Dynamic Contrast-enhanced CT Imaging Research Articles

Diagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With PET With CT Imaging and Dynamic Contrast-Enhanced CT Imaging Using Unenhanced and Contrast-Enhanced CT Imaging

Solitary pulmonary nodules (SPNs) measuring 8 to 30mm in diameter require further workup to determine the likelihood of malignancy. What is the diagnostic performance of a lung cancer prediction convolutional neural network (LCP-CNN) in SPNs using unenhanced and contrast-enhanced CT imaging compared with the current clinical workup? This was a post hoc analysis of the Single Pulmonary Nodule Investigation: Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules trial, a prospective multicenter study comparing the diagnostic accuracy of dynamic contrast-enhanced (DCE) CT imaging with PET imaging in SPNs. The LCP-CNN was designed and validated in an external cohort. LCP-CNN-generated risk scores were created from the noncontrast and contrast-enhanced CT scan images from the DCE CT imaging. The gold standard was histologic analysis or 2 years of follow-up. The area under the receiver operating characteristic curves (AUC) were calculated using LCP-CNN score, maximum standardized uptake value, and DCE CT scan maximum enhancement and were compared using the DeLong test. Two hundred seventy participants (mean ± SD age, 68.3 ± 8.8 years; 49%women) underwent PET with CT scan imaging and DCE CT imaging with CT scan data available centrally for LCP-CNN analysis. The accuracy of the LCP-CNN on the noncontrast images (AUC, 0.83; 95%CI, 0.79-0.88) was superior to that of DCE CT imaging (AUC, 0.76; 95%CI, 0.69-0.82; P= .03) and equal to that of PET with CT scan imaging (AUC, 0.86; 95%CI, 0.81-0.90; P= .35). The presence of contrast resulted in a small reduction in diagnostic accuracy, with the AUC falling from 0.83 (95%CI, 0.79-0.88) on the noncontrast images to 0.80 to 0.83 after contrast (P< .05 for 240s after contrast only). An LCP-CNN algorithm provides an AUC equivalent to PET with CT scan imaging in the diagnosis of solitary pulmonary nodules. ClinicalTrials.gov Identifier; No.: NCT02013063.

Image Features of Dynamic Enhanced Computed Tomography Scanning Combined with Digestive Endoscopy in the Treatment of Gastroesophageal Varices and Nursing of Esophagogastric Gastric Varices Bleeding.

The objective of this study was to investigate the application of dynamic contrast-enhanced CT images in the nursing of patients with gastroesophageal varices (GOV) treated by digestive endoscopy and its role in relieving bleeding symptoms. A total of 60 patients with liver cirrhosis and GOV were selected as the research objects. According to whether CT was used to evaluate the position of tissue adhesion embolism, the patients were divided into the control group (24 cases) and the observation group (36 cases). The treatment effect and bleeding situation of patients in the two groups were analyzed and compared. The results showed that the main portal vein pressure (17.24 ± 1.02 cmH2O), liver function recovery effect (2.84 ± 0.45 points), and total effective rate (100%) in observation group were better than those in control group (9.70 ± 1.22 cmH2O, 0.95 ± 0.72 points, and 79.17%, respectively) (P < 0.05). In addition, the bleeding rate in observation group (0%) was significantly lower than that in control group (16.67%) (P < 0.05). In conclusion, dynamically enhanced CT scan images combined with digestive endoscopy can help improve the therapeutic effect of GOV and reduce postoperative bleeding, which was worthy of clinical application and promotion.

Dynamic contrast-enhanced CT-derived blood flow measurements enable early prediction of long term outcome in metastatic renal cell cancer patients on antiangiogenic treatment.

To evaluate the role of dynamic contrast-enhanced CT (DCE-CT) as an independent non-invasive biomarker in predicting long term outcome in patients with metastatic renal cell carcinoma (mRCC) on antiangiogenic treatment. Eighty two mRCC patients were prospectively enrolled from 09/2011 to 04/2015, out of which 71 were included in the final data analysis; the population was observed until 12/2020 to obtain complete overall survival data. DCE-CT imaging was performed at baseline and 10 to 12 weeks after start of treatment with targeted therapy. DCE-CT included a dynamic acquisition after injection of 50 ml of nonionic contrast agent at 6 ml/s using a 4D spiral mode (10 cm z-axis coverage, acquisition time 43 sec, 100 kVp (abdomen), 80 kVp (chest), 80-100 mAs) on a dual source scanner (Definition FLASH, Siemens). Blood flow (BF) was calculated for target tumor volumes using a deconvolution model. Progression free survival (PFS) and overall survival (OS) were analyzed using Kaplan-Meier statistics (SPSS version 24). Patients were treated with either sunitinib, pazopanib, sorafenib, tivozanib, axitinib, or cabozantinib. A cut-off value of 50% blood flow reduction at follow-up allowed for identification of patients with favorable long-term outcome: Median OS in n = 42 patients with an average blood flow reduction of >50% (mean, 79%) was 34 (range, 14-54) months, while n = 21 patients with an average reduction of less than 50% (mean, 28%) showed a median OS of 12 (range, 6-18) months, and n = 8 patients with an increase in blood flow survived for a median of 7 (range, 3-11) months. Blood flow in metastases measured with DCE-CT at first follow-up is a strong predictor of overall survival in mRCC patients on antiangiogenic treatment.

DeepPrognosis: Preoperative prediction of pancreatic cancer survival and surgical margin via comprehensive understanding of dynamic contrast-enhanced CT imaging and tumor-vascular contact parsing

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and carries a dismal prognosis of ∼10% in five year survival rate. Surgery remains the best option of a potential cure for patients who are evaluated to be eligible for initial resection of PDAC. However, outcomes vary significantly even among the resected patients who were the same cancer stage and received similar treatments. Accurate quantitative preoperative prediction of primary resectable PDACs for personalized cancer treatment is thus highly desired. Nevertheless, there are a very few automated methods yet to fully exploit the contrast-enhanced computed tomography (CE-CT) imaging for PDAC prognosis assessment. CE-CT plays a critical role in PDAC staging and resectability evaluation. In this work, we propose a novel deep neural network model for the survival prediction of primary resectable PDAC patients, named as 3D Contrast-Enhanced Convolutional Long Short-Term Memory network (CE-ConvLSTM), which can derive the tumor attenuation signatures or patterns from patient CE-CT imaging studies. Tumor-vascular relationships, which might indicate the resection margin status, have also been proven to hold strong relationships with the overall survival of PDAC patients. To capture such relationships, we propose a self-learning approach for automated pancreas and peripancreatic anatomy segmentation without requiring any annotations on our PDAC datasets. We then employ a multi-task convolutional neural network (CNN) to accomplish both tasks of survival outcome and margin prediction where the network benefits from learning the resection margin related image features to improve the survival prediction. Our presented framework can improve overall survival prediction performances compared with existing state-of-the-art survival analysis approaches. The new staging biomarker integrating both the proposed risk signature and margin prediction has evidently added values to be combined with the current clinical staging system.

Noninvasive assessment of radiation-induced renal injury in mice

Purpose The kidney is a radiosensitive late-responding normal tissue. Injury is characterized by radiation nephropathy and decline of glomerular filtration rate (GFR). The current study aimed to compare two rapid and cost-effective methodologies of assessing GFR against more conventional biomarker measurements. Methods C57BL/6 mice were treated with bilateral focal X-irradiation (1x14Gy or 5x6Gy). Functional measurements of kidney injury were assessed 20 weeks post-treatment. GFR was estimated using a transcutaneous measurement of fluorescein-isothiocyanate conjugated (FITC)-sinistrin renal excretion and also dynamic contrast-enhanced CT imaging with a contrast agent (ISOVUE-300 Iopamidol). Results Hematoxylin and eosin (H&E) and Periodic acid-Schiff staining identified comparable radiation-induced glomerular atrophy and mesangial matrix accumulation after both radiation schedules, respectively, although the fractionated regimen resulted in less diffuse tubulointerstitial fibrosis. Albumin-to-creatinine ratios (ACR) increased after irradiation (1x14Gy: 100.4 ± 12.2 µg/mg; 6x5Gy: 80.4 ± 3.02 µg/mg) and were double that of nontreated controls (44.9 ± 3.64 µg/mg). GFR defined by both techniques was negatively correlated with BUN, mesangial expansion score, and serum creatinine. The FITC-sinistrin transcutaneous method was more rapid and can be used to assess GFR in conscious animals, dynamic contrast-enhanced CT imaging technique was equally safe and effective. Conclusion This study demonstrated that GFR measured by dynamic contrast-enhanced CT imaging is safe and effective compared to transcutaneous methodology to estimate kidney function.

Fully end-to-end deep-learning-based diagnosis of pancreatic tumors.

Artificial intelligence can facilitate clinical decision making by considering massive amounts of medical imaging data. Various algorithms have been implemented for different clinical applications. Accurate diagnosis and treatment require reliable and interpretable data. For pancreatic tumor diagnosis, only 58.5% of images from the First Affiliated Hospital and the Second Affiliated Hospital, Zhejiang University School of Medicine are used, increasing labor and time costs to manually filter out images not directly used by the diagnostic model.Methods: This study used a training dataset of 143,945 dynamic contrast-enhanced CT images of the abdomen from 319 patients. The proposed model contained four stages: image screening, pancreas location, pancreas segmentation, and pancreatic tumor diagnosis.Results: We established a fully end-to-end deep-learning model for diagnosing pancreatic tumors and proposing treatment. The model considers original abdominal CT images without any manual preprocessing. Our artificial-intelligence-based system achieved an area under the curve of 0.871 and a F1 score of 88.5% using an independent testing dataset containing 107,036 clinical CT images from 347 patients. The average accuracy for all tumor types was 82.7%, and the independent accuracies of identifying intraductal papillary mucinous neoplasm and pancreatic ductal adenocarcinoma were 100% and 87.6%, respectively. The average test time per patient was 18.6 s, compared with at least 8 min for manual reviewing. Furthermore, the model provided a transparent and interpretable diagnosis by producing saliency maps highlighting the regions relevant to its decision.Conclusions: The proposed model can potentially deliver efficient and accurate preoperative diagnoses that could aid the surgical management of pancreatic tumor.

Can Dynamic Contrast-Enhanced CT Quantify Perfusion in a Stimulated Muscle of Limited Size? A Rat Model.

AbstractBackgroundMuscle 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 t

Utility of the portal venous phase for diagnosing pancreatic necrosis in acute pancreatitis using the CT severity index.

The purpose of the study was to evaluate the value of portal venous phase (PVP) images in the diagnosis of pancreatic necrosis in patients with acute pancreatitis using computed tomography severity index (CTSI). This retrospective study was approved by our Institutional Review Board, and written informed consent was waived. Dynamic contrast-enhanced CT images, with the pancreatic parenchymal phase (PPP) and the PVP, were obtained from 56 consecutive patients with acute pancreatitis. Two radiologists reviewed two sets of images, namely PPP images alone (image set A) and combined PPP and PVP images (image set B) to evaluate the CTSI. Cases were categorized as necrotizing pancreatitis if ensuing walled-off necrosis formation was identified 4weeks after onset of symptoms. The relationship between pancreatic necrosis and CTSI was compared between image sets A and B. Logistic regression analysis was performed to evaluate the significance of clinical and radiological factors associated with the diagnosis of pancreatic necrosis. Pancreatic necrosis was confirmed in 14 out of 56 (25%) patients. The area under the receiver-operating-characteristic curve (AUC) for the diagnosis of pancreatic necrosis was 0.70 and 0.78 for image sets A and B, respectively. The AUC for image set B was significantly greater than that for image set A (P = 0.0002). Logistic regression analysis demonstrated that among clinical and radiological factors tested, CTSI for image set B was independently correlated with pancreatic necrosis (P = 0.025). Combined PPP and PVP images significantly improved the diagnostic accuracy of pancreatic necrosis following acute pancreatitis.

Predicting tumor hypoxia in non-small cell lung cancer by combining CT, FDG PET and dynamic contrast-enhanced CT

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 > 1 cm3) 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.

A semi-automatic method for the extraction of the portal venous input function in quantitative dynamic contrast-enhanced CT of the liver.

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.

Imaging and enhancement characteristics of coccidioidomycosis nodules assessed by dynamic contrast-enhanced computed tomography

ObjectiveTo describe the dynamic contrast-enhanced CT (DCECT) appearance of pulmonary nodules related to infection with coccidioidomycosis. MethodsRetrospective review of a database of DCECTs identified a subset of patients with pulmonary nodules related to coccidioidomycosis. General and DCECT imaging characteristics of these nodules were evaluated. Results16 of the 44 nodules identified (36.4%) showed enhancement on DCECT imaging. The majority (74.9%) of enhancing nodules demonstrate delayed (3 or 4 min) peak enhancement. The majority (88.6%) of the 44 nodules were either smooth or lobulated in appearance. Only 1 (2.3%) of the nodules was cavitary. 19 (43.2%) of the nodules had adjacent satellite nodules. DCECT can be a useful study to help characterize pulmonary nodules in areas with endemic Coccidioides with an expected false-positive rate of ∼30–40%. Biopsy could potentially be averted in 60–70% of nodules by evaluating with DCECT prior to tissue sampling. ConclusionWhen combined with morphological, serological, clinical, and historical information DCECT can be a powerful tool for reducing the number of benign nodules being biopsied and/or resected.

Assessment of contrast enhanced respiration managed cone-beam CT for image guided radiotherapy of intrahepatic tumors.

Contrast enhancement and respiration management are widely used during image acquisition for radiotherapy treatment planning of liver tumors along with respiration management at the treatment unit. However, neither respiration management nor intravenous contrast is commonly used during cone-beam CT (CBCT) image acquisition for alignment prior to radiotherapy. In this study, the authors investigate the potential gains of injecting an iodinated contrast agent in combination with respiration management during CBCT acquisition for liver tumor radiotherapy. Five rabbits with implanted liver tumors were subjected to CBCT with and without motion management and contrast injection. The acquired CBCT images were registered to the planning CT to determine alignment accuracy and dosimetric impact. The authors developed a simulation tool for simulating contrast-enhanced CBCT images from dynamic contrast enhanced CT imaging (DCE-CT) to determine optimal contrast injection protocols. The tool was validated against contrast-enhanced CBCT of the rabbit subjects and was used for five human patients diagnosed with hepatocellular carcinoma. In the rabbit experiment, when neither motion management nor contrast was used, tumor centroid misalignment between planning image and CBCT was 9.2 mm. This was reduced to 2.8 mm when both techniques were employed. Tumors were not visualized in clinical CBCT images of human subjects. Simulated contrast-enhanced CBCT was found to improve tumor contrast in all subjects. Different patients were found to require different contrast injections to maximize tumor contrast. Based on the authors' animal study, respiration managed contrast enhanced CBCT improves IGRT significantly. Contrast enhanced CBCT benefits from patient specific tracer kinetics determined from DCE-CT.

Advances in CT for prediction of hematoma expansion in acute intracerebral hemorrhage

Intracerebral haemorrhage accounts for 10–15% of all strokes and is associated with high morbidity and mortality. Early hematoma expansion, which occurs in approximately a third of patients leads to worse outcomes. Prediction of expansion may aid in the identification of patients most likely to benefit from novel medical or surgical therapies. Intrahematoma contrast density seen on CT angiography, coined the ‘spot sign’, has been validated in a prospective multicenter study to predict expansion and poor outcome. Increasing the number of spot signs visualized increases the risk of expansion. Utilization of delayed or dynamic contrast-enhanced CT is able to detect up to 21% additional patients with contrast extravasation, and improves sensitivity for outcome prediction. Different patterns of contrast extravasation may indicate differential rates of hemorrhage. CT angiography or dynamic contrast-enhanced CT imaging may aid in identifying efficacious therapies in intracerebral haemorrhage, and identification ...

Quantitative Perfusion Analysis of Malignant Liver Tumors

To prospectively analyze the correlation between quantitative parameters of perfusion derived from dynamic contrast-enhanced CT (DCE-CT) and contrast-enhanced ultrasound (DCE-US) in patients with malignant liver tumors. Thirty patients (mean age: 59.4 ± 12.3 years) with primary malignant liver tumors or hepatic metastases of various origin underwent DCE-CT (4D spiral mode, scan range, 14.8 cm; 15 scans; cycle time, 3 seconds) and DCE-US (low mechanical index, <0.1, 2.4 mL microbubbles). DCE-CT and DCE-US images were evaluated by 2 radiologists regarding quantitative perfusion parameters including arterial liver perfusion (ALP), portal-venous perfusion (PVP), and total perfusion (P = ALP + PVP) from DCE-CT, as well as blood inflow velocity (B) and the normalized slope within the calculation range (CVan) from DCE-US. Quantitative assessment was possible with DCE-CT in 12/30 (40%) patients before and in all patients after automated motion correction. With DCE-US, quantitative assessment could not be performed in 9/30 (30.0%) patients due to respiratory motion. Interreader agreements for quantitative perfusion analysis were good with DCE-CT (r = 0.640-0.892, each P < 0.001) and DCE-US (r = 0.761-0.909, each P < 0.001). Moderate significant correlations were found between the perfusion parameters from DCE-CT (P, ALP) and DCE-US (B, CVan) (r = 0.446-0.621, each P < 0.05). No significant correlations were found between PVP from CT and perfusion parameters from DCE-US (B, CVan; each P = nonsignificant). Quantitative evaluation of DCE-CT data was feasible in all patients after automated motion correction, whereas DCE-US data could not be quantitatively evaluated in 30% of patients due to respiratory motion and lack of motion correction software. Quantitative arterial perfusion analysis showed moderate significant correlations for blood flow parameters among modalities.

Non-invasive assessment of functionally relevant coronary artery stenoses with quantitative CT perfusion: preliminary clinical experiences

We developed a quantitative Dynamic Contrast-Enhanced CT (DCE-CT) technique for measuring Myocardial Perfusion Reserve (MPR) and Volume Reserve (MVR) and studied their relationship with coronary stenosis. Twenty-six patients with Coronary Artery Disease (CAD) were recruited. Degree of stenosis in each coronary artery was classified from catheter-based angiograms as Non-Stenosed (NS, angiographically normal or mildly irregular), Moderately Stenosed (MS, 50-80% reduction in luminal diameter), Severely Stenosed (SS, >80%) and SS with Collaterals (SSC). DCE-CT at rest and after dipyridamole infusion was performed using 64-slice CT. Mid-diastolic heart images were corrected for beam hardening and analyzed using proprietary software to calculate Myocardial Blood Flow (MBF, in mL∙min(-1)∙100 g(-1)) and Blood Volume (MBV, in mL∙100 g(-1)) parametric maps. MPR and MVR in each coronary territory were calculated by dividing MBF and MBV after pharmacological stress by their respective baseline values. MPR and MVR in MS and SS territories were significantly lower than those of NS territories (p < 0.05 for all). Logistic regression analysis identified MPR∙MVR as the best predictor of ≥50% coronary lesion than MPR or MVR alone. DCE-CT imaging with quantitative CT perfusion analysis could be useful for detecting coronary stenoses that are functionally significant.

Hepatocellular carcinoma: clinical and radiological findings in patients with chronic B viral hepatitis and chronic C viral hepatitis

To compare clinical and radiological findings of newly diagnosed hepatocellular carcinomas (HCCs) in patients with chronic hepatitis B virus (HBV) and chronic hepatitis C virus (HCV) infections. Dynamic contrast-enhanced CT images in 532 patients with newly diagnosed HCC were retrospectively reviewed. Of these patients, 418 had chronic HBV infections and 114 had chronic HCV infections. The number, size, shape (nodular vs. non-nodular) and enhancement pattern (typical vs. atypical) of hepatic lesions were assessed. The presence of portal vein thrombosis and bile duct invasion were determined. The mean age of the HBV group (54.31 [range 27-85], median 54) was younger than that of the HCV group (64.21 [range 30-86], median 64) (P < 0.001). Lesions in patients with HBV were more likely to be multifocal (>5; P < 0.001); larger than 5 cm (P = 0.023); non-nodular shape (P < 0.001); atypical enhancement pattern (P = 0.047), association with portal vein thrombosis (P = 0.004); association with bile duct invasion (P < 0.001). Clinical and radiological findings of HCC differ between patients with HBV and HCV infections.

Monitoring the Longitudinal Intra-tumor Physiological Impulse Response to VEGFR2 Blockade in Breast Tumors Using DCE-CT

The purpose of this study was to quantify and model the longitudinal intra-tumor physiological response to a single dose of a monoclonal antibody specific to the VEGFR2 using dynamic contrast-enhanced CT. Dynamic contrast-enhanced CT imaging was performed on athymic nude mice bearing xenograft VEGF-transfected MCF-7 tumors (MCF7(VEGF)) to quantify intra-tumor physiology pre- and post-injection (days 2, 7, and 14) of a nonspecific (IgG1, controls) and specific (DC101, treated) monoclonal antibody targeting VEGFR2. Parametrical maps of tumor physiology-perfusion (F), permeability surface area (PS), fractional plasma (f(p)), and interstitial space (f (is))-were obtained at four time points over a 2-week period. A temporal multistage recovery process whereby a decoupling of the fractional change in physiological parameters (f (p), F) was observed when comparing treated to control tumors: f (p) and perfusion decreased by a combined 27% (P < 0.01) and 65% (P < 0.01) on day 2, while only perfusion remained reduced by 46% (P < 0.01) on day 7. Intra-tumor heterogeneity defined by the change in variance of perfusion decreased on days 2 and 7; no change in the variance of f(p) was observed. Analysis based on a mathematical model linking perfusion and vascular morphology indicates that a decrease in f(p) and perfusion was consistent with a reduction in blood vessel radius, followed by an increase in the vascular radius and tortuosity resulting in the decoupling of f(p) and perfusion before returning to control levels. Inhibiting VEGFR2 activity results in a temporal decoupling of physiological parameters, which can be explained by a combination of morphological changes influencing perfusion. Such a decoupling has the potential to significantly impact the delivery of pharmaceuticals and oxygen within solid tumors, critical factors in combined anti-angiogenic and radio- and chemotherapies.

Preliminary assessment of dynamic contrast-enhanced CT implementation in pretreatment FDG-PET/CT for outcome prediction in head and neck tumors

Recently published data show some controversy concerning the impact of [18F]-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) in predicting head and neck tumors (HNT) outcome. Assessment of tumor blood supply parameters using dynamic contrast-enhanced CT (DCE-CT) may deliver additional information concerning this important question. To evaluate the contribution of DCE-CT implemented in pretherapeutic FDG-PET/CT protocol for prognosis prediction in patients with HNT. Ten consecutive patients (median age 50 years, range 47-74 years) with histologically proven HNT underwent FDG-PET/CT with DCE-CT before treatment. FDG uptake was measured by maximum standardized uptake value (SUV(max)). Relative tumor blood volume (rTBV) was determined from DCE-CT using Patlak analysis. Intratumoral heterogeneity was assessed by means of lacunarity analysis. Obtained values were compared with time-to-progression and overall survival. PET and DCE-CT images were compared on a pixel-by-pixel basis using Pearson coefficient of correlation. Three patients with lower FDG uptake (SUV(max): 8+/-1) and five patients with higher FDG uptake (SUV(max): 15+/-4, P=0.004) were free of local recurrence for 24 months. Two groups of patients with significantly differing lower (group A: 0.37+/-0.02, n=6) and higher (group B: 0.52+/-0.01, n=4; P<0.01), tumor heterogeneity (lacunarity) were identified. Corresponding mean rTBV was higher in group A (9.6+/-1.8 ml/100 ml) than in group B (6.2+/-0.6 ml/100 ml). All six patients with homogeneous tumor blood supply (lower lacunarity) and higher rTBV were free of local recurrence during 24 months, while two of four patients with heterogeneous tumor blood supply (higher lacunarity) and lower rTBV died during follow-up due to tumor relapse. A weak correlation between FDG-PET and DCE-CT rTBV was observed (R(2)=0.1). FDG-PET/CT and DCT-CT are complementary methods for surveillance assessment in patients with HNT. Implementation of DCE-CT in the pretreatment FDG-PET/CT protocol may improve tumor outcome prediction.

Dynamic contrast-enhanced CT imaging of hepatocellular carcinoma in cirrhosis: feasibility of a prolonged dual-phase imaging protocol with tracer kinetics modeling

Dynamic contrast-enhanced (DCE) CT imaging of four patients with hepatocellular carcinoma (HCC) was performed using a dual-phase imaging protocol designed with initial rapid dynamic imaging to capture the initial increase in contrast medium enhancement in order to assess perfusion, followed by a delayed imaging phase with progressively longer intervals to monitor subsequent tissue enhancement behaviour in order to assess tissue permeability. The DCE CT images were analysed using a dual-input two-compartment distributed parameter model to yield separate estimates for blood flow and permeability, as well as fractional intravascular and extravascular volumes. The HCCs and surrounding cirrhotic liver tissues were found to exhibit enhancement curves that can be appropriately described by two distinct compartments separated by a semipermeable barrier. Early contrast arrival was also found for HCC as compared with background liver. These findings are consistent with the current understanding of sinusoidal capillarization and hepatocarcinogenesis.

A distributed parameter model of cerebral blood-tissue exchange with account of capillary transit time distribution

Quantitative estimates of physiological parameters associated with cerebral blood flow can be derived from the analysis of dynamic contrast-enhanced (DCE) images, using an appropriate model of the underlying tissue impulse residue function. The theoretical formulation of a distributed parameter model of tissue microcirculation, which accounts for the effects of capillary permeability and transit time distribution, is presented here. This model considers a statistical distribution of capillary-tissue units, each described by a distributed parameter model that accounts for convective transport within the capillary and transcapillary axial diffusion. Monte Carlo simulations were performed to study the confidence of the parameter estimates, and the model was used to analyze DCE CT images of patient study cases with metastatic cerebral tumors. The tumors were found to yield significantly higher estimates than normal tissues for the parameters associated with the extravasation of tracer and for the standard deviation of capillary transit times. The proposed model can be used with DCE imaging to study the microcirculatory characteristics of cerebral tumors.