Radiation therapy is crucial in cancer treatment. Experienced experts typically iteratively generate high-quality dose distribution maps, forming the basis for excellent radiation therapy plans. Therefore, automated prediction of dose distribution maps is significant in expediting the treatment process and providing a better starting point for developing radiation therapy plans. With the remarkable results of diffusion models in predicting high-frequency regions of dose distribution maps, dose prediction methods based on diffusion models have been extensively studied. However, existing methods mainly utilize CNNs or Transformers as denoising networks. CNNs lack the capture of global receptive fields, resulting in suboptimal prediction performance. Transformers excel in global modeling but face quadratic complexity with image size, resulting in significant computational overhead. To tackle these challenges, we introduce a novel diffusion model, MD-Dose, based on the Mamba architecture for predicting radiation therapy dose distribution in thoracic cancer patients. In the forward process, MD-Dose adds Gaussian noise to dose distribution maps to obtain pure noise images. In the backward process, MD-Dose utilizes a noise predictor based on the Mamba to predict the noise, ultimately outputting the dose distribution maps. Furthermore, We develop a Mamba encoder to extract structural information and integrate it into the noise predictor for localizing dose regions in the planning target volume (PTV) and organs at risk (OARs). Through extensive experiments on a dataset of 300 thoracic tumor patients, we showcase the superiority of MD-Dose in various metrics and time consumption.

Purpose. To investigate the clinical value of double contrast-enhanced ultrasound (DCEUS) combined with dynamic contrastenhanced multislice CT (MSCT) in preoperative T staging of gastric cancer (GC). Methods. 206 patients with GC confirmed by preoperative gastroscopy from February 2019 to February 2021 were collected, all patients were examined by DCEUS and dynamic contrast-enhanced MSCT before operation, and the invasion depth (T staging) of GC was evaluated. 0e diagnosis results of DCEUS, dynamic contrast-enhanced MSCT, and combined diagnosis of DCEUS and MSCT methods (D&M method) were compared with the pathological staging results (gold standard). Results. 0e correct diagnosis rate of MSCT was 27.27% in T1 staging, 55.56% in T2 staging, 42.11% in T3 staging, 59.29% in T4 staging, and 55.34% in summation.0e correct diagnosis rate of DCEUS was 90.91% in T1 staging, 88.89% in T2 staging, 78.95% in T3 staging, 82.86% in T4 staging, and 83.98% in summation. 0e correct diagnosis rate of the D&Mmethod was 100.00% in T1 staging, 94.44% in T2 staging, 89.47% in T3 staging, 93.57% in T4 staging, and 93.69% in summation. 0e D&M method had higher correct diagnosis rate than MSCT or DCEUS alone, the correct diagnosis rate of the D&Mmethod in T1, T2, T3, and T4 staging was significantly higher than that of MSCT (P< 0.05).0e correct diagnosis rate of the D&Mmethod in T1, T3, and T4 was significantly higher than that of DCEUS (P< 0.05). 0e Youden index of preoperative T1, T2, T3, and T4 staging of GC by the D&M method was 99.49%, 94.44%, 84.13%, and 90.54%, respectively, and the Kappa values of these were 0.954, 0.966, 0.707, and 0.881, respectively. Conclusions. Dynamic contrastenhanced MSCT combined with DCEUS in the diagnosis of preoperative cT staging of GC has more validity, reliability, and revenue than the using of MSCT or DCEUS alone, which is an image evaluation method worthy of clinical promotion.

As we begin to consider modeling large, realistic 3D building scenes, it becomes necessary to consider a more compact representation over the polygonal mesh model. Due to the large amounts of annotated training data, which is costly to obtain, we leverage synthetic data to train our system for the satellite image domain. By utilizing the synthetic data, we formulate the building decomposition as an application of instance segmentation and primitive fitting to decompose a building into a set of primitive shapes. Experimental results on WorldView-3 satellite image dataset demonstrate the effectiveness of our 3D building modeling approach.

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study.

Comparative preliminary analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data collected in the International Breast MR Consortium 6883 multicenter trial was performed to distinguish benign and malignant breast tumors. Prebiopsy DCE-MRI data from 45 patients with suspicious breast lesions were obtained. Semiquantitative mean signal-enhancement ratio ([Formula: see text]) was calculated for all lesions, and quantitative pharmacokinetic, parameters [Formula: see text], [Formula: see text], and [Formula: see text], were calculated for the subset with available [Formula: see text] maps ([Formula: see text]). Diagnostic performance was estimated for DCE-MRI parameters and compared to standard clinical MRI assessment. Quantitative and semiquantitative metrics discriminated benign and malignant lesions, with receiver operating characteristic area under the curve (AUC) values of 0.71, 0.70, and 0.82 for [Formula: see text], [Formula: see text], and [Formula: see text], respectively ([Formula: see text]). At equal 94% sensitivity, the specificity and positive predictive value of [Formula: see text] (53% and 63%, respectively) and Ktrans (42% and 58%) were higher than clinical MRI assessment (32% and 54%). A multivariable model combining [Formula: see text] and clinical MRI assessment had an AUC value of 0.87. Quantitative pharmacokinetic and semiquantitative analyses of DCE-MRI improves discrimination of benign and malignant breast tumors, with our findings suggesting higher diagnostic accuracy using [Formula: see text]. [Formula: see text] has potential to help reduce unnecessary biopsies resulting from routine breast imaging.

Fractional Flow Reserve (FFR), the ratio of arterial pressure distal to a coronary lesion to the proximal pressure, is indicative of its hemodynamic significance. This quantity can be determined from invasive measurements made with a catheter, or by using computational methods incorporating models of the the coronary vasculature. One of the inputs needed by a model-based approach for estimating FFR from Computed Tomography Angiography (CTA) images (denoted FFR-CT) is the geometry of the coronary arteries, which requires segmentation of the coronary lumen. Several algorithms have been proposed for coronary lumen segmentation, including the recent application of machine learning techniques. For evaluating these algorithms or for training machine learning algorithms, manual segmentation of the lumen has been considered as ground truth. However, since there is inter-subject variability in manual segmentation, it would be useful to first assess the extent to which this variability affects the predicted FFR values. In the current study, we evaluated the impact of inter-subject variability in manual segmentation on computed FFR, using datasets with three different manual segmentations provided as part of the Rotterdam Coronary Artery Evaluation Framework. FFR was computed using a coronary blood flow model. Our results indicate that variability in manual segmentations on FFR estimates depend on the FFR value. For FFR ≥ 0.97, variability in manual segmentations does not impact FFR estimates, while, for lower FFR values, the variability in manual segmentations leads to significant variability in FFR. The results of this study indicate that researchers should exercise caution when treating manual segmentations as ground truth for estimating FFR from CTA images.

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.

Abstract Introduction: We investigate the ability of multi-parametric, voxel-based characterizations of tumor heterogeneity from magnetic resonance imaging (MRI) to predict the response of breast cancer patients to neoadjuvant therapy (NAT). In particular, we use high-dimensional analysis of the longitudinal changes in the vascular and cellular characteristics provided by quantitative dynamic contrast enhanced MRI (DCE-MRI) to predict response. Methods: DCE-MRI data was acquired from 34 patients with stage II/III breast cancer before initiating NAT (t1) and after one cycle of NAT (t2). Pathological complete response (pCR) and non-pCR was defined at the time of surgery. Non-pCR patients were further subdivided into partial response (>30% decrease in tumor volume), progressive disease (PD, >20% increase in tumor volume), and stable disease according to the Response Evaluation in Solid Tumors (i.e., RECIST) criteria. For each tumor voxel, DCE-MRI data was modeled to extract the extravascular, extracellular volume fraction, ve, the plasma volume fraction, vp, and the volume transfer coefficients, Ktrans and kep, which correspond to the rate of wash-in and wash-out of contrast agent, respectively. Multi-parametric voxel-based maps of physiological parameters Ktrans, kep, ve, and vp were used to identify different tumor subpopulations. Dimension reduction was completed using t-distributed Stochastic Neighbor Embedding (t-SNE) and subpopulations were identified using DBSCAN clustering. Using the t-SNE generated low-dimensional maps, a subset subpopulations were analyzed based on cluster differences between groups at t2. Based on MRI parameter values, the selected voxel subpopulations fell into one of two categories: high vascularity-high cellularity (HV-HC: high vp , Ktrans , kep values, low ve values) and low vascularity-low cellularity (LV-LC: low vp , Ktrans , kep values, high ve values). The contribution of each cluster to overall tumor volume was analyzed for each patient and quantified as percent tumor volume. Results: No differences were observed between patient groups at t1. At t2, patients with pCR revealed increased percent tumor volumes containing LV-LC subpopulations, compared to PD patients (p = 0.03). Furthermore, PD patients demonstrated significant increase in percent tumor volumes with HV-HC subpopulations, compared to pCR patients (p=0.01). Compared to pCR patients, combined non-pCR patients showed decreased percent tumor volumes of LV-LC subpopulations, trending toward significance (p=0.06). Conclusion: The results indicate that analysis of high-dimensional parameter maps derived from quantitative MRI can be utilized to characterize intratumoral heterogeneity and identify subpopulations of tumor response within patients. These characterizations can potentially be used to define and model tumor habitats and ultimately predict patient treatment response. Citation Format: Anum Syed, Anna G. Sorace, Stephanie L. Barnes, Lori Arlinghaus, Xia Li, Thomas E. Yankeelov. Assessing heterogeneity in DCE-MRI data of breast cancer to predict treatment response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3941. doi:10.1158/1538-7445.AM2017-3941

The cerebral cortex is conventionally divided into a number of domains based on cytoarchitectural features. Diffusion tensor imaging (DTI) enables noninvasive parcellation of the cortex based on white matter connectivity patterns. However, the correspondence between DTI-connectivity-based and cytoarchitectural parcellation has not been systematically established. In this study, we compared histological parcellation of New World monkey neocortex to DTI-connectivity-based classification and clustering in the same brains. First, we used supervised classification to parcellate parieto-frontal cortex based on DTI tractograms and the cytoarchitectural prior (obtained using Nissl staining). We performed both within and across sample classification, showing reasonable classification performance in both conditions. Second, we used unsupervised clustering to parcellate the cortex and compared the clusters to the cytoarchitectonic standard. We then explored the similarities and differences with several post-hoc analyses, highlighting underlying principles that drive the DTI-connectivity-based parcellation. The differences in parcellation between DTI-connectivity and Nissl histology probably represent both DTI's bias toward easily-tracked bundles and true differences between cytoarchitectural and connectivity defined domains. DTI tractograms appear to cluster more according to functional networks, rather than mapping directly onto cytoarchitectonic domains. Our results show that caution should be used when DTI-tractography classification, based on data from another brain, is used as a surrogate for cytoarchitectural parcellation.

This study explores potential multi-parametric magnetic resonance imaging (mpMRI) biomarkers for improved non-invasive prostate cancer (PCa) management, particularly for clinically significant PCa lesions that are missed by the current standard of care mpMRI exams. T2-weighted MRI and diffusion weighted imaging scans were acquired for subjects with Gleason Score 6 to 9 PCa lesions prior to radical prostatectomy. Lesions were examined by experienced clinicians and categorized as those visible on routine mpMRI radiological assessment, viz. mpMRI-detectable (mpMRI-d) and those that could not be satisfactorily diagnosed using routine mpMRI, viz. mpMRI-undetectable (mpMRI-u). A dense set of voxel-wise mpMRI features quantifying local spatial distribution, texture and statistical properties in the prostate were computed and analyzed for their potential to differentiate disease from healthy tissue. Feature selection and training were performed to build classifiers to produce voxel-wise probability-of-malignancy maps over the entire gland to characterize the extent of PCa. Receiver operating characteristic analysis on a cohort of 48 mpMRI exams showed that our classifier improves the sensitivity of the detection of prostate lesions by over 38% for the whole cohort and 124% for the mpMRI-u lesions with only a modest loss in specificity when compared to baseline routine radiological mpMRI assessment.

Objective: To identify features from multimodal MRI data that correlate with clinical symptoms in a mild traumatic brain injury (mTBI) population, with the goal of predicting outcomes. Background: The pathology underlying neurocognitive change after mTBI is poorly understood. Multimodal MRI was used to study the impact of mTBI to the functional and structural organization of the brain. In this study, global, local, and network-specific metrics were extracted from MRI data and correlated to clinical symptoms. Design/Methods: 86 uncomplicated mTBI subjects and 26 controls were recruited. All subjects and controls underwent neurological examination and MRI scans - resting state fMRI (rs-fMRI), T1, T2 FLAIR, diffusion, and perfusion imaging on GE MR750 3T scanners. mTBI subjects were evaluated and scanned at four encounters: 3 days, 7 days, 1 month, 3 months post injury. Controls were scanned twice, one week apart. Pipelines were developed for analysis of rs-fMRI, diffusion, and volumetry of brain structures. Functional and structural networks were computed, and connectivity metrics extracted. The multishell diffusion data allowed computation of radial and orientational quantities, including diffusional kurtosis (DK, a measure of axonal integrity), fiber tractography, and conventional diffusion tensor imaging metrics. These imaging features were correlated with clinical symptom severity score using the SCAT2. Results: Voxel and network-based fMRI metrics (eg fractional amplitude of low frequency fluctuations, p<0.02) were found to correlate with symptom scores. A subset of functional networks displayed broader spatial spread in mTBI subjects than in controls. Whole white-matter DK and FA histograms did not correlate significantly with symptoms; voxel-based analysis is underway (40 subjects). Finally, decreased whole brain, hippocampus, thalamus, caudate, and amygdala volumes correlated with symptom scores only at encounter 4 (p<0.05). Conclusions: A longitudinal, multimodal MRI study in mTBI population was described and statistically significant correlations between clinical measures and select MRI metrics reported.

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.

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 [18F]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.

1316 Objectives To determine: 1) the ability of FDG-PET/CT to predict, early in neoadjuvant treatment (NAT) of locally advanced breast cancer (LABC), pathologic complete response (pCR); and 2) whether the predictive performance of FDG-PET/CT differs according to ER, PR, and/or HER2 status. Methods In this prospective, IRB-approved study (NCT01222416) 37 patients (pts) with LABC received up to six cycles of NAT prior to surgery. FDG-PET/CT was performed prone before (t1) and after the first cycle (t2). ROIs were analyzed using lean body mass-corrected SUV (SULpeak) at t1 and t2 (and their percentage difference, %[t2-t1]/t1) as primary endpoints. Receptor subgroups included HER2+ (n=17), ER+ and/or PR+/HER2- (n=7), and ER-/PR-/HER2- (n=12). Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curve (AUC) and positive and negative predictive value (PPV and NPV, respectively) were calculated. Group AUCs were statistically compared via bootstrapping with 500 replicates. Results Thirty-seven pts completed t1 and surgery; 30 (81%) completed t2 (12±6 days after first dose). Twelve pts (32%) achieved pCR; 25 (68%) were non-responders (NR). Table 1 summarizes the ROC findings. No significant AUC differences between the receptor subgroups relative to control (All Pts) were observed. Conclusions Initial findings suggest that FDG-PET/CT, performed within the first cycle of NAT, is not able to reliably predict pathologic response in LABC. These results contrast with those from recent studies, perhaps owing to the small sample size and/or short t1-to-t2 interval. Research Support NCI U01 CA142565, NCI 1U01 CA174706, NCI P50 CA098131, and NCI P30 CA68485; Kleberg Foundation

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 10-20x 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 x 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(N1.9), 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.

PURPOSE The authors propose a method whereby serially acquired DCE-MRI, DW-MRI, and FDG-PET breast data sets can be spatially and temporally coregistered to enable the comparison of changes in parameter maps at the voxel level. METHODS First, the authors aligned the PET and MR images at each time point rigidly and nonrigidly. To register the MR images longitudinally, the authors extended a nonrigid registration algorithm by including a tumor volume-preserving constraint in the cost function. After the PET images were aligned to the MR images at each time point, the authors then used the transformation obtained from the longitudinal registration of the MRI volumes to register the PET images longitudinally. The authors tested this approach on ten breast cancer patients by calculating a modified Dice similarity of tumor size between the PET and MR images as well as the bending energy and changes in the tumor volume after the application of the registration algorithm. RESULTS The median of the modified Dice in the registered PET and DCE-MRI data was 0.92. For the longitudinal registration, the median tumor volume change was -0.03% for the constrained algorithm, compared to -32.16% for the unconstrained registration algorithms (p = 8 × 10(-6)). The medians of the bending energy were 0.0092 and 0.0001 for the unconstrained and constrained algorithms, respectively (p = 2.84 × 10(-7)). CONCLUSIONS The results indicate that the proposed method can accurately spatially align DCE-MRI, DW-MRI, and FDG-PET breast images acquired at different time points during therapy while preventing the tumor from being substantially distorted or compressed.

Multi-parametric Magnetic Resonance Imaging, and specifically Dynamic Contrast Enhanced (DCE) MRI, play increasingly important roles in detection and staging of prostate cancer (PCa). One of the actively investigated approaches to DCE MRI analysis involves pharmacokinetic (PK) modeling to extract quantitative parameters that may be related to microvascular properties of the tissue. It is well-known that the prescribed arterial blood plasma concentration (or Arterial Input Function, AIF) input can have significant effects on the parameters estimated by PK modeling. The purpose of our study was to investigate such effects in DCE MRI data acquired in a typical clinical PCa setting. First, we investigated how the choice of a semi-automated or fully automated image-based individualized AIF (iAIF) estimation method affects the PK parameter values; and second, we examined the use of method-specific averaged AIF (cohort-based, or cAIF) as a means to attenuate the differences between the two AIF estimation methods. Two methods for automated image-based estimation of individualized (patient-specific) AIFs, one of which was previously validated for brain and the other for breast MRI, were compared. cAIFs were constructed by averaging the iAIF curves over the individual patients for each of the two methods. Pharmacokinetic analysis using the Generalized kinetic model and each of the four AIF choices (iAIF and cAIF for each of the two image-based AIF estimation approaches) was applied to derive the volume transfer rate (K(trans)) and extravascular extracellular volume fraction (ve) in the areas of prostate tumor. Differences between the parameters obtained using iAIF and cAIF for a given method (intra-method comparison) as well as inter-method differences were quantified. The study utilized DCE MRI data collected in 17 patients with histologically confirmed PCa. Comparison at the level of the tumor region of interest (ROI) showed that the two automated methods resulted in significantly different (p<0.05) mean estimates of ve, but not of K(trans). Comparing cAIF, different estimates for both ve, and K(trans) were obtained. Intra-method comparison between the iAIF- and cAIF-driven analyses showed the lack of effect on ve, while K(trans) values were significantly different for one of the methods. Our results indicate that the choice of the algorithm used for automated image-based AIF determination can lead to significant differences in the values of the estimated PK parameters. K(trans) estimates are more sensitive to the choice between cAIF/iAIF as compared to ve, leading to potentially significant differences depending on the AIF method. These observations may have practical consequences in evaluating the PK analysis results obtained in a multi-site setting.

ObjectivesThe 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 MethodsPatients 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 (Ktrans), extravascular extracellular volume fraction (ve), blood plasma volume fraction (vp), and the efflux rate constant (kep = Ktrans/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. ResultsThe 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). ConclusionsThe multiparametric analysis of DCE-MRI and DWI was superior to the single-parameter measurements for predicting pCR after the first cycle of NAC.

The purpose of this study is to investigate the ability of multivariate analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) parametric maps, obtained early in the course of therapy, to predict which patients will achieve pathologic complete response (pCR) at the time of surgery. Thirty-three patients underwent DCE-MRI (to estimate K (trans), v e, k ep, and v p) and DW-MRI [to estimate the apparent diffusion coefficient (ADC)] at baseline (t 1) and after the first cycle of neoadjuvant chemotherapy (t 2). Four analyses were performed and evaluated using receiver-operating characteristic (ROC) analysis to test their ability to predict pCR. First, a region of interest (ROI) level analysis input the mean K (trans), v e, k ep, v p, and ADC into the logistic model. Second, a voxel-based analysis was performed in which a longitudinal registration algorithm aligned serial parameters to a common space for each patient. The voxels with an increase in k ep, K (trans), and v p or a decrease in ADC or v e were then detected and input into the regression model. In the third analysis, both the ROI and voxel level data were included in the regression model. In the fourth analysis, the ROI and voxel level data were combined with selected clinical data in the regression model. The overfitting-corrected area under the ROC curve (AUC) with 95% confidence intervals (CIs) was then calculated to evaluate the performance of the four analyses. The combination of k ep, ADC ROI, and voxel level data achieved the best AUC (95% CI) of 0.87 (0.77-0.98).