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Yan TC, Yu SW, Liu L, Xu JH, Wang ST, Li N, Guan HN, Pan N, Zhang T. Oxygen-Enhanced ZTE-MRI for Pulmonary Nodule Assessment: A Comparative Study with CT. Acad Radiol 2025:S1076-6332(25)00387-3. [PMID: 40348712 DOI: 10.1016/j.acra.2025.04.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2025] [Revised: 04/11/2025] [Accepted: 04/12/2025] [Indexed: 05/14/2025]
Abstract
BACKGROUND Frequent computed tomography (CT) scans for pulmonary nodule monitoring (every 3, 6, and 12 months) lead to increased radiation exposure and a potential risk of malignancy. Although lung magnetic resonance imaging (MRI) is gradually approaching CT in terms of performance, the effectiveness of zero-echo-time (ZTE) sequences remains to be fully optimized, particularly in terms of diagnostic accuracy under the Lung-RADS. OBJECTIVE This study aimed to evaluate the feasibility of oxygen-enhanced (OE) ZTE-MRI at varying oxygen concentrations (21% and 100%) for both the subjective and objective assessment of pulmonary nodules. It further explored the potential of OE-ZTE-MRI in detecting nodules, its diagnostic utility in Lung-RADS classification, and its role in evaluating malignant potential. METHODS A total of 68 participants who underwent CT, ZTE-MRI, or OE-ZTE-MRI, were enrolled in this study. Quantitative MRI parameters (signal-to-noise ratio [SNR] and contrast-to-noise ratio [CNR]) were used to evaluate image quality. Lung nodule detection and Lung-RADS classification were performed by two radiologists independently using observer-based scoring of structural features: nodule type (solid nodule [SN], part-solid nodule [PSN], and ground-glass nodule [GGN]), and nodule size (measured manually on CT, ZTE-MRI, and OE-ZTE-MRI). Statistical analyses included the Wilcoxon signed-rank test, percentage consistency, kappa values, intraclass correlation coefficient (ICC), Spearman`s correlation, and Bland-Altman analysis. Statistical significance was set at p < 0.05. RESULTS Among the 68 patients (80 nodules; 57.2 ± 10.7 years; 27 males), OE-ZTE-MRI demonstrated a significantly higher SNR (p < 0.05) and superior qualitative scoring compared to ZTE-MRI. The nodule detection rate for OE-ZTE-MRI was 87.5%, with a diagnostic performance comparable to that of CT for assessing nodule diameter (ICC: 0.997; r = 0.994). OE-ZTE-MRI showed a high agreement with CT in nodule characterization (kappa = 0.789) and Lung-RADS (kappa = 0.756). Additionally, OE-ZTE-MRI exhibited strong inter-observer consistency in nodule size measurements. CONCLUSION OE-ZTE-MRI, which incorporates oxygen concentration adjustments, outperformed conventional ZTE-MRI in both subjective and objective evaluations. It achieves diagnostic performance comparable to that of CT in terms of nodule size. According to the Lung-RADS classification, OE-ZTE-MRI is gradually approaching the same diagnostic accuracy as CT.
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Affiliation(s)
- Tian-Cai Yan
- Department of Radiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150000, China (T.C.Y., S.W.Y., L.L., J.H.X., S.T.W., N.L., T.Z.)
| | - Si-Wen Yu
- Department of Radiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150000, China (T.C.Y., S.W.Y., L.L., J.H.X., S.T.W., N.L., T.Z.)
| | - Ling Liu
- Department of Radiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150000, China (T.C.Y., S.W.Y., L.L., J.H.X., S.T.W., N.L., T.Z.)
| | - Jia-Heng Xu
- Department of Radiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150000, China (T.C.Y., S.W.Y., L.L., J.H.X., S.T.W., N.L., T.Z.)
| | - Shu-Ting Wang
- Department of Radiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150000, China (T.C.Y., S.W.Y., L.L., J.H.X., S.T.W., N.L., T.Z.)
| | - Na Li
- Department of Radiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150000, China (T.C.Y., S.W.Y., L.L., J.H.X., S.T.W., N.L., T.Z.)
| | - Hao-Nan Guan
- General Electronics (GE) Healthcare MR Research China, Beijing 100176, China (H.N.G.)
| | - Nan Pan
- Department of Thoracic Surgery, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150000, China (N.P.)
| | - Tong Zhang
- Department of Radiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150000, China (T.C.Y., S.W.Y., L.L., J.H.X., S.T.W., N.L., T.Z.).
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Fink N, Sperl JI, Rueckel J, Stüber T, Goller SS, Rudolph J, Escher F, Aschauer T, Hoppe BF, Ricke J, Sabel BO. Artificial intelligence-based automated matching of pulmonary nodules on follow-up chest CT. Eur Radiol Exp 2025; 9:48. [PMID: 40316834 PMCID: PMC12048373 DOI: 10.1186/s41747-025-00579-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2024] [Accepted: 03/18/2025] [Indexed: 05/04/2025] Open
Abstract
BACKGROUND The growing demand for follow-up imaging highlights the need for tools supporting the assessment of pulmonary nodules over time. We evaluated the performance of an artificial intelligence (AI)-based system for automated nodule matching. METHODS In this single-center study, patients with nodules and ≤ 2 chest computed tomography (CT) examinations were retrospectively selected. An AI-based algorithm was used for automated nodule detection and matching. The matching rate and the causes for incorrect matching were evaluated for the ten largest lesions (5-30 mm in diameter) registered on baseline CT. The dependence of the matching rate on nodule number and localization was also analyzed. RESULTS One hundred patients (46 females), with a median age of 62 years (interquartile range 57-69), and 253 CTs were included. Focusing on the ten largest lesions, 1,141 lesions were identified, of which 36 (3.2%) were other structures incorrectly identified as nodules (false-positives). Of the 1,105 identified nodules, 964 (87.2%) were correctly detected and matched. The matching rate for nodules registered in both baseline and follow-up scans was 97.8%. The matching rate per case ranged 80.0-100.0% (median 90.0%). Correct matching rate decreased in follow-up examinations to over 50 nodules (p = 0.003), with an overrepresentation of missed matching. Matching rates were higher in parenchymal (91.8%), peripheral (84.4%), and juxtavascular (82.4%) nodules than in juxtaphrenic nodules (71.1%) (p < 0.001). Missed matching was overrepresented in juxtavascular, and incorrect assignment in juxtaphrenic nodules. CONCLUSION The correct automated-matching rate of metastatic pulmonary nodules in follow-up examinations was high, but it depends on localization and a number of nodules. RELEVANCE STATEMENT The algorithm enables precise follow-up matching of pulmonary nodules, potentially providing a solid basis for standardized and accurate evaluations. Understanding the algorithm's strengths and weaknesses based on nodule localization and number enhances the interpretation of AI-based results. KEY POINTS The AI algorithm achieved a correct nodule matching rate of 87.2% and up to 97.8% when considering nodules detected in both baseline and follow-up scans. Matching accuracy depended on nodule number and localization. This algorithm has the potential to support response evaluation criteria in solid tumor-based evaluations in clinical practice.
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Affiliation(s)
- Nicola Fink
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
- Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany.
| | | | - Johannes Rueckel
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Theresa Stüber
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- Department of Statistics, Statistical Learning & Data Science, LMU Munich, Munich, Germany
| | - Sophia S Goller
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Jan Rudolph
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Felix Escher
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Theresia Aschauer
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Boj F Hoppe
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Bastian O Sabel
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
- Department of Radiology, Asklepios Lung Clinic Munich-Gauting, Gauting, Germany
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Wang X, Cui Y, Wang Y, Liu S, Meng N, Wei W, Bai Y, Shen Y, Guo J, Guo Z, Wang M. Assessment of Lung Nodule Detection and Lung CT Screening Reporting and Data System Classification Using Zero Echo Time Pulmonary MRI. J Magn Reson Imaging 2025; 61:822-829. [PMID: 38602245 DOI: 10.1002/jmri.29388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/27/2024] [Accepted: 03/28/2024] [Indexed: 04/12/2024] Open
Abstract
BACKGROUND The detection rate of lung nodules has increased considerably with CT as the primary method of examination, and the repeated CT examinations at 3 months, 6 months or annually, based on nodule characteristics, have increased the radiation exposure of patients. So, it is urgent to explore a radiation-free MRI examination method that can effectively address the challenges posed by low proton density and magnetic field inhomogeneities. PURPOSE To evaluate the potential of zero echo time (ZTE) MRI in lung nodule detection and lung CT screening reporting and data system (lung-RADS) classification, and to explore the value of ZTE-MRI in the assessment of lung nodules. STUDY TYPE Prospective. POPULATION 54 patients, including 21 men and 33 women. FIELD STRENGTH/SEQUENCE Chest CT using a 16-slice scanner and ZTE-MRI at 3.0T based on fast gradient echo. ASSESSMENT Nodule type (ground-glass nodules, part-solid nodules, and solid nodules), lung-RADS classification, and nodule diameter (manual measurement) on CT and ZTE-MRI images were recorded. STATISTICAL TESTS The percent of concordant cases, Kappa value, intraclass correlation coefficient (ICC), Wilcoxon signed-rank test, Spearman's correlation, and Bland-Altman. The p-value <0.05 is considered significant. RESULTS A total of 54 patients (age, 54.8 ± 11.9 years; 21 men) with 63 nodules were enrolled. Compared with CT, the total nodule detection rate of ZTE-MRI was 85.7%. The intermodality agreement of ZTE-MRI and CT lung nodules type evaluation was substantial (Kappa = 0.761), and the intermodality agreement of ZTE-MRI and CT lung-RADS classification was moderate (Kappa = 0.592). The diameter measurements between ZTE-MRI and CT showed no significant difference and demonstrated a high degree of interobserver (ICC = 0.997-0.999) and intermodality (ICC = 0.956-0.985) agreements. DATA CONCLUSION The measurement of nodule diameter by pulmonary ZTE-MRI is similar to that by CT, but the ability of lung-RADS to classify nodes from MRI images still requires further research. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Xinhui Wang
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Yingying Cui
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Ying Wang
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Shuo Liu
- Department of Medical Imaging, Xinxiang Medical University and Henan Provincial People's Hospital, Zhengzhou, China
| | - Nan Meng
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Wei Wei
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Yan Bai
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Yu Shen
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | | | - Zhiping Guo
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
- Health Management Center of Henan Province, Zhengzhou University People's Hospital and FuWai Central China Cardiovascular Hospital, Zhengzhou, China
| | - Meiyun Wang
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Biomedical Research Institute, Henan Academy of Sciences, Zhengzhou, China
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Paramasamy J, Mandal S, Blomjous M, Mulders T, Bos D, Aerts JGJV, Vanapalli P, Challa V, Sathyamurthy S, Devi R, Jain R, Visser JJ. Validation of a commercially available CAD-system for lung nodule detection and characterization using CT-scans. Eur Radiol 2025; 35:1076-1088. [PMID: 39042303 PMCID: PMC11782423 DOI: 10.1007/s00330-024-10969-0] [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: 03/15/2024] [Revised: 05/27/2024] [Accepted: 06/30/2024] [Indexed: 07/24/2024]
Abstract
OBJECTIVES This study aims to externally validate a commercially available Computer-Aided Detection (CAD)-system for the automatic detection and characterization of solid, part-solid, and ground-glass lung nodules (LN) on CT scans. METHODS This retrospective study encompasses 263 chest CT scans performed between January 2020 and December 2021 at a Dutch university hospital. All scans were read by a radiologist (R1) and compared with the initial radiology report. Conflicting scans were assessed by an adjudicating radiologist (R2). All scans were also processed by CAD. The standalone performance of CAD in terms of sensitivity and false-positive (FP)-rate for detection was calculated together with the sensitivity for characterization, including texture, calcification, speculation, and location. The R1's detection sensitivity was also assessed. RESULTS A total of 183 true nodules were identified in 121 nodule-containing scans (142 non-nodule-containing scans), of which R1 identified 165/183 (90.2%). CAD detected 149 nodules, of which 12 were not identified by R1, achieving a sensitivity of 149/183 (81.4%) with an FP-rate of 49/121 (0.405). CAD's detection sensitivity for solid, part-solid, and ground-glass LNs was 82/94 (87.2%), 42/47 (89.4%), and 25/42 (59.5%), respectively. The classification accuracy for solid, part-solid, and ground-glass LNs was 81/82 (98.8%), 16/42 (38.1%), and 18/25 (72.0%), respectively. Additionally, CAD demonstrated overall classification accuracies of 137/149 (91.9%), 123/149 (82.6%), and 141/149 (94.6%) for calcification, spiculation, and location, respectively. CONCLUSIONS Although the overall detection rate of this system slightly lags behind that of a radiologist, CAD is capable of detecting different LNs and thereby has the potential to enhance a reader's detection rate. While promising characterization performances are obtained, the tool's performance in terms of texture classification remains a subject of concern. CLINICAL RELEVANCE STATEMENT Numerous lung nodule computer-aided detection-systems are commercially available, with some of them solely being externally validated based on their detection performance on solid nodules. We encourage researchers to assess performances by incorporating all relevant characteristics, including part-solid and ground-glass nodules. KEY POINTS Few computer-aided detection (CAD) systems are externally validated for automatic detection and characterization of lung nodules. A detection sensitivity of 81.4% and an overall texture classification sensitivity of 77.2% were measured utilizing CAD. CAD has the potential to increase single reader detection rate, however, improvement in texture classification is required.
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Affiliation(s)
- Jasika Paramasamy
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Souvik Mandal
- Qure.ai, Level 7, Oberoi Commerz II, Goregaon East, Mumbai, 400063, India
| | - Maurits Blomjous
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Ties Mulders
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Daniel Bos
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Joachim G J V Aerts
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Prakash Vanapalli
- Qure.ai, Level 7, Oberoi Commerz II, Goregaon East, Mumbai, 400063, India
| | - Vikash Challa
- Qure.ai, Level 7, Oberoi Commerz II, Goregaon East, Mumbai, 400063, India
| | | | - Ranjana Devi
- Qure.ai, Level 7, Oberoi Commerz II, Goregaon East, Mumbai, 400063, India
| | - Ritvik Jain
- Qure.ai, Level 7, Oberoi Commerz II, Goregaon East, Mumbai, 400063, India
| | - Jacob J Visser
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.
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Afridi WA, Picos SH, Bark JM, Stamoudis DAF, Vasani S, Irwin D, Fielding D, Punyadeera C. Minimally invasive biomarkers for triaging lung nodules-challenges and future perspectives. Cancer Metastasis Rev 2025; 44:29. [PMID: 39888565 PMCID: PMC11785609 DOI: 10.1007/s10555-025-10247-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2024] [Accepted: 01/23/2025] [Indexed: 02/01/2025]
Abstract
CT chest scans are commonly performed worldwide, either in routine clinical practice for a wide range of indications or as part of lung cancer screening programs. Many of these scans detect lung nodules, which are small, rounded opacities measuring 8-30 mm. While the concern about nodules is that they may represent early lung cancer, in screening programs, only 1% of such nodules turn out to be cancer. This leads to a series of complex decisions and, at times, unnecessary biopsies for nodules that are ultimately determined to be benign. Additionally, patients may be anxious about the status of detected lung nodules. The high rate of false positive lung nodule detections has driven advancements in biomarker-based research aimed at triaging lung nodules (benign versus malignant) to identify truly malignant nodules better. Biomarkers found in biofluids and breath hold promise owing to their minimally invasive sampling methods, ease of use, and cost-effectiveness. Although several biomarkers have demonstrated clinical utility, their sensitivity and specificity are still relatively low. Combining multiple biomarkers could enhance the characterisation of small pulmonary nodules by addressing the limitations of individual biomarkers. This approach may help reduce unnecessary invasive procedures and accelerate diagnosis in the future. This review offers a thorough overview of emerging minimally invasive biomarkers for triaging lung nodules, emphasising key challenges and proposing potential solutions for biomarker-based nodule differentiation. It focuses on diagnosis rather than screening, analysing research published primarily in the past five years with some exceptions. The incorporation of biomarkers into clinical practice will facilitate the early detection of malignant nodules, leading to timely interventions and improved outcomes. Further efforts are needed to increase the cost-effectiveness and practicality of many of these applications in clinical settings. However, the range of technologies is advancing rapidly, and they may soon be implemented in clinics in the near future.
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Affiliation(s)
- Waqar Ahmed Afridi
- Saliva and Liquid Biopsy Translational Laboratory, Institute for Biomedicine and Glycomics (IBG), Griffith University, Brisbane, 4111, Australia
- Virtual University of Pakistan, Islamabad, 44000, Pakistan
| | - Samandra Hernandez Picos
- Saliva and Liquid Biopsy Translational Laboratory, Institute for Biomedicine and Glycomics (IBG), Griffith University, Brisbane, 4111, Australia
| | - Juliana Muller Bark
- Saliva and Liquid Biopsy Translational Laboratory, Institute for Biomedicine and Glycomics (IBG), Griffith University, Brisbane, 4111, Australia
| | - Danyelle Assis Ferreira Stamoudis
- Saliva and Liquid Biopsy Translational Laboratory, Institute for Biomedicine and Glycomics (IBG), Griffith University, Brisbane, 4111, Australia
| | - Sarju Vasani
- Department of Otolaryngology, Royal Brisbane and Women's Hospital, Herston, 4006, Australia
| | - Darryl Irwin
- The Agena Biosciences, Bowen Hills, Brisbane, 4006, Australia
| | - David Fielding
- The Royal Brisbane and Women's Hospital, Herston, Brisbane, 4006, Australia
| | - Chamindie Punyadeera
- Saliva and Liquid Biopsy Translational Laboratory, Institute for Biomedicine and Glycomics (IBG), Griffith University, Brisbane, 4111, Australia.
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Ding L, Chen M, Li X, Wu Y, Li J, Deng S, Xu Y, Chen Z, Yan C. Ultra-low dose dual-layer detector spectral CT for pulmonary nodule screening: image quality and diagnostic performance. Insights Imaging 2025; 16:11. [PMID: 39792229 PMCID: PMC11723867 DOI: 10.1186/s13244-024-01888-1] [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: 06/29/2024] [Accepted: 12/15/2024] [Indexed: 01/12/2025] Open
Abstract
OBJECTIVES To investigate the image quality and diagnostic performance with ultra-low dose dual-layer detector spectral CT (DLSCT) by various reconstruction techniques for evaluation of pulmonary nodules. MATERIALS AND METHODS Between April 2023 and December 2023, patients with suspected pulmonary nodules were prospectively enrolled and underwent regular-dose chest CT (RDCT; 120 kVp/automatic tube current) and ultra-low dose CT (ULDCT; 100 kVp/10 mAs) on a DLSCT scanner. ULDCT was reconstructed with hybrid iterative reconstruction (HIR), electron density map (EDM), and virtual monoenergetic images at 40 keV and 70 keV. Quantitative and qualitative image analysis, nodule detectability, and Lung-RADS evaluation were compared using repeated one-way analysis of variance, Friedman test, and weighted kappa coefficient. RESULTS A total of 249 participants (mean age ± standard deviation, 50.0 years ± 12.9; 126 male) with 637 lung nodules were included. ULDCT resulted in a significantly lower mean radiation dose than RDCT (0.3 mSv ± 0.0 vs. 3.6 mSv ± 0.8; p < 0.001). Compared with RDCT, ULDCT EDM showed significantly higher signal-noise-ratio (44.0 ± 77.2 vs. 4.6 ± 6.6; p < 0.001) and contrast-noise-ratio (26.7 ± 17.7 vs. 5.0 ± 4.4; p < 0.001) with qualitative scores ranked higher or equal to the average. Using the regular-dose images as a reference, ULDCT EDM images had a satisfactory nodule detection rate (84.6%) and good inter-observer agreements compared with RDCT (κw > 0.60). CONCLUSION Ultra-low dose dual-layer detector CT with 91.2% radiation dose reduction achieves sufficient image quality and diagnostic performance of pulmonary nodules. CRITICAL RELEVANCE STATEMENT Dual-layer detector spectral CT enables substantial radiation dose reduction without impairing image quality for the follow-up of pulmonary nodules or lung cancer screening. KEY POINTS Radiation dose is a major concern for patients requiring pulmonary nodules CT screening. Ultra-low dose dual-layer detector spectral CT with 91.2% dose reduction demonstrated satisfactory performance. Dual-layer detector spectral CT has the potential for lung cancer screening and management.
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Affiliation(s)
- Li Ding
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Mingwang Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Xiaomei Li
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Yuting Wu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Jingxu Li
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China
| | - Shuting Deng
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Yikai Xu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Zhao Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Chenggong Yan
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
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Avram C, Mederle AO, Mavrea A, Barata PI, Patrascu R. Comparison of Lung-RADS Version 2022 and British Thoracic Society Guidelines in Classifying Solid Pulmonary Nodules Detected at Lung Cancer Screening CT. Life (Basel) 2024; 15:14. [PMID: 39859954 PMCID: PMC11767224 DOI: 10.3390/life15010014] [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: 11/04/2024] [Revised: 12/18/2024] [Accepted: 12/26/2024] [Indexed: 01/27/2025] Open
Abstract
BACKGROUND AND OBJECTIVES Lung cancer screening is critical for early detection and management, particularly through the use of computed tomography (CT). This study aims to compare the Lung Imaging Reporting and Data System (Lung-RADS) Version 2022 with the British Thoracic Society (BTS) guidelines in classifying solid pulmonary nodules detected at lung cancer screening CT examinations. MATERIALS AND METHODS This retrospective study included 224 patients who underwent lung cancer screening CT between 2016 and 2022 and had a reported solid pulmonary nodule. A fellowship-trained thoracic radiologist reviewed the CT images, characterizing nodules by size, location, margins, attenuation, calcification, growth at follow-up, and final pathologic diagnosis if malignant. The sensitivity and specificity of Lung-RADS Version 2022 in detecting malignant nodules were compared with those of the BTS guidelines using the McNemar test. RESULTS Of the 224 patients, 198 (88%) had nodules deemed benign, while 26 (12%) had malignant nodules. The Lung-RADS Version 2022 resulted in higher specificity than the BTS guidelines (85% vs. 65%, p < 0.001), without sacrificing sensitivity (92% for both). Nodules larger than 8 mm, spiculated margins, upper lobe location, and interval growth were associated with higher malignancy risk (p < 0.01). CONCLUSIONS Compared with the BTS guidelines, Lung-RADS Version 2022 reduces the number of false-positive screening CT examinations while maintaining high sensitivity for detecting malignant solid pulmonary nodules.
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Affiliation(s)
- Claudiu Avram
- Doctoral School, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania;
| | - Alexandru Ovidiu Mederle
- Department of Surgery, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania;
| | - Adelina Mavrea
- Department of Internal Medicine I, Cardiology Clinic, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Paula Irina Barata
- Center for Research and Innovation in Precision Medicine of Respiratory Diseases, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania;
- Department of Physiology, Faculty of Medicine, “Vasile Goldis” Western University of Arad, 310025 Arad, Romania
| | - Raul Patrascu
- Department of Functional Science, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania;
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Jungblut L, Rizzo SM, Ebner L, Kobe A, Nguyen-Kim TDL, Martini K, Roos J, Puligheddu C, Afshar-Oromieh A, Christe A, Dorn P, Funke-Chambour M, Hötker A, Frauenfelder T. Advancements in lung cancer: a comprehensive perspective on diagnosis, staging, therapy and follow-up from the SAKK Working Group on Imaging in Diagnosis and Therapy Monitoring. Swiss Med Wkly 2024; 154:3843. [PMID: 39835913 DOI: 10.57187/s.3843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2025] Open
Abstract
In 2015, around 4400 individuals received a diagnosis of lung cancer, and Switzerland recorded approximately 3200 deaths related to lung cancer. Advances in detection, such as lung cancer screening and improved treatments, have led to increased identification of early-stage lung cancer and higher chances of long-term survival. This progress has introduced new considerations in imaging, emphasising non-invasive diagnosis and characterisation techniques like radiomics. Treatment aspects, such as preoperative assessment and the implementation of immune response evaluation criteria in solid tumours (iRECIST), have also seen advancements. For those undergoing curative treatment for lung cancer, guidelines propose follow-up with computed tomography (CT) scans within a specific timeframe. However, discrepancies exist in published guidelines, and there is a lack of universally accepted recommendations for follow-up procedures. This white paper aims to provide a certain standard regarding the use of imaging on the diagnosis, staging, treatment and follow-up of patients with lung cancer.
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Affiliation(s)
- Lisa Jungblut
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stefania Maria Rizzo
- Service of Radiology, Imaging Institute of Southern Switzerland, Clinica Di Radiologia EOC, Lugano, Switzerland
| | - Lukas Ebner
- Department of Radiology and Nuclear Medicine, Luzerner Kantonsspital, Lucerne, Switzerland
| | - Adrian Kobe
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Thi Dan Linh Nguyen-Kim
- Institute of Radiology and Nuclear Medicine, Stadtspital Triemli Zurich, Zurich, Switzerland
| | - Katharina Martini
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Justus Roos
- Department of Radiology and Nuclear Medicine, Luzerner Kantonsspital, Lucerne, Switzerland
| | - Carla Puligheddu
- Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland
| | - Ali Afshar-Oromieh
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Andreas Christe
- Department of Radiology SLS, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Patrick Dorn
- Department of General Thoracic Surgery, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Manuela Funke-Chambour
- Department of Pulmonary Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Andreas Hötker
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Thomas Frauenfelder
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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9
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Hui YM, Guo Y, Li B, Meng YQ, Feng HM, Su ZP, Lin MZ, Chen YZ, Zheng ZZ, Li HT. Comparative analysis of three-dimensional and two-dimensional models for predicting the malignancy probability of subsolid nodules. Clin Radiol 2024; 79:781-790. [PMID: 39068114 DOI: 10.1016/j.crad.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/30/2024]
Abstract
AIM To construct three-dimensional (3D) and two-dimensional (2D) models to predict the malignancy probability of subsolid nodules (SSNs) and compare their effectiveness. MATERIALS AND METHODS A total of 371 SSNs from 332 patients, collected between January 2020 and January 2024, were included in the study. The SSNs were divided into a training set for constructing the models and a test set for validating the models. Models were developed using binary logistic backward regression, based on factors that showed significant differences in univariate analyses. The performance of the models was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC). The AUCs of different models were compared using the DeLong test. RESULTS The AUCs for the two 3D models, one 2D model, and the Brock model were 0.785 (0.733-0.836), 0.776 (0.723-0.829), 0.764 (0.710-0.818), and 0.738 (0.679-0.798) in the training set. In the test set, these AUCs were 0.817 (0.706-0.928), 0.796 (0.679-0.913), 0.771 (0.647-0.895), and 0.790 (0.678-0.903). The two 3D models demonstrated statistically significant differences from the Brock model in the training set (P=0.024 and P=0.046). None of the four models showed significant differences in the test set (all P>0.05). CONCLUSION The 3D models outperform both the 2D model and the Brock model in predicting the malignancy probability of SSNs, and the 3D model incorporating volume, mean CT attenuation value, and lobulation as factors performed the best.
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Affiliation(s)
- Y-M Hui
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - Y Guo
- Department of Radiology, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - B Li
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - Y-Q Meng
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - H-M Feng
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - Z-P Su
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - M-Z Lin
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - Y-Z Chen
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - Z-Z Zheng
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - H-T Li
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
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10
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Resnick K, Shah A, Mason J, Kuhn P, Nieva J, Shishido SN. Circulation of rare events in the liquid biopsy for early detection of lung mass lesions. Thorac Cancer 2024; 15:2100-2109. [PMID: 39233479 PMCID: PMC11471425 DOI: 10.1111/1759-7714.15429] [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: 05/14/2024] [Revised: 07/30/2024] [Accepted: 08/05/2024] [Indexed: 09/06/2024] Open
Abstract
BACKGROUND Lung cancer screening with low-dose computed tomography (CT) scans (LDCT) has reduced mortality for patients with high-risk smoking histories, but it has significant limitations: LDCT screening implementation remains low, high rates of false-positive scans, and current guidelines exclude those without smoking histories. We sought to explore the utility of liquid biopsy (LBx) in early cancer screening and diagnosis of lung cancer. METHODS Using the high-definition single-cell assay workflow, we analyzed 99 peripheral blood samples from three cohorts: normal donors (NDs) with no known pathology (n = 50), screening CT patients (n = 25) with Lung-RADS score of 1-2, and biopsy (BX) patients (n = 24) with abnormal CT scans requiring tissue biopsy. RESULTS For CT and BX patients, demographic information was roughly equivalent; however, average pack-years smoked differed. A total of 14 (58%) BX patients were diagnosed with primary lung cancer (BX+). The comparison of the rare event enumerations among the cohorts revealed a greater incidence of total events, rare cells, and oncosomes, as well as specific cellular phenotypes in the CT and BX cohorts compared with the ND cohort. LBx analytes were also significantly elevated in the BX compared with the CT samples, but there was no difference between BX+ and BX- samples. CONCLUSIONS The data support the utility of the LBx in distinguishing patients with an alveolar lesion from those without, providing a potential avenue for prescreening before LDCT.
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Affiliation(s)
- Karen Resnick
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Anya Shah
- Convergent Science Institute for Cancer, Michelson Center, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Jeremy Mason
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Convergent Science Institute for Cancer, Michelson Center, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Institute of Urology, Catherine & Joseph Aresty Department of UrologyKeck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Peter Kuhn
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Convergent Science Institute for Cancer, Michelson Center, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Institute of Urology, Catherine & Joseph Aresty Department of UrologyKeck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of Biomedical EngineeringViterbi School of Engineering, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of Aerospace and Mechanical EngineeringViterbi School of Engineering, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of Biological SciencesDornsife College of Letters, Arts, and Sciences, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Jorge Nieva
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Stephanie N. Shishido
- Convergent Science Institute for Cancer, Michelson Center, University of Southern CaliforniaLos AngelesCaliforniaUSA
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11
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Ocaña-Tienda B, Eroles-Simó A, Pérez-Beteta J, Arana E, Pérez-García VM. Growth dynamics of lung nodules: implications for classification in lung cancer screening. Cancer Imaging 2024; 24:113. [PMID: 39187900 PMCID: PMC11346294 DOI: 10.1186/s40644-024-00755-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 08/07/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND Lung nodules observed in cancer screening are believed to grow exponentially, and their associated volume doubling time (VDT) has been proposed for nodule classification. This retrospective study aimed to elucidate the growth dynamics of lung nodules and determine the best classification as either benign or malignant. METHODS Data were analyzed from 180 participants (73.7% male) enrolled in the I-ELCAP screening program (140 primary lung cancer and 40 benign) with three or more annual CT examinations before resection. Attenuation, volume, mass and growth patterns (decelerated, linear, subexponential, exponential and accelerated) were assessed and compared as classification methods. RESULTS Most lung cancers (83/140) and few benign nodules (11/40) exhibited an accelerated, faster than exponential, growth pattern. Half (50%) of the benign nodules versus 26.4% of the malignant ones displayed decelerated growth. Differences in growth patterns allowed nodule malignancy to be classified, the most effective individual variable being the increase in volume between two-year-interval scans (ROC-AUC = 0.871). The same metric on the first two follow-ups yielded an AUC value of 0.769. Further classification into solid, part-solid or non-solid, improved results (ROC-AUC of 0.813 in the first year and 0.897 in the second year). CONCLUSIONS In our dataset, most lung cancers exhibited accelerated growth in contrast to their benign counterparts. A measure of volumetric growth allowed discrimination between benign and malignant nodules. Its classification power increased when adding information on nodule compactness. The combination of these two meaningful and easily obtained variables could be used to assess malignancy of lung cancer nodules.
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Affiliation(s)
- Beatriz Ocaña-Tienda
- Mathematical Oncology Laboratory, University of Castilla-La Mancha, Ciudad Real, Spain.
| | - Alba Eroles-Simó
- Instituto de Instrumentación para la Imagen Molecular (i3M), Universitat Politécnica de València, Consejo Superior de Investigaciones Científicas (CSIC), València, Spain
| | - Julián Pérez-Beteta
- Mathematical Oncology Laboratory, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Estanislao Arana
- Department of Radiology, Fundación Instituto Valenciano de Oncología, Valencia, Spain
| | - Víctor M Pérez-García
- Mathematical Oncology Laboratory, University of Castilla-La Mancha, Ciudad Real, Spain
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12
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Bisanzi S, Puliti D, Picozzi G, Romei C, Pistelli F, Deliperi A, Carreras G, Masala G, Gorini G, Zappa M, Sani C, Carrozzi L, Paci E, Kaaks R, Carozzi FM, Mascalchi M. Baseline Cell-Free DNA Can Predict Malignancy of Nodules Observed in the ITALUNG Screening Trial. Cancers (Basel) 2024; 16:2276. [PMID: 38927981 PMCID: PMC11201711 DOI: 10.3390/cancers16122276] [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: 05/15/2024] [Revised: 06/08/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024] Open
Abstract
The role of total plasma cell-free DNA (cfDNA) in lung cancer (LC) screening with low-dose computed tomography (LDCT) is uncertain. We hypothesized that cfDNA could support differentiation between malignant and benign nodules observed in LDCT. The baseline cfDNA was measured in 137 subjects of the ITALUNG trial, including 29 subjects with screen-detected LC (17 prevalent and 12 incident) and 108 subjects with benign nodules. The predictive capability of baseline cfDNA to differentiate malignant and benign nodules was compared to that of Lung-RADS classification and Brock score at initial LDCT (iLDCT). Subjects with prevalent LC showed both well-discriminating radiological characteristics of the malignant nodule (16 of 17 were classified as Lung-RADS 4) and markedly increased cfDNA (mean 18.8 ng/mL). The mean diameters and Brock scores of malignant nodules at iLDCT in subjects who were diagnosed with incident LC were not different from those of benign nodules. However, 75% (9/12) of subjects with incident LC showed a baseline cfDNA ≥ 3.15 ng/mL, compared to 34% (37/108) of subjects with benign nodules (p = 0.006). Moreover, baseline cfDNA was correlated (p = 0.001) with tumor growth, measured with volume doubling time. In conclusion, increased baseline cfDNA may help to differentiate subjects with malignant and benign nodules at LDCT.
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Affiliation(s)
- Simonetta Bisanzi
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, Italy; (S.B.); (G.P.); (G.C.); (G.M.); (G.G.); (M.Z.); (C.S.); (E.P.); (F.M.C.)
| | - Donella Puliti
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, Italy; (S.B.); (G.P.); (G.C.); (G.M.); (G.G.); (M.Z.); (C.S.); (E.P.); (F.M.C.)
| | - Giulia Picozzi
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, Italy; (S.B.); (G.P.); (G.C.); (G.M.); (G.G.); (M.Z.); (C.S.); (E.P.); (F.M.C.)
| | - Chiara Romei
- Division of Radiology, Cisanello Hospital, Azienda Ospedaliera Pisana, 56124 Pisa, Italy; (C.R.); (A.D.)
| | - Francesco Pistelli
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, 56126 Pisa, Italy, (L.C.)
- Pulmonary Unit, Cardiothoracic and Vascular Department, Pisa University Hospital, 56124 Pisa, Italy
| | - Annalisa Deliperi
- Division of Radiology, Cisanello Hospital, Azienda Ospedaliera Pisana, 56124 Pisa, Italy; (C.R.); (A.D.)
| | - Giulia Carreras
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, Italy; (S.B.); (G.P.); (G.C.); (G.M.); (G.G.); (M.Z.); (C.S.); (E.P.); (F.M.C.)
| | - Giovanna Masala
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, Italy; (S.B.); (G.P.); (G.C.); (G.M.); (G.G.); (M.Z.); (C.S.); (E.P.); (F.M.C.)
| | - Giuseppe Gorini
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, Italy; (S.B.); (G.P.); (G.C.); (G.M.); (G.G.); (M.Z.); (C.S.); (E.P.); (F.M.C.)
| | - Marco Zappa
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, Italy; (S.B.); (G.P.); (G.C.); (G.M.); (G.G.); (M.Z.); (C.S.); (E.P.); (F.M.C.)
| | - Cristina Sani
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, Italy; (S.B.); (G.P.); (G.C.); (G.M.); (G.G.); (M.Z.); (C.S.); (E.P.); (F.M.C.)
| | - Laura Carrozzi
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, 56126 Pisa, Italy, (L.C.)
- Pulmonary Unit, Cardiothoracic and Vascular Department, Pisa University Hospital, 56124 Pisa, Italy
| | - Eugenio Paci
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, Italy; (S.B.); (G.P.); (G.C.); (G.M.); (G.G.); (M.Z.); (C.S.); (E.P.); (F.M.C.)
| | - Rudolf Kaaks
- Division of Cancer Epidemiology (C020), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (R.K.); (M.M.)
- Translational Lung Research Center Heidelberg (TLRC-H), German Center for Lung Research (DZL), 69120 Heidelberg, Germany
| | - Francesca Maria Carozzi
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, Italy; (S.B.); (G.P.); (G.C.); (G.M.); (G.G.); (M.Z.); (C.S.); (E.P.); (F.M.C.)
| | - Mario Mascalchi
- Division of Cancer Epidemiology (C020), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (R.K.); (M.M.)
- Department of Clinical and Experimental Biomedical Sciences “Mario Serio”, University of Florence, 50121 Florence, Italy
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O'Neill J, Dhillon SS, Ma CT, Stubbs EGC, Khalidi NA, Ioannidis G, Beattie KA, Carmona R. Axial Spondyloarthritis: Does Magnetic Resonance Imaging Classification Improve Report Interpretation. J Clin Rheumatol 2024; 30:145-150. [PMID: 38595264 DOI: 10.1097/rhu.0000000000002079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
OBJECTIVE The interpretation of magnetic resonance imaging (MRI) reports is crucial for the diagnosis of axial spondyloarthritis, but the subjective nature of narrative reports can lead to varying interpretations. This study presents a validation of a novel MRI reporting system for the sacroiliac joint in clinical practice. METHODS A historical review was conducted on 130 consecutive patients referred by 2 rheumatologists for initial MRI assessment of possible axial spondyloarthritis. The original MRI reports were interpreted by the rheumatologists and the radiologist who originally read the images and then categorized according to the novel system. Two musculoskeletal radiologists then reinterpreted the original MRI scans using the new system, and the resulting reports were interpreted and categorized by the same rheumatologists. The quality of the new framework was assessed by comparing the interpretations of both reports. RESULTS Ninety-two patients met the study criteria. The rheumatologists disagreed on the categorization of the original MRI reports in 12% of cases. The rheumatologists and original radiologists disagreed on the categorization of the initial report in 23.4% of cases. In contrast, there was 100% agreement between the rheumatologists and radiologists on the categorization of the new MRI report. CONCLUSION The new MRI categorization system significantly improved the agreement between the clinician and radiologist in report interpretation. The system provided a standard vocabulary for reporting, reduced variability in report interpretation, and may therefore improve clinical decision-making.
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14
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Chang YC, Chen PT, Hsieh MS, Huang YS, Ko WC, Lin MW, Hsu HH, Chen JS, Chang YC. Discrimination of invasive lung adenocarcinoma from Lung-RADS category 2 nonsolid nodules through visual assessment: a retrospective study. Eur Radiol 2024; 34:3453-3461. [PMID: 37914975 DOI: 10.1007/s00330-023-10317-8] [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: 02/10/2023] [Revised: 09/11/2023] [Accepted: 09/24/2023] [Indexed: 11/03/2023]
Abstract
OBJECTIVES Invasive adenocarcinomas (IADs) have been identified among nonsolid nodules (NSNs) assigned as Lung Imaging Reporting and Data System (Lung-RADS) category 2. This study used visual assessment for differentiating IADs from noninvasive lesions (NILs) in this category. METHODS This retrospective study included 222 patients with 242 NSNs, which were resected after preoperative computed tomography (CT)-guided dye localization. Visual assessment was performed by using the lung and bone window (BW) settings to classify NSNs into BW-visible (BWV) and BW-invisible (BWI) NSNs. In addition, nodule size, shape, border, CT attenuation, and location were evaluated and correlated with histopathological results. Logistic regression was performed for multivariate analysis. A p value of < 0.05 was considered statistically significant. RESULTS A total of 242 NSNs (mean diameter, 7.6 ± 2.8 mm), including 166 (68.6%) BWV and 76 (31.4%) BWI NSNs, were included. IADs accounted for 31% (75) of the nodules. Only 4 (5.3%) IADs were identified in the BWI group and belonged to the lepidic-predominant (n = 3) and acinar-predominant (n = 1) subtypes. In univariate analysis for differentiating IADs from NILs, the nodule size, shape, CT attenuation, and visual classification exhibited statistical significance. Nodule size and visual classification were the significant predictors for IAD in multivariate analysis with logistic regression (p < 0.05). The sensitivity, specificity, positive predictive value, and negative predictive value of visual classification in IAD prediction were 94.7%, 43.1%, 42.8%, and 94.7%, respectively. CONCLUSIONS The window-based visual classification of NSNs is a simple and objective method to discriminate IADs from NILs. CLINICAL RELEVANCE STATEMENT The present study shows that using the bone window to classify nonsolid nodules helps discriminate invasive adenocarcinoma from noninvasive lesions. KEY POINTS • Evidence has shown the presence of lung adenocarcinoma in Lung-RADS category 2 nonsolid nodules. • Nonsolid nodules are classified into the bone window-visible and the bone window-invisible nonsolid nodules, and this classification differentiates invasive adenocarcinoma from noninvasive lesions. • The Lung-RADS category 2 nonsolid nodules are unlikely invasive adenocarcinoma if they show nonvisualization in the bone window.
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Affiliation(s)
- Yu-Chien Chang
- Department of Medical Imaging, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Chung-Shan South Rd., Taipei, 100225, Taiwan
| | - Po-Ting Chen
- Department of Medical Imaging, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Chung-Shan South Rd., Taipei, 100225, Taiwan
| | - Min-Shu Hsieh
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yu-Sen Huang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Chung-Shan South Rd., Taipei, 100225, Taiwan
| | - Wei-Chun Ko
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Chung-Shan South Rd., Taipei, 100225, Taiwan
| | - Mong-Wei Lin
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Hsao-Hsun Hsu
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jin-Shing Chen
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Chung-Shan South Rd., Taipei, 100225, Taiwan.
- Department of Medical Imaging, National Taiwan University Cancer Center, Taipei, Taiwan.
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15
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Triplette M, Kross EK, Snidarich M, Shahrir S, Hippe DS, Crothers K. An alternating-intervention pilot trial on the impact of an informational handout on patient-reported outcomes and follow-up after lung cancer screening. PLoS One 2024; 19:e0300352. [PMID: 38598511 PMCID: PMC11006146 DOI: 10.1371/journal.pone.0300352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 02/20/2024] [Indexed: 04/12/2024] Open
Abstract
INTRODUCTION Lung cancer screening (LCS) can reduce lung cancer mortality; however, poor understanding of results may impact patient experience and follow-up. We sought to determine whether an informational handout accompanying LCS results can improve patient-reported outcomes and adherence to follow-up. STUDY DESIGN This was a prospective alternating intervention pilot trial of a handout to accompany LCS results delivery. SETTING/PARTICIPANTS Patients undergoing LCS in a multisite program over a 6-month period received a mailing containing either: 1) a standardized form letter of LCS results (control) or 2) the LCS results letter and the handout (intervention). INTERVENTION A two-sided informational handout on commonly asked questions after LCS created through iterative mixed-methods evaluation with both LCS patients and providers. OUTCOME MEASURES The primary outcomes of 1)patient understanding of LCS results, 2)correct identification of next steps in screening, and 3)patient distress were measured through survey. Adherence to recommended follow-up after LCS was determined through chart review. Outcomes were compared between the intervention and control group using generalized estimating equations. RESULTS 389 patients were eligible and enrolled with survey responses from 230 participants (59% response rate). We found no differences in understanding of results, identification of next steps in follow-up or distress but did find higher levels of knowledge and understanding on questions assessing individual components of LCS in the intervention group. Follow-up adherence was overall similar between the two arms, though was higher in the intervention group among those with positive findings (p = 0.007). CONCLUSIONS There were no differences in self-reported outcomes between the groups or overall follow-up adherence. Those receiving the intervention did report greater understanding and knowledge of key LCS components, and those with positive results had a higher rate of follow-up. This may represent a feasible component of a multi-level intervention to address knowledge and follow-up for LCS. TRIAL REGISTRATION ClinicalTrials.gov NCT05265897.
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Affiliation(s)
- Matthew Triplette
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States of America
- Department of Medicine, University of Washington, Seattle, WA, United States of America
| | - Erin K. Kross
- Department of Medicine, University of Washington, Seattle, WA, United States of America
- Cambia Palliative Care Center of Excellence at UW Medicine, Seattle, WA, United States of America
| | - Madison Snidarich
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States of America
| | - Shahida Shahrir
- Department of Medicine, University of Washington, Seattle, WA, United States of America
| | - Daniel S. Hippe
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States of America
| | - Kristina Crothers
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States of America
- Veterans Affairs Puget Sound Health Care System, Seattle, WA, United States of America
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16
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Leong TL, McWilliams A, Wright GM. Incidental Pulmonary Nodules: An Opportunity to Complement Lung Cancer Screening. J Thorac Oncol 2024; 19:522-524. [PMID: 38582541 DOI: 10.1016/j.jtho.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 01/02/2024] [Indexed: 04/08/2024]
Affiliation(s)
- Tracy L Leong
- Department of Respiratory Medicine, Austin Health, Heidelberg, Victoria, Australia.
| | - Annette McWilliams
- Department of Respiratory Medicine, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
| | - Gavin M Wright
- Department of Cardiothoracic Surgery, St. Vincent's Hospital, Fitzroy, Victoria, Australia
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17
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Christensen J, Prosper AE, Wu CC, Chung J, Lee E, Elicker B, Hunsaker AR, Petranovic M, Sandler KL, Stiles B, Mazzone P, Yankelevitz D, Aberle D, Chiles C, Kazerooni E. ACR Lung-RADS v2022: Assessment Categories and Management Recommendations. J Am Coll Radiol 2024; 21:473-488. [PMID: 37820837 DOI: 10.1016/j.jacr.2023.09.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/08/2023] [Accepted: 09/21/2023] [Indexed: 10/13/2023]
Abstract
The ACR created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.
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Affiliation(s)
- Jared Christensen
- Vice Chair and Professor of Radiology, Department of Radiology, Duke University, Durham, North Carolina; Chair, ACR Lung-RADS Committee.
| | - Ashley Elizabeth Prosper
- Assistant Professor and Section Chief of Cardiothoracic Imaging, Department of Radiological Sciences, University of California, Los Angeles, California
| | - Carol C Wu
- Professor of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jonathan Chung
- Professor of Radiology Vice Chair of Quality Section Chief of Cardiopulmonary Imaging, University of Chicago, Chicago, Illinois
| | - Elizabeth Lee
- Clinical Associate Professor, Radiology, Michigan Medicine, Ann Arbor, Michigan
| | - Brett Elicker
- Chief of the Cardiac & Pulmonary Imaging Section, University of California, San Francisco, California
| | - Andetta R Hunsaker
- Brigham and Women's Hospital, Boston, Massachusetts; Associate Professor Harvard Medical School Chief Division of Thoracic Imaging
| | - Milena Petranovic
- Instructor, Radiology, Harvard Medical School Divisional Quality Director, Thoracic Imaging and Intervention, Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Kim L Sandler
- Associate Professor, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Brendon Stiles
- Professor and Chair, Thoracic Surgery and Surgical Oncology, Montefiore Health System, Albert Einstein College of Medicine, Bronx, New York
| | | | | | - Denise Aberle
- Professor of Radiology, Department of Radiological Sciences; David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Caroline Chiles
- Professor of Radiology Director, Lung Screening Program, Atrium Health Wake Forest, Winston-Salem, North Carolina
| | - Ella Kazerooni
- Professor of Radiology & Internal Medicine and Associate Chief Clinical Officer for Diagnostics, Michigan Medicine/University of Michigan Medical School, Ann Arbor, Michigan; Clinical Information Management, University of Michigan Medical Group
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18
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Christensen J, Prosper AE, Wu CC, Chung J, Lee E, Elicker B, Hunsaker AR, Petranovic M, Sandler KL, Stiles B, Mazzone P, Yankelevitz D, Aberle D, Chiles C, Kazerooni E. ACR Lung-RADS v2022: Assessment Categories and Management Recommendations. Chest 2024; 165:738-753. [PMID: 38300206 DOI: 10.1016/j.chest.2023.10.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024] Open
Abstract
The American College of Radiology created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.
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Affiliation(s)
- Jared Christensen
- Vice Chair and Professor of Radiology, Department of Radiology, Duke University, Durham, North Carolina; Chair, ACR Lung-RADS Committee.
| | - Ashley Elizabeth Prosper
- Assistant Professor and Section Chief of Cardiothoracic Imaging, Department of Radiological Sciences, University of California, Los Angeles, California
| | - Carol C Wu
- Professor of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jonathan Chung
- Professor of Radiology Vice Chair of Quality Section Chief of Cardiopulmonary Imaging, University of Chicago, Chicago, Illinois
| | - Elizabeth Lee
- Clinical Associate Professor, Radiology, Michigan Medicine, Ann Arbor, Michigan
| | - Brett Elicker
- Chief of the Cardiac & Pulmonary Imaging Section, University of California, San Francisco, California
| | - Andetta R Hunsaker
- Brigham and Women's Hospital, Boston, Massachusetts; Associate Professor Harvard Medical School Chief Division of Thoracic Imaging
| | - Milena Petranovic
- Instructor, Radiology, Harvard Medical School Divisional Quality Director, Thoracic Imaging and Intervention, Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Kim L Sandler
- Associate Professor, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Brendon Stiles
- Professor and Chair, Thoracic Surgery and Surgical Oncology, Montefiore Health System, Albert Einstein College of Medicine, Bronx, New York
| | | | | | - Denise Aberle
- Professor of Radiology, Department of Radiological Sciences; David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Caroline Chiles
- Professor of Radiology Director, Lung Screening Program, Atrium Health Wake Forest, Winston-Salem, North Carolina
| | - Ella Kazerooni
- Professor of Radiology & Internal Medicine and Associate Chief Clinical Officer for Diagnostics, Michigan Medicine/University of Michigan Medical School, Ann Arbor, Michigan; Clinical Information Management, University of Michigan Medical Group
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19
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Yang X, Chu XP, Huang S, Xiao Y, Li D, Su X, Qi YF, Qiu ZB, Wang Y, Tang WF, Wu YL, Zhu Q, Liang H, Zhong WZ. A novel image deep learning-based sub-centimeter pulmonary nodule management algorithm to expedite resection of the malignant and avoid over-diagnosis of the benign. Eur Radiol 2024; 34:2048-2061. [PMID: 37658883 DOI: 10.1007/s00330-023-10026-2] [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: 02/20/2023] [Revised: 05/08/2023] [Accepted: 06/26/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVES With the popularization of chest computed tomography (CT) screening, there are more sub-centimeter (≤ 1 cm) pulmonary nodules (SCPNs) requiring further diagnostic workup. This area represents an important opportunity to optimize the SCPN management algorithm avoiding "one-size fits all" approach. One critical problem is how to learn the discriminative multi-view characteristics and the unique context of each SCPN. METHODS Here, we propose a multi-view coupled self-attention module (MVCS) to capture the global spatial context of the CT image through modeling the association order of space and dimension. Compared with existing self-attention methods, MVCS uses less memory consumption and computational complexity, unearths dimension correlations that previous methods have not found, and is easy to integrate with other frameworks. RESULTS In total, a public dataset LUNA16 from LIDC-IDRI, 1319 SCPNs from 1069 patients presenting to a major referral center, and 160 SCPNs from 137 patients from three other major centers were analyzed to pre-train, train, and validate the model. Experimental results showed that performance outperforms the state-of-the-art models in terms of accuracy and stability and is comparable to that of human experts in classifying precancerous lesions and invasive adenocarcinoma. We also provide a fusion MVCS network (MVCSN) by combining the CT image with the clinical characteristics and radiographic features of patients. CONCLUSION This tool may ultimately aid in expediting resection of the malignant SCPNs and avoid over-diagnosis of the benign ones, resulting in improved management outcomes. CLINICAL RELEVANCE STATEMENT In the diagnosis of sub-centimeter lung adenocarcinoma, fusion MVCSN can help doctors improve work efficiency and guide their treatment decisions to a certain extent. KEY POINTS • Advances in computed tomography (CT) not only increase the number of nodules detected, but also the nodules that are identified are smaller, such as sub-centimeter pulmonary nodules (SCPNs). • We propose a multi-view coupled self-attention module (MVCS), which could model spatial and dimensional correlations sequentially for learning global spatial contexts, which is better than other attention mechanisms. • MVCS uses fewer huge memory consumption and computational complexity than the existing self-attention methods when dealing with 3D medical image data. Additionally, it reaches promising accuracy for SCPNs' malignancy evaluation and has lower training cost than other models.
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Affiliation(s)
- Xiongwen Yang
- School of Medicine, South China University of Technology, Guangzhou, China
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China
| | - Xiang-Peng Chu
- School of Medicine, South China University of Technology, Guangzhou, China
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China
| | - Shaohong Huang
- Department of Cardio-Thoracic Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Yi Xiao
- Department of Cardio-Thoracic Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Dantong Li
- Medical Big Data Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Cardiovascular Institute, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Xiaoyang Su
- Department of Thoracic Surgery, Maoming City People's Hospital, Maoming, China
| | - Yi-Fan Qi
- School of Medicine, South China University of Technology, Guangzhou, China
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China
| | - Zhen-Bin Qiu
- School of Medicine, South China University of Technology, Guangzhou, China
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China
| | - Yanqing Wang
- Department of Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wen-Fang Tang
- Department of Cardio-Thoracic Surgery, Zhongshan City People's Hospital, Zhongshan, China
| | - Yi-Long Wu
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China
| | - Qikui Zhu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
| | - Huiying Liang
- Medical Big Data Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
- Guangdong Cardiovascular Institute, Guangzhou, Guangdong, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
| | - Wen-Zhao Zhong
- School of Medicine, South China University of Technology, Guangzhou, China.
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China.
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20
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Kim M, Kim Y, Kim AR, Kwon WJ, Lim S, Kim W, Yoo C. Cooking oil fume exposure and Lung-RADS distribution among school cafeteria workers of South Korea. Ann Occup Environ Med 2024; 36:e2. [PMID: 38379639 PMCID: PMC10874949 DOI: 10.35371/aoem.2024.36.e2] [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: 08/08/2023] [Revised: 09/29/2023] [Accepted: 01/03/2024] [Indexed: 02/22/2024] Open
Abstract
Background Cooking oil fumes (COFs) from cooking with hot oil may contribute to the pathogenesis of lung cancer. Since 2021, occupational lung cancer for individual cafeteria workers has been recognized in South Korea. In this study, we aimed to identify the distribution of lung-imaging reporting and data system (Lung-RADS) among cafeteria workers and to determine factors related to Lung-RADS distribution. Methods We included 203 female participants who underwent low-dose computed tomography (LDCT) screening at a university hospital and examined the following variables: age, smoking status, second-hand smoke, height, weight, and years of service, mask use, cooking time, heat source, and ventilation. We divided all participants into culinary and non-culinary workers. Binomial logistic regression was conducted to determine the risk factors on LDCT of Category ≥ 3, separately for the overall group and the culinary group. Results In this study, Lung-RADS-positive occurred in 17 (8.4%) individuals, all of whom were culinary workers. Binary logistic regression analyses were performed and no variables were found to have a significant impact on Lung-RADS results. In the subgroup analysis, the Lung-RADS-positive, and -negative groups differed only in ventilation. Binary logistic regression showed that the adjusted odds ratio (aOR) of the Lung-RADS-positive group for inappropriate ventilation at the workplace was 14.89 (95% confidence interval [CI]: 3.296-67.231) compared to appropriate ventilation as the reference, and the aOR for electric appliances at home was 4.59 (95% CI: 1.061-19.890) using liquid fuel as the reference. Conclusions The rate of Lung-RADS-positive was significantly higher among culinary workers who performed actual cooking tasks than among nonculinary workers. In addition, appropriate ventilation at the workplace made the LDCT results differ. More research is needed to identify factors that might influence LDCT findings among culinary workers, including those in other occupations.
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Affiliation(s)
- Minjun Kim
- Department of Occupational and Environmental Medicine, Ulsan University Hospital, Ulsan University School of Medicine, Ulsan, Korea
| | - Yangho Kim
- Department of Occupational and Environmental Medicine, Ulsan University Hospital, Ulsan University School of Medicine, Ulsan, Korea
| | - A Ram Kim
- Department of Occupational and Environmental Medicine, Ulsan University Hospital, Ulsan University School of Medicine, Ulsan, Korea
| | - Woon Jung Kwon
- Department of Diagnostic Radiology, Ulsan University Hospital, Ulsan University School of Medicine, Ulsan, Korea
| | - Soyeoun Lim
- Department of Diagnostic Radiology, Ulsan University Hospital, Ulsan University School of Medicine, Ulsan, Korea
| | - Woojin Kim
- Department of Occupational and Environmental Medicine, Ulsan University Hospital, Ulsan University School of Medicine, Ulsan, Korea
| | - Cheolin Yoo
- Department of Occupational and Environmental Medicine, Ulsan University Hospital, Ulsan University School of Medicine, Ulsan, Korea
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21
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Rendle KA, Saia CA, Vachani A, Burnett-Hartman AN, Doria-Rose VP, Beucker S, Neslund-Dudas C, Oshiro C, Kim RY, Elston-Lafata J, Honda SA, Ritzwoller D, Wainwright JV, Mitra N, Greenlee RT. Rates of Downstream Procedures and Complications Associated With Lung Cancer Screening in Routine Clinical Practice : A Retrospective Cohort Study. Ann Intern Med 2024; 177:18-28. [PMID: 38163370 PMCID: PMC11111256 DOI: 10.7326/m23-0653] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND Lung cancer screening (LCS) using low-dose computed tomography (LDCT) reduces lung cancer mortality but can lead to downstream procedures, complications, and other potential harms. Estimates of these events outside NLST (National Lung Screening Trial) have been variable and lacked evaluation by screening result, which allows more direct comparison with trials. OBJECTIVE To identify rates of downstream procedures and complications associated with LCS. DESIGN Retrospective cohort study. SETTING 5 U.S. health care systems. PATIENTS Individuals who completed a baseline LDCT scan for LCS between 2014 and 2018. MEASUREMENTS Outcomes included downstream imaging, invasive diagnostic procedures, and procedural complications. For each, absolute rates were calculated overall and stratified by screening result and by lung cancer detection, and positive and negative predictive values were calculated. RESULTS Among the 9266 screened patients, 1472 (15.9%) had a baseline LDCT scan showing abnormalities, of whom 140 (9.5%) were diagnosed with lung cancer within 12 months (positive predictive value, 9.5% [95% CI, 8.0% to 11.0%]; negative predictive value, 99.8% [CI, 99.7% to 99.9%]; sensitivity, 92.7% [CI, 88.6% to 96.9%]; specificity, 84.4% [CI, 83.7% to 85.2%]). Absolute rates of downstream imaging and invasive procedures in screened patients were 31.9% and 2.8%, respectively. In patients undergoing invasive procedures after abnormal findings, complication rates were substantially higher than those in NLST (30.6% vs. 17.7% for any complication; 20.6% vs. 9.4% for major complications). LIMITATION Assessment of outcomes was retrospective and was based on procedural coding. CONCLUSION The results indicate substantially higher rates of downstream procedures and complications associated with LCS in practice than observed in NLST. Diagnostic management likely needs to be assessed and improved to ensure that screening benefits outweigh potential harms. PRIMARY FUNDING SOURCE National Cancer Institute and Gordon and Betty Moore Foundation.
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Affiliation(s)
- Katharine A Rendle
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (K.A.R., C.A.S., A.V., S.B., R.Y.K., J.V.W., N.M.)
| | - Chelsea A Saia
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (K.A.R., C.A.S., A.V., S.B., R.Y.K., J.V.W., N.M.)
| | - Anil Vachani
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (K.A.R., C.A.S., A.V., S.B., R.Y.K., J.V.W., N.M.)
| | | | - V Paul Doria-Rose
- Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland (V.P.D.)
| | - Sarah Beucker
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (K.A.R., C.A.S., A.V., S.B., R.Y.K., J.V.W., N.M.)
| | | | - Caryn Oshiro
- Center for Integrated Healthcare Research, Kaiser Permanente Hawaii, Honolulu, Hawaii (C.O.)
| | - Roger Y Kim
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (K.A.R., C.A.S., A.V., S.B., R.Y.K., J.V.W., N.M.)
| | - Jennifer Elston-Lafata
- Henry Ford Health and Henry Ford Cancer Institute, Detroit, Michigan, and Eshelman School of Pharmacy and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (J.E.)
| | - Stacey A Honda
- Center for Integrated Health Care Research, Kaiser Permanente Hawaii, and Hawaii Permanente Medical Group, Honolulu, Hawaii (S.A.H.)
| | - Debra Ritzwoller
- Institute for Health Research, Kaiser Permanente Colorado, Aurora, Colorado (A.N.B., D.R.)
| | - Jocelyn V Wainwright
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (K.A.R., C.A.S., A.V., S.B., R.Y.K., J.V.W., N.M.)
| | - Nandita Mitra
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (K.A.R., C.A.S., A.V., S.B., R.Y.K., J.V.W., N.M.)
| | - Robert T Greenlee
- Center for Clinical Epidemiology and Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin (R.T.G.)
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22
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Hsiao CC, Peng CH, Wu FZ, Cheng DC. Impact of Voxel Normalization on a Machine Learning-Based Method: A Study on Pulmonary Nodule Malignancy Diagnosis Using Low-Dose Computed Tomography (LDCT). Diagnostics (Basel) 2023; 13:3690. [PMID: 38132274 PMCID: PMC10742752 DOI: 10.3390/diagnostics13243690] [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: 10/20/2023] [Revised: 12/04/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023] Open
Abstract
Lung cancer (LC) stands as the foremost cause of cancer-related fatality rates worldwide. Early diagnosis significantly enhances patient survival rate. Nowadays, low-dose computed tomography (LDCT) is widely employed on the chest as a tool for large-scale lung cancer screening. Nonetheless, a large amount of chest radiographs creates an onerous burden for radiologists. Some computer-aided diagnostic (CAD) tools can provide insight to the use of medical images for diagnosis and can augment diagnostic speed. However, due to the variation in the parameter settings across different patients, substantial discrepancies in image voxels persist. We found that different voxel sizes can create a compromise between model generalization and diagnostic efficacy. This study investigates the performance disparities of diagnostic models trained on original images and LDCT images reconstructed to different voxel sizes while making isotropic. We examined the ability of our method to differentiate between benign and malignant nodules. Using 11 features, a support vector machine (SVM) was trained on LDCT images using an isotropic voxel with a side length of 1.5 mm for 225 patients in-house. The result yields a favorable model performance with an accuracy of 0.9596 and an area under the receiver operating characteristic curve (ROC/AUC) of 0.9855. In addition, to furnish CAD tools for clinical application, future research including LDCT images from multi-centers is encouraged.
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Affiliation(s)
- Chia-Chi Hsiao
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan;
| | - Chen-Hao Peng
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 40400, Taiwan;
| | - Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan;
| | - Da-Chuan Cheng
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 40400, Taiwan;
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23
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Islam JY, Yang S, Schabath M, Vadaparampil ST, Lou X, Wu Y, Bian J, Guo Y. Lung cancer screening adherence among people living with and without HIV: An analysis of an integrated health system in Florida, United States (2012-2021). Prev Med Rep 2023; 35:102334. [PMID: 37546581 PMCID: PMC10403735 DOI: 10.1016/j.pmedr.2023.102334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 07/17/2023] [Accepted: 07/18/2023] [Indexed: 08/08/2023] Open
Abstract
Although lung cancer is a leading cause of death among people living with HIV (PLWH), limited research exists characterizing real-world lung cancer screening adherence among PLWH. Our objective was to compare low-dose computed tomography (LDCT) adherence among PLWH to those without HIV treated at one integrated health system. Using the University of Florida's Health Integrated Data Repository (01/01/2012-10/31/2021), we identified PLWH with at least one LDCT procedure, using Current Procedural Terminology codes(S8032/G0297/71271). Lung cancer screening adherence was defined as a second LDCT based on the Lung Imaging Reporting and Data System (Lung-RADS®). Lung-RADS categories were extracted from radiology reports using a natural language processing system. PLWH were matched with 4 randomly selected HIV-negative patients based on (+/- 1 year) age, Lung-RADS category, and calendar year. Seventy-three PLWH and 292 matched HIV-negative adults with at least one LDCT were identified. PLWH were more likely to be male (66% vs.52%,p < 0.04), non-Hispanic Black (53% vs.23%,p < 0.001), and live in an area of high poverty (45% vs.31%,p < 0.001). PLWH were more likely to be diagnosed with lung cancer after first LDCT (8% vs.0%,p < 0.001). Seventeen percent of HIV-negative and 12% of PLWH were adherent to LDCT screenings. Only 25% of PLWH diagnosed with category 4A were adherent compared to 44% of HIV-negative. On multivariable analyses, those with older age (66-80 vs.50-64 years) and with either Medicaid, charity-based, or other government insurance (vs. Medicare) were less likely to be adherent to LDCT screenings. PLWH may have poorer adherence to LDCT compared to their HIV-negative counterparts.
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Affiliation(s)
- Jessica Y. Islam
- Cancer Epidemiology Program, Center for Immunization and Infection in Cancer Research, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Shuang Yang
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Matthew Schabath
- Cancer Epidemiology Program, Center for Immunization and Infection in Cancer Research, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Susan T. Vadaparampil
- Health Outcomes and Behavior, The Office of Community Outreach, Engagement, and Equity (COEE), H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Xiwei Lou
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Yonghui Wu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
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24
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Murphy DR, Zimolzak AJ, Upadhyay DK, Wei L, Jolly P, Offner A, Sittig DF, Korukonda S, Rekha RM, Singh H. Developing electronic clinical quality measures to assess the cancer diagnostic process. J Am Med Inform Assoc 2023; 30:1526-1531. [PMID: 37257883 PMCID: PMC10436145 DOI: 10.1093/jamia/ocad089] [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: 11/27/2022] [Revised: 04/12/2023] [Accepted: 05/08/2023] [Indexed: 06/02/2023] Open
Abstract
OBJECTIVE Measures of diagnostic performance in cancer are underdeveloped. Electronic clinical quality measures (eCQMs) to assess quality of cancer diagnosis could help quantify and improve diagnostic performance. MATERIALS AND METHODS We developed 2 eCQMs to assess diagnostic evaluation of red-flag clinical findings for colorectal (CRC; based on abnormal stool-based cancer screening tests or labs suggestive of iron deficiency anemia) and lung (abnormal chest imaging) cancer. The 2 eCQMs quantified rates of red-flag follow-up in CRC and lung cancer using electronic health record data repositories at 2 large healthcare systems. Each measure used clinical data to identify abnormal results, evidence of appropriate follow-up, and exclusions that signified follow-up was unnecessary. Clinicians reviewed 100 positive and 20 negative randomly selected records for each eCQM at each site to validate accuracy and categorized missed opportunities related to system, provider, or patient factors. RESULTS We implemented the CRC eCQM at both sites, while the lung cancer eCQM was only implemented at the VA due to lack of structured data indicating level of cancer suspicion on most chest imaging results at Geisinger. For the CRC eCQM, the rate of appropriate follow-up was 36.0% (26 746/74 314 patients) in the VA after removing clinical exclusions and 41.1% at Geisinger (1009/2461 patients; P < .001). Similarly, the rate of appropriate evaluation for lung cancer in the VA was 61.5% (25 166/40 924 patients). Reviewers most frequently attributed missed opportunities at both sites to provider factors (84 of 157). CONCLUSIONS We implemented 2 eCQMs to evaluate the diagnostic process in cancer at 2 large health systems. Health care organizations can use these eCQMs to monitor diagnostic performance related to cancer.
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Affiliation(s)
- Daniel R Murphy
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, USA
- Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Andrew J Zimolzak
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, USA
- Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Divvy K Upadhyay
- Division of Quality, Safety and Patient Experience, Geisinger, Danville, Pennsylvania, USA
| | - Li Wei
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, USA
- Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Preeti Jolly
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, USA
- Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Alexis Offner
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, USA
- Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Dean F Sittig
- Department of Clinical and Health Informatics, The University of Texas Health Science Center at Houston’s School of Biomedical Informatics, Houston, Texas, USA
- The UT-Memorial Hermann Center for Healthcare Quality & Safety, Houston, Texas, USA
| | - Saritha Korukonda
- Investigator-Initiated Research Operations, Geisinger, Danville, Pennsylvania, USA
| | - Riyaa Murugaesh Rekha
- Division of Quality, Safety and Patient Experience, Geisinger, Danville, Pennsylvania, USA
| | - Hardeep Singh
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, USA
- Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
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Manyak A, Seaburg L, Bohreer K, Kirtland SH, Hubka M, Gerbino AJ. Invasive Procedures Associated With Lung Cancer Screening in Clinical Practice. Chest 2023; 164:544-555. [PMID: 36781101 DOI: 10.1016/j.chest.2023.02.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/26/2022] [Accepted: 02/07/2023] [Indexed: 02/13/2023] Open
Abstract
BACKGROUND The harm associated with imaging abnormalities related to lung cancer screening (LCS) is not well documented, especially outside the clinical trial and academic setting. RESEARCH QUESTION What is the frequency of invasive procedures and complications associated with a community based LCS program, including procedures for false-positive and benign, but clinically important, incidental findings? STUDY DESIGN AND METHODS We performed a single-center retrospective study of an LCS program at a nonuniversity teaching hospital from 2016 through 2019 to identify invasive procedures prompted by LCS results, including their indication and complications. RESULTS Among 2,003 LCS participants, 58 patients (2.9%) received a diagnosis of lung cancer and 71 patients (3.5%) received a diagnosis of any malignancy. Invasive procedures were performed 160 times in 103 participants (5.1%), including 1.7% of those without malignancy. Eight invasive procedures (0.4% of participants), including four surgeries (12% of diagnostic lung resections), were performed for false-positive lung nodules. Only 1% of Lung Imaging Reporting and Data System category 4A nodules that proved benign were subject to an invasive procedure. Among those without malignancy, an invasive procedure was performed in eight participants for extrapulmonary false-positive findings (0.4%) and in 19 participants (0.9%) to evaluate incidental findings considered benign but clinically important. Procedures for the latter indication resulted in treatment, change in management, or diagnosis in 79% of individuals. Invasive procedures in those without malignancy resulted in three complications (0.15%). Seventy nonsurgical procedures (6% complication rate) and 48 thoracic surgeries (4% major complication rate) were performed in those with malignancy. INTERPRETATION The use of invasive procedures to resolve false-positive findings was uncommon in the clinical practice of a nonuniversity LCS program that adhered to a nodule management algorithm and used a multidisciplinary approach. Incidental findings considered benign but clinically important resulted in invasive procedure rates that were similar to those for false-positive findings and frequently had clinical value.
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Affiliation(s)
- Anton Manyak
- Section of Graduate Medical Education, Virginia Mason Medical Center, Virginia Mason Franciscan Health, Seattle, WA; Department of Graduate Medical Education, Loma Linda University, Loma Linda, CA
| | - Luke Seaburg
- Section of Pulmonary Medicine, Virginia Mason Medical Center, Virginia Mason Franciscan Health, Seattle, WA
| | - Kristin Bohreer
- Section of Pulmonary Medicine, Virginia Mason Medical Center, Virginia Mason Franciscan Health, Seattle, WA
| | - Steve H Kirtland
- Section of Pulmonary Medicine, Virginia Mason Medical Center, Virginia Mason Franciscan Health, Seattle, WA
| | - Michal Hubka
- Section of Thoracic Surgery, Virginia Mason Medical Center, Virginia Mason Franciscan Health, Seattle, WA
| | - Anthony J Gerbino
- Section of Pulmonary Medicine, Virginia Mason Medical Center, Virginia Mason Franciscan Health, Seattle, WA.
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Peters AA, Christe A, von Stackelberg O, Pohl M, Kauczor HU, Heußel CP, Wielpütz MO, Ebner L. "Will I change nodule management recommendations if I change my CAD system?"-impact of volumetric deviation between different CAD systems on lesion management. Eur Radiol 2023; 33:5568-5577. [PMID: 36894752 PMCID: PMC10326095 DOI: 10.1007/s00330-023-09525-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 11/17/2022] [Accepted: 02/05/2023] [Indexed: 03/11/2023]
Abstract
OBJECTIVES To evaluate and compare the measurement accuracy of two different computer-aided diagnosis (CAD) systems regarding artificial pulmonary nodules and assess the clinical impact of volumetric inaccuracies in a phantom study. METHODS In this phantom study, 59 different phantom arrangements with 326 artificial nodules (178 solid, 148 ground-glass) were scanned at 80 kV, 100 kV, and 120 kV. Four different nodule diameters were used: 5 mm, 8 mm, 10 mm, and 12 mm. Scans were analyzed by a deep-learning (DL)-based CAD and a standard CAD system. Relative volumetric errors (RVE) of each system vs. ground truth and the relative volume difference (RVD) DL-based vs. standard CAD were calculated. The Bland-Altman method was used to define the limits of agreement (LOA). The hypothetical impact on LungRADS classification was assessed for both systems. RESULTS There was no difference between the three voltage groups regarding nodule volumetry. Regarding the solid nodules, the RVE of the 5-mm-, 8-mm-, 10-mm-, and 12-mm-size groups for the DL CAD/standard CAD were 12.2/2.8%, 1.3/ - 2.8%, - 3.6/1.5%, and - 12.2/ - 0.3%, respectively. The corresponding values for the ground-glass nodules (GGN) were 25.6%/81.0%, 9.0%/28.0%, 7.6/20.6%, and 6.8/21.2%. The mean RVD for solid nodules/GGN was 1.3/ - 15.2%. Regarding the LungRADS classification, 88.5% and 79.8% of all solid nodules were correctly assigned by the DL CAD and the standard CAD, respectively. 14.9% of the nodules were assigned differently between the systems. CONCLUSIONS Patient management may be affected by the volumetric inaccuracy of the CAD systems and hence demands supervision and/or manual correction by a radiologist. KEY POINTS • The DL-based CAD system was more accurate in the volumetry of GGN and less accurate regarding solid nodules than the standard CAD system. • Nodule size and attenuation have an effect on the measurement accuracy of both systems; tube voltage has no effect on measurement accuracy. • Measurement inaccuracies of CAD systems can have an impact on patient management, which demands supervision by radiologists.
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Affiliation(s)
- Alan A Peters
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, Heidelberg, Germany.
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Marsilius-Arkaden 130, 69120, Heidelberg, Germany.
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, University Hospital of Heidelberg, Thoraxklinik Heidelberg, Roentgenstrasse 1, 69126, Heidelberg, Germany.
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, 3010, Freiburgstrasse, Switzerland.
| | - Andreas Christe
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, 3010, Freiburgstrasse, Switzerland
| | - Oyunbileg von Stackelberg
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Marsilius-Arkaden 130, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, University Hospital of Heidelberg, Thoraxklinik Heidelberg, Roentgenstrasse 1, 69126, Heidelberg, Germany
| | - Moritz Pohl
- Institute of Medical Biometry, University of Heidelberg, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Marsilius-Arkaden 130, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, University Hospital of Heidelberg, Thoraxklinik Heidelberg, Roentgenstrasse 1, 69126, Heidelberg, Germany
| | - Claus Peter Heußel
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Marsilius-Arkaden 130, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, University Hospital of Heidelberg, Thoraxklinik Heidelberg, Roentgenstrasse 1, 69126, Heidelberg, Germany
| | - Mark O Wielpütz
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Marsilius-Arkaden 130, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, University Hospital of Heidelberg, Thoraxklinik Heidelberg, Roentgenstrasse 1, 69126, Heidelberg, Germany
| | - Lukas Ebner
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, 3010, Freiburgstrasse, Switzerland
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Gu JZ, Baird GL, Ge C, Fletcher LM, Agarwal S, Eltorai AEM, Healey TT. ACR Lung CT Screening Reporting and Data System, a Systematic Review and Meta-Analysis Before Change in US Preventative Services Taskforce Eligibility Criteria: 2014 to 2021. J Am Coll Radiol 2023; 20:769-780. [PMID: 37301355 DOI: 10.1016/j.jacr.2023.04.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/24/2023] [Accepted: 04/07/2023] [Indexed: 06/12/2023]
Abstract
OBJECTIVE To review Lung CT Screening Reporting and Data System (Lung-RADS) scores from 2014 to 2021, before changes in eligibility criteria proposed by the US Preventative Services Taskforce. METHODS A registered systematic review and meta-analysis was conducted in MEDLINE, Embase, CINAHL, and Web of Science in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines; eligible studies examined low-dose CT (LDCT) lung cancer screening at institutions in the United States and reported Lung-RADS from 2014 to 2021. Patient and study characteristics, including age, gender, smoking status, pack-years, screening timeline, number of individual patients, number of unique studies, Lung-RADS scores, and positive predictive value (PPV) were extracted. Meta-analysis estimates were derived from generalized linear mixed modeling. RESULTS The meta-analysis included 24 studies yielding 36,211 LDCT examinations for 32,817 patient encounters. The meta-analysis Lung-RADS 1-2 scores were lower than anticipated by ACR guidelines, at 84.4 (95% confidence interval [CI] 83.3-85.6) versus 90% respectively (P < .001). Lung-RADS 3 and 4 scores were both higher than anticipated by the ACR, at 8.7% (95% CI 7.6-10.1) and 6.5% (95% CI 5.707.4), compared with 5% and 4%, respectively (P < .001). The ACR's minimum estimate of PPV for Lung-RADS 3 to 4 is 21% or higher; we observed a rate of 13.1% (95% CI 10.1-16.8). However, our estimated PPV rate for Lung-RADS 4 was 28.6% (95% CI 21.6-36.8). CONCLUSION Lung-RADS scores and PPV rates in the literature are not aligned with the ACR's own estimates, suggesting that perhaps Lung-RADS categorization needs to be reexamined for better concordance with real-world screening populations. In addition to serving as a benchmark before screening guideline broadening, this study provides guidance for future reporting of lung cancer screening and Lung-RADS data.
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Affiliation(s)
- Joey Z Gu
- Warren Alpert Medical School of Brown University, Providence, Rhode Island.
| | - Grayson L Baird
- Associate Professor, Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, Rhode Island, and Lifespan Biostatistics, Epidemiology, and Research Design, Providence, Rhode Island
| | - Connie Ge
- University of Massachusetts Chan Medical School, Worcester, Massachusetts
| | | | - Saurabh Agarwal
- Vice Chair of Diversity and Inclusion, Associate Professor, Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, Rhode Island; Rhode Island Councilor, American College of Radiology, Reston, Virginia
| | - Adam E M Eltorai
- Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Terrance T Healey
- Director of Thoracic Imaging, Assistant Professor, Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, Rhode Island; Society of Thoracic Radiology Councilor, American College of Radiology, Reston, Virginia
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Lastwika KJ, Wu W, Zhang Y, Ma N, Zečević M, Pipavath SNJ, Randolph TW, Houghton AM, Nair VS, Lampe PD, Kinahan PE. Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment. Cancers (Basel) 2023; 15:3418. [PMID: 37444527 PMCID: PMC10341085 DOI: 10.3390/cancers15133418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/23/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody-antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules.
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Affiliation(s)
- Kristin J. Lastwika
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
- Translational Research Program, Public Health Sciences Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Wei Wu
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
| | - Yuzheng Zhang
- Program in Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (Y.Z.); (T.W.R.)
| | - Ningxin Ma
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
| | - Mladen Zečević
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
| | - Sudhakar N. J. Pipavath
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
- Division of Pulmonary, Critical Care & Sleep Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Timothy W. Randolph
- Program in Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (Y.Z.); (T.W.R.)
| | - A. McGarry Houghton
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
- Division of Pulmonary, Critical Care & Sleep Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA
- Human Biology Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Viswam S. Nair
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
- Division of Pulmonary, Critical Care & Sleep Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Paul D. Lampe
- Translational Research Program, Public Health Sciences Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Human Biology Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Paul E. Kinahan
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
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Mikhael PG, Wohlwend J, Yala A, Karstens L, Xiang J, Takigami AK, Bourgouin PP, Chan P, Mrah S, Amayri W, Juan YH, Yang CT, Wan YL, Lin G, Sequist LV, Fintelmann FJ, Barzilay R. Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography. J Clin Oncol 2023; 41:2191-2200. [PMID: 36634294 PMCID: PMC10419602 DOI: 10.1200/jco.22.01345] [Citation(s) in RCA: 81] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/10/2022] [Accepted: 11/29/2022] [Indexed: 01/13/2023] Open
Abstract
PURPOSE Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data. METHODS We developed a model called Sybil using LDCTs from the National Lung Screening Trial (NLST). Sybil requires only one LDCT and does not require clinical data or radiologist annotations; it can run in real time in the background on a radiology reading station. Sybil was validated on three independent data sets: a heldout set of 6,282 LDCTs from NLST participants, 8,821 LDCTs from Massachusetts General Hospital (MGH), and 12,280 LDCTs from Chang Gung Memorial Hospital (CGMH, which included people with a range of smoking history including nonsmokers). RESULTS Sybil achieved area under the receiver-operator curves for lung cancer prediction at 1 year of 0.92 (95% CI, 0.88 to 0.95) on NLST, 0.86 (95% CI, 0.82 to 0.90) on MGH, and 0.94 (95% CI, 0.91 to 1.00) on CGMH external validation sets. Concordance indices over 6 years were 0.75 (95% CI, 0.72 to 0.78), 0.81 (95% CI, 0.77 to 0.85), and 0.80 (95% CI, 0.75 to 0.86) for NLST, MGH, and CGMH, respectively. CONCLUSION Sybil can accurately predict an individual's future lung cancer risk from a single LDCT scan to further enable personalized screening. Future study is required to understand Sybil's clinical applications. Our model and annotations are publicly available. [Media: see text].
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Affiliation(s)
- Peter G. Mikhael
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Jeremy Wohlwend
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Adam Yala
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Ludvig Karstens
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Justin Xiang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Angelo K. Takigami
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Patrick P. Bourgouin
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - PuiYee Chan
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Sofiane Mrah
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Wael Amayri
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Yu-Hsiang Juan
- Chang Gung University, Taoyuan, Taiwan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Cheng-Ta Yang
- Chang Gung University, Taoyuan, Taiwan
- Department of Thoracic Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Yung-Liang Wan
- Chang Gung University, Taoyuan, Taiwan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Gigin Lin
- Chang Gung University, Taoyuan, Taiwan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Lecia V. Sequist
- Harvard Medical School, Boston, MA
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Florian J. Fintelmann
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Regina Barzilay
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
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Inoue A, Johnson TF, Walkoff LA, Levin DL, Hartman TE, Burke KA, Rajendran K, Yu L, McCollough CH, Fletcher JG. Lung Cancer Screening Using Clinical Photon-Counting Detector Computed Tomography and Energy-Integrating-Detector Computed Tomography: A Prospective Patient Study. J Comput Assist Tomogr 2023; 47:229-235. [PMID: 36573321 DOI: 10.1097/rct.0000000000001419] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
OBJECTIVE To evaluate the diagnostic quality of photon-counting detector (PCD) computed tomography (CT) in patients undergoing lung cancer screening compared with conventional energy-integrating detector (EID) CT in a prospective multireader study. MATERIALS Patients undergoing lung cancer screening with conventional EID-CT were prospectively enrolled and scanned on a PCD-CT system using similar automatic exposure control settings and reconstruction kernels. Three thoracic radiologists blinded to CT system compared PCD-CT and EID-CT images and scored examinations using a 5-point Likert comparison score (-2 [left image is worse] to +2 [left image is better]) for artifacts, sharpness, image noise, diagnostic image quality, emphysema visualization, and lung nodule evaluation focusing on the border. Post hoc correction of Likert scores was performed such that they reflected PCD-CT performance in comparison to EID-CT. A nonreader radiologist measured objective image noise. RESULTS Thirty-three patients (mean, 66.9 ± 5.6 years; 11 female; body mass index; 30.1 ± 5.1 kg/m 2 ) were enrolled. Mean volume CT dose index for PCD-CT was lower (0.61 ± 0.21 vs 0.73 ± 0.22; P < 0.001). Pooled reader results showed significant differences between imaging modalities for all comparative rankings ( P < 0.001), with PCD-CT favored for sharpness, image noise, image quality, and emphysema visualization and lung nodule border, but not artifacts. Photon-counting detector CT had significantly lower image noise (74.4 ± 10.5 HU vs 80.1 ± 8.6 HU; P = 0.048). CONCLUSIONS Photon-counting detector CT with similar acquisition and reconstruction settings demonstrated improved image quality and less noise despite lower radiation dose, with improved ability to depict pulmonary emphysema and lung nodule borders compared with EID-CT at low-dose lung cancer CT screening.
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Affiliation(s)
- Akitoshi Inoue
- From the Department of Radiology, Mayo Clinic, Rochester, MN
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Voigt W, Prosch H, Silva M. Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges? Cancers (Basel) 2023; 15:cancers15041218. [PMID: 36831559 PMCID: PMC9954060 DOI: 10.3390/cancers15041218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 02/05/2023] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
As most lung cancer (LC) cases are still detected at advanced and incurable stages, there are increasing efforts to foster detection at earlier stages by low dose computed tomography (LDCT) based LC screening. In this scoping review, we describe current advances in candidate selection for screening (selection phase), technical aspects (screening), and probability evaluation of malignancy of CT-detected pulmonary nodules (PN management). Literature was non-systematically assessed and reviewed for suitability by the authors. For the selection phase, we describe current eligibility criteria for screening, along with their limitations and potential refinements through advanced clinical scores and biomarker assessments. For LC screening, we discuss how the accuracy of computerized tomography (CT) scan reading might be augmented by IT tools, helping radiologists to cope with increasing workloads. For PN management, we evaluate the precision of follow-up scans by semi-automatic volume measurements of CT-detected PN. Moreover, we present an integrative approach to evaluate the probability of PN malignancy to enable safe decisions on further management. As a clear limitation, additional validation studies are required for most innovative diagnostic approaches presented in this article, but the integration of clinical risk models, current imaging techniques, and advancing biomarker research has the potential to improve the LC screening performance generally.
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Affiliation(s)
- Wieland Voigt
- Medical Innovation and Management, Steinbeis University Berlin, Ernst-Augustin-Strasse 15, 12489 Berlin, Germany
- Correspondence:
| | - Helmut Prosch
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, General Hospital, 1090 Vienna, Austria
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, 43121 Parma, Italy
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Jin GY. [Lung Imaging Reporting and Data System (Lung-RADS) in Radiology: Strengths, Weaknesses and Improvement]. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2023; 84:34-50. [PMID: 36818696 PMCID: PMC9935959 DOI: 10.3348/jksr.2022.0136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 12/05/2022] [Accepted: 12/27/2022] [Indexed: 06/18/2023]
Abstract
In 2019, the American College of Radiology announced Lung CT Screening Reporting & Data System (Lung-RADS) 1.1 to reduce lung cancer false positivity compared to that of Lung-RADS 1.0 for effective national lung cancer screening, and in December 2022, announced the new Lung-RADS 1.1, Lung-RADS® 2022 improvement. The Lung-RADS® 2022 measures the nodule size to the first decimal place compared to that of the Lung-RADS 1.0, to category 2 until the juxtapleural nodule size is < 10 mm, increases the size criterion of the ground glass nodule to 30 mm in category 2, and changes categories 4B and 4X to extremely suspicious. The category was divided according to the airway nodules location and shape or wall thickness of atypical pulmonary cysts. Herein, to help radiologists understand the Lung-RADS® 2022, this review will describe its advantages, disadvantages, and future improvements.
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He C, Liu J, Li Y, Lin L, Qing H, Guo L, Hu S, Zhou P. Quantitative parameters of enhanced dual-energy computed tomography for differentiating lung cancers from benign lesions in solid pulmonary nodules. Front Oncol 2022; 12:1027985. [PMID: 36276069 PMCID: PMC9582258 DOI: 10.3389/fonc.2022.1027985] [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: 08/25/2022] [Accepted: 09/26/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives This study aimed to investigate the ability of quantitative parameters of dual-energy computed tomography (DECT) and nodule size for differentiation between lung cancers and benign lesions in solid pulmonary nodules. Materials and Methods A total of 151 pathologically confirmed solid pulmonary nodules including 78 lung cancers and 73 benign lesions from 147 patients were consecutively and retrospectively enrolled who underwent dual-phase contrast-enhanced DECT. The following features were analyzed: diameter, volume, Lung CT Screening Reporting and Data System (Lung-RADS) categorization, and DECT-derived quantitative parameters including effective atomic number (Zeff), iodine concentration (IC), and normalized iodine concentration (NIC) in arterial and venous phases. Multivariable logistic regression analysis was used to build a combined model. The diagnostic performance was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity. Results The independent factors for differentiating lung cancers from benign solid pulmonary nodules included diameter, Lung-RADS categorization of diameter, volume, Zeff in arterial phase (Zeff_A), IC in arterial phase (IC_A), NIC in arterial phase (NIC_A), Zeff in venous phase (Zeff_V), IC in venous phase (IC_V), and NIC in venous phase (NIC_V) (all P < 0.05). The IC_V, NIC_V, and combined model consisting of diameter and NIC_V showed good diagnostic performance with AUCs of 0.891, 0.888, and 0.893, which were superior to the diameter, Lung-RADS categorization of diameter, volume, Zeff_A, and Zeff_V (all P < 0.001). The sensitivities of IC_V, NIC_V, and combined model were higher than those of IC_A and NIC_A (all P < 0.001). The combined model did not increase the AUCs compared with IC_V (P = 0.869) or NIC_V (P = 0.633). Conclusion The DECT-derived IC_V and NIC_V may be useful in differentiating lung cancers from benign lesions in solid pulmonary nodules.
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Affiliation(s)
| | | | | | | | | | | | | | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Intra- and Inter-Reader Variations in Lung Nodule Measurements: Influences of Nodule Size, Location, and Observers. Diagnostics (Basel) 2022; 12:diagnostics12102319. [PMID: 36292008 PMCID: PMC9600531 DOI: 10.3390/diagnostics12102319] [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: 08/29/2022] [Revised: 09/21/2022] [Accepted: 09/21/2022] [Indexed: 11/17/2022] Open
Abstract
(1) Background: Accurate measurement of lung-nodule size is necessary, but whether a three-dimensional volume measurement is better or more reliable than the one-dimensional method is still unclear. This study aimed to investigate the intra- and inter-reader variations according to nodule type, size, three-dimensional volume measurements, and one-dimensional linear measurements. (2) Methods: This retrospective study included computed tomography (CT) examinations of lung nodules and volume measurements performed from October to December 2016. Two radiologists independently performed all measurements. Intra-class correlation coefficients (ICC) and Bland-Altman plots were used for analysis. (3) Results: The overall variability in the calculated volume was larger than when using the semiautomatic volume measurement. Nodules <6 mm tended to have larger variability than nodules ≥6 mm in both one-dimensional and calculated volume measurements. The isolated type showed smaller variability in both intra- and inter-reader comparisons. The juxta-vascular type showed the largest variability in both one-dimensional and calculated volume measurements. The variability was decreased when using the 3D volume semiautomated software. (4) Conclusions: The present study suggests that 3D semiautomatic volume measurements showed lower variability than the calculated volume measurement. Nodule size and location influence measurement variability. The intra- and inter-reader variabilities in nodule volume measurement were considerable.
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Hammer MM, Hunsaker AR. Strategies for Reducing False-Positive Screening Results for Intermediate-Size Nodules Evaluated Using Lung-RADS: A Secondary Analysis of National Lung Screening Trial Data. AJR Am J Roentgenol 2022; 219:397-405. [PMID: 35319912 PMCID: PMC9398972 DOI: 10.2214/ajr.22.27595] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND. Lung-RADS version 1.1 (v1.1) classifies all solid nodules less than 6 mm as category 2. Lung-RADS v1.1 also classifies solid intermediate-size (6 to < 10 mm) nodules as category 2 if they are perifissural and have a triangular, polygonal, or ovoid shape (indicative of intrapulmonary lymph nodes). Additional category 2 criteria could reduce false-positive results of screening examinations. OBJECTIVE. The purpose of this study was to evaluate the impact of proposed strategies for reducing false-positive results for intermediate-size nodules on lung cancer screening CT evaluated using Lung-RADS v1.1. METHODS. This retrospective study entailed secondary analysis of National Lung Screening Trial (NLST) data. Of 1387 solid nodules measuring 6.0-9.5 mm on baseline screening CT examinations in the NLST, all 38 nodules in patients who developed cancer and a random sample of 200 nodules in patients who did not develop cancer were selected for further evaluation. Cancers were required to correspond with the baseline nodule on manual review. After exclusions, the sample included 223 patients (median age, 62 years; 143 men, 80 women; 196 benign nodules, 27 malignant nodules). Two thoracic radiologists independently reviewed baseline examinations to record nodule diameter and volume using semiautomated software and to determine whether nodules had perifissural location; other subpleural location; and triangular, polygonal, or ovoid shape. Different schemes for category 2 assignment were compared. RESULTS. Across readers, standard Lung-RADS v1.1 had sensitivity of 89-93% and specificity of 26-31%. A modification assigning nodules less than 10 mm with triangular, polygonal, or ovoid shape in other subpleural locations (vs only perifissural location) as category 2 had sensitivity of 85-93% and specificity of 47-51%. Lung-RADS v1.1 using volume cutoffs had sensitivity of 89-93% and specificity of 37% (both readers). The sensitivity of both modified Lung-RADS v1.1 and Lung-RADS v1.1 with volume cutoffs was not significantly different from standard Lung-RADS v1.1 (all p > .05). However, both schemes' specificity was significantly better than standard Lung-RADS v1.1 (all p < .05). Combining the two strategies yielded sensitivity of 85-93% and specificity of 58-59%. CONCLUSION. Classifying intermediate-size nodules with triangular, polygonal, or ovoid shape in any subpleural (not just perifissural) location as category 2 and using volume- rather than diameter-based measurements improves Lung-RADS specificity without decreased sensitivity. CLINICAL IMPACT. The findings can help reduce false-positive results, decreasing 6-month follow-up examinations for benign findings.
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Affiliation(s)
- Mark M Hammer
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115
| | - Andetta R Hunsaker
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115
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36
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Choi W, Dahiya N, Nadeem S. CIRDataset: A large-scale Dataset for Clinically-Interpretable lung nodule Radiomics and malignancy prediction. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2022; 2022:13-22. [PMID: 36198166 PMCID: PMC9527770 DOI: 10.1007/978-3-031-16443-9_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules, are good predictors of lung cancer malignancy and hence, are routinely assessed and reported by radiologists as part of the standardized Lung-RADS clinical scoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice assessment by radiologists, manual spiculation/lobulation annotation is a tedious task and thus no public datasets exist to date for probing the importance of these clinically-reported features in the SOTA malignancy prediction algorithms. As part of this paper, we release a large-scale Clinically-Interpretable Radiomics Dataset, CIRDataset, containing 956 radiologist QA/QC'ed spiculation/lobulation annotations on segmented lung nodules from two public datasets, LIDC-IDRI (N=883) and LUNGx (N=73). We also present an end-to-end deep learning model based on multi-class Voxel2Mesh extension to segment nodules (while preserving spikes), classify spikes (sharp/spiculation and curved/lobulation), and perform malignancy prediction. Previous methods have performed malignancy prediction for LIDC and LUNGx datasets but without robust attribution to any clinically reported/actionable features (due to known hyperparameter sensitivity issues with general attribution schemes). With the release of this comprehensively-annotated CIRDataset and end-to-end deep learning baseline, we hope that malignancy prediction methods can validate their explanations, benchmark against our baseline, and provide clinically-actionable insights. Dataset, code, pretrained models, and docker containers are available at https://github.com/nadeemlab/CIR.
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Affiliation(s)
- Wookjin Choi
- Department of Radiation Oncology, Thomas Jefferson University Hospital
| | - Navdeep Dahiya
- School of Electrical and Computer Engineering, Georgia Institute of Technology
| | - Saad Nadeem
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center
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Cao P, Jeon J, Meza R. Evaluation of benefits and harms of adaptive screening schedules for lung cancer: A microsimulation study. J Med Screen 2022; 29:260-267. [PMID: 35989646 PMCID: PMC9574899 DOI: 10.1177/09691413221118194] [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] [Indexed: 11/16/2022]
Abstract
BACKGROUND Although lung cancer screening (LCS) has been proven effective in reducing lung cancer mortality, it is associated with some potential harms, such as false positives and invasive follow-up procedures. Determining the time to next screen based on individual risk could reduce harms while maintaining health gains. Here, we evaluate the benefits and harms of LCS strategies with adaptive schedules, and compare these with those from non-adaptive strategies. METHODS We extended the Lee and Zelen risk threshold method to select screening schedules based on individual's lung cancer risk and life expectancy (adaptive schedules). We compared the health benefits and harms of these adaptive schedules with regular (non-adaptive) schedules (annual, biennial and triennial) using a validated lung cancer microsimulation model. Outcomes include lung cancer deaths (LCD) averted, life years gained (LYG), discounted quality adjusted life years (QALYs) gained, and false positives per LCD averted. We also explored the impact of varying screening-related disutilities. RESULTS In comparison to standard regular screening recommendations, risk-dependent adaptive screening reduced screening harms while maintaining a similar level of health benefits. The net gains and the balance of benefits and harms from LCS with efficient adaptive schedules were improved compared to those from regular screening, especially when the screening-related disutilities are high. CONCLUSIONS Adaptive screening schedules can reduce the associated harms of screening while maintaining its associated lung cancer mortality reductions and years of life gained. Our study identifies individually tailored schedules that optimize the screening benefit/harm trade-offs.
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Affiliation(s)
- Pianpian Cao
- Department of Epidemiology, 1259University of Michigan, Ann Arbor, MI, USA
| | - Jihyoun Jeon
- Department of Epidemiology, 1259University of Michigan, Ann Arbor, MI, USA
| | - Rafael Meza
- Department of Epidemiology, 1259University of Michigan, Ann Arbor, MI, USA
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38
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Wood DE, Kazerooni EA, Aberle D, Berman A, Brown LM, Eapen GA, Ettinger DS, Ferguson JS, Hou L, Kadaria D, Klippenstein D, Kumar R, Lackner RP, Leard LE, Lennes IT, Leung ANC, Mazzone P, Merritt RE, Midthun DE, Onaitis M, Pipavath S, Pratt C, Puri V, Raz D, Reddy C, Reid ME, Sandler KL, Sands J, Schabath MB, Studts JL, Tanoue L, Tong BC, Travis WD, Wei B, Westover K, Yang SC, McCullough B, Hughes M. NCCN Guidelines® Insights: Lung Cancer Screening, Version 1.2022. J Natl Compr Canc Netw 2022; 20:754-764. [PMID: 35830884 DOI: 10.6004/jnccn.2022.0036] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The NCCN Guidelines for Lung Cancer Screening recommend criteria for selecting individuals for screening and provide recommendations for evaluation and follow-up of lung nodules found during initial and subsequent screening. These NCCN Guidelines Insights focus on recent updates to the NCCN Guidelines for Lung Cancer Screening.
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Affiliation(s)
- Douglas E Wood
- Fred Hutchinson Cancer Research Center/Seattle Cancer Care Alliance
| | | | | | - Abigail Berman
- Abramson Cancer Center at the University of Pennsylvania
| | | | | | | | | | - Lifang Hou
- Robert H. Lurie Comprehensive Cancer Center of Northwestern University
| | - Dipen Kadaria
- St. Jude Children's Research Hospital/The University of Tennessee Health Science Center
| | | | | | | | | | | | | | - Peter Mazzone
- Case Comprehensive Cancer Center/University Hospitals Seidman Cancer Center and Cleveland Clinic Taussig Cancer Institute
| | - Robert E Merritt
- The Ohio State University Comprehensive Cancer Center - James Cancer Hospital and Solove Research Institute
| | | | - Mark Onaitis
- Fred Hutchinson Cancer Research Center/Seattle Cancer Care Alliance
| | | | | | - Varun Puri
- Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine
| | - Dan Raz
- City of Hope National Medical Center
| | | | | | | | - Jacob Sands
- Dana-Farber/Brigham and Women's Cancer Center
| | | | | | | | | | | | | | | | - Stephen C Yang
- The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins
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Tanoue LT, Sather P, Cortopassi I, Dicks D, Curtis A, Michaud G, Bader A, Gange C, Detterbeck F, Killam J. Standardizing the Reporting of Incidental, Non-Lung Cancer (Category S) Findings Identified on Lung Cancer Screening Low-Dose CT Imaging. Chest 2022; 161:1697-1706. [DOI: 10.1016/j.chest.2021.12.662] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 12/12/2022] Open
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40
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Yanagawa M. Artificial Intelligence Improves Radiologist Performance for Predicting Malignancy at Chest CT. Radiology 2022; 304:692-693. [PMID: 35608448 DOI: 10.1148/radiol.220571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Masahiro Yanagawa
- From the Department of Radiology, Osaka University Graduate School of Medicine, Yamadaoka, 2-2 Suita, Osaka 565-0871, Japan
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41
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Silva M, Picozzi G, Sverzellati N, Anglesio S, Bartolucci M, Cavigli E, Deliperi A, Falchini M, Falaschi F, Ghio D, Gollini P, Larici AR, Marchianò AV, Palmucci S, Preda L, Romei C, Tessa C, Rampinelli C, Mascalchi M. Low-dose CT for lung cancer screening: position paper from the Italian college of thoracic radiology. LA RADIOLOGIA MEDICA 2022; 127:543-559. [PMID: 35306638 PMCID: PMC8934407 DOI: 10.1007/s11547-022-01471-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 02/18/2022] [Indexed: 12/24/2022]
Abstract
Smoking is the main risk factor for lung cancer (LC), which is the leading cause of cancer-related death worldwide. Independent randomized controlled trials, governmental and inter-governmental task forces, and meta-analyses established that LC screening (LCS) with chest low dose computed tomography (LDCT) decreases the mortality of LC in smokers and former smokers, compared to no-screening, especially in women. Accordingly, several Italian initiatives are offering LCS by LDCT and smoking cessation to about 10,000 high-risk subjects, supported by Private or Public Health Institutions, envisaging a possible population-based screening program. Because LDCT is the backbone of LCS, Italian radiologists with LCS expertise are presenting this position paper that encompasses recommendations for LDCT scan protocol and its reading. Moreover, fundamentals for classification of lung nodules and other findings at LDCT test are detailed along with international guidelines, from the European Society of Thoracic Imaging, the British Thoracic Society, and the American College of Radiology, for their reporting and management in LCS. The Italian College of Thoracic Radiologists produced this document to provide the basics for radiologists who plan to set up or to be involved in LCS, thus fostering homogenous evidence-based approach to the LDCT test over the Italian territory and warrant comparison and analyses throughout National and International practices.
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Affiliation(s)
- Mario Silva
- Department of Medicine and Surgery (DiMeC), University of Parma, Via Gramsci 14, Parma, Italy.
- Unit of "Scienze Radiologiche", University Hospital of Parma, Pad. Barbieri, Via Gramsci 14, 43126, Parma, Italy.
| | - Giulia Picozzi
- Istituto Di Studio Prevenzione E Rete Oncologica, Firenze, Italy
| | - Nicola Sverzellati
- Department of Medicine and Surgery (DiMeC), University of Parma, Via Gramsci 14, Parma, Italy
- Unit of "Scienze Radiologiche", University Hospital of Parma, Pad. Barbieri, Via Gramsci 14, 43126, Parma, Italy
| | | | | | | | | | | | | | - Domenico Ghio
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Anna Rita Larici
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Università Cattolica del Sacro Cuore Di Roma, Roma, Italy
| | - Alfonso V Marchianò
- Department of Radiology, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, MI, Italy
| | - Stefano Palmucci
- UOC Radiologia 1, Dipartimento Scienze Mediche Chirurgiche E Tecnologie Avanzate "GF Ingrassia", Università Di Catania, AOU Policlinico "G. Rodolico-San Marco", Catania, Italy
| | - Lorenzo Preda
- IRCCS Fondazione Policlinico San Matteo, Pavia, Italy
- Dipartimento Di Scienze Clinico-Chirurgiche, Diagnostiche E Pediatriche, Università Degli Studi Di Pavia, Pavia, Italy
| | | | - Carlo Tessa
- Radiologia Apuane E Lunigiana, Azienda USL Toscana Nord Ovest, Pisa, Italy
| | | | - Mario Mascalchi
- Istituto Di Studio Prevenzione E Rete Oncologica, Firenze, Italy
- Università Di Firenze, Firenze, Italy
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Guedes Pinto E, Penha D, Hochhegger B, Monaghan C, Marchiori E, Taborda-Barata L, Irion K. The impact of cardiopulmonary hemodynamic factors in volumetry for pulmonary nodule management. BMC Med Imaging 2022; 22:49. [PMID: 35303820 PMCID: PMC8932130 DOI: 10.1186/s12880-022-00774-w] [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: 11/02/2021] [Accepted: 03/10/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The acceptance of coronary CT angiogram (CCTA) scans in the management of stable angina has led to an exponential increase in studies performed and reported incidental findings, including pulmonary nodules (PN). Using low-dose CT scans, volumetry tools are used in growth assessment and risk stratification of PN between 5 and 8 mm in diameter. Volumetry of PN could also benefit from the increased temporal resolution of CCTA scans, potentially expediting clinical decisions when an incidental PN is first detected on a CCTA scan, and allow for better resource management and planning in a Radiology department. This study aims to investigate how cardiopulmonary hemodynamic factors impact the volumetry of PN using CCTA scans. These factors include the cardiac phase, vascular distance from the main pulmonary artery (MPA) to the nodule, difference of the MPA diameter between systole and diastole, nodule location, and cardiomegaly presence. MATERIALS AND METHODS Two readers reviewed all CCTA scans performed from 2016 to 2019 in a tertiary hospital and detected PN measuring between 5 and 8 mm in diameter. Each observer measured each nodule using two different software packages and in systole and diastole. A multiple linear regression model was applied, and inter-observer and inter-software agreement were assessed using intraclass correlation. RESULTS A total of 195 nodules from 107 patients were included in this retrospective, cross-sectional and observational study. The regression model identified the vascular distance (p < 0.001), the difference of the MPA diameter between systole and diastole (p < 0.001), and the location within the lower or posterior thirds of the field of view (p < 0.001 each) as affecting the volume measurement. The cardiac phase was not significant in the model. There was a very high inter-observer agreement but no reasonable inter-software agreement between measurements. CONCLUSIONS PN volumetry using CCTA scans seems to be sensitive to cardiopulmonary hemodynamic changes independently of the cardiac phase. These might also be relevant to non-gated scans, such as during PN follow-up. The cardiopulmonary hemodynamic changes are a new limiting factor to PN volumetry. In addition, when a patient experiences an acute or deteriorating cardiopulmonary disease during PN follow-up, these hemodynamic changes could affect the PN growth estimation.
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Affiliation(s)
| | - Diana Penha
- Universidade da Beira Interior, Covilhã, Portugal.,Imaging Department, Liverpool Heart and Chest Hospital NHS Foundation Trust: Liverpool, Liverpool, UK
| | - Bruno Hochhegger
- Pontifical Catholic University of Rio Grande Do Sul, Porto Alegre, Brazil
| | - Colin Monaghan
- Imaging Department, Liverpool Heart and Chest Hospital NHS Foundation Trust: Liverpool, Liverpool, UK
| | - Edson Marchiori
- Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Klaus Irion
- Imaging Department, University of Manchester, Manchester, UK
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May M, Heiss R, Koehnen J, Wetzl M, Wiesmueller M, Treutlein C, Braeuer L, Uder M, Kopp M. Personalized Chest Computed Tomography: Minimum Diagnostic Radiation Dose Levels for the Detection of Fibrosis, Nodules, and Pneumonia. Invest Radiol 2022; 57:148-156. [PMID: 34468413 PMCID: PMC8826613 DOI: 10.1097/rli.0000000000000822] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/13/2021] [Accepted: 07/13/2021] [Indexed: 01/08/2023]
Abstract
OBJECTIVES The purpose of this study was to evaluate the minimum diagnostic radiation dose level for the detection of high-resolution (HR) lung structures, pulmonary nodules (PNs), and infectious diseases (IDs). MATERIALS AND METHODS A preclinical chest computed tomography (CT) trial was performed with a human cadaver without known lung disease with incremental radiation dose using tin filter-based spectral shaping protocols. A subset of protocols for full diagnostic evaluation of HR, PN, and ID structures was translated to clinical routine. Also, a minimum diagnostic radiation dose protocol was defined (MIN). These protocols were prospectively applied over 5 months in the clinical routine under consideration of the individual clinical indication. We compared radiation dose parameters, objective and subjective image quality (IQ). RESULTS The HR protocol was performed in 38 patients (43%), PN in 21 patients (24%), ID in 20 patients (23%), and MIN in 9 patients (10%). Radiation dose differed significantly among HR, PN, and ID (5.4, 1.2, and 0.6 mGy, respectively; P < 0.001). Differences between ID and MIN (0.2 mGy) were not significant (P = 0.262). Dose-normalized contrast-to-noise ratio was comparable among all groups (P = 0.087). Overall IQ was perfect for the HR protocol (median, 5.0) and decreased for PN (4.5), ID-CT (4.3), and MIN-CT (2.5). The delineation of disease-specific findings was high in all dedicated protocols (HR, 5.0; PN, 5.0; ID, 4.5). The MIN protocol had borderline IQ for PN and ID lesions but was insufficient for HR structures. The dose reductions were 78% (PN), 89% (ID), and 97% (MIN) compared with the HR protocols. CONCLUSIONS Personalized chest CT tailored to the clinical indications leads to substantial dose reduction without reducing interpretability. More than 50% of patients can benefit from such individual adaptation in a clinical routine setting. Personalized radiation dose adjustments with validated diagnostic IQ are especially preferable for evaluating ID and PN lesions.
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Affiliation(s)
- Matthias May
- From the Department of Radiology, University Hospital Erlangen
| | - Rafael Heiss
- From the Department of Radiology, University Hospital Erlangen
| | - Julia Koehnen
- From the Department of Radiology, University Hospital Erlangen
| | - Matthias Wetzl
- From the Department of Radiology, University Hospital Erlangen
| | | | | | - Lars Braeuer
- Institute of Anatomy, Chair II, Friedrich Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Michael Uder
- From the Department of Radiology, University Hospital Erlangen
| | - Markus Kopp
- From the Department of Radiology, University Hospital Erlangen
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44
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Penha D, Pinto E, Monaghan C, Hochhegger B, Marchiori E, Taborda-Barata L, Irion K, Ravara S, Kauczor HU. Incidental findings on lung cancer screening: pictorial essay and systematic checklist. J Bras Pneumol 2022; 48:e20210371. [PMID: 35137873 PMCID: PMC8836644 DOI: 10.36416/1806-3756/e20210371] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 11/05/2021] [Indexed: 11/23/2022] Open
Abstract
Lung cancer screening (LCS) programs are increasing worldwide. Incidental findings (IFs) on LCS are defined as low-dose CT findings unrelated to the primary purpose of identifying lung cancer. Most IFs on LCS are benign and clinically insignificant but are being increasingly recognized, and some require urgent referral for further diagnostic workup. Other findings are expected and are known as smoking-related comorbidities, including COPD, cardiovascular disease, emphysema, and interstitial lung disease, and their diagnosis can have a significant impact on patient prognosis. The purpose of this pictorial essay is to illustrate the most common IFs on LCS, organized by organ. We will discuss the current literature on IFs on LCS, focusing on their prevalence, appropriate communication, and triggering of clinical pathway systems.
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Affiliation(s)
- Diana Penha
- . Faculdade de Ciências da Saúde, Universidade da Beira Interior, Covilhã, Portugal.,. Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, United Kingdom
| | - Erique Pinto
- . Faculdade de Ciências da Saúde, Universidade da Beira Interior, Covilhã, Portugal
| | - Colin Monaghan
- . Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, United Kingdom
| | - Bruno Hochhegger
- . Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre (RS) Brasil.,. University of Florida. Department of Radiology. Gainesville (FL) USA
| | - Edson Marchiori
- . Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro (RJ) Brasil.,. Faculdade de Medicina, Universidade Federal Fluminense, Niterói (RJ) Brasil
| | - Luís Taborda-Barata
- . Faculdade de Ciências da Saúde, Universidade da Beira Interior, Covilhã, Portugal.,. Centro de Investigação em Ciências da Saúde, Universidade da Beira Interior - CICS-UBI - Covilhã, Portugal
| | - Klaus Irion
- . Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Sofia Ravara
- . Centro de Investigação em Ciências da Saúde, Universidade da Beira Interior - CICS-UBI - Covilhã, Portugal.,. Centro de Investigação em Saúde Pública, Escola Nacional de Saúde Pública, Universidade NOVA de Lisboa, Lisboa, Portugal.,. Setor de Pneumologia, Centro Hospitalar Universitário Cova da Beira, Covilhã, Portugal
| | - Hans-Ulrich Kauczor
- . Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany.,. Translational Lung Research Center, Heidelberg, Germany
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Ohno Y, Takenaka D, Yoshikawa T, Yui M, Koyama H, Yamamoto K, Hamabuchi N, Shigemura C, Watanabe A, Ueda T, Ikeda H, Hattori H, Murayama K, Toyama H. Efficacy of Ultrashort Echo Time Pulmonary MRI for Lung Nodule Detection and Lung-RADS Classification. Radiology 2021; 302:697-706. [PMID: 34846203 DOI: 10.1148/radiol.211254] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background Pulmonary MRI with ultrashort echo time (UTE) has been compared with chest CT for nodule detection and classification. However, direct comparisons of these methods' capabilities for Lung CT Screening Reporting and Data System (Lung-RADS) evaluation remain lacking. Purpose To compare the capabilities of pulmonary MRI with UTE with those of standard- or low-dose thin-section CT for Lung-RADS classification. Materials and Methods In this prospective study, standard- and low-dose chest CT (270 mA and 60 mA, respectively) and MRI with UTE were used to examine consecutive participants enrolled between January 2017 and December 2020 who met American College of Radiology Appropriateness Criteria for lung cancer screening with low-dose CT. Probability of nodule presence was assessed for all methods with a five-point visual scoring system by two board-certified radiologists. All nodules were then evaluated in terms of their Lung-RADS classification using each method. To compare nodule detection capability of the three methods, consensus for performances was rated by using jackknife free-response receiver operating characteristic analysis, and sensitivity was compared by means of the McNemar test. In addition, weighted κ statistics were used to determine the agreement between Lung-RADS classification obtained with each method and the reference standard generated from standard-dose CT evaluated by two radiologists who were not included in the image analysis session. Results A total of 205 participants (mean age: 64 years ± 7 [standard deviation], 106 men) with 1073 nodules were enrolled. Figure of merit (FOM) (P < .001) had significant differences among three modalities (standard-dose CT: FOM = 0.91, low-dose CT: FOM = 0.89, pulmonary MRI with UTE: FOM = 0.94), with no evidence of false-positive findings in participants with all modalities (P > .05). Agreements for Lung-RADS classification between all modalities and the reference standard were almost perfect (standard-dose CT: κ = 0.82, P < .001; low-dose CT: κ = 0.82, P < .001; pulmonary MRI with UTE: κ = 0.82, P < .001). Conclusion In a lung cancer screening population, ultrashort echo time pulmonary MRI was comparable to standard- or low-dose CT for Lung CT Screening Reporting and Data System classification. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Wielpütz in this issue.
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Affiliation(s)
- Yoshiharu Ohno
- From the Department of Radiology (Y.O., N.H., C.S., A.W., T.U., H.I., H.H., H.T.) and Joint Research Laboratory of Advanced Biomedical Imaging (Y.O., K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., T.Y.); Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan (D.T., T.Y.); Canon Medical Systems, Otawara, Japan (M.Y., K.Y.); and Department of Radiology, Osaka Police Hospital, Osaka, Japan (H.K.)
| | - Daisuke Takenaka
- From the Department of Radiology (Y.O., N.H., C.S., A.W., T.U., H.I., H.H., H.T.) and Joint Research Laboratory of Advanced Biomedical Imaging (Y.O., K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., T.Y.); Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan (D.T., T.Y.); Canon Medical Systems, Otawara, Japan (M.Y., K.Y.); and Department of Radiology, Osaka Police Hospital, Osaka, Japan (H.K.)
| | - Takeshi Yoshikawa
- From the Department of Radiology (Y.O., N.H., C.S., A.W., T.U., H.I., H.H., H.T.) and Joint Research Laboratory of Advanced Biomedical Imaging (Y.O., K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., T.Y.); Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan (D.T., T.Y.); Canon Medical Systems, Otawara, Japan (M.Y., K.Y.); and Department of Radiology, Osaka Police Hospital, Osaka, Japan (H.K.)
| | - Masao Yui
- From the Department of Radiology (Y.O., N.H., C.S., A.W., T.U., H.I., H.H., H.T.) and Joint Research Laboratory of Advanced Biomedical Imaging (Y.O., K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., T.Y.); Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan (D.T., T.Y.); Canon Medical Systems, Otawara, Japan (M.Y., K.Y.); and Department of Radiology, Osaka Police Hospital, Osaka, Japan (H.K.)
| | - Hisanobu Koyama
- From the Department of Radiology (Y.O., N.H., C.S., A.W., T.U., H.I., H.H., H.T.) and Joint Research Laboratory of Advanced Biomedical Imaging (Y.O., K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., T.Y.); Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan (D.T., T.Y.); Canon Medical Systems, Otawara, Japan (M.Y., K.Y.); and Department of Radiology, Osaka Police Hospital, Osaka, Japan (H.K.)
| | - Kaori Yamamoto
- From the Department of Radiology (Y.O., N.H., C.S., A.W., T.U., H.I., H.H., H.T.) and Joint Research Laboratory of Advanced Biomedical Imaging (Y.O., K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., T.Y.); Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan (D.T., T.Y.); Canon Medical Systems, Otawara, Japan (M.Y., K.Y.); and Department of Radiology, Osaka Police Hospital, Osaka, Japan (H.K.)
| | - Nayu Hamabuchi
- From the Department of Radiology (Y.O., N.H., C.S., A.W., T.U., H.I., H.H., H.T.) and Joint Research Laboratory of Advanced Biomedical Imaging (Y.O., K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., T.Y.); Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan (D.T., T.Y.); Canon Medical Systems, Otawara, Japan (M.Y., K.Y.); and Department of Radiology, Osaka Police Hospital, Osaka, Japan (H.K.)
| | - Chika Shigemura
- From the Department of Radiology (Y.O., N.H., C.S., A.W., T.U., H.I., H.H., H.T.) and Joint Research Laboratory of Advanced Biomedical Imaging (Y.O., K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., T.Y.); Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan (D.T., T.Y.); Canon Medical Systems, Otawara, Japan (M.Y., K.Y.); and Department of Radiology, Osaka Police Hospital, Osaka, Japan (H.K.)
| | - Ayumi Watanabe
- From the Department of Radiology (Y.O., N.H., C.S., A.W., T.U., H.I., H.H., H.T.) and Joint Research Laboratory of Advanced Biomedical Imaging (Y.O., K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., T.Y.); Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan (D.T., T.Y.); Canon Medical Systems, Otawara, Japan (M.Y., K.Y.); and Department of Radiology, Osaka Police Hospital, Osaka, Japan (H.K.)
| | - Takahiro Ueda
- From the Department of Radiology (Y.O., N.H., C.S., A.W., T.U., H.I., H.H., H.T.) and Joint Research Laboratory of Advanced Biomedical Imaging (Y.O., K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., T.Y.); Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan (D.T., T.Y.); Canon Medical Systems, Otawara, Japan (M.Y., K.Y.); and Department of Radiology, Osaka Police Hospital, Osaka, Japan (H.K.)
| | - Hirotaka Ikeda
- From the Department of Radiology (Y.O., N.H., C.S., A.W., T.U., H.I., H.H., H.T.) and Joint Research Laboratory of Advanced Biomedical Imaging (Y.O., K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., T.Y.); Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan (D.T., T.Y.); Canon Medical Systems, Otawara, Japan (M.Y., K.Y.); and Department of Radiology, Osaka Police Hospital, Osaka, Japan (H.K.)
| | - Hidekazu Hattori
- From the Department of Radiology (Y.O., N.H., C.S., A.W., T.U., H.I., H.H., H.T.) and Joint Research Laboratory of Advanced Biomedical Imaging (Y.O., K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., T.Y.); Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan (D.T., T.Y.); Canon Medical Systems, Otawara, Japan (M.Y., K.Y.); and Department of Radiology, Osaka Police Hospital, Osaka, Japan (H.K.)
| | - Kazuhiro Murayama
- From the Department of Radiology (Y.O., N.H., C.S., A.W., T.U., H.I., H.H., H.T.) and Joint Research Laboratory of Advanced Biomedical Imaging (Y.O., K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., T.Y.); Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan (D.T., T.Y.); Canon Medical Systems, Otawara, Japan (M.Y., K.Y.); and Department of Radiology, Osaka Police Hospital, Osaka, Japan (H.K.)
| | - Hiroshi Toyama
- From the Department of Radiology (Y.O., N.H., C.S., A.W., T.U., H.I., H.H., H.T.) and Joint Research Laboratory of Advanced Biomedical Imaging (Y.O., K.M.), Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., T.Y.); Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan (D.T., T.Y.); Canon Medical Systems, Otawara, Japan (M.Y., K.Y.); and Department of Radiology, Osaka Police Hospital, Osaka, Japan (H.K.)
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Outcomes of Positive and Suspicious Findings in Clinical CT Lung Cancer Screening and the Road Ahead. Ann Am Thorac Soc 2021; 19:1371-1378. [PMID: 34818144 PMCID: PMC9353952 DOI: 10.1513/annalsats.202106-733oc] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Rationale Future optimization of computed tomography (CT) lung cancer screening (CTLS) algorithms will depend on clinical outcomes data. Objectives To report the outcomes of positive and suspicious findings in a clinical CTLS program. Methods We retrospectively reviewed results for patients from our institution undergoing lung cancer screening from January 2012 through December 2018, with follow-up through December 2019. All exams were retrospectively rescored using Lung-RADS v1.1 (LR). Metrics assessed included positive, probably benign, and suspicious exam rates, frequency/nature of care escalation, and lung cancer detection rates after a positive, probably benign, and suspicious exam result and overall. We calculated time required to resolve suspicious exams as malignant or benign. Results were broken down by subcategories, reason for positive/suspicious designation, and screening round. Results During the study period 4,301 individuals underwent a total of 10,897 exams. The number of positive (13.9%), suspicious (5.5%), and significant incidental (6.4%) findings was significantly higher at baseline screening. Cancer detection and false-positive rates were 2.0% and 12.3% at baseline versus 1.3% and 5.1% across subsequent screening rounds, respectively. Baseline solid nodule(s) 6 to <8 mm were the only probably benign findings resulting in lung cancer detection within 12 months. New solid nodules 6 to <8 mm were the only LR category 4A (LR4A) findings falling within the LR predicted cancer detection range of 5–15% (12.8%). 38.5% of LR4A cancers were detected within 3 months. Conclusions Modification of the definition and suggested workup of positive and suspicious lung cancer screening findings appears warranted.
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Yousefirizi F, Pierre Decazes, Amyar A, Ruan S, Saboury B, Rahmim A. AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging:: Towards Radiophenomics. PET Clin 2021; 17:183-212. [PMID: 34809866 DOI: 10.1016/j.cpet.2021.09.010] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Artificial intelligence (AI) techniques have significant potential to enable effective, robust, and automated image phenotyping including the identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of "radiomics" and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be used as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.
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Affiliation(s)
- Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada.
| | - Pierre Decazes
- Department of Nuclear Medicine, Henri Becquerel Centre, Rue d'Amiens - CS 11516 - 76038 Rouen Cedex 1, France; QuantIF-LITIS, Faculty of Medicine and Pharmacy, Research Building - 1st floor, 22 boulevard Gambetta, 76183 Rouen Cedex, France
| | - Amine Amyar
- QuantIF-LITIS, Faculty of Medicine and Pharmacy, Research Building - 1st floor, 22 boulevard Gambetta, 76183 Rouen Cedex, France; General Electric Healthcare, Buc, France
| | - Su Ruan
- QuantIF-LITIS, Faculty of Medicine and Pharmacy, Research Building - 1st floor, 22 boulevard Gambetta, 76183 Rouen Cedex, France
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA; Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada; Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada; Department of Physics, University of British Columbia, Vancouver, British Columbia, Canada
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