Repeatability and Reproducibility Assessment of the Apparent Diffusion Coefficient in the Prostate: A Trial of the ECOG-ACRIN Research Group (ACRIN 6701).

BACKGROUND: Uncertainty regarding the reproducibility of the apparent diffusion coefficient (ADC) hampers the use of quantitative diffusion-weighted imaging (DWI) in evaluation of the prostate with magnetic resonance imaging MRI. The quantitative imaging biomarkers alliance (QIBA) profile for quantitative DWI claims a within-subject coefficient of variation (wCV) for prostate lesion ADC of 0.17. Improved understanding of ADC reproducibility would aid the use of quantitative diffusion in prostate MRI evaluation. PURPOSE: Evaluation of the repeatability (same-day) and reproducibility (multi-day) of whole-prostate and focal-lesion ADC assessment in a multi-site setting. STUDY TYPE: Prospective multi-institutional. SUBJECTS: Twenty-nine males, ages 53 to 80 (median 63) years, following diagnosis of prostate cancer, 10 with focal lesions. FIELD STRENGTH/SEQUENCE: 3T, single-shot spin-echo diffusion-weighted echo-planar sequence with four b-values. ASSESSMENT: Sites qualified for the study using an ice-water phantom with known ADC. Readers performed DWI analyses at visit 1 ("V1") and visit 2 ("V2," 2-14 days after V1), where V2 comprised scans before ("V2pre") and after ("V2post") a "coffee-break" interval with subject removal and repositioning. A single reader segmented the whole prostate. Two readers separately placed region-of-interests for focal lesions. STATISTICAL TESTS: Reproducibility and repeatability coefficients for whole prostate and focal lesions derived from median pixel ADC. We estimated the wCV and 95% confidence interval using a variance stabilizing transformation and assessed interreader reliability of focal lesion ADC using the intraclass correlation coefficient (ICC). RESULTS: The ADC biases from b0 -b600 and b0 -b800 phantom scans averaged 1.32% and 1.44%, respectively; mean b-value dependence was 0.188%. Repeatability and reproducibility of whole prostate median pixel ADC both yielded wCVs of 0.033 (N = 29). In 10 subjects with an evaluable focal lesion, the individual reader wCVs were 0.148 and 0.074 (repeatability) and 0.137 and 0.078 (reproducibility). All time points demonstrated good to excellent interreader reliability for focal lesion ADC (ICCV1  = 0.89; ICCV2pre  = 0.76; ICCV2post  = 0.94). DATA CONCLUSION: This study met the QIBA claim for prostate ADC. Test-retest repeatability and multi-day reproducibility were largely equivalent. Interreader reliability for focal lesion ADC was high across time points. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2 TOC CATEGORY: Pelvis.

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy.

PURPOSE: To evaluate DRAMMS, an attribute-based deformable registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy. METHODS: Breast magnetic resonance images from 14 women undergoing neoadjuvant chemotherapy were analyzed. The accuracy of DRAMMS versus five intensity-based deformable registration methods was evaluated based on 2,380 landmarks independently annotated by two experts, for the entire image volume, different image subregions, and patient subgroups. The registration method with the smallest landmark error was used to quantify tumor changes, by calculating the Jacobian determinant maps of the registration deformation. RESULTS: DRAMMS had the smallest landmark errors (6.05 ± 4.86 mm), followed by the intensity-based methods CC-FFD (8.07 ± 3.86 mm), NMI-FFD (8.21 ± 3.81 mm), SSD-FFD (9.46 ± 4.55 mm), Demons (10.76 ± 6.01 mm), and Diffeomorphic Demons (10.82 ± 6.11 mm). Results show that registration accuracy also depends on tumor versus normal tissue regions and different patient subgroups. CONCLUSIONS: The DRAMMS deformable registration method, driven by attribute-matching and mutual-saliency, can register longitudinal breast magnetic resonance images with a higher accuracy than several intensity-matching methods included in this article. As such, it could be valuable for more accurately quantifying heterogeneous tumor changes as a marker of response to treatment.

Phase 1 and pharmacodynamic trial of everolimus in combination with cetuximab in patients with advanced cancer.

BACKGROUND: Preclinical and clinical studies suggest mTOR (mammalian target of rapamycin) inhibitors may have metabolic and antiangiogenic effects, and synergize with epidermal growth factor pathway inhibitors. Therefore, a phase 1/pharmacodynamic trial of everolimus with cetuximab was performed. METHODS: A total of 29 patients were randomized to a run-in of oral everolimus (30, 50, or 70 mg) or cetuximab (400 mg/m(2) loading, 250 mg/m(2) maintenance) weekly, followed by the combination in this dose-escalation study. Primary endpoints were phase 2 dose and toxicity characterization. [(18)F]Fluorodeoxyglucose positron emission tomography (FDG-PET) was performed as a pharmacodynamic marker of mTOR inhibition, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was performed as an indicator of tumor perfusion changes, at 3 time points. RESULTS: Everolimus and cetuximab were tolerable at full doses, with an expected toxicity profile. Dose-limiting toxicities in the everolimus 70 mg group included grade 3 skin toxicity in 2 patients, and mucositis in 1 patient. Of 16 patients evaluable for response, 5 had stable disease lasting 4 to 19 months. Mean change in maximum standardized uptake value (SUV(max)) for those treated initially with everolimus was -24% (2% to -54%), and with cetuximab was -5% (-23 to 36%). The K(trans) measured by DCE-MRI did not decrease, regardless of run-in drug. CONCLUSIONS: Everolimus and cetuximab can be safely administered at standard doses, and are associated with prolonged disease control. The recommended phase 2 dose of oral weekly everolimus is 70 mg in combination with standard cetuximab. Imaging studies reveal that metabolic inhibition by everolimus alone and in combination with cetuximab predominates over changes in tumor perfusion in this patient population.

Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study.

PURPOSE: To determine the feasibility of using a computer-aided diagnosis (CAD) system to differentiate among triple-negative breast cancer, estrogen receptor (ER)-positive cancer, human epidermal growth factor receptor type 2 (HER2)-positive cancer, and benign fibroadenoma lesions on dynamic contrast material-enhanced (DCE) magnetic resonance (MR) images. MATERIALS AND METHODS: This is a retrospective study of prospectively acquired breast MR imaging data collected from an institutional review board-approved, HIPAA-compliant study between 2002 and 2007. Written informed consent was obtained from all patients. The authors collected DCE MR images from 65 women with 76 breast lesions who had been recruited into a larger study of breast MR imaging. The women had triple-negative (n = 21), ER-positive (n = 25), HER2-positive (n = 18), or fibroadenoma (n = 12) lesions. All lesions were classified as Breast Imaging Reporting and Data System category 4 or higher on the basis of previous imaging. Images were subject to quantitative feature extraction, feed-forward feature selection by means of linear discriminant analysis, and lesion classification by using a support vector machine classifier. The area under the receiver operating characteristic curve (Az) was calculated for each of five lesion classification tasks involving triple-negative breast cancers. RESULTS: For each pair-wise lesion type comparison, linear discriminant analysis helped identify the most discriminatory features, which in conjunction with a support vector machine classifier yielded an Az of 0.73 (95% confidence interval [CI]: 0.59, 0.87) for triple-negative cancer versus all non-triple-negative lesions, 0.74 (95% CI: 0.60, 0.88) for triple-negative cancer versus ER- and HER2-positive cancer, 0.77 (95% CI: 0.63, 0.91) for triple-negative versus ER-positive cancer, 0.74 (95% CI: 0.58, 0.89) for triple-negative versus HER2-positive cancer, and 0.97 (95% CI: 0.91, 1.00) for triple-negative cancer versus fibroadenoma. CONCLUSION: Triple-negative cancers possess certain characteristic features on DCE MR images that can be captured and quantified with CAD, enabling good discrimination of triple-negative cancers from non-triple-negative cancers, as well as between triple-negative cancers and benign fibroadenomas. Such CAD algorithms may provide added diagnostic benefit in identifying the highly aggressive triple-negative cancer phenotype with DCE MR imaging in high-risk women.

Automatic coil selection for streak artifact reduction in radial MRI.

In radial MR imaging, streaking artifacts contaminating the entire field of view can arise from regions at the outer edges of the prescribed field of view. This can occur even when the Nyquist criterion is satisfied within the desired field of view. These artifacts become exacerbated when parts of the object lie in the superior/inferior regions of the scanner where the gradient strengths become weakened. When multiple coil arrays are used for signal reception, coils at the outer edges can be disabled before data acquisition to reduce the artifact levels. However, as the weakened gradient strengths near the edges often distort the object, causing the signal to become highly concentrated into a small region, the streaks are often not completely removed. Data from certain coils can also be excluded during reconstruction by visually inspecting the individual coil images, but this is impractical for routine use. In this work, a postprocessing method is proposed to automatically identify those coils whose images contain high levels of streaking for subsequent exclusion during reconstruction. The proposed method was demonstrated in vivo dynamic contrast enhanced MRI datasets acquired using a three-dimensional hybrid radial sequence. The results demonstrate that the proposed strategy substantially improves the image quality and show excellent agreement with images reconstructed with manually determined coil selection.

Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.

Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of the breast has emerged as an adjunct imaging tool to conventional X-ray mammography due to its high detection sensitivity. Despite the increasing use of breast DCE-MRI, specificity in distinguishing malignant from benign breast lesions is low, and interobserver variability in lesion classification is high. The novel contribution of this paper is in the definition of a new DCE-MRI descriptor that we call textural kinetics, which attempts to capture spatiotemporal changes in breast lesion texture in order to distinguish malignant from benign lesions. We qualitatively and quantitatively demonstrated on 41 breast DCE-MRI studies that textural kinetic features outperform signal intensity kinetics and lesion morphology features in distinguishing benign from malignant lesions. A probabilistic boosting tree (PBT) classifier in conjunction with textural kinetic descriptors yielded an accuracy of 90%, sensitivity of 95%, specificity of 82%, and an area under the curve (AUC) of 0.92. Graph embedding, used for qualitative visualization of a low-dimensional representation of the data, showed the best separation between benign and malignant lesions when using textural kinetic features. The PBT classifier results and trends were also corroborated via a support vector machine classifier which showed that textural kinetic features outperformed the morphological, static texture, and signal intensity kinetics descriptors. When textural kinetic attributes were combined with morphologic descriptors, the resulting PBT classifier yielded 89% accuracy, 99% sensitivity, 76% specificity, and an AUC of 0.91.

STEP: spatiotemporal enhancement pattern for MR-based breast tumor diagnosis.

The authors propose a spatiotemporal enhancement pattern (STEP) for comprehensive characterization of breast tumors in contrast-enhanced MR images. By viewing serial contrast-enhanced MR images as a single spatiotemporal image, they formulate the STEP as a combination of (1) dynamic enhancement and architectural features of a tumor, and (2) the spatial variations of pixelwise temporal enhancements. Although the latter has been widely used by radiologists for diagnostic purposes, it has rarely been employed for computer-aided diagnosis. This article presents two major contributions. First, the STEP features are introduced to capture temporal enhancement and its spatial variations. This is essentially carried out through the Fourier transformation and pharmacokinetic modeling of various temporal enhancement features, followed by the calculation of moment invariants and Gabor texture features. Second, for effectively extracting the STEP features from tumors, we develop a graph-cut based segmentation algorithm that aims at refining coarse manual segmentations of tumors. The STEP features are assessed through their diagnostic performance for differentiating between benign and malignant tumors using a linear classifier (along with a simple ranking-based feature selection) in a leave-one-out cross-validation setting. The experimental results for the proposed features exhibit superior performance, when compared to the existing approaches, with the area under the ROC curve approaching 0.97.

Estimation of the content of fat and parenchyma in breast tissue using MRI T1 histograms and phantoms.

Mammographic breast density has been correlated with breast cancer risk. Estimation of the volumetric composition of breast tissue using three-dimensional MRI has been proposed, but accuracy depends upon the estimation methods employed. The use of segmentation based on T1 relaxation rates allows quantitative estimates of fat and parenchyma volume, but is limited by partial volume effects. An investigation employing phantom breast tissue composed of various combinations of chicken breast (to represent parenchyma) and cooking fats was carried out to elucidate the factors that influence MRI T1 histograms. Using the phantoms, T1 histograms and their known fat and parenchyma composition, a logistic distribution function was derived to describe the apportioning of the T1 histogram to fat and parenchyma. This function and T1 histograms were then used to predict the fat and parenchyma content of breasts from 14 women. Using this method, the composition of the breast tissue in the study population was as follows: fat 69.9+/-22.9% and parenchyma 30.1+/-22.9%.

Improving parenchyma segmentation by simultaneous estimation of tissue property T1 map and group-wise registration of inversion recovery MR breast images.

The parenchyma tissue in the breast has a strong relation with predictive biomarkers of breast cancer. To better segment parenchyma, we perform segmentation on estimated tissue property T1 map. To improve the estimation of tissue property (T1) which is the basis for parenchyma segmentation, we present an integrated algorithm for simultaneous T1 map estimation, T1 map based parenchyma segmentation and group-wise registration on series of inversion recovery magnetic resonance (MR) breast images. The advantage of using this integrated algorithm is that the simultaneous T1 map estimation (E-step) and group-wise registration (R-step) could benefit each other and jointly improve parenchyma segmentation. In particular, in E-step, T1 map based segmentation could help perform an edge-preserving smoothing on the tentatively estimated noisy T1 map, and could also help provide tissue probability maps to be robustly registered in R-step. Meanwhile, the improved estimation of T1 map could help segment parenchyma in a more accurate way. In R-step, for robust registration, the group-wise registration is performed on the tissue probability maps produced in E-step, rather than the original inversion recovery MR images, since tissue probability maps are the intrinsic tissue property which is invariant to the use of different imaging parameters. The better alignment of images achieved in R-step can help improve T1 map estimation and indirectly the T1 map based parenchyma segmentation. By iteratively performing E-step and R-step, we can simultaneously obtain better results for T1 map estimation, T1 map based segmentation, group-wise registration, and finally parenchyma segmentation.

De-enhancing the dynamic contrast-enhanced breast MRI for robust registration.

Dynamic enhancement causes serious problems for registration of contrast enhanced breast MRI, due to variable uptakes of agent on different tissues or even same tissues in the breast. We present an iterative optimization algorithm to de-enhance the dynamic contrast-enhanced breast MRI and then register them for avoiding the effects of enhancement on image registration. In particular, the spatially varying enhancements are modeled by a Markov Random Field, and estimated by a locally smooth function with boundaries using a graph cut algorithm. The de-enhanced images are then registered by conventional B-spline based registration algorithm. These two steps benefit from each other and are repeated until the results converge. Experimental results show that our two-step registration algorithm performs much better than conventional mutual information based registration algorithm. Also, the effects of tumor shrinking in the conventional registration algorithms can be effectively avoided by our registration algorithm.

Segmentation and classification of breast tumor using dynamic contrast-enhanced MR images.

Accuracy of automatic cancer diagnosis is largely determined by two factors, namely, the precision of tumor segmentation, and the suitability of extracted features for discrimination between malignancy and benignancy. In this paper, we propose a new framework for accurate characterization of tumors in contrast enhanced MR images. First, a new graph cut based segmentation algorithm is developed for refining coarse manual segmentation, which allows precise identification of tumor regions. Second, by considering serial contrast-enhanced images as a single spatio-temporal image, a spatio-temporal model of segmented tumor is constructed to extract Spatio-Temporal Enhancement Patterns (STEPs). STEPs are designed to capture not only dynamic enhancement and architectural features, but also spatial variations of pixel-wise temporal enhancement of the tumor. While temporal enhancement features are extracted through Fourier transform, the resulting STEP framework captures spatial patterns of temporal enhancement features via moment invariants and rotation invariant Gabor textures. High accuracy of the proposed framework is a direct consequence of this two pronged approach, which is validated through experiments yielding, for instance, an area of 0.97 under the ROC curve.

Optimal post-contrast timing of breast MR image acquisition for architectural feature analysis.

PURPOSE: To define a post-contrast imaging time span during which diagnostic accuracy of breast magnetic resonance (MR) architectural feature analysis is maintained. MATERIALS AND METHODS: Seventy-five patients with mammographically-visible or palpable findings underwent MR examination. Three sequential post-contrast, fat-saturated, three-dimensional gradient-echo imaging runs were acquired spanning 0-90, 90-180, and 180-270 seconds after contrast injection. Five readers independently predicted the malignant potential of the MR abnormalities. RESULTS: Receiver-operator characteristics (ROC) curves were our primary measure of diagnostic accuracy. The accuracy of four readers was unchanged over the three post-contrast runs. One reader was slightly more accurate using the second and third runs than using the first. CONCLUSION: For most readers, a single post-contrast run performed at any point during the first four minutes and 30 seconds following injection should yield an equivalent diagnostic accuracy. If any time period is less optimal, it is that of our first run, performed between 0-90 seconds after contrast injection.