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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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Ma G, Dou Y, Dang S, Yu N, Guo Y, Han D, Fan Q. Improving Image Quality and Nodule Characterization in Ultra-low-dose Lung CT with Deep Learning Image Reconstruction. Acad Radiol 2024; 31:2944-2952. [PMID: 38429189 DOI: 10.1016/j.acra.2024.01.010] [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: 11/23/2023] [Revised: 12/31/2023] [Accepted: 01/05/2024] [Indexed: 03/03/2024]
Abstract
RATIONALE AND OBJECTIVE To investigate the influence of the deep learning image reconstruction (DLIR) on the image quality and quantitative analysis of pulmonary nodules under ultra-low dose lung CT conditions. MATERIALS AND METHODS This was a prospective study with patient consent and included 56 patients with suspected pulmonary nodules. Patients were examined by both standard-dose CT (SDCT) and ultra-low-dose CT (ULDCT). SDCT images were reconstructed with adaptive statistical iterative reconstruction-V 40% (ASIR-V40%) (group A), while ULDCT images were reconstructed using ASIR-V40% (group B) and high-strength DLIR (DLIR-H) (group C). The three image sets were analyzed using a commercial computer aided diagnosis (CAD) software. Parameters such as nodule length, width, density, volume, risk, and classification were measured. The CAD quantitative data of different nodule types (solid, calcified, and subsolid nodules) and nodule image quality scores evaluated by two physicians on a 5-point scale were compared. RESULT The radiation dose in ULDCT was 0.25 ± 0.08mSv, 7.2% that of the 3.48 ± 1.08mSv in SDCT (P < 0.001). 104 pulmonary nodules were detected (51/53 solid, 26/24 calcified and 27/27 subsolid in Groups A and (B&C), respectively). Group B had lower density for solid, calcified nodules, and lower volume and risk for subsolid nodules than Group A, while Group C had lower density for calcified nodules (P < 0.05), There were no significant differences in other parameters among the three groups (P > 0.05). Group A and C had similar image quality for nodules and were higher than Group B (P < 0.05). CONCLUSION DLIR-H significantly improves image quality than ASIR-V40% and maintains similar nodule detection and characterization with CAD in ULDCT compared to SDCT.
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Affiliation(s)
- Guangming Ma
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China
| | - Yuequn Dou
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China
| | - Shan Dang
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China
| | - Nan Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China
| | - Yanbing Guo
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China
| | - Dong Han
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China
| | - Qiuju Fan
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China.
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Detterbeck FC, Woodard GA, Bader AS, Dacic S, Grant MJ, Park HS, Tanoue LT. The Proposed 9th Edition TNM Classification of Lung Cancer. Chest 2024:S0012-3692(24)00706-2. [PMID: 38885896 DOI: 10.1016/j.chest.2024.05.026] [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: 04/09/2024] [Revised: 05/24/2024] [Accepted: 05/26/2024] [Indexed: 06/20/2024] Open
Abstract
A universal nomenclature of the anatomic extent of lung cancer has been critical for individual patient care as well as research advances. As progress occurs, new details emerge that need to be included in a refined system that aligns with contemporary clinical management issues. The 9th edition TNM classification of lung cancer, which is scheduled to take effect in January 2025, addresses this need. It is based on a large international database, multidisciplinary input, and extensive statistical analyses. Key features of the 9th edition include validation of the significant changes in the T component introduced in the 8th edition, subdivision of N2 after exploration of fundamentally different ways of categorizing the N component, and further subdivision of the M component. This has led to reordering of the TNM combinations included in stage groups, primarily involving stage groups IIA, IIB, IIIA, and IIIB. This article summarizes the analyses and revisions for the TNM classification of lung cancer to familiarize the broader medical community and facilitate implementation of the 9th edition system.
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Affiliation(s)
- Frank C Detterbeck
- Division of Thoracic Surgery, Department of Surgery, Yale University School of Medicine, New Haven, CT.
| | - Gavitt A Woodard
- Division of Thoracic Surgery, Department of Surgery, Yale University School of Medicine, New Haven, CT
| | - Anna S Bader
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT
| | - Sanja Dacic
- Department of Pathology, Yale University School of Medicine, New Haven, CT
| | - Michael J Grant
- Department of Medicine (Medical Oncology), Yale Cancer Center, Yale University School of Medicine, New Haven, CT
| | - Henry S Park
- Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT
| | - Lynn T Tanoue
- Division of Pulmonary Critical Care Medicine, Department of Medicine, Yale University School of Medicine, New Haven, CT
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Azour L, Oh AS, Prosper AE, Toussie D, Villasana-Gomez G, Pourzand L. Subsolid Nodules: Significance and Current Understanding. Clin Chest Med 2024; 45:263-277. [PMID: 38816087 DOI: 10.1016/j.ccm.2024.02.003] [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] [Indexed: 06/01/2024]
Abstract
Subsolid nodules are heterogeneously appearing and behaving entities, commonly encountered incidentally and in high-risk populations. Accurate characterization of subsolid nodules, and application of evolving surveillance guidelines, facilitates evidence-based and multidisciplinary patient-centered management.
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Affiliation(s)
- Lea Azour
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA.
| | - Andrea S Oh
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
| | - Ashley E Prosper
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
| | - Danielle Toussie
- Department of Radiology, New York University Grossman School of Medicine, NYU Langone Health, 660 1st Avenue, New York, NY 10016, USA
| | - Geraldine Villasana-Gomez
- Department of Radiology, New York University Grossman School of Medicine, NYU Langone Health, 660 1st Avenue, New York, NY 10016, USA
| | - Lila Pourzand
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
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Gaffney B, Murphy DJ. Approach to Pulmonary Nodules in Connective Tissue Disease. Semin Respir Crit Care Med 2024; 45:316-328. [PMID: 38547916 DOI: 10.1055/s-0044-1782656] [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: 05/24/2024]
Abstract
The assessment of pulmonary nodules is a common and often challenging clinical scenario. This evaluation becomes even more complex in patients with connective tissue diseases (CTDs), as a range of disease-related factors must also be taken into account. These diseases are characterized by immune-mediated chronic inflammation, leading to tissue damage, collagen deposition, and subsequent organ dysfunction. A thorough examination of nodule features in these patients is required, incorporating anatomic and functional information, along with patient demographics, clinical factors, and disease-specific knowledge. This integrated approach is vital for effective risk stratification and precise diagnosis. This review article addresses specific CTD-related factors that should be taken into account when evaluating pulmonary nodules in this patient group.
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Affiliation(s)
- Brian Gaffney
- Department of Radiology, St Vincent's University Hospital, Dublin, Ireland
| | - David J Murphy
- Department of Radiology, St Vincent's University Hospital, Dublin, Ireland
- School of Medicine, University College, Dublin, Ireland
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Bak SH, Park J, Lee S, Kim JH, Lee HY, Park JY. Clinical usability of 3D gradient-echo-based ultrashort echo time imaging: Is it enough to facilitate diagnostic decision in real-world practice? PLoS One 2024; 19:e0296696. [PMID: 38722966 PMCID: PMC11081383 DOI: 10.1371/journal.pone.0296696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 12/17/2023] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND With recent advances in magnetic resonance imaging (MRI) technology, the practical role of lung MRI is expanding despite the inherent challenges of the thorax. The purpose of our study was to evaluate the current status of the concurrent dephasing and excitation (CODE) ultrashort echo-time sequence and the T1-weighted volumetric interpolated breath-hold examination (VIBE) sequence in the evaluation of thoracic disease by comparing it with the gold standard computed tomography (CT). METHODS Twenty-four patients with lung cancer and mediastinal masses underwent both CT and MRI including T1-weighted VIBE and CODE. For CODE images, data were acquired in free breathing and end-expiratory images were reconstructed using retrospective respiratory gating. All images were evaluated through qualitative and quantitative approaches regarding various anatomical structures and lesions (nodule, mediastinal mass, emphysema, reticulation, honeycombing, bronchiectasis, pleural plaque and lymphadenopathy) inside the thorax in terms of diagnostic performance in making specific decisions. RESULTS Depiction of the lung parenchyma, mediastinal and pleural lesion was not significant different among the three modalities (p > 0.05). Intra-tumoral and peritumoral features of lung nodules were not significant different in the CT, VIBE or CODE images (p > 0.05). However, VIBE and CODE had significantly lower image quality and poorer depiction of airway, great vessels, and emphysema compared to CT (p < 0.05). Image quality of central airways and depiction of bronchi were significantly better in CODE than in VIBE (p < 0.001 and p = 0.005). In contrast, the depiction of the vasculature was better for VIBE than CODE images (p = 0.003). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were significant greater in VIBE than CODE except for SNRlung and SNRnodule (p < 0.05). CONCLUSIONS Our study showed the potential of CODE and VIBE sequences in the evaluation of localized thoracic abnormalities including solid pulmonary nodules.
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Affiliation(s)
- So Hyeon Bak
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Jinil Park
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Seokwon Lee
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Jong Hee Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Ho Yun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Korea
| | - Jang-Yeon Park
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
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Xie Y, Tang W, Ma J, Chen Y. A retrospective study of 68Ga-FAPI PET/CT in differentiating the nature of pulmonary lesions. Front Oncol 2024; 14:1373286. [PMID: 38779097 PMCID: PMC11109402 DOI: 10.3389/fonc.2024.1373286] [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: 01/19/2024] [Accepted: 04/03/2024] [Indexed: 05/25/2024] Open
Abstract
Purpose This study aimed to investigate the characteristics of various pulmonary lesions as revealed by 68Ga-FAPI PET/CT and to determine the utility of 68Ga-FAPI PET/CT in distinguishing the nature of these pulmonary lesions. Methods A retrospective analysis was conducted on 99 patients with pulmonary lesions, who were categorized into three distinct groups: primary lung tumors (G1), metastatic lung tumors (G2), and benign lesions (G3). Each participant underwent a 68Ga-FAPI PET/CT scan. Among these groups, variables such as the Tumor/Background Ratio (TBR), Maximum Standardized Uptake Value (SUVmax), and the true positive rate of the lesions were compared. Furthermore, the FAPI uptake in nodular-like pulmonary lesions (d<3cm) and those with irregular borders was evaluated across the groups. A correlation analysis sought to understand the relationship between FAPI uptake in primary and pulmonary metastatic lesions. Results The study's participants were composed of 52 males and 47 females, with an average age of 56.8 ± 13.2 years. A higher uptake and detection rate for pulmonary lesions were exhibited by Group G1 compared to the other groups (SUVmax [G1 vs. G2 vs. G3: 9.1 ± 4.1 vs. 6.1 ± 4.1 vs. 5.3 ± 5.8], P<0.05; TBR [G1 vs. G2 vs. G3: 6.2 ± 2.4 vs. 4.1 ± 2.2 vs. 3.2 ± 2.7], P<0.01; true positive rate 95.1% vs. 88% vs. 75.6%]. In nodular-like lung lesions smaller than 3 cm, G1 showed a significantly higher FAPI uptake compared to G2 and G3 (SUVmax [G1 vs. G2 vs. G3: 8.8 ± 4.3 vs. 5.2 ± 3.2 vs. 4.9 ± 6.1], P<0.01; TBR [G1 vs. G2 vs. G3: 5.7 ± 2.7 vs. 3.7 ± 2.1 vs. 3.3 ± 4.4], P<0.05). Both G1 and G2 demonstrated significantly elevated FAPI agent activity in irregular-bordered pulmonary lesions when compared to G3 (SUVmax [G1 vs. G2 vs. G3: 10.9 ± 3.3 vs. 8.5 ± 2.7 vs. 4.6 ± 2.7], P<0.01; TBR [G1 vs. G2 vs. G3: 7.2 ± 2.1 vs. 6.4 ± 1.3 vs. 3.2 ± 2.4], P<0.01). A positive correlation was identified between the level of 68Ga-FAPI uptake in primary lesions and the uptake in pulmonary metastatic lesions within G2 (r=0.856, P<0.05). Conclusion 68Ga-FAPI PET/CT imaging proves to be of significant value in the evaluation of pulmonary lesions, offering distinctive insights into their nature.
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Affiliation(s)
- Yang Xie
- Department of Nuclear Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, Sichuan, China
- Academician (Expert) Workstation of Sichuan Province, Luzhou, Sichuan, China
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Wenxin Tang
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
- Institute of Nuclear Medicine, Fudan University, Shanghai, China
| | - Jiao Ma
- Department of Nuclear Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, Sichuan, China
- Academician (Expert) Workstation of Sichuan Province, Luzhou, Sichuan, China
| | - Yue Chen
- Department of Nuclear Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, Sichuan, China
- Academician (Expert) Workstation of Sichuan Province, Luzhou, Sichuan, China
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8
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Polanco D, González J, Gracia-Lavedan E, Pinilla L, Plana R, Molina M, Pardina M, Barbé F. Multidisciplinary virtual management of pulmonary nodules. Pulmonology 2024; 30:239-246. [PMID: 35115280 DOI: 10.1016/j.pulmoe.2021.12.003] [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: 08/25/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 10/19/2022] Open
Abstract
INTRODUCTION AND OBJECTIVES Multidisciplinary nodule clinics provide high-quality care and favor adherence to guidelines. Virtual care has shown savings benefits along with patient satisfaction. Our aim is to describe the first year of operation of a multidisciplinary virtual lung nodule clinic, the population evaluated and issued decisions. Secondarily, among discharged patients, we aimed to analyze their follow-up prior to the existence of our consultation, evaluating its adherence to guidelines. MATERIALS AND METHODS Observational study including all patients evaluated at the Virtual Lung Nodule Clinic (VLNC) (March 2018- March 2019). Clinical and radiological data were recorded. Recommendations, based on 2017 Fleischner Society guidelines, were categorized into follow-up, discharge or referral to lung cancer consultation. Discharged patients were classified according to adherence to guidelines of their previous management, into adequate, prolonged and non-indicated follow-up. RESULTS A total of 365 patients (58.9% men; median age 64.0 years) were included. Sixty-four percent had smoking history and 23% had chronic obstructive pulmonary disease (COPD). Most nodules were solid (87.4%) and multiple (57.5%). The median diameter was 6.00 mm. 43.8% of patients were discharged following first VLNC evaluation. Among them, 27.5% had received appropriate follow-up, but 66.9% had received poor management. Patients with prolonged follow-up (33.1%) were older (67.0 vs 60.5 years) and had larger nodules (6.00 mm vs 5.00). Non-indicated follow-up patients (33.8%) were more non-smokers (77.8% vs 31.8%) and presented smaller nodules (4.00 vs 5.00 mm). CONCLUSIONS During its first year of operation, the VLNC has evaluated a population with a relevant risk profile for lung cancer development, management of which should be cautious and adhere to guidelines. After the first VLNC assessment, approximately one-half of this population was discharged. It was noticeable that previous follow-up of discharged patients was found poorly adherent to guidelines, with a marked tendency to overmanagement.
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Affiliation(s)
- D Polanco
- Respiratory Department, University Hospital Arnau de Vilanova. Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain; Group of Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Spain
| | - J González
- Respiratory Department, University Hospital Arnau de Vilanova. Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain; Group of Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Spain
| | - E Gracia-Lavedan
- Group of Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Spain; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Spain
| | - L Pinilla
- Group of Precision Medicine in Chronic Diseases, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Spain; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Spain
| | - R Plana
- Respiratory Department, University Hospital Arnau de Vilanova. Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain; Group of Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Spain
| | - M Molina
- Respiratory Department, University Hospital Arnau de Vilanova. Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain
| | - M Pardina
- Department of Radiology, Arnau de Vilanova University Hospital, IRBLleida
| | - F Barbé
- Respiratory Department, University Hospital Arnau de Vilanova. Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain; Group of Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Spain; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Spain.
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Sun JD, Sugarbaker E, Byrne SC, Gagné A, Leo R, Swanson SJ, Hammer MM. Clinical Outcomes of Resected Pure Ground-Glass, Heterogeneous Ground-Glass, and Part-Solid Pulmonary Nodules. AJR Am J Roentgenol 2024; 222:e2330504. [PMID: 38323785 PMCID: PMC11161307 DOI: 10.2214/ajr.23.30504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
BACKGROUND. Increased (but not definitively solid) attenuation within pure ground-glass nodules (pGGNs) may indicate invasive adenocarcinoma and the need for resection rather than surveillance. OBJECTIVE. The purpose of this study was to compare the clinical outcomes among resected pGGNs, heterogeneous ground-glass nodules (GGNs), and part-solid nodules (PSNs). METHODS. This retrospective study included 469 patients (335 female patients and 134 male patients; median age, 68 years [IQR, 62.5-73.5 years]) who, between January 2012 and December 2020, underwent resection of lung adenocarcinoma that appeared as a subsolid nodule on CT. Two radiologists, using lung windows, independently classified each nodule as a pGGN, a heterogeneous GGN, or a PSN, resolving discrepancies through discussion. A heterogeneous GGN was defined as a GGN with internal increased attenuation not quite as dense as that of pulmonary vessels, and a PSN was defined as having an internal solid component with the same attenuation as that of the pulmonary vessels. Outcomes included pathologic diagnosis of invasive adenocarcinoma, 5-year recurrence rates (locoregional or distant), and recurrence-free survival (RFS) and overall survival (OS) over 7 years, as analyzed by Kaplan-Meier and Cox proportional hazards regression analyses, with censoring of patients with incomplete follow-up. RESULTS. Interobserver agreement for nodule type, expressed as a kappa coefficient, was 0.69. Using consensus assessments, 59 nodules were pGGNs, 109 were heterogeneous GGNs, and 301 were PSNs. The frequency of invasive adenocarcinoma was 39.0% in pGGNs, 67.9% in heterogeneous GGNs, and 75.7% in PSNs (for pGGNs vs heterogeneous GGNs, p < .001; for pGGNs vs PSNs, p < .001; and for heterogeneous GGNs vs PSNs, p = .28). The 5-year recurrence rate was 0.0% in patients with pGGNs, 6.3% in those with heterogeneous GGNs, and 10.8% in those with PSNs (for pGGNs vs heterogeneous GGNs, p = .06; for pGGNs vs PSNs, p = .02; and for heterogeneous GGNs vs PSNs, p = .18). At 7 years, RFS was 97.7% in patients with pGGNs, 82.0% in those with heterogeneous GGNs, and 79.4% in those with PSNs (for pGGNs vs heterogeneous GGNs, p = .02; for pGGNs vs PSNs, p = .006; and for heterogeneous GGNs vs PSNs, p = .40); OS was 98.0% in patients with pGGNs, 84.6% in those with heterogeneous GGNs, and 82.9% in those with PSNs (for pGGNs vs heterogeneous GGNs, p = .04; for pGGNs vs PSNs, p = .01; and for heterogeneous GGNs vs PSNs, p = .50). CONCLUSION. Resected pGGNs had excellent clinical outcomes. Heterogeneous GGNs had relatively worse outcomes, more closely resembling outcomes for PSNs. CLINICAL IMPACT. The findings support surveillance for truly homogeneous pGGNs versus resection for GGNs showing internal increased attenuation even if not having a true solid component.
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Affiliation(s)
| | | | - Suzanne C. Byrne
- Departments of Radiology (J.D.S., S.C.B., M.M.H.), Surgery (E.S., R.L., S.J.S.), and Pathology (A.G.), Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., Boston, MA 02115
| | - Andréanne Gagné
- Departments of Radiology (J.D.S., S.C.B., M.M.H.), Surgery (E.S., R.L., S.J.S.), and Pathology (A.G.), Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., Boston, MA 02115
| | - Rachel Leo
- Departments of Radiology (J.D.S., S.C.B., M.M.H.), Surgery (E.S., R.L., S.J.S.), and Pathology (A.G.), Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., Boston, MA 02115
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10
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Kifjak D, El Kaddouri B, Madani SP, de Margerie-Mellon C, Heidinger BH. From text to texture: a glossary transforms the pulmonary nodule paradigm. Eur Radiol 2024:10.1007/s00330-024-10763-y. [PMID: 38649472 DOI: 10.1007/s00330-024-10763-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 03/10/2024] [Accepted: 03/20/2024] [Indexed: 04/25/2024]
Affiliation(s)
- Daria Kifjak
- Department of Radiology, University of Massachusetts Memorial Health and University of Massachusetts Chan Medical School, Worcester, MA, USA.
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
| | - Bilal El Kaddouri
- Department of Radiology, University of Massachusetts Memorial Health and University of Massachusetts Chan Medical School, Worcester, MA, USA
- Department of Radiology, Erasmus Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Seyedeh Panid Madani
- Department of Radiology, University of Massachusetts Memorial Health and University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | - Benedikt H Heidinger
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
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11
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Xie K, Cui C, Li X, Yuan Y, Wang Z, Zeng L. MRI-Based Clinical-Imaging-Radiomics Nomogram Model for Discriminating Between Benign and Malignant Solid Pulmonary Nodules or Masses. Acad Radiol 2024:S1076-6332(24)00207-1. [PMID: 38644089 DOI: 10.1016/j.acra.2024.03.042] [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: 02/29/2024] [Revised: 03/29/2024] [Accepted: 03/30/2024] [Indexed: 04/23/2024]
Abstract
RATIONALE AND OBJECTIVES Pulmonary nodules or masses are highly prevalent worldwide, and differential diagnosis of benign and malignant lesions remains difficult. Magnetic resonance imaging (MRI) can provide functional and metabolic information of pulmonary lesions. This study aimed to establish a nomogram model based on clinical features, imaging features, and multi-sequence MRI radiomics to identify benign and malignant solid pulmonary nodules or masses. MATERIALS AND METHODS A total of 145 eligible patients (76 male; mean age, 58.4 years ± 13.7 [SD]) with solid pulmonary nodules or masses were retrospectively analyzed. The patients were randomized into two groups (training cohort, n = 102; validation cohort, n = 43). The nomogram was used for predicting malignant pulmonary lesions. The diagnostic performance of different models was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS Of these patients, 95 patients were diagnosed with benign lesions and 50 with malignant lesions. Multivariate analysis showed that age, DWI value, LSR value, and ADC value were independent predictors of malignant lesions. Among the radiomics models, the multi-sequence MRI-based model (T1WI+T2WI+ADC) achieved the best diagnosis performance with AUCs of 0.858 (95%CI: 0.775, 0.919) and 0.774 (95%CI: 0.621, 0.887) for the training and validation cohorts, respectively. Combining multi-sequence radiomics, clinical and imaging features, the predictive efficacy of the clinical-imaging-radiomics model was significantly better than the clinical model, imaging model and radiomics model (all P < 0.05). CONCLUSION The MRI-based clinical-imaging-radiomics model is helpful to differentiate benign and malignant solid pulmonary nodules or masses, and may be useful for precision medicine of pulmonary diseases.
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Affiliation(s)
- Kexin Xie
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Can Cui
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Xiaoqing Li
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Yongfeng Yuan
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Zhongqiu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Liang Zeng
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China.
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12
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Niimi T, Samejima J, Wakabayashi M, Miyoshi T, Tane K, Aokage K, Taki T, Nakai T, Ishii G, Kikuchi A, Yoshioka E, Yokose T, Ito H, Tsuboi M. Ten-year follow-up outcomes of limited resection trial for radiologically less-invasive lung cancer. Jpn J Clin Oncol 2024; 54:479-488. [PMID: 38183216 DOI: 10.1093/jjco/hyad187] [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: 08/04/2023] [Accepted: 12/13/2023] [Indexed: 01/07/2024] Open
Abstract
BACKGROUND The JCOG0804/WJOG4507L single-arm confirmatory trial indicated a satisfactory 10-year prognosis for patients who underwent limited resection for radiologically less-invasive lung cancer. However, only one prospective trial has reported a 10-year prognosis. METHODS We conducted a multicenter prospective study coordinated by the National Cancer Center Hospital East and Kanagawa Cancer Center. We analyzed the long-term prognosis of 100 patients who underwent limited resection of a radiologically less-invasive lung cancer in the peripheral lung field. We defined radiologically less-invasive lung cancer as lung adenocarcinoma with a maximum tumor diameter of ≤2 cm, tumor disappearance ratio of ≥0.5 and cN0. The primary endpoint was the 10-year local recurrence-free survival. RESULTS Our patients, with a median age of 62 years, included 39 males. A total of 58 patients were non-smokers; 87 had undergone wide wedge resection and 9 underwent segmentectomy. A total of four cases were converted to lobectomy because of the presence of poorly differentiated components in the frozen specimen or insufficient margin with segmentectomy. The median follow-up duration was 120.9 months. The 10-year recurrence-free survival and overall survival rates of patients with lung cancer were both 96.0%. Following the 10-year long-term follow-up, two patients experienced recurrences at resection ends after wedge resection. CONCLUSIONS Limited resection imparted a satisfactory prognosis for patients with radiologically less-invasive lung cancer, except two cases of local recurrence >5 years after surgery. These findings suggest that patients with this condition who underwent limited resection may require continued follow-up >5 years after surgery.
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Affiliation(s)
- Takahiro Niimi
- Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Chiba
| | - Joji Samejima
- Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba
| | - Masashi Wakabayashi
- Biostatistics Division, Center for Research Administration and Support, National Cancer Center Hospital East, Kashiwa
| | - Tomohiro Miyoshi
- Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba
| | - Kenta Tane
- Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba
| | - Keiju Aokage
- Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba
| | - Tetsuro Taki
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Chiba
| | - Tokiko Nakai
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Chiba
| | - Genichiro Ishii
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Chiba
- Division of Innovative Pathology and Laboratory Medicine, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiba, Chiba
| | - Akitomo Kikuchi
- Department of Pathology, Kanagawa Cancer Center, Yokohama, Kanagawa
| | - Emi Yoshioka
- Department of Pathology, Kanagawa Cancer Center, Yokohama, Kanagawa
| | - Tomoyuki Yokose
- Department of Pathology, Kanagawa Cancer Center, Yokohama, Kanagawa
| | - Hiroyuki Ito
- Department of Thoracic Surgery, Kanagawa Cancer Center, Yokohama, Kanagawa, Japan
| | - Masahiro Tsuboi
- Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba
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13
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Yang H, Wang Q, Zhang Y, An Z, Liu C, Zhang X, Zhou SK. Lung Nodule Segmentation and Uncertain Region Prediction With an Uncertainty-Aware Attention Mechanism. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1284-1295. [PMID: 37966939 DOI: 10.1109/tmi.2023.3332944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2023]
Abstract
Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty. Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations. However, these approaches fail to leverage the valuable information inherent in the consensus and disagreements among the multiple annotations. In this paper, we propose an Uncertainty-Aware Attention Mechanism (UAAM) that utilizes consensus and disagreements among multiple annotations to facilitate better segmentation. To this end, we introduce the Multi-Confidence Mask (MCM), which combines a Low-Confidence (LC) Mask and a High-Confidence (HC) Mask. The LC mask indicates regions with low segmentation confidence, where radiologists may have different segmentation choices. Following UAAM, we further design an Uncertainty-Guide Multi-Confidence Segmentation Network (UGMCS-Net), which contains three modules: a Feature Extracting Module that captures a general feature of a lung nodule, an Uncertainty-Aware Module that produces three features for the annotations' union, intersection, and annotation set, and an Intersection-Union Constraining Module that uses distances between the three features to balance the predictions of final segmentation and MCM. To comprehensively demonstrate the performance of our method, we propose a Complex-Nodule Validation on LIDC-IDRI, which tests UGMCS-Net's segmentation performance on lung nodules that are difficult to segment using common methods. Experimental results demonstrate that our method can significantly improve the segmentation performance on nodules that are difficult to segment using conventional methods.
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14
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Dal Lago EA, Sousa LG, Yang Z, Hoff CO, Bonini F, Sawyer M, Wang K, Lewis W, Wahid KA, Hanna EY, El-Naggar A, Fuller CD, Kundu S, Godoy M, Ferrarotto R. Prognostic value of tumor volume doubling time in lung-metastatic adenoid cystic carcinoma. Oral Oncol 2024; 151:106759. [PMID: 38507991 PMCID: PMC11195296 DOI: 10.1016/j.oraloncology.2024.106759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/31/2024] [Accepted: 03/12/2024] [Indexed: 03/22/2024]
Abstract
OBJECTIVES Lung metastases in adenoid cystic carcinoma (ACC) usually have indolent growth and the optimal timing to start systemic therapy is not established. We assessed ACC lung metastasis tumor growth dynamics and compared the prognostic value of time to progression (TTP) and tumor volume doubling time (TVDT). METHODS The study included ACC patients with ≥1 pulmonary metastasis (≥5 mm) and at least 2 chest computed tomography scans. Radiology assessment was performed from the first scan showing metastasis until treatment initiation or death. Up to 5 lung nodules per patient were segmented for TVDT calculation. To assess tumor growth rate (TGR), the correlation coefficient (r) and coefficient of determination (R2) were calculated for measured lung nodules. TTP was assessed per RECIST 1.1; TVDT was calculated using the Schwartz formula. Overall survival was analyzed using the Kaplan-Meier method. RESULTS The study included 75 patients. Sixty-seven patients (89%) had lung-only metastasis on first CT scan. The TGR was overall constant (median R2 = 0.974). Median TTP and TVDT were 11.2 months and 7.5 months. Shorter TVDT (<6 months) was associated with poor overall survival (HR = 0.48; p = 0.037), but TTP was not associated with survival (HR = 1.02; p = 0.96). Cox regression showed that TVDT but not TTP significantly correlated with OS. TVDT calculated using estimated tumor volume correlated with TVDT obtained by segmentation. CONCLUSION Most ACC lung metastases have a constant TGR. TVDT may be a better prognostic indicator than TTP in lung-metastatic ACC. TVDT can be estimated by single longitudinal measurement in clinical practice.
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Affiliation(s)
- Eduardo A Dal Lago
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Luana G Sousa
- Department of Thoracic and Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Zixi Yang
- Biostatistics Department, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Camilla O Hoff
- Department of Thoracic and Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Flavia Bonini
- Department of Thoracic and Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Matthew Sawyer
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Kaiwen Wang
- Department of Pharmacy, The University of Texas Health Science Center at Houston, TX, USA
| | - Whitney Lewis
- Department of Pharmacy, The University of Texas Health Science Center at Houston, TX, USA
| | - Kareem A Wahid
- Radiation Oncology, The University of Texas Health Science Center at Houston, TX, USA
| | - Ehab Y Hanna
- Department of Head and Neck Surgery, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Adel El-Naggar
- Department of Pathology, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Clifton D Fuller
- Radiation Oncology, The University of Texas Health Science Center at Houston, TX, USA
| | - Suprateek Kundu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Myrna Godoy
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Renata Ferrarotto
- Department of Thoracic and Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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15
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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:S1076-6332(24)00086-2. [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] [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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16
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Park H, Hwang EJ, Goo JM. Deep Learning-Based Kernel Adaptation Enhances Quantification of Emphysema on Low-Dose Chest CT for Predicting Long-Term Mortality. Invest Radiol 2024; 59:278-286. [PMID: 37428617 DOI: 10.1097/rli.0000000000001003] [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: 07/12/2023]
Abstract
OBJECTIVES The aim of this study was to ascertain the predictive value of quantifying emphysema using low-dose computed tomography (LDCT) post deep learning-based kernel adaptation on long-term mortality. MATERIALS AND METHODS This retrospective study investigated LDCTs obtained from asymptomatic individuals aged 60 years or older during health checkups between February 2009 and December 2016. These LDCTs were reconstructed using a 1- or 1.25-mm slice thickness alongside high-frequency kernels. A deep learning algorithm, capable of generating CT images that resemble standard-dose and low-frequency kernel images, was applied to these LDCTs. To quantify emphysema, the lung volume percentage with an attenuation value less than or equal to -950 Hounsfield units (LAA-950) was gauged before and after kernel adaptation. Low-dose chest CTs with LAA-950 exceeding 6% were deemed emphysema-positive according to the Fleischner Society statement. Survival data were sourced from the National Registry Database at the close of 2021. The risk of nonaccidental death, excluding causes such as injury or poisoning, was explored according to the emphysema quantification results using multivariate Cox proportional hazards models. RESULTS The study comprised 5178 participants (mean age ± SD, 66 ± 3 years; 3110 males). The median LAA-950 (18.2% vs 2.6%) and the proportion of LDCTs with LAA-950 exceeding 6% (96.3% vs 39.3%) saw a significant decline after kernel adaptation. There was no association between emphysema quantification before kernel adaptation and the risk of nonaccidental death. Nevertheless, after kernel adaptation, higher LAA-950 (hazards ratio for 1% increase, 1.01; P = 0.045) and LAA-950 exceeding 6% (hazards ratio, 1.36; P = 0.008) emerged as independent predictors of nonaccidental death, upon adjusting for age, sex, and smoking status. CONCLUSIONS The application of deep learning for kernel adaptation proves instrumental in quantifying pulmonary emphysema on LDCTs, establishing itself as a potential predictive tool for long-term nonaccidental mortality in asymptomatic individuals.
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Affiliation(s)
- Hyungin Park
- From the Department of Radiology, Seoul National University Hospital, Seoul, South Korea (H.P., E.J.H., J.M.G.); and Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.)
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17
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Suh YJ, Han K, Kwon Y, Kim H, Lee S, Hwang SH, Kim MH, Shin HJ, Lee CY, Shim HS. Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas. Yonsei Med J 2024; 65:163-173. [PMID: 38373836 PMCID: PMC10896671 DOI: 10.3349/ymj.2023.0368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/19/2023] [Accepted: 10/25/2023] [Indexed: 02/21/2024] Open
Abstract
PURPOSE To assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets. MATERIALS AND METHODS This retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS Rad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722-0.846] vs. AUC: 0.815 (95% CI: 0.759-0.872), p=0.042}. In the temporal validation set, the combined model showed a significantly higher AUC than that of the conventional model (p=0.001). The combined model showed a higher AUC than the conventional model in the test and internal validation sets, albeit with no statistical significance. CONCLUSION A quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas.
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Affiliation(s)
- Young Joo Suh
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Yonghan Kwon
- Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, Korea
| | - Hwiyoung Kim
- Department of Biomedical System Informatics, Yonsei University College of Medicine, Seoul, Korea
| | - Suji Lee
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Ho Hwang
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Myung Hyun Kim
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
| | - Hyun Joo Shin
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
| | - Chang Young Lee
- Thoracic and Cardiovascular Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hyo Sup Shim
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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18
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Barcelos RR, Sugarbaker E, Kennedy KF, McAllister M, Kim S, Herrera-Zamora J, Leo R, Swanson S, Ugalde Figueroa P. Time between imaging and surgery is not a risk factor for upstaging of clinical stage IA non-small-cell lung cancer. Eur J Cardiothorac Surg 2024; 65:ezae057. [PMID: 38407382 DOI: 10.1093/ejcts/ezae057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 12/05/2023] [Accepted: 02/06/2024] [Indexed: 02/27/2024] Open
Abstract
OBJECTIVES The timing of preoperative imaging in patients with lung cancer is a debated topic, as there are limited data on cancer progression during the interval between clinical staging by imaging and pathological staging after resection. We quantified disease progression during this interval in patients with early stage non-small-cell lung cancer (NSCLC) to better understand if its length impacts upstaging. METHODS We retrospectively reviewed our institutional database to identify patients who underwent surgery for clinically staged T1N0M0 NSCLC from January 2015 through September 2022. Tumour upstaging between chest computed tomography (CT) and surgery were analysed as a function of time (<30, 30-59, ≥60 days) for different nodule subtypes. We analysed data across 3 timeframes using Pearson's chi-squared and analysis of variance tests. RESULTS During the study period, 622 patients underwent surgery for clinically staged T1N0M0 NSCLC. CT-to-surgery interval was <30 days in 228 (36.7%), 30-59 days in 242 (38.9%) and ≥60 days in 152 (24.4%) with no differences in patient or nodule characteristics observed between these groups. T-stage increased in 346 patients (55.6%) between CT imaging and surgery. Among these patients, 126 (36.4%) had ground-glass nodules, 147 (42.5%) had part-solid nodules and 73 (21.1%) had solid nodules. CT-to-surgery interval length was not associated with upstaging of any nodule subtype (full-cohort, P = 0.903; ground-glass, P = 0.880; part-solid, P = 0.858; solid, P = 0.959). CONCLUSIONS This single-centre experience suggests no significant association between tumour upstaging and time from imaging to lung resection in patients with clinical stage IA NSCLC. Further studies are needed to better understand the risk factors for upstaging.
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Affiliation(s)
- Rafael R Barcelos
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Evert Sugarbaker
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Miles McAllister
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Sangmin Kim
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Rachel Leo
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Scott Swanson
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, MA, USA
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19
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Bankier AA, MacMahon H, Colby T, Gevenois PA, Goo JM, Leung AN, Lynch DA, Schaefer-Prokop CM, Tomiyama N, Travis WD, Verschakelen JA, White CS, Naidich DP. Fleischner Society: Glossary of Terms for Thoracic Imaging. Radiology 2024; 310:e232558. [PMID: 38411514 PMCID: PMC10902601 DOI: 10.1148/radiol.232558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/17/2024] [Accepted: 01/31/2024] [Indexed: 02/28/2024]
Abstract
Members of the Fleischner Society have compiled a glossary of terms for thoracic imaging that replaces previous glossaries published in 1984, 1996, and 2008, respectively. The impetus to update the previous version arose from multiple considerations. These include an awareness that new terms and concepts have emerged, others have become obsolete, and the usage of some terms has either changed or become inconsistent to a degree that warranted a new definition. This latest glossary is focused on terms of clinical importance and on those whose meaning may be perceived as vague or ambiguous. As with previous versions, the aim of the present glossary is to establish standardization of terminology for thoracic radiology and, thereby, to facilitate communications between radiologists and clinicians. Moreover, the present glossary aims to contribute to a more stringent use of terminology, increasingly required for structured reporting and accurate searches in large databases. Compared with the previous version, the number of images (chest radiography and CT) in the current version has substantially increased. The authors hope that this will enhance its educational and practical value. All definitions and images are hyperlinked throughout the text. Click on each figure callout to view corresponding image. © RSNA, 2024 Supplemental material is available for this article. See also the editorials by Bhalla and Powell in this issue.
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Affiliation(s)
- Alexander A. Bankier
- From the Dept of Radiology, University of Massachusetts Memorial
Health and University of Massachusetts Chan Medical School, 55 Lake Ave N,
Worcester, MA 01655 (A.A.B.); Dept of Radiology, University of Chicago, Chicago,
Ill (H.M.); Dept of Pathology, Mayo Clinic Scottsdale, Scottsdale, Ariz (T.C.);
Dept of Pulmonology, Université Libre de Bruxelles, Brussels, Belgium
(P.A.G.); Dept of Radiology, Seoul National University Hospital, Seoul, Korea
(J.M.G.); Center for Academic Medicine, Dept of Radiology, Stanford University,
Palo Alto, Calif (A.N.C.L.); Dept of Radiology, National Jewish Medical and
Research Center, Denver, Colo (D.A.L.); Dept of Radiology, Meander Medical
Centre Amersfoort, Amersfoort, the Netherlands (C.M.S.P.); Dept of Radiology,
Osaka University Graduate School of Medicine, Suita, Japan (N.T.); Dept of
Pathology, Memorial Sloan Kettering Cancer Center, New York, NY (W.D.T.); Dept
of Radiology, Catholic University Leuven, University Hospital Gasthuisberg,
Leuven, Belgium (J.A.V.); Dept of Diagnostic Radiology, University of Maryland
Hospital, Baltimore, Md (C.S.W.); and Dept of Radiology, NYU Langone Medical
Center/Tisch Hospital, New York, NY (D.P.N.)
| | - Heber MacMahon
- From the Dept of Radiology, University of Massachusetts Memorial
Health and University of Massachusetts Chan Medical School, 55 Lake Ave N,
Worcester, MA 01655 (A.A.B.); Dept of Radiology, University of Chicago, Chicago,
Ill (H.M.); Dept of Pathology, Mayo Clinic Scottsdale, Scottsdale, Ariz (T.C.);
Dept of Pulmonology, Université Libre de Bruxelles, Brussels, Belgium
(P.A.G.); Dept of Radiology, Seoul National University Hospital, Seoul, Korea
(J.M.G.); Center for Academic Medicine, Dept of Radiology, Stanford University,
Palo Alto, Calif (A.N.C.L.); Dept of Radiology, National Jewish Medical and
Research Center, Denver, Colo (D.A.L.); Dept of Radiology, Meander Medical
Centre Amersfoort, Amersfoort, the Netherlands (C.M.S.P.); Dept of Radiology,
Osaka University Graduate School of Medicine, Suita, Japan (N.T.); Dept of
Pathology, Memorial Sloan Kettering Cancer Center, New York, NY (W.D.T.); Dept
of Radiology, Catholic University Leuven, University Hospital Gasthuisberg,
Leuven, Belgium (J.A.V.); Dept of Diagnostic Radiology, University of Maryland
Hospital, Baltimore, Md (C.S.W.); and Dept of Radiology, NYU Langone Medical
Center/Tisch Hospital, New York, NY (D.P.N.)
| | - Thomas Colby
- From the Dept of Radiology, University of Massachusetts Memorial
Health and University of Massachusetts Chan Medical School, 55 Lake Ave N,
Worcester, MA 01655 (A.A.B.); Dept of Radiology, University of Chicago, Chicago,
Ill (H.M.); Dept of Pathology, Mayo Clinic Scottsdale, Scottsdale, Ariz (T.C.);
Dept of Pulmonology, Université Libre de Bruxelles, Brussels, Belgium
(P.A.G.); Dept of Radiology, Seoul National University Hospital, Seoul, Korea
(J.M.G.); Center for Academic Medicine, Dept of Radiology, Stanford University,
Palo Alto, Calif (A.N.C.L.); Dept of Radiology, National Jewish Medical and
Research Center, Denver, Colo (D.A.L.); Dept of Radiology, Meander Medical
Centre Amersfoort, Amersfoort, the Netherlands (C.M.S.P.); Dept of Radiology,
Osaka University Graduate School of Medicine, Suita, Japan (N.T.); Dept of
Pathology, Memorial Sloan Kettering Cancer Center, New York, NY (W.D.T.); Dept
of Radiology, Catholic University Leuven, University Hospital Gasthuisberg,
Leuven, Belgium (J.A.V.); Dept of Diagnostic Radiology, University of Maryland
Hospital, Baltimore, Md (C.S.W.); and Dept of Radiology, NYU Langone Medical
Center/Tisch Hospital, New York, NY (D.P.N.)
| | - Pierre Alain Gevenois
- From the Dept of Radiology, University of Massachusetts Memorial
Health and University of Massachusetts Chan Medical School, 55 Lake Ave N,
Worcester, MA 01655 (A.A.B.); Dept of Radiology, University of Chicago, Chicago,
Ill (H.M.); Dept of Pathology, Mayo Clinic Scottsdale, Scottsdale, Ariz (T.C.);
Dept of Pulmonology, Université Libre de Bruxelles, Brussels, Belgium
(P.A.G.); Dept of Radiology, Seoul National University Hospital, Seoul, Korea
(J.M.G.); Center for Academic Medicine, Dept of Radiology, Stanford University,
Palo Alto, Calif (A.N.C.L.); Dept of Radiology, National Jewish Medical and
Research Center, Denver, Colo (D.A.L.); Dept of Radiology, Meander Medical
Centre Amersfoort, Amersfoort, the Netherlands (C.M.S.P.); Dept of Radiology,
Osaka University Graduate School of Medicine, Suita, Japan (N.T.); Dept of
Pathology, Memorial Sloan Kettering Cancer Center, New York, NY (W.D.T.); Dept
of Radiology, Catholic University Leuven, University Hospital Gasthuisberg,
Leuven, Belgium (J.A.V.); Dept of Diagnostic Radiology, University of Maryland
Hospital, Baltimore, Md (C.S.W.); and Dept of Radiology, NYU Langone Medical
Center/Tisch Hospital, New York, NY (D.P.N.)
| | - Jin Mo Goo
- From the Dept of Radiology, University of Massachusetts Memorial
Health and University of Massachusetts Chan Medical School, 55 Lake Ave N,
Worcester, MA 01655 (A.A.B.); Dept of Radiology, University of Chicago, Chicago,
Ill (H.M.); Dept of Pathology, Mayo Clinic Scottsdale, Scottsdale, Ariz (T.C.);
Dept of Pulmonology, Université Libre de Bruxelles, Brussels, Belgium
(P.A.G.); Dept of Radiology, Seoul National University Hospital, Seoul, Korea
(J.M.G.); Center for Academic Medicine, Dept of Radiology, Stanford University,
Palo Alto, Calif (A.N.C.L.); Dept of Radiology, National Jewish Medical and
Research Center, Denver, Colo (D.A.L.); Dept of Radiology, Meander Medical
Centre Amersfoort, Amersfoort, the Netherlands (C.M.S.P.); Dept of Radiology,
Osaka University Graduate School of Medicine, Suita, Japan (N.T.); Dept of
Pathology, Memorial Sloan Kettering Cancer Center, New York, NY (W.D.T.); Dept
of Radiology, Catholic University Leuven, University Hospital Gasthuisberg,
Leuven, Belgium (J.A.V.); Dept of Diagnostic Radiology, University of Maryland
Hospital, Baltimore, Md (C.S.W.); and Dept of Radiology, NYU Langone Medical
Center/Tisch Hospital, New York, NY (D.P.N.)
| | - Ann N.C. Leung
- From the Dept of Radiology, University of Massachusetts Memorial
Health and University of Massachusetts Chan Medical School, 55 Lake Ave N,
Worcester, MA 01655 (A.A.B.); Dept of Radiology, University of Chicago, Chicago,
Ill (H.M.); Dept of Pathology, Mayo Clinic Scottsdale, Scottsdale, Ariz (T.C.);
Dept of Pulmonology, Université Libre de Bruxelles, Brussels, Belgium
(P.A.G.); Dept of Radiology, Seoul National University Hospital, Seoul, Korea
(J.M.G.); Center for Academic Medicine, Dept of Radiology, Stanford University,
Palo Alto, Calif (A.N.C.L.); Dept of Radiology, National Jewish Medical and
Research Center, Denver, Colo (D.A.L.); Dept of Radiology, Meander Medical
Centre Amersfoort, Amersfoort, the Netherlands (C.M.S.P.); Dept of Radiology,
Osaka University Graduate School of Medicine, Suita, Japan (N.T.); Dept of
Pathology, Memorial Sloan Kettering Cancer Center, New York, NY (W.D.T.); Dept
of Radiology, Catholic University Leuven, University Hospital Gasthuisberg,
Leuven, Belgium (J.A.V.); Dept of Diagnostic Radiology, University of Maryland
Hospital, Baltimore, Md (C.S.W.); and Dept of Radiology, NYU Langone Medical
Center/Tisch Hospital, New York, NY (D.P.N.)
| | - David A. Lynch
- From the Dept of Radiology, University of Massachusetts Memorial
Health and University of Massachusetts Chan Medical School, 55 Lake Ave N,
Worcester, MA 01655 (A.A.B.); Dept of Radiology, University of Chicago, Chicago,
Ill (H.M.); Dept of Pathology, Mayo Clinic Scottsdale, Scottsdale, Ariz (T.C.);
Dept of Pulmonology, Université Libre de Bruxelles, Brussels, Belgium
(P.A.G.); Dept of Radiology, Seoul National University Hospital, Seoul, Korea
(J.M.G.); Center for Academic Medicine, Dept of Radiology, Stanford University,
Palo Alto, Calif (A.N.C.L.); Dept of Radiology, National Jewish Medical and
Research Center, Denver, Colo (D.A.L.); Dept of Radiology, Meander Medical
Centre Amersfoort, Amersfoort, the Netherlands (C.M.S.P.); Dept of Radiology,
Osaka University Graduate School of Medicine, Suita, Japan (N.T.); Dept of
Pathology, Memorial Sloan Kettering Cancer Center, New York, NY (W.D.T.); Dept
of Radiology, Catholic University Leuven, University Hospital Gasthuisberg,
Leuven, Belgium (J.A.V.); Dept of Diagnostic Radiology, University of Maryland
Hospital, Baltimore, Md (C.S.W.); and Dept of Radiology, NYU Langone Medical
Center/Tisch Hospital, New York, NY (D.P.N.)
| | - Cornelia M. Schaefer-Prokop
- From the Dept of Radiology, University of Massachusetts Memorial
Health and University of Massachusetts Chan Medical School, 55 Lake Ave N,
Worcester, MA 01655 (A.A.B.); Dept of Radiology, University of Chicago, Chicago,
Ill (H.M.); Dept of Pathology, Mayo Clinic Scottsdale, Scottsdale, Ariz (T.C.);
Dept of Pulmonology, Université Libre de Bruxelles, Brussels, Belgium
(P.A.G.); Dept of Radiology, Seoul National University Hospital, Seoul, Korea
(J.M.G.); Center for Academic Medicine, Dept of Radiology, Stanford University,
Palo Alto, Calif (A.N.C.L.); Dept of Radiology, National Jewish Medical and
Research Center, Denver, Colo (D.A.L.); Dept of Radiology, Meander Medical
Centre Amersfoort, Amersfoort, the Netherlands (C.M.S.P.); Dept of Radiology,
Osaka University Graduate School of Medicine, Suita, Japan (N.T.); Dept of
Pathology, Memorial Sloan Kettering Cancer Center, New York, NY (W.D.T.); Dept
of Radiology, Catholic University Leuven, University Hospital Gasthuisberg,
Leuven, Belgium (J.A.V.); Dept of Diagnostic Radiology, University of Maryland
Hospital, Baltimore, Md (C.S.W.); and Dept of Radiology, NYU Langone Medical
Center/Tisch Hospital, New York, NY (D.P.N.)
| | - Noriyuki Tomiyama
- From the Dept of Radiology, University of Massachusetts Memorial
Health and University of Massachusetts Chan Medical School, 55 Lake Ave N,
Worcester, MA 01655 (A.A.B.); Dept of Radiology, University of Chicago, Chicago,
Ill (H.M.); Dept of Pathology, Mayo Clinic Scottsdale, Scottsdale, Ariz (T.C.);
Dept of Pulmonology, Université Libre de Bruxelles, Brussels, Belgium
(P.A.G.); Dept of Radiology, Seoul National University Hospital, Seoul, Korea
(J.M.G.); Center for Academic Medicine, Dept of Radiology, Stanford University,
Palo Alto, Calif (A.N.C.L.); Dept of Radiology, National Jewish Medical and
Research Center, Denver, Colo (D.A.L.); Dept of Radiology, Meander Medical
Centre Amersfoort, Amersfoort, the Netherlands (C.M.S.P.); Dept of Radiology,
Osaka University Graduate School of Medicine, Suita, Japan (N.T.); Dept of
Pathology, Memorial Sloan Kettering Cancer Center, New York, NY (W.D.T.); Dept
of Radiology, Catholic University Leuven, University Hospital Gasthuisberg,
Leuven, Belgium (J.A.V.); Dept of Diagnostic Radiology, University of Maryland
Hospital, Baltimore, Md (C.S.W.); and Dept of Radiology, NYU Langone Medical
Center/Tisch Hospital, New York, NY (D.P.N.)
| | - William D. Travis
- From the Dept of Radiology, University of Massachusetts Memorial
Health and University of Massachusetts Chan Medical School, 55 Lake Ave N,
Worcester, MA 01655 (A.A.B.); Dept of Radiology, University of Chicago, Chicago,
Ill (H.M.); Dept of Pathology, Mayo Clinic Scottsdale, Scottsdale, Ariz (T.C.);
Dept of Pulmonology, Université Libre de Bruxelles, Brussels, Belgium
(P.A.G.); Dept of Radiology, Seoul National University Hospital, Seoul, Korea
(J.M.G.); Center for Academic Medicine, Dept of Radiology, Stanford University,
Palo Alto, Calif (A.N.C.L.); Dept of Radiology, National Jewish Medical and
Research Center, Denver, Colo (D.A.L.); Dept of Radiology, Meander Medical
Centre Amersfoort, Amersfoort, the Netherlands (C.M.S.P.); Dept of Radiology,
Osaka University Graduate School of Medicine, Suita, Japan (N.T.); Dept of
Pathology, Memorial Sloan Kettering Cancer Center, New York, NY (W.D.T.); Dept
of Radiology, Catholic University Leuven, University Hospital Gasthuisberg,
Leuven, Belgium (J.A.V.); Dept of Diagnostic Radiology, University of Maryland
Hospital, Baltimore, Md (C.S.W.); and Dept of Radiology, NYU Langone Medical
Center/Tisch Hospital, New York, NY (D.P.N.)
| | - Johny A. Verschakelen
- From the Dept of Radiology, University of Massachusetts Memorial
Health and University of Massachusetts Chan Medical School, 55 Lake Ave N,
Worcester, MA 01655 (A.A.B.); Dept of Radiology, University of Chicago, Chicago,
Ill (H.M.); Dept of Pathology, Mayo Clinic Scottsdale, Scottsdale, Ariz (T.C.);
Dept of Pulmonology, Université Libre de Bruxelles, Brussels, Belgium
(P.A.G.); Dept of Radiology, Seoul National University Hospital, Seoul, Korea
(J.M.G.); Center for Academic Medicine, Dept of Radiology, Stanford University,
Palo Alto, Calif (A.N.C.L.); Dept of Radiology, National Jewish Medical and
Research Center, Denver, Colo (D.A.L.); Dept of Radiology, Meander Medical
Centre Amersfoort, Amersfoort, the Netherlands (C.M.S.P.); Dept of Radiology,
Osaka University Graduate School of Medicine, Suita, Japan (N.T.); Dept of
Pathology, Memorial Sloan Kettering Cancer Center, New York, NY (W.D.T.); Dept
of Radiology, Catholic University Leuven, University Hospital Gasthuisberg,
Leuven, Belgium (J.A.V.); Dept of Diagnostic Radiology, University of Maryland
Hospital, Baltimore, Md (C.S.W.); and Dept of Radiology, NYU Langone Medical
Center/Tisch Hospital, New York, NY (D.P.N.)
| | - Charles S. White
- From the Dept of Radiology, University of Massachusetts Memorial
Health and University of Massachusetts Chan Medical School, 55 Lake Ave N,
Worcester, MA 01655 (A.A.B.); Dept of Radiology, University of Chicago, Chicago,
Ill (H.M.); Dept of Pathology, Mayo Clinic Scottsdale, Scottsdale, Ariz (T.C.);
Dept of Pulmonology, Université Libre de Bruxelles, Brussels, Belgium
(P.A.G.); Dept of Radiology, Seoul National University Hospital, Seoul, Korea
(J.M.G.); Center for Academic Medicine, Dept of Radiology, Stanford University,
Palo Alto, Calif (A.N.C.L.); Dept of Radiology, National Jewish Medical and
Research Center, Denver, Colo (D.A.L.); Dept of Radiology, Meander Medical
Centre Amersfoort, Amersfoort, the Netherlands (C.M.S.P.); Dept of Radiology,
Osaka University Graduate School of Medicine, Suita, Japan (N.T.); Dept of
Pathology, Memorial Sloan Kettering Cancer Center, New York, NY (W.D.T.); Dept
of Radiology, Catholic University Leuven, University Hospital Gasthuisberg,
Leuven, Belgium (J.A.V.); Dept of Diagnostic Radiology, University of Maryland
Hospital, Baltimore, Md (C.S.W.); and Dept of Radiology, NYU Langone Medical
Center/Tisch Hospital, New York, NY (D.P.N.)
| | - David P. Naidich
- From the Dept of Radiology, University of Massachusetts Memorial
Health and University of Massachusetts Chan Medical School, 55 Lake Ave N,
Worcester, MA 01655 (A.A.B.); Dept of Radiology, University of Chicago, Chicago,
Ill (H.M.); Dept of Pathology, Mayo Clinic Scottsdale, Scottsdale, Ariz (T.C.);
Dept of Pulmonology, Université Libre de Bruxelles, Brussels, Belgium
(P.A.G.); Dept of Radiology, Seoul National University Hospital, Seoul, Korea
(J.M.G.); Center for Academic Medicine, Dept of Radiology, Stanford University,
Palo Alto, Calif (A.N.C.L.); Dept of Radiology, National Jewish Medical and
Research Center, Denver, Colo (D.A.L.); Dept of Radiology, Meander Medical
Centre Amersfoort, Amersfoort, the Netherlands (C.M.S.P.); Dept of Radiology,
Osaka University Graduate School of Medicine, Suita, Japan (N.T.); Dept of
Pathology, Memorial Sloan Kettering Cancer Center, New York, NY (W.D.T.); Dept
of Radiology, Catholic University Leuven, University Hospital Gasthuisberg,
Leuven, Belgium (J.A.V.); Dept of Diagnostic Radiology, University of Maryland
Hospital, Baltimore, Md (C.S.W.); and Dept of Radiology, NYU Langone Medical
Center/Tisch Hospital, New York, NY (D.P.N.)
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Liu M, Yang L, Sun X, Liang X, Li C, Feng Q, Li M, Zhang L. Evaluation of Prognosis in Patients with Lung Adenocarcinoma with Atypical Solid Nodules on Thin-Section CT Images. Radiol Cardiothorac Imaging 2024; 6:e220234. [PMID: 38206165 PMCID: PMC10912885 DOI: 10.1148/ryct.220234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 04/03/2023] [Accepted: 08/23/2023] [Indexed: 01/12/2024]
Abstract
Purpose To evaluate the clinicopathologic characteristics and prognosis of patients with clinical stage IA lung adenocarcinoma with atypical solid nodules (ASNs) on thin-section CT images. Materials and Methods Data from patients with clinical stage IA lung adenocarcinoma who underwent resection between January 2005 and December 2012 were retrospectively reviewed. According to their manifestations on thin-section CT images, nodules were classified as ASNs, subsolid nodules (SSNs), and typical solid nodules (TSNs). The clinicopathologic characteristics of the ASNs were investigated, and the differences across the three groups were analyzed. The Kaplan-Meier method and multivariable Cox analysis were used to evaluate survival differences among patients with ASNs, SSNs, and TSNs. Results Of the 254 patients (median age, 58 years [IQR, 53-66]; 152 women) evaluated, 49 had ASNs, 123 had SSNs, and 82 had TSNs. Compared with patients with SSNs, those with ASNs were more likely to have nonsmall adenocarcinoma (P < .001), advanced-stage adenocarcinoma (P = .004), nonlepidic growth adenocarcinoma (P < .001), and middle- or low-grade differentiation tumors (P < .001). Compared with patients with TSNs, those with ASNs were more likely to have no lymph node involvement (P = .009) and epidermal growth factor receptor mutation positivity (P = .018). Average disease-free survival in patients with ASNs was significantly longer than that in patients with TSNs (P < .001) but was not distinguishable from that in patients with SSNs (P = .051). Conclusion ASNs were associated with better clinical outcomes than TSNs in patients with clinical stage IA lung adenocarcinoma. Keywords: Adenocarcinoma, Atypical Solid Nodules, CT, Disease-free Survival, Lung, Prognosis, Pulmonary Supplemental material is available for this article. Published under a CC BY 4.0 license.
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Affiliation(s)
- Mengwen Liu
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
| | - Lin Yang
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
| | - Xujie Sun
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
| | - Xin Liang
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
| | - Cong Li
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
| | - Qianqian Feng
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
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Song F, Yang Q, Gong T, Sun K, Zhang W, Liu M, Lv F. Comparison of different classification systems for pulmonary nodules: a multicenter retrospective study in China. Cancer Imaging 2024; 24:15. [PMID: 38254185 PMCID: PMC10801946 DOI: 10.1186/s40644-023-00634-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/05/2023] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND To compare the diagnostic performance of Lung-RADS (lung imaging-reporting and data system) 2022 and PNI-GARS (pulmonary node imaging-grading and reporting system). METHODS Pulmonary nodules (PNs) were selected at four centers, namely, CQ Center (January 1, 2018-December 31, 2021), HB Center (January 1, 2021-June 30, 2022), SC Center (September 1, 2021-December 31, 2021), and SX Center (January 1, 2021-December 31, 2021). PNs were divided into solid nodules (SNs), partial solid nodules (PSNs) and ground-glass nodules (GGNs), and they were then classified by the Lung-RADS and PNI-GARS. The sensitivity, specificity and agreement rate were compared between the two systems by the χ2 test. RESULTS For SN and PSN, the sensitivity of PNI-GARS and Lung-RADS was close (SN 99.8% vs. 99.4%, P < 0.001; PSN 99.9% vs. 98.4%, P = 0.015), but the specificity (SN 51.2% > 35.1%, PSN 13.3% > 5.7%, all P < 0.001) and agreement rate (SN 81.1% > 74.5%, P < 0.001, PSN 94.6% > 92.7%, all P < 0.05) of PNI-GARS were superior to those of Lung-RADS. For GGN, the sensitivity (96.5%) and agreement rate (88.6%) of PNI-GARS were better than those of Lung-RADS (0, 18.5%, P < 0.001). For the whole sample, the sensitivity (98.5%) and agreement rate (87.0%) of PNI-GARS were better than Lung-RADS (57.5%, 56.5%, all P < 0.001), whereas the specificity was slightly lower (49.8% < 53.4%, P = 0.003). CONCLUSION PNI-GARS was superior to Lung-RADS in diagnostic performance, especially for GGN.
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Affiliation(s)
- Feipeng Song
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No.1 YouYi Road, Chongqing, 400010, China
| | - Qian Yang
- Department of Radiology, Hubei Cancer Hospital, Wuhan, China
| | - Tong Gong
- Department of Radiology, Sichuan Provincial People's Hospital, Chengdu, China
| | - Kai Sun
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Wenjia Zhang
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Mengxi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No.1 YouYi Road, Chongqing, 400010, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No.1 YouYi Road, Chongqing, 400010, China.
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22
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Antonicelli A, Muriana P, Favaro G, Mangiameli G, Lanza E, Profili M, Bianchi F, Fina E, Ferrante G, Ghislandi S, Pistillo D, Finocchiaro G, Condorelli G, Lembo R, Novellis P, Dieci E, De Santis S, Veronesi G. The Smokers Health Multiple ACtions (SMAC-1) Trial: Study Design and Results of the Baseline Round. Cancers (Basel) 2024; 16:417. [PMID: 38254906 PMCID: PMC10814085 DOI: 10.3390/cancers16020417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/13/2024] [Accepted: 01/15/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Lung cancer screening with low-dose helical computed tomography (LDCT) reduces mortality in high-risk subjects. Cigarette smoking is linked to up to 90% of lung cancer deaths. Even more so, it is a key risk factor for many other cancers and cardiovascular and pulmonary diseases. The Smokers health Multiple ACtions (SMAC-1) trial aimed to demonstrate the feasibility and effectiveness of an integrated program based on the early detection of smoking-related thoraco-cardiovascular diseases in high-risk subjects, combined with primary prevention. A new multi-component screening design was utilized to strengthen the framework on conventional lung cancer screening programs. We report here the study design and the results from our baseline round, focusing on oncological findings. METHODS High-risk subjects were defined as being >55 years of age and active smokers or formers who had quit within 15 years (>30 pack/y). A PLCOm2012 threshold >2% was chosen. Subject outreach was streamlined through media campaign and general practitioners' engagement. Eligible subjects, upon written informed consent, underwent a psychology consultation, blood sample collection, self-evaluation questionnaire, spirometry, and LDCT scan. Blood samples were analyzed for pentraxin-3 protein levels, interleukins, microRNA, and circulating tumor cells. Cardiovascular risk assessment and coronary artery calcium (CAC) scoring were performed. Direct and indirect costs were analyzed focusing on the incremental cost-effectiveness ratio per quality-adjusted life years gained in different scenarios. Personalized screening time-intervals were determined using the "Maisonneuve risk re-calculation model", and a threshold <0.6% was chosen for the biennial round. RESULTS In total, 3228 subjects were willing to be enrolled. Out of 1654 eligible subjects, 1112 participated. The mean age was 64 years (M/F 62/38%), with a mean PLCOm2012 of 5.6%. Former and active smokers represented 23% and 77% of the subjects, respectively. At least one nodule was identified in 348 subjects. LDCTs showed no clinically significant findings in 762 subjects (69%); thus, they were referred for annual/biennial LDCTs based on the Maisonneuve risk (mean value = 0.44%). Lung nodule active surveillance was indicated for 122 subjects (11%). Forty-four subjects with baseline suspicious nodules underwent a PET-FDG and twenty-seven a CT-guided lung biopsy. Finally, a total of 32 cancers were diagnosed, of which 30 were lung cancers (2.7%) and 2 were extrapulmonary cancers (malignant pleural mesothelioma and thymoma). Finally, 25 subjects underwent lung surgery (2.25%). Importantly, there were zero false positives and two false negatives with CT-guided biopsy, of which the patients were operated on with no stage shift. The final pathology included lung adenocarcinomas (69%), squamous cell carcinomas (10%), and others (21%). Pathological staging showed 14 stage I (47%) and 16 stage II-IV (53%) cancers. CONCLUSIONS LDCTs continue to confirm their efficacy in safely detecting early-stage lung cancer in high-risk subjects, with a negligible risk of false-positive results. Re-calculating the risk of developing lung cancer after baseline LDCTs with the Maisonneuve model allows us to optimize time intervals to subsequent screening. The Smokers health Multiple ACtions (SMAC-1) trial offers solid support for policy assessments by policymakers. We trust that this will help in developing guidelines for the large-scale implementation of lung cancer screening, paving the way for better outcomes for lung cancer patients.
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Affiliation(s)
- Alberto Antonicelli
- Faculty of Medicine and Surgery, School of Thoracic Surgery, Università Vita-Salute San Raffaele, 20132 Milan, Italy; (A.A.); (G.V.)
- Department of Thoracic Surgery, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (P.N.); (E.D.); (S.D.S.)
| | - Piergiorgio Muriana
- Department of Thoracic Surgery, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (P.N.); (E.D.); (S.D.S.)
| | - Giovanni Favaro
- Department of Anesthesia and Intensive Care, IRCCS Istituto Oncologico Veneto (IOV), 35128 Padua, Italy;
| | - Giuseppe Mangiameli
- Division of Thoracic Surgery, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy; (G.M.); (E.F.)
- Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, Italy; (E.L.); (G.F.); (G.C.)
| | - Ezio Lanza
- Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, Italy; (E.L.); (G.F.); (G.C.)
- Department of Interventional Radiology, IRCCS Humanitas Clinical and Research Center, 20089 Rozzano, Italy;
| | - Manuel Profili
- Department of Interventional Radiology, IRCCS Humanitas Clinical and Research Center, 20089 Rozzano, Italy;
| | - Fabrizio Bianchi
- Unit of Cancer Biomarkers, Fondazione IRCCS Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, Italy;
| | - Emanuela Fina
- Division of Thoracic Surgery, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy; (G.M.); (E.F.)
| | - Giuseppe Ferrante
- Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, Italy; (E.L.); (G.F.); (G.C.)
- Cardio Center, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy
| | - Simone Ghislandi
- CERGAS and Department of Social and Political Sciences, Bocconi University, 20136 Milan, Italy;
| | - Daniela Pistillo
- Center for Biological Resources, Humanitas Cancer Center, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy;
| | - Giovanna Finocchiaro
- Department of Medical Oncology, Humanitas Cancer Center, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy;
| | - Gianluigi Condorelli
- Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, Italy; (E.L.); (G.F.); (G.C.)
- Cardio Center, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy
| | - Rosalba Lembo
- Department of Anesthesia and Intensive Care, Section of Biostatistics, Università Vita-Salute San Raffaele, 20132 Milan, Italy;
| | - Pierluigi Novellis
- Department of Thoracic Surgery, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (P.N.); (E.D.); (S.D.S.)
| | - Elisa Dieci
- Department of Thoracic Surgery, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (P.N.); (E.D.); (S.D.S.)
| | - Simona De Santis
- Department of Thoracic Surgery, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (P.N.); (E.D.); (S.D.S.)
| | - Giulia Veronesi
- Faculty of Medicine and Surgery, School of Thoracic Surgery, Università Vita-Salute San Raffaele, 20132 Milan, Italy; (A.A.); (G.V.)
- Department of Thoracic Surgery, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (P.N.); (E.D.); (S.D.S.)
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23
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Willner J, Narula N, Moreira AL. Updates on lung adenocarcinoma: invasive size, grading and STAS. Histopathology 2024; 84:6-17. [PMID: 37872108 DOI: 10.1111/his.15077] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 09/29/2023] [Accepted: 10/04/2023] [Indexed: 10/25/2023]
Abstract
Advancements in the classification of lung adenocarcinoma have resulted in significant changes in pathological reporting. The eighth edition of the tumour-node-metastasis (TNM) staging guidelines calls for the use of invasive size in staging in place of total tumour size. This shift improves prognostic stratification and requires a more nuanced approach to tumour measurements in challenging situations. Similarly, the adoption of new grading criteria based on the predominant and highest-grade pattern proposed by the International Association for the Study of Lung Cancer (IASLC) shows improved prognostication, and therefore clinical utility, relative to previous grading systems. Spread through airspaces (STAS) is a form of tumour invasion involving tumour cells spreading through the airspaces, which has been highly researched in recent years. This review discusses updates in pathological T staging, adenocarcinoma grading and STAS and illustrates the utility and limitations of current concepts in lung adenocarcinoma.
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Affiliation(s)
- Jonathan Willner
- Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
| | - Navneet Narula
- Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
| | - Andre L Moreira
- Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
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24
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Han X, Wang X, Li Z, Dou W, Shi H, Liu Y, Sun K. Risk prediction of intraoperative pain in percutaneous microwave ablation of lung tumors under CT guidance. Eur Radiol 2023; 33:8693-8702. [PMID: 37382619 DOI: 10.1007/s00330-023-09874-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 04/05/2023] [Accepted: 05/04/2023] [Indexed: 06/30/2023]
Abstract
OBJECTIVES To evaluate the effect of intraoperative pain in microwave ablation of lung tumors (MWALT) on local efficacy and establish the pain risk prediction model. METHODS It was a retrospectively study. Consecutive patients with MWALT from September 2017 to December 2020 were divided into mild and severe pain groups. Local efficacy was evaluated by comparing technical success, technical effectiveness, and local progression-free survival (LPFS) in two groups. All cases were randomly allocated into training and validation cohorts at a ratio of 7:3. A nomogram model was established using predictors identified by logistics regression in training dataset. The calibration curves, C-statistic, and decision curve analysis (DCA) were used to evaluate the accuracy, ability, and clinical value of the nomogram. RESULTS A total of 263 patients (mild pain group: n = 126; severe pain group: n = 137) were included in the study. Technical success rate and technical effectiveness rate were 100% and 99.2% in the mild pain group and 98.5% and 97.8% in the severe pain group. LPFS rates at 12 and 24 months were 97.6% and 87.6% in the mild pain group and 91.9% and 79.3% in the severe pain group (p = 0.034; HR: 1.90). The nomogram was established based on three predictors: depth of nodule, puncture depth, and multi-antenna. The prediction ability and accuracy were verified by C-statistic and calibration curve. DCA curve suggested the proposed prediction model was clinically useful. CONCLUSIONS Severe intraoperative pain in MWALT reduced the local efficacy. An established prediction model could accurately predict severe pain and assist physicians in choosing a suitable anesthesia type. CLINICAL RELEVANCE STATEMENT This study firstly provides a prediction model for the risk of severe intraoperative pain in MWALT. Physicians can choose a suitable anesthesia type based on pain risk, in order to improve patients' tolerance as well as local efficacy of MWALT. KEY POINTS • The severe intraoperative pain in MWALT reduced the local efficacy. • Predictors of severe intraoperative pain in MWALT were the depth of nodule, puncture depth, and multi-antenna. • The prediction model established in this study can accurately predict the risk of severe pain in MWALT and assist physicians in choosing a suitable anesthesia type.
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Affiliation(s)
- Xujian Han
- Department of Medical Intervention, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324 Jingwu Road, Jinan, Shandong, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324 Jingwu Road, Jinan, Shandong, China.
| | - Zhenjia Li
- Department of Medical Intervention, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324 Jingwu Road, Jinan, Shandong, China.
| | - Weitao Dou
- Department of Medical Intervention, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324 Jingwu Road, Jinan, Shandong, China
| | - Honglu Shi
- Department of Medical Intervention, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324 Jingwu Road, Jinan, Shandong, China
| | - Yuanqing Liu
- Department of Medical Intervention, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324 Jingwu Road, Jinan, Shandong, China
| | - Kui Sun
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324 Jingwu Road, Jinan, Shandong, China
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Guo C, Xu L, Li X, Fu Y, Wang H, Han R, Li G, Feng Z, Li M, Ren W, Peng Z. Computed tomography imaging and clinical characteristics of pulmonary ground-glass nodules ≤2 cm with micropapillary pattern. Thorac Cancer 2023; 14:3433-3444. [PMID: 37876115 PMCID: PMC10719660 DOI: 10.1111/1759-7714.15136] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 09/28/2023] [Accepted: 10/03/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND The aim of this study was to investigate the imaging features, lymph node metastasis, and genetic mutations in micropapillary lung adenocarcinoma (imaging with mixed ground-glass nodules) ≤2 cm, to provide a more precise and refined basis for the selection of lung segment resection. METHODS A retrospective analysis of 162 patients with surgically resected pathologically confirmed cancers ≤2.0 cm in diameter (50 cases of micropapillary mixed ground-glass nodules [mGGNs], 50 cases of nonmicropapillary mGGNs, and 62 cases of micropapillary SNs [solid nodules]) was performed. mGGNs were classified into five categories according to imaging features. The distribution of these five morphologies in micropapillary with mGGN and nonmicropapillary with mGGN was analyzed. The postoperative pathology and prognosis of lymph node metastasis were also compared between micropapillary mGGNs and micropapillary with SNs. After searching the TCGA database, we demonstrated heterogeneity, high malignancy and high risk of microcapillary lung cancer cancers. RESULTS Different pathological subtypes of mGGN differed in morphological features (p < 0.05). The rate of lymph node metastasis was significantly higher in micropapillary mGGNs than in nonmicropapillary mGGNs. In the TCGA database samples, lactate transmembrane protein activity, collagen transcription score, and fibroblast EMT score were remarkably higher in micropapillary adenocarcinoma. Other pathological subtypes had a better survival prognosis and longer disease-free survival compared with micropapillary adenocarcinoma. CONCLUSION mGGNs ≤2 cm with a micropapillary pattern have a higher risk of lymph node metastasis compared with SNs, and computed tomography (CT) imaging features can assist in their diagnosis.
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Affiliation(s)
- Chen‐ran Guo
- Department of Thoracic Surgery, Shandong Provincial HospitalShandong UniversityJinanChina
| | - Lin Xu
- Department of Thoracic Surgery, Shandong Provincial HospitalShandong UniversityJinanChina
| | - Xiao Li
- Department of Thoracic Surgery, Shandong Provincial HospitalShandong UniversityJinanChina
| | - Yi‐lin Fu
- Department of Thoracic SurgeryShandong Provincial HospitalJinanChina
| | - Hui Wang
- Department of Thoracic SurgeryShandong Provincial HospitalJinanChina
| | - Rui Han
- Peking Union Medical CollegeBeijingChina
| | - Geng‐sheng Li
- Department of AnesthesiologyShandong Provincial HospitalJinanChina
| | - Zhen Feng
- Department of Thoracic SurgeryShandong Provincial HospitalJinanChina
| | - Meng Li
- Department of Thoracic SurgeryShandong Provincial HospitalJinanChina
| | - Wan‐gang Ren
- Department of Thoracic SurgeryShandong Provincial HospitalJinanChina
| | - Zhong‐min Peng
- Department of Thoracic Surgery, Shandong Provincial HospitalShandong UniversityJinanChina
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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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27
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Ma ZJ, Ma ZX, Sun YL, Li DC, Jin L, Gao P, Li C, Li M. Prediction of subsolid pulmonary nodule growth rate using radiomics. BMC Med Imaging 2023; 23:177. [PMID: 37936095 PMCID: PMC10629176 DOI: 10.1186/s12880-023-01143-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/27/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND Pulmonary nodule growth rate assessment is critical in the management of subsolid pulmonary nodules (SSNs) during clinical follow-up. The present study aimed to develop a model to predict the growth rate of SSNs. METHODS A total of 273 growing SSNs with clinical information and 857 computed tomography (CT) scans were retrospectively analyzed. The images were randomly divided into training and validation sets. All images were categorized into fast-growth (volume doubling time (VDT) ≤ 400 days) and slow-growth (VDT > 400 days) groups. Models for predicting the growth rate of SSNs were developed using radiomics and clinical features. The models' performance was evaluated using the area under the curve (AUC) values for the receiver operating characteristic curve. RESULTS The fast- and slow-growth groups included 108 and 749 scans, respectively, and 10 radiomics features and three radiographic features (nodule density, presence of spiculation, and presence of vascular changes) were selected to predict the growth rate of SSNs. The nomogram integrating radiomics and radiographic features (AUC = 0.928 and AUC = 0.905, respectively) performed better than the radiographic (AUC = 0.668 and AUC = 0.689, respectively) and radiomics (AUC = 0.888 and AUC = 0.816, respectively) models alone in both the training and validation sets. CONCLUSION The nomogram model developed by combining radiomics with radiographic features can predict the growth rate of SSNs more accurately than traditional radiographic models. It can also optimize clinical treatment decisions for patients with SSNs and improve their long-term management.
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Affiliation(s)
- Zong Jing Ma
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Zhuang Xuan Ma
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Ying Li Sun
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - De Chun Li
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Liang Jin
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Pan Gao
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Cheng Li
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Ming Li
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China.
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28
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Cardillo G, Petersen RH, Ricciardi S, Patel A, Lodhia JV, Gooseman MR, Brunelli A, Dunning J, Fang W, Gossot D, Licht PB, Lim E, Roessner ED, Scarci M, Milojevic M. European guidelines for the surgical management of pure ground-glass opacities and part-solid nodules: Task Force of the European Association of Cardio-Thoracic Surgery and the European Society of Thoracic Surgeons. Eur J Cardiothorac Surg 2023; 64:ezad222. [PMID: 37243746 DOI: 10.1093/ejcts/ezad222] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/10/2023] [Accepted: 05/26/2023] [Indexed: 05/29/2023] Open
Affiliation(s)
- Giuseppe Cardillo
- Unit of Thoracic Surgery, Azienda Ospedaliera San Camillo Forlanini, Rome, Italy
- Unicamillus-Saint Camillus University of Health Sciences, Rome, Italy
| | - René Horsleben Petersen
- Department of Cardiothoracic Surgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Denmark
| | - Sara Ricciardi
- Unit of Thoracic Surgery, Azienda Ospedaliera San Camillo Forlanini, Rome, Italy
- Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Akshay Patel
- Department of Thoracic Surgery, University Hospitals Birmingham, England, United Kingdom
- Institute of Immunology and Immunotherapy, University of Birmingham, United Kingdom
| | - Joshil V Lodhia
- Department of Thoracic Surgery, St James University Hospital, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Michael R Gooseman
- Department of Thoracic Surgery, Hull University Teaching Hospitals NHS Trust, and Hull York Medical School, University of Hull, Hull, United Kingdom
| | - Alessandro Brunelli
- Department of Thoracic Surgery, St James University Hospital, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Joel Dunning
- James Cook University Hospital Middlesbrough, United Kingdom
| | - Wentao Fang
- Department of Thoracic Surgery, Shanghai Chest Hospital, Jiaotong University Medical School, Shangai, China
| | - Dominique Gossot
- Department of Thoracic Surgery, Curie-Montsouris Thoracic Institute, Paris, France
| | - Peter B Licht
- Department of Cardiothoracic Surgery, Odense University Hospital, Odense, Denmark
| | - Eric Lim
- Academic Division of Thoracic Surgery, The Royal Brompton Hospital and Imperial College London, United Kingdom
| | - Eric Dominic Roessner
- Department of Thoracic Surgery, Center for Thoracic Diseases, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Marco Scarci
- Division of Thoracic Surgery, Imperial College NHS Healthcare Trust and National Heart and Lung Institute, Hammersmith Hospital, London, United Kingdom
| | - Milan Milojevic
- Department of Cardiac Surgery and Cardiovascular Research, Dedinje Cardiovascular Institute, Belgrade, Serbia
- Department of Cardiothoracic Surgery, Erasmus University Medical Center, Rotterdam, the Netherlands
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29
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Schütte W, Gütz S, Nehls W, Blum TG, Brückl W, Buttmann-Schweiger N, Büttner R, Christopoulos P, Delis S, Deppermann KM, Dickgreber N, Eberhardt W, Eggeling S, Fleckenstein J, Flentje M, Frost N, Griesinger F, Grohé C, Gröschel A, Guckenberger M, Hecker E, Hoffmann H, Huber RM, Junker K, Kauczor HU, Kollmeier J, Kraywinkel K, Krüger M, Kugler C, Möller M, Nestle U, Passlick B, Pfannschmidt J, Reck M, Reinmuth N, Rübe C, Scheubel R, Schumann C, Sebastian M, Serke M, Stoelben E, Stuschke M, Thomas M, Tufman A, Vordermark D, Waller C, Wolf J, Wolf M, Wormanns D. [Prevention, Diagnosis, Therapy, and Follow-up of Lung Cancer - Interdisciplinary Guideline of the German Respiratory Society and the German Cancer Society - Abridged Version]. Pneumologie 2023; 77:671-813. [PMID: 37884003 DOI: 10.1055/a-2029-0134] [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: 10/28/2023]
Abstract
The current S3 Lung Cancer Guidelines are edited with fundamental changes to the previous edition based on the dynamic influx of information to this field:The recommendations include de novo a mandatory case presentation for all patients with lung cancer in a multidisciplinary tumor board before initiation of treatment, furthermore CT-Screening for asymptomatic patients at risk (after federal approval), recommendations for incidental lung nodule management , molecular testing of all NSCLC independent of subtypes, EGFR-mutations in resectable early stage lung cancer in relapsed or recurrent disease, adjuvant TKI-therapy in the presence of common EGFR-mutations, adjuvant consolidation treatment with checkpoint inhibitors in resected lung cancer with PD-L1 ≥ 50%, obligatory evaluation of PD-L1-status, consolidation treatment with checkpoint inhibition after radiochemotherapy in patients with PD-L1-pos. tumor, adjuvant consolidation treatment with checkpoint inhibition in patients withPD-L1 ≥ 50% stage IIIA and treatment options in PD-L1 ≥ 50% tumors independent of PD-L1status and targeted therapy and treatment option immune chemotherapy in first line SCLC patients.Based on the current dynamic status of information in this field and the turnaround time required to implement new options, a transformation to a "living guideline" was proposed.
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Affiliation(s)
- Wolfgang Schütte
- Klinik für Innere Medizin II, Krankenhaus Martha Maria Halle-Dölau, Halle (Saale)
| | - Sylvia Gütz
- St. Elisabeth-Krankenhaus Leipzig, Abteilung für Innere Medizin I, Leipzig
| | - Wiebke Nehls
- Klinik für Palliativmedizin und Geriatrie, Helios Klinikum Emil von Behring
| | - Torsten Gerriet Blum
- Helios Klinikum Emil von Behring, Klinik für Pneumologie, Lungenklinik Heckeshorn, Berlin
| | - Wolfgang Brückl
- Klinik für Innere Medizin 3, Schwerpunkt Pneumologie, Klinikum Nürnberg Nord
| | | | - Reinhard Büttner
- Institut für Allgemeine Pathologie und Pathologische Anatomie, Uniklinik Köln, Berlin
| | | | - Sandra Delis
- Helios Klinikum Emil von Behring, Klinik für Pneumologie, Lungenklinik Heckeshorn, Berlin
| | | | - Nikolas Dickgreber
- Klinik für Pneumologie, Thoraxonkologie und Beatmungsmedizin, Klinikum Rheine
| | | | - Stephan Eggeling
- Vivantes Netzwerk für Gesundheit, Klinikum Neukölln, Klinik für Thoraxchirurgie, Berlin
| | - Jochen Fleckenstein
- Klinik für Strahlentherapie und Radioonkologie, Universitätsklinikum des Saarlandes und Medizinische Fakultät der Universität des Saarlandes, Homburg
| | - Michael Flentje
- Klinik und Poliklinik für Strahlentherapie, Universitätsklinikum Würzburg, Würzburg
| | - Nikolaj Frost
- Medizinische Klinik mit Schwerpunkt Infektiologie/Pneumologie, Charite Universitätsmedizin Berlin, Berlin
| | - Frank Griesinger
- Klinik für Hämatologie und Onkologie, Pius-Hospital Oldenburg, Oldenburg
| | | | - Andreas Gröschel
- Klinik für Pneumologie und Beatmungsmedizin, Clemenshospital, Münster
| | | | | | - Hans Hoffmann
- Klinikum Rechts der Isar, TU München, Sektion für Thoraxchirurgie, München
| | - Rudolf M Huber
- Medizinische Klinik und Poliklinik V, Thorakale Onkologie, LMU Klinikum Munchen
| | - Klaus Junker
- Klinikum Oststadt Bremen, Institut für Pathologie, Bremen
| | - Hans-Ulrich Kauczor
- Klinikum der Universität Heidelberg, Abteilung Diagnostische Radiologie, Heidelberg
| | - Jens Kollmeier
- Helios Klinikum Emil von Behring, Klinik für Pneumologie, Lungenklinik Heckeshorn, Berlin
| | | | - Marcus Krüger
- Klinik für Thoraxchirurgie, Krankenhaus Martha-Maria Halle-Dölau, Halle-Dölau
| | | | - Miriam Möller
- Krankenhaus Martha-Maria Halle-Dölau, Klinik für Innere Medizin II, Halle-Dölau
| | - Ursula Nestle
- Kliniken Maria Hilf, Klinik für Strahlentherapie, Mönchengladbach
| | | | - Joachim Pfannschmidt
- Klinik für Thoraxchirurgie, Lungenklinik Heckeshorn, Helios Klinikum Emil von Behring, Berlin
| | - Martin Reck
- Lungeclinic Grosshansdorf, Pneumologisch-onkologische Abteilung, Grosshansdorf
| | - Niels Reinmuth
- Klinik für Pneumologie, Thorakale Onkologie, Asklepios Lungenklinik Gauting, Gauting
| | - Christian Rübe
- Klinik für Strahlentherapie und Radioonkologie, Universitätsklinikum des Saarlandes, Homburg/Saar, Homburg
| | | | | | - Martin Sebastian
- Medizinische Klinik II, Universitätsklinikum Frankfurt, Frankfurt
| | - Monika Serke
- Zentrum für Pneumologie und Thoraxchirurgie, Lungenklinik Hemer, Hemer
| | | | - Martin Stuschke
- Klinik und Poliklinik für Strahlentherapie, Universitätsklinikum Essen, Essen
| | - Michael Thomas
- Thoraxklinik am Univ.-Klinikum Heidelberg, Thorakale Onkologie, Heidelberg
| | - Amanda Tufman
- Medizinische Klinik und Poliklinik V, Thorakale Onkologie, LMU Klinikum München
| | - Dirk Vordermark
- Universitätsklinik und Poliklinik für Strahlentherapie, Universitätsklinikum Halle, Halle
| | - Cornelius Waller
- Klinik für Innere Medizin I, Universitätsklinikum Freiburg, Freiburg
| | | | - Martin Wolf
- Klinikum Kassel, Klinik für Onkologie und Hämatologie, Kassel
| | - Dag Wormanns
- Evangelische Lungenklinik, Radiologisches Institut, Berlin
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Jreige M, Darçot E, Lovis A, Simons J, Nicod-Lalonde M, Schaefer N, Buela F, Long O, Beigelman-Aubry C, Prior JO. Lung CT stabilization with high-frequency non-invasive ventilation (HF-NIV) and breath-hold (BH) in lung nodule assessment by PET/CT. Eur J Hybrid Imaging 2023; 7:16. [PMID: 37661217 PMCID: PMC10475447 DOI: 10.1186/s41824-023-00175-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/21/2023] [Indexed: 09/05/2023] Open
Abstract
PURPOSE To evaluate the effect of lung stabilization using high-frequency non-invasive ventilation (HF-NIV) and breath-hold (BH) techniques on lung nodule detection and texture assessment in PET/CT compared to a free-breathing (FB) standard lung CT acquisition in PET/CT. MATERIALS AND METHODS Six patients aged 65 ± 7 years, addressed for initial assessment of at least one suspicious lung nodule with 18F-FDG PET/CT, underwent three consecutive lung PET/CT acquisitions with FB, HF-NIV and BH. Lung nodules were assessed on all three CT acquisitions of the PET/CT and characterized for any size, volume and solid/sub-solid nature. RESULTS BH detected a significantly higher number of nodules (n = 422) compared to HF-NIV (n = 368) and FB (n = 191) (p < 0.001). The mean nodule size (mm) was 2.4 ± 2.1, 2.6 ± 1.9 and 3.2 ± 2.4 in BH, HF-NIV and FB, respectively, for long axis and 1.5 ± 1.3, 1.6 ± 1.2 and 2.1 ± 1.7 in BH, HF-NIV and FB, respectively, for short axis. Long- and short-axis diameters were significantly different between BH and FB (p < 0.001) and between HF-NIV and FB (p < 0.001 and p = 0.008), but not between BH and HF-NIV. A trend for higher volume was shown in FB compared to BH (p = 0.055) and HF-NIV (p = 0.068) without significant difference between BH and HF-NIV (p = 1). We found a significant difference in detectability of sub-solid nodules between the three acquisitions, with BH showing a higher number of sub-solid nodules (n = 128) compared to HF-NIV (n = 72) and FB (n = 44) (p = 0.002). CONCLUSION We observed a higher detection rate of pulmonary nodules on CT under BH or HF-NIV conditions applied to PET/CT than with FB. BH and HF-NIV demonstrated comparable texture assessment and performed better than FB in assessing size and volume. BH showed a better performance for detecting sub-solid nodules compared to HF-NIV and FB. The addition of BH or HF-NIV to PET/CT can help improve the detection and texture characterization of lung nodules by CT, therefore improving the accuracy of oncological lung disease assessment. The ease of use of BH and its added value should prompt its use in routine practice.
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Affiliation(s)
- Mario Jreige
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Emeline Darçot
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Alban Lovis
- Department of Pulmonology, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Julien Simons
- Department of Physiotherapy, Lausanne University Hospital, Lausanne, Switzerland
| | - Marie Nicod-Lalonde
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Rue du Bugnon 46, 1011, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Niklaus Schaefer
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Rue du Bugnon 46, 1011, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Flore Buela
- Department of Physiotherapy, Lausanne University Hospital, Lausanne, Switzerland
| | - Olivier Long
- Department of Physiotherapy, Lausanne University Hospital, Lausanne, Switzerland
| | - Catherine Beigelman-Aubry
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - John O Prior
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Rue du Bugnon 46, 1011, Lausanne, Switzerland.
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
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31
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Feng KP, Fu K, Xu C, Ding C, Zhu XY, Pan B, Jia XY, Zhao J, Li C. NSCLC patients with a changing T grade after operation may represent a special subset of tumor staging. J Cancer Res Clin Oncol 2023; 149:9991-9998. [PMID: 37258719 DOI: 10.1007/s00432-023-04925-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 05/23/2023] [Indexed: 06/02/2023]
Abstract
BACKGROUND There is consensus that postoperative adjuvant therapy is not recommended in patients with stage 1a non-small cell lung cancer (NSCLC). Meanwhile, it is still controversial whether postoperative adjuvant chemotherapy is recommended for NSCLC patients with T2aN0M0 (stage 1b). In some patients with stage 1b NSCLC without pleural invasion, tumor diameter was measured between 3 and 4 cm by preoperative imaging and less than 3 cm by postoperative pathology specimens. TNM staging in such patients is both radiologic stage 1b and pathologic stage 1a. Thoracic surgeons are often confused about whether such patients with NSCLC will require subsequent treatment and how the survival prognosis for this group of patients will be. METHODS All data of radiographic TNM stage 1b patients who underwent radical R0 resection at the department of thoracic surgery, the First Affiliated Hospital of Soochow University between January 2013 and July 2017 were retrieved, and 208 patients were finally included in the study. Clinical data, including imaging data, pathology data, were obtained by reviewing the patients' electronic medical records. Disease-free survival (DFS) and overall survival (OS) were obtained by telephone interview. Statistical analysis was performed using SPSS (SPSS 26.0 for windows, SPSS). RESULTS A total of 208 patients were included in this study, 61 patients with T-stage migration (observation group) and 147 patients without T-stage migration (control group). There were significant statistical differences between the two groups in terms of preoperative FEV1/FVC and tumor diameter (specimens, CT and 3-dimensional measurements). Logistic regression results showed that lower FEV1/FVC and smaller CT measurements would make the patient's T stage more likely to migrate. Bland-Altman plots showed that tumor length measured by imaging was significantly higher than that measured by pathological specimens. Taking DFS as the outcome, the survival curve of the observation group was significantly better than that of the control group. Similarly, there was a significant difference in OS between the two groups. CONCLUSIONS For NSCLC patients whose preoperative imaging evaluation was stage 1b (tumor diameter more than 3 cm, no main bronchus, pleura, no atelectasis), the presence of lung tissue with smaller tumor diameter and/or higher air content may indicate that the postoperative pathological staging may be changed to stage 1a (tumor diameter less than 3 cm). These patients had better survival prognosis than those who did not undergo TNM stage change and were diagnosed with stage 1b non-small cell lung cancer before and after surgery.
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Affiliation(s)
- Kun-Peng Feng
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Medical College of Soochow University, Suzhou, 215000, China
- Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Kai Fu
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Medical College of Soochow University, Suzhou, 215000, China
- Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Chun Xu
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Medical College of Soochow University, Suzhou, 215000, China
- Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Cheng Ding
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Medical College of Soochow University, Suzhou, 215000, China
- Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xin-Yu Zhu
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Medical College of Soochow University, Suzhou, 215000, China
- Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Bin Pan
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Medical College of Soochow University, Suzhou, 215000, China
- Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xin-Yu Jia
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Medical College of Soochow University, Suzhou, 215000, China
- Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jun Zhao
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Medical College of Soochow University, Suzhou, 215000, China.
- Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.
| | - Chang Li
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Medical College of Soochow University, Suzhou, 215000, China.
- Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.
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32
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Baidya Kayal E, Ganguly S, Sasi A, Sharma S, DS D, Saini M, Rangarajan K, Kandasamy D, Bakhshi S, Mehndiratta A. A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models. Front Oncol 2023; 13:1212526. [PMID: 37671060 PMCID: PMC10476362 DOI: 10.3389/fonc.2023.1212526] [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: 04/26/2023] [Accepted: 07/31/2023] [Indexed: 09/07/2023] Open
Abstract
The presence of lung metastases in patients with primary malignancies is an important criterion for treatment management and prognostication. Computed tomography (CT) of the chest is the preferred method to detect lung metastasis. However, CT has limited efficacy in differentiating metastatic nodules from benign nodules (e.g., granulomas due to tuberculosis) especially at early stages (<5 mm). There is also a significant subjectivity associated in making this distinction, leading to frequent CT follow-ups and additional radiation exposure along with financial and emotional burden to the patients and family. Even 18F-fluoro-deoxyglucose positron emission technology-computed tomography (18F-FDG PET-CT) is not always confirmatory for this clinical problem. While pathological biopsy is the gold standard to demonstrate malignancy, invasive sampling of small lung nodules is often not clinically feasible. Currently, there is no non-invasive imaging technique that can reliably characterize lung metastases. The lung is one of the favored sites of metastasis in sarcomas. Hence, patients with sarcomas, especially from tuberculosis prevalent developing countries, can provide an ideal platform to develop a model to differentiate lung metastases from benign nodules. To overcome the lack of optimal specificity of CT scan in detecting pulmonary metastasis, a novel artificial intelligence (AI)-based protocol is proposed utilizing a combination of radiological and clinical biomarkers to identify lung nodules and characterize it as benign or metastasis. This protocol includes a retrospective cohort of nearly 2,000-2,250 sample nodules (from at least 450 patients) for training and testing and an ambispective cohort of nearly 500 nodules (from 100 patients; 50 patients each from the retrospective and prospective cohort) for validation. Ground-truth annotation of lung nodules will be performed using an in-house-built segmentation tool. Ground-truth labeling of lung nodules (metastatic/benign) will be performed based on histopathological results or baseline and/or follow-up radiological findings along with clinical outcome of the patient. Optimal methods for data handling and statistical analysis are included to develop a robust protocol for early detection and classification of pulmonary metastasis at baseline and at follow-up and identification of associated potential clinical and radiological markers.
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Affiliation(s)
- Esha Baidya Kayal
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Shuvadeep Ganguly
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Archana Sasi
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Swetambri Sharma
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Dheeksha DS
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Manish Saini
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Krithika Rangarajan
- Radiodiagnosis, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | | | - Sameer Bakhshi
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Amit Mehndiratta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, Delhi, India
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33
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Chiong J, Ramkumar PG, Weir NW, Weir-McCall JR, Nania A, Shaw LJ, Einstein AJ, Dweck MR, Mills NL, Newby DE, van Beek EJR, Roditi G, Williams MC. Evaluating Radiation Exposure in Patients with Stable Chest Pain in the SCOT-HEART Trial. Radiology 2023; 308:e221963. [PMID: 37526539 PMCID: PMC10478793 DOI: 10.1148/radiol.221963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 05/19/2023] [Accepted: 06/09/2023] [Indexed: 08/02/2023]
Abstract
Background In the Scottish Computed Tomography of the Heart (SCOT-HEART) trial in individuals with stable chest pain, a treatment strategy based on coronary CT angiography (CTA) led to improved outcomes. Purpose To assess 5-year cumulative radiation doses of participants undergoing investigation for suspected angina due to coronary artery disease with or without coronary CTA. Materials and Methods This secondary analysis of the SCOT-HEART trial included data from six of 12 recruiting sites and two of three imaging sites. Participants were recruited between November 18, 2010, and September 24, 2014, with follow-up through January 31, 2018. Study participants had been randomized (at a one-to-one ratio) to standard care with CT (n = 1466) or standard care alone (n = 1428). Imaging was performed on a 64-detector (n = 223) or 320-detector row scanner (n = 1466). Radiation dose from CT (dose-length product), SPECT (injected activity), and invasive coronary angiography (ICA; kerma-area product) was assessed for 5 years after enrollment. Effective dose was calculated using conversion factors appropriate for the imaging modality and body region imaged (using 0.026 mSv/mGy · cm for cardiac CT). Results Cumulative radiation dose was assessed in 2894 participants. Median effective dose was 3.0 mSv (IQR, 2.6-3.3 mSv) for coronary calcium scoring, 4.1 mSv (IQR, 2.6-6.1 mSv) for coronary CTA, 7.4 mSv (IQR, 6.2-8.5 mSv) for SPECT, and 4.1 mSv (IQR, 2.5-6.8 mSv) for ICA. After 5 years, total per-participant cumulative dose was higher in the CT group (median, 8.1 mSv; IQR, 5.5-12.4 mSv) compared with standard-care group (median, 0 mSv; IQR, 0-4.5 mSv; P < .001). In participants who underwent any imaging, cumulative radiation exposure was higher in the CT group (n = 1345; median, 8.6 mSv; IQR, 6.1-13.3 mSv) compared with standard-care group (n = 549; median, 6.4 mSv; IQR, 3.4-9.2 mSv; P < .001). Conclusion In the SCOT-HEART trial, the 5-year cumulative radiation dose from cardiac imaging was higher in the coronary CT angiography group compared with the standard-care group, largely because of the radiation exposure from CT. Clinical trial registration no. NCT01149590 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Dodd and Bosserdt in this issue.
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Affiliation(s)
- Justin Chiong
- From the British Heart Foundation Centre for Cardiovascular Science,
University of Edinburgh, Chancellor's Building, 49 Little France
Crescent, Edinburgh, UK (J.C., M.R.D., N.L.M., D.E.N., E.J.R.v.B., M.C.W.);
Department of Radiology, Ninewells Hospital, Dundee, UK (P.G.R.); Clinical
Research Imaging Facility, University of Dundee, UK (P.G.R.); Department of
Medical Physics, NHS Lothian, Edinburgh, UK (N.W.W.); Edinburgh Imaging Facility
QMRI, University of Edinburgh, Edinburgh, UK (N.W.W., M.R.D., N.L.M., D.E.N.,
E.J.R.v.B., M.C.W.); University of Cambridge, Cambridge, UK (J.R.W.M.); Royal
Papworth Hospital, Cambridge, UK (J.R.W.M.); Department of Radiology, Royal
Infirmary of Scotland, Edinburgh, UK (A.N., E.J.R.v.B., M.C.W.); Blavatnik
Family Women's Health Research Institute, Icahn School of Medicine at
Mount Sinai, New York, NY (L.J.S.); Seymour, Paul and Gloria Milstein Division
of Cardiology, Department of Medicine, and Department of Radiology, Columbia
University Irving Medical Center and New York-Presbyterian Hospital, New York,
NY (A.J.E.); and Institute of Clinical Sciences, University of Glasgow, UK
(G.R.)
| | - Prasad Guntur Ramkumar
- From the British Heart Foundation Centre for Cardiovascular Science,
University of Edinburgh, Chancellor's Building, 49 Little France
Crescent, Edinburgh, UK (J.C., M.R.D., N.L.M., D.E.N., E.J.R.v.B., M.C.W.);
Department of Radiology, Ninewells Hospital, Dundee, UK (P.G.R.); Clinical
Research Imaging Facility, University of Dundee, UK (P.G.R.); Department of
Medical Physics, NHS Lothian, Edinburgh, UK (N.W.W.); Edinburgh Imaging Facility
QMRI, University of Edinburgh, Edinburgh, UK (N.W.W., M.R.D., N.L.M., D.E.N.,
E.J.R.v.B., M.C.W.); University of Cambridge, Cambridge, UK (J.R.W.M.); Royal
Papworth Hospital, Cambridge, UK (J.R.W.M.); Department of Radiology, Royal
Infirmary of Scotland, Edinburgh, UK (A.N., E.J.R.v.B., M.C.W.); Blavatnik
Family Women's Health Research Institute, Icahn School of Medicine at
Mount Sinai, New York, NY (L.J.S.); Seymour, Paul and Gloria Milstein Division
of Cardiology, Department of Medicine, and Department of Radiology, Columbia
University Irving Medical Center and New York-Presbyterian Hospital, New York,
NY (A.J.E.); and Institute of Clinical Sciences, University of Glasgow, UK
(G.R.)
| | - Nicholas W. Weir
- From the British Heart Foundation Centre for Cardiovascular Science,
University of Edinburgh, Chancellor's Building, 49 Little France
Crescent, Edinburgh, UK (J.C., M.R.D., N.L.M., D.E.N., E.J.R.v.B., M.C.W.);
Department of Radiology, Ninewells Hospital, Dundee, UK (P.G.R.); Clinical
Research Imaging Facility, University of Dundee, UK (P.G.R.); Department of
Medical Physics, NHS Lothian, Edinburgh, UK (N.W.W.); Edinburgh Imaging Facility
QMRI, University of Edinburgh, Edinburgh, UK (N.W.W., M.R.D., N.L.M., D.E.N.,
E.J.R.v.B., M.C.W.); University of Cambridge, Cambridge, UK (J.R.W.M.); Royal
Papworth Hospital, Cambridge, UK (J.R.W.M.); Department of Radiology, Royal
Infirmary of Scotland, Edinburgh, UK (A.N., E.J.R.v.B., M.C.W.); Blavatnik
Family Women's Health Research Institute, Icahn School of Medicine at
Mount Sinai, New York, NY (L.J.S.); Seymour, Paul and Gloria Milstein Division
of Cardiology, Department of Medicine, and Department of Radiology, Columbia
University Irving Medical Center and New York-Presbyterian Hospital, New York,
NY (A.J.E.); and Institute of Clinical Sciences, University of Glasgow, UK
(G.R.)
| | - Jonathan R. Weir-McCall
- From the British Heart Foundation Centre for Cardiovascular Science,
University of Edinburgh, Chancellor's Building, 49 Little France
Crescent, Edinburgh, UK (J.C., M.R.D., N.L.M., D.E.N., E.J.R.v.B., M.C.W.);
Department of Radiology, Ninewells Hospital, Dundee, UK (P.G.R.); Clinical
Research Imaging Facility, University of Dundee, UK (P.G.R.); Department of
Medical Physics, NHS Lothian, Edinburgh, UK (N.W.W.); Edinburgh Imaging Facility
QMRI, University of Edinburgh, Edinburgh, UK (N.W.W., M.R.D., N.L.M., D.E.N.,
E.J.R.v.B., M.C.W.); University of Cambridge, Cambridge, UK (J.R.W.M.); Royal
Papworth Hospital, Cambridge, UK (J.R.W.M.); Department of Radiology, Royal
Infirmary of Scotland, Edinburgh, UK (A.N., E.J.R.v.B., M.C.W.); Blavatnik
Family Women's Health Research Institute, Icahn School of Medicine at
Mount Sinai, New York, NY (L.J.S.); Seymour, Paul and Gloria Milstein Division
of Cardiology, Department of Medicine, and Department of Radiology, Columbia
University Irving Medical Center and New York-Presbyterian Hospital, New York,
NY (A.J.E.); and Institute of Clinical Sciences, University of Glasgow, UK
(G.R.)
| | - Alberto Nania
- From the British Heart Foundation Centre for Cardiovascular Science,
University of Edinburgh, Chancellor's Building, 49 Little France
Crescent, Edinburgh, UK (J.C., M.R.D., N.L.M., D.E.N., E.J.R.v.B., M.C.W.);
Department of Radiology, Ninewells Hospital, Dundee, UK (P.G.R.); Clinical
Research Imaging Facility, University of Dundee, UK (P.G.R.); Department of
Medical Physics, NHS Lothian, Edinburgh, UK (N.W.W.); Edinburgh Imaging Facility
QMRI, University of Edinburgh, Edinburgh, UK (N.W.W., M.R.D., N.L.M., D.E.N.,
E.J.R.v.B., M.C.W.); University of Cambridge, Cambridge, UK (J.R.W.M.); Royal
Papworth Hospital, Cambridge, UK (J.R.W.M.); Department of Radiology, Royal
Infirmary of Scotland, Edinburgh, UK (A.N., E.J.R.v.B., M.C.W.); Blavatnik
Family Women's Health Research Institute, Icahn School of Medicine at
Mount Sinai, New York, NY (L.J.S.); Seymour, Paul and Gloria Milstein Division
of Cardiology, Department of Medicine, and Department of Radiology, Columbia
University Irving Medical Center and New York-Presbyterian Hospital, New York,
NY (A.J.E.); and Institute of Clinical Sciences, University of Glasgow, UK
(G.R.)
| | - Leslee J. Shaw
- From the British Heart Foundation Centre for Cardiovascular Science,
University of Edinburgh, Chancellor's Building, 49 Little France
Crescent, Edinburgh, UK (J.C., M.R.D., N.L.M., D.E.N., E.J.R.v.B., M.C.W.);
Department of Radiology, Ninewells Hospital, Dundee, UK (P.G.R.); Clinical
Research Imaging Facility, University of Dundee, UK (P.G.R.); Department of
Medical Physics, NHS Lothian, Edinburgh, UK (N.W.W.); Edinburgh Imaging Facility
QMRI, University of Edinburgh, Edinburgh, UK (N.W.W., M.R.D., N.L.M., D.E.N.,
E.J.R.v.B., M.C.W.); University of Cambridge, Cambridge, UK (J.R.W.M.); Royal
Papworth Hospital, Cambridge, UK (J.R.W.M.); Department of Radiology, Royal
Infirmary of Scotland, Edinburgh, UK (A.N., E.J.R.v.B., M.C.W.); Blavatnik
Family Women's Health Research Institute, Icahn School of Medicine at
Mount Sinai, New York, NY (L.J.S.); Seymour, Paul and Gloria Milstein Division
of Cardiology, Department of Medicine, and Department of Radiology, Columbia
University Irving Medical Center and New York-Presbyterian Hospital, New York,
NY (A.J.E.); and Institute of Clinical Sciences, University of Glasgow, UK
(G.R.)
| | - Andrew J. Einstein
- From the British Heart Foundation Centre for Cardiovascular Science,
University of Edinburgh, Chancellor's Building, 49 Little France
Crescent, Edinburgh, UK (J.C., M.R.D., N.L.M., D.E.N., E.J.R.v.B., M.C.W.);
Department of Radiology, Ninewells Hospital, Dundee, UK (P.G.R.); Clinical
Research Imaging Facility, University of Dundee, UK (P.G.R.); Department of
Medical Physics, NHS Lothian, Edinburgh, UK (N.W.W.); Edinburgh Imaging Facility
QMRI, University of Edinburgh, Edinburgh, UK (N.W.W., M.R.D., N.L.M., D.E.N.,
E.J.R.v.B., M.C.W.); University of Cambridge, Cambridge, UK (J.R.W.M.); Royal
Papworth Hospital, Cambridge, UK (J.R.W.M.); Department of Radiology, Royal
Infirmary of Scotland, Edinburgh, UK (A.N., E.J.R.v.B., M.C.W.); Blavatnik
Family Women's Health Research Institute, Icahn School of Medicine at
Mount Sinai, New York, NY (L.J.S.); Seymour, Paul and Gloria Milstein Division
of Cardiology, Department of Medicine, and Department of Radiology, Columbia
University Irving Medical Center and New York-Presbyterian Hospital, New York,
NY (A.J.E.); and Institute of Clinical Sciences, University of Glasgow, UK
(G.R.)
| | - Marc R. Dweck
- From the British Heart Foundation Centre for Cardiovascular Science,
University of Edinburgh, Chancellor's Building, 49 Little France
Crescent, Edinburgh, UK (J.C., M.R.D., N.L.M., D.E.N., E.J.R.v.B., M.C.W.);
Department of Radiology, Ninewells Hospital, Dundee, UK (P.G.R.); Clinical
Research Imaging Facility, University of Dundee, UK (P.G.R.); Department of
Medical Physics, NHS Lothian, Edinburgh, UK (N.W.W.); Edinburgh Imaging Facility
QMRI, University of Edinburgh, Edinburgh, UK (N.W.W., M.R.D., N.L.M., D.E.N.,
E.J.R.v.B., M.C.W.); University of Cambridge, Cambridge, UK (J.R.W.M.); Royal
Papworth Hospital, Cambridge, UK (J.R.W.M.); Department of Radiology, Royal
Infirmary of Scotland, Edinburgh, UK (A.N., E.J.R.v.B., M.C.W.); Blavatnik
Family Women's Health Research Institute, Icahn School of Medicine at
Mount Sinai, New York, NY (L.J.S.); Seymour, Paul and Gloria Milstein Division
of Cardiology, Department of Medicine, and Department of Radiology, Columbia
University Irving Medical Center and New York-Presbyterian Hospital, New York,
NY (A.J.E.); and Institute of Clinical Sciences, University of Glasgow, UK
(G.R.)
| | - Nicholas L. Mills
- From the British Heart Foundation Centre for Cardiovascular Science,
University of Edinburgh, Chancellor's Building, 49 Little France
Crescent, Edinburgh, UK (J.C., M.R.D., N.L.M., D.E.N., E.J.R.v.B., M.C.W.);
Department of Radiology, Ninewells Hospital, Dundee, UK (P.G.R.); Clinical
Research Imaging Facility, University of Dundee, UK (P.G.R.); Department of
Medical Physics, NHS Lothian, Edinburgh, UK (N.W.W.); Edinburgh Imaging Facility
QMRI, University of Edinburgh, Edinburgh, UK (N.W.W., M.R.D., N.L.M., D.E.N.,
E.J.R.v.B., M.C.W.); University of Cambridge, Cambridge, UK (J.R.W.M.); Royal
Papworth Hospital, Cambridge, UK (J.R.W.M.); Department of Radiology, Royal
Infirmary of Scotland, Edinburgh, UK (A.N., E.J.R.v.B., M.C.W.); Blavatnik
Family Women's Health Research Institute, Icahn School of Medicine at
Mount Sinai, New York, NY (L.J.S.); Seymour, Paul and Gloria Milstein Division
of Cardiology, Department of Medicine, and Department of Radiology, Columbia
University Irving Medical Center and New York-Presbyterian Hospital, New York,
NY (A.J.E.); and Institute of Clinical Sciences, University of Glasgow, UK
(G.R.)
| | - David E. Newby
- From the British Heart Foundation Centre for Cardiovascular Science,
University of Edinburgh, Chancellor's Building, 49 Little France
Crescent, Edinburgh, UK (J.C., M.R.D., N.L.M., D.E.N., E.J.R.v.B., M.C.W.);
Department of Radiology, Ninewells Hospital, Dundee, UK (P.G.R.); Clinical
Research Imaging Facility, University of Dundee, UK (P.G.R.); Department of
Medical Physics, NHS Lothian, Edinburgh, UK (N.W.W.); Edinburgh Imaging Facility
QMRI, University of Edinburgh, Edinburgh, UK (N.W.W., M.R.D., N.L.M., D.E.N.,
E.J.R.v.B., M.C.W.); University of Cambridge, Cambridge, UK (J.R.W.M.); Royal
Papworth Hospital, Cambridge, UK (J.R.W.M.); Department of Radiology, Royal
Infirmary of Scotland, Edinburgh, UK (A.N., E.J.R.v.B., M.C.W.); Blavatnik
Family Women's Health Research Institute, Icahn School of Medicine at
Mount Sinai, New York, NY (L.J.S.); Seymour, Paul and Gloria Milstein Division
of Cardiology, Department of Medicine, and Department of Radiology, Columbia
University Irving Medical Center and New York-Presbyterian Hospital, New York,
NY (A.J.E.); and Institute of Clinical Sciences, University of Glasgow, UK
(G.R.)
| | - Edwin J. R. van Beek
- From the British Heart Foundation Centre for Cardiovascular Science,
University of Edinburgh, Chancellor's Building, 49 Little France
Crescent, Edinburgh, UK (J.C., M.R.D., N.L.M., D.E.N., E.J.R.v.B., M.C.W.);
Department of Radiology, Ninewells Hospital, Dundee, UK (P.G.R.); Clinical
Research Imaging Facility, University of Dundee, UK (P.G.R.); Department of
Medical Physics, NHS Lothian, Edinburgh, UK (N.W.W.); Edinburgh Imaging Facility
QMRI, University of Edinburgh, Edinburgh, UK (N.W.W., M.R.D., N.L.M., D.E.N.,
E.J.R.v.B., M.C.W.); University of Cambridge, Cambridge, UK (J.R.W.M.); Royal
Papworth Hospital, Cambridge, UK (J.R.W.M.); Department of Radiology, Royal
Infirmary of Scotland, Edinburgh, UK (A.N., E.J.R.v.B., M.C.W.); Blavatnik
Family Women's Health Research Institute, Icahn School of Medicine at
Mount Sinai, New York, NY (L.J.S.); Seymour, Paul and Gloria Milstein Division
of Cardiology, Department of Medicine, and Department of Radiology, Columbia
University Irving Medical Center and New York-Presbyterian Hospital, New York,
NY (A.J.E.); and Institute of Clinical Sciences, University of Glasgow, UK
(G.R.)
| | - Giles Roditi
- From the British Heart Foundation Centre for Cardiovascular Science,
University of Edinburgh, Chancellor's Building, 49 Little France
Crescent, Edinburgh, UK (J.C., M.R.D., N.L.M., D.E.N., E.J.R.v.B., M.C.W.);
Department of Radiology, Ninewells Hospital, Dundee, UK (P.G.R.); Clinical
Research Imaging Facility, University of Dundee, UK (P.G.R.); Department of
Medical Physics, NHS Lothian, Edinburgh, UK (N.W.W.); Edinburgh Imaging Facility
QMRI, University of Edinburgh, Edinburgh, UK (N.W.W., M.R.D., N.L.M., D.E.N.,
E.J.R.v.B., M.C.W.); University of Cambridge, Cambridge, UK (J.R.W.M.); Royal
Papworth Hospital, Cambridge, UK (J.R.W.M.); Department of Radiology, Royal
Infirmary of Scotland, Edinburgh, UK (A.N., E.J.R.v.B., M.C.W.); Blavatnik
Family Women's Health Research Institute, Icahn School of Medicine at
Mount Sinai, New York, NY (L.J.S.); Seymour, Paul and Gloria Milstein Division
of Cardiology, Department of Medicine, and Department of Radiology, Columbia
University Irving Medical Center and New York-Presbyterian Hospital, New York,
NY (A.J.E.); and Institute of Clinical Sciences, University of Glasgow, UK
(G.R.)
| | - Michelle C. Williams
- From the British Heart Foundation Centre for Cardiovascular Science,
University of Edinburgh, Chancellor's Building, 49 Little France
Crescent, Edinburgh, UK (J.C., M.R.D., N.L.M., D.E.N., E.J.R.v.B., M.C.W.);
Department of Radiology, Ninewells Hospital, Dundee, UK (P.G.R.); Clinical
Research Imaging Facility, University of Dundee, UK (P.G.R.); Department of
Medical Physics, NHS Lothian, Edinburgh, UK (N.W.W.); Edinburgh Imaging Facility
QMRI, University of Edinburgh, Edinburgh, UK (N.W.W., M.R.D., N.L.M., D.E.N.,
E.J.R.v.B., M.C.W.); University of Cambridge, Cambridge, UK (J.R.W.M.); Royal
Papworth Hospital, Cambridge, UK (J.R.W.M.); Department of Radiology, Royal
Infirmary of Scotland, Edinburgh, UK (A.N., E.J.R.v.B., M.C.W.); Blavatnik
Family Women's Health Research Institute, Icahn School of Medicine at
Mount Sinai, New York, NY (L.J.S.); Seymour, Paul and Gloria Milstein Division
of Cardiology, Department of Medicine, and Department of Radiology, Columbia
University Irving Medical Center and New York-Presbyterian Hospital, New York,
NY (A.J.E.); and Institute of Clinical Sciences, University of Glasgow, UK
(G.R.)
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34
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Song F, Fu B, Liu M, Liu X, Liu S, Lv F. Proposal of Modified Lung-RADS in Assessing Pulmonary Nodules of Patients with Previous Malignancies: A Primary Study. Diagnostics (Basel) 2023; 13:2210. [PMID: 37443604 DOI: 10.3390/diagnostics13132210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/12/2023] [Accepted: 06/16/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND In addition to the diameters of pulmonary nodules, the number and morphology of blood vessels in pure ground-glass nodules (pGGNs) were closely related to the occurrence of lung cancer. Moreover, the benign and malignant signs of nodules were also valuable for the identification of nodules. Based on these two points, we tried to revise Lung-RADS 2022 and proposed our Modified Lung-RADS. The aim of the study was to verify the diagnostic performance of Modified Lung-RADS for pulmonary solid nodules (SNs) and pure ground-glass nodules (pGGNs) in patients with previous malignancies. METHODS The chest CT and clinical data of patients with prior cancer who underwent pulmonary nodulectomies from 1 January 2018 to 30 November 2021 were enrolled according to inclusion and exclusion criteria. A total of 240 patients with 293 pulmonary nodules were included in this study. In contrast with the original version, the risk classification of pGGNs based on the GGN-vascular relationships (GVRs), and the SNs without burrs and with benign signs, could be downgraded to category 2. The sensitivity, specificity, and agreement rate of the original Lung-RADS 2022 and Modified Lung-RADS for pGGNs and SNs were calculated and compared. RESULTS Compared with the original version, the sensitivity and agreement rate of the Modified version for pGGNs increased from 0 and 23.33% to 97.10% and 92.22%, respectively, while the specificity decreased from 100% to 76.19%. As regards SNs, the specificity and agreement rate of the Modified version increased from 44.44% to 75.00% (p < 0.05) and 88.67% to 94.09% (p = 0.052), respectively, while the sensitivity was unchanged (98.20%). CONCLUSIONS In general, the diagnostic efficiency of Modified Lung-RADS was superior to that of the original version, and Modified Lung-RADS could be a preliminary attempt to improve Lung-RADS 2022.
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Affiliation(s)
- Feipeng Song
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1 YouYi Road, Chongqing 400010, China
| | - Binjie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1 YouYi Road, Chongqing 400010, China
| | - Mengxi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1 YouYi Road, Chongqing 400010, China
| | - Xiangling Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1 YouYi Road, Chongqing 400010, China
| | - Sizhu Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1 YouYi Road, Chongqing 400010, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1 YouYi Road, Chongqing 400010, China
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35
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Silva JAM, Marchiori E, Amorim VB, Barreto MM. CT features of osteosarcoma lung metastasis: a retrospective study of 127 patients. J Bras Pneumol 2023; 49:e20220433. [PMID: 37132704 PMCID: PMC10171270 DOI: 10.36416/1806-3756/e20220433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/26/2023] [Indexed: 03/31/2023] Open
Abstract
Objective: Osteosarcoma lung metastases have a wide variety of CT presentations, representing a challenge for radiologists. Knowledge of atypical CT patterns of lung metastasis is important to differentiate it from benign lung disease and synchronous lung cancer, as well as to determine the extent of primary disease. The objective of this study was to analyze CT features of osteosarcoma lung metastasis before and during chemotherapy. Methods: Two radiologists independently reviewed chest CT images of 127 patients with histopathologically confirmed osteosarcoma treated between May 10, 2012 and November 13, 2020. The images were divided into two groups for analysis: images obtained before chemotherapy and images obtained during chemotherapy (initial CT examination). Results: Seventy-five patients were diagnosed with synchronous or metachronous lung metastases. The most common CT findings were nodules (in 95% of the patients), distributed bilaterally (in 86%), with no predominance regarding craniocaudal distribution (in 71%). Calcification was observed in 47%. Less common findings included intravascular lesions (in 16%), cavitation (in 7%), and the halo sign (in 5%). The primary tumor size was significantly greater (i.e., > 10 cm) in patients with lung metastasis. Conclusions: On CT scans, osteosarcoma lung metastases typically appear as bilateral solid nodules. However, they can have atypical presentations, with calcification being the most common. Knowledge of the typical and atypical CT features of osteosarcoma lung metastasis could play a key role in improving image interpretation in these cases.
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Affiliation(s)
| | - Edson Marchiori
- . Departamento de Radiologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro (RJ) Brasil
| | - Viviane Brandão Amorim
- . Departamento de Radiologia, Instituto Nacional do Câncer, Rio de Janeiro (RJ) Brasil
- . Departamento de Radiologia, Grupo Fleury S.A., Rio de Janeiro (RJ) Brasil
| | - Miriam Menna Barreto
- . Departamento de Radiologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro (RJ) Brasil
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36
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Duignan JA, Ryan DT, O'Riordan B, O'Brien A, Healy GM, O'Brien C, Butler M, Keane MP, McCarthy C, Murphy DJ, Dodd JD. Combined autologous blood patch-immediate patient rollover does not reduce the pneumothorax or chest drain rate following CT-guided lung biopsy compared to immediate patient rollover alone. Eur J Radiol 2023; 160:110691. [PMID: 36640713 DOI: 10.1016/j.ejrad.2023.110691] [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: 11/14/2022] [Revised: 12/27/2022] [Accepted: 01/07/2023] [Indexed: 01/11/2023]
Abstract
PUPROSE The purpose of this study was to evaluate a combined autologous blood-patch (ABP)-immediate patient rollover (IPR) technique compared with the IPR technique alone on the incidence of pneumothorax and chest drainage following CT-guided lung biopsy. METHODS In this interventional cohort study of both prospectively and retrospectively acquired data, 652 patients underwent CT-guided lung biopsy. Patient demographics, lesion characteristics and technical biopsy variables including the combined ABP-IPR versus IPR alone were evaluated as predictors of pneumothorax and chest drain rates using regression analysis. RESULTS The combined ABP-IPR technique was performed in 259 (39.7 %) patients whilst 393 (60.3 %) underwent IPR alone. There was no significant difference in pneumothorax rate or chest drains required between the combined ABP-IPR vs IPR groups (p =.08, p =.60 respectively). Predictors of pneumothorax adjusted for the combined ABP-IPR and IPR alone groups included age (p =.02), lesion size (p =.01), location (p =.005), patient position (p =.008), emphysema along the needle track (p =.005) and lesion distance from the pleura (p =.02). Adjusted predictors of chest drain insertion included lesion location (p =.09), patient position (p =.002), bullae crossed (p =.02) and lesion distance from the pleura (p =.02). CONCLUSION The combined ABP-IPR technique does not reduce the pneumothorax or chest drain rate compared to the IPR technique alone. Utilising IPR without an ABP following CT-guided lung biopsy results in similar pneumothorax and chest drain rates while minimising the potential risk of systemic air embolism.
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Affiliation(s)
- John A Duignan
- Department of Radiology, St. Vincent's University Hospital, Dublin, Ireland.
| | - David T Ryan
- Department of Radiology, St. Vincent's University Hospital, Dublin, Ireland
| | - Brian O'Riordan
- Department of Radiology, St. Vincent's University Hospital, Dublin, Ireland; School of Medicine, University College Dublin, Ireland
| | - Amy O'Brien
- Department of Radiology, St. Vincent's University Hospital, Dublin, Ireland
| | - Gerard M Healy
- Department of Radiology, St. Vincent's University Hospital, Dublin, Ireland
| | - Cormac O'Brien
- Department of Radiology, St. Vincent's University Hospital, Dublin, Ireland
| | - Marcus Butler
- Department of Respiratory Medicine, St. Vincent's University Hospital, Dublin, Ireland; School of Medicine, University College Dublin, Ireland
| | - Michael P Keane
- Department of Respiratory Medicine, St. Vincent's University Hospital, Dublin, Ireland; School of Medicine, University College Dublin, Ireland
| | - Cormac McCarthy
- Department of Respiratory Medicine, St. Vincent's University Hospital, Dublin, Ireland; School of Medicine, University College Dublin, Ireland
| | - David J Murphy
- Department of Radiology, St. Vincent's University Hospital, Dublin, Ireland; School of Medicine, University College Dublin, Ireland
| | - Jonathan D Dodd
- Department of Radiology, St. Vincent's University Hospital, Dublin, Ireland; School of Medicine, University College Dublin, Ireland.
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37
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Ke X, Hu W, Su X, Huang F, Lai Q. Potential of artificial intelligence based on chest computed tomography to predict the nature of part-solid nodules. THE CLINICAL RESPIRATORY JOURNAL 2023; 17:320-328. [PMID: 36740215 PMCID: PMC10113279 DOI: 10.1111/crj.13597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 01/05/2023] [Accepted: 01/30/2023] [Indexed: 02/07/2023]
Abstract
BACKGROUND The potential of artificial intelligence (AI) to predict the nature of part-solid nodules based on chest computed tomography (CT) is still under exploration. OBJECTIVE To determine the potential of AI to predict the nature of part-solid nodules. METHODS Two hundred twenty-three patients diagnosed with part-solid nodules (241) by chest CT were retrospectively collected that were divided into benign group (104) and malignant group (137). Intraclass correlation coefficient (ICC) was used to assess the agreement in predicting malignancy, and the predictive effectiveness was compared between AI and senior radiologists. The parameters measured by AI and the size of solid components measured by senior radiologists were compared between two groups. Receiver operating characteristic (ROC) curve was chosen for calculating the Youden index of each quantitative parameter, which has statistical significance between two groups. Binary logistic regression performed on the significant indicators to suggest predictors of malignancy. RESULTS AI was in moderate agreement with senior radiologists (ICC = 0.686). The sensitivity, specificity and accuracy of two groups were close (p > 0.05). The longest diameter, volume and mean CT attenuation value and the largest diameter of solid components between benign and malignant groups were different significantly (p < 0.001). Logistic regression analysis showed that the longest diameter and mean CT attenuation value and the largest diameter of solid components were indicators for malignant part-solid nodules, the threshold of which were 9.45 mm, 425.0 HU and 3.45 mm, respectively. CONCLUSION Potential of quantitative parameter measured by AI to predict malignant part-solid nodules can provide a certain value for the clinical management.
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Affiliation(s)
- Xiaoting Ke
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Weiyi Hu
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Xianyan Su
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Fang Huang
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Qingquan Lai
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
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38
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Adams SJ, Stone E, Baldwin DR, Vliegenthart R, Lee P, Fintelmann FJ. Lung cancer screening. Lancet 2023; 401:390-408. [PMID: 36563698 DOI: 10.1016/s0140-6736(22)01694-4] [Citation(s) in RCA: 88] [Impact Index Per Article: 88.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/26/2022] [Accepted: 08/25/2022] [Indexed: 12/24/2022]
Abstract
Randomised controlled trials, including the National Lung Screening Trial (NLST) and the NELSON trial, have shown reduced mortality with lung cancer screening with low-dose CT compared with chest radiography or no screening. Although research has provided clarity on key issues of lung cancer screening, uncertainty remains about aspects that might be critical to optimise clinical effectiveness and cost-effectiveness. This Review brings together current evidence on lung cancer screening, including an overview of clinical trials, considerations regarding the identification of individuals who benefit from lung cancer screening, management of screen-detected findings, smoking cessation interventions, cost-effectiveness, the role of artificial intelligence and biomarkers, and current challenges, solutions, and opportunities surrounding the implementation of lung cancer screening programmes from an international perspective. Further research into risk models for patient selection, personalised screening intervals, novel biomarkers, integrated cardiovascular disease and chronic obstructive pulmonary disease assessments, smoking cessation interventions, and artificial intelligence for lung nodule detection and risk stratification are key opportunities to increase the efficiency of lung cancer screening and ensure equity of access.
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Affiliation(s)
- Scott J Adams
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Emily Stone
- Faculty of Medicine, University of New South Wales and Department of Lung Transplantation and Thoracic Medicine, St Vincent's Hospital, Sydney, NSW, Australia
| | - David R Baldwin
- Respiratory Medicine Unit, David Evans Research Centre, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | | | - Pyng Lee
- Division of Respiratory and Critical Care Medicine, National University Hospital and National University of Singapore, Singapore
| | - Florian J Fintelmann
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
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39
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Ma X, Gong J, Hu F, Tang W, Gu Y, Peng W. Pretreatment Multiparametric MRI-Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors. J Magn Reson Imaging 2023; 57:633-645. [PMID: 35657093 DOI: 10.1002/jmri.28286] [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: 03/11/2022] [Revised: 05/20/2022] [Accepted: 05/23/2022] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Preoperative pathological grading assessment is important for patients with breast phyllodes tumors (PTs). PURPOSE To develop and validate a clinical-radiomics model based on multiparametric MRI and clinical information for the pretreatment differential diagnosis of PTs. STUDY TYPE Retrospective. POPULATION A total of 216 patients with PTs, 133 in the training cohort (55 benign PTs [BPTs] and 78 borderline/malignant PTs [BMPTs]) and 83 in the validation cohort (28 BPTs and 55 BMPTs). FIELD STRENGTH/SEQUENCE 1.5 T and 3 T; T2-weighted imaging (T2WI), precontrast T1-weighted imaging (T1WI) and dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI). ASSESSMENT A total of 3138 radiomics features were computed to decode the imaging phenotypes of PTs. To build the classification models, the following workflow was followed: minimum-maximum scaling normalization method, recursive feature elimination based on ridge regression (Ridge-RFE), synthetic minority oversampling technique, and support vector machine classifier. We established several models based on the statistically significant features (Ridge-RFE selected) of each sequence to distinguish BPTs from BMPTs, including precontrast T1WI model, DCE-T1WI phase 1 model, T1WI feature fusion model, T2WI model, T1WI + T2WI model, clinical feature model, conventional MRI characteristics model, and combined clinical-radiomics model. STATISTICAL TESTS Univariate analysis was utilized to compare variables between the BPT and BMPT groups. The receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of these models. RESULTS In the training cohort, the clinical-radiomics model had excellent diagnostic efficiency, with an area under ROC (AUC) of 0.91 ± 0.02 (95% CI: 0.87-0.94). In the validation cohort, the AUCs were 0.79 ± 0.05 (95% CI: 0.70-0.87) for the combined model and 0.77 ± 0.05 (95% CI: 0.67-0.85) for the radiomics model. DATA CONCLUSION Compared with conventional MRI characteristics, radiomics features extracted from multiparametric MRI are helpful for improving the accuracy of differentiating the pathological grades of PTs preoperatively. The model based on radiomics and clinical information is expected to become a potential noninvasive tool for the assessment of PTs grades. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Xiaowen Ma
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Feixiang Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Tang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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40
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Abstract
Pulmonary nodules are a common finding on CT scans of the chest. In the United Kingdom, management should follow British Thoracic Society Guidelines, which were published in 2015. This review covers key aspects of nodule management also looks at new and emerging evidence since then.
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Affiliation(s)
- Emma L O’Dowd
- Department of Respiratory Medicine, David Evans Building, Nottingham City Hospital, Nottingham, United Kingdom
| | - David R Baldwin
- Department of Respiratory Medicine, David Evans Building, Nottingham City Hospital, Nottingham, United Kingdom
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41
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Jin GY. [Lung Imaging Reporting and Data System (Lung-RADS) in Radiology: Strengths, Weaknesses and Improvement]. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2023; 84:34-50. [PMID: 36818696 PMCID: PMC9935959 DOI: 10.3348/jksr.2022.0136] [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: 10/09/2022] [Revised: 12/05/2022] [Accepted: 12/27/2022] [Indexed: 06/18/2023]
Abstract
In 2019, the American College of Radiology announced Lung CT Screening Reporting & Data System (Lung-RADS) 1.1 to reduce lung cancer false positivity compared to that of Lung-RADS 1.0 for effective national lung cancer screening, and in December 2022, announced the new Lung-RADS 1.1, Lung-RADS® 2022 improvement. The Lung-RADS® 2022 measures the nodule size to the first decimal place compared to that of the Lung-RADS 1.0, to category 2 until the juxtapleural nodule size is < 10 mm, increases the size criterion of the ground glass nodule to 30 mm in category 2, and changes categories 4B and 4X to extremely suspicious. The category was divided according to the airway nodules location and shape or wall thickness of atypical pulmonary cysts. Herein, to help radiologists understand the Lung-RADS® 2022, this review will describe its advantages, disadvantages, and future improvements.
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42
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Borgbjerg J, Steinkohl E, Olesen SS, Akisik F, Bethke A, Bieliuniene E, Christensen HS, Engjom T, Haldorsen IS, Kartalis N, Lisitskaya MV, Naujokaite G, Novovic S, Ozola-Zālīte I, Phillips AE, Swensson JK, Drewes AM, Frøkjær JB. Inter- and intra-observer variability of computed tomography-based parenchymal- and ductal diameters in chronic pancreatitis: a multi-observer international study. ABDOMINAL RADIOLOGY (NEW YORK) 2023; 48:306-317. [PMID: 36138242 DOI: 10.1007/s00261-022-03667-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/25/2022] [Accepted: 08/27/2022] [Indexed: 01/21/2023]
Abstract
PURPOSE The need for incorporation of quantitative imaging biomarkers of pancreatic parenchymal and ductal structures has been highlighted in recent proposals for new scoring systems in chronic pancreatitis (CP). To quantify inter- and intra-observer variability in CT-based measurements of ductal- and gland diameters in CP patients. MATERIALS AND METHODS Prospectively acquired pancreatic CT examinations from 50 CP patients were reviewed by 12 radiologists and four pancreatologists from 10 institutions. Assessment entailed measuring maximum diameter in the axial plane of four structures: (1) pancreatic head (PDhead), (2) pancreatic body (PDbody), (3) main pancreatic duct in the pancreatic head (MPDhead), and (4) body (MPDbody). Agreement was assessed by the 95% limits of agreement with the mean (LOAM), representing how much a single measurement for a specific subject may plausibly deviate from the mean of all measurements on the specific subject. Bland-Altman limits of agreement (LoA) were generated for intra-observer pairs. RESULTS The 16 observers completed 6400 caliper placements comprising a first and second measurement session. The widest inter-observer LOAM was seen with PDhead (± 9.1 mm), followed by PDbody (± 5.1 mm), MPDhead (± 3.2 mm), and MPDbody (± 2.6 mm), whereas the mean intra-observer LoA width was ± 7.3, ± 5.1, ± 3.7, and ± 2.4 mm, respectively. CONCLUSION Substantial intra- and inter-observer variability was observed in pancreatic two-point measurements. This was especially pronounced for parenchymal and duct diameters of the pancreatic head. These findings challenge the implementation of two-point measurements as the foundation for quantitative imaging scoring systems in CP.
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Affiliation(s)
- Jens Borgbjerg
- Department of Radiology, Akershus University Hospital, 1478, Nordbyhagen, Norway
| | - Emily Steinkohl
- Department of Radiology, Aalborg University Hospital, Hobrovej 18-22, PO. Box 365, 9000, Aalborg, Denmark.,Department of Gastroenterology and Hepatology, Centre for Pancreatic Diseases, Aalborg University Hospital, Mølleparkvej 4, 9000, Aalborg, Denmark.,Department of Clinical Medicine, Aalborg University, Søndre Skovvej 11, 9000, Aalborg, Denmark
| | - Søren S Olesen
- Department of Gastroenterology and Hepatology, Centre for Pancreatic Diseases, Aalborg University Hospital, Mølleparkvej 4, 9000, Aalborg, Denmark.,Department of Clinical Medicine, Aalborg University, Søndre Skovvej 11, 9000, Aalborg, Denmark
| | - Fatih Akisik
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N University Blvd, Ste 0663, Indianapolis, IN, 46202, USA
| | - Anne Bethke
- Department of Radiology, Akershus University Hospital, 1478, Nordbyhagen, Norway
| | - Edita Bieliuniene
- Department of Radiology, Lithuanian University of Health Sciences, Eivenių g. 2, 50161, Kaunas, Lithuania
| | - Heidi S Christensen
- Department of Clinical Medicine, Faculty of Medicine, Aalborg University, Aalborg, Denmark.,Department of Haematology, Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark
| | - Trond Engjom
- Department of Medicine, University of Bergen, Jonas Lies vei 65, 5021, Bergen, Norway.,Department of Clinical Medicine, University of Bergen, Jonas Lies vei 87, 5021, Bergen, Norway
| | - Ingfrid S Haldorsen
- Department of Clinical Medicine, University of Bergen, Jonas Lies vei 87, 5021, Bergen, Norway.,Department of Radiology, Mohn Medical Imaging and Visualization Centre, Haukeland University Hospital, Ulriksdal 8, 5009, Bergen, Norway
| | - Nikolaos Kartalis
- Division of Radiology, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, O-huset 42, 14186, Stockholm, Sweden.,Department of Radiology Huddinge, Karolinska University Hospital, O-huset 42, 14186, Stockholm, Sweden
| | - Maria V Lisitskaya
- Department of Radiology, Aalborg University Hospital, Hobrovej 18-22, PO. Box 365, 9000, Aalborg, Denmark.,Department of Gastroenterology and Hepatology, Centre for Pancreatic Diseases, Aalborg University Hospital, Mølleparkvej 4, 9000, Aalborg, Denmark
| | - Gintare Naujokaite
- Department of Radiology, Aalborg University Hospital, Hobrovej 18-22, PO. Box 365, 9000, Aalborg, Denmark
| | - Srdan Novovic
- Department of Gastroenterology and Gastrointestinal Surgery, Copenhagen University Hospital Hvidovre, Kettegård Allé 30, 2650, Hvidovre, Denmark
| | - Imanta Ozola-Zālīte
- Centre of Gastroenterology, Hepatology and Nutrition, Pauls Stradins Clinical University Hospital, Pilsoņu iela 13, Zemgales priekšpilsēta, Riga, 1002, Latvia
| | - Anna E Phillips
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jordan K Swensson
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N University Blvd, Ste 0663, Indianapolis, IN, 46202, USA
| | - Asbjørn M Drewes
- Department of Gastroenterology and Hepatology, Centre for Pancreatic Diseases, Aalborg University Hospital, Mølleparkvej 4, 9000, Aalborg, Denmark.,Department of Clinical Medicine, Aalborg University, Søndre Skovvej 11, 9000, Aalborg, Denmark
| | - Jens B Frøkjær
- Department of Radiology, Aalborg University Hospital, Hobrovej 18-22, PO. Box 365, 9000, Aalborg, Denmark. .,Department of Clinical Medicine, Aalborg University, Søndre Skovvej 11, 9000, Aalborg, Denmark.
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Wu L, Gao C, Kong N, Lou X, Xu M. The long-term course of subsolid nodules and predictors of interval growth on chest CT: a systematic review and meta-analysis. Eur Radiol 2023; 33:2075-2088. [PMID: 36136107 PMCID: PMC9935651 DOI: 10.1007/s00330-022-09138-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 07/26/2022] [Accepted: 09/02/2022] [Indexed: 12/01/2022]
Abstract
OBJECTIVES To calculate the pooled incidence of interval growth after long-term follow-up and identify predictors of interval growth in subsolid nodules (SSNs) on chest CT. METHODS A search of MEDLINE (PubMed), Cochrane Library, Web of Science Core Collection, and Embase was performed on November 08, 2021, for relevant studies. Patient information, CT scanner, and SSN follow-up information were extracted from each included study. A random-effects model was applied along with subgroup and meta-regression analyses. Study quality was assessed by the Newcastle-Ottawa scale, and publication bias was assessed by Egger's test. RESULTS Of the 6802 retrieved articles, 16 articles were included and analyzed, providing a total of 2898 available SSNs. The pooled incidence of growth in the 2898 SSNs was 22% (95% confidence interval [CI], 15-29%). The pooled incidence of growth in the subgroup analysis of pure ground-glass nodules was 26% (95% CI: 12-39%). The incidence of SSN growth after 2 or more years of stability was only 5% (95% CI: 3-7%). An initially large SSN size was found to be the most frequent risk factor affecting the incidence of SSN growth and the time of growth. CONCLUSIONS The pooled incidence of SSN growth was as high as 22%, with a 26% incidence reported for pure ground-glass nodules. Although the incidence of growth was only 5% after 2 or more years of stability, long-term follow-up is needed in certain cases. Moreover, the initial size of the SSN was the most frequent risk factor for growth. KEY POINTS • Based on a meta-analysis of 2898 available subsolid nodules in the literature, the pooled incidence of growth was 22% for all subsolid nodules and 26% for pure ground-glass nodules. • After 2 or more years of stability on follow-up CT, the pooled incidence of subsolid nodule growth was only 5%. • Given the incidence of subsolid nodule growth, management of these lesions with long-term follow-up is preferred.
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Affiliation(s)
- Linyu Wu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, China
| | - Chen Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, China
| | - Ning Kong
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xinjing Lou
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), 54 Youdian Road, Hangzhou, China.
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, China.
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44
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Li X, Zhang G, Gao S, Xue Q, He J. Knowledge mapping visualization of the pulmonary ground-glass opacity published in the web of science. Front Oncol 2022; 12:1075350. [PMID: 36620580 PMCID: PMC9815441 DOI: 10.3389/fonc.2022.1075350] [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: 10/20/2022] [Accepted: 12/01/2022] [Indexed: 12/24/2022] Open
Abstract
Objectives With low-dose computed tomography(CT) lung cancer screening, many studies with an increasing number of patients with ground-glass opacity (GGO) are published. Hence, the present study aimed to analyze the published studies on GGO using bibliometric analysis. The findings could provide a basis for future research in GGO and for understanding past advances and trends in the field. Methods Published studies on GGO were obtained from the Web of Science Core Collection. A bibliometric analysis was conducted using the R package and VOSviewer for countries, institutions, journals, authors, keywords, and articles relevant to GGO. In addition, a bibliometric map was created to visualize the relationship. Results The number of publications on GGO has been increasing since 2011. China is ranked as the most prolific country; however, Japan has the highest number of citations for its published articles. Seoul National University and Professor Jin Mo Goo from Korea had the highest publications. Most top 10 journals specialized in the field of lung diseases. Radiology is a comprehensive journal with the greatest number of citations and highest H-index than other journals. Using bibliometric analysis, research topics on "prognosis and diagnosis," "artificial intelligence," "treatment," "preoperative positioning and minimally invasive surgery," and "pathology of GGO" were identified. Artificial intelligence diagnosis and minimally invasive treatment may be the future of GGO. In addition, most top 10 literatures in this field were guidelines for lung cancer and pulmonary nodules. Conclusions The publication volume of GGO has increased rapidly. The top three countries with the highest number of published articles were China, Japan, and the United States. Japan had the most significant number of citations for published articles. Most key journals specialized in the field of lung diseases. Artificial intelligence diagnosis and minimally invasive treatment may be the future of GGO.
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Affiliation(s)
| | | | | | - Qi Xue
- *Correspondence: Qi Xue, ; Jie He,
| | - Jie He
- *Correspondence: Qi Xue, ; Jie He,
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45
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Zhang Z, Zhou L, Yang F, Li X. The natural growth history of persistent pulmonary subsolid nodules: Radiology, genetics, and clinical management. Front Oncol 2022; 12:1011712. [PMID: 36568242 PMCID: PMC9772280 DOI: 10.3389/fonc.2022.1011712] [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: 08/04/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022] Open
Abstract
The high detection rate of pulmonary subsolid nodules (SSN) is an increasingly crucial clinical issue due to the increased number of screening tests and the growing popularity of low-dose computed tomography (LDCT). The persistence of SSN strongly suggests the possibility of malignancy. Guidelines have been published over the past few years and guide the optimal management of SSNs, but many remain controversial and confusing for clinicians. Therefore, in-depth research on the natural growth history of persistent pulmonary SSN can help provide evidence-based medical recommendations for nodule management. In this review, we briefly describe the differential diagnosis, growth patterns and rates, genetic characteristics, and factors that influence the growth of persistent SSN. With the advancement of radiomics and artificial intelligence (AI) technology, individualized evaluation of SSN becomes possible. These technologies together with liquid biopsy, will promote the transformation of current diagnosis and follow-up strategies and provide significant progress in the precise management of subsolid nodules in the early stage of lung cancer.
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46
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Ottilinger T, Martini K, Baessler B, Sartoretti T, Bauer RW, Leschka S, Sartoretti E, Walter JE, Frauenfelder T, Wildermuth S, Alkadhi H, Messerli M. Semi-automated volumetry of pulmonary nodules: Intra-individual comparison of standard dose and chest X-ray equivalent ultralow dose chest CT scans. Eur J Radiol 2022; 156:110549. [PMID: 36272226 DOI: 10.1016/j.ejrad.2022.110549] [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: 04/15/2022] [Revised: 09/05/2022] [Accepted: 09/26/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE To assess the performance of semi-automated volumetry of solid pulmonary nodules on single-energy tin-filtered ultralow dose (ULD) chest CT scans at a radiation dose equivalent to chest X-ray relative to standard dose (SD) chest CT scans and assess the impact of kernel and iterative reconstruction selection. METHODS Ninety-four consecutive patients from a prospective single-center study were included and underwent clinically indicated SD chest CT (1.9 ± 0.8 mSv) and additional ULD chest CT (0.13 ± 0.01 mSv) in the same session. All scans were reconstructed with a soft tissue (Br40) and lung (Bl64) kernel as well as with Filtered Back Projection (FBP) and Iterative Reconstruction (ADMIRE-3 and ADMIRE-5). One hundred and forty-eight solid pulmonary nodules were identified and analysed by semi-automated volumetry on all reconstructions. Nodule volumes were compared amongst all reconstructions thereby focusing on the agreement between SD and ULD scans. RESULTS Nodule volumes ranged from 58.5 (28.8-126) mm3 for ADMIRE-5 Br40 ULD reconstructions to 72.5 (39-134) mm3 for FBP Bl64 SD reconstructions with significant differences between reconstructions (p < 0.001). Interscan agreement of volumes between two given reconstructions ranged from ICC = 0.605 to ICC = 0.999. Between SD and ULD scans, agreement of nodule volumes was highest for FBP Br40 (ICC = 0.995), FBP Bl64 (ICC = 0.939) and ADMIRE-5 Bl64 (ICC = 0.994) reconstructions. ADMIRE-3 reconstructions exhibited reduced interscan agreement of nodule volumes (ICCs from 0.788 - 0.882). CONCLUSIONS The interscan agreement of node volumes between SD and ULD is high depending on the choice of kernel and reconstruction algorithm. However, caution should be exercised when comparing two image series that were not identically reconstructed.
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Affiliation(s)
- Thorsten Ottilinger
- Division of Radiology and Nuclear Medicine, Cantonal Hospital St. Gallen, Switzerland; University Zurich, Zurich, Switzerland
| | - Katharina Martini
- University Zurich, Zurich, Switzerland; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Switzerland
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Switzerland; Department of Diagnostic and Interventional Radiology, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Thomas Sartoretti
- University Zurich, Zurich, Switzerland; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Switzerland; Department of Nuclear Medicine, University Hospital Zurich, Switzerland
| | - Ralf W Bauer
- RNS, Private Radiology and Radiation Therapy Group, Wiesbaden, Germany
| | - Sebastian Leschka
- Division of Radiology and Nuclear Medicine, Cantonal Hospital St. Gallen, Switzerland
| | - Elisabeth Sartoretti
- University Zurich, Zurich, Switzerland; Department of Nuclear Medicine, University Hospital Zurich, Switzerland
| | - Joan E Walter
- Department of Nuclear Medicine, University Hospital Zurich, Switzerland
| | - Thomas Frauenfelder
- University Zurich, Zurich, Switzerland; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Switzerland
| | - Simon Wildermuth
- Division of Radiology and Nuclear Medicine, Cantonal Hospital St. Gallen, Switzerland
| | - Hatem Alkadhi
- University Zurich, Zurich, Switzerland; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Switzerland
| | - Michael Messerli
- University Zurich, Zurich, Switzerland; Department of Nuclear Medicine, University Hospital Zurich, Switzerland.
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Russ DH, Barta JA, Evans NR, Stapp RT, Kane GC. Volume Doubling Time of Pulmonary Carcinoid Tumors Measured by Computed Tomography. Clin Lung Cancer 2022; 23:e453-e459. [PMID: 35922364 DOI: 10.1016/j.cllc.2022.06.006] [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: 03/21/2022] [Revised: 06/20/2022] [Accepted: 06/22/2022] [Indexed: 01/27/2023]
Abstract
INTRODUCTION Pulmonary carcinoid tumor (PCT) is a rare neuroendocrine lung neoplasm comprising approximately 2% of lung cancer diagnoses. It is classified as either localized low-grade (typical) or intermediate-grade (atypical) subtypes. PCT is known clinically to be a slow-growing cancer, however few studies have established its true growth rate when followed over time by computed tomography (CT). Therefore, we sought to determine the volume doubling time for PCTs as visualized on CT imaging. MATERIALS AND METHODS We conducted a retrospective analysis of all PCTs treated at our institution between 2006 and 2020. Nodule dimensions were measured using a Picture Archiving and Communication System or retrieved from radiology reports. Volume doubling time was calculated using the Schwartz formula for PCTs followed by successive CT scans during radiographic surveillance. Consistent with Fleischner Society guidelines, tumors were considered to have demonstrated definitive growth by CT only when the interval change in tumor diameter was greater than or equal to 2 mm. RESULTS The median volume doubling time of 13 typical PCTs was 977 days, or 2.7 years. Five atypical PCTs were followed longitudinally, with a median doubling time of 327 days, or 0.9 years. CONCLUSIONS Typical pulmonary carcinoid features a remarkably slow growth rate as compared to more common lung cancers. Our analysis of atypical pulmonary carcinoid included too few cases to offer definitive conclusions. It is conceivable that clinicians following current nodule surveillance guidelines may mistake incidentally detected typical carcinoids for benign non-growing lesions when followed for less than 2 years in low-risk patients.
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Affiliation(s)
- Douglas H Russ
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA.
| | - Julie A Barta
- Division of Pulmonary, Allergy and Critical Care, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA
| | - Nathaniel R Evans
- Division of Thoracic Surgery, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA
| | - Robert T Stapp
- Department of Pathology, Anatomy, and Cell Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA
| | - Gregory C Kane
- The Jane and Leonard Korman Respiratory Institute at Thomas Jefferson University, Philadelphia, PA
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Prediction of Lung Nodule Progression with an Uncertainty-Aware Hierarchical Probabilistic Network. Diagnostics (Basel) 2022; 12:diagnostics12112639. [DOI: 10.3390/diagnostics12112639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 11/16/2022] 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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49
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Yang R, Hui D, Li X, Wang K, Li C, Li Z. Prediction of single pulmonary nodule growth by CT radiomics and clinical features - a one-year follow-up study. Front Oncol 2022; 12:1034817. [PMID: 36387220 PMCID: PMC9650464 DOI: 10.3389/fonc.2022.1034817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 10/05/2022] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND With the development of imaging technology, an increasing number of pulmonary nodules have been found. Some pulmonary nodules may gradually grow and develop into lung cancer, while others may remain stable for many years. Accurately predicting the growth of pulmonary nodules in advance is of great clinical significance for early treatment. The purpose of this study was to establish a predictive model using radiomics and to study its value in predicting the growth of pulmonary nodules. MATERIALS AND METHODS According to the inclusion and exclusion criteria, 228 pulmonary nodules in 228 subjects were included in the study. During the one-year follow-up, 69 nodules grew larger, and 159 nodules remained stable. All the nodules were randomly divided into the training group and validation group in a proportion of 7:3. For the training data set, the t test, Chi-square test and Fisher exact test were used to analyze the sex, age and nodule location of the growth group and stable group. Two radiologists independently delineated the ROIs of the nodules to extract the radiomics characteristics using Pyradiomics. After dimension reduction by the LASSO algorithm, logistic regression analysis was performed on age and ten selected radiological features, and a prediction model was established and tested in the validation group. SVM, RF, MLP and AdaBoost models were also established, and the prediction effect was evaluated by ROC analysis. RESULTS There was a significant difference in age between the growth group and the stable group (P < 0.05), but there was no significant difference in sex or nodule location (P > 0.05). The interclass correlation coefficients between the two observers were > 0.75. After dimension reduction by the LASSO algorithm, ten radiomic features were selected, including two shape-based features, one gray-level-cooccurence-matrix (GLCM), one first-order feature, one gray-level-run-length-matrix (GLRLM), three gray-level-dependence-matrix (GLDM) and two gray-level-size-zone-matrix (GLSZM). The logistic regression model combining age and radiomics features achieved an AUC of 0.87 and an accuracy of 0.82 in the training group and an AUC of 0.82 and an accuracy of 0.84 in the verification group for the prediction of nodule growth. For nonlinear models, in the training group, the AUCs of the SVM, RF, MLP and boost models were 0.95, 1.0, 1.0 and 1.0, respectively. In the validation group, the AUCs of the SVM, RF, MLP and boost models were 0.81, 0.77, 0.81, and 0.71, respectively. CONCLUSIONS In this study, we established several machine learning models that can successfully predict the growth of pulmonary nodules within one year. The logistic regression model combining age and imaging parameters has the best accuracy and generalization. This model is very helpful for the early treatment of pulmonary nodules and has important clinical significance.
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Affiliation(s)
- Ran Yang
- Department of Radiology, Second People’s Hospital of JiuLongPo District, Chongqing, China
| | - Dongming Hui
- Department of Radiology, Second People’s Hospital of JiuLongPo District, Chongqing, China
| | - Xing Li
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Kun Wang
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Caiyong Li
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Zhichao Li
- Department of Radiology, Second People’s Hospital of JiuLongPo District, Chongqing, China
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50
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Liao RQ, Li AW, Yan HH, Lin JT, Liu SY, Wang JW, Fang JS, Liu HB, Hou YH, Song C, Yang HF, Li B, Jiang BY, Dong S, Nie Q, Zhong WZ, Wu YL, Yang XN. Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images. Front Oncol 2022; 12:1002953. [PMID: 36313666 PMCID: PMC9597322 DOI: 10.3389/fonc.2022.1002953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Background Estimating the growth of pulmonary sub-solid nodules (SSNs) is crucial to the successful management of them during follow-up periods. The purpose of this study is to (1) investigate the measurement sensitivity of diameter, volume, and mass of SSNs for identifying growth and (2) seek to establish a deep learning-based model to predict the growth of SSNs. Methods A total of 2,523 patients underwent at least 2-year examination records retrospectively collected with sub-solid nodules. A total of 2,358 patients with 3,120 SSNs from the NLST dataset were randomly divided into training and validation sets. Patients from the Yibicom Health Management Center and Guangdong Provincial People’s Hospital were collected as an external test set (165 patients with 213 SSN). Trained models based on LUNA16 and Lndb19 datasets were employed to automatically obtain the diameter, volume, and mass of SSNs. Then, the increase rate in measurements between cancer and non-cancer groups was studied to evaluate the most appropriate way to identify growth-associated lung cancer. Further, according to the selected measurement, all SSNs were classified into two groups: growth and non-growth. Based on the data, the deep learning-based model (SiamModel) and radiomics model were developed and verified. Results The double time of diameter, volume, and mass were 711 vs. 963 days (P = 0.20), 552 vs. 621 days (P = 0.04) and 488 vs. 623 days (P< 0.001) in the cancer and non-cancer groups, respectively. Our proposed SiamModel performed better than the radiomics model in both the NLST validation set and external test set, with an AUC of 0.858 (95% CI 0.786–0.921) and 0.760 (95% CI 0.646–0.857) in the validation set and 0.862 (95% CI 0.789–0.927) and 0.681 (95% CI 0.506–0.841) in the external test set, respectively. Furthermore, our SiamModel could use the data from first-time CT to predict the growth of SSNs, with an AUC of 0.855 (95% CI 0.793–0.908) in the NLST validation set and 0.821 (95% CI 0.725–0.904) in the external test set. Conclusion Mass increase rate can reflect more sensitively the growth of SSNs associated with lung cancer than diameter and volume increase rates. A deep learning-based model has a great potential to predict the growth of SSNs.
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Affiliation(s)
- Ri-qiang Liao
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - An-wei Li
- Guangzhou Shiyuan Electronics Co., Ltd, Guangzhou, China
| | - Hong-hong Yan
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jun-tao Lin
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Si-yang Liu
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jing-wen Wang
- Guangzhou Shiyuan Electronics Co., Ltd, Guangzhou, China
| | | | - Hong-bo Liu
- Guangzhou Shiyuan Electronics Co., Ltd, Guangzhou, China
| | - Yong-he Hou
- Yibicom Health Management Center, CVTE, Guangzhou, China
| | - Chao Song
- Yibicom Health Management Center, CVTE, Guangzhou, China
| | - Hui-fang Yang
- Yibicom Health Management Center, CVTE, Guangzhou, China
| | - Bin Li
- Automation Science and Engineering, South China University of Technology, Guangzhou, China
| | - Ben-yuan Jiang
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Song Dong
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Qiang Nie
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Wen-zhao Zhong
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yi-long Wu
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- *Correspondence: Xue-ning Yang, ; Yi-long Wu,
| | - Xue-ning Yang
- Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- *Correspondence: Xue-ning Yang, ; Yi-long Wu,
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