Computed Tomography Imaging Parameters Research Articles

Initial computed tomography imaging details during first-line systemic therapy is of significant prognostic value in patients with naïve, unresectable metastatic renal cell carcinoma.

PurposeWe aimed to determine the prognostic significance of computed tomography imaging parameters of unresectable primary renal tumor lesions, obtained at baseline and at first follow-up, on overall survival in naïve, unresectable metastatic renal cell carcinoma patients during first-line systemic therapy.Materials and methodsClinicopathological parameters of 56 patients treated between 2007 and 2015, including imaging parameters (such as the longest tumor diameter, necrotic area diameter, and attenuation in primary renal tumor lesions on baseline vs. follow-up computed tomography), were retrospectively reviewed to derive predictive factors of overall survival. The best overall response was measured according to the RECIST v1.1.ResultsThe median treatment period was 206.3 days and the median follow-up was 14.6 months. Forty-four (78.6%) patients progressed after a median 4.6 months of progression-free survival, and 6 (10.7%) patients survived with a median overall survival of 12.5 months. Multivariate analysis showed that the baseline tumor diameter (hazard ratio [HR] 0.903) and mean attenuation (HR 0.936), change of tumor diameter (HR 0.714) and necrosis diameter (HR 0.861), change in the percentage of tumor diameter (HR 1.483) and of necrosis diameter (HR 1.028) between baseline and follow-up computed tomography images; treatment duration (HR 0.986) and baseline serum hemoglobin (HR 1.790) and albumin level (HR 0.060) were significant factors for overall survival (p<0.05).ConclusionThe study showed that baseline and first follow-up computed tomography findings of primary renal lesions during first-line systemic therapy are useful and significant predictors of OS in patients with naïve unresectable mRCC.

A Lesion-Based Response Prediction Model Using Pretherapy PET/CT Image Features for Y90 Radioembolization to Hepatic Malignancies

We present a probabilistic approach to identify patients with primary and secondary hepatic malignancies as responders or nonresponders to yttrium-90 radioembolization therapy. Recent advances in computer-aided detection have decreased false-negative and false-positive rates of perceived abnormalities; however, there is limited research in using similar concepts to predict treatment response. Our approach is driven by the goal of precision medicine to determine pretherapy fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography imaging parameters to facilitate the identification of patients who would benefit most from yttrium-90 radioembolization therapy, while avoiding complex and costly procedures for those who would not. Our algorithm seeks to predict a patient's response by discovering common co-occurring image patterns in the lesions of baseline fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography scans by extracting invariant shape and texture features. The extracted imaging features were represented as a distribution of each subject based on the bag-of-feature paradigm. The distribution was applied in a multinomial naive Bayes classifier to predict whether a patient would be a responder or nonresponder to yttrium-90 radioembolization therapy based on the imaging features of a pretherapy fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography scan. Comprehensive published criteria were used to determine lesion-based clinical treatment response based on fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography imaging findings. Our results show that the model is able to predict a patient with liver cancer as a responder or nonresponder to yttrium-90 radioembolization therapy with a sensitivity of 0.791 using extracted invariant imaging features from the pretherapy fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography test. The sensitivity increased to 0.821 when combining extracted invariant image features with variable features of tumor volume.

Computed tomography imaging parameters for inhomogeneity correction in radiation treatment planning.

Modern treatment planning systems provide accurate dosimetry in heterogeneous media (such as a patient' body) with the help of tissue characterization based on computed tomography (CT) number. However, CT number depends on the type of scanner, tube voltage, field of view (FOV), reconstruction algorithm including artifact reduction and processing filters. The impact of these parameters on CT to electron density (ED) conversion had been subject of investigation for treatment planning in various clinical situations. This is usually performed with a tissue characterization phantom with various density plugs acquired with different tube voltages (kilovoltage peak), FOV reconstruction and different scanners to generate CT number to ED tables. This article provides an overview of inhomogeneity correction in the context of CT scanning and a new evaluation tool, difference volume dose-volume histogram (DVH), dV-DVH. It has been concluded that scanner and CT parameters are important for tissue characterizations, but changes in ED are minimal and only pronounced for higher density materials. For lungs, changes in CT number are minimal among scanners and CT parameters. Dosimetric differences for lung and prostate cases are usually insignificant (<2%) in three-dimensional conformal radiation therapy and < 5% for intensity-modulated radiation therapy (IMRT) with CT parameters. It could be concluded that CT number variability is dependent on acquisition parameters, but its dosimetric impact is pronounced only in high-density media and possibly in IMRT. In view of such small dosimetric changes in low-density medium, the acquisition of additional CT data for financially difficult clinics and countries may not be warranted.

2D versus 3D imaging of hip displacement in children with cerebral palsy

2D versus 3D imaging of hip displacement in children with cerebral palsy

Lung metastases detection in CT images using 3D template matching

The aim of this study is to demonstrate a novel, fully automatic computer detection method applicable to metastatic tumors to the lung with a diameter of 4-20 mm in high-risk patients using typical computed tomography (CT) scans of the chest. Three-dimensional (3D) spherical tumor appearance models (templates) of various sizes were created to match representative CT imaging parameters and to incorporate partial volume effects. Taking into account the variability in the location of CT sampling planes cut through the spherical models, three offsetting template models were created for each appearance model size. Lung volumes were automatically extracted from computed tomography images and the correlation coefficients between the subregions around each voxel in the lung volume and the set of appearance models were calculated using a fast frequency domain algorithm. To determine optimal parameters for the templates, simulated tumors of varying sizes and eccentricities were generated and superposed onto a representative human chest image dataset. The method was applied to real image sets from 12 patients with known metastatic disease to the lung. A total of 752 slices and 47 identifiable tumors were studied. Spherical templates of three sizes (6, 8, and 10 mm in diameter) were used on the patient image sets; all 47 true tumors were detected with the inclusion of only 21 false positives. This study demonstrates that an automatic and straightforward 3D template-matching method, without any complex training or postprocessing, can be used to detect small lung metastases quickly and reliably in the clinical setting.

Accuracy limits for the determination of cortical width and density: the influence of object size and CT imaging parameters

In this study we analysed the accuracy of computed tomography (CT) measurements in assessing cortical bone. We determined the dependency of thickness and density measurements on the true width and density of the cortex and on the spatial resolution in the CT images using two optimized segmentation methods. As a secondary goal, we assessed the ability of CT to reflect small changes in cortical thickness. Two different bone-mimicking phantoms with varying cortical thickness were scanned with single-slice CT on a Somatom Plus 4 scanner. Images were reconstructed with both a standard and a high-resolution convolution kernel. Two special operator-independent segmentation methods were used to automatically detect the edges of the cortical shell. We measured cortical thickness and density and compared the phantom measurements with theoretical computations by simulating a cross-sectional shape of the cortical shell. Based on the simulations, we calculated CT's power to detect small changes in cortical thickness. Simulations and phantom measurements were in very good agreement. Cortical thickness could be measured with an error of less than 10% if the true thickness was larger than 0.9 (0.7) mm for the standard (high-resolution) kernel which is close to the full width at half maximum (FWHM) of the point spread functions for these kernels and our scanner. Density measurements yielded errors of less than 10% for true cortical thickness values above two to three times the FWHM corresponding to 2.5 (2) mm in our case. The simulations showed that a 10% change in cortical width would not be detected with satisfying probability in bones with a cortical shell thinner than 1.2 mm. An accurate determination of the cortical thickness is limited to bones with a thickness higher than the FWHM of the scanner's point spread function. Therefore, the use of a high-resolution reconstruction kernel is crucial. Cortical bone mineral density can only be measured accurately in bones two to three times thicker than this number. In thinner bones, the measured density becomes dependent on the thickness. Changes in cortical thickness can only be assessed if the change is rather large or if the measured bone has sufficient thickness. Therefore, assessing density or thickness of the vertebral shell by CT should be treated with caution.

Rupture of posterior tibial tendon: CT and MR imaging with surgical correlation.

Computed tomography (CT) and magnetic resonance (MR) imaging were performed in 32 cases of clinically suspected chronic tears of the posterior tibial tendon. Surgery was performed in 22 patients (69%). Each case was classified radiographically and surgically as normal or a type 1, type 2, or type 3 rupture. The sensitivity and specificity of CT were 90% and 100%, respectively, while those of MR imaging were 95% and 100%. The accuracy in detecting ruptures was 91% for CT and 96% for MR imaging. The overall accuracy, which reflected the percentage of cases correctly diagnosed as well as those correctly classified, was 59% for CT and 73% for MR imaging. Although the differences between the CT and MR imaging parameters were not statistically significant (possibly due to the small population), the results suggest that MR imaging is the method of choice for detecting ruptures of the posterior tibial tendon. MR imaging provided greater definition of tendon outline, vertical splits, synovial fluid, edema, and degenerated tissue. CT was superior to MR imaging in showing associated bone abnormalities such as periostitis, subtalar osteoarthritis, and subtalar dislocation.

Mediastinal masses: A system for diagnosis based on computed tomography

A revised system for mediastinal mass differentiation has been developed and used at the Veterans Administration Medical Center, San Diego, and University Hospital of the University of California, San Diego. Contrast medium infusion or bolus computed tomography of the entire mediastinum is obtained, except when the mass is probably a thyroid mass (1231 scan then obtained first) or an esophageal mass (barium swallow evaluation then obtained first). Next computed tomography density is determined, and only subsequently are masses of similar density subdivided by location. Further differentiation is based on computed tomography imaging parameters. A new algorithm for evaluation of mediastinal masses has resulted from use of this system. The system has been shown to increase specificity of differential diagnosis and to result in increased efficiency of evaluation of patients with mediastinal masses.