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Peters AA, Wiescholek N, Müller M, Klaus J, Strodka F, Macek A, Primetis E, Drakopulos D, Huber AT, Obmann VC, Ruder TD, Roos JE, Heverhagen JT, Christe A, Ebner L. Impact of artificial intelligence assistance on pulmonary nodule detection and localization in chest CT: a comparative study among radiologists of varying experience levels. Sci Rep 2024; 14:22447. [PMID: 39341945 PMCID: PMC11439040 DOI: 10.1038/s41598-024-73435-3] [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: 05/10/2024] [Accepted: 09/17/2024] [Indexed: 10/01/2024] Open
Abstract
The study aimed to evaluate the impact of AI assistance on pulmonary nodule detection rates among radiology residents and senior radiologists, along with assessing the effectiveness of two different commercialy available AI software systems in improving detection rates and LungRADS classification in chest CT. The study cohort included 198 participants with 221 pulmonary nodules. Residents' mean detection rate increased significantly from 64 to 77% with AI assist, while seniors' detection rate remained largely unchanged (85% vs. 86%). Residents showed significant improvement in segmental nodule localization with AI assistance, seniors did not. Software 2 slightly outperformed software 1 in increasing detection rates (67-77% vs. 80-86%), but neither significantly affected LungRADS classification. The study suggests that clinical experience mitigates the need for additional AI software, with the combination of CAD with residents being the most beneficial approach. Both software systems performed similarly, with software 2 showing a slightly higher but non-significant increase in detection rates.
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Affiliation(s)
- Alan Arthur Peters
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010, Bern, Switzerland.
| | - Nina Wiescholek
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010, Bern, Switzerland
| | - Martin Müller
- Department of Emergency Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Jeremias Klaus
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010, Bern, Switzerland
| | - Felix Strodka
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010, Bern, Switzerland
| | - Ana Macek
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010, Bern, Switzerland
- Institute of Radiology, Cantonal Hospital Münsterlingen, Münsterlingen, Switzerland
| | - Elias Primetis
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010, Bern, Switzerland
| | - Dionysios Drakopulos
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010, Bern, Switzerland
| | - Adrian Thomas Huber
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010, Bern, Switzerland
- Radiology and Nuclear Medicine, Luzerner Kantonsspital, Luzern, Switzerland
| | - Verena Carola Obmann
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010, Bern, Switzerland
| | - Thomas Daniel Ruder
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010, Bern, Switzerland
| | | | - Johannes Thomas Heverhagen
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010, Bern, Switzerland
- Department of BioMedical Research, Experimental Radiology, University of Bern, Bern, Switzerland
- Department of Radiology, The Ohio State University, Columbus, OH, USA
| | - Andreas Christe
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010, Bern, Switzerland
| | - Lukas Ebner
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010, Bern, Switzerland
- Radiology and Nuclear Medicine, Luzerner Kantonsspital, Luzern, Switzerland
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Wang Y, Lyu D, Yu D, Hu S, Ma Y, Huang W, Duan S, Zhou T, Tu W, Zhou X, Xiao Y, Fan L, Liu S. Intratumoral and peritumoral radiomics combined with computed tomography features for predicting the invasiveness of lung adenocarcinoma presenting as a subpleural ground-glass nodule with a consolidation-to-tumor ratio ≤50. J Thorac Dis 2024; 16:5122-5137. [PMID: 39268144 PMCID: PMC11388221 DOI: 10.21037/jtd-24-243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 06/28/2024] [Indexed: 09/15/2024]
Abstract
Background Preoperative accurate judgment of the degree of invasiveness in subpleural ground-glass lung adenocarcinoma (LUAD) with a consolidation-to-tumor ratio (CTR) ≤50% is very important for the choice of surgical timing and planning. This study aims to investigate the performance of intratumoral and peritumoral radiomics combined with computed tomography (CT) features for predicting the invasiveness of LUAD presenting as a subpleural ground-glass nodule (GGN) with a CTR ≤50%. Methods A total of 247 patients with LUAD from our hospital were randomly divided into two groups, i.e., the training cohort (n=173) and the internal validation cohort (n=74) (7:3 ratio). Furthermore, 47 patients from three other hospitals were collected as the external validation cohort. In the training cohort, the differences in clinical-radiological features were compared using univariate and multivariate analyses. The gross tumor volume (GTV) and gross peritumoral tumor volume (GPTV5, GPTV10, and GPTV15) radiomics models were constructed based on intratumoral and peritumoral (5, 10, and 15 mm) radiomics features. Additionally, the radscore of the best radiomics model and clinical risk factors were used to construct a combined model and the predictive efficacy of the model was evaluated in the validation cohorts. Finally, the receiver operating characteristics (ROC) curve and area under the curve (AUC) value were used to evaluate the discriminative ability of the model. Results Tumor size and CTR were independent risk factors for predicting the invasiveness of LUAD. The GPTV10 model outperformed the other radiomics models, with AUC values of 0.910, 0.870, and 0.887 in the three cohorts. The AUC values of the combined model were 0.912, 0.874, and 0.892. Conclusions A nomogram based on GPTV10-radscore, tumor size, and CTR exhibited high predictive efficiency for predicting the invasiveness of LUAD.
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Affiliation(s)
- Yun Wang
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China
| | - Deng Lyu
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China
| | - Dan Yu
- Department of Radiology, Hangzhou Lin'an District First People's Hospital, Hangzhou, China
| | - Su Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yanqing Ma
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Wenjun Huang
- Department of Radiology, The Second People's Hospital of Deyang, Deyang, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Taohu Zhou
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China
| | - Wenting Tu
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China
| | - Xiuxiu Zhou
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China
| | - Yi Xiao
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China
| | - Li Fan
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China
| | - Shiyuan Liu
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China
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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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Hop JF, Walstra ANH, Pelgrim GJ, Xie X, Panneman NA, Schurink NW, Faby S, van Straten M, de Bock GH, Vliegenthart R, Greuter MJW. Detectability and Volumetric Accuracy of Pulmonary Nodules in Low-Dose Photon-Counting Detector Computed Tomography: An Anthropomorphic Phantom Study. Diagnostics (Basel) 2023; 13:3448. [PMID: 37998584 PMCID: PMC10669978 DOI: 10.3390/diagnostics13223448] [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: 10/16/2023] [Revised: 11/05/2023] [Accepted: 11/13/2023] [Indexed: 11/25/2023] Open
Abstract
The aim of this phantom study was to assess the detectability and volumetric accuracy of pulmonary nodules on photon-counting detector CT (PCD-CT) at different low-dose levels compared to conventional energy-integrating detector CT (EID-CT). In-house fabricated artificial nodules of different shapes (spherical, lobulated, spiculated), sizes (2.5-10 mm and 5-1222 mm3), and densities (-330 HU and 100 HU) were randomly inserted into an anthropomorphic thorax phantom. The phantom was scanned with a low-dose chest protocol with PCD-CT and EID-CT, in which the dose with PCD-CT was lowered from 100% to 10% with respect to the EID-CT reference dose. Two blinded observers independently assessed the CT examinations of the nodules. A third observer measured the nodule volumes using commercial software. The influence of the scanner type, dose, observer, physical nodule volume, shape, and density on the detectability and volumetric accuracy was assessed by a multivariable regression analysis. In 120 CT examinations, 642 nodules were present. Observer 1 and 2 detected 367 (57%) and 289 nodules (45%), respectively. With PCD-CT and EID-CT, the nodule detectability was similar. The physical nodule volumes were underestimated by 20% (range 8-52%) with PCD-CT and 24% (range 9-52%) with EID-CT. With PCD-CT, no significant decrease in the detectability and volumetric accuracy was found at dose reductions down to 10% of the reference dose (p > 0.05). The detectability and volumetric accuracy were significantly influenced by the observer, nodule volume, and a spiculated nodule shape (p < 0.05), but not by dose, CT scanner type, and nodule density (p > 0.05). Low-dose PCD-CT demonstrates potential to detect and assess the volumes of pulmonary nodules, even with a radiation dose reduction of up to 90%.
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Affiliation(s)
- Joost F. Hop
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands; (A.N.H.W.); (G.-J.P.); (N.A.P.); (R.V.); (M.J.W.G.)
| | - Anna N. H. Walstra
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands; (A.N.H.W.); (G.-J.P.); (N.A.P.); (R.V.); (M.J.W.G.)
| | - Gert-Jan Pelgrim
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands; (A.N.H.W.); (G.-J.P.); (N.A.P.); (R.V.); (M.J.W.G.)
| | - Xueqian Xie
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China;
| | - Noor A. Panneman
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands; (A.N.H.W.); (G.-J.P.); (N.A.P.); (R.V.); (M.J.W.G.)
| | - Niels W. Schurink
- Siemens Healthineers Nederland B.V., 2595 BN Den Haag, The Netherlands
| | - Sebastian Faby
- Computed Tomography, Siemens Healthcare GmbH, 91301 Forchheim, Germany;
| | - Marcel van Straten
- Department of Radiology, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands;
| | - Geertruida H. de Bock
- Department of Epidemiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands;
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands; (A.N.H.W.); (G.-J.P.); (N.A.P.); (R.V.); (M.J.W.G.)
| | - Marcel J. W. Greuter
- Department of Radiology, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands; (A.N.H.W.); (G.-J.P.); (N.A.P.); (R.V.); (M.J.W.G.)
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Zhang C, Luan K, Li S, Wang Z, Chen S, Zhang W, Zhao C, Liu A, Jiao W. Different nodal upstaging rates and prognoses for patients with clinical T1N0M0 lung adenocarcinoma classified according to the presence of solid components in the lung and mediastinal windows. J Thorac Dis 2023; 15:3612-3626. [PMID: 37559610 PMCID: PMC10407531 DOI: 10.21037/jtd-23-18] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 05/06/2023] [Indexed: 08/11/2023]
Abstract
BACKGROUND Little is known about the correlation between nodal upstaging and pulmonary nodules classified according to the presence of solid components in the lung and mediastinal windows. This study thus aimed to analyze the risk factors of nodal upstaging and prognosis based on different imaging features, clinical characteristics, and pathological results from patients with clinical stage T1N0M0 lung adenocarcinoma. METHODS A total of 340 patients between January 2016 and June 2017 were selected from the Affiliated Hospital of Qingdao University database. Imaging features, clinical characteristics, and pathological results were collected for survival and analysis of nodal upstaging risk factors. We used logistic regression models to identify important metastatic risk factors for nodal upstaging. Survival rates were calculated using Kaplan-Meier (KM) survival curves and compared with the log-rank test. Significant prognostic risk factors were identified using the Cox proportional hazards model. RESULTS A total of 340 patients, with an average age of 64.89 (±8.775) years, were enrolled. Among them, nonnodal upstaging occurred both in 77 (22.6%) patients with pure ground-glass nodules (pGGNs) and in 30 (8.8%) patients with heterogenous ground-glass nodules (hGGNs). Compared to the 92 (27.1%) patients with real part-solid nodules (rPSNs), the 141 (41.5%) patients with solid nodules were significantly different in terms of in nodal upstaging (P<0.001). Moreover, preoperative carcinoembryonic antigen (CEA) level >3.4 µg/L [odds ratio (OR): 2.931; 95% confidence interval (CI): 1.511-5.688; P=0.001], imaging tumor size >18.3 mm (OR, 3.482; 95% CI: 1.609-7.535; P=0.002), and consolidation tumor ratio (CTR) >0.788 (OR 8.791; 95% CI: 3.570-21.651; P<0.001) were independent risk factors for nodal upstaging. The KM survival curve results showed that patients with pGGNs and those with hGGNs had a much better 5-year disease-free survival (DFS) and 5-year overall survival (OS) than did those with rPSNs and those with solid nodules (DFS: 98.7% vs. 100% vs. 81.4% vs. 73.7%, P<0.001; OS: 97.4% vs. 100% vs. 90.2% vs. 83.7%, P=0.003). In the multivariate Cox regression analysis of patients with rPSNs and solid nodules, tumor location and pathological lymph node grade were found to be independent risk factors for DFS and OS. CONCLUSIONS Patients with pGGNs and those with hGGNs were more likely to be free of nodal upstaging and had better prognosis than did those with clinical stage IA rPSNs and solid nodules. The patients with pGGNs or hGGNs with preoperative CEA level <3.4 µg/L, imaging tumor size <18.3 mm, and CTR <0.788 can choose systematic lymph node sampling (SLNS) or decline lymph node dissection to avoid postoperative complications.
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Affiliation(s)
- Chenyu Zhang
- Department of Thoracic Surgery, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China
| | - Kun Luan
- Department of Thoracic Surgery, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China
| | - Shaoxiang Li
- Department of Thoracic Surgery, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China
| | - Zipeng Wang
- Department of Thoracic Surgery, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China
| | - Sheng Chen
- Department of Thoracic Surgery, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China
| | - Wenxi Zhang
- Department of Thoracic Surgery, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China
| | - Ce Zhao
- Department of Thoracic Surgery, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China
| | - Ao Liu
- Department of Thoracic Surgery, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China
| | - Wenjie Jiao
- Department of Thoracic Surgery, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China
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Zhang Y, Feng W, Wu Z, Li W, Tao L, Liu X, Zhang F, Gao Y, Huang J, Guo X. Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1088. [PMID: 37374292 DOI: 10.3390/medicina59061088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 06/29/2023]
Abstract
Background and Objectives: Lung cancer remains a leading cause of cancer mortality worldwide. Accurately classifying benign pulmonary nodules and malignant ones is crucial for early diagnosis and improved patient outcomes. The purpose of this study is to explore the deep-learning model of ResNet combined with a convolutional block attention module (CBAM) for the differentiation between benign and malignant lung cancer, based on computed tomography (CT) images, morphological features, and clinical information. Methods and materials: In this study, 8241 CT slices containing pulmonary nodules were retrospectively included. A random sample comprising 20% (n = 1647) of the images was used as the test set, and the remaining data were used as the training set. ResNet combined CBAM (ResNet-CBAM) was used to establish classifiers on the basis of images, morphological features, and clinical information. Nonsubsampled dual-tree complex contourlet transform (NSDTCT) combined with SVM classifier (NSDTCT-SVM) was used as a comparative model. Results: The AUC and the accuracy of the CBAM-ResNet model were 0.940 and 0.867, respectively, in test set when there were only images as inputs. By combining the morphological features and clinical information, CBAM-ResNet shows better performance (AUC: 0.957, accuracy: 0.898). In comparison, a radiomic analysis using NSDTCT-SVM achieved AUC and accuracy values of 0.807 and 0.779, respectively. Conclusions: Our findings demonstrate that deep-learning models, combined with additional information, can enhance the classification performance of pulmonary nodules. This model can assist clinicians in accurately diagnosing pulmonary nodules in clinical practice.
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Affiliation(s)
- Yanfei Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Wei Feng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Zhiyuan Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Weiming Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Lixin Tao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Xiangtong Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Feng Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Yan Gao
- Department of Nuclear Medicine, Xuanwu Hospital Capital Medical University, Beijing 100053, China
| | - Jian Huang
- School of Mathematical Sciences, University College Cork, T12 YN60 Cork, Ireland
| | - Xiuhua Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
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Yost CC, Bhagat R, Blitzer D, Louis C, Han J, Wilder FG, Meguid RA. A primer for the student joining the general thoracic surgery service tomorrow: Primer 2 of 7. JTCVS OPEN 2023; 14:293-313. [PMID: 37425458 PMCID: PMC10328966 DOI: 10.1016/j.xjon.2023.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 04/02/2023] [Accepted: 04/08/2023] [Indexed: 07/11/2023]
Affiliation(s)
- Colin C. Yost
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pa
| | - Rohun Bhagat
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic, Cleveland, Ohio
| | - David Blitzer
- Division of Cardiovascular Surgery, Columbia University, New York, NY
| | - Clauden Louis
- Division of Cardiothoracic Surgery, Brigham and Women’s Hospital, Boston, Mass
| | - Jason Han
- Division of Cardiothoracic Surgery, University of Pennsylvania, Philadelphia, Pa
| | - Fatima G. Wilder
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, Mass
| | - Robert A. Meguid
- Division of Cardiothoracic Surgery, Department of Surgery, University of Colorado School of Medicine, Aurora, Colo
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Yang L, Liu H, Han J, Xu S, Zhang G, Wang Q, Du Y, Yang F, Zhao X, Shi G. Ultra-low-dose CT lung screening with artificial intelligence iterative reconstruction: evaluation via automatic nodule-detection software. Clin Radiol 2023:S0009-9260(23)00031-4. [PMID: 36948944 DOI: 10.1016/j.crad.2023.01.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 01/04/2023] [Accepted: 01/15/2023] [Indexed: 02/05/2023]
Abstract
AIM To test the feasibility of ultra-low-dose (ULD) computed tomography (CT) combined with an artificial intelligence iterative reconstruction (AIIR) algorithm for screening pulmonary nodules using computer-assisted diagnosis (CAD). MATERIALS AND METHODS A chest phantom with artificial pulmonary nodules was first scanned using the routine protocol and the ULD protocol (3.28 versus 0.18 mSv) to compare the image quality and to test the acceptability of the ULD CT protocol. Next, 147 lung-screening patients were enrolled prospectively, undergoing an additional ULD CT immediately after their routine CT examination for clinical validation. Images were reconstructed with filtered back-projection (FBP), hybrid iterative reconstruction (HIR), the AIIR, and were imported to the CAD software for preliminary nodule detection. Subjective image quality on the phantom was scored using a five-point scale and compared using the Mann-Whitney U-test. Nodule detection using CAD was evaluated for ULD HIR and AIIR images using the routine dose image as reference. RESULTS Higher image quality was scored for AIIR than for FBP and HIR at ULD (p<0.001). As reported by CAD, 107 patients were presented with more than five nodules on routine dose images and were chosen to represent the challenging cases at an early stage of pulmonary disease. Among such, the performance of nodule detection by CAD on ULD HIR and AIIR images was 75.2% and 92.2% of the routine dose image, respectively. CONCLUSION Combined with AIIR, it was feasible to use an ULD CT protocol with 95% dose reduction for CAD-based screening of pulmonary nodules.
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Affiliation(s)
- L Yang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - H Liu
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - J Han
- United Imaging Healthcare, Shanghai, China
| | - S Xu
- United Imaging Healthcare, Shanghai, China
| | - G Zhang
- United Imaging Healthcare, Shanghai, China
| | - Q Wang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Y Du
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - F Yang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - X Zhao
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - G Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
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Ren C, Xu M, Zhang J, Zhang F, Song S, Sun Y, Wu K, Cheng J. Classification of solid pulmonary nodules using a machine-learning nomogram based on 18F-FDG PET/CT radiomics integrated clinicobiological features. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1265. [PMID: 36618813 PMCID: PMC9816842 DOI: 10.21037/atm-22-2647] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 10/13/2022] [Indexed: 11/24/2022]
Abstract
Background To develop and validate an 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and clinico-biological features-based nomogram for distinguishing solid benign pulmonary nodules (BPNs) from malignant pulmonary nodules (MPNs). Methods A total of 280 patients with BPN (n=128) or MPN (n=152) were collected retrospectively and randomized into the training set (n=196) and validation set (n=84). Pretherapeutic clinicobiological markers, PET/CT metabolic features and radiomic features were analyzed and selected to develop prediction models by the machine-learning method [Least Absolute Shrinkage and Selection Operator (LASSO) regression]. These prediction models were validated using the area under the curve (AUC) of the receiver-operator characteristic (ROC) analysis and decision curve analysis (DCA). Then, the factors of the model with the optimal predictive efficiency were used to constructed a nomogram to provide a visually quantitative tool for distinguishing BPN from MPN patients. Results We developed 3 independent models (Clinical Model, Radiomics Model and Combined Model) to distinguish patients with BPN from those with MPN in the training set. The Combined Model was validated to hold the optimal efficiency and clinical utility with the lowest false positive rate (FPR) in classifying the solid pulmonary nodules in two sets (AUCs of 0.91 and 0.94, FPRs of 18.68% and 5.41%, respectively; P<0.05). Thus, the quantitative nomogram was developed based on the Combined Model, and a good consistency between the predictions and the actual observations was validated by the calibration curves. Conclusions This study presents a machine-learning nomogram integrated clinico-biologico-radiological features that can improve the efficiency and reduce the FPR in the noninvasive differentiation of BPN from MPN.
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Affiliation(s)
- Caiyue Ren
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, China;,Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China;,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
| | - Mingxia Xu
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, China;,Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China;,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
| | - Jiangang Zhang
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, China;,Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China;,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
| | - Fuquan Zhang
- College of Physics, Sichuan University, Chengdu, China
| | - Shaoli Song
- Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China;,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China;,Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China;,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China;,Center for Biomedical Imaging, Fudan University, Shanghai, China;,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
| | - Yun Sun
- Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China;,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China;,Department of Research and Development, Shanghai Proton and Heavy Ion Center, Shanghai, China
| | - Kailiang Wu
- Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China;,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China;,Department of Radiotherapy, Shanghai Proton and Heavy Ion Center, Shanghai, China
| | - Jingyi Cheng
- Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China;,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China;,Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China;,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China;,Center for Biomedical Imaging, Fudan University, Shanghai, China;,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
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10
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Hall H, Ruparel M, Quaife SL, Dickson JL, Horst C, Tisi S, Batty J, Woznitza N, Ahmed A, Burke S, Shaw P, Soo MJ, Taylor M, Navani N, Bhowmik A, Baldwin DR, Duffy SW, Devaraj A, Nair A, Janes SM. The role of computer-assisted radiographer reporting in lung cancer screening programmes. Eur Radiol 2022; 32:6891-6899. [PMID: 35567604 PMCID: PMC9474336 DOI: 10.1007/s00330-022-08824-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/11/2022] [Accepted: 04/13/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVES Successful lung cancer screening delivery requires sensitive, timely reporting of low-dose computed tomography (LDCT) scans, placing a demand on radiology resources. Trained non-radiologist readers and computer-assisted detection (CADe) software may offer strategies to optimise the use of radiology resources without loss of sensitivity. This report examines the accuracy of trained reporting radiographers using CADe support to report LDCT scans performed as part of the Lung Screen Uptake Trial (LSUT). METHODS In this observational cohort study, two radiographers independently read all LDCT performed within LSUT and reported on the presence of clinically significant nodules and common incidental findings (IFs), including recommendations for management. Reports were compared against a 'reference standard' (RS) derived from nodules identified by study radiologists without CADe, plus consensus radiologist review of any additional nodules identified by the radiographers. RESULTS A total of 716 scans were included, 158 of which had one or more clinically significant pulmonary nodules as per our RS. Radiographer sensitivity against the RS was 68-73.7%, with specificity of 92.1-92.7%. Sensitivity for detection of proven cancers diagnosed from the baseline scan was 83.3-100%. The spectrum of IFs exceeded what could reasonably be covered in radiographer training. CONCLUSION Our findings highlight the complexity of LDCT reporting requirements, including the limitations of CADe and the breadth of IFs. We are unable to recommend CADe-supported radiographers as a sole reader of LDCT scans, but propose potential avenues for further research including initial triage of abnormal LDCT or reporting of follow-up surveillance scans. KEY POINTS • Successful roll-out of mass screening programmes for lung cancer depends on timely, accurate CT scan reporting, placing a demand on existing radiology resources. • This observational cohort study examines the accuracy of trained radiographers using computer-assisted detection (CADe) software to report lung cancer screening CT scans, as a potential means of supporting reporting workflows in LCS programmes. • CADe-supported radiographers were less sensitive than radiologists at identifying clinically significant pulmonary nodules, but had a low false-positive rate and good sensitivity for detection of confirmed cancers.
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Affiliation(s)
- Helen Hall
- Lungs for Living Research Centre, UCL Respiratory, Rayne Institute, University College London, 5 University Street, London, WC1E 6JF, UK
| | - Mamta Ruparel
- Lungs for Living Research Centre, UCL Respiratory, Rayne Institute, University College London, 5 University Street, London, WC1E 6JF, UK
| | - Samantha L Quaife
- Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Jennifer L Dickson
- Lungs for Living Research Centre, UCL Respiratory, Rayne Institute, University College London, 5 University Street, London, WC1E 6JF, UK
| | - Carolyn Horst
- Lungs for Living Research Centre, UCL Respiratory, Rayne Institute, University College London, 5 University Street, London, WC1E 6JF, UK
| | - Sophie Tisi
- Lungs for Living Research Centre, UCL Respiratory, Rayne Institute, University College London, 5 University Street, London, WC1E 6JF, UK
| | - James Batty
- Department of Radiology, University College London Hospital, London, UK
| | | | - Asia Ahmed
- Department of Radiology, University College London Hospital, London, UK
| | - Stephen Burke
- Department of Radiology, Homerton University Hospital, London, UK
| | - Penny Shaw
- Department of Radiology, University College London Hospital, London, UK
| | - May Jan Soo
- Department of Radiology, Homerton University Hospital, London, UK
| | - Magali Taylor
- Department of Radiology, University College London Hospital, London, UK
| | - Neal Navani
- Lungs for Living Research Centre, UCL Respiratory, Rayne Institute, University College London, 5 University Street, London, WC1E 6JF, UK
- Department of Thoracic Medicine, University College London Hospital, London, UK
| | - Angshu Bhowmik
- Department of Thoracic Medicine, Homerton University Hospital, London, UK
| | - David R Baldwin
- Respiratory Medicine Unit, David Evans Research Centre, Nottingham University Hospitals, Nottingham, UK
| | - Stephen W Duffy
- Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Anand Devaraj
- Department of Radiology, Royal Brompton Hospital, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Arjun Nair
- Department of Radiology, University College London Hospital, London, UK
| | - Sam M Janes
- Lungs for Living Research Centre, UCL Respiratory, Rayne Institute, University College London, 5 University Street, London, WC1E 6JF, UK.
- Department of Thoracic Medicine, University College London Hospital, London, UK.
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11
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Bai Y, Li D, Duan Q, Chen X. Analysis of high-resolution reconstruction of medical images based on deep convolutional neural networks in lung cancer diagnostics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 217:106592. [PMID: 35172253 DOI: 10.1016/j.cmpb.2021.106592] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 12/04/2021] [Accepted: 12/15/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE To study the diagnostic effect of 64-slice spiral CT and MRI high-resolution images based on deep convolutional neural networks(CNN) in lung cancer. METHODS In this paper, we Select 74 patients with highly suspected lung cancer who were treated in our hospital from January 2017 to January 2021 as the research objects. The enhanced 64-slice spiral CT and MRI were used to detect and diagnose respectively, and the images and accuracy of CT diagnosis and MRI diagnosis were retrospectively analyzed. RESULTS The accuracy of CT diagnosis is 94.6% (70/74), and the accuracy of MRI diagnosis is 89.2% (66/74). CT examination has the advantages of non-invasive, convenient operation and fast examination. MRI is showing there are advantages in the relationship between the chest wall and the mediastinum, and the relationship between the lesion and the large blood vessels. CONCLUSION Enhanced CT and MRI examinations based on convolutional neural networks(CNN) to improve image clarity have high application value in the diagnosis of lung cancer patients, but the focus of performance is different.
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Affiliation(s)
- Yang Bai
- Department of Nursing, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110000 China
| | - Dan Li
- Department of Nursing, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110000 China
| | - Qiongyu Duan
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110000 China
| | - Xiaodong Chen
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110000 China.
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12
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Li S, Ding X, Zhao Y, Chen X, Huang J. Intravenous patient-controlled analgesia plus psychoeducational intervention for acute postoperative pain in patients with pulmonary nodules after thoracoscopic surgery: a retrospective cohort study. BMC Anesthesiol 2021; 21:281. [PMID: 34773972 PMCID: PMC8590357 DOI: 10.1186/s12871-021-01505-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 11/03/2021] [Indexed: 11/10/2022] Open
Abstract
Background The association of psychological factors with postoperative pain has been well documented. The incorporation of psychoeducational intervention into a standard analgesia protocol seems to be an attractive approach for the management of acute postoperative pain. Our study aimed to evaluate the impact of psychoeducational intervention on acute postoperative pain in pulmonary nodule (PN) patients treated with thoracoscopic surgery. Methods In this study, 76 PN patients treated with thoracoscopic surgery and intravenous patient-controlled analgesia (IV-PCA) plus psychoeducational evaluation and intervention were selected as the psychoeducational intervention group (PG). Another 76 PN patients receiving IV-PCA without psychoeducational intervention after thoracoscopic surgery, treated as the control group (CG), were identified from the hospital database and matched pairwise with PG patients according to age, sex, preoperative body mass index (BMI), opioid medications used for IV-PCA and the educational attainment of patients. Results The most common psychological disorders were anxiety and interpersonal sensitivity, which were recorded from 82.9% (63/76) and 63.2% (48/76) of PG patients. The numerical rating scale (NRS) pain scores of the PG patients were significantly lower than those of the CG patients at 2 and 24 h after surgery (P < 0.001). Total opioid consumption for acute postoperative pain in the PG was 52.1 mg of morphine equivalent, which was significantly lower than that (67.8 mg) in the CG (P = 0.038). PG patients had a significantly lower incidence of rescue analgesia than CG patients (28.9% vs. 44.7%, P = 0.044). Nausea/vomiting was the most common side effect of opioid medications, recorded for 3 (3.9%) PG patients and 10 (13.2%) CG patients (P = 0.042). In addition, no significant difference was observed between PG and CG patients in terms of grade 2 or higher postoperative complications (10.5% vs. 17.1%, P = 0.240). Conclusions Psychoeducational intervention for PN patients treated with thoracoscopic surgery resulted in reduced acute postoperative pain, less opioid consumption and fewer opioid-related side effects.
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Affiliation(s)
- Sha Li
- Department of Anesthesiology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, Wuxi, 214125, Jiangsu, People's Republic of China
| | - Xian Ding
- Department of Anesthesiology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, Wuxi, 214125, Jiangsu, People's Republic of China
| | - Yong Zhao
- Department of Thoracic and Cardiovascular Surgery, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Xiao Chen
- Department of Anesthesiology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, Wuxi, 214125, Jiangsu, People's Republic of China.
| | - Jianfeng Huang
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, Wuxi, 214125, Jiangsu, People's Republic of China.
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13
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Use of a Dual Artificial Intelligence Platform to Detect Unreported Lung Nodules. J Comput Assist Tomogr 2021; 45:318-322. [PMID: 33273162 DOI: 10.1097/rct.0000000000001118] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To investigate the performance of Dual-AI Deep Learning Platform in detecting unreported pulmonary nodules that are 6 mm or greater, comprising computer-vision (CV) algorithm to detect pulmonary nodules, with positive results filtered by natural language processing (NLP) analysis of the dictated report. METHODS Retrospective analysis of 5047 chest CT scans and corresponding reports. Cases which were both CV algorithm positive (nodule ≥ 6 mm) and NLP negative (nodule not reported), were outputted for review by 2 chest radiologists. RESULTS The CV algorithm detected nodules that are 6 mm or greater in 1830 (36.3%) of 5047 cases. Three hundred fifty-five (19.4%) were unreported by the radiologist, as per NLP algorithm. Expert review determined that 139 (39.2%) of 355 cases were true positives (2.8% of all cases). One hundred thirty (36.7%) of 355 cases were unnecessary alerts-vague language in the report confounded the NLP algorithm. Eighty-six (24.2%) of 355 cases were false positives. CONCLUSIONS Dual-AI platform detected actionable unreported nodules in 2.8% of chest CT scans, yet minimized intrusion to radiologist's workflow by avoiding alerts for most already-reported nodules.
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14
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Couraud S, Ferretti G, Milleron B, Cortot A, Girard N, Gounant V, Laurent F, Leleu O, Quoix E, Revel MP, Wislez M, Westeel V, Zalcman G, Scherpereel A, Khalil A. [Recommendations of French specialists on screening for lung cancer]. Rev Mal Respir 2021; 38:310-325. [PMID: 33637394 DOI: 10.1016/j.rmr.2021.02.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 01/25/2021] [Indexed: 12/17/2022]
Affiliation(s)
- S Couraud
- Service de pneumologie aiguë spécialisée et cancérologie thoracique, hospices civils de Lyon, hôpital Lyon Sud, Pierre-Bénite, France; Intergroupe francophone de cancérologie thoracique, Paris, France.
| | - G Ferretti
- Intergroupe francophone de cancérologie thoracique, Paris, France; Service de radiologie diagnostique et interventionnel, CHU de Grenoble-Alpes, Grenoble, France
| | - B Milleron
- Intergroupe francophone de cancérologie thoracique, Paris, France
| | - A Cortot
- Intergroupe francophone de cancérologie thoracique, Paris, France; Service de pneumologie et oncologie thoracique, CHU de Lille, Lille, France
| | - N Girard
- Intergroupe francophone de cancérologie thoracique, Paris, France; Unité d'oncologie thoracique, institut Curie, Paris, France
| | - V Gounant
- Intergroupe francophone de cancérologie thoracique, Paris, France; Service d'oncologie thoracique, groupe hospitalier Bichat-Claude-Bernard, AP-HP, Paris, France
| | - F Laurent
- Service de radiologie, CHU de Bordeaux, Pessac, France
| | - O Leleu
- Intergroupe francophone de cancérologie thoracique, Paris, France; Service de pneumologie, centre hospitalier Abbeville, Abbeville, France
| | - E Quoix
- Intergroupe francophone de cancérologie thoracique, Paris, France; Service de pneumologie, CHRU Strasbourg, Strasbourg, France
| | - M-P Revel
- Service de radiologie, hôpital Cochin, Paris, France
| | - M Wislez
- Intergroupe francophone de cancérologie thoracique, Paris, France; Service d'oncologie thoracique, hôpital Cochin, Paris, France
| | - V Westeel
- Intergroupe francophone de cancérologie thoracique, Paris, France; Service de pneumologie et cancérologie thoracique, CHU de Besançon, Besançon, France
| | - G Zalcman
- Intergroupe francophone de cancérologie thoracique, Paris, France; Service d'oncologie thoracique, groupe hospitalier Bichat-Claude-Bernard, AP-HP, Paris, France
| | - A Scherpereel
- Intergroupe francophone de cancérologie thoracique, Paris, France; Service de pneumologie et oncologie thoracique, CHU de Lille, Lille, France
| | - A Khalil
- Intergroupe francophone de cancérologie thoracique, Paris, France; Service de radiologie, groupe hospitalier Bichat-Claude-Bernard, AP-HP, Paris, France
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15
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Intergroupe francophone de cancérologie thoracique, Société de pneumologie de langue française, and Société d'imagerie thoracique statement paper on lung cancer screening. Diagn Interv Imaging 2021; 102:199-211. [PMID: 33648872 DOI: 10.1016/j.diii.2021.01.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 01/21/2021] [Accepted: 01/29/2021] [Indexed: 12/17/2022]
Abstract
Following the American National Lung Screening Trial results in 2011 a consortium of French experts met to edit a statement. Recent results of other randomized trials gave the opportunity for our group to meet again in order to edit updated guidelines. After literature review, we provide here a new update on lung cancer screening in France. Notably, in accordance with all international guidelines, the experts renew their recommendation in favor of individual screening for lung cancer in France as per the conditions laid out in this document. In addition, the experts recommend the very rapid organization and funding of prospective studies, which, if conclusive, will enable the deployment of lung cancer screening organized at the national level.
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16
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Lu Y, Huang J, Li F, Wang Y, Ding M, Zhang J, Yin H, Zhang R, Ren X. EGFR-specific single-chain variable fragment antibody-conjugated Fe 3O 4/Au nanoparticles as an active MRI contrast agent for NSCLC. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2021; 34:581-591. [PMID: 33624188 PMCID: PMC7902179 DOI: 10.1007/s10334-021-00916-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 02/04/2021] [Accepted: 02/05/2021] [Indexed: 12/24/2022]
Abstract
Overexpression of epidermal growth factor receptor (EGFR) is closely associated with a poor prognosis in non-small cell lung cancer (NSCLC), thus making it a promising biomarker for NSCLC diagnosis. Here, we conjugated a single-chain antibody (scFv) targeting EGFR with Fe3O4/Au nanoparticles to form an EGFR-specific molecular MRI bioprobe (scFv@Fe3O4/Au) to better detect EGFR-positive NSCLC tumors in vivo. In vitro, we demonstrated that the EGFR-specific scFv could specifically deliver Fe3O4/Au to EGFR-positive NSCLC cells. In vivo experiments showed that the accumulation of scFv@Fe3O4/Au in tumor tissue was detectable by magnetic resonance imaging (MRI) at the indicated time points after systemic injection. The T2W signal-to-noise ratio (SNR) of EGFR-positive SPC-A1 tumors was significantly decreased after scFv@Fe3O4/Au injection, which was not observed in the tumors of mice injected with BSA@Fe3O4/Au. Furthermore, transmission electron microscopy (TEM) analysis showed the specific localization of scFv@Fe3O4/Au in the SPC-A1 tumor cell cytoplasm. Collectively, the results of our study demonstrated that scFv@Fe3O4/Au might be a useful probe for the noninvasive diagnosis of EGFP-positive NSCLC.
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Affiliation(s)
- Yuan Lu
- Department of Respiratory and Critical Care Medicine, Zhongda Hospital, Southeast University, Nanjing, Jiangsu, China
| | - Jing Huang
- Department of Respiratory and Critical Care Medicine, Zhongda Hospital, Southeast University, Nanjing, Jiangsu, China
| | - Fakai Li
- Department of Respiratory and Critical Care Medicine, Jinhua Guangfu Hospital, Jinhua, Zhejiang, China
| | - Yuan Wang
- The Second Section of Internal Medicine, Xi'an Thoracic Hospital, Xi'an, Shannxi, China
| | - Ming Ding
- Department of Respiratory and Critical Care Medicine, Zhongda Hospital, Southeast University, Nanjing, Jiangsu, China
| | - Jian Zhang
- Department of Respiratory and Critical Care Medicine, Xijing Hospital, Air Force Medical University of PLA (the Fourth Military Medical University), Xi'an, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Air Force Medical University of PLA (the Fourth Military Medical University), Xi'an, Shannxi, China.
| | - Rui Zhang
- The State Key Laboratory of Cancer Biology, Department of Immunology, Air Force Medical University of PLA (the Fourth Military Medical University), Xi'an, Shannxi, China.
| | - Xinling Ren
- Department of Respiratory, Shenzhen University General Hospital, Shenzhen University, Xueyuan Ave. 1098, Shenzhen, 518055, Guangdong, China.
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17
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Vonder M, Dorrius MD, Vliegenthart R. Latest CT technologies in lung cancer screening: protocols and radiation dose reduction. Transl Lung Cancer Res 2021; 10:1154-1164. [PMID: 33718053 PMCID: PMC7947397 DOI: 10.21037/tlcr-20-808] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The aim of this review is to provide clinicians and technicians with an overview of the development of CT protocols in lung cancer screening. CT protocols have evolved from pre-fixed settings in early lung cancer screening studies starting in 2004 towards automatic optimized settings in current international guidelines. The acquisition protocols of large lung cancer screening studies and guidelines are summarized. Radiation dose may vary considerably between CT protocols, but has reduced gradually over the years. Ultra-low dose acquisition can be achieved by applying latest dose reduction techniques. The use of low tube current or tin-filter in combination with iterative reconstruction allow to reduce the radiation dose to a submilliSievert level. However, one should be cautious in reducing the radiation dose to ultra-low dose settings since performed studies lacked generalizability. Continuous efforts are made by international radiology organizations to streamline the CT data acquisition and image quality assurance and to keep track of new developments in CT lung cancer screening. Examples like computer-aided diagnosis and radiomic feature extraction are discussed and current limitations are outlined. Deep learning-based solutions in post-processing of CT images are provided. Finally, future perspectives and recommendations are provided for lung cancer screening CT protocols.
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Affiliation(s)
- Marleen Vonder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Monique D Dorrius
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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18
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Chien SC, Su YJ. Hemoptysis in an elderly woman. J Am Coll Emerg Physicians Open 2021. [PMID: 33392603 DOI: 10.1002/emp2.12319.] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Shih-Chao Chien
- Poison Center Department of Emergency Medicine MacKay Memorial Hospital Taipei Taiwan
| | - Yu-Jang Su
- Poison Center Department of Emergency Medicine MacKay Memorial Hospital Taipei Taiwan.,Department of Medicine MacKay Medical College New Taipei City Taiwan
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19
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Chen J, Pan X, Gu C, Zheng X, Yuan H, Yang J, Sun J. The feasibility of navigation bronchoscopy-guided pulmonary microcoil localization of small pulmonary nodules prior to thoracoscopic surgery. Transl Lung Cancer Res 2020; 9:2380-2390. [PMID: 33489800 PMCID: PMC7815366 DOI: 10.21037/tlcr-20-1206] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Background Accurate preoperative localization of small pulmonary nodules facilitates the rapid and precise video-assisted thoracoscopic surgery (VATS). This study aims to evaluate the feasibility, safety, and efficacy of navigation bronchoscopy-guided pulmonary microcoil placement for preoperative pulmonary nodule localization. Methods Twelve lung lesions were simulated by mixing lipiodol in three porcine models. After 1 week, two microcoils per lesion were deployed under bronchoscopic guidance. Computed tomography scans were then performed 1 day, 1 week, 2 weeks, and 4 weeks after the deployment to assess the position of the microcoils relative to the lesions. Surgical resection of the simulated lesions was performed under fluoroscopy 5 weeks after the deployment and the accuracy, stability, and associated complications of the microcoil localization were evaluated. Following this, an exploratory clinical study was conducted on three patients with pure ground-glass pulmonary nodules. Results The mean diameter of the twelve simulated lung lesions was 9.55±2.36 mm, and the mean distance from the pleura to the lesions was 8.29±2.99 mm. Twenty-four pulmonary microcoils were implanted in the bronchi surrounding the lesions. Four weeks later, the mean distance between the microcoils and the center of the lesions was 16.12±8.97 mm and the average migration of the microcoils relative to the baseline position (1 day after implantation) was 3.48±4.56 mm. All microcoils and target lesions were successfully resected in both the animal experiment and clinical study and no complications, such as pneumothorax, were observed during marker implantation or postoperative follow-up. Conclusions The preoperative localization of pulmonary nodules by navigation bronchoscopy-guided microcoil placement is a safe, stable, and effective technique with minimal complication risk. This procedure can assist subsequent thoracoscopic resection.
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Affiliation(s)
- Junxiang Chen
- Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China
| | - Xufeng Pan
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Chuanjia Gu
- Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China
| | - Xiaoxuan Zheng
- Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China
| | - Haibin Yuan
- Department of Emergency, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Yang
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Jiayuan Sun
- Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China
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20
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Chien S, Su Y. Hemoptysis in an elderly woman. J Am Coll Emerg Physicians Open 2020; 1:1774-1775. [PMID: 33392603 PMCID: PMC7771748 DOI: 10.1002/emp2.12319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 10/21/2020] [Accepted: 10/26/2020] [Indexed: 02/05/2023] Open
Affiliation(s)
- Shih‐Chao Chien
- Poison CenterDepartment of Emergency MedicineMacKay Memorial HospitalTaipeiTaiwan
| | - Yu‐Jang Su
- Poison CenterDepartment of Emergency MedicineMacKay Memorial HospitalTaipeiTaiwan
- Department of MedicineMacKay Medical CollegeNew Taipei CityTaiwan
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21
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Grob D, Oostveen LJ, Jacobs C, Scholten E, Prokop M, Schaefer-Prokop CM, Sechopoulos I, Brink M. Pulmonary nodule enhancement in subtraction CT and dual-energy CT: A comparison study. Eur J Radiol 2020; 134:109443. [PMID: 33310553 DOI: 10.1016/j.ejrad.2020.109443] [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: 08/18/2020] [Revised: 11/10/2020] [Accepted: 11/25/2020] [Indexed: 10/22/2022]
Abstract
OBJECTIVE To compare nodule enhancement on subtraction CT iodine maps to that on dual-energy CT iodine maps using CT datasets acquired simultaneously. METHODS A previously-acquired set of lung subtraction and dual-energy CT maps consisting of thirty patients with 95 solid pulmonary nodules (≥4 mm diameter) was used. Nodules were annotated and segmented on CT angiography, and mean nodule enhancement in the iodine maps calculated. Three radiologists scored nodule visibility with both techniques on a 4-point scale. RESULTS Mean nodule enhancement was higher (p < 0.001) at subtraction CT (34.9 ± 12.9 HU) than at dual-energy CT (25.4 ± 21.0 HU). Nodule enhancement at subtraction CT was judged more often to be "highly visible" for each observers (p < 0.001) with an area under the curve of 0.81. CONCLUSIONS Subtraction CT is able to depict iodine enhancement in pulmonary nodules better than dual-energy CT.
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Affiliation(s)
- Dagmar Grob
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands.
| | - Luuk J Oostveen
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands.
| | - Colin Jacobs
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands.
| | - Ernst Scholten
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands.
| | - Mathias Prokop
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands.
| | - Cornelia M Schaefer-Prokop
- Department of Radiology and Nuclear Medicine, Meander Medical Centre, Maatweg 3, 3813 TZ, Amersfoort, the Netherlands.
| | - Ioannis Sechopoulos
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands.
| | - Monique Brink
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands.
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22
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Lung cancer LDCT screening and mortality reduction - evidence, pitfalls and future perspectives. Nat Rev Clin Oncol 2020; 18:135-151. [PMID: 33046839 DOI: 10.1038/s41571-020-00432-6] [Citation(s) in RCA: 221] [Impact Index Per Article: 55.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2020] [Indexed: 12/17/2022]
Abstract
In the past decade, the introduction of molecularly targeted agents and immune-checkpoint inhibitors has led to improved survival outcomes for patients with advanced-stage lung cancer; however, this disease remains the leading cause of cancer-related mortality worldwide. Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening in high-risk populations - the US National Lung Screening Trial (NLST) and NELSON - have provided evidence of a statistically significant mortality reduction in patients. LDCT-based screening programmes for individuals at a high risk of lung cancer have already been implemented in the USA. Furthermore, implementation programmes are currently underway in the UK following the success of the UK Lung Cancer Screening (UKLS) trial, which included the Liverpool Health Lung Project, Manchester Lung Health Check, the Lung Screen Uptake Trial, the West London Lung Cancer Screening pilot and the Yorkshire Lung Screening trial. In this Review, we focus on the current evidence on LDCT-based lung cancer screening and discuss the clinical developments in high-risk populations worldwide; additionally, we address aspects such as cost-effectiveness. We present a framework to define the scope of future implementation research on lung cancer screening programmes referred to as Screening Planning and Implementation RAtionale for Lung cancer (SPIRAL).
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23
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Aspergillus Mimicking a Rasmussen Aneurysm in an Immunocompromised Setting Causing Massive Hemoptysis. INFECTIOUS DISEASES IN CLINICAL PRACTICE 2020. [DOI: 10.1097/ipc.0000000000000878] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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24
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Campbell-Washburn AE. 2019 American Thoracic Society BEAR Cage Winning Proposal: Lung Imaging Using High-Performance Low-Field Magnetic Resonance Imaging. Am J Respir Crit Care Med 2020; 201:1333-1336. [PMID: 32298594 PMCID: PMC7258650 DOI: 10.1164/rccm.201912-2505ed] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
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25
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Cui S, Ming S, Lin Y, Chen F, Shen Q, Li H, Chen G, Gong X, Wang H. Development and clinical application of deep learning model for lung nodules screening on CT images. Sci Rep 2020; 10:13657. [PMID: 32788705 PMCID: PMC7423892 DOI: 10.1038/s41598-020-70629-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 07/29/2020] [Indexed: 12/11/2022] Open
Abstract
Lung cancer screening based on low-dose CT (LDCT) has now been widely applied because of its effectiveness and ease of performance. Radiologists who evaluate a large LDCT screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small nodules, lack of consistent criteria, etc. It requires an efficient method for helping radiologists improve nodule detection accuracy with efficiency and cost-effectiveness. Many novel deep neural network-based systems have demonstrated the potential for use in the proposed technique to detect lung nodules. However, the effectiveness of clinical practice has not been fully recognized or proven. Therefore, the aim of this study to develop and assess a deep learning (DL) algorithm in identifying pulmonary nodules (PNs) on LDCT and investigate the prevalence of the PNs in China. Radiologists and algorithm performance were assessed using the FROC score, ROC-AUC, and average time consumption. Agreement between the reference standard and the DL algorithm in detecting positive nodules was assessed per-study by Bland-Altman analysis. The Lung Nodule Analysis (LUNA) public database was used as the external test. The prevalence of NCPNs was investigated as well as other detailed information regarding the number of pulmonary nodules, their location, and characteristics, as interpreted by two radiologists.
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Affiliation(s)
- Sijia Cui
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, 310013, China
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Shuai Ming
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, 310013, China
| | - Yi Lin
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, 310013, China
| | - Fanghong Chen
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, 310013, China
| | - Qiang Shen
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, 310013, China
| | - Hui Li
- Hangzhou Yitu Healthcare Technology Co., Ltd, Hangzhou, 310000, China
| | - Gen Chen
- Hangzhou Yitu Healthcare Technology Co., Ltd, Hangzhou, 310000, China
| | - Xiangyang Gong
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, 310013, China.
- Institute of Artificial Intelligence and Remote Imaging, Hangzhou Medical College, Hangzhou, 310000, China.
| | - Haochu Wang
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, 310013, China.
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26
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Tugwell-Allsup J, Owen BW, England A. Low-dose chest CT and the impact on nodule visibility. Radiography (Lond) 2020; 27:24-30. [PMID: 32499090 DOI: 10.1016/j.radi.2020.05.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/07/2020] [Accepted: 05/08/2020] [Indexed: 11/16/2022]
Abstract
INTRODUCTION The need to continually optimise CT protocols is essential to ensure the lowest possible radiation dose for the clinical task and individual patient. The aim of this study was to explore the effect of reducing effective mAs on nodule detection and radiation dose across six scanners. METHODS An anthropomorphic chest phantom was scanned using a low-dose chest CT protocol, with the effective mAs lowered to the lowest permissible level. All other acquisition parameters remained consistent. Images were evaluated by five radiologists to determine their sensitivity in detecting six simulated nodules within the phantom. Image noise was calculated together with DLP. RESULTS The lowest possible mAs achievable ranged from 7 to 19 mAs. The two highest mAs setting (17 mAs + 19 mAs) had kV modulation enabled (100 kV instead of 120 kV) which consequently resulted in a higher nodule detection rate. Overall nodule detection averaged at 91% (range 80-97%). Out of a possible 180 nodules, 16 were missed, with 12 of those 16 being the same nodule. Noise was double for the Somatom Sensation scanner when compared to the others; however, this scanner did not have iterative reconstruction and it was installed over 10 years ago. There was a strong correlation between image noise and scanner age. CONCLUSION This study highlighted that nodules can be detected at very low effective mAs (<20 mAs) but only when other acquisition parameters are optimised i.e. iterative reconstruction and kV modulation. Nodule detection rates were affected by nodule location and image noise. IMPLICATIONS FOR PRACTICE This study consolidates previous findings on how to successfully optimise low-dose chest CT. It also highlights the difficulty with standardisation owing to factors such as scanner age and different vendor attributes.
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Affiliation(s)
- J Tugwell-Allsup
- Betsi Cadwaladr University Health Board, Bangor, Gwynedd, Wales, LL57 2PW, UK.
| | - B W Owen
- Betsi Cadwaladr University Health Board, Bangor, Gwynedd, Wales, LL57 2PW, UK.
| | - A England
- School of Health Sciences, Salford University, Manchester, M6 6PU, UK.
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27
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Abstract
The 2010's saw demonstration of the power of lung cancer screening to reduce mortality. However, with implementation of lung cancer screening comes the challenge of diagnosing millions of lung nodules every year. When compared to other cancers with widespread screening strategies (breast, colorectal, cervical, prostate, and skin), obtaining a lung nodule tissue biopsy to confirm a positive screening test remains associated with higher morbidity and cost. Therefore, non-invasive diagnostic biomarkers may have a unique opportunity in lung cancer to greatly improve the management of patients at risk. This review covers recent advances in the field of liquid biomarkers and computed tomographic imaging features, with special attention to new methods for combination of biomarkers as well as the use of artificial intelligence for the discrimination of benign from malignant nodules.
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Affiliation(s)
- Michael N Kammer
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA.,Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Pierre P Massion
- Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.,Cancer Early Detection and Prevention Initiative, Vanderbilt Ingram Cancer Center, Nashville, TN, USA.,Medical Service, Tennessee Valley Healthcare Systems, Nashville Campus, Nashville, TN, USA
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28
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Comparison of the diagnostic accuracy of diffusion-weighted magnetic resonance imaging and positron emission tomography/computed tomography in pulmonary nodules: a prospective study. Pol J Radiol 2020; 84:e498-e503. [PMID: 32082446 PMCID: PMC7016491 DOI: 10.5114/pjr.2019.91200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 10/21/2019] [Indexed: 11/30/2022] Open
Abstract
Purpose Computed tomography (CT) and positron emission tomography (PET) are the mainstay imaging methods in the evaluation and follow-up of pulmonary nodules. But they both have high radiation risk for patients. Diffusion- weighted magnetic resonance imaging (DW-MRI), on the other hand, is a radiation free imaging method that gives information about the biological structure of tissues at the molecular level by measuring random movement of water in biological tissues. In this prospective study we aimed to compare the computed tomography characteristics of the nodules in terms of malignancy and to compare the accuracy of DW-MRI and PET/CT results in those patients. Material and methods Seventy-six patients suspicious for lung cancer on thorax CT imaging were prospectively further evaluated by thorax diffusion-weighted imaging and PET/CT. Pulmonary lesion characteristics, apparent diffusion coefficient (ADC), and maximum standardised uptake values (SUVmax) were compared with histopathological results. Results There was statistically significant moderate negative correlation between PET-SUVmax and ADC values of lung lesions. ADC values below the cut-off was 97.1%, specificity was 97.6%, positive predictive value was 97.1%, and the negative predictive value was 97.6%. Conclusions DAG-MRI and PET/CT have similar success in the differentiation of benign and malignant lung lesions.
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29
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A fully-automated system for identification and classification of subsolid nodules in lung computed tomographic scans. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.101586] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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30
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Chiles C, Munden RF. Lung cancer screening: the path forward. Transl Lung Cancer Res 2018; 7:216-219. [PMID: 30050760 PMCID: PMC6037961 DOI: 10.21037/tlcr.2018.06.02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Affiliation(s)
- Caroline Chiles
- Department of Radiology, Wake Forest Health Sciences Center, Winston-Salem, NC, USA
| | - Reginald F Munden
- Department of Radiology, Wake Forest Health Sciences Center, Winston-Salem, NC, USA
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