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Coolens C, Mohseni H, Dhodi S, Ma S, Keller H, Jaffray DA. Quantification accuracy for dynamic contrast enhanced (DCE) CT imaging: phantom and quality assurance framework. Eur J Radiol 2018; 106:192-198. [PMID: 30150044 DOI: 10.1016/j.ejrad.2018.08.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 08/05/2018] [Indexed: 01/01/2023]
Abstract
PURPOSE Standardization and protocol optimization is essential for quantification of Dynamic Contrast Enhanced CT as an imaging biomarker. Currently, no commercially available quality assurance (QA) phantoms can provide for testing a complete set of imaging parameters pertaining to routine quality control for contrast-enhanced (CE) CT, as well as spatiotemporal accuracy. The purpose of this work was, therefore: (a) developing a solid calibration phantom for routine CE CT quality assurance; (b) investigating the sensitivity of CECT to organ motion, and (c) characterizing a volumetric CT scanner for CECT. METHODS CECT calibration phantom consisting of an acrylic uniform cylinder containing multiple capsules of varying diameters and orientations was designed and built. The capsules contain different solid density materials mimicking iodine contrast enhancement. Sensitivity and accuracy of CECT measurements on all capsules was performed using a 320-slice CT scanner for a range of scan parameters both with and without phantom motion along the transaxial axis of the scanner. RESULTS Routine commissioning tests such as uniformity, spatial resolution and image noise were successfully determined using the CECT phantom. Partial volume effect and motion blurring both contribute to a general decrease in contrast enhancement and this was further dependent on capsule orientation (least pronounced for the transaxial orientation). Scanning with a rotation time of less than 0.5 s, the effect of blurring is less than 3% for all orientations and phantom speeds. CONCLUSION A new robust contrast calibration phantom was developed and used to evaluate the performance of a 320-slice volumetric CT scanner for DCE-CT.
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Affiliation(s)
- C Coolens
- Department of Medical Physics, Princess Margaret Cancer Centre, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; TECHNA Institute, University Health Network, Toronto, Ontario, Canada.
| | - H Mohseni
- Department of Medical Physics, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - S Dhodi
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - S Ma
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - H Keller
- Department of Medical Physics, Princess Margaret Cancer Centre, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - D A Jaffray
- Department of Medical Physics, Princess Margaret Cancer Centre, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada; TECHNA Institute, University Health Network, Toronto, Ontario, Canada
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Validation of automated lobe segmentation on paired inspiratory-expiratory chest CT in 8-14 year-old children with cystic fibrosis. PLoS One 2018; 13:e0194557. [PMID: 29630630 PMCID: PMC5890971 DOI: 10.1371/journal.pone.0194557] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 03/06/2018] [Indexed: 01/02/2023] Open
Abstract
Objectives Densitometry on paired inspiratory and expiratory multidetector computed tomography (MDCT) for the quantification of air trapping is an important approach to assess functional changes in airways diseases such as cystic fibrosis (CF). For a regional analysis of functional deficits, an accurate lobe segmentation algorithm applicable to inspiratory and expiratory scans is beneficial. Materials and methods We developed a fully automated lobe segmentation algorithm, and subsequently validated automatically generated lobe masks (ALM) against manually corrected lobe masks (MLM). Paired inspiratory and expiratory CTs from 16 children with CF (mean age 11.1±2.4) acquired at 4 time-points (baseline, 3mon, 12mon, 24mon) with 2 kernels (B30f, B60f) were segmented, resulting in 256 ALM. After manual correction spatial overlap (Dice index) and mean differences in lung volume and air trapping were calculated for ALM vs. MLM. Results The mean overlap calculated with Dice index between ALM and MLM was 0.98±0.02 on inspiratory, and 0.86±0.07 on expiratory CT. If 6 lobes were segmented (lingula treated as separate lobe), the mean overlap was 0.97±0.02 on inspiratory, and 0.83±0.08 on expiratory CT. The mean differences in lobar volumes calculated in accordance with the approach of Bland and Altman were generally low, ranging on inspiratory CT from 5.7±52.23cm3 for the right upper lobe to 17.41±14.92cm3 for the right lower lobe. Higher differences were noted on expiratory CT. The mean differences for air trapping were even lower, ranging from 0±0.01 for the right upper lobe to 0.03±0.03 for the left lower lobe. Conclusions Automatic lobe segmentation delivers excellent results for inspiratory and good results for expiratory CT. It may become an important component for lobe-based quantification of functional deficits in cystic fibrosis lung disease, reducing necessity for user-interaction in CT post-processing.
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Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier. Phys Med 2016; 32:1502-1509. [PMID: 27856118 DOI: 10.1016/j.ejmp.2016.11.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2016] [Revised: 11/01/2016] [Accepted: 11/01/2016] [Indexed: 11/24/2022] Open
Abstract
Improving the performance of computer-aided detection (CAD) system for pulmonary nodules is still an important issue for its future clinical applications. This study aims to develop a new CAD scheme for pulmonary nodule detection based on dynamic self-adaptive template matching and Fisher linear discriminant analysis (FLDA) classifier. We first segment and repair lung volume by using OTSU algorithm and three-dimensional (3D) region growing. Next, the suspicious regions of interest (ROIs) are extracted and filtered by applying 3D dot filtering and thresholding method. Then, pulmonary nodule candidates are roughly detected with 3D dynamic self-adaptive template matching. Finally, we optimally select 11 image features and apply FLDA classifier to reduce false positive detections. The performance of the new method is validated by comparing with other methods through experiments using two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. By a 10-fold cross-validation experiment, the new CAD scheme finally has achieved a sensitivity of 90.24% and a false-positive (FP) of 4.54 FP/scan on average for the former dataset, and a sensitivity of 84.1% with 5.59 FP/scan for the latter. By comparing with other previously reported CAD schemes tested on the same datasets, the study proves that this new scheme can yield higher and more robust results in detecting pulmonary nodules.
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Ma X, Siegelman J, Paik DS, Mulshine JL, St Pierre S, Buckler AJ. Volumes Learned: It Takes More Than Size to "Size Up" Pulmonary Lesions. Acad Radiol 2016; 23:1190-8. [PMID: 27287713 DOI: 10.1016/j.acra.2016.04.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Revised: 04/08/2016] [Accepted: 04/10/2016] [Indexed: 12/17/2022]
Abstract
RATIONALE AND OBJECTIVES This study aimed to review the current understanding and capabilities regarding use of imaging for noninvasive lesion characterization and its relationship to lung cancer screening and treatment. MATERIALS AND METHODS Our review of the state of the art was broken down into questions about the different lung cancer image phenotypes being characterized, the role of imaging and requirements for increasing its value with respect to increasing diagnostic confidence and quantitative assessment, and a review of the current capabilities with respect to those needs. RESULTS The preponderance of the literature has so far been focused on the measurement of lesion size, with increasing contributions being made to determine the formal performance of scanners, measurement tools, and human operators in terms of bias and variability. Concurrently, an increasing number of investigators are reporting utility and predictive value of measures other than size, and sensitivity and specificity is being reported. Relatively little has been documented on quantitative measurement of non-size features with corresponding estimation of measurement performance and reproducibility. CONCLUSIONS The weight of the evidence suggests characterization of pulmonary lesions built on quantitative measures adds value to the screening for, and treatment of, lung cancer. Advanced image analysis techniques may identify patterns or biomarkers not readily assessed by eye and may also facilitate management of multidimensional imaging data in such a way as to efficiently integrate it into the clinical workflow.
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Affiliation(s)
- Xiaonan Ma
- Elucid Bioimaging Inc., 225 Main Street, Wenham, MA 01984.
| | - Jenifer Siegelman
- Department of Radiology, Brigham and Women's Hospital, Boston Massachusetts; Department of Radiology (hospital-based), Harvard Medical School, Boston, Massachusetts
| | - David S Paik
- Elucid Bioimaging Inc., 225 Main Street, Wenham, MA 01984
| | - James L Mulshine
- Department of Internal Medicine, Rush University, Chicago, Illinois
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Prakashini K, Babu S, Rajgopal KV, Kokila KR. Role of Computer Aided Diagnosis (CAD) in the detection of pulmonary nodules on 64 row multi detector computed tomography. Lung India 2016; 33:391-7. [PMID: 27578931 PMCID: PMC4948226 DOI: 10.4103/0970-2113.184872] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
AIMS AND OBJECTIVES To determine the overall performance of an existing CAD algorithm with thin-section computed tomography (CT) in the detection of pulmonary nodules and to evaluate detection sensitivity at a varying range of nodule density, size, and location. MATERIALS AND METHODS A cross-sectional prospective study was conducted on 20 patients with 322 suspected nodules who underwent diagnostic chest imaging using 64-row multi-detector CT. The examinations were evaluated on reconstructed images of 1.4 mm thickness and 0.7 mm interval. Detection of pulmonary nodules, initially by a radiologist of 2 years experience (RAD) and later by CAD lung nodule software was assessed. Then, CAD nodule candidates were accepted or rejected accordingly. Detected nodules were classified based on their size, density, and location. The performance of the RAD and CAD system was compared with the gold standard that is true nodules confirmed by consensus of senior RAD and CAD together. The overall sensitivity and false-positive (FP) rate of CAD software was calculated. OBSERVATIONS AND RESULTS Of the 322 suspected nodules, 221 were classified as true nodules on the consensus of senior RAD and CAD together. Of the true nodules, the RAD detected 206 (93.2%) and 202 (91.4%) by the CAD. CAD and RAD together picked up more number of nodules than either CAD or RAD alone. Overall sensitivity for nodule detection with the CAD program was 91.4%, and FP detection per patient was 5.5%. The CAD showed comparatively higher sensitivity for nodules of size 4-10 mm (93.4%) and nodules in hilar (100%) and central (96.5%) location when compared to RAD's performance. CONCLUSION CAD performance was high in detecting pulmonary nodules including the small size and low-density nodules. CAD even with relatively high FP rate, assists and improves RAD's performance as a second reader, especially for nodules located in the central and hilar region and for small nodules by saving RADs time.
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Affiliation(s)
- K Prakashini
- Department of Radiodiagnosis and Imaging, Kasturba Medical College, Manipal University, Manipal, Udupi, Karnataka, India
| | - Satish Babu
- Department of Radiodiagnosis and Imaging, Kasturba Medical College, Manipal University, Manipal, Udupi, Karnataka, India
| | - K V Rajgopal
- Department of Radiodiagnosis and Imaging, Kasturba Medical College, Manipal University, Manipal, Udupi, Karnataka, India
| | - K Raja Kokila
- Consultant Radiologist, Jansons Health (P) Ltd., Erode, Tamil Nadu, India
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Niesterok C, Piesnack S, Köhler C, Ludewig E, Alef M, Kiefer I. [Computed tomography with computer-assisted detection of pulmonary nodules in dogs and cats]. TIERARZTLICHE PRAXIS. AUSGABE K, KLEINTIERE/HEIMTIERE 2015; 43:381-388. [PMID: 26582331 DOI: 10.15654/tpk-150048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Accepted: 05/15/2015] [Indexed: 06/05/2023]
Abstract
OBJECTIVE The aim of this study was to assess the potential benefit of computer-assisted detection (CAD) of pulmonary nodules in veterinary medicine. Therefore, the CAD rate was compared to the detection rates of two individual examiners in terms of its sensitivity and false-positive findings. MATERIALS AND METHODS We included 51 dogs and 16 cats with pulmonary nodules previously diagnosed by computed tomography. First, the number of nodules ≥ 3 mm was recorded for each patient by two independent examiners. Subsequently, each examiner used the CAD software for automated nodule detection. With the knowledge of the CAD results, a final consensus decision on the number of nodules was achieved. The software used was a commercially available CAD program. RESULTS The sensitivity of examiner 1 was 89.2%, while that of examiner 2 reached 87.4%. CAD had a sensitivity of 69.4%. With CAD, the sensitivity of examiner 1 increased to 94.7% and that of examiner 2 to 90.8%. CONCLUSION AND CLINICAL RELEVANCE The CAD-system, which we used in our study, had a moderate sensitivity of 69.4%. Despite its severe limitations, with a high level of false-positive and false-negative results, CAD increased the examiners' sensitivity. Therefore, its supportive role in diagnostics appears to be evident.
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Affiliation(s)
- C Niesterok
- Christian Niesterok, Klinik für Kleintiere, Veterinärmedizinische Fakultät der Universität Leipzig, An den Tierkliniken 23, 04103 Leipzig, E-Mail:
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Hwang SH, Oh YW, Ham SY, Kang EY, Lee KY. Effect of the high-pitch mode in dual-source computed tomography on the accuracy of three-dimensional volumetry of solid pulmonary nodules: a phantom study. Korean J Radiol 2015; 16:641-7. [PMID: 25995695 PMCID: PMC4435995 DOI: 10.3348/kjr.2015.16.3.641] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Accepted: 02/16/2015] [Indexed: 12/05/2022] Open
Abstract
Objective To evaluate the influence of high-pitch mode (HPM) in dual-source computed tomography (DSCT) on the accuracy of three-dimensional (3D) volumetry for solid pulmonary nodules. Materials and Methods A lung phantom implanted with 45 solid pulmonary nodules (n = 15 for each of 4-mm, 6-mm, and 8-mm in diameter) was scanned twice, first in conventional pitch mode (CPM) and then in HPM using DSCT. The relative percentage volume errors (RPEs) of 3D volumetry were compared between the HPM and CPM. In addition, the intermode volume variability (IVV) of 3D volumetry was calculated. Results In the measurement of the 6-mm and 8-mm nodules, there was no significant difference in RPE (p > 0.05, respectively) between the CPM and HPM (IVVs of 1.2 ± 0.9%, and 1.7 ± 1.5%, respectively). In the measurement of the 4-mm nodules, the mean RPE in the HPM (35.1 ± 7.4%) was significantly greater (p < 0.01) than that in the CPM (18.4 ± 5.3%), with an IVV of 13.1 ± 6.6%. However, the IVVs were in an acceptable range (< 25%), regardless of nodule size. Conclusion The accuracy of 3D volumetry with HPM for solid pulmonary nodule is comparable to that with CPM. However, the use of HPM may adversely affect the accuracy of 3D volumetry for smaller (< 5 mm in diameter) nodule.
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Affiliation(s)
- Sung Ho Hwang
- Department of Radiology, Korea University Anam Hospital, Seoul 136-705, Korea
| | - Yu-Whan Oh
- Department of Radiology, Korea University Anam Hospital, Seoul 136-705, Korea
| | - Soo-Youn Ham
- Department of Radiology, Korea University Anam Hospital, Seoul 136-705, Korea
| | - Eun-Young Kang
- Department of Radiology, Korea University Guro Hospital, Seoul 152-703, Korea
| | - Ki Yeol Lee
- Department of Radiology, Korea University Ansan Hospital, Ansan 425-707, Korea
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Badura P, Pietka E. Soft computing approach to 3D lung nodule segmentation in CT. Comput Biol Med 2014; 53:230-43. [PMID: 25173811 DOI: 10.1016/j.compbiomed.2014.08.005] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2014] [Revised: 08/07/2014] [Accepted: 08/07/2014] [Indexed: 11/25/2022]
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CT Quantification of Large Opacities and Emphysema in Silicosis: Correlations among Clinical, Functional, and Radiological Parameters. Lung 2014; 192:543-51. [DOI: 10.1007/s00408-014-9590-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2014] [Accepted: 04/21/2014] [Indexed: 10/25/2022]
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Small irregular pulmonary nodules in low-dose CT: observer detection sensitivity and volumetry accuracy. AJR Am J Roentgenol 2014; 202:W202-9. [PMID: 24555615 DOI: 10.2214/ajr.13.10830] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE The purpose of this study is to evaluate observer detection and volume measurement of small irregular solid artificial pulmonary nodules on 64-MDCT in an anthropomorphic thoracic phantom. MATERIALS AND METHODS Forty in-house-made solid pulmonary nodules (lobulated and spiculated; actual volume, 5.1-88.4 mm3; actual CT densities, -51 to 157 HU) were randomly placed inside an anthropomorphic thoracic phantom with pulmonary vasculature. The phantom was examined on two 64-MDCT scanners, using a scan protocol as applied in lung cancer screening. Two independent blinded observers screened for pulmonary nodules. Nodule volume was evaluated semiautomatically using dedicated software and was compared with the actual volume using an independent-samples t test. The interscanner and interobserver agreement of volumetry was assessed using Bland-Altman analysis. RESULTS Observer detection sensitivity increased along with increasing size of irregular nodules. Sensitivity was 100% when the actual volume was at least 69 mm3, regardless of specific observer, scanner, nodule shape, and density. Overall, nodule volume was underestimated by (mean±SD) 18.9±11.8 mm3 (39%±21%; p<0.001). The relative interscanner difference of volumetry was 3.3% (95% CI, -33.9% to 40.4%). The relative interobserver difference was 0.6% (-33.3% to 34.5%). CONCLUSION Small irregular solid pulmonary nodules with an actual volume of at least 69 mm3 are reliably detected on 64-MDCT. However, CT-derived volume of those small nodules is largely underestimated, with considerable variation.
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Abstract
Low-dose CT (LDCT) is effective in the early detection of lung cancer, providing higher resectability and long-term survival rates. The National Lung Screening Trial shows a statistically significant mortality reduction in LDCT compared with chest radiography. The efficacy and safety of annual LDCT screening in heavy smokers must be explored, and the magnitude of benefit compared with the cost of large-scale screening. Trials in Europe have different study designs and an observational arm. Strategies to reduce lung cancer mortality should combine early detection with primary prevention and innovative biologic approaches.
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Affiliation(s)
- Ugo Pastorino
- Division of Thoracic Surgery, Istituto Nazionale Tumori, Via Venezian 1, 20133 Milan, Italy.
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Jo HH, Hong H, Goo JM. Pulmonary nodule registration in serial CT scans using global rib matching and nodule template matching. Comput Biol Med 2013; 45:87-97. [PMID: 24480168 DOI: 10.1016/j.compbiomed.2013.10.028] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2013] [Revised: 10/28/2013] [Accepted: 10/30/2013] [Indexed: 10/26/2022]
Abstract
We propose an automatic nodule registration method between baseline and follow-up chest CT scans. Initial alignment using the center of the lung volume corrects the gross translational mismatch, and rigid registration using coronal and sagittal maximum intensity projection images effectively refines the rigid motion of the lungs. Nodule correspondences are established by finding the most similar region in terms of density as well as the geometrical constraint. The proposed nodule registration method increased the nodule hit rate (the ratio of the number of successfully matched nodules to total nodule number) from 26% to 100%.
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Affiliation(s)
- Hyun Hee Jo
- Department of Multimedia Engineering, College of Information and Media, Seoul Women's University, 126 Gongreung-dong, Nowon-gu, Seoul 139-774, Republic of Korea.
| | - Helen Hong
- Department of Multimedia Engineering, College of Information and Media, Seoul Women's University, 126 Gongreung-dong, Nowon-gu, Seoul 139-774, Republic of Korea.
| | - Jin Mo Goo
- Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul 110-744, Republic of Korea.
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Xie X, Willemink MJ, Zhao Y, de Jong PA, van Ooijen PMA, Oudkerk M, Greuter MJW, Vliegenthart R. Inter- and intrascanner variability of pulmonary nodule volumetry on low-dose 64-row CT: an anthropomorphic phantom study. Br J Radiol 2013; 86:20130160. [PMID: 23884758 DOI: 10.1259/bjr.20130160] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To assess inter- and intrascanner variability in volumetry of solid pulmonary nodules in an anthropomorphic thoracic phantom using low-dose CT. METHODS Five spherical solid artificial nodules [diameters 3, 5, 8, 10 and 12 mm; CT density +100 Hounsfield units (HU)] were randomly placed inside an anthropomorphic thoracic phantom in different combinations. The phantom was examined on two 64-row multidetector CT (64-MDCT) systems (CT-A and CT-B) from different vendors with a low-dose protocol. Each CT examination was performed three times. The CT examinations were evaluated twice by independent blinded observers. Nodule volume was semi-automatically measured by dedicated software. Interscanner variability was evaluated by Bland-Altman analysis and expressed as 95% confidence interval (CI) of relative differences. Intrascanner variability was expressed as 95% CI of relative variation from the mean. RESULTS No significant difference in CT-derived volume was found between CT-A and CT-B, except for the 3-mm nodules (p<0.05). The 95% CI of interscanner variability was within ±41.6%, ±18.2% and ±4.9% for 3, 5 and ≥8 mm nodules, respectively. The 95% CI of intrascanner variability was within ±28.6%, ±13.4% and ±2.6% for 3, 5 and ≥8 mm nodules, respectively. CONCLUSION Different 64-MDCT scanners in low-dose settings yield good agreement in volumetry of artificial pulmonary nodules between 5 mm and 12 mm in diameter. Inter- and intrascanner variability decreases at a larger nodule size to a maximum of 4.9% for ≥8 mm nodules. ADVANCES IN KNOWLEDGE The commonly accepted cut-off of 25% to determine nodule growth has the potential to be reduced for ≥8 mm nodules. This offers the possibility of reducing the interval for repeated CT scans in lung cancer screenings.
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Affiliation(s)
- X Xie
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
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Keshani M, Azimifar Z, Tajeripour F, Boostani R. Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system. Comput Biol Med 2013; 43:287-300. [PMID: 23369568 DOI: 10.1016/j.compbiomed.2012.12.004] [Citation(s) in RCA: 115] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2011] [Revised: 10/24/2012] [Accepted: 12/09/2012] [Indexed: 11/26/2022]
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Coche E. Advances and perspectives in lung cancer imaging using multidetector row computed tomography. Expert Rev Anticancer Ther 2013; 12:1313-26. [PMID: 23176619 DOI: 10.1586/era.12.112] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The introduction of multidetector row computed tomography (CT) into clinical practice has revolutionized many aspects of the clinical work-up. Lung cancer imaging has benefited from various breakthroughs in computing technology, with advances in the field of lung cancer detection, tissue characterization, lung cancer staging and response to therapy. Our paper discusses the problems of radiation, image visualization and CT examination comparison. It also reviews the most significant advances in lung cancer imaging and highlights the emerging clinical applications that use state of the art CT technology in the field of lung cancer diagnosis and follow-up.
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Affiliation(s)
- Emmanuel Coche
- Department of Medical Imaging, Cliniques Universitaires St-Luc, Université Catholique de Louvain, Avenue Hippocrate, 10, 1200 Brussels, Belgium.
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Wiemker R, Klinder T, Bergtholdt M, Meetz K, Carlsen IC, Bülow T. A radial structure tensor and its use for shape-encoding medical visualization of tubular and nodular structures. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2013; 19:353-366. [PMID: 22689078 DOI: 10.1109/tvcg.2012.136] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
The concept of curvature and shape-based rendering is beneficial for medical visualization of CT and MRI image volumes. Color-coding of local shape properties derived from the analysis of the local Hessian can implicitly highlight tubular structures such as vessels and airways, and guide the attention to potentially malignant nodular structures such as tumors, enlarged lymph nodes, or aneurysms. For some clinical applications, however, the evaluation of the Hessian matrix does not yield satisfactory renderings, in particular for hollow structures such as airways, and densely embedded low contrast structures such as lymph nodes. Therefore, as a complement to Hessian-based shape-encoding rendering, this paper introduces a combination of an efficient sparse radial gradient sampling scheme in conjunction with a novel representation, the radial structure tensor (RST). As an extension of the well-known general structure tensor, which has only positive definite eigenvalues, the radial structure tensor correlates position and direction of the gradient vectors in a local neighborhood, and thus yields positive and negative eigenvalues which can be used to discriminate between different shapes. As Hessian-based rendering, also RST-based rendering is ideally suited for GPU implementation. Feedback from clinicians indicates that shape-encoding rendering can be an effective image navigation tool to aid diagnostic workflow and quality assurance.
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Wiemker R, Dharaiya ED, Bülow T. Informatics in Radiology: Hesse Rendering for Computer-aided Visualization and Analysis of Anomalies at Chest CT and Breast MR Imaging. Radiographics 2012; 32:289-304. [DOI: 10.1148/rg.321105076] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Niemeijer M, Loog M, Abramoff MD, Viergever MA, Prokop M, van Ginneken B. On combining computer-aided detection systems. IEEE TRANSACTIONS ON MEDICAL IMAGING 2011; 30:215-223. [PMID: 20813633 DOI: 10.1109/tmi.2010.2072789] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Computer-aided detection (CAD) is increasingly used in clinical practice and for many applications a multitude of CAD systems have been developed. In practice, CAD systems have different strengths and weaknesses and it is therefore interesting to consider their combination. In this paper, we present generic methods to combine multiple CAD systems and investigate what kind of performance increase can be expected. Experimental results are presented using data from the ANODE09 and ROC09 online CAD challenges for the detection of pulmonary nodules in computed tomography scans and red lesions in retinal images, respectively. For both applications, combination results in a large and significant increase in performance when compared to the best individual CAD system.
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Affiliation(s)
- Meindert Niemeijer
- University Medical Center Utrecht, Image Sciences Institute, 3584 CX Utrecht, The Netherlands.
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Park SC, Tan J, Wang X, Lederman D, Leader JK, Kim SH, Zheng B. Computer-aided detection of early interstitial lung diseases using low-dose CT images. Phys Med Biol 2011; 56:1139-53. [PMID: 21263171 DOI: 10.1088/0031-9155/56/4/016] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
This study aims to develop a new computer-aided detection (CAD) scheme to detect early interstitial lung disease (ILD) using low-dose computed tomography (CT) examinations. The CAD scheme classifies each pixel depicted on the segmented lung areas into positive or negative groups for ILD using a mesh-grid-based region growth method and a multi-feature-based artificial neural network (ANN). A genetic algorithm was applied to select optimal image features and the ANN structure. In testing each CT examination, only pixels selected by the mesh-grid region growth method were analyzed and classified by the ANN to improve computational efficiency. All unselected pixels were classified as negative for ILD. After classifying all pixels into the positive and negative groups, CAD computed a detection score based on the ratio of the number of positive pixels to all pixels in the segmented lung areas, which indicates the likelihood of the test case being positive for ILD. When applying to an independent testing dataset of 15 positive and 15 negative cases, the CAD scheme yielded the area under receiver operating characteristic curve (AUC = 0.884 ± 0.064) and 80.0% sensitivity at 85.7% specificity. The results demonstrated the feasibility of applying the CAD scheme to automatically detect early ILD using low-dose CT examinations.
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Affiliation(s)
- Sang Cheol Park
- School of Electronics and Computer Engineering, Chonnam National University, Gwangju, Korea
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Gavrielides MA, Zeng R, Kinnard LM, Myers KJ, Petrick N. Information-theoretic approach for analyzing bias and variance in lung nodule size estimation with CT: a phantom study. IEEE TRANSACTIONS ON MEDICAL IMAGING 2010; 29:1795-807. [PMID: 20562039 DOI: 10.1109/tmi.2010.2052466] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
This work is a part of our more general effort to probe the interrelated factors impacting the accuracy and precision of lung nodule measurement tasks. For such a task a low-bias size estimator is needed so that the true effect of factors such as acquisition and reconstruction parameters, nodule characteristics and others can be assessed. Towards this goal, we have developed a matched filter based on an adaptive model of the object acquisition and reconstruction process. Our model derives simulated reconstructed data of nodule objects (templates) which are then matched to computed tomography data produced from imaging the actual nodule in a phantom study using corresponding imaging parameters. This approach incorporates the properties of the imaging system and their effect on the discrete 3-D representation of the object of interest. Using a sum of absolute differences cost function, the derived matched filter demonstrated low bias and variance in the volume estimation of spherical synthetic nodules ranging in density from -630 to +100 HU and in size from 5 to 10 mm. This work could potentially lead to better understanding of sources of error in the task of lung nodule size measurements and may lead to new techniques to account for those errors.
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Affiliation(s)
- Marios A Gavrielides
- Division of Imaging and Applied Mathematics (DIAM), Office of Science and Engineering Laboratories (OSEL), Center for Devices and Radiological Health (CDRH), U.S. Food and Drug Administration (FDA), Silver Spring, MD 20993, USA.
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21
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Tan J, Pu J, Zheng B, Wang X, Leader JK. Computerized comprehensive data analysis of lung imaging database consortium (LIDC). Med Phys 2010; 37:3802-8. [PMID: 20831088 DOI: 10.1118/1.3455701] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Lung Image Database Consortium (LIDC) is the largest public CT image database of lung nodules. In this study, the authors present a comprehensive and the most updated analysis of this dynamically growing database under the help of a computerized tool, aiming to assist researchers to optimally use this database for lung cancer related investigations. METHODS The authors developed a computer scheme to automatically match the nodule outlines marked manually by radiologists on CT images. A large variety of characteristics regarding the annotated nodules in the database including volume, spiculation level, elongation, interobserver variability, as well as the intersection of delineated nodule voxels and overlapping ratio between the same nodules marked by different radiologists are automatically calculated and summarized. The scheme was applied to analyze all 157 examinations with complete annotation data currently available in LIDC dataset. RESULTS The scheme summarizes the statistical distributions of the abovementioned geometric and diagnosis features. Among the 391 nodules, (1) 365 (93.35%) have principal axis length < or =20 mm; (2) 120, 75, 76, and 120 were marked by one, two, three, and four radiologists, respectively; and (3) 122 (32.48%) have the maximum volume overlapping ratios -80% for the delineations of two radiologists, while 198 (50.64%) have the maximum volume overlapping ratios <60%. The results also showed that 72.89% of the nodules were assessed with malignancy score between 2 and 4, and only 7.93% of these nodules were considered as severely malignant (malignancy > or =4). CONCLUSIONS This study demonstrates that LIDC contains examinations covering a diverse distribution of nodule characteristics and it can be a useful resource to assess the performance of the nodule detection and/or segmentation schemes.
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Affiliation(s)
- Jun Tan
- Department of Radiology, Imaging Research Division, University of Pittsburgh, 3362 Fifth Avenue, Pittsburgh, Pennsylvania 15213, USA.
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22
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van Ginneken B, Armato SG, de Hoop B, van Amelsvoort-van de Vorst S, Duindam T, Niemeijer M, Murphy K, Schilham A, Retico A, Fantacci ME, Camarlinghi N, Bagagli F, Gori I, Hara T, Fujita H, Gargano G, Bellotti R, Tangaro S, Bolaños L, De Carlo F, Cerello P, Cristian Cheran S, Lopez Torres E, Prokop M. Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study. Med Image Anal 2010; 14:707-22. [PMID: 20573538 DOI: 10.1016/j.media.2010.05.005] [Citation(s) in RCA: 129] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2009] [Revised: 05/14/2010] [Accepted: 05/25/2010] [Indexed: 12/21/2022]
Abstract
Numerous publications and commercial systems are available that deal with automatic detection of pulmonary nodules in thoracic computed tomography scans, but a comparative study where many systems are applied to the same data set has not yet been performed. This paper introduces ANODE09 ( http://anode09.isi.uu.nl), a database of 55 scans from a lung cancer screening program and a web-based framework for objective evaluation of nodule detection algorithms. Any team can upload results to facilitate benchmarking. The performance of six algorithms for which results are available are compared; five from academic groups and one commercially available system. A method to combine the output of multiple systems is proposed. Results show a substantial performance difference between algorithms, and demonstrate that combining the output of algorithms leads to marked performance improvements.
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Affiliation(s)
- Bram van Ginneken
- Image Sciences Institute, University Medical Center Utrecht, The Netherlands.
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Lee MC, Boroczky L, Sungur-Stasik K, Cann AD, Borczuk AC, Kawut SM, Powell CA. Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction. Artif Intell Med 2010; 50:43-53. [PMID: 20570118 DOI: 10.1016/j.artmed.2010.04.011] [Citation(s) in RCA: 92] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2008] [Revised: 04/04/2010] [Accepted: 04/04/2010] [Indexed: 12/20/2022]
Abstract
OBJECTIVE Accurate classification methods are critical in computer-aided diagnosis (CADx) and other clinical decision support systems. Previous research has reported on methods for combining genetic algorithm (GA) feature selection with ensemble classifier systems in an effort to increase classification accuracy. In this study, we describe a CADx system for pulmonary nodules using a two-step supervised learning system combining a GA with the random subspace method (RSM), with the aim of exploring algorithm design parameters and demonstrating improved classification performance over either the GA or RSM-based ensembles alone. METHODS AND MATERIALS We used a retrospective database of 125 pulmonary nodules (63 benign; 62 malignant) with CT volumes and clinical history. A total of 216 features were derived from the segmented image data and clinical history. Ensemble classifiers using RSM or GA-based feature selection were constructed and tested via leave-one-out validation with feature selection and classifier training executed within each iteration. We further tested a two-step approach using a GA ensemble to first assess the relevance of the features, and then using this information to control feature selection during a subsequent RSM step. The base classification was performed using linear discriminant analysis (LDA). RESULTS The RSM classifier alone achieved a maximum leave-one-out Az of 0.866 (95% confidence interval: 0.794-0.919) at a subset size of s=36 features. The GA ensemble yielded an Az of 0.851 (0.775-0.907). The proposed two-step algorithm produced a maximum Az value of 0.889 (0.823-0.936) when the GA ensemble was used to completely remove less relevant features from the second RSM step, with similar results obtained when the GA-LDA results were used to reduce but not eliminate the occurrence of certain features. After accounting for correlations in the data, the leave-one-out Az in the two-step method was significantly higher than in the RSM and the GA-LDA. CONCLUSIONS We have developed a CADx system for evaluation of pulmonary nodule based on a two-step feature selection and ensemble classifier algorithm. We have shown that by combining classifier ensemble algorithms in this two-step manner, it is possible to predict the malignancy for solitary pulmonary nodules with a performance exceeding that of either of the individual steps.
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Affiliation(s)
- Michael C Lee
- Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY 10510-2099, USA.
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24
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Abstract
Lung cancer is the primary cause of cancer mortality in developed countries. First diagnosis only when disease has already reached the metastatic phase is the main reason for failure in treatment. To this regard, although low-dose spiral computed tomography (CT) has proven to be effective in the early detection of lung cancer (providing both higher resectability and higher long-term survival rates), the capacity of annual CT screening to reduce lung cancer mortality in heavy smokers has yet to be demonstrated. Numerous ongoing large-scale randomised trials are under way in high-risk individuals with different study designs. The initial results should be available within the next 2 years.
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Affiliation(s)
- U Pastorino
- Division of Thoracic Surgery, Istituto Nazionale Tumori, Via Venezian 1, 20133, Milan, Italy.
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Hein PA, Krug LD, Romano VC, Kandel S, Hamm B, Rogalla P. Computer-aided Detection in Computed Tomography Colonography with Full Fecal Tagging: Comparison of Standalone Performance of 3 Automated Polyp Detection Systems. Can Assoc Radiol J 2010; 61:102-8. [DOI: 10.1016/j.carj.2009.10.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2009] [Revised: 10/05/2009] [Accepted: 10/06/2009] [Indexed: 01/25/2023] Open
Abstract
Purpose We sought to compare the performance of 3 computer-aided detection (CAD) polyp algorithms in computed tomography colonography (CTC) with fecal tagging. Methods CTC data sets of 33 patients were retrospectively analysed by 3 different CAD systems: system 1, MedicSight; system 2, Colon CAD; and system 3, Polyp Enhanced View. The polyp database comprised 53 lesions, including 6 cases of colorectal cancer, and was established by consensus reading and comparison with colonoscopy. Lesions ranged from 6-40 mm, with 25 lesions larger than 10 mm in size. Detection and false-positive (FP) rates were calculated. Results CAD systems 1 and 2 could be set to have varying sensitivities with higher FP rates for higher sensitivity levels. Sensitivities for system 1 ranged from 73%–94% for all lesions (78%–100% for lesions ≥10 mm) and, for system 2, from 64%–94% (78%–100% for lesions ≥10 mm). System 3 reached an overall sensitivity of 76% (100% for lesions ≥10 mm). The mean FP rate per patient ranged from 8–32 for system 1, from 1–8 for system 2, and was 5 for system 3. At the highest sensitivity level for all polyps (94%), system 2 showed a statistically significant lower FP rate compared with system 1 ( P = .001). When analysing lesions ≥10 mm, system 3 had significantly fewer FPs than systems 1 and 2 ( P < .012). Conclusions Standalone CTC-CAD analysis in the selected patient collective showed the 3 systems tested to have a variable but overall promising performance with respect to sensitivity and the FP rate.
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Affiliation(s)
- Patrick A. Hein
- Department of Radiology, Charité-University Hospital, Campus Mitte, Berlin, Germany
| | - Lasse D. Krug
- Department of Radiology, Charité-University Hospital, Campus Mitte, Berlin, Germany
| | - Valentina C. Romano
- Department of Radiology, Charité-University Hospital, Campus Mitte, Berlin, Germany
| | - Sonja Kandel
- Department of Radiology, Charité-University Hospital, Campus Mitte, Berlin, Germany
| | - Bernd Hamm
- Department of Radiology, Charité-University Hospital, Campus Mitte, Berlin, Germany
| | - Patrik Rogalla
- Department of Radiology, Charité-University Hospital, Campus Mitte, Berlin, Germany
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Edey AJ, Hansell DM. Incidentally detected small pulmonary nodules on CT. Clin Radiol 2009; 64:872-84. [PMID: 19664477 DOI: 10.1016/j.crad.2009.03.006] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2008] [Revised: 03/25/2009] [Accepted: 03/31/2009] [Indexed: 12/21/2022]
Abstract
The widespread use of multidetector computed tomography for imaging of the chest has lead to a significant increase in the number of incidentally detected pulmonary nodules. The significance of these nodules is often uncertain and further investigations may be required. This article will review the spectrum of imaging appearances of small pulmonary nodules, and highlight the few features that allow confident characterization of a nodule as benign or malignant; current guidelines for the management of incidentally detected nodules will also be discussed.
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Affiliation(s)
- A J Edey
- Department of Radiology, Royal Brompton Hospital, London, UK
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Zheng B. Computer-Aided Diagnosis in Mammography Using Content-based Image Retrieval Approaches: Current Status and Future Perspectives. ALGORITHMS 2009; 2:828-849. [PMID: 20305801 PMCID: PMC2841362 DOI: 10.3390/a2020828] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
As the rapid advance of digital imaging technologies, the content-based image retrieval (CBIR) has became one of the most vivid research areas in computer vision. In the last several years, developing computer-aided detection and/or diagnosis (CAD) schemes that use CBIR to search for the clinically relevant and visually similar medical images (or regions) depicting suspicious lesions has also been attracting research interest. CBIR-based CAD schemes have potential to provide radiologists with "visual aid" and increase their confidence in accepting CAD-cued results in the decision making. The CAD performance and reliability depends on a number of factors including the optimization of lesion segmentation, feature selection, reference database size, computational efficiency, and relationship between the clinical relevance and visual similarity of the CAD results. By presenting and comparing a number of approaches commonly used in previous studies, this article identifies and discusses the optimal approaches in developing CBIR-based CAD schemes and assessing their performance. Although preliminary studies have suggested that using CBIR-based CAD schemes might improve radiologists' performance and/or increase their confidence in the decision making, this technology is still in the early development stage. Much research work is needed before the CBIR-based CAD schemes can be accepted in the clinical practice.
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Affiliation(s)
- Bin Zheng
- Imaging Research Center, Department of Radiology, University of Pittsburgh, 3362 Fifth Avenue, Room 128, Pittsburgh, PA 15213, USA
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Gavrielides MA, Kinnard LM, Myers KJ, Petrick N. Noncalcified lung nodules: volumetric assessment with thoracic CT. Radiology 2009; 251:26-37. [PMID: 19332844 DOI: 10.1148/radiol.2511071897] [Citation(s) in RCA: 139] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Lung nodule volumetry is used for nodule diagnosis, as well as for monitoring tumor response to therapy. Volume measurement precision and accuracy depend on a number of factors, including image-acquisition and reconstruction parameters, nodule characteristics, and the performance of algorithms for nodule segmentation and volume estimation. The purpose of this article is to provide a review of published studies relevant to the computed tomographic (CT) volumetric analysis of lung nodules. A number of underexamined areas of research regarding volumetric accuracy are identified, including the measurement of nonsolid nodules, the effects of pitch and section overlap, and the effect of respiratory motion. The need for public databases of phantom scans, as well as of clinical data, is discussed. The review points to the need for continued research to examine volumetric accuracy as a function of a multitude of interrelated variables involved in the assessment of lung nodules. Understanding and quantifying the sources of volumetric measurement error in the assessment of lung nodules with CT would be a first step toward the development of methods to minimize that error through system improvements and to correctly account for any remaining error.
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Affiliation(s)
- Marios A Gavrielides
- National Institute of Biomedical Imaging and Bioengineering/Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993-0002, USA.
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Marchianò A, Calabrò E, Civelli E, Di Tolla G, Frigerio LF, Morosi C, Tafaro F, Ferri E, Sverzellati N, Camerini T, Mariani L, Lo Vullo S, Pastorino U. Pulmonary nodules: volume repeatability at multidetector CT lung cancer screening. Radiology 2009; 251:919-25. [PMID: 19380692 DOI: 10.1148/radiol.2513081313] [Citation(s) in RCA: 58] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
PURPOSE To assess in vivo volumetric repeatability of an automated software algorithm in pulmonary nodules detected during a lung cancer screening trial. MATERIALS AND METHODS This study was approved by an institutional review board. Written informed consent was obtained from all participants. Data were collected from the Multicentric Italian Lung Detection project, a randomized controlled lung cancer screening trial. The first 1236 consecutive baseline computed tomographic (CT) studies performed at the Istituto Nazionale Tumori of Milan were evaluated. Among the enrolled participants, those who underwent repeat low-dose CT after 3 months and had at least one indeterminate nodule with a volume of more than 60 mm(3) (diameter of 4.8 mm or greater) were considered. Nonsolid, part-solid, and pleural-based nodules were excluded from this study. A descriptive analysis was performed by calculating means and standard deviations of nodule volumes at three assessment times (at baseline and 3 and 12 months later). The volume measurement repeatability was determined by using the approach described by Bland and Altman. RESULTS One hundred one subjects (70 men, 31 women; mean age, 58 years) with 233 eligible nodules (mean volume, 98.3 mm(3); range, 5-869 mm(3)) were identified. The 95% confidence interval for difference in measured volumes was in the range of +/-27%. About 70% of measurements had a relative difference in nodule volume of less than 10%. No malignant lesions were registered during the follow-up of these subjects. CONCLUSION Semiautomatic volumetry is sufficiently accurate and repeatable and may be useful in assisting with lung nodule management in a lung cancer screening program.
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Affiliation(s)
- Alfonso Marchianò
- Department of Diagnostic Imaging and Radiotherapy, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian 1, 20133 Milan, Italy.
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Nakamura M, Wada S, Miki T, Shimada Y, Suda Y, Tamura G. Automated segmentation and morphometric analysis of the human airway tree from multidetector CT images. J Physiol Sci 2008; 58:493-8. [PMID: 19055856 DOI: 10.2170/physiolsci.rp007408] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2008] [Accepted: 12/03/2008] [Indexed: 11/05/2022]
Abstract
Remarkable advances in computed tomography (CT) technology geared our research toward investigating the integrative function of the lung and the development of a database of the airway tree incorporating anatomical and functional data with computational models. As part of this project, we are developing the algorithm to construct an anatomically realistic geometric model of airways from CT images. The basic concept of the algorithm is to segment as many airway trees as possible from CT images and later correct quantified parameters based on CT values. CT images are acquired with a 64-channel multidetector CT, and the airway is then extracted from them by the region-growing method while maintaining connectivity. Using this method, we extracted 428 airways up to the 14th branching generation. Although the airway diameters up to the 4th generation showed good agreement with those reported in an autopsy study, those in later generations were all greater than the reported values because of the limited resolution of the CT images. We corrected the errors in diameters by assessing the relationship between the diameter and median value of Hounsfield unit (HU) intensity of each airway; consequently, the diameters up to generation 8 agreed well with the reported values. Based on these results, we conclude that the use of HU-based correction algorithm combined with rough segmentation can be another way to improve data accuracy in the morphometric analysis of airways from CTs.
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Affiliation(s)
- Masanori Nakamura
- The Center for Advanced Medical Engineering and Informatics, Osaka University, Toyonaka 560-8531, Japan.
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32
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Impact of segmentation uncertainties on computer-aided diagnosis of pulmonary nodules. Int J Comput Assist Radiol Surg 2008. [DOI: 10.1007/s11548-008-0257-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Difficulties encountered managing nodules detected during a computed tomography lung cancer screening program. J Thorac Cardiovasc Surg 2008; 136:611-7. [DOI: 10.1016/j.jtcvs.2008.02.082] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2007] [Revised: 01/20/2008] [Accepted: 02/07/2008] [Indexed: 01/03/2023]
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35
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Wiemker R, Paulus T, Kabus S, Bülow T, Apostolova I, Buchert R, Klutmann S. Combined motion blur and partial volume correction for computer aided diagnosis of pulmonary nodules in PET/CT. Int J Comput Assist Radiol Surg 2008. [DOI: 10.1007/s11548-008-0212-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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36
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Detection of Bone Graft Failure in Lumbar Spondylodesis: Spatial Resolution with High-Resolution Peripheral Quantitative CT. AJR Am J Roentgenol 2008; 190:1255-9. [PMID: 18430840 DOI: 10.2214/ajr.07.2701] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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37
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Pauls S, Kürschner C, Dharaiya E, Muche R, Schmidt SA, Krüger S, Brambs HJ, Aschoff AJ. Comparison of manual and automated size measurements of lung metastases on MDCT images: Potential influence on therapeutic decisions. Eur J Radiol 2008; 66:19-26. [PMID: 17606351 DOI: 10.1016/j.ejrad.2007.05.022] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2006] [Revised: 02/09/2007] [Accepted: 05/25/2007] [Indexed: 10/23/2022]
Abstract
PURPOSE The goal of this study was to evaluate the influence of automated measurement of diameter, area, and volume from chest CT scans on therapeutic decisions of lung nodules as compared to manual 2-D measurements. PATIENTS AND METHOD The retrospective study involved 25 patients with 75 lung metastases. Contrast enhanced CT scans (16 row) of the lung were performed three times during chemotherapy with a mean time interval of 67.9 days between scans. In each patient, three metastases were evaluated (n=225). Automatic measurements were compared to manual assessment for the following parameters: diameter, area, and density. The influence on the therapeutic decisions was evaluated using the RECIST criteria. RESULTS The maximum diameter measured by the automatic application was on an average 27% (S.D. 39; CI: 0.22-0.32; p<0.0001) higher than the maximum diameter with manual assessment, and the differences depended on metastases size. Based on diameter calculation, manual and automated assessment disagreed in up to 32% of therapeutic decisions. Volumetric assessment tended towards more changes in therapy as compared to diameter calculation. The calculation of mean transversal area of metastases was 36% (S.D. 0.305; CI: -0.40 to -0.32; p<0.0001) less with automated measurement. Therapeutic strategy would be changed in up to 25.7% of nodules using automated area calculation. Automated assessment of nodules' area and volume could influence the therapeutic decisions in up to 51.4% of all nodules. Density of the nodules was not validated to determine the influence on therapeutic decisions. CONCLUSION There is a discrepancy between the manual and automated size measurement of lung metastases which could be significant.
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Affiliation(s)
- Sandra Pauls
- Department of Diagnostic and Interventional Radiology, University of Ulm, Robert-Koch-Strasse 8, 89081 Ulm, Germany.
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Matthews S, Morcos SK. Lung Cancer. Cancer Imaging 2008. [DOI: 10.1016/b978-012374212-4.50022-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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39
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Abe Y, Tamura K, Sakata I, Ishida J, Nagata M, Nakamura M, Machida K, Ogata T. Lung Cancer. Cancer Imaging 2008. [DOI: 10.1016/b978-012374212-4.50025-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Capobianco J, Jasinowodolinski D, Szarf G. Diagnóstico auxiliado por computador na detecção de nódulos pulmonares pela tomografia computadorizada com múltiplos detectores: estudo preliminar de 24 casos. J Bras Pneumol 2008; 34:27-33. [DOI: 10.1590/s1806-37132008000100006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2007] [Accepted: 05/14/2007] [Indexed: 11/22/2022] Open
Abstract
OBJETIVOS: Avaliar o desempenho de um programa para auxílio na detecção de nódulos pulmonares em tomografia computadorizada com múltiplos detectores (TCMD). MÉTODOS: Foram avaliadas 24 tomografias computadorizadas de tórax consecutivas realizadas no Centro de Medicina Diagnóstica Fleury no período de 07/10/2006 a 19/10/2006 usando um tomógrafo helicoidal multidetectores de 64 canais. O estudo compreendeu 12 pacientes do sexo feminino e 12 do sexo masculino, com idades variando entre 35 e 77 anos, idade média de 57,9. As imagens foram analisadas independentemente pelo método da dupla leitura e pelo programa diagnóstico auxiliado por computador (DAC). Os nódulos encontrados nos diferentes processos foram registrados e os dados comparados. RESULTADOS: A sensibilidade total da detecção de nódulos pelo DAC nesse trabalho foi de 16,5%, 55% excluindo os nódulos medindo <4 mm. A sensibilidade separada por tamanho foi de 6,5% para nódulos <4 mm, 45% para nódulos de 4 a 6 mm, 100% para nódulos de 6 mm a 1 cm, e 0% para nódulos >1 cm. Menos de 1% dos nódulos verdadeiros destacados pelo DAC não haviam sido registrados no processo de dupla leitura. CONCLUSÕES: Neste trabalho preliminar de 24 casos, o programa testado não conseguiu superar de forma significativa a sensibilidade da dupla leitura realizada de rotina neste serviço.
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Affiliation(s)
| | | | - Gilberto Szarf
- Centro de Medicina Diagnóstica Fleury, Brasil; Universidade Federal de São Paulo
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Hurwitz LM, Reiman RE, Yoshizumi TT, Goodman PC, Toncheva G, Nguyen G, Lowry C. Radiation Dose from Contemporary Cardiothoracic Multidetector CT Protocols with an Anthropomorphic Female Phantom: Implications for Cancer Induction. Radiology 2007; 245:742-50. [PMID: 17923509 DOI: 10.1148/radiol.2453062046] [Citation(s) in RCA: 192] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Lynne M Hurwitz
- Department of Radiology, Duke University Medical Center, Box 3808, Durham, NC 27710, USA.
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42
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Ohkubo M, Wada S, Kunii M, Matsumoto T, Nishizawa K. Imaging of small spherical structures in CT: simulation study using measured point spread function. Med Biol Eng Comput 2007; 46:273-82. [DOI: 10.1007/s11517-007-0283-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2007] [Accepted: 10/22/2007] [Indexed: 10/22/2022]
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43
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Bolte H, Jahnke T, Schäfer FKW, Wenke R, Hoffmann B, Freitag-Wolf S, Dicken V, Kuhnigk JM, Lohmann J, Voss S, Knöss N, Heller M, Biederer J. Interobserver-variability of lung nodule volumetry considering different segmentation algorithms and observer training levels. Eur J Radiol 2007; 64:285-95. [PMID: 17433595 DOI: 10.1016/j.ejrad.2007.02.031] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2006] [Revised: 02/22/2007] [Accepted: 02/23/2007] [Indexed: 10/23/2022]
Abstract
OBJECTIVE The aim of this study was to investigate the interobserver variability of CT based diameter and volumetric measurements of artificial pulmonary nodules. A special interest was the consideration of different measurement methods, observer experience and training levels. MATERIALS AND METHODS For this purpose 46 artificial small solid nodules were examined in a dedicated ex-vivo chest phantom with multislice-spiral CT (20 mAs, 120 kV, collimation 16 mm x 0.75 mm, table feed 15 mm, reconstructed slice thickness 1mm, reconstruction increment 0.7 mm, intermediate reconstruction kernel). Two observer groups of different radiologic experience (0 and more than 5 years of training, 3 observers each) analysed all lesions with digital callipers and 2 volumetry software packages (click-point depending and robust volumetry) in a semi-automatic and manually corrected mode. For data analysis the variation coefficient (VC) was calculated in per cent for each group and a Wilcoxon test was used for analytic statistics. RESULTS Click-point robust volumetry showed with a VC of <0.01% in both groups the smallest interobserver variability. Between experienced and un-experienced observers interobserver variability was significantly different for diameter measurements (p=0.023) but not for semi-automatic and manual corrected volumetry. A significant training effect was revealed for diameter measurements (p=0.003) and semi-automatic measurements of click-point depending volumetry (p=0.007) in the un-experienced observer group. CONCLUSIONS Compared to diameter measurements volumetry achieves a significantly smaller interobserver variance and advanced volumetry algorithms are independent of observer experience.
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Affiliation(s)
- H Bolte
- Department of Diagnostic Radiology, University Hospital Schleswig-Holstein Campus Kiel, Arnold-Heller Strasse 9, 24105 Kiel, Germany.
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44
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Heyer CM, Mohr PS, Lemburg SP, Peters SA, Nicolas V. Image Quality and Radiation Exposure at Pulmonary CT Angiography with 100- or 120-kVp Protocol: Prospective Randomized Study. Radiology 2007; 245:577-83. [PMID: 17940308 DOI: 10.1148/radiol.2452061919] [Citation(s) in RCA: 232] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Christoph M Heyer
- Institute of Diagnostic Radiology, Interventional Radiology and Nuclear Medicine, BG Clinics Bergmannsheil, Ruhr-University of Bochum, Buerkle-de-la-Camp Platz 1, D-44789, Bochum, Germany.
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45
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M05-01: Nodule detection using assisted computed diagnosis. J Thorac Oncol 2007. [DOI: 10.1097/01.jto.0000282939.16528.c0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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46
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Bolte H, Riedel C, Riede C, Müller-Hülsbeck S, Freitag-Wolf S, Kohl G, Drews T, Heller M, Biederer J, Bieder J. Precision of computer-aided volumetry of artificial small solid pulmonary nodules inex vivoporcine lungs. Br J Radiol 2007; 80:414-21. [PMID: 17684075 DOI: 10.1259/bjr/23933268] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
The purpose of this study was to investigate the precision of CT-based volumetric measurements of artificial small pulmonary nodules under ex vivo conditions. We implanted 322 artificial nodules in 23 inflated ex vivo porcine lungs in a dedicated chest phantom. The lungs were examined with a multislice spiral CT (20 mAs, collimation 16x0.75 mm, 1 mm slice thickness, 0.7 mm increment). A commercial volumetry software package (LungCARE VA70C-W; Siemens, Erlangen, Germany) was used for volume analysis in a semi-automatic and a manual corrected mode. After imaging, the lungs were dissected to harvest the nodules for gold standard determination. The volumes of 202 solitary, solid and well-defined lesions without contact with the pleura, greater bronchi or vessels were compared with the results of volumetry. A mean nodule diameter of 8.3 mm (+/-2.1 mm) was achieved. The mean relative deviation from the true lesion volume was -9.2% (+/-10.6%) for semi-automatic and -0.3% (+/-6.5%) for manual corrected volumetry. The subgroup of lesions from 5 mm to <10 mm in diameter showed a mean relative deviation of -8.7% (+/-10.9%) for semi-automatic volumetry and -0.3% (+/-6.9%) for manually corrected volumetry. We conclude that the presented software allowed for precise volumetry of artificial nodules in ex vivo lung tissue. This result is comparable to the findings of previous in vitro studies.
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Affiliation(s)
- H Bolte
- Department of Diagnostic Radiology, University Hospital Schleswig-Holstein Campus Kiel, Arnold-Heller Strasse 9, 24105 Kiel, Germany.
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47
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Brown MS, McNitt-Gray MF, Pais R, Shah SK, Qing P, Da Costa I, Aberle DR, Goldin JG. CAD in clinical trials: Current role and architectural requirements. Comput Med Imaging Graph 2007; 31:332-7. [PMID: 17418527 DOI: 10.1016/j.compmedimag.2007.02.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Computer-aided diagnosis (CAD) technology is becoming an important tool to assess treatment response in clinical trials. However, CAD software alone is not sufficient to conduct an imaging-based clinical trial. There are a number of architectural requirements such as image receive (from multiple field sites), a database for storing quantitative measures, and data mining and reporting capabilities. In this paper we describe the architectural requirements to incorporate CAD into clinical trials and illustrate their functionality in therapeutic trials for emphysema.
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48
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Das M, Ley-Zaporozhan J, Gietema HA, Czech A, Mühlenbruch G, Mahnken AH, Katoh M, Bakai A, Salganicoff M, Diederich S, Prokop M, Kauczor HU, Günther RW, Wildberger JE. Accuracy of automated volumetry of pulmonary nodules across different multislice CT scanners. Eur Radiol 2007; 17:1979-84. [PMID: 17206420 DOI: 10.1007/s00330-006-0562-1] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2006] [Revised: 12/04/2006] [Accepted: 12/05/2006] [Indexed: 12/21/2022]
Abstract
The purpose of this study was to compare the accuracy of an automated volumetry software for phantom pulmonary nodules across various 16-slice multislice spiral CT (MSCT) scanners from different vendors. A lung phantom containing five different nodule categories (intraparenchymal, around a vessel, vessel attached, pleural, and attached to the pleura), with each category comprised of 7-9 nodules (total, n = 40) of varying sizes (diameter 3-10 mm; volume 6.62 mm(3)-525 mm(3)), was scanned with four different 16-slice MSCT scanners (Siemens, GE, Philips, Toshiba). Routine and low-dose chest protocols with thin and thick collimations were applied. The data from all scanners were used for further analysis using a dedicated prototype volumetry software. Absolute percentage volume errors (APE) were calculated and compared. The mean APE for all nodules was 8.4% (+/-7.7%) for data acquired with the 16-slice Siemens scanner, 14.3% (+/-11.1%) for the GE scanner, 9.7% (+/-9.6%) for the Philips scanner and 7.5% (+/-7.2%) for the Toshiba scanner, respectively. The lowest APEs were found within the diameter size range of 5-10 mm and volumes >66 mm(3). Nodule volumetry is accurate with a reasonable volume error in data from different scanner vendors. This may have an important impact for intraindividual follow-up studies.
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Affiliation(s)
- Marco Das
- Department of Diagnostic Radiology, RWTH Aachen University, Pauwelsstrasse 30, 52072 Aachen, Germany.
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Stein PD, Woodard PK, Weg JG, Wakefield TW, Tapson VF, Sostman HD, Sos TA, Quinn DA, Leeper KV, Hull RD, Hales CA, Gottschalk A, Goodman LR, Fowler SE, Buckley JD. Diagnostic Pathways in Acute Pulmonary Embolism: Recommendations of the PIOPED II Investigators. Radiology 2007; 242:15-21. [PMID: 17185658 DOI: 10.1148/radiol.2421060971] [Citation(s) in RCA: 214] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Paul D Stein
- Department of Research, St. Joseph Mercy Oakland Hospital, 44405 Woodward Ave, Pontiac, MI 48341-5023, and Department of Medicine, Wayne State University, Detroit, MI, USA.
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50
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Chabriais J. [Informatics and medical imaging: a year of transition?]. JOURNAL DE RADIOLOGIE 2006; 87:889-90. [PMID: 16888577 DOI: 10.1016/s0221-0363(06)74103-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Affiliation(s)
- J Chabriais
- Département d'Imagerie Médicale, Centre Hospitalier Henri Mondor d'Aurillac, BP 229, 15002 Aurillac Cedex, France.
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