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Background: BRCA1/2 deleterious variants account for most of the hereditary breast and ovarian cancer cases. Prediction models and guidelines for the assessment of genetic risk rely heavily on criteria with high variability such as family cancer history. Here we investigated the efficacy of MRI (magnetic resonance imaging) texture features as a predictor for BRCA mutation status., Methods: A total of 41 female breast cancer individuals at high genetic risk, sixteen with a BRCA1/2 pathogenic variant and twenty five controls were included. From each MRI 4225 computer-extracted voxels were analyzed. Non-imaging features including clinical, family cancer history variables and triple negative receptor status (TNBC) were complementarily used. Lasso-principal component regression (L-PCR) analysis was implemented to compare the predictive performance, assessed as area under the curve (AUC), when imaging features were used, and lasso logistic regression or conventional logistic regression for the remaining analyses., Results: Lasso-selected imaging principal components showed the highest predictive value (AUC 0.86), surpassing family cancer history. Clinical variables comprising age at disease onset and bilateral breast cancer yielded a relatively poor AUC (~ 0.56). Combination of imaging with the non-imaging variables led to an improvement of predictive performance in all analyses, with TNBC along with the imaging components yielding the highest AUC (0.94). Replacing family history variables with imaging components yielded an improvement of classification performance of ~ 4%, suggesting that imaging compensates the predictive information arising from family cancer structure., Conclusions: The L-PCR model uncovered evidence for the utility of MRI texture features in distinguishing between BRCA1/2 positive and negative high-risk breast cancer individuals, which may suggest value to diagnostic routine. Integration of computer-extracted texture analysis from MRI modalities in prediction models and inclusion criteria might play a role in reducing false positives or missed cases especially when established risk variables such as family history are missing.

Purpose: The purpose of this research was to evaluate whether a commercially available advanced cardiac software package for coronary CT angiography (CTA) interpretation may reliably assist inexperienced readers to screen for significant coronary artery stenoses., Methods: Coronary CTA data sets of 61 consecutive patients with suspected coronary artery disease were evaluated by three novice readers with no experience in cardiac CT interpretation. In the first 15 patients, the novice readers were trained to use the advanced cardiac software package (includes automatic detection of coronary vessels, curved MPR and VRT reconstructions and a measurement too) knowing the results of an expert read. In the next 46 patients, the novice readers had to state whether there is a significant coronary artery stenosis ([Formula: see text]50 %) and if they are confident with their diagnosis. The results of the novice readers were compared to the expert read., Results: The 46 coronary CTA data sets contained 184 vessels with 15 stenoses in 9 patients. On a per-vessel analysis, novice reader 1/2/3 demonstrated 60 %/100 %/ 93% sensitivity, and 98 %/90 %/86 % specificity. Per patient, the readers diagnosed 36/28/29 cases correctly as free of stenoses, 6/9/8 correctly as having at least one stenosis, missed 3/0/1 cases with a stenosis and overdiagnosed 1/9/8 patients. Cohen's kappa values for the three readers versus the expert were 0.60, 0.61 and 0.54. The three novice readers felt confident in the diagnosis of 36/33/30 patients. In these patients, they missed one significant stenosis, showed a sensitivity of 100 %/100 %/75 % and a specificity of 100 %/92 %/88 %., Conclusions: The evaluated advanced cardiac software package successfully assists novice readers in interpreting coronary CTA data sets especially in ruling out significant coronary artery stenosis.

Rationale and Objectives: To evaluate a computer-aided diagnosis (CADx) system for the characterization of liver lesions in computed tomography (CT) scans. The stand-alone predictive performance of the CADx system was assessed and compared to that of three radiologists who were provided with the same amount of image information to which the CADx system had access., Materials and Methods: The CADx system operates as an image search engine exploiting texture analysis of liver lesion image data for the lesion in question and lesions from a database. A region of interest drawn around an indeterminate liver lesion is used as input query. The CADx system retrieves lesions of similar histology (benign/malignant), density (hypodense/hyperdense), or type (cyst/hemangioma/metastasis). The system's performance was evaluated with leave-one-patient-out receiver operating characteristic area under the curve on 685 CT scans from 372 patients that contained 2325 liver lesions (193 <1 cm(3)). Sensitivity, specificity, and positive and negative predictive values were evaluated separately for subcentimeter lesions. Results were compared to those of three radiologists who rated 83 liver lesions (20 hemangiomas, 20 metastases, 20 cysts, 20 hepatocellular carcinomas, and 3 focal nodular hyperplasias) displaying only the liver., Results: The CADx system's leave-one-patient-out receiver operating characteristic area under the curve was 97.1% for density, 91.4% for histology, and 95.5% for lesion type. For subcentimeter lesions, input of additional semantic information improved the system's performance. The CADx system has been proved to significantly outperform radiologists in discriminating lesion histology and type, provided the radiologists have no access to information other than the image. The radiologists were most reliable in diagnosing hemangioma given the limited image data., Conclusions: The CADx system under study discriminated reliably between various liver lesions, even outperforming radiologists when accessing the same image information and demonstrated promising performance in classifying subcentimeter lesions in particular., (Copyright © 2013 AUR. Published by Elsevier Inc. All rights reserved.)

Objective: To evaluate a computer-aided detection (CADe) system for lytic and blastic spinal metastases on computed tomography (CT)., Methods: We retrospectively evaluated the CADe system on 20 consecutive patients with 42 lytic and on 30 consecutive patients with 172 blastic metastases. The CADe system was trained using CT images of 114 subjects with 102 lytic and 308 blastic spinal metastases. Lesions were annotated by experienced radiologists. Detected benign lesions were considered false-positive findings. Detector sensitivity and the number of false-positive findings were calculated as the criteria for detector performance, and free-response receiver operating characteristic (FROC) analysis was conducted. Detailed analysis of false-positive and false-negative findings was performed., Results: Algorithm runtime is 3 ± 0.5 min per patient. The system achieves a sensitivity of 83 % at 3.5 false positives per patient on average for blastic metastases and a sensitivity of 88 % at 3.7 false positives for lytic metastases. False positives appeared predominantly in the area of degenerative changes in the case of the blastic metastasis detector and in osteoporotic areas in the case of the lytic metastasis detector., Conclusion: The CADe system reliably detects thoracolumbar spine metastases in real time. An additional study is planned to evaluate how the bone lesion CADe system improves radiologists' accuracy and efficiency in a clinical setting., Key Points: • Computer-aided detection (CADe) of bone metastases has been developed for spinal CT. • The CADe system exhibits high sensitivity with a tolerable false-positive rate. • Analysis of false-positive detection may further improve the system. • CADe may reduce the number of missed spinal metastases at CT interpretation.

Being able to segment the esophagus without user interaction from 3-D CT data is of high value to radiologists during oncological examinations of the mediastinum. The segmentation can serve as a guideline and prevent confusion with pathological tissue. However, limited contrast to surrounding structures and versatile shape and appearance make segmentation a challenging problem. This paper presents a multistep method. First, a detector that is trained to learn a discriminative model of the appearance is combined with an explicit model of the distribution of respiratory and esophageal air. In the next step, prior shape knowledge is incorporated using a Markov chain model. We follow a "detect and connect" approach to obtain the maximum a posteriori estimate of the approximate esophagus shape from hypothesis about the esophagus contour in axial image slices. Finally, the surface of this approximation is nonrigidly deformed to better fit the boundary of the organ. The method is compared to an alternative approach that uses a particle filter instead of a Markov chain to infer the approximate esophagus shape, to the performance of a human observer and also to state of the art methods, which are all semiautomatic. Cross-validation on 144 CT scans showed that the Markov chain based approach clearly outperforms the particle filter. It segments the esophagus with a mean error of 1.80 mm in less than 16 s on a standard PC. This is only 1 mm above the interobserver variability and can compete with the results of previously published semiautomatic methods.

In this paper, a method is described to automatically estimate the visible body region of a computed tomography (CT) volume image. In order to quantify the body region, a body coordinate (BC) axis is used that runs in longitudinal direction. Its origin and unit length are patient-specific and depend on anatomical landmarks. The body region of a test volume is estimated by registering it only along the longitudinal axis to a set of reference CT volume images with known body coordinates. During these 1D registrations, an axial image slice of the test volume is compared to an axial slice of a reference volume by extracting a descriptor from both slices and measuring the similarity of the descriptors. A slice descriptor consists of histograms of visual words. Visual words are code words of a quantized feature space and can be thought of as classes of image patches with similar appearance. A slice descriptor is formed by sampling a slice on a regular 2D grid and extracting a Speeded Up Robust Features (SURF) descriptor at each sample point. The codebook, or visual vocabulary, is generated in a training step by clustering SURF descriptors. Each SURF descriptor extracted from a slice is classified into the closest visual word (or cluster center) and counted in a histogram. A slice is finally described by a spatial pyramid of such histograms. We introduce an extension of the SURF descriptors to an arbitrary number of dimensions (N-SURF). Here, we make use of 2-SURF and 3-SURF descriptors. Cross-validation on 84 datasets shows the robustness of the results. The body portion can be estimated with an average error of 15.5mm within 9s. Possible applications of this method are automatic labeling of medical image databases and initialization of subsequent image analysis algorithms., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Automatic segmentation of the esophagus from CT data is a challenging problem. Its wall consists of muscle tissue, which has low contrast in CT. Sometimes it is filled with air or remains of orally given contrast agent. While air holes are a clear hint to a human when searching for the esophagus, we found that they are rather distracting to discriminative models of the appearance because of their similarity to the trachea and to lung tissue. However, air inside the respiratory organs can be segmented easily. In this paper, we propose to combine a model based segmentation algorithm of the esophagus with a spatial probability map generated from detected air. Threefold cross-validation on 144 datasets showed that this probability map, combined with a technique that puts more focus on hard cases, increases accuracy by 22%. In contrast to prior work, our method is not only automatic on a manually selected region of interest, but on a whole thoracic CT scan, while our mean segmentation error of 1.80mm is even better.

Automated segmentation of the esophagus in CT images is of high value to radiologists for oncological examinations of the mediastinum. It can serve as a guideline and prevent confusion with pathological tissue. However, segmentation is a challenging problem due to low contrast and versatile appearance of the esophagus. In this paper, a two step method is proposed which first finds the approximate shape using a "detect and connect" approach. A classifier is trained to find short segments of the esophagus which are approximated by an elliptical model. Recently developed techniques in discriminative learning and pruning of the search space enable a rapid detection of possible esophagus segments. Prior shape knowledge of the complete esophagus is modeled using a Markov chain framework, which allows efficient inferrence of the approximate shape from the detected candidate segments. In a refinement step, the surface of the detected shape is non-rigidly deformed to better fit the organ boundaries. In contrast to previously proposed methods, no user interaction is required. It was evaluated on 117 datasets and achieves a mean segmentation error of 2.28mm with less than 9s computation time.