Ekta Dharaiya

Lung nodule CAD software as a second reader: a multicenter study.

RATIONALE AND OBJECTIVES The purpose of this multicenter, multireader study was to evaluate the performance of computed tomography (CT) lung nodule computer-aided detection (CAD) software as a second reader. METHODS AND MATERIALS The study involved 109 patients from four sites. The data were collected from a variety of multidetector CT scanners and had different scan parameters. Each chest CT scan was divided into four quadrants. A group of three expert thoracic radiologists identified nodules between 4 and 30 mm in maximum diameter within each quadrant. The standard of reference was established by a consensus read of these experienced radiologists. The cases were then interpreted by 10 other radiologist readers with varying degrees of experience, without and then with CAD software. These readers identified nodules and assigned an actionability rating to each quadrant before and after using CAD software. Receiver operating characteristic curves were used to measure the performance of the readers without and with CAD software. RESULTS The average increase in area under the curve for the 10 readers with CAD software was 1.9% for a 95% confidence interval (0.8-8.0%). The area under the curve without CAD software was 86.7% and with CAD software was 88.7%. A nonsignificant correlation was observed between the improvement in sensitivity and experience of the radiologists. The readers also showed a greater improvement in patients with cancer as compared to those without cancer. CONCLUSIONS In this multicenter trial, CAD software was shown to be effective as a second reader by improving the sensitivity of the radiologists in detecting pulmonary nodules.

Assessment of COPD severity by computed tomography: correlation with lung functional testing

CT scans of 474 patients with suspected chronic obstructive pulmonary disease (COPD) were retrospectively evaluated by automated software. There was a correlation between the total lung capacity (TLC) and the total lung volume (TLV) (r=.675, P<.001), between the TLC and the total emphysema volume (r=.571, P<.001), as well as between the TLC and the emphysema index (r=.532, P<.001), respectively. The correlation between the TLC and the TLV was dependent on the COPD severity according to the Global Initiative for Chronic Obstructive Lung Disease classification (chi(2)=6.3079, P=.043). The TLC allows a prediction of clinical illness severity.

Low radiation dose ECG-gated chest CT angiography on a 256-slice multidetector CT scanner

Computed tomography angiography (CTA) of the thorax other than cardiac CTA, is utilized for a multitude of conditions and ranges in application from a diagnostic test, to presurgical planning and postsurgical follow-up. Helical CTA without electrocardiogram (ECG) gating has been routinely utilized for the evaluation of thoracic vasculature. However, its applicability can be limited in the evaluation of the thoracic aorta and pulmonary vasculature because of the artifacts resulting from cardiac motion. Traditional retrospective ECG-gated helical scans address this issue but at the price of a high radiation dose to the patient. In this paper we review CTA dose reduction strategies for non-coronary indications, examine field of view requirements, and discuss breath hold challenges for ECG-gated acquisitions. In addition, we present clinical examples performed using low-dose prospective gating technique for evaluation of the aorta acquired on a 256-slice multidetector computed tomography system.

The Vancouver Lung Cancer Risk Prediction Model: Assessment by Using a Subset of the National Lung Screening Trial Cohort.

Purpose To assess the likelihood of malignancy among a subset of nodules in the National Lung Screening Trial (NLST) by using a risk calculator based on nodule and patient characteristics. Materials and Methods All authors received approval for use of NLST data. An institutional review board exemption and a waiver for informed consent were granted to the author with an academic appointment. Nodule characteristics and patient attributes with regard to benign and malignant nodules in the NLST were applied to a nodule risk calculator from a group in Vancouver, Canada. Patient populations and their nodule characteristics were compared between the NLST and Vancouver cohorts. Multiple thresholds were tested to distinguish benign nodules from malignant nodules. An optimized threshold value was used to determine positive and negative predictive values, and a full logistic regression model was applied to the NLST data set. Results Sufficient data were available for 4431 nodules (4315 benign nodules and 116 malignant nodules) from the NLST data set. The NLST and Vancouver data sets differed in that the former included fewer nodules per study, fewer nonsolid nodules, and more nodule spiculation and emphysema. A composite risk score threshold of 10% was determined to be optimal, demonstrating sensitivity, specificity, positive predictive value, and negative predictive value of 85.3%, 93.9%, 27.4%, and 99.6%, respectively. The receiver operating characteristic curve for the full regression model applied to the NLST database demonstrated an area under the receiver operating characteristic curve of 0.963 (95% confidence interval: 0.945, 0.974). Conclusion Application of an NLST data subset to the Vancouver risk calculator yielded a high discriminant value, which supports the use of a risk calculator method as a valuable approach to distinguish between benign and malignant nodules.

Lung nodule computer-aided detection as a second reader: influence on radiology residents.

Objective: The purpose of this study was to evaluate the use of a computed tomographic lung nodule computer-aided detection (CAD) software as a second reader for radiology residents. Methods: The study involved 110 cases from 4 sites. Three expert radiologists identified nodules that were 4 to 30 mm in maximum diameter to form the ground truth. These cases were then interpreted by 6 board-certified radiologists and 6 radiology residents. The residents read each case without and then with a CAD software (Lung Nodule Assesment, Extended Brilliance Workspace; Philips Healthcare, Highlands Heights, OH) to identify nodules that were 4 to 30 mm in maximum diameter. Results: The experts identified 91 nodules as the ground truth for the study. The mean sensitivity of the 6 board-certified radiologists was 89%. The mean sensitivity of the residents was 85% without the CAD and 90% (P < 0.05) with the CAD as a second reader. Conclusions: The CAD software can help improve the sensitivity of residents in the detection of pulmonary nodules on computed tomography, making them comparable with board-certified radiologists.

Filter learning and evaluation of the computer aided visualization and analysis (CAVA) paradigm for pulmonary nodules using the LIDC-IDRI database

We present a simple rendering scheme for thoracic CT datasets which yields a color coding based on local differential geometry features rather than Hounsfield densities. The local curvatures are computed on several resolution scales and mapped onto different colors, thereby enhancing nodular and tubular structures. The rendering can be used as a navigation device to quickly access points of possible chest anomalies, in particular lung nodules and lymph nodes. The underlying principle is to use the nodule enhancing overview as a possible alternative to classical CAD approaches by avoiding explicit graphical markers. For performance evaluation we have used the LIDC-IDRI lung nodule data base. Our results indicate that the nodule-enhancing overview correlates well with the projection images produced from the IDRI expert annotations, and that we can use this measure to optimize the combination of differential geometry filters.

Toward computer-aided emphysema quantification on ultralow-dose CT: reproducibility of ventrodorsal gravity effect measurement and correction

Computer aided quantification of emphysema in high resolution CT data is based on identifying low attenuation areas below clinically determined Hounsfield thresholds. However, the emphysema quantification is prone to error since a gravity effect can influence the mean attenuation of healthy lung parenchyma up to ± 50 HU between ventral and dorsal lung areas. Comparing ultra-low-dose (7 mAs) and standard-dose (70 mAs) CT scans of each patient we show that measurement of the ventrodorsal gravity effect is patient specific but reproducible. It can be measured and corrected in an unsupervised way using robust fitting of a linear function.

Performance study of a globally elastic locally rigid matching algorithm for follow-up chest CT

A real-time matching algorithm for follow-up chest CT scans can significantly reduce the workload on radiologists by automatically finding the corresponding location in the first or second scan, respectively. The objective of this study was to assess the accuracy of a fast and versatile single-point registration algorithm for thoracic CT scans. The matching algorithm is based on automatic lung segmentations in both CT scans, individually for left and right lung. Whenever the user clicks on an arbitrary structure in the lung, the coarse position of the corresponding point in the other scan is identified by comparing the volume percentiles of the lungs. Then the position is refined by optimizing the gray value cross-correlation of a local volume of interest. The algorithm is able to register any structure in or near the lungs, but is of clinical interest in particular with respect to lung nodules and airways. For validation, CT scan pairs were used in which the patients were scanned twice in one session, using low-dose non-contrast-enhanced chest CT scans (0.75 mm collimation). Between these scans, patients got off and on the table to simulate a follow-up scan. 291 nodules were evaluated. Average nodule diameter was 9.5 mm (range 2.9 - 74.1 mm). Automatic registration succeeded in 95.2% of all cases (277 / 291). In successful registered nodules, average registration consistency was 1.1 mm. The real-time matching proved to be an accurate and useful tool for radiologists evaluating follow-up chest CT scans to assess possible nodule growth.

Informatics in Radiology: Hesse Rendering for Computer-aided Visualization and Analysis of Anomalies at Chest CT and Breast MR Imaging

A volume-rendering (VR) technique known as Hesse rendering applies image-enhancement filters to three-dimensional imaging volumes and depicts the filter responses in a color-coded fashion. Unlike direct VR, which makes use of intensities, Hesse rendering operates on the basis of shape properties, such that nodular structures in the resulting renderings have different colors than do tubular structures and thus are easily visualized. The renderings are mouse-click sensitive and can be used to navigate to locations of possible anomalies in the original images. Hesse rendering is meant to complement rather than replace conventional section-by-section viewing or VR. Although it is a pure visualization technique that involves no internal segmentation or explicit object detection, Hesse rendering, like computer-aided detection, may be effective for quickly calling attention to points of interest in large stacks of images and for helping radiologists to avoid oversights.

Repeatability and noise robustness of spicularity features for computer aided characterization of pulmonary nodules in CT

Computer aided characterization aims to support the differential diagnosis of indeterminate pulmonary nodules. A number of published studies have correlated automatically computed features from image processing with clinical diagnoses of malignancy vs. benignity. Often, however, a high number of features was trained on a relatively small number of diagnosed nodules, raising a certain skepticism as to how salient and numerically robust the various features really are. On the way towards computer aided diagnosis which is trusted in clinical practice, the credibility of the individual numerical features has to be carefully established. Nodule volume is the most crucial parameter for nodule characterization, and a number of studies are testing its repeatability. Apart from functional parameters (such as dynamic CT enhancement and PET uptake values), the next most widely used parameter is the surface characteristic (vascularization, spicularity, lobulation, smoothness). In this study, we test the repeatability of two simple surface smoothness features which can discriminate between smoothly delineated nodules and those with a high degree of surface irregularity. Robustness of the completely automatically computed features was tested with respect to the following aspects: (a) repeated CT scan of the same patient with equal dose, (b) repeated CT scan with much lower dose and much higher noise, (c) repeated automatic segmentation of the nodules using varying segmentation parameters, resulting in differing nodule surfaces. The tested nodules (81) were all solid or partially solid and included a high number of sub- and juxtapleural nodules. We found that both tested surface characterization features correlated reasonably well with each other (80%), and that in particular the mean-surface-shape-index showed an excellent repeatability: 98% correlation between equal dose CT scans, 93% between standard-dose and low-dose scan (without systematic shift), and 97% between varying HU-threshold of the automatic segmentation, which makes it a reliable feature to be used in computer aided diagnosis.