MRI-cytometry: Mapping nonparametric cell size distributions using diffusion MRI.

PURPOSE: This report introduces and validates a new diffusion MRI-based method, termed MRI-cytometry, which can noninvasively map intravoxel, nonparametric cell size distributions in tissues. METHODS: MRI was used to acquire diffusion MRI signals with a range of diffusion times and gradient factors, and a model was fit to these data to derive estimates of cell size distributions. We implemented a 2-step fitting method to avoid noise-induced artificial peaks and provide reliable estimates of tumor cell size distributions. Computer simulations in silico, experimental measurements on cultured cells in vitro, and animal xenografts in vivo were used to validate the accuracy and precision of the method. Tumors in 7 patients with breast cancer were also imaged and analyzed using this MRI-cytometry approach on a clinical 3 Tesla MRI scanner. RESULTS: Simulations and experimental results confirm that MRI-cytometry can reliably map intravoxel, nonparametric cell size distributions and has the potential to discriminate smaller and larger cells. The application in breast cancer patients demonstrates the feasibility of direct translation of MRI-cytometry to clinical applications. CONCLUSION: The proposed MRI-cytometry method can characterize nonparametric cell size distributions in human tumors, which potentially provides a practical imaging approach to derive specific histopathological information on biological tissues.

Quantitative Comparison of Prone and Supine PERCIST Measurements in Breast Cancer.

Positron emission tomography (PET) is typically performed in the supine position. However, breast magnetic resonance imaging (MRI) is performed in prone, as this improves visibility of deep breast tissues. With the emergence of hybrid scanners that integrate molecular information from PET and functional information from MRI, it is of great interest to determine if the prognostic utility of prone PET is equivalent to supine. We compared PERCIST (PET Response Criteria in Solid Tumors) measurements between prone and supine FDG-PET in patients with breast cancer and the effect of orientation on predicting pathologic complete response (pCR). In total, 47 patients were enrolled and received up to 6 cycles of neoadjuvant therapy. Prone and supine FDG-PET were performed at baseline (t0 ; n = 46), after cycle 1 (t1 ; n = 1) or 2 (t2 ; n = 10), or after all neoadjuvant therapy (t3 ; n = 19). FDG uptake was quantified by maximum and peak standardized uptake value (SUV) with and without normalization to lean body mass; that is, SUVmax , SUVpeak , SULmax , and SULpeak . PERCIST measurements were performed for each paired baseline and post-treatment scan. Receiver operating characteristic analysis for the prediction of pCR was performed using logistic regression that included age and tumor size as covariates. SUV and SUL metrics were significantly different between orientation (P < .001), but were highly correlated (P > .98). Importantly, no differences were observed with the PERCIST measurements (P > .6). Overlapping 95% confidence intervals for the receiver operating characteristic analysis suggested no difference at predicting pCR. Therefore, prone and supine PERCIST in this data set were not statistically different.

Magnetic resonance imaging of mean cell size in human breast tumors.

PURPOSE: Cell size is a fundamental characteristic of all tissues, and changes in cell size in cancer reflect tumor status and response to treatments, such as apoptosis and cell-cycle arrest. Unfortunately, cell size can currently be obtained only by pathological evaluation of tumor tissue samples obtained invasively. Previous imaging approaches are limited to preclinical MRI scanners or require relatively long acquisition times that are impractical for clinical imaging. There is a need to develop cell-size imaging for clinical applications. METHODS: We propose a clinically feasible IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) approach that can characterize mean cell sizes in solid tumors. We report the use of a combination of pulse sequences, using different gradient waveforms implemented on clinical MRI scanners and analytical equations based on these waveforms to analyze diffusion-weighted MRI signals and derive specific microstructural parameters such as cell size. We also describe comprehensive validations of this approach using computer simulations, cell experiments in vitro, and animal experiments in vivo and demonstrate applications in preoperative breast cancer patients. RESULTS: With fast acquisitions (~7 minutes), IMPULSED can provide high-resolution (1.3 mm in-plane) mapping of mean cell size of human tumors in vivo on clinical 3T MRI scanners. All validations suggest that IMPULSED provides accurate and reliable measurements of mean cell size. CONCLUSION: The proposed IMPULSED method can assess cell-size variations in tumors of breast cancer patients, which may have the potential to assess early response to neoadjuvant therapy.

Toward quantitative quasistatic elastography with a gravity-induced deformation source for image-guided breast surgery.

Biomechanical breast models have been employed for applications in image registration and diagnostic analysis, breast augmentation simulation, and for surgical and biopsy guidance. Accurate applications of stress-strain relationships of tissue within the breast can improve the accuracy of biomechanical models that attempt to simulate breast deformations. Reported stiffness values for adipose, glandular, and cancerous tissue types vary greatly. Variations in reported stiffness properties have been attributed to differences in testing methodologies and assumptions, measurement errors, and natural interpatient differences in tissue elasticity. Therefore, the ability to determine patient-specific in vivo breast tissue properties would be advantageous for these procedural applications. While some in vivo elastography methods are not quantitative and others do not measure material properties under deformation conditions that are appropriate to the application of concern, in this study, we developed an elasticity estimation method that is performed using deformations representative of supine therapeutic procedures. More specifically, reconstruction of mechanical properties appropriate for the standard-of-care supine lumpectomy was performed by iteratively fitting two anatomical images before and after deformations taking place in the supine breast configuration. The method proposed is workflow-friendly, quantitative, and uses a noncontact, gravity-induced deformation source.

Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results.

Pathologic complete response following neoadjuvant therapy (NAT) is used as a short-term surrogate marker of eventual outcome in patients with breast cancer. Analyzing voxel-level heterogeneity in MRI-derived parametric maps, obtained before and after the first cycle of NAT ([Formula: see text]), in conjunction with receptor status, may improve the predictive accuracy of tumor response to NAT. Toward that end, we incorporated two MRI-derived parameters, the apparent diffusion coefficient and efflux rate constant, with receptor status in a logistic ridge-regression model. The area under the curve (AUC) and Brier score of the model computed via 10-fold cross validation were 0.94 (95% CI: 0.85, 0.99) and 0.11 (95% CI: 0.06, 0.16), respectively. These two statistics strongly support the hypothesis that our proposed model outperforms the other models that we investigated (namely, models without either receptor information or voxel-level information). The contribution of the receptor information was manifested by an 8% to 15% increase in AUC and a 14% to 21% decrease in Brier score. These data indicate that combining multiparametric MRI with hormone receptor status has a high likelihood of improved prediction of pathologic response to NAT in breast cancer.

Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis.

This meta-analysis assesses the prognostic value of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) performed during neoadjuvant therapy (NAT) of locally advanced breast cancer. A systematic literature search was conducted to identify studies of quantitative DCE-MRI and DW-MRI performed during breast cancer NAT that report the sensitivity and specificity for predicting pathological complete response (pCR). Details of the study population and imaging parameters were extracted from each study for subsequent meta-analysis. Metaregression analysis, subgroup analysis, study heterogeneity, and publication bias were assessed. Across 10 studies that met the stringent inclusion criteria for this meta-analysis (out of 325 initially identified studies), we find that MRI had a pooled sensitivity of 0.91 [95% confidence interval (CI), 0.80 to 0.96] and specificity of 0.81(95% CI, 0.68 to 0.89) when adjusted for covariates. Quantitative DCE-MRI exhibits greater specificity for predicting pCR than semiquantitative DCE-MRI ([Formula: see text]). Quantitative DCE-MRI and DW-MRI are able to predict, early in the course of NAT, the eventual response of breast tumors, with a high level of specificity and sensitivity. However, there is a high degree of heterogeneity in published studies highlighting the lack of standardization in the field.

Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network.

Diffusion weighted MRI has become ubiquitous in many areas of medicine, including cancer diagnosis and treatment response monitoring. Reproducibility of diffusion metrics is essential for their acceptance as quantitative biomarkers in these areas. We examined the variability in the apparent diffusion coefficient (ADC) obtained from both postprocessing software implementations utilized by the NCI Quantitative Imaging Network and online scan time-generated ADC maps. Phantom and in vivo breast studies were evaluated for two ([Formula: see text]) and four ([Formula: see text]) [Formula: see text]-value diffusion metrics. Concordance of the majority of implementations was excellent for both phantom ADC measures and in vivo [Formula: see text], with relative biases [Formula: see text] ([Formula: see text]) and [Formula: see text] (phantom [Formula: see text]) but with higher deviations in ADC at the lowest phantom ADC values. In vivo [Formula: see text] concordance was good, with typical biases of [Formula: see text] to 3% but higher for online maps. Multiple b-value ADC implementations were separated into two groups determined by the fitting algorithm. Intergroup mean ADC differences ranged from negligible for phantom data to 2.8% for [Formula: see text] in vivo data. Some higher deviations were found for individual implementations and online parametric maps. Despite generally good concordance, implementation biases in ADC measures are sometimes significant and may be large enough to be of concern in multisite studies.

Dual Src and EGFR inhibition in combination with gemcitabine in advanced pancreatic cancer: phase I results : A phase I clinical trial.

Pancreatic adenocarcinoma remains a major therapeutic challenge, as the poor (<8%) 5-year survival rate has not improved over the last three decades. Our previous preclinical data showed cooperative attenuation of pancreatic tumor growth when dasatinib (Src inhibitor) was added to erlotinib (EGFR inhibitor) and gemcitabine. Thus, this study was designed to determine the maximum-tolerated dose of the triplet combination. Standard 3 + 3 dose escalation was used, starting with daily oral doses of 70 mg dasatinib and 100 mg erlotinib with gemcitabine on days 1, 8, and 15 (800 mg/m2) of a 28-day cycle (L0). Nineteen patients were enrolled, yet 18 evaluable for dose-limiting toxicities (DLTs). One DLT observed at L0, however dasatinib was reduced to 50 mg (L-1) given side effects observed in the first two patients. At L-1, a DLT occurred in 1/6 patients and dose was re-escalated to L0, where zero DLTs reported in next four patients. Dasatinib was escalated to 100 mg (L1) where 1/6 patients experienced a DLT. Although L1 was tolerable, dose escalation was stopped as investigators felt L1 was within the optimal therapeutic window. Most frequent toxicities were anemia (89%), elevated aspartate aminotransferase (79%), fatigue (79%), nausea (79%), elevated alanine aminotransferase (74%), lymphopenia (74%), leukopenia (74%), neutropenia (63%), and thrombocytopenia (63%), most Grade 1/2. Stable disease as best response was observed in 69% (9/13). Median progression-free and overall survival was 3.6 and 8 months, respectively. Dasatinib, erlotinib, and gemcitabine was safe with manageable side effects, and with encouraging preliminary clinical activity in advanced pancreatic cancer.

Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast.

PURPOSE: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used in cancer imaging to probe tumor vascular properties. Compressed sensing (CS) theory makes it possible to recover MR images from randomly undersampled k-space data using nonlinear recovery schemes. The purpose of this paper is to quantitatively evaluate common temporal sparsity-promoting regularizers for CS DCE-MRI of the breast. METHODS: We considered five ubiquitous temporal regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast DCE-MRI data: Fourier transform (FT), Haar wavelet transform (WT), total variation (TV), second-order total generalized variation (TGV α2), and nuclear norm (NN). We measured the signal-to-error ratio (SER) of the reconstructed images, the error in tumor mean, and concordance correlation coefficients (CCCs) of the derived pharmacokinetic parameters Ktrans (volume transfer constant) and ve (extravascular-extracellular volume fraction) across a population of random sampling schemes. RESULTS: NN produced the lowest image error (SER: 29.1), while TV/TGV α2 produced the most accurate Ktrans (CCC: 0.974/0.974) and ve (CCC: 0.916/0.917). WT produced the highest image error (SER: 21.8), while FT produced the least accurate Ktrans (CCC: 0.842) and ve (CCC: 0.799). CONCLUSION: TV/TGV α2 should be used as temporal constraints for CS DCE-MRI of the breast.

Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation.

Tissue stiffness interrogation is fundamental in breast cancer diagnosis and treatment. Furthermore, biomechanical models for predicting breast deformations have been created for several breast cancer applications. Within these applications, constitutive mechanical properties must be defined and the accuracy of this estimation directly impacts the overall performance of the model. In this study, we present an image-derived computational framework to obtain quantitative, patient specific stiffness properties for application in image-guided breast cancer surgery and interventions. The method uses two MR acquisitions of the breast in different supine gravity-loaded configurations to fit mechanical properties to a biomechanical breast model. A reproducibility assessment of the method was performed in a test-retest study using healthy volunteers and was further characterized in simulation. In five human data sets, the within subject coefficient of variation ranged from 10.7% to 27% and the intraclass correlation coefficient ranged from 0.91-0.944 for assessment of fibroglandular and adipose tissue stiffness. In simulation, fibroglandular content and deformation magnitude were shown to have significant effects on the shape and convexity of the objective function defined by image similarity. These observations provide an important step forward in characterizing the use of nonrigid image registration methodologies in conjunction with biomechanical models to estimate tissue stiffness. In addition, the results suggest that stiffness estimation methods using gravity-induced excitation can reliably and feasibly be implemented in breast cancer surgery/intervention workflows.

Towards real-time topical detection and characterization of FDG dose infiltration prior to PET imaging.

PURPOSE: To dynamically detect and characterize 18F-fluorodeoxyglucose (FDG) dose infiltrations and evaluate their effects on positron emission tomography (PET) standardized uptake values (SUV) at the injection site and in control tissue. METHODS: Investigational gamma scintillation sensors were topically applied to patients with locally advanced breast cancer scheduled to undergo limited whole-body FDG-PET as part of an ongoing clinical study. Relative to the affected breast, sensors were placed on the contralateral injection arm and ipsilateral control arm during the resting uptake phase prior to each patient's PET scan. Time-activity curves (TACs) from the sensors were integrated at varying intervals (0-10, 0-20, 0-30, 0-40, and 30-40 min) post-FDG and the resulting areas under the curve (AUCs) were compared to SUVs obtained from PET. RESULTS: In cases of infiltration, observed in three sensor recordings (30 %), the injection arm TAC shape varied depending on the extent and severity of infiltration. In two of these cases, TAC characteristics suggested the infiltration was partially resolving prior to image acquisition, although it was still apparent on subsequent PET. Areas under the TAC 0-10 and 0-20 min post-FDG were significantly different in infiltrated versus non-infiltrated cases (Mann-Whitney, p < 0.05). When normalized to control, all TAC integration intervals from the injection arm were significantly correlated with SUVpeak and SUVmax measured over the infiltration site (Spearman ρ ≥ 0.77, p < 0.05). Receiver operating characteristic (ROC) analyses, testing the ability of the first 10 min of post-FDG sensor data to predict infiltration visibility on the ensuing PET, yielded an area under the ROC curve of 0.92. CONCLUSIONS: Topical sensors applied near the injection site provide dynamic information from the time of FDG administration through the uptake period and may be useful in detecting infiltrations regardless of PET image field of view. This dynamic information may also complement the static PET image to better characterize the true extent of infiltrations.

Phase I trial of vorinostat added to chemoradiation with capecitabine in pancreatic cancer.

BACKGROUND AND PURPOSE: This single institution phase I trial determined the maximum tolerated dose (MTD) of concurrent vorinostat and capecitabine with radiation in non-metastatic pancreatic cancer. MATERIAL AND METHODS: Twenty-one patients received escalating doses of vorinostat (100-400mg daily) during radiation. Capecitabine was given 1000mg q12 on the days of radiation. Radiation consisted of 30Gy in 10 fractions. Vorinostat dose escalation followed the standard 3+3 design. No dose escalation beyond 400mg vorinostat was planned. Diffusion-weighted (DW)-MRI pre- and post-treatment was used to evaluate in vivo tumor cellularity. RESULTS: The MTD of vorinostat was 400mg. Dose limiting toxicities occurred in one patient each at dose levels 100mg, 300mg, and 400mg: 2 gastrointestinal toxicities and one thrombocytopenia. The most common adverse events were lymphopenia (76%) and nausea (14%). The apparent diffusion coefficient (ADC) increased in most tumors. Nineteen (90%) patients had stable disease, and two (10%) had progressive disease at time of surgery. Eleven patients underwent surgical exploration with four R0 resections and one R1 resection. Median overall survival was 1.1years (95% confidence interval 0.78-1.35). CONCLUSIONS: The combination of vorinostat 400mg daily M-F and capecitabine 1000mg q12 M-F with radiation (30Gy in 10 fractions) was well tolerated with encouraging median overall survival.

Quantitative Magnetization Transfer Imaging of the Breast at 3.0 T: Reproducibility in Healthy Volunteers.

Quantitative magnetization transfer magnetic resonance imaging provides a means for indirectly detecting changes in the macromolecular content of tissue noninvasively. A potential application is the diagnosis and assessment of treatment response in breast cancer; however, before quantitative magnetization transfer imaging can be reliably used in such settings, the technique's reproducibility in healthy breast tissue must be established. Thus, this study aims to establish the reproducibility of the measurement of the macromolecular-to-free water proton pool size ratio (PSR) in healthy fibroglandular (FG) breast tissue. Thirteen women with no history of breast disease were scanned twice within a single scanning session, with repositioning between scans. Eleven women had appreciable FG tissue for test-retest measurements. Mean PSR values for the FG tissue ranged from 9.5% to 16.7%. The absolute value of the difference between 2 mean PSR measurements for each volunteer ranged from 0.1% to 2.1%. The 95% confidence interval for the mean difference was ±0.75%, and the repeatability value was 2.39%. These results indicate that the expected measurement variability would be ±0.75% for a cohort of a similar size and would be ±2.39% for an individual, suggesting that future studies of change in PSR in patients with breast cancer are feasible.

MR Imaging Biomarkers in Oncology Clinical Trials.

The authors discuss eight areas of quantitative MR imaging that are currently used (RECIST, DCE-MR imaging, DSC-MR imaging, diffusion MR imaging) in clinical trials or emerging (CEST, elastography, hyperpolarized MR imaging, multiparameter MR imaging) as promising techniques in diagnosing cancer and assessing or predicting response of cancer to therapy. Illustrative applications of the techniques in the clinical setting are summarized before describing the current limitations of the methods.

Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model.

Although there are considerable data on the use of mathematical modeling to describe tumor growth and response to therapy, previous approaches are often not of the form that can be easily applied to clinical data to generate testable predictions in individual patients. Thus, there is a clear need to develop and apply clinically relevant oncologic models that are amenable to available patient data and yet retain the most salient features of response prediction. In this study we show how a biomechanical model of tumor growth can be initialized and constrained by serial patient-specific magnetic resonance imaging data, obtained at two time points early in the course of therapy (before initiation and following one cycle of therapy), to predict the response for individual patients with breast cancer undergoing neoadjuvant therapy. Using our mechanics coupled modeling approach, we are able to predict, after the first cycle of therapy, breast cancer patients that would eventually achieve a complete pathologic response and those who would not, with receiver operating characteristic area under the curve (AUC) of 0.87, sensitivity of 92%, and specificity of 84%. Our approach significantly outperformed the AUCs achieved by standard (i.e., not mechanically coupled) reaction-diffusion predictive modeling (0.75), simple analysis of the tumor cellularity estimated from imaging data (0.73), and the Response Evaluation Criteria in Solid Tumors (0.71). Thus, we show the potential for mathematical model prediction for use as a prognostic indicator of response to therapy. The work indicates the considerable promise of image-driven biophysical modeling for predictive frameworks within therapeutic applications.

Comparison of prone versus supine 18F-FDG-PET of locally advanced breast cancer: Phantom and preliminary clinical studies.

PURPOSE: Previous studies have demonstrated how imaging of the breast with patients lying prone using a supportive positioning device markedly facilitates longitudinal and/or multimodal image registration. In this contribution, the authors' primary objective was to determine if there are differences in the standardized uptake value (SUV) derived from [(18)F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) in breast tumors imaged in the standard supine position and in the prone position using a specialized positioning device. METHODS: A custom positioning device was constructed to allow for breast scanning in the prone position. Rigid and nonrigid phantom studies evaluated differences in prone and supine PET. Clinical studies comprised 18F-FDG-PET of 34 patients with locally advanced breast cancer imaged in the prone position (with the custom support) followed by imaging in the supine position (without the support). Mean and maximum values (SUVpeak and SUVmax, respectively) were obtained from tumor regions-of-interest for both positions. Prone and supine SUV were linearly corrected to account for the differences in 18F-FDG uptake time. Correlation, Bland-Altman, and nonparametric analyses were performed on uptake time-corrected and uncorrected data. RESULTS: SUV from the rigid PET breast phantom imaged in the prone position with the support device was 1.9% lower than without the support device. In the nonrigid PET breast phantom, prone SUV with the support device was 5.0% lower than supine SUV without the support device. In patients, the median (range) difference in uptake time between prone and supine scans was 16.4 min (13.4-30.9 min), which was significantly-but not completely-reduced by the linear correction method. SUVpeak and SUVmax from prone versus supine scans were highly correlated, with concordance correlation coefficients of 0.91 and 0.90, respectively. Prone SUVpeak and SUVmax were significantly lower than supine in both original and uptake time-adjusted data across a range of index times (P < < 0.0001, Wilcoxon signed rank test). Before correcting for uptake time differences, Bland-Altman analyses revealed proportional bias between prone and supine measurements (SUVpeak and SUVmax) that increased with higher levels of FDG uptake. After uptake time correction, this bias was significantly reduced (P < 0.01). Significant prone-supine differences, with regard to the spatial distribution of lesions relative to isocenter, were observed between the two scan positions, but this was poorly correlated with the residual (uptake time-corrected) prone-supine SUVpeak difference (P = 0.78). CONCLUSIONS: Quantitative 18F-FDG-PET/CT of the breast in the prone position is not deleteriously affected by the support device but yields SUV that is consistently lower than those obtained in the standard supine position. SUV differences between scans arising from FDG uptake time differences can be substantially reduced, but not removed entirely, with the current correction method. SUV from the two scan orientations is quantitatively different and should not be assumed equivalent or interchangeable within the same subject. These findings have clinical relevance in that they underscore the importance of patient positioning while scanning as a clinical variable that must be accounted for with longitudinal PET measurement, for example, in the assessment of treatment response.

Realization of a biomechanical model-assisted image guidance system for breast cancer surgery using supine MRI.

PURPOSE: Unfortunately, the current re-excision rates for breast conserving surgeries due to positive margins average 20-40 %. The high re-excision rates arise from difficulty in localizing tumor boundaries intraoperatively and lack of real-time information on the presence of residual disease. The work presented here introduces the use of supine magnetic resonance (MR) images, digitization technology, and biomechanical models to investigate the capability of using an image guidance system to localize tumors intraoperatively. METHODS: Preoperative supine MR images were used to create patient-specific biomechanical models of the breast tissue, chest wall, and tumor. In a mock intraoperative setup, a laser range scanner was used to digitize the breast surface and tracked ultrasound was used to digitize the chest wall and tumor. Rigid registration combined with a novel nonrigid registration routine was used to align the preoperative and intraoperative patient breast and tumor. The registration framework is driven by breast surface data (laser range scan of visible surface), ultrasound chest wall surface, and MR-visible fiducials. Tumor localizations by tracked ultrasound were only used to evaluate the fidelity of aligning preoperative MR tumor contours to physical patient space. The use of tracked ultrasound to digitize subsurface features to constrain our nonrigid registration approach and to assess the fidelity of our framework makes this work unique. Two patient subjects were analyzed as a preliminary investigation toward the realization of this supine image-guided approach. RESULTS: An initial rigid registration was performed using adhesive MR-visible fiducial markers for two patients scheduled for a lumpectomy. For patient 1, the rigid registration resulted in a root-mean-square fiducial registration error (FRE) of 7.5 mm and the difference between the intraoperative tumor centroid as visualized with tracked ultrasound imaging and the registered preoperative MR counterpart was 6.5 mm. Nonrigid correction resulted in a decrease in FRE to 2.9 mm and tumor centroid difference to 5.5 mm. For patient 2, rigid registration resulted in a FRE of 8.8 mm and a 3D tumor centroid difference of 12.5 mm. Following nonrigid correction for patient 2, the FRE was reduced to 7.4 mm and the 3D tumor centroid difference was reduced to 5.3 mm. CONCLUSION: Using our prototype image-guided surgery platform, we were able to align intraoperative data with preoperative patient-specific models with clinically relevant accuracy; i.e., tumor centroid localizations of approximately 5.3-5.5 mm.

Prone Versus Supine Breast FDG-PET/CT for Assessing Locoregional Disease Distribution in Locally Advanced Breast Cancer.

RATIONALE AND OBJECTIVES: Prone (18)F fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) may have advantages for breast imaging because of improved separation of deep anatomic structures. There are limited data on whether prone and supine FDG-PET/CT provide similar information regarding breast and axillary disease in the setting of locally advanced breast cancer (LABC). The purpose of this study was to compare the information on locoregional disease distribution provided by prone versus supine FDG-PET in newly diagnosed LABC. MATERIALS AND METHODS: In an Institutional Review Board-approved prospective trial, 24 patients with newly diagnosed LABC underwent both supine and prone FDG-PET/CT at the same scanning session. Three readers performed an independent review of all scans and categorized the locoregional disease distribution as breast only (BO)-unifocal, BO-multifocal, BO-multicentric, or breast + axillary involvement. For breast + axillary disease, the readers also assessed the number of involved axillary lymph nodes. Interobserver discrepancies were resolved at a consensus reading session. RESULTS: Two scanning sessions were excluded because the prone scan had omitted part of the axilla from the field of view. In the remaining 22 patients, the consensus categorization of anatomic disease distribution was concordant between prone and supine scanning in 21 patients (linear kappa 0.91, 95% confidence interval [0.79-1]). In the 16 patients with breast + axillary disease, equal numbers of involved lymph nodes were identified on prone and supine scanning in 12 patients, whereas in the remaining four patients, prone scanning resulted in a higher number of visualized lymph nodes. CONCLUSIONS: Prone and supine FDG-PET/CT provided statistically identical information on locoregional disease distribution in LABC. However, prone scanning may perform better than supine for assessing the number of involved lymph nodes. Prone FDG-PET/CT may be useful in future clinical and research efforts, including hybrid PET-magnetic resonance imaging (MRI) applications.

DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis.

We present a fast, validated, open-source toolkit for processing dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data. We validate it against the Quantitative Imaging Biomarkers Alliance (QIBA) Standard and Extended Tofts-Kety phantoms and find near perfect recovery in the absence of noise, with an estimated 10-20× speedup in run time compared to existing tools. To explain the observed trends in the fitting errors, we present an argument about the conditioning of the Jacobian in the limit of small and large parameter values. We also demonstrate its use on an in vivo data set to measure performance on a realistic application. For a 192 × 192 breast image, we achieved run times of <1 s. Finally, we analyze run times scaling with problem size and find that the run time per voxel scales as O(N (1.9)), where N is the number of time points in the tissue concentration curve. DCEMRI.jl was much faster than any other analysis package tested and produced comparable accuracy, even in the presence of noise.

Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer.

OBJECTIVES: The purpose of this study was to determine whether multiparametric magnetic resonance imaging (MRI) using dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI), obtained before and after the first cycle of neoadjuvant chemotherapy (NAC), is superior to single-parameter measurements for predicting pathologic complete response (pCR) in patients with breast cancer. MATERIALS AND METHODS: Patients with stage II/III breast cancer were enrolled in an institutional review board-approved study in which 3-T DCE-MRI and DWI data were acquired before (n = 42) and after 1 cycle (n = 36) of NAC. Estimates of the volume transfer rate (K), extravascular extracellular volume fraction (ve), blood plasma volume fraction (vp), and the efflux rate constant (kep = K/ve) were generated from the DCE-MRI data using the Extended Tofts-Kety model. The apparent diffusion coefficient (ADC) was estimated from the DWI data. The derived parameter kep/ADC was compared with single-parameter measurements for its ability to predict pCR after the first cycle of NAC. RESULTS: The kep/ADC after the first cycle of NAC discriminated patients who went on to achieve a pCR (P < 0.001) and achieved a sensitivity, specificity, positive predictive value, and area under the receiver operator curve (AUC) of 0.92, 0.78, 0.69, and 0.88, respectively. These values were superior to the single parameters kep (AUC, 0.76) and ADC (AUC, 0.82). The AUCs between kep/ADC and kep were significantly different on the basis of the bootstrapped 95% confidence intervals (0.018-0.23), whereas the AUCs between kep/ADC and ADC trended toward significance (-0.11 to 0.24). CONCLUSIONS: The multiparametric analysis of DCE-MRI and DWI was superior to the single-parameter measurements for predicting pCR after the first cycle of NAC.