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Purpose To investigate diagnostic performance by using a deep learning method with a convolutional neural network (CNN) for the differentiation of liver masses at dynamic contrast agent-enhanced computed tomography (CT). Materials and Methods This clinical retrospective study used CT image sets of liver masses over three phases (noncontrast-agent enhanced, arterial, and delayed). Masses were diagnosed according to five categories (category A, classic hepatocellular carcinomas [HCCs]; category B, malignant liver tumors other than classic and early HCCs; category C, indeterminate masses or mass-like lesions [including early HCCs and dysplastic nodules] and rare benign liver masses other than hemangiomas and cysts; category D, hemangiomas; and category E, cysts). Supervised training was performed by using 55 536 image sets obtained in 2013 (from 460 patients, 1068 sets were obtained and they were augmented by a factor of 52 [rotated, parallel-shifted, strongly enlarged, and noise-added images were generated from the original images]). The CNN was composed of six convolutional, three maximum pooling, and three fully connected layers. The CNN was tested with 100 liver mass image sets obtained in 2016 (74 men and 26 women; mean age, 66.4 years ± 10.6 [standard deviation]; mean mass size, 26.9 mm ± 25.9; 21, nine, 35, 20, and 15 liver masses for categories A, B, C, D, and E, respectively). Training and testing were performed five times. Accuracy for categorizing liver masses with CNN model and the area under receiver operating characteristic curve for differentiating categories A-B versus categories C-E were calculated. Results Median accuracy of differential diagnosis of liver masses for test data were 0.84. Median area under the receiver operating characteristic curve for differentiating categories A-B from C-E was 0.92. Conclusion Deep learning with CNN showed high diagnostic performance in differentiation of liver masses at dynamic CT.

BACKGROUND: Muscle injury may result in damage to the vasculature, rendering it unable to meet the metabolic demands of muscle regeneration and healing. Therefore, therapies frequently aim to maintain, restore, or improve blood supply to the injured muscle. Although there are several options to assess the vascular outcomes of these therapies, few are capable of spatially assessing perfusion in large volumes of tissue. QUESTIONS/PURPOSES: Can dynamic contrast-enhanced CT (DCE-CT) imaging acquired with a clinical CT scanner be used in a rat model to quantify perfusion in the anterior tibialis muscle at spatially relevant volumes, as assessed by (1) the blood flow rate and tissue blood volume in the muscle after three levels of muscle stimulation (low, medium, and maximum) relative to baseline as determined by the non-stimulated contralateral leg; and (2) how do these measurements compare with those obtained by the more standard approach of microsphere perfusion? METHODS: The right anterior tibialis muscles of adult male Sprague Dawley rats were randomized to low- (n = 10), medium- (n = 6), or maximum- (n = 3) level (duty cycles of 2.5%, 5.0%, and 20%, respectively) nerve electrode coupled muscle stimulation directly followed by DCE-CT imaging. Tissue blood flow and blood volume maps were created using commercial software and volumetrically measured using NIH software. Although differences in blood flow were detectable across the studied levels of muscle stimulation, a review of the evidence suggested the absolute blood flow quantified was underestimated. Therefore, at a later date, a separate set of adult male Sprague Dawley rats were randomized for microsphere perfusion (n = 7) to define blood flow in the animal model with an accepted standard. With this technique, intra-arterial particles sized to freely flow in blood but large enough to lodge in tissue capillaries were injected. Simultaneously, blood sampling at a fixed flow rate was simultaneously performed to provide a fixed blood flow rate sample. The tissues of interest were then explanted and assessed for the total number of particles per tissue volume. Tissue blood flow rate was then calculated based on the particle count ratio within the reference sample. Note that a tissue’s blood volume cannot be calculated with this method. Comparison analysis to the non-stimulated baseline leg was performed using two-tailed paired student t-test. An ANOVA was used to compare difference between stimulation groups. RESULTS: DCE-CT measured (mean ± SD) increasing tissue blood flow differences in stimulated anterior tibialis muscle at 2.5% duty cycle (32 ± 5 cc/100 cc/min), 5.0% duty cycle (46 ± 13 cc/100 cc/min), and 20% duty cycle (73 ± 3 cc/100 cc/min) compared with the paired contralateral non-stimulated anterior tibialis muscle (10 ± 2 cc/100 cc/min, mean difference 21 cc/100 cc/min [95% CI 17.08 to 25.69]; 9 ± 1 cc/100 cc/min, mean difference 37 cc/100 cc/min [95% CI 23.06 to 50.11]; and 11 ± 2 cc/100 cc/min, mean difference 62 cc/100 cc/min [95% CI 53.67 to 70.03]; all p < 0.001). Similarly, DCE-CT showed increasing differences in tissue blood volumes within the stimulated anterior tibialis muscle at 2.5% duty cycle (23.2 ± 4.2 cc/100 cc), 5.0% duty cycle (39.2 ± 7.2 cc/100 cc), and 20% duty cycle (52.5 ± 13.1 cc/100 cc) compared with the paired contralateral non-stimulated anterior tibialis muscle (3.4 ± 0.7 cc/100 cc, mean difference 19.8 cc/100 cc [95% CI 16.46 to 23.20]; p < 0.001; 3.5 ± 0.4 cc/100 cc, mean difference 35.7 cc/100 cc [95% CI 28.44 to 43.00]; p < 0.001; and 4.2 ± 1.3 cc/100 cc, mean difference 48.3 cc/100 cc [95% CI 17.86 to 78.77]; p = 0.010). Microsphere perfusion measurements also showed an increasing difference in tissue blood flow in the stimulated anterior tibialis muscle at 2.5% duty cycle (62 ± 43 cc/100 cc/min), 5.0% duty cycle (89 ± 52 cc/100 cc/min), and 20% duty cycle (313 ± 269 cc/100 cc/min) compared with the paired contralateral non-stimulated anterior tibialis muscle (8 ± 4 cc/100 cc/min, mean difference 55 cc/100 cc/min [95% CI 15.49 to 94.24]; p = 0.007; 9 ± 9 cc/100 cc/min, mean difference 79 cc/100 cc/min [95% CI 33.83 to 125.09]; p = 0.003; and 18 ± 18 cc/100 cc/min, mean difference 295 cc/100 cc/min [95% CI 8.45 to 580.87]; p = 0.023). Qualitative comparison between the methods suggests that DCE-CT values underestimate tissue blood flow with a post-hoc ANOVA showing DCE-CT blood flow values within the 2.5% duty cycle group (32 ± 5 cc/100 cc/min) to be less than the microsphere perfusion value (62 ± 43 cc/100 cc/min) with a mean difference of 31 cc/100 cc/min (95% CI 2.46 to 60.23; p = 0.035). CONCLUSIONS: DCE-CT using a clinical scanner is a feasible modality to measure incremental changes of blood flow and tissue blood volume within a spatially challenged small animal model. Care should be taken in studies where true blood flow values are needed, as this particular small-volume muscle model suggests true blood flow is underestimated using the specific adaptions of DCE-CT acquisition and image processing chosen. CLINICAL RELEVANCE: CT perfusion is a clinically available modality allowing for translation of science from bench to bedside. Adapting the modality to fit small animal models that are relevant to muscle healing may hasten time to clinical utility.

Background: Most solid tumors contain inadequately oxygenated (i.e., hypoxic) regions, which tend to be more aggressive and treatment resistant. Hypoxia PET allows visualization of hypoxia and may enable treatment adaptation. However, hypoxia PET imaging is expensive, time-consuming and not widely available. We aimed to predict hypoxia levels in non-small cell lung cancer (NSCLC) using more easily available imaging modalities: FDG-PET/CT and dynamic contrast-enhanced CT (DCE-CT).Material and methods: For 34 NSCLC patients, included in two clinical trials, hypoxia HX4-PET/CT, planning FDG-PET/CT and DCE-CT scans were acquired before radiotherapy. Scans were non-rigidly registered to the planning CT. Tumor blood flow (BF) and blood volume (BV) were calculated by kinetic analysis of DCE-CT images. Within the gross tumor volume, independent clusters, i. e., supervoxels, were created based on FDG-PET/CT. For each supervoxel, tumor-to-background ratios (TBR) were calculated (median SUV/aorta SUVmean) for HX4-PET/CT and supervoxel features (median, SD, entropy) for the other modalities. Two random forest models (cross-validated: 10 folds, five repeats) were trained to predict the hypoxia TBR; one based on CT, FDG, BF and BV, and one with only CT and FDG features. Patients were split in a training (trial NCT01024829) and independent test set (trial NCT01210378). For each patient, predicted, and observed hypoxic volumes (HV) (TBR> 1.2) were compared.Results: Fifteen patients (3291 supervoxels) were used for training and 19 patients (1502 supervoxels) for testing. The model with all features (RMSE training: 0.19 +/- 0.01, test: 0.27) outperformed the model with only CT and FDG-PET features (RMSE training: 0.20 +/- 0.01, test: 0.29). All tumors of the test set were correctly classified as normoxic or hypoxic (HV> 1cm(3)) by the best performing model.Conclusions: We created a data-driven methodology to predict hypoxia levels and hypoxia spatial patterns using CT, FDG-PET and DCE-CT features in NSCLC. The model correctly classifies all tumors, and could therefore, aid tumor hypoxia classification and patient stratification.

Objective To evaluate a dynamic contrast-enhanced CT-protocol and compare this method with standard of care monophasic portovenous CT for detection of colorectal liver metastases. Materials and methods A dynamic contrast-enhanced CT protocol was developed to detect liver metastasis in patients suffering from colorectal cancer, in clinical practice. The study was approved by the Hospital Ethics Committee. Written informed consent was obtained from all patients. 135 patients were included in this prospective study. All patients were naive to treatment. A dynamic contrast-enhanced CT was performed, followed by routine monophasic portovenous CT of thorax-abdomen-pelvis. 42 of these patients presented with liver metastasis. The number and lesion conspicuity of detected liver metastasis on dynamic contrast-enhanced CT using perfusion maps, was compared to monophasic CT. Results 135 patients were included, of which 42 presented with metastases to the liver. Dynamic contrast-enhanced CT outperformed portovenous CT for detection as well as conspicuity of colorectal liver metastasis, at a relatively low dose increment. Wilcoxon Signed Rank test had a p-value of 0.016 and Conclusion Dynamic contrast-enhanced CT increases the detection of colorectal liver metastasis, especially for lesions smaller than 15 mm, when compared to monophasic portovenous CT. Dynamic contrast-enhanced CT also has the added advantage of improved lesion conspicuity, which can positively influence reader confidence and clinical workflow.

Purpose: To quantify the blood velocity in the arteries of the lower extremities and to investigate the correlation with peripheral arterial disease (PAD). Methods and Materials: 7 Patients with suspicion of PAD underwent timeresolved Computed Tomography Angiography (CTA) in addition to their standard run-off CTA, consisting of 18 repeated axial acquisitions at 2s interphase delay at the level of the calves with 16cm collimation (Revolution CT, GE Healthcare). Image data was processed using in-house developed processing tools. Motion during the acquisitions was compensated by applying registration, after which the anterior tibial artery, posterior tibial artery and peroneal artery were automatically segmented by identifying large intensity changes over time. Subsequently, blood velocity was quantified in the arteries by creating time attenuation curves (TAC) of the arterial cross-sections in every slice and determining the time to peak (TTP). Results: The described method led to plausible velocity estimations in all arteries (n=18) for patients that were considered healthy (n=3). For pathological patients (n=4), only 12 out of 22 arteries could be quantified due to suboptimal bolus arrival, severe calcification or occlusions. For arteries of healthy patients, blood velocity was normally distributed (p=0.282) with a median value of 82.3 mm/s (95% CI= 55.5- 89.9 mm/s). Blood velocity in arteries of pathological patients was observed to be significantly lower, 30.7 mm/s (95% CI= 27.4-45.7 mm/s), p

Purpose To evaluate the diagnostic value of contrast-enhanced computed tomography gastrography (CE-CTG) to predict the histological type of gastric cancer. Materials and methods We analyzed 47 consecutive patients with resectable advanced gastric cancer preoperatively evaluated by multiphasic dynamic contrast-enhanced CT. Two radiologists independently reviewed the CT images and they determined the peak enhancement phase, and then measured the CT attenuation value of the gastric lesion for each phase. The histological types of gastric cancers were assigned to three groups as differentiated-type, undifferentiated-type, and mixed-type. We compared the peak enhancement phase of the three types and compared the CT attenuation values in each phase. Results The peak enhancement was significantly different between the three types of gastric cancers for both readers (reader 1, p = 0.001; reader 2, p = 0.009); most of the undifferentiated types had peak enhancement in the delayed phase. The CT attenuation values of undifferentiated type were significantly higher than those of differentiated or mixed type in the delayed phase according to both readers (reader 1, p = 0.002; reader 2, p = 0.004). Conclusion CE-CTG could provide helpful information in diagnosing the histological type of gastric cancers preoperatively.

We describe the peculiar enhancement pattern of a subepithelial colonic glomus tumour on dynamic contrast-enhanced computed tomography. Discontinuous, peripheral nodular enhancement of the colonic lesion followed by subsequent centripetal filling-in of contrast was reminiscent of a hepatic haemangioma, which has not been described with a glomus or any other subepithelial tumour of the colon. When encountered, this tumour enhancement pattern raises the possibility of a colonic glomus tumour prior to histological confirmation.

PurposeDynamic contrast-enhanced CT (DCE-CT) quantifies vasculature properties of tumors, whereas static FDG-PET/CT defines metabolic activity. Both imaging modalities are capable of showing intra-tumor heterogeneity. We investigated differences in vasculature properties within primary non-small cell lung cancer (NSCLC) tumors measured by DCE-CT and metabolic activity from FDG-PET/CT.MethodsThirty three NSCLC patients were analyzed prior to treatment. FDG-PET/CT and DCE-CT were co-registered. The tumor was delineated and metabolic activity was segmented on the FDG-PET/CT in two regions: low (

Rationale and Objective To develop and evaluate a method for predicting and reducing motion artifacts in free-breathing liver perfusion computed tomography (CT) scanning with couch shuttling and to compare tumor and liver parenchyma perfusion values. Materials and Methods Thirty patients (23 males, 7 females, median age of 74 years) with primary or metastatic intrahepatic tumors underwent dynamic contrast enhanced CT scans with axial shuttling. A semiautomatic respiratory motion correction algorithm was applied to align the acquired images along the z-axis. Perfusion maps were generated using the dual-input Johnson-Wilson model. Root mean squared deviation (RMSD) maps of the model fit to the pixel time-density curves were calculated. Results Precorrection RMSD correlated positively with magnitude of change in functional values resulting from motion. Blood flow, arterial blood flow, and permeability surface product were significantly increased in tumor compared to normal tissue (P < .05), blood volume was significantly reduced in tumor compared to normal tissue (P < .05). In a subgroup of patients with high-amplitude motion significant difference was observed between uncorrected and motion correction blood flow maps. Conclusions Patients can breathe freely during hepatic perfusion imaging if retrospective motion correction is applied to reduce motion artifacts. RMSD provides a regional assessment of motion induced artifacts in liver perfusion maps.

The aim of this systematic review is to provide an overview of the use of Dynamic Contrast-enhanced Computed Tomography (DCE-CT) in patients with pancreatic cancer. This study was composed according to the PRISMA guidelines 2009. The literature search was conducted in PubMed, Cochrane Library, EMBASE, and Web of Science databases to identify all relevant publications. The QUADAS-2 tool was implemented to assess the risk of bias and applicability concerns of each included study. The initial literature search yielded 483 publications. Thirteen articles were included. Articles were categorized into three groups: nine articles concerning primary diagnosis or staging, one article about tumor response to treatment, and three articles regarding scan techniques. In exocrine pancreatic tumors, measurements of blood flow in eight studies and blood volume in seven studies were significantly lower in tumor tissue, compared with measurements in pancreatic tissue outside of tumor, or normal pancreatic tissue in control groups of healthy volunteers. The studies were heterogeneous in the number of patients enrolled and scan protocols. Perfusion parameters measured and analyzed by DCE-CT might be useful in the investigation of characteristic vascular patterns of exocrine pancreatic tumors. Further clinical studies are desired for investigating the potential of DCE-CT in pancreatic tumors.

International audience; Toward an efficient clinical management of hepatocellular carcinoma (HCC), we propose a classification framework dedicated to tumor necrosis rate estimation from dynamic contrast-enhanced CT scans. Based on machine learning, it requires weak interaction efforts to segment healthy, active and necrotic liver tissues. Our contributions are two-fold. First, we apply random forest (RF) on supervoxels using multi-phase supervoxel-based features that discriminate tissues based on their dynamic in response to contrast agent injection. Second, we extend this technique in a hierarchical multi-scale fashion to deal with multiple spatial extents and appearance heterogeneity. It translates in an adaptive data sampling scheme combining RF and hierarchical multi-scale tree resulting from recursive supervoxel decomposition. By concatenating multi-phase features across the hierarchical multi-scale tree to describe leaf supervoxels, we enable RF to automatically infer the most informative scales without defining any explicit rules on how to combine them. Assessment on clinical data confirms the benefits of multi-phase information embedded in a multi-scale supervoxel representation for HCC tumor segmentation. Dedicated but not limited only to HCC management, both contributions reach further steps toward more accurate multi-label tissue classification.

Breast cancer is a complex disease and molecular imaging may contribute to better management through providing new insight for early detection. Fluorodeoxyglucose (FDG)-PET/CT has made great strides as a functional anatomic technique and recently gained attention in the diagnosis, staging, and follow-up of breast cancer. FDG-PET and CT complement each other's strengths in integrated FDGPET/CT. One-stop-shop whole-body FDG-PET/CT coupled with integrated FDG-PET/CT mammography has also been advocated.

Incidental indeterminate solitary pulmonary nodules (SPN) that measure less than 3 cm in size are an increasingly common finding on computed tomography (CT) worldwide. Once identified there are a number of imaging strategies that can be performed to help with nodule characterization. These include interval CT, dynamic contrast enhanced computed tomography (DCE-CT), 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG-PET-CT). To date the most cost effective and efficient non-invasive test or combination of tests for optimal nodule characterization has yet to be determined. DCE-CT is a functional test that involves the acquisition of a dynamic series of images of a nodule before and following the administration of intravenous iodinated contrast medium. This article provides an overview of the current indications and limitations of DCE- CT in nodule characterization and a systematic approach to how to perform, analyse and interpret a DCE-CT scan. Electronic supplementary material The online version of this article (doi:10.1186/s40644-016-0074-4) contains supplementary material, which is available to authorized users.

The aim of this study was to provide an overview of the literature available on dynamic contrast-enhanced computed tomography (DCE-CT) as a tool to evaluate treatment response in patients with lung cancer. This systematic review was compiled according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles concerning treatment response in patients with lung cancer assessed with DCE-CT were included. To assess the validity of each study we implemented Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). The initial search yielded 651 publications, and 16 articles were included in this study. The articles were divided into groups of treatment. In studies where patients were treated with systemic chemotherapy with or without anti-angiogenic drugs, four out of the seven studies found a significant decrease in permeability after treatment. Four out of five studies that measured blood flow post anti-angiogenic treatments found that blood flow was significantly decreased. DCE-CT may be a useful tool in assessing treatment response in patients with lung cancer. It seems that particularly permeability and blood flow are important perfusion values for predicting treatment outcome. However, the heterogeneity in scan protocols, scan parameters, and time between scans makes it difficult to compare the included studies.

International audience; This paper addresses multi-label tissue classification in the context of liver tumor segmentation for patients with hepato-cellular carcinoma (HCC). Covering such issue in an interactive perspective through supervoxel-based random forest (RF) requires an adaptive data sampling scheme to deal with multiple spatial extents and appearance heterogeneity. We propose a simple and efficient strategy combining standard RF and hierarchical multi-scale tree resulting from recursive 3D SLIC supervoxel decomposition. By concatenating features across the hierarchical multi-scale tree to describe leaf super-voxels, we enable RF to automatically infer the most informative scales discriminating tissues based on their intrinsic properties. Our method does not require any explicit rules on how to combine the different scales. Quantitative assessment on expert ground truth annotations demonstrates improved results compared to standard single-scale strategies for HCC tumor segmentation in dynamic contrast-enhanced CT scans.

We report the dynamic contrast-enhanced CT and histopathologic findings of a rare case of recurrent pulmonary capillary hemangiomas. The findings consisted of peripheral nodular enhancement at the early arterial phase and a subsequent "central filling-in" enhancement pattern on the delayed scans, which was identical to the well-known enhancement pattern of hemangiomas of the liver. Although there was no evidence of histological malignancy, pulmonary capillary hemangiomas manifested as multiple nodular lesions and showed postoperative recurrence.

Dynamic contrast-enhanced MRI (DCE-MRI) is frequently used to provide response biomarkers in clinical trials of novel cancer therapeutics but assessment of their physiological accuracy is difficult. DCE-CT provides an independent probe of similar pharmacokinetic processes and may be modeled in the same way as DCE-MRI to provide purportedly equivalent physiological parameters. In this study, DCE-MRI and DCE-CT were directly compared in subjects with primary bladder cancer to assess the degree to which the model parameters report modeled physiology rather than artefacts of the measurement technique and to determine the interchangeability of the techniques in a clinical trial setting. The biomarker K(trans) obtained by fitting an extended version of the Kety model voxelwise to both DCE-MRI and DCE-CT data was in excellent agreement (mean across subjects was 0.085 � 0.030 min(-1) for DCE-MRI and 0.087 � 0.033 min(-1) for DCE-CT, intermodality coefficient of variation 9%). The parameter v(p) derived from DCE-CT was significantly greater than that derived from DCE-MRI (0.018 � 0.006 compared to 0.009 � 0.008, P = 0.0007) and v(e) was in reasonable agreement only for low values. The study provides evidence that the biomarker K(trans) is a robust parameter indicative of the underlying physiology and relatively independent of the method of measurement. Magn Reson Med, 2011. � 2011 Wiley-Liss, Inc.

To evaluate the relationship between image noise, voxel size, and voxel-wise repeatability of a dynamic contrast agent-enhanced (DCE) computed tomographic (CT) examination for prostate cancer.This prospective study was approved by the local research ethics committee, and all patients gave written informed consent. Twenty-nine patients (mean age, 69.1 years; range, 56-80 years) with biopsy-proved prostate cancer underwent two DCE CT examinations within 1 week prior to radiation therapy. Parameter maps of transfer constant (K(trans)), the fraction of blood plasma (v(p)), the fraction of extravascular extracellular space (v(e)), and the flux rate constant between the extravascular extracellular space and plasma (k(ep)) were calculated at 15 different image resolutions, with kernel sizes ranging from 0.002 to 2.57 cm(3). Statistical analysis to quantify the voxel-wise repeatability was performed by using a Bland-Altman analysis on all tracer kinetic model parameter maps of each patient. From this analysis, the within-voxel standard deviation (wSD) was calculated as a function of spatial resolution.A kernel size in the range of 0.1-0.3 cm(3) yields reliable information. At 0.15 cm(3), the median wSDs of K(trans), k(ep), v(p), and v(e) are 0.047 min(-1), 0.144 min(-1), 0.011, and 0.104, respectively. With increasing kernel size, these values reach stable levels of approximately 0.02 min(-1), 0.05 min(-1), 0.005, and 0.05, respectively.There is a high voxel-wise repeatability of the DCE CT imaging technique for prostate cancer for kernel sizes as small as 0.1 cm(3). With the relationship between kernel size, image noise and voxel-wise repeatability, it becomes possible to estimate for alternative DCE CT protocols (eg, those with a reduced radiation dose) at what kernel size a sufficient repeatability can be obtained.

Our aim was to assess the protective effect of hydrogen-rich water against cisplatin-induced nephrotoxicity in rats using dynamic contrast-enhanced CT (DCE-CT). DCE-CT studies were performed in 30 rats (8 weeks old) on days 0, 2, 4 and 7 using multidetector row CT. The rats were divided into three groups: a control group (n = 6) with free access to standard water and without cisplatin injection, a non-treatment group (n = 12) with free access to standard water and injected with cisplatin (3.6 mg kg(-1) body weight) intraperitoneally on day 0 and a treatment group (n = 12) with free access to hydrogen-rich water starting from 7 days before cisplatin injection. The contrast clearance per unit renal volume (K(1)) was estimated from the DCE-CT data using the Patlak model. The contrast clearance of the entire kidney (K) was obtained by multiplying K(1) by the renal volume. The serum creatinine level was also measured on day 7. The K(1) and K values normalised by those on day 0 in the treatment group were significantly greater than those in the non-treatment group on days 2, 4 and 7. There were no significant differences in the normalised K value between the treatment and control groups on days 2 and 7. The serum creatinine level in the treatment group was significantly lower than that in the non-treatment group and was not significantly different from that in the control group. This study demonstrated that hydrogen-rich water ameliorates renal dysfunction due to cisplatin-induced nephrotoxicity in rats.

To determine the relationship between the fractional interstitial volume (Fis), as calculated at dynamic contrast material-enhanced (DCE) computed tomography (CT), and tumor-associated stroma and to analyze its spatial relationship with tumor hypoxia in several xenograft tumor models.All animal experiments were approved by the animal research committee. Mice with three different xenograft tumors (U251, CFPAC-1, and BxPC-3; n = 6, n = 8, and n = 6, respectively) underwent DCE CT then hypoxia imaging with fluorine 18 ((18)F) fluoromisonidazole (FMISO) positron emission tomography (PET) within 24 hours. Immunohistochemical analysis was performed in harvested tumors to detect hypoxia markers and to quantify microvascular and stromal density. Two DCE CT parameters (amount of interstitial space associated with the amount of stroma [Fis] and flow velocity [Fv]) were identified and quantitatively validated by using immunohistochemistry. FMISO uptake within the tumor was also assessed in relation to DCE CT parameters. Imaging and immunohistochemical parameters were assessed by using the Kruskal-Wallis test, Wilcoxon rank-sum test with Bonferroni correction, and Pearson correlation coefficient.Almost no α-smooth muscle actin-positive cells were found in the U251 xenograft, while abundant stroma was found in the entire BxPC-3 xenograft and in the periphery of the CFPAC-1 xenograft. Quantitative analysis showed a significant correlation (R = 0.83, P.0001) between Fis and stromal density. FMISO uptake had a negative correlation with Fis (R = -0.58, P.0001) and Fv (R = -0.53, P.0001).DCE CT can be used to quantify parameters associated with tumor-associated stroma. Tumor hypoxia was Complementarily localized in tumor-associated stroma in these models.

The aim of this review article is to define the technical prerequisites of modern state-of-the-art CT perfusion imaging in oncology at reasonable dose levels. The focus is mainly on abdominal and thoracic tumor imaging, as they pose the largest challenges with respect to attenuation and patient motion. We will show that low kV dynamic scanning in conjunction with detection technology optimized for low photon fluxes has the highest impact on reducing dose independently of other choices made in the protocol selection. We discuss, derived from relatively simple first principles, on what appropriate temporal sampling and total scan duration depend on and why optimized contrast medium injection protocols are also essential in limiting dose. Finally we will examine the possibility of simultaneously extracting standard morphological and functional information from one single 4D examination as a potential enabler for a more widespread use of dynamic contrast enhanced CT in oncology.

Dynamic contrast enhanced CT (DCE-CT) can be used to estimate blood perfusion and vessel permeability in tumors. Tumor induced angiogenesis is generally associated with disorganized microvasculature with increased permeability or leakage. Estimated vascular leakage (K(trans)) values and their reliability greatly depend on the perfusion model used. To identify the preferred model for larynx tumor analysis, several perfusion models frequently used for estimating permeability were compared in this study. DCE-CT scans were acquired for 16 larynx cancer patients. Larynx tumors were delineated based on whole-mount histopathology after laryngectomy. DCE-CT data within these delineated volumes were analyzed using the Patlak and Logan plots, the Extended Tofts Model (ETM), the Adiabatic Approximation to the Tissue Homogeneity model (AATH) and a variant of AATH with fixed transit time (AATHFT). Akaike's Information Criterion (AIC) was used to identify the best fitting model. K(trans) values from all models were compared with this best fitting model. Correlation strength was tested with two-tailed Spearman's rank correlation and further examined using Bland-Altman plots. AATHFT was found to be the best fitting model. The overall median of individual patient medians K(trans) estimates were 14.3, 15.1, 16.1, 2.6 and 22.5 mL/100 g min( - 1) for AATH, AATHFT, ETM, Patlak and Logan, respectively. K(trans) estimates for all models except Patlak were strongly correlated (P 0.001). Bland-Altman plots show large biases but no significant deviating trend for any model other than Patlak. AATHFT was found to be the preferred model among those tested for estimation of K(trans) in larynx tumors.

Both Dual Energy CT (DECT) and Volume Perfusion CT (VPCT) have gained interest in recent years with several studies providing evidence of benefits for both in a variety of oncological settings. These technologies open a new spectrum of diagnostic opportunities aiming at an improved detection and characterization of suspected tumor lesions, early evaluation of therapy response and generally more accurate treatment monitoring. Here, we review and discuss current advances, beneficial aspects and potential shortcomings of both imaging modalities with regard to their clinical use in oncology.

To study the feasibility and clinical value of dynamic contrast-enhanced (DCE) computed tomography (CT) for early evaluation of targeted therapy efficacy in non-small cell lung cancer (NSCLC). We measured tumor diameter, peak height (PH), time to peak (TP), tumor mass–aortic peak height ratio (M/A), and blood perfusion (BP) in 20 patients with advanced NSCLC using DCE-CT before and 7 days after treatment. Therapy efficacy was assessed with conventional CT 4–6 weeks post-treatment. Patients were grouped into those with partial response (PR), stable disease (SD), and progressive disease (PD) according to the therapy efficacy assessment at 4–6 weeks post-treatment. The PR group primary tumor diameter (P = 0.0007) and BP (P = 0.0225) were reduced at 7 days post-treatment; the SD group DCE-CT value changes were not significant. The PD group M/A (P = 0.0443) and BP (P = 0.0268) were increased 7 days post-treatment. The BP decrease group had significantly longer progression-free survival than the BP increase group (median, 54 vs. 6 weeks). DCE-CT can evaluate targeted therapy efficacy at 7 days post-treatment. Decreased primary tumor diameter and BP indicate tumor sensitivity to therapy; increased BP with unchanged tumor diameter suggests the tumor is not sensitive to therapy. Reduced BP suggests treatment effectiveness.

Functional imaging has the potential to be a practical and widely available method of studying the pathphysiology of disease using modern CT technologies. With the high temporal resolution achievable by these technologies, a two-compartment distributed-parameter model, which more accurately represents the tracer concentration within the vascular space, was applied on five patients’ data with extracranial head and neck tumors. The parametric maps successfully generated were more informative than the current commercial software packages and the commonly used lumped-parameter compartmental models.

Extrapleural solitary fibrous tumor, formerly referred to as "hemangiopericytoma" is a rare soft tissue vascular tumor which has elevated malignancy potential arising from the capillary pericytes. Extrapleural solitary fibrous tumors, originating from the spleen, are extremely rare. We report on a rare case of a patient presenting with splenic extrapleural solitary fibrous tumors with intratumoral irregular calcification detected by dynamic contrast-enhanced computed tomography.

International audience; Pre-operative locoregional treatments (PLT) delay the tumor progression by necrosis for patients with hepato-cellular carcinoma (HCC). Toward an efficient evaluation of PLT response, we address the estimation of liver tumor necrosis (TN) from CT scans. The TN rate could shortly supplant standard criteria (RECIST, mRECIST, EASL or WHO) since it has recently shown higher correlation to survival rates. To overcome the inter-expert variability induced by visual qualitative assessment, we propose a semi-automatic method that requires weak interaction efforts to segment parenchyma, tumoral active and necrotic tissues. By combining SLIC supervoxels and random decision forest, it involves discriminative multi-phase cluster-wise features extracted from registered dynamic contrast-enhanced CT scans. Quantitative assessment on expert groundtruth annotations confirms the benefits of exploiting multi-phase information from semantic regions to accurately segment HCC liver tumors.

To aid the extraction of the portal venous input function (PVIF) from axial dynamic contrast-enhanced CT images of the liver, eliminating the need for full manual outlining of the vessel across time points.A cohort of 20 patients undergoing perfusion CT imaging of the liver was examined. Dynamic images of the liver were reformatted into contiguous thin slices. A region of interest was defined within a transverse section of the portal vein on a single contrast-enhanced image. This region of interest was then computationally projected across all thin slices for all time points to yield a semi-automated PVIF curve. This was compared against the "gold-standard" PVIF curve obtained by conventional manual outlining.Bland-Altman plots of curve characteristics indicated no substantial difference between automated and manual PVIF curves [concordance correlation coefficient in the range (0.66, 0.98)]. No substantial differences were shown by Bland-Altman plots of derived pharmacokinetic parameters when a suitable kinetic model was applied in each case [concordance correlation coefficient in range (0.92, 0.95)].This semi-automated method of extracting the PVIF performed equivalently to a "gold-standard" manual method for assessing liver function. Advances in knowledge: This technique provides a quick, simple and effective solution to the problems incurred by respiration motion and partial volume factors in the determination of the PVIF in liver dynamic contrast-enhanced CT.

Dynamic contrast enhanced CT can be used to measure perfusion, thus enabling a quantitative assessment of vascular integrity. Conventional dynamic CT involves the reconstruction of dynamic sequences of 3D images from complete projections acquired at relatively short time intervals. This approach is suitable for spiral scanners but it is unfeasible for most of small-animal cone-beam scanners, as the distribution of the contrast agent changes during the acquisition and leads to inconsistent projections. We present a new algorithm to achieve 4D reconstructions of dynamic CT in case of slow cone-beam scanners.

OBJECTIVE. The purpose of this article is to review advanced imaging of pulmonary nodules, including pathologic and pharmacokinetic background, conventional contrast-enhanced CT and MRI assessment, dynamic contrast-enhanced CT and MRI techniques, and dual-source and area-detector CT systems for pulmonary nodule evaluation. CONCLUSION. Clinicians need to understand the underlying principles and pathologic and pharmacokinetic backgrounds of contrast-enhanced CT and MRI to further improve diagnostic performance. With adjustments in image acquisition and postprocessing, contrast-enhanced CT and MRI, especially the dynamic versions, can have enhanced clinical application for pulmonary nodules and expanded clinical relevance for other thoracic diseases.

The modeling of tracer kinetics with use of low-temporal-resolution data is of central importance for patient dose reduction in dynamic contrast-enhanced CT (DCE-CT) study. Tracer kinetic models of the liver vary according to the physiologic assumptions imposed on the model, and they can substantially differ in the ways how the input for blood supply and tissue compartments are modeled. In this study, single-input flow-limited (FL), Tofts-Kety (TK), extended TK (ETK), Hayton-Brady (HB), two compartment exchange (2CX), and adiabatic approximation to the tissue homogeneity (AATH) models were applied to the analysis of liver 4-phase DCE-CT data with fully continuous-time parameter formulation, including the bolus arrival time. The bolus arrival time for the 2CX and AATH models was described by modifying the vascular transport operator theory. Initial results indicate that single-input tracer kinetic modeling is feasible for distinguishing between hepatocellular carcinoma and normal liver parenchyma.

To examine whether dynamic contrast-enhanced CT (DCE-CT) could be used to characterise and safely distinguish between malignant and benign lung tumours in patients with suspected lung cancer.Using a quantitative approach to DCE-CT, two separate sets of regions of interest (ROIs) in tissues were placed in each tumour: large ROIs over the entire tumour and small ROIs over the maximally perfused parts of the tumour. Using mathematical modelling techniques and dedicated perfusion software, this yielded a plethora of results.First, because of their non-normal distribution, DCE-CT measurements must be analysed using log scale data transformation. Second, there were highly significant differences between large ROI and small ROI measurements (p0.001). Thus, the ROI method used in a given study should always be specified in advance. Third, neither quantitative parameters (blood flow and blood volume) nor semi-quantitative parameters (peak enhancement) could be used to distinguish between malignant and benign tumours. This was irrespective of the method of quantification used for large ROIs (0.13p0.76) and small ROIs (0.084p0.31). Fourth, although there were no indications of systematic reproducibility bias, the 95% limits of agreement were so broad that the risk of disagreement between the measurements could affect the clinical use of the measurements. This lack of reproducibility should be addressed. CONCLUSION AND ADVANCES IN KNOWLEDGE: A quantitative approach to DCE-CT is not a clinically usable method for characterising lung tumours.

To compare four different tracer kinetic models for the analysis of dynamic contrast material-enhanced computed tomographic (CT) data with respect to the prediction of 5-year overall survival in primary colorectal cancer.This study was approved by the ethical review board. Archival dynamic contrast-enhanced CT data from 46 patients with colorectal cancer, obtained as part of a research study, were analyzed retrospectively by using the distributed parameter, conventional compartmental, adiabatic tissue homogeneity, and generalized kinetic models. Blood flow, blood volume, mean transit time (MTT), permeability-surface area product, extraction fraction, extravascular extracellular volume (v(e)), and volume transfer constant (K(trans)) were compared by using the Friedman test, with statistical significance at 5%. Following receiver operating characteristic analysis, parameters of the different kinetic models and tumor stage were compared with respect to overall survival discrimination, with use of Kaplan Meier analysis and a univariate Cox proportional hazard model, with additional cross-validation and permutation testing.Blood flow was lower with the distributed parameter model than with the conventional compartmental and adiabatic tissue homogeneity models (P.0001), and blood flow values determined with the conventional compartmental and adiabatic tissue homogeneity models were similar. Conversely, MTT was longer with the distributed parameter model than with the conventional compartmental and adiabatic tissue homogeneity models (P.0001). Blood volume, permeability-surface area product, and v(e) were higher with the conventional compartmental model than with the adiabatic tissue homogeneity, distributed parameter, or generalized kinetic models (P.0001). The extraction fraction was higher with the distributed parameter model than with the adiabatic tissue homogeneity model. With respect to 5-year overall survival, only the distributed parameter model-derived v(e) was predictive of 5-year overall survival with a threshold value of 15.48 mL/100 mL after cross-validation and permutation testing.Parameter values differ significantly between models. Of the models investigated, the distributed parameter model was the best predictor of 5-year overall survival.http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12120186/-/DC1.

A distinct feature of the tumor vasculature is its tortuosity and irregular branching of vessels, which can translate to a wider dispersion and higher variability of blood flow in the tumor. To enable tumor blood flow variability to be assessed in vivo by imaging, a tracer kinetic model that accounts for flow dispersion is developed for use with dynamic contrast-enhanced (DCE) CT. The proposed model adopts a multiple-pathway approach and allows for the quantification of relative dispersion in the blood flow distribution, which reflects flow variability in the tumor vasculature. Monte Carlo simulation experiments were performed to study the possibility of reducing the number of model parameters based on the Akaike information criterion approach and to explore possible noise and tissue conditions in which the model might be applicable. The model was used for region-of-interest analysis and to generate perfusion parameter maps for three patient DCE CT cases with cerebral tumors, to illustrate clinical applicability. Accepted version

INTRODUCTION: Solitary pulmonary nodules (SPNs) are common on CT. The most cost-effective investigation algorithm is still to be determined. Dynamic contrast-enhanced CT (DCE-CT) is an established diagnostic test not widely available in the UK currently.METHODS AND ANALYSIS: The SPUtNIk study will assess the diagnostic accuracy, clinical utility and cost-effectiveness of DCE-CT, alongside the current CT and 18-flurodeoxyglucose-positron emission tomography) ((18)FDG-PET)-CT nodule characterisation strategies in the National Health Service (NHS). Image acquisition and data analysis for (18)FDG-PET-CT and DCE-CT will follow a standardised protocol with central review of 10% to ensure quality assurance. Decision analytic modelling will assess the likely costs and health outcomes resulting from incorporation of DCE-CT into management strategies for patients with SPNs.ETHICS AND DISSEMINATION: Approval has been granted by the South West Research Ethics Committee. Ethics reference number 12/SW/0206. The results of the trial will be presented at national and international meetings and published in an Health Technology Assessment (HTA) Monograph and in peer-reviewed journals.TRIAL REGISTRATION NUMBER: ISRCTN30784948; Pre-results.

To evaluate the application of perfusion CT for gross tumor volume (GTV) delineation for radiotherapy of intrahepatic tumors.15 radiotherapy patients with confirmed liver tumors underwent contrast enhanced 4D-CT (Philips Brilliance Big-bore) as well as dynamic contrast enhanced (DCE) CT (GE 750HD). Perfusion maps were generated with CT perfusion v5 from GE. Five observers delineated GTVs of all intrahepatic foci on the 4D-CT, time-averaged DCE-CT and perfusion CT for every patient. STAPLE consensus contours were generated. Dice's coefficients were compared between GTVs generated by observers on each image set and the corresponding consensus GTVs. Comparisons were also performed with patients stratified by hepatocellular carcinoma (HCC) metastatic tumors, and by tumor volume.Overall, mean Dice's coefficients were 0.81±0.14, 0.84±0.10, and 0.81±0.14 for 4D-CT, DCECT and perfusion. DCE-CT performed significantly better than 4D-CT and perfusion (p=0.005 and p=0.01 respectively). For patients with HCC, DCE-CT reduced interobserver variability significantly compared to 4D-CT (Dice's coefficients 0.87 vs. 0.84, p0.05). For patients with metastatic disease time-averaged DCE-CT images decreased variability compared to 4D-CT (Dice's coefficient 0.81 vs. 0.76, p0.05), especially true for tumors100cc. The smaller tumors results are important to be included here.DCE-CT imaging of liver perfusion reduced interobserver variability in GTV delineation for both HCC and metastatic liver tumors.

We objectively evaluate a straightforward registration method for correcting respiration-induced movement of abdominal organs in CT perfusion studies by measuring the distributions of alignment errors between corresponding landmark pairs. We introduce the concept and describe the advantages of using the surface-normal component of distance between pairs of corresponding landmarks selected so that their surface normal is in one of the three coordinate axis directions, and show that such landmarks can be precisely placed with respect to the surface normal. Using a large population of landmark pairs on a substantial quantity of 4D dynamic contrast-enhanced CT volume data, we quantify the average alignment errors of abdominal organs that remain uncorrected by registration.

Background. Molecular and functional imaging techniques such as dynamic positron emission tomography (DPET) and dynamic contrast enhanced computed tomography (DCECT) may provide improved characterization of tumors compared to conventional anatomic imaging. The purpose of the current work was to compare spatiotemporal uptake patterns in DPET and DCECT images. Materials and methods. A PET/CT protocol comprising DCECT with an iodine based contrast agent and DPET with 18 F-fl uorodeoxyglucose was set up. The imaging protocol was used for examination of three dogs with spontaneous tumors of the head and neck at sessions prior to and after fractionated radiotherapy. Software tools were developed for downsampling the DCECT image series to the PET image dimensions, for segmentation of tracer uptake pattern in the tumors and for spatiotemporal correlation analysis of DCECT and DPET images. Results. DCECT images evaluated one minute post injection qualitatively resembled the DPET images at most imaging sessions. Segmentation by region growing gave similar tumor extensions in DCECT and DPET images, with a median Dice similarity coeffi cient of 0.81. A relatively high correlation (median 0.85) was found between temporal tumor uptake patterns from DPET and DCECT. The heterogeneity in tumor uptake was not signifi cantly different in the DPET and DCECT images. The median of the spatial correlation was 0.72. Conclusions. DCECT and DPET gave similar temporal wash-in characteristics, and the images also showed a relatively high spatial correlation. Hence, if the limited spatial resolution of DPET is considered adequate, a single DPET scan only for assessing both tumor perfusion and metabolic activity may be considered. However, further work on a larger number of cases is needed to verify the correlations observed in the present study.

International audience; Purpose: To develop a new automated filtering technique and to evaluate its ability to compensate for the known low contrast-to-noise ratio (CNR) in dynamic contrast material-enhanced (DCE) magnetic resonance (MR) and computed tomographic (CT) data, without substantial loss of information.Materials and methods: Clinical data acquisition for this study was approved by the institutional review board. Principal component analysis (PCA) was combined with the fraction of residual information (FRI) criterion to optimize the balance between noise reduction efficiency and information conservation. The PCA FRI filter was evaluated in 15 DCE MR imaging data sets and 15 DCE CT data sets by two radiologists who performed visual analysis and quantitative assessment of noise reduction after filtering.Results: Visual evaluation revealed a substantial noise reduction while conserving information in 90% of MR imaging cases and 87% of CT cases for image analysis and in 93% of MR imaging cases and 90% of CT cases for signal analysis. Efficient denoising enabled improvement in structure characterization in 60% of MR imaging cases and 77% of CT cases. After filtering, CNR was improved by 2.06 ± 0.89 for MR imaging (P < .01) and by 5.72 ± 4.82 for CT (P < .01).Conclusion: This PCA FRI filter demonstrates noise reduction efficiency and information conservation for both DCE MR data and DCE CT data. FRI analysis enabled automated optimization of the parameters for the PCA filter and provided an optional visual control of residual information losses. The robust and fast PCA FRI filter may improve qualitative or quantitative analysis of DCE imaging in a clinical context.

Purpose. To assess whether the differences in vascular-metabolic relationships between lymphoma masses and colorectal liver metastases predicted from previous histopathological studies can be demonstrated by dynamic contrast-enhanced CT (DCE-CT) combined with fluorodeoxyglucose positron emission tomography (FDG-PET). Methods. DCE-CT and FDG-PET studies were drawn from an imaging archive for patients with either lymphoma masses (n=11) or hepatic metastases from colorectal cancer (CRM: n=12). Tumour vascularity was assessed using DCE-CT measurements of perfusion. Tumour glucose metabolism was expressed as the mean FDG Standardised Uptake Value (SUVFDG). The relationship between metabolism and vascularity in each group was assessed from SUVFDG /perfusion ratios and Pearson correlation coefficients. Results. An SUVFDG threshold of 3.0 was used to designate lymphoma masses as active (AL, n=6) or inactive lymphoma (IL, n=5). Tumour perfusion was significantly higher in AL (0.65 mL/min/mL) than CRM (0.37 mL/min/mL: P=.031) despite similar SUVFDG (5.05 and 5.33, resp.). AL demonstrated higher perfusion values than IL (0.24 mL/min/mL: P=.006). SUVFDG/perfusion was significantly higher in CRM (15.3 min) than IL (4.2 min, P<.01). There was no correlation between SUVFDG and perfusion for any patient group.

Dynamic contrast-enhanced computed tomography (CT) was compared with 1.5-T magnetic resonance (MR) imaging with FLASH (fast low-angle shot), gadolinium-enhanced FLASH (Gd-FLASH), and T2-weighted fat-suppression (T2FS) sequences in 11 patients with hematologic malignancies, five with biopsy-confirmed hepatosplenic candidiasis treated with antifungal chemotherapy and six with a clinical history suggestive of acute hepatosplenic candidiasis. CT and MR images were separately interpreted in a prospective fashion. MR imaging showed lesions compatible with candidiasis in the liver in six patients, the spleen in five, and the kidneys in one. CT showed candidiasis-compatible lesions in the liver in three patients and the spleen in one; no renal lesions were shown. Differences between acute and treated candidal lesions were observed. Gd-FLASH images showed the most liver lesions (n = 106), followed by FLASH (n = 85), T2FS (n = 20), and CT (n = 18). MR imaging performed better than CT in distinguishing candidal hepatic lesions from recurrent lymphoblastic lymphoma in one patient and from hepatic infarcts in another. The results suggest that MR imaging may be superior to CT in detecting lesions of hepatosplenic candidiasis.

To investigate the value of 5-mm contrast material-enhanced computed tomography (CT) in patients with moderate to low probability of aortic laceration after a substantial deceleration injury, scans were obtained through the upper mediastinum in 160 consecutive patients. Thoracic angiography and aortography were performed in patients with evidence of mediastinal hemorrhage at CT. There was no evidence of mediastinal hemorrhage in 132 patients with normal admission chest radiographs. In the 28 patients with abnormal admission chest radiographs, CT helped exclude mediastinal hemorrhage in 22 patients (78%), and 19 patients (68%) were treated without undergoing angiography. Six patients had mediastinal hematoma at CT. Only one had an aortic laceration at angiography. The authors conclude that 5-mm contrast-enhanced CT can help exclude mediastinal hemorrhage and reduce the angiography rate in low-to-moderate-risk patients with a widened or indeterminate mediastinum. There were no unsuspected cases of mediastinal hemorrhage in patients with normal chest radiographs. Angiography is recommended for patients considered to be at high risk for aortic laceration.