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Milanese G, Silva M, Ledda RE, Iezzi E, Bortolotto C, Mauro LA, Valentini A, Reali L, Bottinelli OM, Ilardi A, Basile A, Palmucci S, Preda L, Sverzellati N. Study rationale and design of the PEOPLHE trial. LA RADIOLOGIA MEDICA 2024; 129:411-419. [PMID: 38319494 PMCID: PMC10943160 DOI: 10.1007/s11547-024-01764-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 01/03/2024] [Indexed: 02/07/2024]
Abstract
PURPOSE Lung cancer screening (LCS) by low-dose computed tomography (LDCT) demonstrated a 20-40% reduction in lung cancer mortality. National stakeholders and international scientific societies are increasingly endorsing LCS programs, but translating their benefits into practice is rather challenging. The "Model for Optimized Implementation of Early Lung Cancer Detection: Prospective Evaluation Of Preventive Lung HEalth" (PEOPLHE) is an Italian multicentric LCS program aiming at testing LCS feasibility and implementation within the national healthcare system. PEOPLHE is intended to assess (i) strategies to optimize LCS workflow, (ii) radiological quality assurance, and (iii) the need for dedicated resources, including smoking cessation facilities. METHODS PEOPLHE aims to recruit 1.500 high-risk individuals across three tertiary general hospitals in three different Italian regions that provide comprehensive services to large populations to explore geographic, demographic, and socioeconomic diversities. Screening by LDCT will target current or former (quitting < 10 years) smokers (> 15 cigarettes/day for > 25 years, or > 10 cigarettes/day for > 30 years) aged 50-75 years. Lung nodules will be volumetric measured and classified by a modified PEOPLHE Lung-RADS 1.1 system. Current smokers will be offered smoking cessation support. CONCLUSION The PEOPLHE program will provide information on strategies for screening enrollment and smoking cessation interventions; administrative, organizational, and radiological needs for performing a state-of-the-art LCS; collateral and incidental findings (both pulmonary and extrapulmonary), contributing to the LCS implementation within national healthcare systems.
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Affiliation(s)
- Gianluca Milanese
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy
| | - Mario Silva
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy
| | - Roberta Eufrasia Ledda
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy
| | | | - Chandra Bortolotto
- Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy
- Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Letizia Antonella Mauro
- Radiology Unit 1, University Hospital Policlinico G. Rodolico-San Marco, Catania, Catania, Italy
| | - Adele Valentini
- Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Linda Reali
- Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Olivia Maria Bottinelli
- Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy
| | - Adriana Ilardi
- Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Antonio Basile
- Radiology Unit 1-Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Stefano Palmucci
- UOSD I.P.T.R.A.-Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Lorenzo Preda
- Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy
- Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Nicola Sverzellati
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy.
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Lee JH, Choi Y, Hong H, Kim YT, Goo JM, Kim H. Prognostic value of CT-defined ground-glass opacity in early-stage lung adenocarcinomas: a single-center study and meta-analysis. Eur Radiol 2024; 34:1905-1920. [PMID: 37650971 DOI: 10.1007/s00330-023-10160-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: 09/28/2022] [Revised: 05/23/2023] [Accepted: 07/18/2023] [Indexed: 09/01/2023]
Abstract
OBJECTIVES The prognostic value of ground-glass opacity at preoperative chest CT scans in early-stage lung adenocarcinomas is a matter of debate. We aimed to clarify the existing evidence through a single-center, retrospective cohort study and to quantitatively summarize the body of literature by conducting a meta-analysis. METHODS In a retrospective cohort study, patients with clinical stage I lung adenocarcinoma were identified, and the prognostic value of ground-glass opacity was analyzed using multivariable Cox regression. Commercial artificial intelligence software was adopted as the second reader for the presence of ground-glass opacity. The primary end points were freedom from recurrence (FFR) and lung cancer-specific survival (LCSS). In a meta-analysis, we systematically searched Embase and OVID-MEDLINE up to December 30, 2021, for the studies based on the eighth-edition staging system. The pooled hazard ratios (HRs) of solid nodules (i.e., absence of ground-glass opacity) for various end points were calculated with a multi-level random effects model. RESULTS In a cohort of 612 patients, solid nodules were associated with worse outcomes for FFR (adjusted HR, 1.98; 95% CI: 1.17-3.51; p = 0.01) and LCSS (adjusted HR, 1.937; 95% CI: 1.002-4.065; p = 0.049). The artificial intelligence assessment and multiple sensitivity analyses revealed consistent results. The meta-analysis included 13 studies with 12,080 patients. The pooled HR of solid nodules was 2.13 (95% CI: 1.69-2.67; I2 = 30.4%) for overall survival, 2.45 (95% CI: 1.52-3.95; I2 = 0.0%) for FFR, and 2.50 (95% CI: 1.28-4.91; I2 = 30.6%) for recurrence-free survival. CONCLUSIONS The absence of ground-glass opacity in early-stage lung adenocarcinomas is associated with worse postoperative survival. CLINICAL RELEVANCE STATEMENT Early-stage lung adenocarcinomas manifesting as solid nodules at preoperative chest CT, which indicates the absence of ground-glass opacity, were associated with poor postoperative survival. There is room for improvement of the clinical T categorization in the next edition staging system. KEY POINTS • In a retrospective study of 612 patients with stage I lung adenocarcinoma, solid nodules were associated with shorter freedom from recurrence (adjusted hazard ratio [HR], 1.98; p = 0.01) and lung cancer-specific survival (adjusted HR, 1.937; p = 0.049). • Artificial intelligence-assessed solid nodules also showed worse prognosis (adjusted HR for freedom from recurrence, 1.94 [p = 0.01]; adjusted HR for lung cancer-specific survival, 1.93 [p = 0.04]). • In meta-analyses, the solid nodules were associated with shorter freedom from recurrence (HR, 2.45) and shorter overall survival (HR, 2.13).
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Affiliation(s)
- Jong Hyuk Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Korea
| | - Yunhee Choi
- Medical Research Collaborating Center, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Korea
| | - Hyunsook Hong
- Medical Research Collaborating Center, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Korea
| | - Young Tae Kim
- Seoul National University Cancer Research Institute, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Korea
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Korea
- Seoul National University Cancer Research Institute, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Korea
- Department of Radiology, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Korea
| | - Hyungjin Kim
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Korea.
- Department of Radiology, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Korea.
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Quanyang W, Lina Z, Yao H, Jiawei W, Wei T, Linlin Q, Zewei Z, Donghui H, Hongjia L, Shuluan C, Jiaxing Z, Shijun Z. Application of computer-aided detection for NCCN-based follow-up recommendation in subsolid nodules: Effect on inter-observer agreement. Cancer Med 2024; 13:e6967. [PMID: 38348960 PMCID: PMC10832308 DOI: 10.1002/cam4.6967] [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: 09/27/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 02/15/2024] Open
Abstract
RATIONALE AND OBJECTIVES Computer-aided detection (CAD) of pulmonary nodules reduces the impact of observer variability, improving the reliability and reproducibility of nodule assessments in clinical practice. Therefore, this study aimed to assess the impact of CAD on inter-observer agreement in the follow-up management of subsolid nodules. MATERIALS AND METHODS A dataset comprising 60 subsolid nodule cases was constructed based on the National Cancer Center lung cancer screening data. Five observers independently assessed all low-dose computed tomography scans and assigned follow-up management strategies to each case according to the National Comprehensive Cancer Network (NCCN) guidelines, using both manual measurements and CAD assistance. The linearly weighted Cohen's kappa test was used to measure agreement between paired observers. Agreement among multiple observers was evaluated using the Fleiss kappa statistic. RESULTS The agreement of the five observers for NCCN follow-up management categorization was moderate when measured manually, with a Fleiss kappa score of 0.437. Utilizing CAD led to a notable enhancement in agreement, achieving a substantial consensus with a Fleiss kappa value of 0.623. After using CAD, the proportion of major and substantial management discrepancies decreased from 27.5% to 15.8% and 4.8% to 1.5%, respectively (p < 0.01). In 23 lung cancer cases presenting as part-solid nodules, CAD significantly elevates the average sensitivity in detecting lung cancer cases presenting as part-solid nodules (overall sensitivity, 82.6% vs. 92.2%; p < 0.05). CONCLUSION The application of CAD significantly improves inter-observer agreement in the follow-up management strategy for subsolid nodules. It also demonstrates the potential to reduce substantial management discrepancies and increase detection sensitivity in lung cancer cases presenting as part-solid nodules.
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Affiliation(s)
- Wu Quanyang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhou Lina
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Huang Yao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Wang Jiawei
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Tang Wei
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Qi Linlin
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhang Zewei
- PET‐CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Hou Donghui
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Li Hongjia
- PET‐CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Chen Shuluan
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhang Jiaxing
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhao Shijun
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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Pezeshkian F, McAllister M, Singh A, Theeuwen H, Abdallat M, Figueroa PU, Gill RR, Kim AW, Jaklitsch MT. What's new in thoracic oncology. J Surg Oncol 2024; 129:128-137. [PMID: 38031889 DOI: 10.1002/jso.27535] [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: 11/03/2023] [Revised: 11/08/2023] [Accepted: 11/08/2023] [Indexed: 12/01/2023]
Abstract
Many changes have occurred in the field of thoracic surgery over the last several years. In this review, we will discuss new diagnostic techniques for lung cancer, innovations in surgery, and major updates on latest treatment options including immunotherapy. All these have significantly started to change our approach toward the management of lung cancer and have great potential to improve the lives of our patients afflicted with this disease.
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Affiliation(s)
- Fatemehsadat Pezeshkian
- Division of Thoracic Surgery, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Miles McAllister
- Division of Thoracic Surgery, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Anupama Singh
- Division of Thoracic Surgery, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Hailey Theeuwen
- Division of Thoracic Surgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Mohammad Abdallat
- Division of Thoracic Surgery, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Paula Ugalde Figueroa
- Division of Thoracic Surgery, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Ritu R Gill
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Anthony W Kim
- Division of Thoracic Surgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Michael T Jaklitsch
- Division of Thoracic Surgery, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA
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Lin CY, Guo SM, Lien JJJ, Lin WT, Liu YS, Lai CH, Hsu IL, Chang CC, Tseng YL. Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT. LA RADIOLOGIA MEDICA 2024; 129:56-69. [PMID: 37971691 PMCID: PMC10808169 DOI: 10.1007/s11547-023-01730-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 09/21/2023] [Indexed: 11/19/2023]
Abstract
OBJECTIVES The study aimed to develop a combined model that integrates deep learning (DL), radiomics, and clinical data to classify lung nodules into benign or malignant categories, and to further classify lung nodules into different pathological subtypes and Lung Imaging Reporting and Data System (Lung-RADS) scores. MATERIALS AND METHODS The proposed model was trained, validated, and tested using three datasets: one public dataset, the Lung Nodule Analysis 2016 (LUNA16) Grand challenge dataset (n = 1004), and two private datasets, the Lung Nodule Received Operation (LNOP) dataset (n = 1027) and the Lung Nodule in Health Examination (LNHE) dataset (n = 1525). The proposed model used a stacked ensemble model by employing a machine learning (ML) approach with an AutoGluon-Tabular classifier. The input variables were modified 3D convolutional neural network (CNN) features, radiomics features, and clinical features. Three classification tasks were performed: Task 1: Classification of lung nodules into benign or malignant in the LUNA16 dataset; Task 2: Classification of lung nodules into different pathological subtypes; and Task 3: Classification of Lung-RADS score. Classification performance was determined based on accuracy, recall, precision, and F1-score. Ten-fold cross-validation was applied to each task. RESULTS The proposed model achieved high accuracy in classifying lung nodules into benign or malignant categories in LUNA 16 with an accuracy of 92.8%, as well as in classifying lung nodules into different pathological subtypes with an F1-score of 75.5% and Lung-RADS scores with an F1-score of 80.4%. CONCLUSION Our proposed model provides an accurate classification of lung nodules based on the benign/malignant, different pathological subtypes, and Lung-RADS system.
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Affiliation(s)
- Chia-Ying Lin
- Department of Medical Imaging, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Shu-Mei Guo
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Jenn-Jier James Lien
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Wen-Tsen Lin
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Yi-Sheng Liu
- Department of Medical Imaging, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Chao-Han Lai
- Department of Surgery, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - I-Lin Hsu
- Department of Surgery, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Chao-Chun Chang
- Division of Thoracic Surgery, Department of Surgery, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, No.1, University Road, Tainan City, 701, Taiwan, R.O.C..
| | - Yau-Lin Tseng
- Division of Thoracic Surgery, Department of Surgery, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, No.1, University Road, Tainan City, 701, Taiwan, R.O.C
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Milanese G, Ledda RE, Sabia F, Ruggirello M, Sestini S, Silva M, Sverzellati N, Marchianò AV, Pastorino U. Ultra-low dose computed tomography protocols using spectral shaping for lung cancer screening: Comparison with low-dose for volumetric LungRADS classification. Eur J Radiol 2023; 161:110760. [PMID: 36878153 DOI: 10.1016/j.ejrad.2023.110760] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023]
Abstract
PURPOSE To compare Low-Dose Computed Tomography (LDCT) with four different Ultra-Low-Dose Computed Tomography (ULDCT) protocols for PN classification according to the Lung Reporting and Data System (LungRADS). METHODS Three hundred sixty-one participants of an ongoing lung cancer screening (LCS) underwent single-breath-hold double chest Computed Tomography (CT), including LDCT (120kVp, 25mAs; CTDIvol 1,62 mGy) and one ULDCT among: fully automated exposure control ("ULDCT1"); fixed tube-voltage and current according to patient size ("ULDCT2"); hybrid approach with fixed tube-voltage ("ULDCT3") and tube current automated exposure control ("ULDCT4"). Two radiologists (R1, R2) assessed LungRADS 2022 categories on LDCT, and then after 2 weeks on ULDCT using two different kernels (R1: Qr49ADMIRE 4; R2: Br49ADMIRE 3). Intra-subject agreement for LungRADS categories between LDCT and ULDCT was measured by the k-Cohen Index with Fleiss-Cohen weights. RESULTS LDCT-dominant PNs were detected in ULDCT in 87 % of cases on Qr49ADMIRE 4 and 88 % on Br49ADMIRE 3. The intra-subject agreement was: κULDCT1 = 0.89 [95 %CI 0.82-0.96]; κULDCT2 = 0.90 [0.81-0.98]; κULDCT3 = 0.91 [0.84-0.99]; κULDCT4 = 0.88 [0.78-0.97] on Qr49ADMIRE 4, and κULDCT1 = 0.88 [0.80-0.95]; κULDCT2 = 0.91 [0.86-0.96]; κULDCT3 = 0.87 [0.78-0.95]; and κULDCT4 = 0.88 [0.82-0.94] on Br49ADMIRE 3. LDCT classified as LungRADS 4B were correctly identified as LungRADS 4B at ULDCT3, with the lowest radiation exposure among the tested protocols (median effective doses were 0.31, 0.36, 0.27 and 0.37 mSv for ULDCT1, ULDCT2, ULDCT3, and ULDCT4, respectively). CONCLUSIONS ULDCT by spectral shaping allows the detection and characterization of PNs with an excellent agreement with LDCT and can be proposed as a feasible approach in LCS.
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Affiliation(s)
- Gianluca Milanese
- Scienze Radiologiche, Department of Medicine and Surgery, University of Parma, Parma, Italy; Fondazione IRCCS Istituto Nazionale dei Tumori, Thoracic Surgery, Milan, Lombardia, Italy.
| | - Roberta Eufrasia Ledda
- Scienze Radiologiche, Department of Medicine and Surgery, University of Parma, Parma, Italy; Fondazione IRCCS Istituto Nazionale dei Tumori, Thoracic Surgery, Milan, Lombardia, Italy.
| | - Federica Sabia
- Fondazione IRCCS Istituto Nazionale dei Tumori, Thoracic Surgery, Milan, Lombardia, Italy.
| | - Margherita Ruggirello
- Fondazione IRCCS Istituto Nazionale dei Tumori, Department of Diagnostic Imaging and Radiotherapy, Milan, Italy.
| | - Stefano Sestini
- Fondazione IRCCS Istituto Nazionale dei Tumori, Thoracic Surgery, Milan, Lombardia, Italy.
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery, University of Parma, Parma, Italy.
| | - Nicola Sverzellati
- Scienze Radiologiche, Department of Medicine and Surgery, University of Parma, Parma, Italy.
| | - Alfonso Vittorio Marchianò
- Fondazione IRCCS Istituto Nazionale dei Tumori, Department of Diagnostic Imaging and Radiotherapy, Milan, Italy.
| | - Ugo Pastorino
- Fondazione IRCCS Istituto Nazionale dei Tumori, Thoracic Surgery, Milan, Lombardia, Italy.
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Choe J, Lee SM, Hwang HJ, Lee SM, Yun J, Kim N, Seo JB. Artificial Intelligence in Lung Imaging. Semin Respir Crit Care Med 2022; 43:946-960. [PMID: 36174647 DOI: 10.1055/s-0042-1755571] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging. Some of these have been approved by governments and are now commercially available in the marketplace. In the field of chest radiology, there are various tasks and purposes that are suitable for AI: initial evaluation/triage of certain diseases, detection and diagnosis, quantitative assessment of disease severity and monitoring, and prediction for decision support. While AI is a powerful technology that can be applied to medical imaging and is expected to improve our current clinical practice, some obstacles must be addressed for the successful implementation of AI in workflows. Understanding and becoming familiar with the current status and potential clinical applications of AI in chest imaging, as well as remaining challenges, would be essential for radiologists and clinicians in the era of AI. This review introduces the potential clinical applications of AI in chest imaging and also discusses the challenges for the implementation of AI in daily clinical practice and future directions in chest imaging.
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Affiliation(s)
- Jooae Choe
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Hye Jeon Hwang
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jihye Yun
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.,Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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Bonney A, Malouf R, Marchal C, Manners D, Fong KM, Marshall HM, Irving LB, Manser R. Impact of low-dose computed tomography (LDCT) screening on lung cancer-related mortality. Cochrane Database Syst Rev 2022; 8:CD013829. [PMID: 35921047 PMCID: PMC9347663 DOI: 10.1002/14651858.cd013829.pub2] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Lung cancer is the most common cause of cancer-related death in the world, however lung cancer screening has not been implemented in most countries at a population level. A previous Cochrane Review found limited evidence for the effectiveness of lung cancer screening with chest radiography (CXR) or sputum cytology in reducing lung cancer-related mortality, however there has been increasing evidence supporting screening with low-dose computed tomography (LDCT). OBJECTIVES: To determine whether screening for lung cancer using LDCT of the chest reduces lung cancer-related mortality and to evaluate the possible harms of LDCT screening. SEARCH METHODS We performed the search in collaboration with the Information Specialist of the Cochrane Lung Cancer Group and included the Cochrane Lung Cancer Group Trial Register, Cochrane Central Register of Controlled Trials (CENTRAL, the Cochrane Library, current issue), MEDLINE (accessed via PubMed) and Embase in our search. We also searched the clinical trial registries to identify unpublished and ongoing trials. We did not impose any restriction on language of publication. The search was performed up to 31 July 2021. SELECTION CRITERIA: Randomised controlled trials (RCTs) of lung cancer screening using LDCT and reporting mortality or harm outcomes. DATA COLLECTION AND ANALYSIS: Two review authors were involved in independently assessing trials for eligibility, extraction of trial data and characteristics, and assessing risk of bias of the included trials using the Cochrane RoB 1 tool. We assessed the certainty of evidence using GRADE. Primary outcomes were lung cancer-related mortality and harms of screening. We performed a meta-analysis, where appropriate, for all outcomes using a random-effects model. We only included trials in the analysis of mortality outcomes if they had at least 5 years of follow-up. We reported risk ratios (RRs) and hazard ratios (HRs), with 95% confidence intervals (CIs) and used the I2 statistic to investigate heterogeneity. MAIN RESULTS: We included 11 trials in this review with a total of 94,445 participants. Trials were conducted in Europe and the USA in people aged 40 years or older, with most trials having an entry requirement of ≥ 20 pack-year smoking history (e.g. 1 pack of cigarettes/day for 20 years or 2 packs/day for 10 years etc.). One trial included male participants only. Eight trials were phase three RCTs, with two feasibility RCTs and one pilot RCT. Seven of the included trials had no screening as a comparison, and four trials had CXR screening as a comparator. Screening frequency included annual, biennial and incrementing intervals. The duration of screening ranged from 1 year to 10 years. Mortality follow-up was from 5 years to approximately 12 years. None of the included trials were at low risk of bias across all domains. The certainty of evidence was moderate to low across different outcomes, as assessed by GRADE. In the meta-analysis of trials assessing lung cancer-related mortality, we included eight trials (91,122 participants), and there was a reduction in mortality of 21% with LDCT screening compared to control groups of no screening or CXR screening (RR 0.79, 95% CI 0.72 to 0.87; 8 trials, 91,122 participants; moderate-certainty evidence). There were probably no differences in subgroups for analyses by control type, sex, geographical region, and nodule management algorithm. Females appeared to have a larger lung cancer-related mortality benefit compared to males with LDCT screening. There was also a reduction in all-cause mortality (including lung cancer-related) of 5% (RR 0.95, 95% CI 0.91 to 0.99; 8 trials, 91,107 participants; moderate-certainty evidence). Invasive tests occurred more frequently in the LDCT group (RR 2.60, 95% CI 2.41 to 2.80; 3 trials, 60,003 participants; moderate-certainty evidence). However, analysis of 60-day postoperative mortality was not significant between groups (RR 0.68, 95% CI 0.24 to 1.94; 2 trials, 409 participants; moderate-certainty evidence). False-positive results and recall rates were higher with LDCT screening compared to screening with CXR, however there was low-certainty evidence in the meta-analyses due to heterogeneity and risk of bias concerns. Estimated overdiagnosis with LDCT screening was 18%, however the 95% CI was 0 to 36% (risk difference (RD) 0.18, 95% CI -0.00 to 0.36; 5 trials, 28,656 participants; low-certainty evidence). Four trials compared different aspects of health-related quality of life (HRQoL) using various measures. Anxiety was pooled from three trials, with participants in LDCT screening reporting lower anxiety scores than in the control group (standardised mean difference (SMD) -0.43, 95% CI -0.59 to -0.27; 3 trials, 8153 participants; low-certainty evidence). There were insufficient data to comment on the impact of LDCT screening on smoking behaviour. AUTHORS' CONCLUSIONS: The current evidence supports a reduction in lung cancer-related mortality with the use of LDCT for lung cancer screening in high-risk populations (those over the age of 40 with a significant smoking exposure). However, there are limited data on harms and further trials are required to determine participant selection and optimal frequency and duration of screening, with potential for significant overdiagnosis of lung cancer. Trials are ongoing for lung cancer screening in non-smokers.
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Affiliation(s)
- Asha Bonney
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Parkville, Australia
- Department of Medicine, University of Melbourne, Melbourne, Australia
| | - Reem Malouf
- National Perinatal Epidemiology Unit (NPEU), University of Oxford, Oxford, UK
| | | | - David Manners
- Respiratory Medicine, Midland St John of God Public and Private Hospital, Midland, Australia
| | - Kwun M Fong
- Thoracic Medicine Program, The Prince Charles Hospital, Brisbane, Australia
- UQ Thoracic Research Centre, School of Medicine, The University of Queensland, Brisbane, Australia
| | - Henry M Marshall
- School of Medicine, The University of Queensland, Brisbane, Australia
| | - Louis B Irving
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Parkville, Australia
| | - Renée Manser
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Parkville, Australia
- Department of Haematology and Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia
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