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Au C, Reeves R, Li Z, Gingold E, Halpern E, Sundaram B. Impact of multidetector computed tomography scan parameters, novel reconstruction settings, and lung nodule characteristics on nodule diameter measurements: A Phantom Study. Med Phys 2022; 49:3936-3943. [PMID: 35358333 DOI: 10.1002/mp.15639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 03/09/2022] [Accepted: 03/18/2022] [Indexed: 11/12/2022] Open
Abstract
PURPOSE Novel CT reconstruction techniques strive to maintain image quality and processing efficiency. The purpose of this study is to investigate the impact of a newer hybrid iterative reconstruction technique, Adaptive Statistical Iterative Reconstruction-V (ASIR-V) in combination with various CT scan parameters on the semi-automated quantification using various lung nodules. METHODS A chest phantom embedded with eight spherical objects was scanned using varying CT parameters such as tube current and ASIR-V levels. We calculated absolute percentage error (APE) and mean APE (MAPE) using differences between the semi-automated measured diameters and known dimensions. Predictive variables were assessed using a multivariable general linear model. The linear regression slope coefficients (β) were reported to demonstrate effect size and directionality. RESULTS The APE of the semi-automated measured diameters was higher in ground-glass than solid nodules (β = 9.000, p<0.001). APE had an inverse relationship with nodule diameter (mm; β = -3.499, p<0.001) and tube current (mA; β = -0.006, p<0.001). MAPE did not vary based on the ASIR-V level (range: 5.7-13.1%). CONCLUSION Error is dominated by nodule characteristics with a small effect of tube current. Regardless of phantom size, nodule size accuracy is not affected by tube voltage or ASIR-V level, maintaining accuracy while maximizing radiation dose reduction. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Cherry Au
- Department of Internal Medicine, Rush University Medical Center, 1620 W Harrison St, Chicago, IL, 60612
| | - Russell Reeves
- Department of Radiology, Thomas Jefferson University Hospital, 111 S 11th St, Philadelphia, PA, 19107
| | - Zhenteng Li
- Department of Radiology, Thomas Jefferson University Hospital, 111 S 11th St, Philadelphia, PA, 19107.,The Vascular Center, St. Luke's Anderson Campus - Medical Office Building, 1700 St. Luke's Boulevard, Suite 301, Easton, PA
| | - Eric Gingold
- Department of Radiology, Thomas Jefferson University Hospital, 111 S 11th St, Philadelphia, PA, 19107
| | - Ethan Halpern
- Department of Radiology, Thomas Jefferson University Hospital, 111 S 11th St, Philadelphia, PA, 19107
| | - Baskaran Sundaram
- Department of Radiology, Thomas Jefferson University Hospital, 111 S 11th St, Philadelphia, PA, 19107
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Schreuder A, Scholten ET, van Ginneken B, Jacobs C. Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice? Transl Lung Cancer Res 2021; 10:2378-2388. [PMID: 34164285 PMCID: PMC8182724 DOI: 10.21037/tlcr-2020-lcs-06] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Lung cancer computed tomography (CT) screening trials using low-dose CT have repeatedly demonstrated a reduction in the number of lung cancer deaths in the screening group compared to a control group. With various countries currently considering the implementation of lung cancer screening, recurring discussion points are, among others, the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of lung cancer screening. We discuss the performance levels of AI algorithms for various tasks related to the interpretation of lung screening CT scans, how they compare to human experts, and how AI and humans may complement each other. We discuss how AI may be used in the lung cancer CT screening workflow according to the current evidence and describe the additional research that will be required before AI can take a more prominent role in the analysis of lung screening CT scans.
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Affiliation(s)
- Anton Schreuder
- Department of Radiology, Nuclear Medicine, and Anatomy, Radboudumc, Nijmegen, The Netherlands
| | - Ernst T Scholten
- Department of Radiology, Nuclear Medicine, and Anatomy, Radboudumc, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Department of Radiology, Nuclear Medicine, and Anatomy, Radboudumc, Nijmegen, The Netherlands.,Fraunhofer MEVIS, Bremen, Germany
| | - Colin Jacobs
- Department of Radiology, Nuclear Medicine, and Anatomy, Radboudumc, Nijmegen, The Netherlands
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Snoeckx A, Franck C, Silva M, Prokop M, Schaefer-Prokop C, Revel MP. The radiologist's role in lung cancer screening. Transl Lung Cancer Res 2021; 10:2356-2367. [PMID: 34164283 PMCID: PMC8182709 DOI: 10.21037/tlcr-20-924] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Lung cancer is still the deadliest cancer in men and women worldwide. This high mortality is related to diagnosis in advanced stages, when curative treatment is no longer an option. Large randomized controlled trials have shown that lung cancer screening (LCS) with low-dose computed tomography (CT) can detect lung cancers at earlier stages and reduce lung cancer-specific mortality. The recent publication of the significant reduction of cancer-related mortality by 26% in the Dutch-Belgian NELSON LCS trial has increased the likelihood that implementation of LCS in Europe will move forward. Radiologists are important stakeholders in numerous aspects of the LCS pathway. Their role goes beyond nodule detection and nodule management. Being part of a multidisciplinary team, radiologists are key players in numerous aspects of implementation of a high quality LCS program. In this non-systematic review we discuss the multifaceted role of radiologists in LCS.
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Affiliation(s)
- Annemiek Snoeckx
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Caro Franck
- Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Mathias Prokop
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Marie-Pierre Revel
- Department of Radiology, Cochin Hospital, APHP Centre, Université de Paris, Paris, France
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Prospective Pilot Evaluation of Radiologists and Computer-aided Pulmonary Nodule Detection on Ultra–low-Dose CT With Tin Filtration. J Thorac Imaging 2018; 33:396-401. [DOI: 10.1097/rti.0000000000000348] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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Computer-aided detection of lung nodules on multidetector CT in concurrent-reader and second-reader modes: A comparative study. Eur J Radiol 2013; 82:1332-7. [DOI: 10.1016/j.ejrad.2013.02.005] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2012] [Revised: 02/02/2013] [Accepted: 02/04/2013] [Indexed: 11/18/2022]
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Bogoni L, Ko JP, Alpert J, Anand V, Fantauzzi J, Florin CH, Koo CW, Mason D, Rom W, Shiau M, Salganicoff M, Naidich DP. Impact of a computer-aided detection (CAD) system integrated into a picture archiving and communication system (PACS) on reader sensitivity and efficiency for the detection of lung nodules in thoracic CT exams. J Digit Imaging 2013; 25:771-81. [PMID: 22710985 DOI: 10.1007/s10278-012-9496-0] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The objective of this study is to assess the impact on nodule detection and efficiency using a computer-aided detection (CAD) device seamlessly integrated into a commercially available picture archiving and communication system (PACS). Forty-eight consecutive low-dose thoracic computed tomography studies were retrospectively included from an ongoing multi-institutional screening study. CAD results were sent to PACS as a separate image series for each study. Five fellowship-trained thoracic radiologists interpreted each case first on contiguous 5 mm sections, then evaluated the CAD output series (with CAD marks on corresponding axial sections). The standard of reference was based on three-reader agreement with expert adjudication. The time to interpret CAD marking was automatically recorded. A total of 134 true-positive nodules, measuring 3 mm and larger were included in our study; with 85 ≥ 4 and 50 ≥ 5 mm in size. Readers detection improved significantly in each size category when using CAD, respectively, from 44 to 57 % for ≥3 mm, 48 to 61 % for ≥4 mm, and 44 to 60 % for ≥5 mm. CAD stand-alone sensitivity was 65, 68, and 66 % for nodules ≥3, ≥4, and ≥5 mm, respectively, with CAD significantly increasing the false positives for two readers only. The average time to interpret and annotate a CAD mark was 15.1 s, after localizing it in the original image series. The integration of CAD into PACS increases reader sensitivity with minimal impact on interpretation time and supports such implementation into daily clinical practice.
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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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Zhao Y, de Bock GH, Vliegenthart R, van Klaveren RJ, Wang Y, Bogoni L, de Jong PA, Mali WP, van Ooijen PMA, Oudkerk M. Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume. Eur Radiol 2012; 22:2076-84. [PMID: 22814824 PMCID: PMC3431468 DOI: 10.1007/s00330-012-2437-y] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2011] [Revised: 12/24/2011] [Accepted: 01/08/2012] [Indexed: 11/29/2022]
Abstract
Objective To evaluate performance of computer-aided detection (CAD) beyond double reading for pulmonary nodules on low-dose computed tomography (CT) by nodule volume. Methods A total of 400 low-dose chest CT examinations were randomly selected from the NELSON lung cancer screening trial. CTs were evaluated by two independent readers and processed by CAD. A total of 1,667 findings marked by readers and/or CAD were evaluated by a consensus panel of expert chest radiologists. Performance was evaluated by calculating sensitivity of pulmonary nodule detection and number of false positives, by nodule characteristics and volume. Results According to the screening protocol, 90.9 % of the findings could be excluded from further evaluation, 49.2 % being small nodules (less than 50 mm3). Excluding small nodules reduced false-positive detections by CAD from 3.7 to 1.9 per examination. Of 151 findings that needed further evaluation, 33 (21.9 %) were detected by CAD only, one of them being diagnosed as lung cancer the following year. The sensitivity of nodule detection was 78.1 % for double reading and 96.7 % for CAD. A total of 69.7 % of nodules undetected by readers were attached nodules of which 78.3 % were vessel-attached. Conclusions CAD is valuable in lung cancer screening to improve sensitivity of pulmonary nodule detection beyond double reading, at a low false-positive rate when excluding small nodules. Key Points • Computer-aided detection (CAD) has known advantages for computed tomography (CT). • Combined CAD/nodule size cut-off parameters assist CT lung cancer screening. • This combination improves the sensitivity of pulmonary nodule detection by CT. • It increases the positive predictive value for cancer detection.
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Affiliation(s)
- Yingru Zhao
- Center for Medical Imaging - North East Netherlands, Department of Radiology, University of Groningen/University Medical Center Groningen, P.O. Box 30.001, 9700RB, Groningen, the Netherlands
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Biermann C, Tsiflikas I, Thomas C, Kasperek B, Heuschmid M, Claussen CD. Evaluation of computer-assisted quantification of carotid artery stenosis. J Digit Imaging 2012; 25:250-7. [PMID: 21786073 DOI: 10.1007/s10278-011-9413-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Abstract
The purpose of this study was to evaluate the influence of advanced software assistance on the assessment of carotid artery stenosis; particularly, the inter-observer variability of readers with different level of experience is to be investigated. Forty patients with suspected carotid artery stenosis received head and neck dual-energy CT angiography as part of their pre-interventional workup. Four blinded readers with different levels of experience performed standard imaging interpretation. At least 1 day later, they performed quantification using an advanced vessel analysis software including automatic dual-energy bone and hard plaque removal, automatic and semiautomatic vessel segmentation, as well as creation of curved planar reformation. Results were evaluated for the reproducibility of stenosis quantification of different readers by calculating the kappa and correlation values. Consensus reading of the two most experienced readers was used as the standard of reference. For standard imaging interpretation, experienced readers reached very good (k = 0.85) and good (k = 0.78) inter-observer variability. Inexperienced readers achieved moderate (k = 0.6) and fair (k = 0.24) results. Sensitivity values 80%, 91%, 83%, 77% and specificity values 100%, 84%, 82%, 53% were achieved for significant area stenosis >70%. For grading using advanced vessel analysis software, all readers achieved good inter-observer variability (k = 0.77, 0.72, 0.71, and 0.77). Specificity values of 97%, 95%, 95%, 93% and sensitivity values of 84%, 78%, 86%, 92% were achieved. In conclusion, when supported by advanced vessel analysis software, experienced readers are able to achieve good reproducibility. Even inexperienced readers are able to achieve good results in the assessment of carotid artery stenosis when using advanced vessel analysis software.
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Affiliation(s)
- Christina Biermann
- Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany.
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Assessing the use of digital radiography and a real-time interactive pulmonary nodule analysis system for large population lung cancer screening. Eur J Radiol 2012; 81:e451-6. [DOI: 10.1016/j.ejrad.2011.04.031] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2010] [Accepted: 04/06/2011] [Indexed: 11/23/2022]
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Biomedical imaging research: a fast-emerging area for interdisciplinary collaboration. Biomed Imaging Interv J 2011; 7:e21. [PMID: 22279498 PMCID: PMC3265193 DOI: 10.2349/biij.7.3.e21] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2010] [Accepted: 05/20/2011] [Indexed: 11/17/2022] Open
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