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1.  Automated Lung Segmentation of Diseased and Artifact-Corrupted MR Sections 
Medical physics  2006;33(9):3085-3093.
Segmentation of the lungs within magnetic resonance (MR) scans is a necessary step in the computer-based analysis of thoracic MR images. This process is often confounded by image acquisition artifacts and disease-induced morphological deformation. We have developed an automated method for lung segmentation that is insensitive to these complications. The automated method was applied to 23 thoracic MR scans (413 sections) obtained from 10 patients. Two radiologists manually outlined the lung regions in a random sample of 101 sections (n=202 lungs), and the extent to which disease or artifact confounded lung border visualization was evaluated. Accuracy of lung regions extracted by the automated segmentation method was quantified by comparison with the radiologist-defined lung regions using an area overlap measure (AOM) that ranged from 0 (disjoint lung regions) to 1 (complete overlap). The AOM between each observer and the automated method was 0.82 when averaged over all lungs. The average AOM in the lung bases, where lung segmentation is most difficult, was 0.73.
PMCID: PMC3985425  PMID: 17022200
segmentation; magnetic resonance imaging (MRI); image processing; cardiac motion artifact; pulmonary motion artifact; computer-aided diagnosis (CAD)
2.  Lung Volume Measurements as a Surrogate Marker for Patient Response in Malignant Pleural Mesothelioma 
The purpose of this study was to investigate continuous changes in three distinct response assessment methods during treatment as a marker of response for mesothelioma patients. Linear tumor thickness measurements, disease volume measurements, and lung volume measurements (a physiological correlate of disease volumes) were investigated in this study.
Serial CT scans were obtained during the course of clinically standard chemotherapy for 61 patients. For each of the 216 CT scans the aerated lung volumes were segmented using a fully automated method, and the pleural disease volume was segmented using a semi-automated method. Modified RECIST linear thickness measurements were acquired clinically. Diseased (ipsilateral) lung volumes were normalized by the respective contralateral lung volumes to account for differences in inspiration between scans for each patient. Relative changes in each metric from baseline were tracked over the course of follow-up imaging. Survival modeling was performed using Cox proportional hazards models with time-varying covariates.
Median survival from pre-treatment baseline imaging was 12.7 months. A negative correlation was observed between measurements of lung volume and disease volume, and a positive correlation was observed between linear thickness measurements and disease volume. As continuous numerical parameters, all three response assessment methods were significant imaging biomarkers of patient prognosis in independent survival models.
Analysis of trajectories of linear thickness measurements, disease volume measurements, and lung volume measurements during chemotherapy for patients with mesothelioma indicates that increasing linear thickness, increasing disease volume, and decreasing lung volume are all significantly and independently associated with poor patient prognosis.
PMCID: PMC3597989  PMID: 23486268
3.  Multicenter, Double-Blind, Placebo-Controlled, Randomized Phase II Trial of Gemcitabine/Cisplatin Plus Bevacizumab or Placebo in Patients With Malignant Mesothelioma 
Journal of Clinical Oncology  2012;30(20):2509-2515.
Gemcitabine plus cisplatin is active in malignant mesothelioma (MM), although single-arm phase II trials have reported variable outcomes. Vascular endothelial growth factor (VEGF) inhibitors have activity against MM in preclinical models. We added the anti-VEGF antibody bevacizumab to gemcitabine/cisplatin in a multicenter, double-blind, placebo-controlled randomized phase II trial in patients with previously untreated, unresectable MM.
Patients and Methods
Eligible patients had an Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 1 and no thrombosis, bleeding, or major blood vessel invasion. The primary end point was progression-free survival (PFS). Patients were stratified by ECOG performance status (0 v 1) and histologic subtype (epithelial v other). Patients received gemcitabine 1,250 mg/m2 on days 1 and 8 every 21 days, cisplatin 75 mg/m2 every 21 days, and bevacizumab 15 mg/kg or placebo every 21 days for six cycles, and then bevacizumab or placebo every 21 days until progression.
One hundred fifteen patients were enrolled at 11 sites; 108 patients were evaluable. Median PFS time was 6.9 months for the bevacizumab arm and 6.0 months for the placebo arm (P = .88). Median overall survival (OS) times were 15.6 and 14.7 months in the bevacizumab and placebo arms, respectively (P = .91). Partial response rates were similar (24.5% for bevacizumab v 21.8% for placebo; P = .74). A higher pretreatment plasma VEGF concentration (n = 56) was associated with shorter PFS (P = .02) and OS (P = .0066), independent of treatment arm. There were no statistically significant differences in toxicity of grade 3 or greater.
The addition of bevacizumab to gemcitabine/cisplatin in this trial did not significantly improve PFS or OS in patients with advanced MM.
PMCID: PMC3397785  PMID: 22665541
4.  Optimization of Response Classification Criteria for Patients with Malignant Pleural Mesothelioma 
Response assessment metrics play an important role in clinical trials and routine patient management. For patients with malignant pleural mesothelioma (MPM), the standard for response assessment is image-based measurements of tumor thickness made according to the modified RECIST (Response Evaluation Criteria in Solid Tumors) protocol. To classify tumor response, changes in tumor thickness are compared with the standard RECIST −30% and +20% cut-points for partial response (PR) and progressive disease (PD), respectively, which are not specific to MPM. The purpose of this work is to optimize the correlation between tumor response and patient survival by assessing the validity of existing response criteria in MPM, and proposing alternative criteria.
CT measurements of tumor thickness were acquired at baseline and throughout treatment for 78 patients undergoing standard of care chemotherapy. Overall survival was correlated with best response and first follow-up response using Harrell’s C statistic. The response criteria for PD and PR were each varied in 1% increments to obtain the optimal classification criteria. The performance was cross-validated using a leave-one-out approach.
Median survival was 14.9 months. The performance of the standard RECIST criteria in correlating response with survival was 0.778, while the optimized performance was obtained with criteria of −64% for PR and +50% for PD, yielding a performance of 0.855. After cross-validation, this performance was slightly reduced to 0.829.
New tumor response classification criteria were obtained for patients with MPM. These criteria improve the correlation between image-based response and patient survival.
PMCID: PMC3473122  PMID: 23059782
5.  Consensus Versus Disagreement in Imaging Research: a Case Study Using the LIDC Database 
Journal of Digital Imaging  2011;25(3):423-436.
Traditionally, image studies evaluating the effectiveness of computer-aided diagnosis (CAD) use a single label from a medical expert compared with a single label produced by CAD. The purpose of this research is to present a CAD system based on Belief Decision Tree classification algorithm, capable of learning from probabilistic input (based on intra-reader variability) and providing probabilistic output. We compared our approach against a traditional decision tree approach with respect to a traditional performance metric (accuracy) and a probabilistic one (area under the distance–threshold curve—AuCdt). The probabilistic classification technique showed notable performance improvement in comparison with the traditional one with respect to both evaluation metrics. Specifically, when applying cross-validation technique on the training subset of instances, boosts of 28.26% and 30.28% were noted for the probabilistic approach with respect to accuracy and AuCdt, respectively. Furthermore, on the validation subset of instances, boosts of 20.64% and 23.21% were noted again for the probabilistic approach with respect to the same two metrics. In addition, we compared our CAD system results with diagnostic data available for a small subset of the Lung Image Database Consortium database. We discovered that when our CAD system errs, it generally does so with low confidence. Predictions produced by the system also agree with diagnoses of truly benign nodules more often than radiologists, offering the possibility of reducing the false positives.
PMCID: PMC3348979  PMID: 22193755
Chest CT; Computer-aided diagnosis (CAD); Feature extraction; Image analysis; Machine learning; Radiographic image interpretation; Computer-assisted
6.  Assessment of Radiologist Performance in the Detection of Lung Nodules: Dependence on the Definition of “Truth” 
Academic radiology  2009;16(1):28-38.
Rationale and Objectives
Studies that evaluate the lung-nodule-detection performance of radiologists or computerized methods depend on an initial inventory of the nodules within the thoracic images (the “truth”). The purpose of this study was to analyze (1) variability in the “truth” defined by different combinations of experienced thoracic radiologists and (2) variability in the performance of other experienced thoracic radiologists based on these definitions of “truth” in the context of lung nodule detection on computed tomography (CT) scans.
Materials and Methods
Twenty-five thoracic CT scans were reviewed by four thoracic radiologists, who independently marked lesions they considered to be nodules ≥ 3 mm in maximum diameter. Panel “truth” sets of nodules then were derived from the nodules marked by different combinations of two and three of these four radiologists. The nodule-detection performance of the other radiologists was evaluated based on these panel “truth” sets.
The number of “true” nodules in the different panel “truth” sets ranged from 15–89 (mean: 49.8±25.6). The mean radiologist nodule-detection sensitivities across radiologists and panel “truth” sets for different panel “truth” conditions ranged from 51.0–83.2%; mean false-positive rates ranged from 0.33–1.39 per case.
Substantial variability exists across radiologists in the task of lung nodule identification in CT scans. The definition of “truth” on which lung nodule detection studies are based must be carefully considered, since even experienced thoracic radiologists may not perform well when measured against the “truth” established by other experienced thoracic radiologists.
PMCID: PMC2658894  PMID: 19064209
lung nodule; computed tomography (CT); thoracic imaging; inter-observer variability; computer-aided diagnosis (CAD)
7.  Three-Dimensional Stereoscopic Volume Rendering of Malignant Pleural Mesothelioma 
International Surgery  2012;97(1):65-70.
Our objective was to investigate the application of three-dimensional (3D) stereoscopic volume rendering with perceptual colorization on preoperative imaging for malignant pleural mesothelioma. At present, we have prospectively enrolled 6 patients being considered for resection of malignant pleural mesothelioma that have undergone a multidetector-row computed tomography (CT) scan of the chest. The CT data sets were volume rendered without preprocessing. The resultant 3D rendering was displayed stereoscopically and used to provide information regarding tumor extent, morphology, and anatomic involvement. To demonstrate this technique, this information was compared with the corresponding two-dimensional CT grayscale axial images from two of these patients. Three-dimensional stereoscopic reconstructions of the CT data sets provided detailed information regarding the local extent of tumor that could be used for preoperative surgical planning. Three-dimensional stereoscopic volume rendering for malignant pleural mesothelioma is a novel approach. Combined with our innovative perceptual colorization algorithm, stereoscopic volumetric analysis potentially allows for the accurate determination of the extent of pleural mesothelioma with results difficult to duplicate using grayscale, multiplanar CT images.
PMCID: PMC3723194  PMID: 23102002
Mesothelioma; Imaging; Lung cancer; Diagnosis; Computed tomography; CAT scan; Imaging
8.  The Lung Image Database Consortium (LIDC): Ensuring the integrity of expert-defined “truth” 
Academic radiology  2007;14(12):1455-1463.
Rationale and Objectives
Computer-aided diagnostic (CAD) systems fundamentally require the opinions of expert human observers to establish “truth” for algorithm development, training, and testing. The integrity of this “truth,” however, must be established before investigators commit to this “gold standard” as the basis for their research. The purpose of this study was to develop a quality assurance (QA) model as an integral component of the “truth” collection process concerning the location and spatial extent of lung nodules observed on computed tomography (CT) scans to be included in the Lung Image Database Consortium (LIDC) public database.
Materials and Methods
One hundred CT scans were interpreted by four radiologists through a two-phase process. For the first of these reads (the “blinded read phase”), radiologists independently identified and annotated lesions, assigning each to one of three categories: “nodule ≥ 3mm,” “nodule < 3mm,” or “non-nodule ≥ 3mm.” For the second read (the “unblinded read phase”), the same radiologists independently evaluated the same CT scans but with all of the annotations from the previously performed blinded reads presented; each radiologist could add marks, edit or delete their own marks, change the lesion category of their own marks, or leave their marks unchanged. The post-unblinded-read set of marks was grouped into discrete nodules and subjected to the QA process, which consisted of (1) identification of potential errors introduced during the complete image annotation process (such as two marks on what appears to be a single lesion or an incomplete nodule contour) and (2) correction of those errors. Seven categories of potential error were defined; any nodule with a mark that satisfied the criterion for one of these categories was referred to the radiologist who assigned that mark for either correction or confirmation that the mark was intentional.
A total of 105 QA issues were identified across 45 (45.0%) of the 100 CT scans. Radiologist review resulted in modifications to 101 (96.2%) of these potential errors. Twenty-one lesions erroneously marked as lung nodules after the unblinded reads had this designation removed through the QA process.
The establishment of “truth” must incorporate a QA process to guarantee the integrity of the datasets that will provide the basis for the development, training, and testing of CAD systems.
PMCID: PMC2151472  PMID: 18035275
lung nodule; computed tomography (CT); thoracic imaging; database construction; computer-aided diagnosis (CAD); annnotation; quality assurance (QA)
9.  The Lung Image Database Consortium (LIDC) 
Academic radiology  2007;14(11):1409-1421.
Rationale and Objectives
The purpose of this study was to analyze the variability of experienced thoracic radiologists in the identification of lung nodules on CT scans and thereby to investigate variability in the establishment of the “truth” against which nodule-based studies are measured.
Materials and Methods
Thirty CT scans were reviewed twice by four thoracic radiologists through a two-phase image annotation process. During the initial “blinded read” phase, radiologists independently marked lesions they identified as “nodule ≥ 3mm (diameter),” “nodule < 3mm,” or “non-nodule ≥ 3mm.” During the subsequent “unblinded read” phase, the blinded read results of all radiologists were revealed to each of the four radiologists, who then independently reviewed their marks along with the anonymous marks of their colleagues; a radiologist’s own marks then could be deleted, added, or left unchanged. This approach was developed to identify, as completely as possible, all nodules in a scan without requiring forced consensus.
After the initial blinded read phase, a total of 71 lesions received “nodule ≥ 3mm” marks from at least one radiologist; however, all four radiologists assigned such marks to only 24 (33.8%) of these lesions. Following the unblinded reads, a total of 59 lesions were marked as “nodule ≥ 3 mm” by at least one radiologist. 27 (45.8%) of these lesions received such marks from all four radiologists, 3 (5.1%) were identified as such by three radiologists, 12 (20.3%) were identified by two radiologists, and 17 (28.8%) were identified by only a single radiologist.
The two-phase image annotation process yields improved agreement among radiologists in the interpretation of nodules ≥ 3mm. Nevertheless, substantial variabilty remains across radiologists in the task of lung nodule identification.
PMCID: PMC2290739  PMID: 17964464
lung nodule; computed tomography (CT); thoracic imaging; inter-observer variability; computer-aided diagnosis (CAD)
10.  Utilisation of a thoracic oncology database to capture radiological and pathological images for evaluation of response to chemotherapy in patients with malignant pleural mesothelioma 
BMJ Open  2012;2(5):e001620.
An area of need in cancer informatics is the ability to store images in a comprehensive database as part of translational cancer research. To meet this need, we have implemented a novel tandem database infrastructure that facilitates image storage and utilisation.
We had previously implemented the Thoracic Oncology Program Database Project (TOPDP) database for our translational cancer research needs. While useful for many research endeavours, it is unable to store images, hence our need to implement an imaging database which could communicate easily with the TOPDP database.
The Thoracic Oncology Research Program (TORP) imaging database was designed using the Research Electronic Data Capture (REDCap) platform, which was developed by Vanderbilt University. To demonstrate proof of principle and evaluate utility, we performed a retrospective investigation into tumour response for malignant pleural mesothelioma (MPM) patients treated at the University of Chicago Medical Center with either of two analogous chemotherapy regimens and consented to at least one of two UCMC IRB protocols, 9571 and 13473A.
A cohort of 22 MPM patients was identified using clinical data in the TOPDP database. After measurements were acquired, two representative CT images and 0–35 histological images per patient were successfully stored in the TORP database, along with clinical and demographic data.
We implemented the TORP imaging database to be used in conjunction with our comprehensive TOPDP database. While it requires an additional effort to use two databases, our database infrastructure facilitates more comprehensive translational research.
The investigation described herein demonstrates the successful implementation of this novel tandem imaging database infrastructure, as well as the potential utility of investigations enabled by it. The data model presented here can be utilised as the basis for further development of other larger, more streamlined databases in the future.
PMCID: PMC3488720  PMID: 23103606
Basic Sciences
11.  Quantitative Measurement of Lung Re-Expansion in Malignant Pleural Mesothelioma Patients Undergoing Pleurectomy/Decortication 
Academic radiology  2010;18(3):294-298.
Rationale and Objectives
Malignant pleural mesothelioma (MPM) is a neoplasm that grows circumferentially along the pleura. The tumor and concurrent pleural effusion may reduce lung function by restricting or preventing lung expansion. The purpose of this study was to provide objective evidence that pleurectomy/decortication (P/D) allows trapped lung to re-expand, quantify the re-expansion based on computed tomography (CT) scans, and investigate whether the expansion persists after surgery.
Materials and Methods
A database of 12 patients demonstrating unilateral MPM was collected. Each patient underwent a pre-surgical CT scan, surgical debulking by P/D, and two post-surgical CT scans (at one and four months). The lung volume was measured in each scan using an automated algorithm and compared for each patient across time.
An increase in the ipsilateral post-surgical lung volume was observed for 10 of 12 patients (83%) one month after surgery. The median ipsilateral volume increase was 44% relative to the pre-surgical ipsilateral volume and 21% relative to the contralateral volume. A statistically significant change in ipsilateral lung volume was not observed between 1 -month and 4-month post-surgical scans, implying that the volume improvement persisted months after surgery.
Debulking of MPM with P/D substantially increased the ipsilateral lung volume relative to both the pre-surgical, ipsilateral volume and the contralateral lung volume. This improvement persisted months after surgery.
PMCID: PMC3075578  PMID: 21145765
12.  Computed Tomography Assessment of Response to Therapy: Tumor Volume Change Measurement, Truth Data, and Error1 
Translational Oncology  2009;2(4):216-222.
RATIONALE AND OBJECTIVES: This article describes issues and methods that are specific to the measurement of change in tumor volume as measured from computed tomographic (CT) images and how these would relate to the establishment of CT tumor volumetrics as a biomarker of patient response to therapy. The primary focus is on the measurement of lung tumors, but the approach should be generalizable to other anatomic regions. MATERIALS AND METHODS: The first issues addressed are the various sources of bias and variance in the measurement of tumor volumes, which are discussed in the context of measurement variation and its impact on the early detection of response to therapy. RESULTS AND RESOURCES: Research that seeks to identify the magnitude of some of these sources of error is ongoing, and several of these efforts are described herein. In addition, several resources for these investigations are being made available through the National Institutes of Health-funded Reference Image Database to Evaluate Response to therapy in cancer project, and these are described as well. Other measures derived from CT image data that might be predictive of patient response are described briefly, as well as the additional issues that each of these metrics may encounter in real-life applications. CONCLUSIONS: The article concludes with a brief discussion of moving from the assessment of measurement variation to the steps necessary to establish the efficacy of a metric as a biomarker for response.
PMCID: PMC2781084  PMID: 19956381
13.  Quantitative Imaging to Assess Tumor Response to Therapy: Common Themes of Measurement, Truth Data, and Error Sources1 
Translational Oncology  2009;2(4):198-210.
RATIONALE: Early detection of tumor response to therapy is a key goal. Finding measurement algorithms capable of early detection of tumor response could individualize therapy treatment as well as reduce the cost of bringing new drugs to market. On an individual basis, the urgency arises from the desire to prevent continued treatment of the patient with a high-cost and/or high-risk regimen with no demonstrated individual benefit and rapidly switch the patient to an alternative efficacious therapy for that patient. In the context of bringing new drugs to market, such algorithms could demonstrate efficacy in much smaller populations, which would allow phase 3 trials to achieve statistically significant decisions with fewer subjects in shorter trials. MATERIALS AND METHODS: This consensus-based article describes multiple, image modality-independent means to assess the relative performance of algorithms for measuring tumor change in response to therapy. In this setting, we describe specifically the example of measurement of tumor volume change from anatomic imaging as well as provide an overview of other promising generic analytic methods that can be used to assess change in heterogeneous tumors. To support assessment of the relative performance of algorithms for measuring small tumor change, data sources of truth are required. RESULTS: Very short interval clinical imaging examinations and phantom scans provide known truth for comparative evaluation of algorithms. CONCLUSIONS: For a given category of measurement methods, the algorithm that has the smallest measurement noise and least bias on average will perform best in early detection of true tumor change.
PMCID: PMC2781075  PMID: 19956379
14.  PET/CT Assessment of Response to Therapy: Tumor Change Measurement, Truth Data, and Error1 
Translational Oncology  2009;2(4):223-230.
We describe methods and issues that are relevant to the measurement of change in tumor uptake of 18F-fluorodeoxyglucose (FDG) or other radiotracers, as measured from positron emission tomography/computed tomography (PET/CT) images, and how this would relate to the establishment of PET/CT tumor imaging as a biomarker of patient response to therapy. The primary focus is on the uptake of FDG by lung tumors, but the approach can be applied to diseases other than lung cancer and to tracers other than FDG. The first issue addressed is the sources of bias and variance in the measurement of tumor uptake of FDG, and where there are still gaps in our knowledge. These are discussed in the context of measurement variation and how these would relate to the early detection of response to therapy. Some of the research efforts currently underway to identify the magnitude of some of these sources of error are described. In addition, we describe resources for these investigations that are being made available through the Reference Image Database for the Evaluation of Response project. Measures derived from PET image data that might be predictive of patient response as well as the additional issues that each of these metrics may encounter are described briefly. The relationship between individual patient response to therapy and utility for multicenter trials is discussed. We conclude with a discussion of moving from assessing measurement variation to the steps necessary to establish the efficacy of PET/CT imaging as a biomarker for response.
PMCID: PMC2781074  PMID: 19956382
15.  The Lung Image Database Consortium (LIDC): A comparison of different size metrics for pulmonary nodule measurements 
Academic radiology  2007;14(12):1475-1485.
Rationale and Objectives
To investigate the effects of choosing between different metrics in estimating the size of pulmonary nodules as a factor both of nodule characterization and of performance of computer aided detection systems, since the latters are always qualified with respect to a given size range of nodules.
Materials and Methods
This study used 265 whole-lung CT scans documented by the Lung Image Database Consortium using their protocol for nodule evaluation. Each inspected lesion was reviewed independently by four experienced radiologists who provided boundary markings for nodules larger than 3 mm. Four size metrics, based on the boundary markings, were considered: a uni-dimensional and two bi-dimensional measures on a single image slice and a volumetric measurement based on all the image slices. The radiologist boundaries were processed and those with four markings were analyzed to characterize the inter-radiologist variation, while those with at least one marking were used to examine the difference between the metrics.
The processing of the annotations found 127 nodules marked by all of the four radiologists and an extended set of 518 nodules each having at least one observation with three-dimensional sizes ranging from 2.03 to 29.4 mm (average 7.05 mm, median 5.71 mm). A very high inter-observer variation was observed for all these metrics: 95% of estimated standard deviations were in the following ranges [0.49, 1.25], [0.67, 2.55], [0.78, 2.11], and [0.96, 2.69] for the three-dimensional, the uni-dimensional, and the two bi-dimensional size metrics respectively (in mm). Also a very large difference among the metrics was observed: 0.95 probability-coverage region widths for the volume estimation conditional on uni-dimensional, and the two bi-dimensional size measurements of 10mm were 7.32, 7.72, and 6.29 mm respectively.
The selection of data subsets for performance evaluation is highly impacted by the size metric choice. The LIDC plans to include a single size measure for each nodule in its database. This metric is not intended as a gold standard for nodule size; rather, it is intended to facilitate the selection of unique repeatable size limited nodule subsets.
PMCID: PMC2222556  PMID: 18035277
Quantitative image analysis; X-ray CT; Detection; Lung nodule annotation; Size metrics
17.  Evaluation of Lung MDCT Nodule Annotation Across Radiologists and Methods1 
Academic radiology  2006;13(10):1254-1265.
Rationale and Objectives
Integral to the mission of the National Institutes of Health–sponsored Lung Imaging Database Consortium is the accurate definition of the spatial location of pulmonary nodules. Because the majority of small lung nodules are not resected, a reference standard from histopathology is generally unavailable. Thus assessing the source of variability in defining the spatial location of lung nodules by expert radiologists using different software tools as an alternative form of truth is necessary.
Materials and Methods
The relative differences in performance of six radiologists each applying three annotation methods to the task of defining the spatial extent of 23 different lung nodules were evaluated. The variability of radiologists’ spatial definitions for a nodule was measured using both volumes and probability maps (p-map). Results were analyzed using a linear mixed-effects model that included nested random effects.
Across the combination of all nodules, volume and p-map model parameters were found to be significant at P < .05 for all methods, all radiologists, and all second-order interactions except one. The radiologist and methods variables accounted for 15% and 3.5% of the total p-map variance, respectively, and 40.4% and 31.1% of the total volume variance, respectively.
Radiologists represent the major source of variance as compared with drawing tools independent of drawing metric used. Although the random noise component is larger for the p-map analysis than for volume estimation, the p-map analysis appears to have more power to detect differences in radiologist-method combinations. The standard deviation of the volume measurement task appears to be proportional to nodule volume.
PMCID: PMC1994157  PMID: 16979075
LIDC drawing experiment; lung nodule annotation; edge mask; p-map; volume; linear mixed-effects model
18.  Computerized analysis of abnormal asymmetry in digital chest radiographs: Evaluation of potential utility 
Journal of Digital Imaging  1999;12(1):34-42.
The purpose of this study was to develop and test a computerized method for the fully automated analysis of abnormal asymmetry in digital posteroanterior (PA) chest radiographs. An automated lung segmentation method was used to identify the aerated lung regions in 600 chest radiographs. Minimal apriori lung morphology information was required for this gray-level thresholding-based segmentation. Consequently, segmentation was applicable to grossly abnormal cases. The relative areas of segmented right and left lung regions in each image were compared with the corresponding area distributions of normal images to determine the presence of abnormal assymetry. Computerized diagnoses were compared with image ratings assigned by a radiologist. The ability of the automated method to distinguish normal from asymmetrically abnormal cases was evaluated by using receiver operating characteristic (ROC) analysis, which yielded an area under the ROC curve of 0.84. This automated method demonstrated promising performance in its ability to detect abnormal asymmetry in PA chest images. We believe this method could play a role in a picture archiving and communications (PACS) environment to immediately identify abnormal cases and to function as one component of a multifaceted computeraided diagnostic scheme.
PMCID: PMC3452427  PMID: 10036666
computer-aided diagnosis; lung segmentation; gross abnormality; chest radiography

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