung kernel having the smallest error, followed by the bone and standard kernels. The results of this investigation provide guidance for CT protocol development and may guide the development of more advanced techniques to promote quantitatively accurate CT volumetric analysis of tumors. PACS number: 87.57.Q‐

Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.

PURPOSE Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region. METHODS Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLongs test was used to compare the different ROCs in terms of AUC performance. RESULTS The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p < 0.001). The lattice-based texture features also outperform the single-ROI texture features when extracted from the retroareolar or the central breast region (AUC: 0.60-0.74, STD: 0.03). Adding breast PD% does not make a significant performance improvement to the lattice-based texture features or the single-ROI features (p > 0.05). CONCLUSIONS The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare to standard demographic and reproductive risk factors.

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices

Abstract. An analytical framework is presented for evaluating the equivalence of parenchymal texture features across different full-field digital mammography (FFDM) systems using a physical breast phantom. Phantom images (FOR PROCESSING) are acquired from three FFDM systems using their automated exposure control setting. A panel of texture features, including gray-level histogram, co-occurrence, run length, and structural descriptors, are extracted. To identify features that are robust across imaging systems, a series of equivalence tests are performed on the feature distributions, in which the extent of their intersystem variation is compared to their intrasystem variation via the Hodges–Lehmann test statistic. Overall, histogram and structural features tend to be most robust across all systems, and certain features, such as edge enhancement, tend to be more robust to intergenerational differences between detectors of a single vendor than to intervendor differences. Texture features extracted from larger regions of interest (i.e., >63  pixels2) and with a larger offset length (i.e., >7  pixels), when applicable, also appear to be more robust across imaging systems. This framework and observations from our experiments may benefit applications utilizing mammographic texture analysis on images acquired in multivendor settings, such as in multicenter studies of computer-aided detection and breast cancer risk assessment.

Analysis of breast CT lesions using computer-aided diagnosis: an application of neural networks on extracted morphologic and texture features

Dedicated cone-beam breast CT (bCT) scanners have been developed as a potential alternative imaging modality to conventional X-ray mammography in breast cancer diagnosis. As with other modalities, quantitative imaging (QI) analysis can potentially be utilized as a tool to extract useful numeric information concerning diagnosed lesions from high quality 3D tomographic data sets. In this work, preliminary QI analysis was done by designing and implementing a computer-aided diagnosis (CADx) system consisting of image preprocessing, object(s) of interest (i.e. masses, microcalcifications) segmentation, structural analysis of the segmented object(s), and finally classification into benign or malignant disease. Image sets were acquired from bCT patient scans with diagnosed lesions. Iterative watershed segmentation (IWS), a hybridization of the watershed method using observer-set markers and a gradient vector flow (GVF) approach, was used as the lesion segmentation method in 3D. Eight morphologic parameters and six texture features based on gray level co-occurrence matrix (GLCM) calculations were obtained per segmented lesion and combined into multi-dimensional feature input data vectors. Artificial neural network (ANN) classifiers were used by performing cross validation and network parameter optimization to maximize area under the curve (AUC) values of the resulting receiver-operating characteristic (ROC) curves. Within these ANNs, biopsy-proven diagnoses of malignant and benign lesions were recorded as target data while the feature vectors were saved as raw input data. With the image data separated into post-contrast (n = 55) and pre-contrast sets (n = 39), a maximum AUC of 0.70 ± 0.02 and 0.80 ± 0.02 were achieved, respectively, for each data set after ANN application.

Association between Breast Parenchymal Complexity and False-Positive Recall From Digital Mammography Versus Breast Tomosynthesis: Preliminary Investigation in the ACRIN PA 4006 Trial.

RATIONALE AND OBJECTIVES We investigate associations between measures of mammographic parenchymal complexity and false-positive (FP) recall from screening with digital mammography (DM) versus digital breast tomosynthesis (DBT). MATERIALS AND METHODS We retrospectively analyzed data from 541 women recruited by the American College of Radiology Imaging Network 4006 trial, designed to evaluate callback and detection rates from screening with DM versus combined DM and DBT. Of these, 68 and 56 were FPs based on DM alone versus the combined DM/DBT readings, respectively. Mammographic complexity was quantified with computerized texture analysis and percent density. Logistic regression was performed to evaluate associations between extracted features and FP recall, after adjusting for age and number of previous benign biopsies. Odds ratios and area under the curve (AUC) of the receiver operating characteristic were used to assess association strength. RESULTS For DM, age, previous benign biopsies and texture features of correlation, inverse difference moment, sum average, and sum variance were deemed as significant predictors (P <.05) of FP recall, with an AUC = 0.77. For DBT, age was the only significant predictor of FP recall with AUC = 0.64. Using exploratory receiver operating characteristic thresholds for which no true-positives would be missed, a potential FP reduction of 23.5% and 8.9% was demonstrated, respectively, for DM alone versus DM/DBT. CONCLUSION Measures of breast complexity measured on 2D digital mammograms are indicative of the likelihood for FP recall from screening with DM, and could help identify women who could benefit from supplemental screening, including DBT.

Application of computer-extracted breast tissue texture features in predicting false-positive recalls from screening mammography

Mammographic texture features have been shown to have value in breast cancer risk assessment. Previous models have also been developed that use computer-extracted mammographic features of breast tissue complexity to predict the risk of false-positive (FP) recall from breast cancer screening with digital mammography. This work details a novel locallyadaptive parenchymal texture analysis algorithm that identifies and extracts mammographic features of local parenchymal tissue complexity potentially relevant for false-positive biopsy prediction. This algorithm has two important aspects: (1) the adaptive nature of automatically determining an optimal number of region-of-interests (ROIs) in the image and each ROI’s corresponding size based on the parenchymal tissue distribution over the whole breast region and (2) characterizing both the local and global mammographic appearances of the parenchymal tissue that could provide more discriminative information for FP biopsy risk prediction. Preliminary results show that this locallyadaptive texture analysis algorithm, in conjunction with logistic regression, can predict the likelihood of false-positive biopsy with an ROC performance value of AUC=0.92 (p<0.001) with a 95% confidence interval [0.77, 0.94]. Significant texture feature predictors (p<0.05) included contrast, sum variance and difference average. Sensitivity for false-positives was 51% at the 100% cancer detection operating point. Although preliminary, clinical implications of using prediction models incorporating these texture features may include the future development of better tools and guidelines regarding personalized breast cancer screening recommendations. Further studies are warranted to prospectively validate our findings in larger screening populations and evaluate their clinical utility.

The Impact of Acquisition Dose on Quantitative Breast Density Estimation with Digital Mammography: Results from ACRIN PA 4006

Purpose To investigate the impact of radiation dose on breast density estimation in digital mammography. Materials and Methods With institutional review board approval and Health Insurance Portability and Accountability Act compliance under waiver of consent, a cohort of women from the American College of Radiology Imaging Network Pennsylvania 4006 trial was retrospectively analyzed. All patients underwent breast screening with a combination of dose protocols, including standard full-field digital mammography, low-dose digital mammography, and digital breast tomosynthesis. A total of 5832 images from 486 women were analyzed with previously validated, fully automated software for quantitative estimation of density. Clinical Breast Imaging Reporting and Data System (BI-RADS) density assessment results were also available from the trial reports. The influence of image acquisition radiation dose on quantitative breast density estimation was investigated with analysis of variance and linear regression. Pairwise comparisons of density estimations at different dose levels were performed with Student t test. Agreement of estimation was evaluated with quartile-weighted Cohen kappa values and Bland-Altman limits of agreement. Results Radiation dose of image acquisition did not significantly affect quantitative density measurements (analysis of variance, P = .37 to P = .75), with percent density demonstrating a high overall correlation between protocols (r = 0.88-0.95; weighted κ = 0.83-0.90). However, differences in breast percent density (1.04% and 3.84%, P < .05) were observed within high BI-RADS density categories, although they were significantly correlated across the different acquisition dose levels (r = 0.76-0.92, P < .05). Conclusion Precision and reproducibility of automated breast density measurements with digital mammography are not substantially affected by variations in radiation dose; thus, the use of low-dose techniques for the purpose of density estimation may be feasible. (©) RSNA, 2016 Online supplemental material is available for this article.

Parameter optimization of parenchymal texture analysis for prediction of false-positive recalls from screening mammography

This work details a methodology to obtain optimal parameter values for a locally-adaptive texture analysis algorithm that extracts mammographic texture features representative of breast parenchymal complexity for predicting falsepositive (FP) recalls from breast cancer screening with digital mammography. The algorithm has two components: (1) adaptive selection of localized regions of interest (ROIs) and (2) Haralick texture feature extraction via Gray- Level Co-Occurrence Matrices (GLCM). The following parameters were systematically varied: mammographic views used, upper limit of the ROI window size used for adaptive ROI selection, GLCM distance offsets, and gray levels (binning) used for feature extraction. Each iteration per parameter set had logistic regression with stepwise feature selection performed on a clinical screening cohort of 474 non-recalled women and 68 FP recalled women; FP recall prediction was evaluated using area under the curve (AUC) of the receiver operating characteristic (ROC) and associations between the extracted features and FP recall were assessed via odds ratios (OR). A default instance of mediolateral (MLO) view, upper ROI size limit of 143.36 mm (2048 pixels2), GLCM distance offset combination range of 0.07 to 0.84 mm (1 to 12 pixels) and 16 GLCM gray levels was set. The highest ROC performance value of AUC=0.77 [95% confidence intervals: 0.71-0.83] was obtained at three specific instances: the default instance, upper ROI window equal to 17.92 mm (256 pixels2), and gray levels set to 128. The texture feature of sum average was chosen as a statistically significant (p<0.05) predictor and associated with higher odds of FP recall for 12 out of 14 total instances.

A comparative analysis of 2D and 3D CAD for calcifications in digital breast tomosynthesis

Many medical centers offer digital breast tomosynthesis (DBT) and 2D digital mammography acquired under the same compression (i.e., “Combo” examination) for screening. This paper compares a conventional 2D CAD algorithm (Hologic® ImageChecker® CAD v9.4) for calcification detection against a prototype 3D algorithm (Hologic® ImageChecker® 3D Calc CAD v1.0). Due to the newness of DBT, the development of this 3D CAD algorithm is ongoing, and it is currently not FDA-approved in the United States. For this study, DBT screening cases with suspicious calcifications were identified retrospectively at the University of Pennsylvania. An expert radiologist (E.F.C.) reviewed images with both 2D and DBT CAD marks, and compared the marks to biopsy results. Control cases with one-year negative follow-up were also studied; these cases either possess clearly benign calcifications or lacked calcifications. To allow the user to alter the sensitivity for cancer detection, an operating point is assigned to each CAD mark. As expected from conventional 2D CAD, increasing the operating point in 3D CAD increases sensitivity and reduces specificity. Additionally, we showed that some cancers are occult to 2D CAD at all operating points. By contrast, 3D CAD allows for detection of some cancers that are missed on 2D CAD. We also demonstrated that some non-cancerous CAD marks in 3D are not present at analogous locations in the 2D image. Hence, there are additional marks when using both 2D and 3D CAD in combination, leading to lower specificity than with conventional 2D CAD alone.