Automated detection of mass lesions in dedicated breast CT: a preliminary study.
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ABSTRACT:
SUBMITTER: Reiser I
PROVIDER: S-EPMC3277607 | biostudies-other | 2012 Feb
REPOSITORIES: biostudies-other
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Project description:PurposeTo develop and evaluate the diagnostic performance of an algorithm for multi-marker radiomic-based classification of breast masses in dedicated breast computed tomography (bCT) images.MethodsOver 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, and margin sharpness were developed and implemented. These included well-established texture and shape feature descriptors, which were supplemented with additional approaches for contour irregularity quantification, spicule and lobe detection, characterization of degree of infiltration, and differences in peritumoral compartments. All descriptors were extracted from a training set of 202 bCT masses (133 benign and 69 malignant), and their individual diagnostic performance was investigated in terms of area under the receiver operating characteristics (ROC) curve (AUC) of single-feature-based linear discriminant analysis (LDA) classifiers. Subsequently, the most relevant descriptors were selected through a multiple-step feature selection process (including stability analysis, statistical significance, evaluation of feature interaction, and dimensionality reduction), and used to develop a final LDA radiomic model for classification of benign and malignant masses, which was then tested on an independent test set of 82 cases (45 benign and 37 malignant).ResultsThe majority of the individual radiomic descriptors showed, on the training set, an AUC value deriving from a linear decision boundary higher than 0.65, with the lower limit of the associated 95% confidence interval (C.I.) not overlapping with random chance (AUC = 0.5). The final LDA radiomic model resulted in a test set AUC of 0.90 (95% C.I. 0.80-0.96).ConclusionsThe proposed multi-marker radiomic approach achieved high diagnostic accuracy in bCT mass classification, using a radiomic signature based on different feature types. While future studies with larger datasets are needed to further validate these results, quantitative radiomics applied to bCT shows potential to improve the breast cancer diagnosis pipeline.
| S-EPMC7898616 | biostudies-literature
Project description:To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images.The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors' classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images.The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors' automatic classification and manual segmentation were 91.6% ± 2.0%.A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution.
| S-EPMC3477198 | biostudies-other
Project description:To develop a system to facilitate the retrieval of radiologic images that contain similar-appearing lesions and to perform a preliminary evaluation of this system with a database of computed tomographic (CT) images of the liver and an external standard of image similarity.Institutional review board approval was obtained for retrospective analysis of deidentified patient images. Thereafter, 30 portal venous phase CT images of the liver exhibiting one of three types of liver lesions (13 cysts, seven hemangiomas, 10 metastases) were selected. A radiologist used a controlled lexicon and a tool developed for complete and standardized description of lesions to identify and annotate each lesion with semantic features. In addition, this software automatically computed image features on the basis of image texture and boundary sharpness. Semantic and computer-generated features were weighted and combined into a feature vector representing each image. An independent reference standard was created for pairwise image similarity. This was used in a leave-one-out cross-validation to train weights that optimized the rankings of images in the database in terms of similarity to query images. Performance was evaluated by using precision-recall curves and normalized discounted cumulative gain (NDCG), a common measure for the usefulness of information retrieval.When used individually, groups of semantic, texture, and boundary features resulted in various levels of performance in retrieving relevant lesions. However, combining all features produced the best overall results. Mean precision was greater than 90% at all values of recall, and mean, best, and worst case retrieval accuracy was greater than 95%, 100%, and greater than 78%, respectively, with NDCG.Preliminary assessment of this approach shows excellent retrieval results for three types of liver lesions visible on portal venous CT images, warranting continued development and validation in a larger and more comprehensive database.
| S-EPMC2897688 | biostudies-other
Project description:Computed tomographic (CT) images are widely used for the identification of abnormal brain tissue following infarct and hemorrhage in stroke. Manual lesion delineation is currently the standard approach, but is both time-consuming and operator-dependent. To address these issues, we present a method that can automatically delineate infarct and hemorrhage in stroke CT images. The key elements of this method are the accurate normalization of CT images from stroke patients into template space and the subsequent voxelwise comparison with a group of control CT images for defining areas with hypo- or hyper-intense signals. Our validation, using simulated and actual lesions, shows that our approach is effective in reconstructing lesions resulting from both infarct and hemorrhage and yields lesion maps spatially consistent with those produced manually by expert operators. A limitation is that, relative to manual delineation, there is reduced sensitivity of the automated method in regions close to the ventricles and the brain contours. However, the automated method presents a number of benefits in terms of offering significant time savings and the elimination of the inter-operator differences inherent to manual tracing approaches. These factors are relevant for the creation of large-scale lesion databases for neuropsychological research. The automated delineation of stroke lesions from CT scans may also enable longitudinal studies to quantify changes in damaged tissue in an objective and reproducible manner.
| S-EPMC3984449 | biostudies-literature
Project description:Dedicated breast PET/CT is expected to have utility in local staging, surgical planning, monitoring of therapy response, and detection of residual disease for breast cancer. Quantitative metrics will be integral to several such applications. The authors present a validation of fully 3D data correction schemes for a custom built dedicated breast PET/CT (DbPET/CT) scanner via (18)F-FDG phantom scans.A component-based normalization was implemented, live-time was estimated with a multicomponent model, and a variance reduced randoms estimate was computed from delayed coincidences. Attenuation factors were calculated by using a CT based segmentation scheme while scatter was computed using a Monte Carlo (MC) simulation method. As no performance standard currently exists for breast PET systems, custom performance tests were created based on prior patient imaging results. Count-rate linearity for live-time and randoms corrections was measured with a decay experiment for a solid polyethylene cylinder phantom with an offset line source. A MC simulation was used to validate attenuation correction, a multicompartment phantom with asymmetric activity distribution provided an assessment of scatter correction, and image uniformity after geometric and detector normalization was measured from a high count scan of a uniform cylinder phantom. Raw data were reconstructed with filtered back projection (FBP) after Fourier rebinning. To quantify performance absolute activity concentrations, contrast recovery coefficients and image uniformity were calculated through region of interest analysis.The most significant source of error was attributed to mispositioning of events due to pile-up, presenting in count-related axial and transaxial nonuniformities that were not corrected for with the normalization method used here. Within the range of singles counts observed during clinical trials residual error after applying all corrections was comparable to that of a commercial whole body PET/CT system.The results suggest that DbPET/CT is capable of producing quantitative images under the operating conditions expected during patient imaging.
| S-EPMC3350540 | biostudies-other
Project description:To quantify contrast material enhancement of breast lesions scanned with dedicated breast computed tomography (CT) and to compare their conspicuity with that at unenhanced breast CT and mammography.Approval of the institutional review board and the Radiation Use Committee and written informed consent were obtained for this HIPAA-compliant study. Between September 2006 and April 2009, 46 women (mean age, 53.2 years; age range, 35-72 years) with Breast Imaging Reporting and Data System category 4 or 5 lesions underwent unenhanced breast CT and contrast material-enhanced breast CT before biopsy. Two radiologists independently scored lesion conspicuity for contrast-enhanced breast CT versus mammography and for contrast-enhanced breast CT versus unenhanced breast CT. Mean lesion voxel intensity was measured in Hounsfield units and normalized to adipose tissue intensity on manually segmented images obtained before and after administration of contrast material. Regression models focused on conspicuity and quantified enhancement were used to estimate the effect of pathologic diagnosis (benign vs malignant), lesion type (mass vs calcifications), breast density, and interradiologist variability.Fifty-four lesions (25 benign, 29 malignant) in 46 subjects were analyzed. Malignant lesions were seen significantly better at contrast-enhanced breast CT than at unenhanced breast CT (P < .001) or mammography (P < .001). Malignant calcifications (malignant lesions manifested mammographically as microcalcifications only, n = 7) were seen better at contrast-enhanced breast CT than at unenhanced breast CT (P < .001) and were seen similarly at contrast-enhanced breast CT and mammography. Malignant lesions enhanced 55.9 HU +/- 4.0 (standard error), whereas benign lesions enhanced 17.6 HU +/- 6.1 (P < .001). Ductal carcinoma in situ (n = 5) enhanced a mean of 59.6 HU +/- 2.8. Receiver operating characteristic curve analysis of lesion enhancement yielded an area under the receiver operating characteristic curve of 0.876.Conspicuity of malignant breast lesions, including ductal carcinoma in situ, is significantly improved at contrast-enhanced breast CT. Quantifying lesion enhancement may aid in the detection and diagnosis of breast cancer.
| S-EPMC2923727 | biostudies-other
Project description:Dedicated breast CT has great potential for improving the detection and diagnosis of breast cancer. Statistical iterative reconstruction (SIR) in dedicated breast CT is a promising alternative to traditional filtered backprojection (FBP). One of the difficulties in using SIR is the presence of free parameters in the algorithm that control the appearance of the resulting image. These parameters require tuning in order to achieve high quality reconstructions. In this study, the authors investigated the penalized maximum likelihood (PML) method with two commonly used types of roughness penalty functions: hyperbolic potential and anisotropic total variation (TV) norm. Reconstructed images were compared with images obtained using standard FBP. Optimal parameters for PML with the hyperbolic prior are reported for the task of detecting microcalcifications embedded in breast tissue.Computer simulations were used to acquire projections in a half-cone beam geometry. The modeled setup describes a realistic breast CT benchtop system, with an x-ray spectra produced by a point source and an a-Si, CsI:Tl flat-panel detector. A voxelized anthropomorphic breast phantom with 280 μm microcalcification spheres embedded in it was used to model attenuation properties of the uncompressed woman's breast in a pendant position. The reconstruction of 3D images was performed using the separable paraboloidal surrogates algorithm with ordered subsets. Task performance was assessed with the ideal observer detectability index to determine optimal PML parameters.The authors' findings suggest that there is a preferred range of values of the roughness penalty weight and the edge preservation threshold in the penalized objective function with the hyperbolic potential, which resulted in low noise images with high contrast microcalcifications preserved. In terms of numerical observer detectability index, the PML method with optimal parameters yielded substantially improved performance (by a factor of greater than 10) compared to FBP. The hyperbolic prior was also observed to be superior to the TV norm. A few of the best-performing parameter pairs for the PML method also demonstrated superior performance for various radiation doses. In fact, using PML with certain parameter values results in better images, acquired using 2 mGy dose, than FBP-reconstructed images acquired using 6 mGy dose.A range of optimal free parameters for the PML algorithm with hyperbolic and TV norm-based potentials is presented for the microcalcification detection task, in dedicated breast CT. The reported values can be used as starting values of the free parameters, when SIR techniques are used for image reconstruction. Significant improvement in image quality can be achieved by using PML with optimal combination of parameters, as compared to FBP. Importantly, these results suggest improved detection of microcalcifications can be obtained by using PML with lower radiation dose to the patient, than using FBP with higher dose.
| S-EPMC3716792 | biostudies-other
Project description:Dedicated breast CT prototypes used in clinical investigations utilize single circular source trajectory and cone-beam geometry with flat-panel detectors that do not satisfy data-sufficiency conditions and could lead to cone beam artifacts. Hence, this work investigated the glandular dose characteristics of a circle-plus-line trajectory that fulfills data-sufficiency conditions for image reconstruction in dedicated breast CT.Monte Carlo-based computer simulations were performed using the GEANT4 toolkit and was validated with previously reported normalized glandular dose coefficients for one prototype breast CT system. Upon validation, Monte Carlo simulations were performed to determine the normalized glandular dose coefficients as a function of x-ray source position along the line scan. The source-to-axis of rotation distance and the source-to-detector distance were maintained constant at 65 and 100 cm, respectively, in all simulations. The ratio of the normalized glandular dose coefficient at each source position along the line scan to that for the circular scan, defined as relative normalized glandular dose coefficient (RD(g)N), was studied by varying the diameter of the breast at the chest wall, chest-wall to nipple distance, skin thickness, x-ray beam energy, and glandular fraction of the breast.The RD(g)N metric when stated as a function of source position along the line scan, relative to the maximum length of line scan needed for data sufficiency, was found to be minimally dependent on breast diameter, chest-wall to nipple distance, skin thickness, glandular fraction, and x-ray photon energy. This observation facilitates easy estimation of the average glandular dose of the line scan. Polynomial fit equations for computing the RD(g)N and hence the average glandular dose are provided.For a breast CT system that acquires 300-500 projections over 2π for the circular scan, the addition of a line trajectory with equal source spacing and constant x-ray beam quality (kVp and HVL) and mAs matched to the circular scan, will result in less than 0.18% increase in average glandular dose to the breast per projection along the line scan.
| S-EPMC3306439 | biostudies-other
Project description:To determine the mean and range of volumetric glandular fraction (VGF) of the breast in a diagnostic population using a high-resolution flat-panel cone-beam dedicated breast CT system. This information is important for Monte Carlo-based estimation of normalized glandular dose coefficients and for investigating the dependence of VGF on breast dimensions, race, and pathology.Image data from a clinical trial investigating the role of dedicated breast CT that enrolled 150 women were retrospectively analyzed to determine the VGF. The study was conducted in adherence to a protocol approved by the institutional human subjects review boards and written informed consent was obtained from all study participants. All participants in the study were assigned BI-RADS(®) 4 or 5 as per the American College of Radiology assessment categories after standard diagnostic work-up and underwent dedicated breast CT exam prior to biopsy. A Gaussian-kernel based fuzzy c-means algorithm was used to partition the breast CT images into adipose and fibroglandular tissue after segmenting the skin. Upon determination of the accuracy of the algorithm with a phantom, it was applied to 137 breast CT volumes from 136 women. VGF was determined for each breast and the mean and range were determined. Pathology results with classification as benign, malignant, and hyperplasia were available for 132 women, and were used to investigate if the distributions of VGF varied with pathology.The algorithm was accurate to within ±1.9% in determining the volume of an irregular shaped phantom. The study mean (± inter-breast SD) for the VGF was 0.172 ± 0.142 (range: 0.012-0.719). VGF was found to be negatively correlated with age, breast dimensions (chest-wall to nipple length, pectoralis to nipple length, and effective diameter at chest-wall), and total breast volume, and positively correlated with fibroglandular volume. Based on pathology, pairwise statistical analysis (Mann-Whitney test) indicated that at the 0.05 significance level, there was no significant difference in distributions of VGF without adjustment for age between malignant and nonmalignant breasts (p = 0.41). Pairwise comparisons of the distributions of VGF in increasing order of mammographic breast density indicated all comparisons were statistically significant (p < 0.002).This study used a different clinical prototype breast CT system than that in previous studies to image subjects from a different geographical region, and used a different algorithm for analysis of image data. The mean VGF estimated from this study is within the range reported in previous studies, indicating that the choice of 50% glandular weight fraction to represent an average breast for Monte Carlo-based estimation of normalized glandular dose coefficients in mammography needs revising. In the study, the distributions of VGF did not differ significantly with pathology.
| S-EPMC3524966 | biostudies-other
Project description:BackgroundTo investigate the correlation between musculoskeletal mass and perfusion using quantitative computer tomography (QCT) and CT perfusion (CTP) in patients with gastrointestinal malignancy.MethodsIn this prospective study, 96 patients (mean age 66 years, range 25-90; 63.5% male) with gastrointestinal malignancy underwent QCT and CTP between May 2019 and February 2021. Bone mineral density (BMD) and body composition [perivertebral muscular mass index (PMI), skeletal muscular mass index (SMI) and muscular fat fraction] were evaluated through QCT. Musculoskeletal perfusion parameters were measured by CTP. Differences in these parameters between (or among) two (or three) groups (grouped by BMD, SMI, and TNM staging) were analyzed.ResultsThere were significant differences in PMI and muscular fat fraction among normal (n = 30), osteopenia (n = 43), and osteoporosis (n = 23) groups (both P < 0.001). Blood flow (r = 0.336, P = 0.001; adjusted for age and gender, r = 0.383, P < 0.001), blood volume (r = 0.238, P = 0.011; adjusted for age and gender, r = 0.329, P = 0.001), and flow extraction product (r = 0.217, P = 0.034; adjusted for age and gender, r = 0.320, P = 0.002) vaules of vertebral perfusion showed positive correlation with BMD. However, the relationships between PMI and perfusion parameters of perivertebral muscle were not significant. No significant differences were found in musculoskeletal mass and perfusion parameters between different TNM staging.ConclusionsThe changes of bone mass and perivertebral muscular mass in patients with gastrointestinal malignancy are synchronous. Decreased vertebral bone mass is accompanied with reduced perivertebral muscular mass, increased muscular fat, and decreased bone perfusion. However, the changes of perfusion in vertebra and perivertebral muscles are asynchronous. Musculoskeletal mass and perfusion have no correlation with TNM staging of the patients with gastrointestinal malignancy.Trial registrationSHSY-IEC-4.1/20-242/01 (Registered 09-12-2020, Retrospectively registered).
| S-EPMC8991557 | biostudies-literature