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Jiang Q, Sun H, Deng W, Chen L, Li Q, Xie J, Pan X, Cheng Y, Chen X, Wang Y, Li Y, Wang X, Liu S, Xiao Y. Super Resolution of Pulmonary Nodules Target Reconstruction Using a Two-Channel GAN Models. Acad Radiol 2024; 31:3427-3437. [PMID: 38458886 DOI: 10.1016/j.acra.2024.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 02/09/2024] [Accepted: 02/09/2024] [Indexed: 03/10/2024]
Abstract
RATIONALE AND OBJECTIVES To develop a Dual generative-adversarial-network (GAN) Cascaded Network (DGCN) for generating super-resolution computed tomography (SRCT) images from normal-resolution CT (NRCT) images and evaluate the performance of DGCN in multi-center datasets. MATERIALS AND METHODS This retrospective study included 278 patients with chest CT from two hospitals between January 2020 and June 2023, and each patient had all three NRCT (512×512 matrix CT images with a resolution of 0.70 mm, 0.70 mm,1.0 mm), high-resolution CT (HRCT, 1024×1024 matrix CT images with a resolution of 0.35 mm, 0.35 mm,1.0 mm), and ultra-high-resolution CT (UHRCT, 1024×1024 matrix CT images with a resolution of 0.17 mm, 0.17 mm, 0.5 mm) examinations. Initially, a deep chest CT super-resolution residual network (DCRN) was built to generate HRCT from NRCT. Subsequently, we employed the DCRN as a pre-trained model for the training of DGCN to further enhance resolution along all three axes, ultimately yielding SRCT. PSNR, SSIM, FID, subjective evaluation scores, and objective evaluation parameters related to pulmonary nodule segmentation in the testing set were recorded and analyzed. RESULTS DCRN obtained a PSNR of 52.16, SSIM of 0.9941, FID of 137.713, and an average diameter difference of 0.0981 mm. DGCN obtained a PSNR of 46.50, SSIM of 0.9990, FID of 166.421, and an average diameter difference of 0.0981 mm on 39 testing cases. There were no significant differences between the SRCT and UHRCT images in subjective evaluation. CONCLUSION Our model exhibited a significant enhancement in generating HRCT and SRCT images and outperformed established methods regarding image quality and clinical segmentation accuracy across both internal and external testing datasets.
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Affiliation(s)
- Qinling Jiang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai 200003, China
| | - Hongbiao Sun
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai 200003, China
| | - Wei Deng
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai 200232, China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai 200232, China
| | - Qingchu Li
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai 200003, China
| | - Jicai Xie
- Department of Radiology, The Second People's Hospital of Yuhuan, 317699, China
| | - Xianpan Pan
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai 200232, China
| | - Yuxin Cheng
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai 200003, China
| | - Xin Chen
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai 200003, China
| | - Yunmeng Wang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai 200003, China
| | - Yanran Li
- Univerisity of Queensland, Brisbane 4072, Australia
| | - Xiang Wang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai 200003, China
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai 200003, China
| | - Yi Xiao
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai 200003, China.
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Lim RS, Rosenberg J, Willemink MJ, Cheng SN, Guo HH, Hollett PD, Lin MC, Madani MH, Martin L, Pogatchnik BP, Pohlen M, Shen J, Tsai EB, Berry GJ, Scott G, Leung AN. Volumetric Analysis: Effect on Diagnosis and Management of Indeterminate Solid Pulmonary Nodules in Routine Clinical Practice. J Comput Assist Tomogr 2024:00004728-990000000-00335. [PMID: 38968327 DOI: 10.1097/rct.0000000000001630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2024]
Abstract
OBJECTIVE To evaluate the effect of volumetric analysis on the diagnosis and management of indeterminate solid pulmonary nodules in routine clinical practice. METHODS This was a retrospective study with 107 computed tomography (CT) cases of solid pulmonary nodules (range, 6-15 mm), 57 pathology-proven malignancies (lung cancer, n = 34; metastasis, n = 23), and 50 benign nodules. Nodules were evaluated on a total of 309 CT scans (average number of CTs/nodule, 2.9 [range, 2-7]). CT scans were from multiple institutions with variable technique. Nine radiologists (attendings, n = 3; fellows, n = 3; residents, n = 3) were asked their level of suspicion for malignancy (low/moderate or high) and management recommendation (no follow-up, CT follow-up, or care escalation) for baseline and follow-up studies first without and then with volumetric analysis data. Effect of volumetry on diagnosis and management was assessed by generalized linear and logistic regression models. RESULTS Volumetric analysis improved sensitivity (P = 0.009) and allowed earlier recognition (P < 0.05) of malignant nodules. Attending radiologists showed higher sensitivity in recognition of malignant nodules (P = 0.03) and recommendation of care escalation (P < 0.001) compared with trainees. Volumetric analysis altered management of high suspicion nodules only in the fellow group (P = 0.008). κ Statistics for suspicion for malignancy and recommended management were fair to substantial (0.38-0.66) and fair to moderate (0.33-0.50). Volumetric analysis improved interobserver variability for identification of nodule malignancy from 0.52 to 0.66 (P = 0.004) only on the second follow-up study. CONCLUSIONS Volumetric analysis of indeterminate solid pulmonary nodules in routine clinical practice can result in improved sensitivity and earlier identification of malignant nodules. The effect of volumetric analysis on management recommendations is variable and influenced by reader experience.
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Affiliation(s)
| | - Jarrett Rosenberg
- From the Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Martin J Willemink
- From the Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Sarah N Cheng
- From the Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Henry H Guo
- From the Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Philip D Hollett
- From the Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Margaret C Lin
- From the Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | | | - Lynne Martin
- From the Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Brian P Pogatchnik
- From the Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Michael Pohlen
- From the Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Jody Shen
- From the Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Emily B Tsai
- From the Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Gerald J Berry
- Department of Pathology, Stanford University School of Medicine, Stanford, CA
| | | | - Ann N Leung
- From the Department of Radiology, Stanford University School of Medicine, Stanford, CA
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Jirapatnakul A, Yip R, Myers KJ, Cai S, Henschke CI, Yankelevitz D. Assessing the impact of nodule features and software algorithm on pulmonary nodule measurement uncertainty for nodules sized 20 mm or less. Quant Imaging Med Surg 2024; 14:5057-5071. [PMID: 39022249 PMCID: PMC11250315 DOI: 10.21037/qims-23-1501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 05/22/2024] [Indexed: 07/20/2024]
Abstract
Background Measurements are not exact, so that if a measurement is repeated, one would get a different value each time. The spread of these values is the measurement uncertainty. Understanding measurement uncertainty of pulmonary nodules is important for proper interpretation of size and growth measurements. Larger amounts of measurement uncertainty may require longer follow-up intervals to be confident that any observed growth is due to actual growth rather than measurement uncertainty. We examined the influence of nodule features and software algorithm on measurement uncertainty of small, solid pulmonary nodules. Methods Volumes of 107 nodules were measured on 4-6 repeated computed tomography (CT) scans (Siemens Somatom AS, 100 kVp, 120 mA, 1.0 mm slice thickness reconstruction) prospectively obtained during CT-guided fine needle aspiration biopsy between 2015-2021 at Department of Diagnostic, Molecular, and Interventional Radiology in Icahn School of Medicine at Mount Sinai, using two different automated volumetric algorithms. For each, the coefficient of variation (standard deviation divided by the mean) of nodule volume measurements was determined. The following features were considered: diameter, location, vessel and pleural attachments, nodule surface area, and extent of the nodule in the three acquisition dimensions of the scanner. Results Median volume of 107 nodules was 515.23 and 535.53 mm3 for algorithm #1 and #2, respectively with excellent agreement (intraclass correlation coefficient =0.98). Median coefficient of variation of nodule volume was low for the two algorithms, but significantly different (4.6% vs. 8.7%, P<0.001). Both algorithms had a trend of decreasing coefficient of variation of nodule volume with increasing nodule diameter, though only significant for algorithm #2. Coefficient of variation of nodule volume was significantly associated with nodule volume (P=0.02), attachment to blood vessels (P=0.02), and nodule surface area (P=0.001) for algorithm #2 using a multiple linear regression model. Correlation between the coefficient of variation (CoV) of nodule volume and the CoV of the x, y, z measurements for algorithm #1 were 0.29 (P=0.0021), 0.25 (P=0.009), and 0.80 (P<0.001) respectively, and for algorithm #2, 0.46 (P<0.001), 0.52 (P<0.001), and 0.58 (P<0.001), respectively. Conclusions Even in the best-case scenario represented in this study, using the same measurement algorithm, scanner, and scanning protocol, considerable measurement uncertainty exists in nodule volume measurement for nodules sized 20 mm or less. We found that measurement uncertainty was affected by interactions between nodule volume, algorithm, and shape complexity.
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Affiliation(s)
- Artit Jirapatnakul
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rowena Yip
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Siyang Cai
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Claudia I. Henschke
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David Yankelevitz
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Hinsen M, Nagel AM, May MS, Wiesmueller M, Uder M, Heiss R. Lung Nodule Detection With Modern Low-Field MRI (0.55 T) in Comparison to CT. Invest Radiol 2024; 59:215-222. [PMID: 37490031 DOI: 10.1097/rli.0000000000001006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
OBJECTIVES The aim of this study was to evaluate the accuracy of modern low-field magnetic resonance imaging (MRI) for lung nodule detection and to correlate nodule size measurement with computed tomography (CT) as reference. MATERIALS AND METHODS Between November 2020 and July 2021, a prospective clinical trial using low-field MRI at 0.55 T was performed in patients with known pulmonary nodules from a single academic medical center. Every patient underwent MRI and CT imaging on the same day. The primary aim was to evaluate the detection accuracy of pulmonary nodules using MRI with transversal periodically rotated overlapping parallel lines with enhanced reconstruction in combination with coronal half-Fourier acquired single-shot turbo spin-echo MRI sequences. The secondary outcome was the correlation of the mean lung nodule diameter with CT as reference according to the Lung Imaging Reporting and Data System. Nonparametric Mann-Whitney U test, Spearman rank correlation coefficient, and Bland-Altman analysis were applied to analyze the results. RESULTS A total of 46 participants (mean age ± SD, 66 ± 11 years; 26 women) were included. In a blinded analysis of 964 lung nodules, the detection accuracy was 100% for those ≥6 mm (126/126), 80% (159/200) for those ≥4-<6 mm, and 23% (147/638) for those <4 mm in MRI compared with reference CT. Spearman correlation coefficient of MRI and CT size measurement was r = 0.87 ( P < 0.001), and the mean difference was 0.16 ± 0.9 mm. CONCLUSIONS Modern low-field MRI shows excellent accuracy in lesion detection for lung nodules ≥6 mm and a very strong correlation with CT imaging for size measurement, but could not compete with CT in the detection of small nodules.
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Affiliation(s)
- Maximilian Hinsen
- From the Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany (M.H., A.M.N., M.S.M., M.W., M.U., R.H.); and Division of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany (A.M.N.)
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Li S, Chen M, Wang Y, Li X, Gao G, Luo X, Tang L, Liu X, Wu N. An Effective Malignancy Prediction Model for Incidentally Detected Pulmonary Subsolid Nodules Based on Current and Prior CT Scans. Clin Lung Cancer 2023; 24:e301-e310. [PMID: 37596166 DOI: 10.1016/j.cllc.2023.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/30/2023] [Accepted: 08/01/2023] [Indexed: 08/20/2023]
Abstract
INTRODUCTION It is challenging to diagnose and manage incidentally detected pulmonary subsolid nodules due to their indolent nature and heterogeneity. The objective of this study is to construct a decision tree-based model to predict malignancy of a subsolid nodule based on radiomics features and evolution over time. MATERIALS AND METHODS We derived a training set (2947 subsolid nodules), a test set (280 subsolid nodules) from a cohort of outpatient CT scans, and a second test set (5171 subsolid nodules) from the National Lung Cancer Screening Trial (NLST). A Computer-Aided Diagnosis system (CADs) automatically extracted 28 preselected radiomics features, and we calculated the feature change rates as the change of the quantitative measure per time unit between the prior and current CT scans. We built classification models based on XGBoost and employed 5-fold cross validation to optimize the parameters. RESULTS The model that combined radiomics features with their change rates performed the best. The Areas Under Curve (AUCs) on the outpatient test set and on the NLST test set were 0.977 (95% CI, 0.958-0.996) and 0.955 (95% CI, 0.930-0.980), respectively. The model performed consistently well on subgroups stratified by nodule diameters, solid components, and CT scan intervals. CONCLUSION This decision tree-based model trained with the outpatient dataset gives promising predictive performance on the malignancy of pulmonary subsolid nodules. Additionally, it can assist clinicians to deliver more accurate diagnoses and formulate more in-depth follow-up strategies.
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Affiliation(s)
- Shaolei Li
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Mailin Chen
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yaqi Wang
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Xiang Li
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | | | | | - Lei Tang
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | | | - Nan Wu
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China.
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Guedes Pinto E, Penha D, Ravara S, Monaghan C, Hochhegger B, Marchiori E, Taborda-Barata L, Irion K. Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis. Insights Imaging 2023; 14:152. [PMID: 37741928 PMCID: PMC10517915 DOI: 10.1186/s13244-023-01480-z] [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: 04/18/2023] [Accepted: 07/08/2023] [Indexed: 09/25/2023] Open
Abstract
Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance. CRITICAL RELEVANCE STATEMENT: Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence. KEY POINTS: • Factors influencing the volumetry of pulmonary nodules have been extensively investigated. • Just 11% of studies test clinical significance (wrongly diagnosing growth). • Nodule size interacts with most other influencing factors (especially for smaller nodules). • Heterogeneity among studies makes comparison and consolidation of results challenging. • Future research should focus on clinical applicability, screening, and updated technology.
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Affiliation(s)
- Erique Guedes Pinto
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal.
| | - Diana Penha
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal
- Liverpool Heart and Chest Hospital NHS Foundation Trust, Thomas Dr, Liverpool, L14 3PE, UK
| | - Sofia Ravara
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal
| | - Colin Monaghan
- Liverpool Heart and Chest Hospital NHS Foundation Trust, Thomas Dr, Liverpool, L14 3PE, UK
| | | | - Edson Marchiori
- Faculdade de Medicina, Universidade Federal Do Rio de Janeiro, Bloco K - Av. Carlos Chagas Filho, 373 - 2º Andar, Sala 49 - Cidade Universitária da Universidade Federal Do Rio de Janeiro, Rio de Janeiro - RJ, 21044-020, Brasil
- Faculdade de Medicina, Universidade Federal Fluminense, Av. Marquês Do Paraná, 303 - Centro, Niterói - RJ, 24220-000, Brasil
| | - Luís Taborda-Barata
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal
| | - Klaus Irion
- Manchester University NHS Foundation Trust, Manchester Royal Infirmary, Oxford Rd, Manchester, M13 9WL, UK
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Liu YC, Liang CH, Wu YJ, Chen CS, Tang EK, Wu FZ. Managing Persistent Subsolid Nodules in Lung Cancer: Education, Decision Making, and Impact of Interval Growth Patterns. Diagnostics (Basel) 2023; 13:2674. [PMID: 37627933 PMCID: PMC10453827 DOI: 10.3390/diagnostics13162674] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
With the popularization of lung cancer screening, many persistent subsolid nodules (SSNs) have been identified clinically, especially in Asian non-smokers. However, many studies have found that SSNs exhibit heterogeneous growth trends during long-term follow ups. This article adopted a narrative approach to extensively review the available literature on the topic to explore the definitions, rationale, and clinical application of different interval growths of subsolid pulmonary nodule management and follow-up strategies. The development of SSN growth thresholds with different growth patterns could support clinical decision making with follow-up guidelines to reduce over- and delayed diagnoses. In conclusion, using different SSN growth thresholds could optimize the follow-up management and clinical decision making of SSNs in lung cancer screening programs. This could further reduce the lung cancer mortality rate and potential harm from overdiagnosis and over management.
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Affiliation(s)
- Yung-Chi Liu
- Department of Radiology, Xiamen Chang Gung Hospital, Xiamen 361028, China;
- Department of Imaging Technology Division, Xiamen Chang Gung Hospital, Xiamen 361028, China
- Department of Healthcare Administration Department, Xiamen Chang Gung Hospital, Xiamen 361028, China
| | - Chia-Hao Liang
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei 112304, Taiwan;
| | - Yun-Ju Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan;
- Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung 80201, Taiwan
| | - Chi-Shen Chen
- Physical Examination Center, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan;
| | - En-Kuei Tang
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan;
| | - Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan;
- School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Education, National Sun Yat-Sen University, Kaohsiung 804241, Taiwan
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Han D, Cai J, Heus A, Heuvelmans M, Imkamp K, Dorrius M, Pelgrim GJ, de Jonge G, Oudkerk M, van den Berge M, Vliegenthart R. Detection and size quantification of pulmonary nodules in ultralow-dose versus regular-dose CT: a comparative study in COPD patients. Br J Radiol 2023; 96:20220709. [PMID: 36728829 PMCID: PMC10078877 DOI: 10.1259/bjr.20220709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVE To evaluate detectability and semi-automatic diameter and volume measurements of pulmonary nodules in ultralow-dose CT (ULDCT) vs regular-dose CT (RDCT). METHODS Fifty patients with chronic obstructive pulmonary disease (COPD) underwent RDCT on 64-multidetector CT (120 kV, filtered back projection), and ULDCT on third-generation dual source CT (100 kV with tin filter, advanced modeled iterative reconstruction). One radiologist evaluated the presence of nodules on both scans in random order, with discrepancies judged by two independent radiologists and consensus reading. Sensitivity of nodule detection on RDCT and ULDCT was compared to reader consensus. Systematic error in semi-automatically derived diameter and volume, and 95% limits of agreement (LoA) were evaluated. Nodule classification was compared by κ statistics. RESULTS ULDCT resulted in 83.1% (95% CI: 81.0-85.2) dose reduction compared to RDCT (p < 0.001). 45 nodules were present, with diameter range 4.0-25.3 mm and volume range 16.0-4483.0 mm3. Detection sensitivity was non-significant (p = 0.503) between RDCT 88.8% (95% CI: 76.0-96.3) and ULDCT 95.5% (95% CI: 84.9-99.5). No systematic bias in diameter measurements (median difference: -0.2 mm) or volumetry (median difference: -6 mm3) was found for ULDCT compared to RDCT. The 95% LoA for diameter and volume measurements were ±3.0 mm and ±33.5%, respectively. κ value for nodule classification was 0.852 for diameter measurements and 0.930 for volumetry. CONCLUSION ULDCT based on Sn100 kV enables comparable detectability of solid pulmonary nodules in COPD patients, at 83% reduced radiation dose compared to RDCT, without relevant difference in nodule measurement and size classification. ADVANCES IN KNOWLEDGE Pulmonary nodule detectability and measurements in ULDCT are comparable to RDCT.
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Affiliation(s)
- Daiwei Han
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jiali Cai
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Anne Heus
- Department of Radiology, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Marjolein Heuvelmans
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Pulmonology, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Kai Imkamp
- Department of Pulmonology, University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD, Groningen, The Netherlands
| | - Monique Dorrius
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Gert-Jan Pelgrim
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Gonda de Jonge
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Matthijs Oudkerk
- Institute for Diagnostic Accuracy Research B.V., Groningen, The Netherlands
- University of Groningen, Groningen, The Netherlands
| | - Maarten van den Berge
- Department of Pulmonology, University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD, Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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Voigt W, Prosch H, Silva M. Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges? Cancers (Basel) 2023; 15:cancers15041218. [PMID: 36831559 PMCID: PMC9954060 DOI: 10.3390/cancers15041218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 02/05/2023] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
As most lung cancer (LC) cases are still detected at advanced and incurable stages, there are increasing efforts to foster detection at earlier stages by low dose computed tomography (LDCT) based LC screening. In this scoping review, we describe current advances in candidate selection for screening (selection phase), technical aspects (screening), and probability evaluation of malignancy of CT-detected pulmonary nodules (PN management). Literature was non-systematically assessed and reviewed for suitability by the authors. For the selection phase, we describe current eligibility criteria for screening, along with their limitations and potential refinements through advanced clinical scores and biomarker assessments. For LC screening, we discuss how the accuracy of computerized tomography (CT) scan reading might be augmented by IT tools, helping radiologists to cope with increasing workloads. For PN management, we evaluate the precision of follow-up scans by semi-automatic volume measurements of CT-detected PN. Moreover, we present an integrative approach to evaluate the probability of PN malignancy to enable safe decisions on further management. As a clear limitation, additional validation studies are required for most innovative diagnostic approaches presented in this article, but the integration of clinical risk models, current imaging techniques, and advancing biomarker research has the potential to improve the LC screening performance generally.
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Affiliation(s)
- Wieland Voigt
- Medical Innovation and Management, Steinbeis University Berlin, Ernst-Augustin-Strasse 15, 12489 Berlin, Germany
- Correspondence:
| | - Helmut Prosch
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, General Hospital, 1090 Vienna, Austria
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, 43121 Parma, Italy
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10
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Zhang G, Luo L, Zhang L, Liu Z. Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence. Diagnostics (Basel) 2023; 13:diagnostics13030357. [PMID: 36766460 PMCID: PMC9914063 DOI: 10.3390/diagnostics13030357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/06/2023] [Accepted: 01/16/2023] [Indexed: 01/21/2023] Open
Abstract
Machine Learning (ML) is an algorithm based on big data, which learns patterns from the previously observed data through classifying, predicting, and optimizing to accomplish specific tasks. In recent years, there has been rapid development in the field of ML in medicine, including lung imaging analysis, intensive medical monitoring, mechanical ventilation, and there is need for intubation etiology prediction evaluation, pulmonary function evaluation and prediction, obstructive sleep apnea, such as biological information monitoring and so on. ML can have good performance and is a great potential tool, especially in the imaging diagnosis of interstitial lung disease. Idiopathic pulmonary fibrosis (IPF) is a major problem in the treatment of respiratory diseases, due to the abnormal proliferation of fibroblasts, leading to lung tissue destruction. The diagnosis mainly depends on the early detection of imaging and early treatment, which can effectively prolong the life of patients. If the computer can be used to assist the examination results related to the effects of fibrosis, a timely diagnosis of such diseases will be of great value to both doctors and patients. We also previously proposed a machine learning algorithm model that can play a good clinical guiding role in early imaging prediction of idiopathic pulmonary fibrosis. At present, AI and machine learning have great potential and ability to transform many aspects of respiratory medicine and are the focus and hotspot of research. AI needs to become an invisible, seamless, and impartial auxiliary tool to help patients and doctors make better decisions in an efficient, effective, and acceptable way. The purpose of this paper is to review the current application of machine learning in various aspects of respiratory diseases, with the hope to provide some help and guidance for clinicians when applying algorithm models.
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Affiliation(s)
- Gerui Zhang
- Department of Critical Care Unit, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China
| | - Lin Luo
- Department of Critical Care Unit, The Second Hospital of Dalian Medical University, 467 Zhongshan Road, Shahekou District, Dalian 116023, China
| | - Limin Zhang
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China
| | - Zhuo Liu
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China
- Correspondence:
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11
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Rafael-Palou X, Aubanell A, Ceresa M, Ribas V, Piella G, Ballester MAG. Prediction of Lung Nodule Progression with an Uncertainty-Aware Hierarchical Probabilistic Network. Diagnostics (Basel) 2022; 12:2639. [PMID: 36359482 PMCID: PMC9689366 DOI: 10.3390/diagnostics12112639] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 09/08/2024] Open
Abstract
Predicting whether a lung nodule will grow, remain stable or regress over time, especially early in its follow-up, would help doctors prescribe personalized treatments and better surgical planning. However, the multifactorial nature of lung tumour progression hampers the identification of growth patterns. In this work, we propose a deep hierarchical generative and probabilistic network that, given an initial image of the nodule, predicts whether it will grow, quantifies its future size and provides its expected semantic appearance at a future time. Unlike previous solutions, our approach also estimates the uncertainty in the predictions from the intrinsic noise in medical images and the inter-observer variability in the annotations. The evaluation of this method on an independent test set reported a future tumour growth size mean absolute error of 1.74 mm, a nodule segmentation Dice's coefficient of 78% and a tumour growth accuracy of 84% on predictions made up to 24 months ahead. Due to the lack of similar methods for providing future lung tumour growth predictions, along with their associated uncertainty, we adapted equivalent deterministic and alternative generative networks (i.e., probabilistic U-Net, Bayesian test dropout and Pix2Pix). Our method outperformed all these methods, corroborating the adequacy of our approach.
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Affiliation(s)
- Xavier Rafael-Palou
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08108 Barcelona, Spain
- Eurecat Centre Tecnològic de Catalunya, Digital Health Unit, 08005 Barcelona, Spain
| | - Anton Aubanell
- Vall d’Hebron University Hospital, 08035 Barcelona, Spain
| | - Mario Ceresa
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08108 Barcelona, Spain
| | - Vicent Ribas
- Eurecat Centre Tecnològic de Catalunya, Digital Health Unit, 08005 Barcelona, Spain
| | - Gemma Piella
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08108 Barcelona, Spain
| | - Miguel A. González Ballester
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08108 Barcelona, Spain
- ICREA, 08690 Barcelona, Spain
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12
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Lee JH, Hwang EJ, Lim WH, Goo JM. Determination of the optimum definition of growth evaluation for indeterminate pulmonary nodules detected in lung cancer screening. PLoS One 2022; 17:e0274583. [PMID: 36108077 PMCID: PMC9477274 DOI: 10.1371/journal.pone.0274583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 08/30/2022] [Indexed: 11/19/2022] Open
Abstract
Objective
To determine the optimum definition of growth for indeterminate pulmonary nodules detected in lung cancer screening.
Materials and methods
Individuals with indeterminate nodules as defined by volume of 50–500 mm3 (solid nodules) and solid component volume of 50–500 mm3 or average diameter of non-solid component ≥8 mm (part-solid nodules) on baseline lung cancer screening low-dose chest CT (LDCT) were included. The average diameters and volumes of the nodules were measured on baseline and follow-up LDCTs with semi-automated segmentation. Sensitivities and specificities for lung cancer diagnosis of nodule growth defined by a) percentage volume growth ≥25% (defined in the NELSON study); b) absolute diameter growth >1.5 mm (defined in the Lung-RADS version 1.1); and c) subjective decision by a radiologist were evaluated. Sensitivities and specificities of diagnostic referral based on various thresholds of volume doubling time (VDT) were also evaluated.
Results
Altogether, 115 nodules (one nodule per individual; 93 solid and 22 part-solid nodules; 105 men; median age, 68 years) were evaluated (median follow-up interval: 201 days; interquartile range: 127–371 days). Percentage volume growth ≥25% exhibited higher sensitivity but lower specificity than those of diametrical measurement compared to absolute diameter growth >1.5 mm (sensitivity, 69.2% vs. 42.3%, p = 0.023; specificity, 82.0% vs. 96.6%, p = 0.002). The radiologist had an equivalent sensitivity (53.9%; p = 0.289) but higher specificity (98.9%; p = 0.002) compared to those of volume growth, but did not differ from those of diameter growth (p>0.05 both in sensitivity and specificity). Compared to the VDT threshold of 600 days (sensitivity, 61.5%; specificity, 87.6%), VDT thresholds ≤200 and ≤300 days exhibited significantly lower sensitivity (30.8%, p = 0.013) and higher specificity (94.4%, p = 0.041), respectively.
Conclusion
Growth evaluation of screening-detected indeterminate nodules with volumetric measurement exhibited higher sensitivity but lower specificity compared to diametric measurements.
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Affiliation(s)
- Jong Hyuk Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
| | - Eui Jin Hwang
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
- * E-mail:
| | - Woo Hyeon Lim
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
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13
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Kim J, Kim T, Jang TW, Kang H, Kim MH, Yoon SH, Son CH, Lee HK, Kim HK, Lee SY, Shin KC, Han JY, Kang EJ. Clinical outcomes of hyperprogression based on volumetry in non-small cell lung cancer after immune checkpoint inhibitor treatment. Thorac Cancer 2022; 13:2170-2179. [PMID: 35785522 PMCID: PMC9346184 DOI: 10.1111/1759-7714.14539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Hyperprogressive disease (HPD) is a novel pattern of the treatment course after immune checkpoint inhibitor (ICI) therapy in patients with non-small cell lung cancer (NSCLC). This study aimed to investigate the clinical characteristics, outcomes, and associated factors of HPD using a semiautomatic volume measurement. METHODS This retrospective study enrolled patients with recurrent and/or metastatic NSCLC treated with ICIs between January 2015 and August 2019 at eight tertiary centers in Korea. HPD was defined according to the tumor growth kinetics and time to treatment failure. Tumor volume was measured using a semiautomatic software. RESULTS A total of 219 NSCLC patients with 35 HPD by volumetric measurement (HPDv) (15.9%) were enrolled. The median duration of overall survival (OS) and OS after ICI treatment (ICI-OS) were 34.5 and 18.4 months, respectively. HPDv patients had significantly worse progression-free survival (PFS) than progressive disease patients without HPDv (1.16 vs. 1.82 months, p-value <0.001). ICI-OS did not significantly differ between patients with HPDv and those without HPDv (2.66 vs. 5.4 months, p = 0.105). PD-L1 expression lower than 50%, more than three metastatic sites, neutrophil-to-lymphocyte ratio equal to or higher than 3.3, and hemoglobin level lower than 10 were found to be associated with HPDv. CONCLUSIONS There is no standardized definition of HPD. However, defining HPD in NSCLC patients treated with ICI using a semiautomatic volume measurement software is feasible.
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Affiliation(s)
- Jehun Kim
- Division of Pulmonology, Department of Internal Medicine, Kosin University College of Medicine, Kosin University Gospel Hospital, Busan, South Korea
| | - Taeyun Kim
- Department of Internal Medicine, The Armed Forces Goyang Hospital, Goyang-si, South Korea
| | - Tae Won Jang
- Division of Pulmonology, Department of Internal Medicine, Kosin University College of Medicine, Kosin University Gospel Hospital, Busan, South Korea
| | - Hee Kang
- Department of Radiology, Kosin University College of Medicine, Kosin University Gospel Hospital, Busan, South Korea
| | - Mi Hyun Kim
- Division of Pulmonology, Department of Internal Medicine, Pusan National University Hospital, Pusan, South Korea
| | - Seong Hoon Yoon
- Division of Pulmonology, Department of Internal Medicine, Pusan National University Yangsan Hospital, Yangsan, South Korea
| | - Choon-Hee Son
- Division of Pulmonology, Department of Internal Medicine, Gwanghye General Hospital, Pusan, South Korea
| | - Hyun-Kyung Lee
- Division of Pulmonology, Department of Internal Medicine, Inje University Busan Paik Hospital, Busan, South Korea
| | - Hyun-Kuk Kim
- Division of Pulmonology, Department of Internal Medicine, Inje University Haeundae Paik Hospital, Busan, South Korea
| | - Shin Yup Lee
- Division of Pulmonology, Department of Internal Medicine, Kyungpook National University Chilgok Hospital, Daegu, South Korea
| | - Kyeong Choel Shin
- Division of Pulmonology, Department of Internal Medicine, Yeungnam University Hospital, Daegu, South Korea
| | - Ji-Yeon Han
- Department of Radiology, Inje University Busan Paik Hospital, Busan, South Korea
| | - Eun-Ju Kang
- Department of Radiology, Dong-A University College of Medicine, Busan, South Korea
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14
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Lancaster HL, Heuvelmans MA, Oudkerk M. Low-dose computed tomography lung cancer screening: Clinical evidence and implementation research. J Intern Med 2022; 292:68-80. [PMID: 35253286 PMCID: PMC9311401 DOI: 10.1111/joim.13480] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Lung cancer causes more deaths than breast, cervical, and colorectal cancer combined. Nevertheless, population-based lung cancer screening is still not considered standard practice in most countries worldwide. Early lung cancer detection leads to better survival outcomes: patients diagnosed with stage 1A lung cancer have a >75% 5-year survival rate, compared to <5% at stage 4. Low-dose computed tomography (LDCT) thorax imaging for the secondary prevention of lung cancer has been studied at length, and has been shown to significantly reduce lung cancer mortality in high-risk populations. The US National Lung Screening Trial reported a 20% overall reduction in lung cancer mortality when comparing LDCT to chest X-ray, and the Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) trial more recently reported a 24% reduction when comparing LDCT to no screening. Hence, the focus has now shifted to implementation research. Consequently, the 4-IN-THE-LUNG-RUN consortium based in five European countries, has set up a large-scale multicenter implementation trial. Successful implementation of and accessibility to LDCT lung cancer screening are dependent on many factors, not limited to population selection, recruitment strategy, computed tomography screening frequency, lung-nodule management, participant compliance, and cost effectiveness. This review provides an overview of current evidence for LDCT lung cancer screening, and draws attention to major factors that need to be addressed to successfully implement standardized, effective, and accessible screening throughout Europe. Evidence shows that through the appropriate use of risk-prediction models and a more personalized approach to screening, efficacy could be improved. Furthermore, extending the screening interval for low-risk individuals to reduce costs and associated harms is a possibility, and through the use of volumetric-based measurement and follow-up, false positive results can be greatly reduced. Finally, smoking cessation programs could be a valuable addition to screening programs and artificial intelligence could offer a solution to the added workload pressures radiologists are facing.
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Affiliation(s)
- Harriet L Lancaster
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Institute for Diagnostic Accuracy, Groningen, The Netherlands
| | - Marjolein A Heuvelmans
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Institute for Diagnostic Accuracy, Groningen, The Netherlands
| | - Matthijs Oudkerk
- Institute for Diagnostic Accuracy, Groningen, The Netherlands.,Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands
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15
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Diao K, Chen Y, Liu Y, Chen BJ, Li WJ, Zhang L, Qu YL, Zhang T, Zhang Y, Wu M, Li K, Song B. Diagnostic study on clinical feasibility of an AI-based diagnostic system as a second reader on mobile CT images: a preliminary result. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:668. [PMID: 35845492 PMCID: PMC9279799 DOI: 10.21037/atm-22-2157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 06/06/2022] [Indexed: 02/05/2023]
Abstract
Background Artificial intelligence (AI) has breathed new life into the lung nodules detection and diagnosis. However, whether the output information from AI will translate into benefits for clinical workflow or patient outcomes in a real-world setting remains unknown. This study was to demonstrate the feasibility of an AI-based diagnostic system deployed as a second reader in imaging interpretation for patients screened for pulmonary abnormalities in a clinical setting. Methods The study included patients from a lung cancer screening program conducted in Sichuan Province, China using a mobile computed tomography (CT) scanner which traveled to medium-size cities between July 10th, 2020 and September 10th, 2020. Cases that were suspected to have malignant nodules by junior radiologists, senior radiologists or AI were labeled a high risk (HR) tag as HR-junior, HR-senior and HR-AI, respectively, and included into final analysis. The diagnosis efficacy of the AI was evaluated by calculating negative predictive value and positive predictive value when referring to the senior readers’ final results as the gold standard. Besides, characteristics of the lesions were compared among cases with different HR labels. Results In total, 251/3,872 patients (6.48%, male/female: 91/160, median age, 66 years) with HR lung nodules were included. The AI algorithm achieved a negative predictive value of 88.2% [95% confidence interval (CI): 62.2–98.0%] and a positive predictive value of 55.6% (95% CI: 49.0–62.0%). The diagnostic duration was significantly reduced when AI was used as a second reader (223±145.6 vs. 270±143.17 s, P<0.001). The information yielded by AI affected the radiologist’s decision-making in 35/145 cases. Lesions of HR cases had a higher volume [309.9 (214.9–732.5) vs. 141.3 (79.3–380.8) mm3, P<0.001], lower average CT number [−511.0 (−576.5 to −100.5) vs. −191.5 (−487.3 to 22.5), P=0.010], and pure ground glass opacity rather than solid. Conclusions The AI algorithm had high negative predictive value but low positive predictive value in diagnosing HR lung lesions in a clinical setting. Deploying AI as a second reader could help avoid missed diagnoses, reduce diagnostic duration, and strengthen diagnostic confidence for radiologists.
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Affiliation(s)
- Kaiyue Diao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ying Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Bo-Jiang Chen
- Department of Respiratory Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Wan-Jiang Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Lin Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ya-Li Qu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yun Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Min Wu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Huaxi MR Research Center, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Kang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, Sanya People's Hospital (West China Sanya Hospital of Sichuan University), Chengdu, China
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16
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The Importance of Accurate Tumor Measurements and Staging in Oncologic Imaging: Impact on Patients' Health. Acad Radiol 2021; 28:767-768. [PMID: 33468419 DOI: 10.1016/j.acra.2021.01.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 01/08/2021] [Indexed: 12/20/2022]
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17
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Silva M, Milanese G, Ledda RE, Pastorino U, Sverzellati N. Screen-detected solid nodules: from detection of nodule to structured reporting. Transl Lung Cancer Res 2021; 10:2335-2346. [PMID: 34164281 PMCID: PMC8182712 DOI: 10.21037/tlcr-20-296] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Lung cancer screening (LCS) is gaining some interest worldwide after positive results from International trials. Unlike other screening practices, LCS is performed by an extremely sensitive test, namely low-dose computed tomography (LDCT) that can detect the smallest nodules in lung parenchyma. Up-to-date detection approaches, such as computer aided detection systems, have been increasingly employed for lung nodule automatic identification and are largely used in most LCS programs as a complementary tool to visual reading. Solid nodules of any size are represented in the vast majority of subjects undergoing LDCT. However, less than 1% of solid nodules will be diagnosed lung cancer. This fact calls for specific characterization of nodules to avoid false positives, overinvestigation, and reduce the risks associated with nodule work up. Recent research has been exploring the potential of artificial intelligence, including deep learning techniques, to enhance the accuracy of both detection and characterisation of lung nodule. Computer aided detection and diagnosis algorithms based on artificial intelligence approaches have demonstrated the ability to accurately detect and characterize parenchymal nodules, reducing the number of false positives, and to outperform some of the currently used risk models for prediction of lung cancer risk, potentially reducing the proportion of surveillance CT scans. These forthcoming approaches will eventually integrate a new reasoning for development of future guidelines, which are expected to evolve into precision and personalized stratification of lung cancer risk stratification by continuous fashion, as opposed to the current format with a limited number of risk classes within fixed thresholds of nodule size. This review aims to detail the standard of reference for optimal management of solid nodules by low-dose computed and its projection into the fine selection of candidates for work up.
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Affiliation(s)
- Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Gianluca Milanese
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Roberta E Ledda
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Ugo Pastorino
- Section of Thoracic Surgery, IRCCS Istituto Nazionale Tumori, Milano, Italy
| | - Nicola Sverzellati
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
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18
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Hwang EJ, Goo JM, Kim HY, Yi J, Kim Y. Optimum diameter threshold for lung nodules at baseline lung cancer screening with low-dose chest CT: exploration of results from the Korean Lung Cancer Screening Project. Eur Radiol 2021; 31:7202-7212. [PMID: 33738597 DOI: 10.1007/s00330-021-07827-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 01/01/2021] [Accepted: 02/22/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To explore the optimum diameter threshold for solid nodules to define positive results at baseline screening low-dose CT (LDCT) and to compare two-dimensional and volumetric measurement of lung nodules for the diagnosis of lung cancers. METHODS We included consecutive participants from the Korean Lung Cancer Screening project between 2017 and 2018. The average transverse diameter and effective diameter (diameter of a sphere with the same volume) of lung nodules were measured by semi-automated segmentation. Diagnostic performances for lung cancers diagnosed within 1 year after LDCT were evaluated using area under receiver-operating characteristic curves (AUCs), sensitivities, and specificities, with diameter thresholds for solid nodules ranging from 6 to 10 mm. The reduction of unnecessary follow-up LDCTs and the diagnostic delay of lung cancers were estimated for each threshold. RESULTS Fifty-two lung cancers were diagnosed among 10,424 (10,141 men; median age 62 years) participants within 1 year after LDCT. Average transverse (0.980) and effective diameters (0.981) showed similar AUCs (p = .739). Elevating the average transverse diameter threshold from 6 to 9 mm resulted in a significantly increased specificity (91.7 to 96.7%, p < .001), a modest reduction in sensitivity (96.2 to 94.2%, p = .317), a 60.2% estimated reduction of unnecessary follow-up LDCTs, and a diagnostic delay in 1.9% of lung cancers. Elevating the threshold to 10 mm led to a significant reduction in sensitivity (86.5%, p = .025). CONCLUSIONS Elevating the diameter threshold for solid nodules from 6 to 9 mm may lead to a substantial reduction in unnecessary follow-up LDCTs with a small proportion of diagnostic delay of lung cancers. KEY POINTS • Elevation of the diameter threshold for solid nodules from 6 to 9 mm can substantially reduce unnecessary follow-up LDCTs with a small proportion of diagnostic delay of lung cancers. • The average transverse and effective diameters of lung nodules showed similar performances for the prediction of a lung cancer diagnosis.
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Affiliation(s)
- Eui Jin Hwang
- Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Jin Mo Goo
- Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea. .,Cancer Research Institute, Seoul National University, Seoul, South Korea.
| | - Hyae Young Kim
- Department of Radiology, National Cancer Center, Goyang, South Korea
| | | | - Yeol Kim
- National Cancer Control Institute, National Cancer Center, Goyang, South Korea
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19
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Fischer AM, Yacoub B, Savage RH, Martinez JD, Wichmann JL, Sahbaee P, Grbic S, Varga-Szemes A, Schoepf UJ. Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow Considerations. J Thorac Imaging 2021; 35 Suppl 1:S21-S27. [PMID: 32317574 DOI: 10.1097/rti.0000000000000498] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The constantly increasing number of computed tomography (CT) examinations poses major challenges for radiologists. In this article, the additional benefits and potential of an artificial intelligence (AI) analysis platform for chest CT examinations in routine clinical practice will be examined. Specific application examples include AI-based, fully automatic lung segmentation with emphysema quantification, aortic measurements, detection of pulmonary nodules, and bone mineral density measurement. This contribution aims to appraise this AI-based application for value-added diagnosis during routine chest CT examinations and explore future development perspectives.
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Affiliation(s)
- Andreas M Fischer
- Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC
| | - Basel Yacoub
- Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC
| | - Rock H Savage
- Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC
| | - John D Martinez
- Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC
| | | | | | | | - Akos Varga-Szemes
- Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC
| | - U Joseph Schoepf
- Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC
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20
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Lee H, An C, Ryu SJ. The Effect of Lung Volume on the Size and Volume of Pulmonary Subsolid Nodules on CT: Intraindividual Comparison between Total Lung Capacity and Tidal Volume. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2021; 82:1534-1544. [PMID: 36238880 PMCID: PMC9431968 DOI: 10.3348/jksr.2021.0141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/10/2021] [Accepted: 09/11/2021] [Indexed: 11/15/2022]
Abstract
Purpose Materials and Methods Results Conclusion
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Affiliation(s)
- Hyunji Lee
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Chansik An
- Department of Radiology, National Health Insurance Service Ilsan Hospital, Goyang, Korea
| | - Seok Jong Ryu
- Department of Radiology, National Health Insurance Service Ilsan Hospital, Goyang, Korea
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21
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Gierada DS, Rydzak CE, Zei M, Rhea L. Improved Interobserver Agreement on Lung-RADS Classification of Solid Nodules Using Semiautomated CT Volumetry. Radiology 2020; 297:675-684. [PMID: 32930652 PMCID: PMC7706890 DOI: 10.1148/radiol.2020200302] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Revised: 07/05/2020] [Accepted: 07/29/2020] [Indexed: 11/11/2022]
Abstract
Background Classification of lung cancer screening CT scans depends on measurement of lung nodule size. Information about interobserver agreement is limited. Purpose To assess interobserver agreement in the measurements and American College of Radiology Lung CT Screening Reporting and Data System (Lung-RADS) classifications of solid lung nodules detected at lung cancer screening using manual measurements of average diameter and computer-aided semiautomated measurements of average diameter and volume (CT volumetry). Materials and Methods Two radiologists and one radiology resident retrospectively measured lung nodules from screening CT scans obtained between September 2016 and June 2018 with a Lung-RADS (version 1.0) classification of 2, 3, 4A, or 4B in the clinical setting. Average manual diameter and semiautomated computer-aided diameter and volume measurements were converted to the corresponding Lung-RADS categories. Interobserver agreement in raw measurements was assessed using intraclass correlation and Bland-Altman indexes, and interobserver agreement in Lung-RADS classification was assessed using bi-rater κ. Results One hundred twenty patients (mean age, 63 years ± 6 [standard deviation]; 67 women) were evaluated. All manual, semiautomated diameter, and semiautomated volume measurements were obtained by all three readers in 120 of 147 nodules (82%). Intraclass correlation coefficients were greater than or equal to 0.95 for all reader pairs using all measurement methods and were highest using volumetry. Bias and 95% limits of agreement for average diameter were smaller with semiautomated measurements than with manual measurements. κ values across all Lung-RADS classifications were greater than or equal to 0.81, with the lowest being for manual measurements and the highest being for volumetric measurements. Forty-three of 120 (36%) of the nodules were classified into a lower Lung-RADS category on the basis of volumetry compared with using manual diameter measurements by at least one reader, whereas the reverse occurred for four of 120 (3%) of the nodules. Conclusion Interobserver agreement was high with manual diameter measurements and increased with semiautomated CT volumetric measurements. Semiautomated CT volumetry enabled classification of more nodules into lower Lung CT Screening Reporting and Data System categories than manual or semiautomated diameter measurements. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Nishino in this issue.
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Affiliation(s)
- David S. Gierada
- From the Mallinckrodt Institute of Radiology (D.S.G., C.E.R., M.Z.) and Department of Biostatistics (L.R.), School of Medicine, Washington University, 510 S Kingshighway Blvd, St Louis, MO 63110
| | | | - Markus Zei
- From the Mallinckrodt Institute of Radiology (D.S.G., C.E.R., M.Z.) and Department of Biostatistics (L.R.), School of Medicine, Washington University, 510 S Kingshighway Blvd, St Louis, MO 63110
| | - Lee Rhea
- From the Mallinckrodt Institute of Radiology (D.S.G., C.E.R., M.Z.) and Department of Biostatistics (L.R.), School of Medicine, Washington University, 510 S Kingshighway Blvd, St Louis, MO 63110
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Tandon YK, Bartholmai BJ, Koo CW. Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules. J Thorac Dis 2020; 12:6954-6965. [PMID: 33282401 PMCID: PMC7711413 DOI: 10.21037/jtd-2019-cptn-03] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 07/10/2020] [Indexed: 12/18/2022]
Abstract
Lung cancer remains the leading cause of cancer related death world-wide despite advances in treatment. This largely relates to the fact that many of these patients already have advanced diseases at the time of initial diagnosis. As most lung cancers present as nodules initially, an accurate classification of pulmonary nodules as early lung cancers is critical to reducing lung cancer morbidity and mortality. There have been significant recent advances in artificial intelligence (AI) for lung nodule evaluation. Deep learning (DL) and convolutional neural networks (CNNs) have shown promising results in pulmonary nodule detection and have also excelled in segmentation and classification of pulmonary nodules. This review aims to provide an overview of progress that has been made in AI recently for pulmonary nodule detection and characterization with the ultimate goal of lung cancer prediction and classification while outlining some of the pitfalls and challenges that remain to bring such advancements to routine clinical use.
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Affiliation(s)
| | | | - Chi Wan Koo
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
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23
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Dyer SC, Bartholmai BJ, Koo CW. Implications of the updated Lung CT Screening Reporting and Data System (Lung-RADS version 1.1) for lung cancer screening. J Thorac Dis 2020; 12:6966-6977. [PMID: 33282402 PMCID: PMC7711402 DOI: 10.21037/jtd-2019-cptn-02] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Lung cancer remains the leading cause of cancer death in the United States. Screening with low-dose computed tomography (LDCT) has been proven to aid in early detection of lung cancer and reduce disease specific mortality. In 2014, the American College of Radiology (ACR) released version 1.0 of the Lung CT Screening Reporting and Data System (Lung-RADS) as a quality tool to standardize the reporting of lung cancer screening LDCT. In 2019, 5 years after the implementation of Lung-RADS version 1.0 the ACR released the updated Lung-RADS version 1.1 which incorporates initial experience with lung cancer screening. In this review, we outline the implications of the changes and additions in Lung-RADS version 1.1 and examine relevant literature for many of the updates. We also highlight several challenges and opportunities as Lung-RADS version 1.1 is implemented in lung cancer screening programs.
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Affiliation(s)
- Spencer C Dyer
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Chi Wan Koo
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
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24
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Lung cancer LDCT screening and mortality reduction - evidence, pitfalls and future perspectives. Nat Rev Clin Oncol 2020; 18:135-151. [PMID: 33046839 DOI: 10.1038/s41571-020-00432-6] [Citation(s) in RCA: 221] [Impact Index Per Article: 55.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2020] [Indexed: 12/17/2022]
Abstract
In the past decade, the introduction of molecularly targeted agents and immune-checkpoint inhibitors has led to improved survival outcomes for patients with advanced-stage lung cancer; however, this disease remains the leading cause of cancer-related mortality worldwide. Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening in high-risk populations - the US National Lung Screening Trial (NLST) and NELSON - have provided evidence of a statistically significant mortality reduction in patients. LDCT-based screening programmes for individuals at a high risk of lung cancer have already been implemented in the USA. Furthermore, implementation programmes are currently underway in the UK following the success of the UK Lung Cancer Screening (UKLS) trial, which included the Liverpool Health Lung Project, Manchester Lung Health Check, the Lung Screen Uptake Trial, the West London Lung Cancer Screening pilot and the Yorkshire Lung Screening trial. In this Review, we focus on the current evidence on LDCT-based lung cancer screening and discuss the clinical developments in high-risk populations worldwide; additionally, we address aspects such as cost-effectiveness. We present a framework to define the scope of future implementation research on lung cancer screening programmes referred to as Screening Planning and Implementation RAtionale for Lung cancer (SPIRAL).
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25
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Ambrosi F, Lissenberg-Witte B, Comans E, Sprengers R, Dickhoff C, Bahce I, Radonic T, Thunnissen E. Tumor Atelectasis Gives Rise to a Solid Appearance in Pulmonary Adenocarcinomas on High-Resolution Computed Tomography. JTO Clin Res Rep 2020; 1:100018. [PMID: 34589925 PMCID: PMC8474473 DOI: 10.1016/j.jtocrr.2020.100018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 02/11/2020] [Indexed: 10/31/2022] Open
Abstract
Introduction Ground-glass opacities in a high-resolution computed tomography (HR-CT) scan correlate, if malignant, with adenocarcinoma in situ. The solid appearance in the HR-CT is often considered indicative of an invasive component. This study aims to compare the radiologic features revealed in the HR-CT and the histologic features of primary adenocarcinomas in resection specimens to find the presence of tumor atelectasis in ground-glass nodules (GGNs) and part-solid and solid nodules. Methods HR-CT imaging was evaluated, and lung nodules were classified as GGNs, part-solid nodules, and solid nodules, whereas adenocarcinomas were classified according to WHO classification. Lepidic growth pattern with collapse was considered if there was reduction of air in the histologic section with maintained pulmonary architecture (without signs of pleural or vascular invasion). Results Radiologic and histologic features were compared in 47 lesions of 41 patients. The number of GGN, part-solid, and solid nodules were two, eight, and 37, respectively. Lepidic growth pattern with collapse was observed in both GGN, seven of the eight part-solid (88%) and 24 of the 37 solid (65%) lesions. Remarkably, more than 50% of the adenocarcinomas with a solid appearance in HR-CT imaging had a preexisting pulmonary architecture with adenocarcinoma with a predominant lepidic growth pattern. In these cases, the solid component can be explained by tumor-related collapse in vivo (tumor atelectasis on radiologic examination). Conclusions Tumor atelectasis is a frequent finding in pulmonary adenocarcinomas and may beside a ground glass opacity also result in a solid appearance in HR-CT imaging. A solid appearance on HR-CT cannot be attributed to invasion alone, as has been the assumption until now.
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Affiliation(s)
- Francesca Ambrosi
- Experimental, Diagnostic, and Specialty Medicine Department, University of Bologna Medical Center, Bologna, Italy
| | - Birgit Lissenberg-Witte
- Department of Epidemiology and Biostatistics, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Emile Comans
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Ralf Sprengers
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Chris Dickhoff
- Department of Surgery and Cardiothoracic Surgery, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Idris Bahce
- Department of Pulmonology, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Teodora Radonic
- Department of Pathology, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Erik Thunnissen
- Department of Pathology, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
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26
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González Maldonado S, Delorme S, Hüsing A, Motsch E, Kauczor HU, Heussel CP, Kaaks R. Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography. JAMA Netw Open 2020; 3:e1921221. [PMID: 32058555 DOI: 10.1001/jamanetworkopen.2019.21221] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
IMPORTANCE Malignancy prediction models based on participant-related characteristics and imaging parameters from low-dose computed tomography (CT) may improve decision-making regarding nodule management and diagnosis in lung cancer screening. OBJECTIVE To externally validate 5 malignancy prediction models that were developed in screening settings, compared with 3 models that were developed in clinical settings, in terms of discrimination and absolute risk calibration among participants in the German Lung Cancer Screening Intervention trial. DESIGN, SETTING, AND PARTICIPANTS In this population-based diagnostic study, malignancy probabilities were estimated by applying 8 prediction models to data from 1159 participants in the intervention arm of the Lung Cancer Screening Intervention trial, a randomized clinical trial conducted from October 23, 2007, to April 30, 2016, with ongoing follow-up. This analysis considers end points up to 1 year after individuals' last screening visit. Inclusion criteria for participants were at least 1 noncalcified pulmonary nodule detected on any of 5 annual screening visits, receiving a lung cancer diagnosis within the active screening phase of the Lung Cancer Screening Intervention trial, and an unequivocal identification of the malignant nodules. Data analysis was performed from February 1, 2019, through December 5, 2019. INTERVENTIONS Five annual rounds of low-dose multislice CT. MAIN OUTCOMES AND MEASURES Discrimination ability and calibration of malignancy probabilities estimated by 5 models developed in data from screening studies (4 Pan-Canadian Early Detection of Lung Cancer Study [PanCan] models using a parsimonious approach including nodule spiculation [PanCan-1b] or a comprehensive approach including nodule spiculation [PanCan-2b], and PanCan-2b replacing the nodule diameter variable with mean diameter [PanCan-MD] or volume [PanCan-VOL], as well as a model developed by the UK Lung Cancer Screening trial) and 3 models developed in clinical settings (US Department of Veterans Affairs, Mayo Clinic, and Peking University People's Hospital). RESULTS A total of 1159 participants (median [range] age, 57.63 [50.34-71.89] years; 763 [65.8%] men) with 3903 pulmonary nodules were included in this study. For nodules detected in the prevalence round of CT, the PanCan models showed excellent discrimination (PanCan-1b: area under the curve [AUC], 0.93 [95% CI, 0.87-0.99]; PanCan-2b: AUC, 0.94 [95% CI, 0.89-0.99]; PanCan-MD: AUC, 0.94 [95% CI, 0.91-0.98]; PanCan-VOL: AUC, 0.94 [95% CI, 0.90-0.98]), and all of the screening models except PanCan-MD and PanCan-VOL showed acceptable calibration (PanCan-1b: Spiegelhalter z = -1.081; P = .28; PanCan-2b: Spiegelhalter z = 0.436; P = .67; PanCan-MD: Spiegelhalter z = 3.888; P < .001; PanCan-VOL: Spiegelhalter z = 1.978; P = .05; UK Lung Cancer Screening trial: Spiegelhalter z = -1.076; P = .28), whereas the other models showed worse discrimination and calibration, from an AUC of 0.58 (95% CI, 0.46-0.70) for the UK Lung Cancer Screening trial model to an AUC of 0.89 (95% CI, 0.82-0.97) for the Mayo Clinic model. CONCLUSIONS AND RELEVANCE This diagnostic study found that PanCan models showed excellent discrimination and calibration in prevalence screenings, confirming their ability to improve nodule management in screening settings, although calibration to nodules detected in follow-up scans should be improved. The models developed by the Mayo Clinic, Peking University People's Hospital, Department of Veterans Affairs, and UK Lung Cancer Screening Trial did not perform as well.
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Affiliation(s)
- Sandra González Maldonado
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
| | - Stefan Delorme
- Division of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Anika Hüsing
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
| | - Erna Motsch
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Claus-Peter Heussel
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik-Heidelberg GmbH, Heidelberg, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
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Robbins HA, Katki HA, Cheung LC, Landy R, Berg CD. Insights for Management of Ground-Glass Opacities From the National Lung Screening Trial. J Thorac Oncol 2019; 14:1662-1665. [PMID: 31125735 PMCID: PMC6909540 DOI: 10.1016/j.jtho.2019.05.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 04/12/2019] [Accepted: 05/11/2019] [Indexed: 11/20/2022]
Abstract
BACKGROUND In the National Lung Screening Trial (NLST), screen-detected cancers that would not have been identified by the Lung Computed Tomographic Screening Reporting and Data System (Lung-RADS) nodule management guidelines were frequently ground-glass opacities (GGOs). Lung-RADS suggests that GGOs with diameter less than 20 mm return for annual screening, and GGOs greater than or equal to 20 mm receive 6-month follow-up. We examined whether this 20-mm threshold gives consistent management of GGOs compared with solid nodules. METHODS First, we calculated diameter-specific malignancy probabilities for GGOs and solid nodules in the NLST. Using the solid-nodule malignancy risks as benchmarks, we suggested risk-based management categories for GGOs based on their probability of malignancy. Second, we compared lung-cancer mortality between GGOs and solid nodules in the same risk-based category. RESULTS Using the Lung-RADS v1.0 classifications, malignancy probability is higher for GGOs than solid nodules within the same category. A risk-based classification of GGOs would assign annual screening for GGOs 4 to 5 mm (0.4% malignancy risk); 6-month follow-up for GGOs 6 to 7 mm (1.1%), 8 to 14 mm (3.0%), and 15 to 19 mm (5.2%); and 3-month follow-up for greater than or equal to 20 mm (10.9%). This reclassification would have assigned similarly fatal cancers to 3-month follow-up (hazard ratio = 2.0 for lung-cancer death in GGOs versus solid-nodule cancers, 95% confidence interval: 0.4-8.7), but for 6-month follow-up, mortality was lower in GGO cancers (hazard ratio = 0.18, 95% confidence interval: 0.05-0.67). CONCLUSIONS If Lung-RADS categories for GGOs were based on malignancy probability, then 6- to 19-mm GGOs would receive 6-month follow-up and greater than or equal to 20-mm GGOs would receive 3-month follow-up. Such risk-based management for GGOs could improve the sensitivity of Lung-RADS, especially for large GGO cancers. However, small GGO cancers were less aggressive than their solid-nodule counterparts.
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Affiliation(s)
| | - Hormuzd A Katki
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | - Li C Cheung
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | - Rebecca Landy
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | - Christine D Berg
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
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28
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Screening for Early Lung Cancer, Chronic Obstructive Pulmonary Disease, and Cardiovascular Disease (the Big-3) Using Low-dose Chest Computed Tomography. J Thorac Imaging 2019; 34:160-169. [DOI: 10.1097/rti.0000000000000379] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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29
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McNulty W, Baldwin D. Management of pulmonary nodules. BJR Open 2019; 1:20180051. [PMID: 33178935 PMCID: PMC7592490 DOI: 10.1259/bjro.20180051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 03/17/2019] [Accepted: 03/19/2019] [Indexed: 11/05/2022] Open
Abstract
Pulmonary nodules are frequently detected during clinical practice and require a structured approach in their management in order to identify early lung cancers and avoid harm from over investigation. The article reviews the 2015 British Thoracic Society guidelines for the management of pulmonary nodules and the evidence behind them.
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Affiliation(s)
- William McNulty
- King’s College Hospital NHS Foundation Trust, Denmark Hill, London, UK
| | - David Baldwin
- Nottingham University Hospitals NHS Trust, City Campus, Hucknall Road, Nottingham, England
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30
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Heuvelmans MA, Oudkerk M. Pulmonary nodules measurements in CT lung cancer screening. J Thorac Dis 2018; 10:S2100-S2102. [PMID: 30023131 DOI: 10.21037/jtd.2018.05.166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Marjolein A Heuvelmans
- Center for Medical Imaging-North East Netherlands, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Matthijs Oudkerk
- Center for Medical Imaging-North East Netherlands, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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31
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Heuvelmans MA, Walter JE, Vliegenthart R, van Ooijen PMA, De Bock GH, de Koning HJ, Oudkerk M. Disagreement of diameter and volume measurements for pulmonary nodule size estimation in CT lung cancer screening. Thorax 2017; 73:779-781. [DOI: 10.1136/thoraxjnl-2017-210770] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 10/03/2017] [Accepted: 10/09/2017] [Indexed: 12/26/2022]
Abstract
We studied 2240 indeterminate solid nodules (volume 50–500mm3) to determine the correlation of diameter and semi-automated volume measurements for pulmonary nodule size estimation. Intra-nodular diameter variation, defined as maximum minus minimum diameter through the nodule’s center, varied by 2.8 mm (median, IQR:2.2–3.7 mm), so above the 1.5 mm cutoff for nodule growth used in Lung CT Screening Reporting and Data System (Lung-RADS). Using mean or maximum axial diameter to assess nodule volume led to a substantial mean overestimation of nodule volume of 47.2% and 85.1%, respectively, compared to semi-automated volume. Thus, size of indeterminate nodules is poorly represented by diameter.Trial registration numberPre-results, ISRCTN63545820.
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