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Kim JY, Ryu WS, Kim D, Kim EY. Better performance of deep learning pulmonary nodule detection using chest radiography with pixel level labels in reference to computed tomography: data quality matters. Sci Rep 2024; 14:15967. [PMID: 38987309 PMCID: PMC11237128 DOI: 10.1038/s41598-024-66530-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: 05/03/2023] [Accepted: 07/02/2024] [Indexed: 07/12/2024] Open
Abstract
Labeling errors can significantly impact the performance of deep learning models used for screening chest radiographs. The deep learning model for detecting pulmonary nodules is particularly vulnerable to such errors, mainly because normal chest radiographs and those with nodules obscured by ribs appear similar. Thus, high-quality datasets referred to chest computed tomography (CT) are required to prevent the misclassification of nodular chest radiographs as normal. From this perspective, a deep learning strategy employing chest radiography data with pixel-level annotations referencing chest CT scans may improve nodule detection and localization compared to image-level labels. We trained models using a National Institute of Health chest radiograph-based labeling dataset and an AI-HUB CT-based labeling dataset, employing DenseNet architecture with squeeze-and-excitation blocks. We developed four models to assess whether CT versus chest radiography and pixel-level versus image-level labeling would improve the deep learning model's performance to detect nodules. The models' performance was evaluated using two external validation datasets. The AI-HUB dataset with image-level labeling outperformed the NIH dataset (AUC 0.88 vs 0.71 and 0.78 vs. 0.73 in two external datasets, respectively; both p < 0.001). However, the AI-HUB data annotated at the pixel level produced the best model (AUC 0.91 and 0.86 in external datasets), and in terms of nodule localization, it significantly outperformed models trained with image-level annotation data, with a Dice coefficient ranging from 0.36 to 0.58. Our findings underscore the importance of accurately labeled data in developing reliable deep learning algorithms for nodule detection in chest radiography.
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Affiliation(s)
- Jae Yong Kim
- Artificial Intelligence Research Center, JLK Inc., 5 Teheran-ro 33-gil, Seoul, Republic of Korea
| | - Wi-Sun Ryu
- Artificial Intelligence Research Center, JLK Inc., 5 Teheran-ro 33-gil, Seoul, Republic of Korea.
| | - Dongmin Kim
- Artificial Intelligence Research Center, JLK Inc., 5 Teheran-ro 33-gil, Seoul, Republic of Korea
| | - Eun Young Kim
- Department of Radiology, Incheon Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, 21080, Republic of Korea.
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Qutob RA, Almehaidib IA, Alzahrani SS, Alabdulkarim SM, Abuhemid HA, Alassaf RA, Alaryni A, Alghamdi A, Alsolamy E, Bukhari A, Alotay AA, Alhajery MA, Alanazi A, Faqihi FA, Almaimani MK. Knowledge, Attitudes, and Practice Patterns of Lung Cancer Screening Among Physicians in Saudi Arabia. Cureus 2024; 16:e51842. [PMID: 38327913 PMCID: PMC10848281 DOI: 10.7759/cureus.51842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/08/2024] [Indexed: 02/09/2024] Open
Abstract
BACKGROUND Lung cancer remains the primary cause of death connected to cancer on a worldwide scale. Obtaining a deep understanding of the knowledge, attitudes, and behavior patterns of doctors is essential for developing successful strategies to improve lung cancer screening. This study aims to identify the attitudes, beliefs, referral practices, and knowledge of lung cancer screening among physicians in Saudi Arabia. METHODS An online survey was conducted from July to December 2023 to investigate the attitudes, beliefs, referral practices, and knowledge of lung cancer screening, and adherence to lung cancer screening recommendations among physicians in Saudi Arabia. Internal medicine, family medicine, and pulmonology physicians of all levels (consultants, senior registrars, and residents) who are currently practicing medicine in Saudi Arabia formed the study population. This study employed a previously developed questionnaire. Binary logistic regression analysis was employed to identify factors that indicate a better degree of knowledge and a positive attitude toward lung cancer screening. RESULTS This study involved a total of 96 physicians. The study participants demonstrated a significant degree of understanding regarding lung cancer screening, with an average knowledge score of 5.8 (SD: 1.7) out of 8, equivalent to 72.5% of the highest possible score. The accuracy rate for knowledge items varied from 44.8% to 91.7%. The study participants had a moderately favorable attitude toward lung cancer screening, as shown by a mean attitude score of 14.4 (SD: 3.7) out of a maximum possible score of 30, which corresponds to 48.0% of the highest achievable score. Around 36.5% of the survey participants reported engaging in the practice of discussing the results of lung cancer screening with patients. The primary obstacles frequently cited were challenges in patient scheduling, insufficient time to discuss lung cancer screening during clinic appointments, and patient refusal, constituting 59.4%, 53.1%, and 53.1% of the identified barriers, respectively. Physicians in Saudi Arabia, particularly those employed in private hospitals, demonstrated a higher level of knowledge of lung cancer screening compared to others (p < 0.05). In contrast, individuals with 11-15 years of experience were shown to have a 78.0% lower likelihood of being educated about lung cancer screening compared to their counterparts (p < 0.05). CONCLUSION The study's results indicate that there is a need for the development of specialized educational initiatives aimed at Saudi Arabian physicians, particularly those with 11 to 15 years of experience who exhibit a limited understanding of lung cancer screening. Utilizing programs that provide continuing medical education would aid in their education. There is a need to facilitate communication between physicians and patients. It is critical to address the identified issues, such as streamlining the appointment scheduling process and ensuring patients have sufficient time during clinic visits. Furthermore, it is critical for the success of nationwide screening initiatives to foster collaboration between the public and private healthcare sectors.
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Affiliation(s)
- Rayan A Qutob
- Department of Internal Medicine, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU
| | - Ibrahim Ali Almehaidib
- Department of Internal Medicine, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU
| | - Sarah Saad Alzahrani
- Department of Internal Medicine, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU
| | - Sara Mohammed Alabdulkarim
- Department of Internal Medicine, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU
| | - Haifa Abdulrahman Abuhemid
- Department of Internal Medicine, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU
| | - Reema Abdulrahman Alassaf
- Department of Internal Medicine, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU
| | - Abdullah Alaryni
- Department of Internal Medicine, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU
| | - Abdullah Alghamdi
- Department of Internal Medicine, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU
| | - Eysa Alsolamy
- Department of Internal Medicine, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU
| | - Abdullah Bukhari
- Department of Internal Medicine, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU
| | - Abdulwahed Abdulaziz Alotay
- Department of Internal Medicine, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU
| | - Mohammad A Alhajery
- Department of Internal Medicine, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU
| | - Abdulrahman Alanazi
- Department of Internal Medicine, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU
| | - Fahad Ali Faqihi
- Department of Internal Medicine and Adult Critical Care Medicine, Dr. Sulaiman Al Habib Medical Group Holding Company, Riyadh, SAU
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Higuchi M, Nagata T, Iwabuchi K, Sano A, Maekawa H, Idaka T, Yamasaki M, Seko C, Sato A, Suzuki J, Anzai Y, Yabuki T, Saito T, Suzuki H. Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography. Fukushima J Med Sci 2023; 69:177-183. [PMID: 37853640 PMCID: PMC10694515 DOI: 10.5387/fms.2023-14] [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: 04/11/2023] [Accepted: 09/15/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND In this study, we aimed to develop a novel artificial intelligence (AI) algorithm to support pulmonary nodule detection, which will enable physicians to efficiently interpret chest radiographs for lung cancer diagnosis. METHODS We analyzed chest X-ray images obtained from a health examination center in Fukushima and the National Institutes of Health (NIH) Chest X-ray 14 dataset. We categorized these data into two types: type A included both Fukushima and NIH datasets, and type B included only the Fukushima dataset. We also demonstrated pulmonary nodules in the form of a heatmap display on each chest radiograph and calculated the positive probability score as an index value. RESULTS Our novel AI algorithms had a receiver operating characteristic (ROC) area under the curve (AUC) of 0.74, a sensitivity of 0.75, and a specificity of 0.60 for the type A dataset. For the type B dataset, the respective values were 0.79, 0.72, and 0.74. The algorithms in both the type A and B datasets were superior to the accuracy of radiologists and similar to previous studies. CONCLUSIONS The proprietary AI algorithms had a similar accuracy for interpreting chest radiographs when compared with previous studies and radiologists. Especially, we could train a high quality AI algorithm, even with our small type B data set. However, further studies are needed to improve and further validate the accuracy of our AI algorithm.
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Affiliation(s)
- Mitsunori Higuchi
- Department of Thoracic Surgery, Aizu Medical Center, Fukushima Medical University
| | - Takeshi Nagata
- University of Tsukuba School of Integrative and Global Majors
- Mizuho Research and Technologies, Ltd.
| | | | | | | | | | | | | | - Atsushi Sato
- Fukushima Preservative Service Association of Health
| | - Junzo Suzuki
- Fukushima Preservative Service Association of Health
| | | | | | - Takuro Saito
- Department of Surgery, Aizu Medical Center, Fukushima Medical University
| | - Hiroyuki Suzuki
- Department of Chest Surgery, Fukushima Medical University School of Medicine
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Lam DCL, Liam CK, Andarini S, Park S, Tan DSW, Singh N, Jang SH, Vardhanabhuti V, Ramos AB, Nakayama T, Nhung NV, Ashizawa K, Chang YC, Tscheikuna J, Van CC, Chan WY, Lai YH, Yang PC. Lung Cancer Screening in Asia: An Expert Consensus Report. J Thorac Oncol 2023; 18:1303-1322. [PMID: 37390982 DOI: 10.1016/j.jtho.2023.06.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 05/23/2023] [Accepted: 06/10/2023] [Indexed: 07/02/2023]
Abstract
INTRODUCTION The incidence and mortality of lung cancer are highest in Asia compared with Europe and USA, with the incidence and mortality rates being 34.4 and 28.1 per 100,000 respectively in East Asia. Diagnosing lung cancer at early stages makes the disease amenable to curative treatment and reduces mortality. In some areas in Asia, limited availability of robust diagnostic tools and treatment modalities, along with variations in specific health care investment and policies, make it necessary to have a more specific approach for screening, early detection, diagnosis, and treatment of patients with lung cancer in Asia compared with the West. METHOD A group of 19 advisors across different specialties from 11 Asian countries, met on a virtual Steering Committee meeting, to discuss and recommend the most affordable and accessible lung cancer screening modalities and their implementation, for the Asian population. RESULTS Significant risk factors identified for lung cancer in smokers in Asia include age 50 to 75 years and smoking history of more than or equal to 20 pack-years. Family history is the most common risk factor for nonsmokers. Low-dose computed tomography screening is recommended once a year for patients with screening-detected abnormality and persistent exposure to risk factors. However, for high-risk heavy smokers and nonsmokers with risk factors, reassessment scans are recommended at an initial interval of 6 to 12 months with subsequent lengthening of reassessment intervals, and it should be stopped in patients more than 80 years of age or are unable or unwilling to undergo curative treatment. CONCLUSIONS Asian countries face several challenges in implementing low-dose computed tomography screening, such as economic limitations, lack of efforts for early detection, and lack of specific government programs. Various strategies are suggested to overcome these challenges in Asia.
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Affiliation(s)
- David Chi-Leung Lam
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Chong-Kin Liam
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Sita Andarini
- Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Indonesia - Persahabatan Hospital, Jakarta, Indonesia
| | - Samina Park
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Daniel S W Tan
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore; Division of Medical Oncology, National Cancer Centre Singapore, Duke-NUS Medical School, Singapore
| | - Navneet Singh
- Lung Cancer Clinic, Department of Pulmonary Medicine, Postgraduate Institute of Medical Education & Research, Chandigarh, India
| | - Seung Hun Jang
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Anyang, Korea
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, People's Republic of China
| | - Antonio B Ramos
- Department of Thoracic Surgery and Anesthesia, Lung Center of the Philippines, Quezon City, Philippines
| | - Tomio Nakayama
- Division of Screening Assessment and Management, National Cancer Center Institute for Cancer Control, Japan
| | - Nguyen Viet Nhung
- Vietnam National Lung Hospital, University of Medicine and Pharmacy, VNU Hanoi, Vietnam
| | - Kazuto Ashizawa
- Department of Clinical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jamsak Tscheikuna
- Division of Respiratory Disease and Tuberculosis, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | | | - Wai Yee Chan
- Imaging Department, Gleneagles Hospital Kuala Lumpur, Jalan Ampang, 50450 Kuala Lumpur; Department of Biomedical Imaging, University of Malaya, Kuala Lumpur, Malaysia
| | - Yeur-Hur Lai
- School of Nursing, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Nursing, National Taiwan University Cancer Center, Taipei, Taiwan
| | - Pan-Chyr Yang
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan; Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan & National Taiwan University Hospital, Taipei, Taiwan.
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Lin LP, Tan MTT. Biosensors for the detection of lung cancer biomarkers: A review on biomarkers, transducing techniques and recent graphene-based implementations. Biosens Bioelectron 2023; 237:115492. [PMID: 37421797 DOI: 10.1016/j.bios.2023.115492] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 06/07/2023] [Accepted: 06/19/2023] [Indexed: 07/10/2023]
Abstract
Lung cancer remains the leading cause of cancer-related death. In addition to chest X-rays and computerised tomography, the detection of cancer biomarkers serves as an emerging diagnostic tool for lung cancer. This review explores biomarkers including the rat sarcoma gene, the tumour protein 53 gene, the epidermal growth factor receptor, the neuron-specific enolase, the cytokeratin-19 fragment 21-1 and carcinoembryonic antigen as potential indicators of lung cancer. Biosensors, which utilise various transduction techniques, present a promising solution for the detection of lung cancer biomarkers. Therefore, this review also explores the working principles and recent implementations of transducers in the detection of lung cancer biomarkers. The transducing techniques explored include optical techniques, electrochemical techniques and mass-based techniques for detecting biomarkers and cancer-related volatile organic compounds. Graphene has outstanding properties in terms of charge transfer, surface area, thermal conductivity and optical characteristics, on top of allowing easy incorporation of other nanomaterials. Exploiting the collective merits of both graphene and biosensor is an emerging trend, as evidenced by the growing number of studies on graphene-based biosensors for the detection of lung cancer biomarkers. This work provides a comprehensive review of these studies, including information on modification schemes, nanomaterials, amplification strategies, real sample applications, and sensor performance. The paper concludes with a discussion of the challenges and future outlook of lung cancer biosensors, including scalable graphene synthesis, multi-biomarker detection, portability, miniaturisation, financial support, and commercialisation.
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Affiliation(s)
- Lih Poh Lin
- Faculty of Engineering and Technology, Tunku Abdul Rahman University of Management and Technology, 53300, Kuala Lumpur, Malaysia; Centre for Multimodal Signal Processing, Tunku Abdul Rahman University of Management and Technology, 53300, Kuala Lumpur, Malaysia
| | - Michelle Tien Tien Tan
- Faculty of Science and Engineering, University of Nottingham Malaysia, 43500, Semenyih, Malaysia.
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Behrendt F, Bengs M, Bhattacharya D, Krüger J, Opfer R, Schlaefer A. A systematic approach to deep learning-based nodule detection in chest radiographs. Sci Rep 2023; 13:10120. [PMID: 37344565 DOI: 10.1038/s41598-023-37270-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 06/19/2023] [Indexed: 06/23/2023] Open
Abstract
Lung cancer is a serious disease responsible for millions of deaths every year. Early stages of lung cancer can be manifested in pulmonary lung nodules. To assist radiologists in reducing the number of overseen nodules and to increase the detection accuracy in general, automatic detection algorithms have been proposed. Particularly, deep learning methods are promising. However, obtaining clinically relevant results remains challenging. While a variety of approaches have been proposed for general purpose object detection, these are typically evaluated on benchmark data sets. Achieving competitive performance for specific real-world problems like lung nodule detection typically requires careful analysis of the problem at hand and the selection and tuning of suitable deep learning models. We present a systematic comparison of state-of-the-art object detection algorithms for the task of lung nodule detection. In this regard, we address the critical aspect of class imbalance and and demonstrate a data augmentation approach as well as transfer learning to boost performance. We illustrate how this analysis and a combination of multiple architectures results in state-of-the-art performance for lung nodule detection, which is demonstrated by the proposed model winning the detection track of the Node21 competition. The code for our approach is available at https://github.com/FinnBehrendt/node21-submit.
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Affiliation(s)
- Finn Behrendt
- Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology, 21073, Hamburg, Germany.
| | - Marcel Bengs
- Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology, 21073, Hamburg, Germany
| | - Debayan Bhattacharya
- Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology, 21073, Hamburg, Germany
| | | | | | - Alexander Schlaefer
- Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology, 21073, Hamburg, Germany
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Leveraging Deep Learning Decision-Support System in Specialized Oncology Center: A Multi-Reader Retrospective Study on Detection of Pulmonary Lesions in Chest X-ray Images. Diagnostics (Basel) 2023; 13:diagnostics13061043. [PMID: 36980351 PMCID: PMC10047277 DOI: 10.3390/diagnostics13061043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/27/2023] [Accepted: 03/07/2023] [Indexed: 03/12/2023] Open
Abstract
Chest X-ray (CXR) is considered to be the most widely used modality for detecting and monitoring various thoracic findings, including lung carcinoma and other pulmonary lesions. However, X-ray imaging shows particular limitations when detecting primary and secondary tumors and is prone to reading errors due to limited resolution and disagreement between radiologists. To address these issues, we developed a deep-learning-based automatic detection algorithm (DLAD) to automatically detect and localize suspicious lesions on CXRs. Five radiologists were invited to retrospectively evaluate 300 CXR images from a specialized oncology center, and the performance of individual radiologists was subsequently compared with that of DLAD. The proposed DLAD achieved significantly higher sensitivity (0.910 (0.854–0.966)) than that of all assessed radiologists (RAD 10.290 (0.201–0.379), p < 0.001, RAD 20.450 (0.352–0.548), p < 0.001, RAD 30.670 (0.578–0.762), p < 0.001, RAD 40.810 (0.733–0.887), p = 0.025, RAD 50.700 (0.610–0.790), p < 0.001). The DLAD specificity (0.775 (0.717–0.833)) was significantly lower than for all assessed radiologists (RAD 11.000 (0.984–1.000), p < 0.001, RAD 20.970 (0.946–1.000), p < 0.001, RAD 30.980 (0.961–1.000), p < 0.001, RAD 40.975 (0.953–0.997), p < 0.001, RAD 50.995 (0.985–1.000), p < 0.001). The study results demonstrate that the proposed DLAD could be utilized as a decision-support system to reduce radiologists’ false negative rate.
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Kim H, Lee KH, Han K, Lee JW, Kim JY, Im DJ, Hong YJ, Choi BW, Hur J. Development and Validation of a Deep Learning-Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs. JAMA Netw Open 2023; 6:e2253820. [PMID: 36719681 PMCID: PMC9890286 DOI: 10.1001/jamanetworkopen.2022.53820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 12/01/2022] [Indexed: 02/01/2023] Open
Abstract
Importance Dual-energy chest radiography exhibits better sensitivity than single-energy chest radiography, partly due to its ability to remove overlying anatomical structures. Objectives To develop and validate a deep learning-based synthetic bone-suppressed (DLBS) nodule-detection algorithm for pulmonary nodule detection on chest radiographs. Design, Setting, and Participants This decision analytical modeling study used data from 3 centers between November 2015 and July 2019 from 1449 patients. The DLBS nodule-detection algorithm was trained using single-center data (institute 1) of 998 chest radiographs. The DLBS algorithm was validated using 2 external data sets (institute 2, 246 patients; and institute 3, 205 patients). Statistical analysis was performed from March to December 2021. Exposures DLBS nodule-detection algorithm. Main Outcomes and Measures The nodule-detection performance of DLBS model was compared with the convolution neural network nodule-detection algorithm (original model). Reader performance testing was conducted by 3 thoracic radiologists assisted by the DLBS algorithm or not. Sensitivity and false-positive markings per image (FPPI) were compared. Results Training data consisted of 998 patients (539 men [54.0%]; mean [SD] age, 54.2 [9.82] years), and 2 external validation data sets consisted of 246 patients (133 men [54.1%]; mean [SD] age, 55.3 [8.7] years) and 205 patients (105 men [51.2%]; mean [SD] age, 51.8 [9.1] years). Using the external validation data set of institute 2, the bone-suppressed model showed higher sensitivity compared with that of the original model for nodule detection (91.5% [109 of 119] vs 79.8% [95 of 119]; P < .001). The overall mean of FPPI with the bone-suppressed model was reduced compared with the original model (0.07 [17 of 246] vs 0.09 [23 of 246]; P < .001). For the observer performance testing with the data of institute 3, the mean sensitivity of 3 radiologists was 77.5% (95% [CI], 69.9%-85.2%), whereas that of radiologists assisted by DLBS modeling was 92.1% (95% CI, 86.3%-97.3%; P < .001). The 3 radiologists had a reduced number of FPPI when assisted by the DLBS model (0.071 [95% CI, 0.041-0.111] vs 0.151 [95% CI, 0.111-0.210]; P < .001). Conclusions and Relevance This decision analytical modeling study found that the DLBS model was more sensitive to detecting pulmonary nodules on chest radiographs compared with the original model. These findings suggest that the DLBS model could be beneficial to radiologists in the detection of lung nodules in chest radiographs without need of the specialized equipment or increase of radiation dose.
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Affiliation(s)
- Hwiyoung Kim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Kye Ho Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Department of Radiology, Dankook University Hospital, Cheonan, Chungnam Province, Republic of Korea
| | - Kyunghwa Han
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Ji Won Lee
- Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Korea
- Medical Research Institute, Busan, Korea
| | - Jin Young Kim
- Department of Radiology, Dongsan Medical Center, Keimyung University College of Medicine, Daegu, Korea
| | - Dong Jin Im
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Yoo Jin Hong
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Byoung Wook Choi
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jin Hur
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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Mridha MF, Prodeep AR, Hoque ASMM, Islam MR, Lima AA, Kabir MM, Hamid MA, Watanobe Y. A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5905230. [PMID: 36569180 PMCID: PMC9788902 DOI: 10.1155/2022/5905230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/17/2022] [Accepted: 11/09/2022] [Indexed: 12/23/2022]
Abstract
Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.
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Affiliation(s)
- M. F. Mridha
- Department of Computer Science and Engineering, American International University Bangladesh, Dhaka 1229, Bangladesh
| | - Akibur Rahman Prodeep
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - A. S. M. Morshedul Hoque
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - Md. Rashedul Islam
- Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh
| | - Aklima Akter Lima
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - Muhammad Mohsin Kabir
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - Md. Abdul Hamid
- Department of Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Yutaka Watanobe
- Department of Computer Science and Engineering, University of Aizu, Aizuwakamatsu 965-8580, Japan
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Blais MA, Akhloufi MA. Deep Learning and Binary Relevance Classification of Multiple Diseases using Chest X-Ray images . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2794-2797. [PMID: 34891829 DOI: 10.1109/embc46164.2021.9629846] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Disease detection using chest X-ray (CXR) images is one of the most popular radiology methods to diagnose diseases through a visual inspection of abnormal symptoms in the lung region. A wide variety of diseases such as pneumonia, heart failure and lung cancer can be detected using CXRs. Although CXRs can show the symptoms of a variety of diseases, detecting and manually classifying those diseases can be difficult and time-consuming adding to clinicians' work burden. Research shows that nearly 90% of mistakes made in a lung cancer diagnosis involved chest radiography. A variety of algorithms and computer-assisted diagnosis tools (CAD) were proposed to assist radiologists in the interpretation of medical images to reduce diagnosis errors. In this work, we propose a deep learning approach to screen multiple diseases using more than 220,000 images from the CheXpert dataset. The proposed binary relevance approach using Deep Convolutional Neural Networks (CNNs) achieves high performance results and outperforms past published work in this area.Clinical relevance- This application can be used to support physicians ans speed-up the diagnosis work. The proposed CAD can increase the confidence in the diagnosis or suggest a second opinion. The CAD can also be used in emergency situations when a radiologist is not available immediately.
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Ueda D, Yamamoto A, Shimazaki A, Walston SL, Matsumoto T, Izumi N, Tsukioka T, Komatsu H, Inoue H, Kabata D, Nishiyama N, Miki Y. Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: a retrospective clinical validation study. BMC Cancer 2021; 21:1120. [PMID: 34663260 PMCID: PMC8524996 DOI: 10.1186/s12885-021-08847-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 10/07/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND We investigated the performance improvement of physicians with varying levels of chest radiology experience when using a commercially available artificial intelligence (AI)-based computer-assisted detection (CAD) software to detect lung cancer nodules on chest radiographs from multiple vendors. METHODS Chest radiographs and their corresponding chest CT were retrospectively collected from one institution between July 2017 and June 2018. Two author radiologists annotated pathologically proven lung cancer nodules on the chest radiographs while referencing CT. Eighteen readers (nine general physicians and nine radiologists) from nine institutions interpreted the chest radiographs. The readers interpreted the radiographs alone and then reinterpreted them referencing the CAD output. Suspected nodules were enclosed with a bounding box. These bounding boxes were judged correct if there was significant overlap with the ground truth, specifically, if the intersection over union was 0.3 or higher. The sensitivity, specificity, accuracy, PPV, and NPV of the readers' assessments were calculated. RESULTS In total, 312 chest radiographs were collected as a test dataset, including 59 malignant images (59 nodules of lung cancer) and 253 normal images. The model provided a modest boost to the reader's sensitivity, particularly helping general physicians. The performance of general physicians was improved from 0.47 to 0.60 for sensitivity, from 0.96 to 0.97 for specificity, from 0.87 to 0.90 for accuracy, from 0.75 to 0.82 for PPV, and from 0.89 to 0.91 for NPV while the performance of radiologists was improved from 0.51 to 0.60 for sensitivity, from 0.96 to 0.96 for specificity, from 0.87 to 0.90 for accuracy, from 0.76 to 0.80 for PPV, and from 0.89 to 0.91 for NPV. The overall increase in the ratios of sensitivity, specificity, accuracy, PPV, and NPV were 1.22 (1.14-1.30), 1.00 (1.00-1.01), 1.03 (1.02-1.04), 1.07 (1.03-1.11), and 1.02 (1.01-1.03) by using the CAD, respectively. CONCLUSION The AI-based CAD was able to improve the ability of physicians to detect nodules of lung cancer in chest radiographs. The use of a CAD model can indicate regions physicians may have overlooked during their initial assessment.
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Affiliation(s)
- Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan.
| | - Akira Yamamoto
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Akitoshi Shimazaki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Shannon Leigh Walston
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Toshimasa Matsumoto
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Nobuhiro Izumi
- Department of Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Takuma Tsukioka
- Department of Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Hiroaki Komatsu
- Department of Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Hidetoshi Inoue
- Department of Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Daijiro Kabata
- Department of Medical Statistics, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Noritoshi Nishiyama
- Department of Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
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A Highly Reliable Convolutional Neural Network Based Soft Tissue Sarcoma Metastasis Detection from Chest X-ray Images: A Retrospective Cohort Study. Cancers (Basel) 2021; 13:cancers13194961. [PMID: 34638445 PMCID: PMC8508001 DOI: 10.3390/cancers13194961] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/29/2021] [Accepted: 09/30/2021] [Indexed: 12/15/2022] Open
Abstract
Simple Summary Soft tissue sarcomas are relatively rare malignant diseases. Part of the diagnosis and follow-up includes medical imaging of the thorax for detection of lung metastases. A Python script was created and trained using a set of lung X-rays and concordant CT scans from a high-volume German-speaking sarcoma center. It is capable of detecting malignant metastasis in the lung with a precision of 71.2%, specificity of 90.5%, sensitivity of 94% and accuracy of 91.2%. Furthermore, the program was able to detect even small nodules with a size <1 cm in conventional X-rays of the thorax. This algorithm was implemented into our daily clinical practice alongside with the radiologists’ findings. With this tool we aim to improve the quality of our service and reduce the expenditure of time. Abstract Introduction: soft tissue sarcomas are a subset of malignant tumors that are relatively rare and make up 1% of all malignant tumors in adulthood. Due to the rarity of these tumors, there are significant differences in quality in the diagnosis and treatment of these tumors. One paramount aspect is the diagnosis of hematogenous metastases in the lungs. Guidelines recommend routine lung imaging by means of X-rays. With the ever advancing AI-based diagnostic support, there has so far been no implementation for sarcomas. The aim of the study was to utilize AI to obtain analyzes regarding metastasis on lung X-rays in the most possible sensitive and specific manner in sarcoma patients. Methods: a Python script was created and trained using a set of lung X-rays with sarcoma metastases from a high-volume German-speaking sarcoma center. 26 patients with lung metastasis were included. For all patients chest X-ray with corresponding lung CT scans, and histological biopsies were available. The number of trainable images were expanded to 600. In order to evaluate the biological sensitivity and specificity, the script was tested on lung X-rays with a lung CT as control. Results: in this study we present a new type of convolutional neural network-based system with a precision of 71.2%, specificity of 90.5%, sensitivity of 94%, recall of 94% and accuracy of 91.2%. A good detection of even small findings was determined. Discussion: the created script establishes the option to check lung X-rays for metastases at a safe level, especially given this rare tumor entity.
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Hwang EJ, Lee JS, Lee JH, Lim WH, Kim JH, Choi KS, Choi TW, Kim TH, Goo JM, Park CM. Deep Learning for Detection of Pulmonary Metastasis on Chest Radiographs. Radiology 2021; 301:455-463. [PMID: 34463551 DOI: 10.1148/radiol.2021210578] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background A computer-aided detection (CAD) system may help surveillance for pulmonary metastasis at chest radiography in situations where there is limited access to CT. Purpose To evaluate whether a deep learning (DL)-based CAD system can improve diagnostic yield for newly visible lung metastasis on chest radiographs in patients with cancer. Materials and Methods A regulatory-approved CAD system for lung nodules was implemented to interpret chest radiographs from patients referred by the medical oncology department in clinical practice. In this retrospective diagnostic cohort study, chest radiographs interpreted with assistance from a CAD system after the implementation (January to April 2019, CAD-assisted interpretation group) and those interpreted before the implementation (September to December 2018, conventional interpretation group) of the CAD system were consecutively included. The diagnostic yield (frequency of true-positive detections) and false-referral rate (frequency of false-positive detections) of formal reports of chest radiographs for newly visible lung metastasis were compared between the two groups using generalized estimating equations. Propensity score matching was performed between the two groups for age, sex, and primary cancer. Results A total of 2916 chest radiographs from 1521 patients (1546 men, 1370 women; mean age, 62 years) and 5681 chest radiographs from 3456 patients (2941 men, 2740 women; mean age, 62 years) were analyzed in the CAD-assisted interpretation and conventional interpretation groups, respectively. The diagnostic yield for newly visible metastasis was higher in the CAD-assisted interpretation group (0.86%, 25 of 2916 [95% CI: 0.58, 1.3] vs 0.32%, 18 of 568 [95% CI: 0.20, 0.50%]; P = .004). The false-referral rate in the CAD-assisted interpretation group (0.34%, 10 of 2916 [95% CI: 0.19, 0.64]) was not inferior to that in the conventional interpretation group (0.25%, 14 of 5681 [95% CI: 0.15, 0.42]) at the noninferiority margin of 0.5% (95% CI of difference: -0.15, 0.35). Conclusion A deep learning-based computer-aided detection system improved the diagnostic yield for newly visible metastasis on chest radiographs in patients with cancer with a similar false-referral rate. © RSNA, 2021 Online supplemental material is available for this article.
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Affiliation(s)
- Eui Jin Hwang
- From the Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., J.S.L., J.H.L., W.H.L., J.H.K., K.S.C., T.W.C., T.H.K., J.M.G., C.M.P.); Department of Radiology, Namwon Medical Center, Namwon, Korea (W.H.L.); Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (K.S.C.); and Department of Radiology, Naval Pohang Hospital, Pohang, Korea (T.H.K.)
| | - Jeong Su Lee
- From the Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., J.S.L., J.H.L., W.H.L., J.H.K., K.S.C., T.W.C., T.H.K., J.M.G., C.M.P.); Department of Radiology, Namwon Medical Center, Namwon, Korea (W.H.L.); Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (K.S.C.); and Department of Radiology, Naval Pohang Hospital, Pohang, Korea (T.H.K.)
| | - Jong Hyuk Lee
- From the Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., J.S.L., J.H.L., W.H.L., J.H.K., K.S.C., T.W.C., T.H.K., J.M.G., C.M.P.); Department of Radiology, Namwon Medical Center, Namwon, Korea (W.H.L.); Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (K.S.C.); and Department of Radiology, Naval Pohang Hospital, Pohang, Korea (T.H.K.)
| | - Woo Hyeon Lim
- From the Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., J.S.L., J.H.L., W.H.L., J.H.K., K.S.C., T.W.C., T.H.K., J.M.G., C.M.P.); Department of Radiology, Namwon Medical Center, Namwon, Korea (W.H.L.); Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (K.S.C.); and Department of Radiology, Naval Pohang Hospital, Pohang, Korea (T.H.K.)
| | - Jae Hyun Kim
- From the Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., J.S.L., J.H.L., W.H.L., J.H.K., K.S.C., T.W.C., T.H.K., J.M.G., C.M.P.); Department of Radiology, Namwon Medical Center, Namwon, Korea (W.H.L.); Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (K.S.C.); and Department of Radiology, Naval Pohang Hospital, Pohang, Korea (T.H.K.)
| | - Kyu Sung Choi
- From the Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., J.S.L., J.H.L., W.H.L., J.H.K., K.S.C., T.W.C., T.H.K., J.M.G., C.M.P.); Department of Radiology, Namwon Medical Center, Namwon, Korea (W.H.L.); Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (K.S.C.); and Department of Radiology, Naval Pohang Hospital, Pohang, Korea (T.H.K.)
| | - Tae Won Choi
- From the Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., J.S.L., J.H.L., W.H.L., J.H.K., K.S.C., T.W.C., T.H.K., J.M.G., C.M.P.); Department of Radiology, Namwon Medical Center, Namwon, Korea (W.H.L.); Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (K.S.C.); and Department of Radiology, Naval Pohang Hospital, Pohang, Korea (T.H.K.)
| | - Tae-Hyung Kim
- From the Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., J.S.L., J.H.L., W.H.L., J.H.K., K.S.C., T.W.C., T.H.K., J.M.G., C.M.P.); Department of Radiology, Namwon Medical Center, Namwon, Korea (W.H.L.); Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (K.S.C.); and Department of Radiology, Naval Pohang Hospital, Pohang, Korea (T.H.K.)
| | - Jin Mo Goo
- From the Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., J.S.L., J.H.L., W.H.L., J.H.K., K.S.C., T.W.C., T.H.K., J.M.G., C.M.P.); Department of Radiology, Namwon Medical Center, Namwon, Korea (W.H.L.); Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (K.S.C.); and Department of Radiology, Naval Pohang Hospital, Pohang, Korea (T.H.K.)
| | - Chang Min Park
- From the Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., J.S.L., J.H.L., W.H.L., J.H.K., K.S.C., T.W.C., T.H.K., J.M.G., C.M.P.); Department of Radiology, Namwon Medical Center, Namwon, Korea (W.H.L.); Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (K.S.C.); and Department of Radiology, Naval Pohang Hospital, Pohang, Korea (T.H.K.)
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Augmenting lung cancer diagnosis on chest radiographs: positioning artificial intelligence to improve radiologist performance. Clin Radiol 2021; 76:607-614. [PMID: 33993997 DOI: 10.1016/j.crad.2021.03.021] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/26/2021] [Indexed: 01/18/2023]
Abstract
AIM To evaluate the role that artificial intelligence (AI) could play in assisting radiologists as the first reader of chest radiographs (CXRs), to increase the accuracy and efficiency of lung cancer diagnosis by flagging positive cases before passing the remaining examinations to standard reporting. MATERIALS AND METHODS A dataset of 400 CXRs including 200 difficult lung cancer cases was curated. Examinations were reviewed by three FRCR radiologists and an AI algorithm to establish performance in tumour identification. AI and radiologist labels were combined retrospectively to simulate the proposed AI triage workflow. RESULTS When used as a standalone algorithm, AI classification was equivalent to the average radiologist performance. The best overall performances were achieved when AI was combined with radiologists, with an average reduction of missed cancers of 60%. Combination with AI also standardised the performance of radiologists. The greatest improvements were observed when common sources of errors were present, such as distracting findings. DISCUSSION The proposed AI implementation pathway stands to reduce radiologist errors and improve clinician reporting performance. Furthermore, taking a radiologist-centric approach in the development of clinical AI holds promise for catching systematically missed lung cancers. This represents a tremendous opportunity to improve patient outcomes for lung cancer diagnosis.
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Sung J, Park S, Lee SM, Bae W, Park B, Jung E, Seo JB, Jung KH. Added Value of Deep Learning-based Detection System for Multiple Major Findings on Chest Radiographs: A Randomized Crossover Study. Radiology 2021; 299:450-459. [PMID: 33754828 DOI: 10.1148/radiol.2021202818] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background Previous studies assessing the effects of computer-aided detection on observer performance in the reading of chest radiographs used a sequential reading design that may have biased the results because of reading order or recall bias. Purpose To compare observer performance in detecting and localizing major abnormal findings including nodules, consolidation, interstitial opacity, pleural effusion, and pneumothorax on chest radiographs without versus with deep learning-based detection (DLD) system assistance in a randomized crossover design. Materials and Methods This study included retrospectively collected normal and abnormal chest radiographs between January 2016 and December 2017 (https://cris.nih.go.kr/; registration no. KCT0004147). The radiographs were randomized into two groups, and six observers, including thoracic radiologists, interpreted each radiograph without and with use of a commercially available DLD system by using a crossover design with a washout period. Jackknife alternative free-response receiver operating characteristic (JAFROC) figure of merit (FOM), area under the receiver operating characteristic curve (AUC), sensitivity, specificity, false-positive findings per image, and reading times of observers with and without the DLD system were compared by using McNemar and paired t tests. Results A total of 114 normal (mean patient age ± standard deviation, 51 years ± 11; 58 men) and 114 abnormal (mean patient age, 60 years ± 15; 75 men) chest radiographs were evaluated. The radiographs were randomized to two groups: group A (n = 114) and group B (n = 114). Use of the DLD system improved the observers' JAFROC FOM (from 0.90 to 0.95, P = .002), AUC (from 0.93 to 0.98, P = .002), per-lesion sensitivity (from 83% [822 of 990 lesions] to 89.1% [882 of 990 lesions], P = .009), per-image sensitivity (from 80% [548 of 684 radiographs] to 89% [608 of 684 radiographs], P = .009), and specificity (from 89.3% [611 of 684 radiographs] to 96.6% [661 of 684 radiographs], P = .01) and reduced the reading time (from 10-65 seconds to 6-27 seconds, P < .001). The DLD system alone outperformed the pooled observers (JAFROC FOM: 0.96 vs 0.90, respectively, P = .007; AUC: 0.98 vs 0.93, P = .003). Conclusion Observers including thoracic radiologists showed improved performance in the detection and localization of major abnormal findings on chest radiographs and reduced reading time with use of a deep learning-based detection system. © RSNA, 2021 Online supplemental material is available for this article.
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Affiliation(s)
- Jinkyeong Sung
- From the R&D Center, VUNO, 507 Gangnamdae-ro, Seocho-gu, Seoul 06536, South Korea (J.S., W.B., B.P., E.J., K.H.J.); and Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.P., S.M.L., J.B.S.)
| | - Sohee Park
- From the R&D Center, VUNO, 507 Gangnamdae-ro, Seocho-gu, Seoul 06536, South Korea (J.S., W.B., B.P., E.J., K.H.J.); and Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.P., S.M.L., J.B.S.)
| | - Sang Min Lee
- From the R&D Center, VUNO, 507 Gangnamdae-ro, Seocho-gu, Seoul 06536, South Korea (J.S., W.B., B.P., E.J., K.H.J.); and Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.P., S.M.L., J.B.S.)
| | - Woong Bae
- From the R&D Center, VUNO, 507 Gangnamdae-ro, Seocho-gu, Seoul 06536, South Korea (J.S., W.B., B.P., E.J., K.H.J.); and Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.P., S.M.L., J.B.S.)
| | - Beomhee Park
- From the R&D Center, VUNO, 507 Gangnamdae-ro, Seocho-gu, Seoul 06536, South Korea (J.S., W.B., B.P., E.J., K.H.J.); and Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.P., S.M.L., J.B.S.)
| | - Eunkyung Jung
- From the R&D Center, VUNO, 507 Gangnamdae-ro, Seocho-gu, Seoul 06536, South Korea (J.S., W.B., B.P., E.J., K.H.J.); and Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.P., S.M.L., J.B.S.)
| | - Joon Beom Seo
- From the R&D Center, VUNO, 507 Gangnamdae-ro, Seocho-gu, Seoul 06536, South Korea (J.S., W.B., B.P., E.J., K.H.J.); and Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.P., S.M.L., J.B.S.)
| | - Kyu-Hwan Jung
- From the R&D Center, VUNO, 507 Gangnamdae-ro, Seocho-gu, Seoul 06536, South Korea (J.S., W.B., B.P., E.J., K.H.J.); and Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.P., S.M.L., J.B.S.)
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Lee JH, Sun HY, Park S, Kim H, Hwang EJ, Goo JM, Park CM. Performance of a Deep Learning Algorithm Compared with Radiologic Interpretation for Lung Cancer Detection on Chest Radiographs in a Health Screening Population. Radiology 2020; 297:687-696. [PMID: 32960729 DOI: 10.1148/radiol.2020201240] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Background The performance of a deep learning algorithm for lung cancer detection on chest radiographs in a health screening population is unknown. Purpose To validate a commercially available deep learning algorithm for lung cancer detection on chest radiographs in a health screening population. Materials and Methods Out-of-sample testing of a deep learning algorithm was retrospectively performed using chest radiographs from individuals undergoing a comprehensive medical check-up between July 2008 and December 2008 (validation test). To evaluate the algorithm performance for visible lung cancer detection, the area under the receiver operating characteristic curve (AUC) and diagnostic measures, including sensitivity and false-positive rate (FPR), were calculated. The algorithm performance was compared with that of radiologists using the McNemar test and the Moskowitz method. Additionally, the deep learning algorithm was applied to a screening cohort undergoing chest radiography between January 2008 and December 2012, and its performances were calculated. Results In a validation test comprising 10 285 radiographs from 10 202 individuals (mean age, 54 years ± 11 [standard deviation]; 5857 men) with 10 radiographs of visible lung cancers, the algorithm's AUC was 0.99 (95% confidence interval: 0.97, 1), and it showed comparable sensitivity (90% [nine of 10 radiographs]) to that of the radiologists (60% [six of 10 radiographs]; P = .25) with a higher FPR (3.1% [319 of 10 275 radiographs] vs 0.3% [26 of 10 275 radiographs]; P < .001). In the screening cohort of 100 525 chest radiographs from 50 070 individuals (mean age, 53 years ± 11; 28 090 men) with 47 radiographs of visible lung cancers, the algorithm's AUC was 0.97 (95% confidence interval: 0.95, 0.99), and its sensitivity and FPR were 83% (39 of 47 radiographs) and 3% (2999 of 100 478 radiographs), respectively. Conclusion A deep learning algorithm detected lung cancers on chest radiographs with a performance comparable to that of radiologists, which will be helpful for radiologists in healthy populations with a low prevalence of lung cancer. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Armato in this issue.
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Affiliation(s)
- Jong Hyuk Lee
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., H.K., E.J.H., J.M.G., C.M.P.); Department of Radiology, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea (H.Y.S.); and Lunit Inc, Seoul, Korea (S.P.)
| | - Hye Young Sun
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., H.K., E.J.H., J.M.G., C.M.P.); Department of Radiology, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea (H.Y.S.); and Lunit Inc, Seoul, Korea (S.P.)
| | - Sunggyun Park
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., H.K., E.J.H., J.M.G., C.M.P.); Department of Radiology, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea (H.Y.S.); and Lunit Inc, Seoul, Korea (S.P.)
| | - Hyungjin Kim
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., H.K., E.J.H., J.M.G., C.M.P.); Department of Radiology, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea (H.Y.S.); and Lunit Inc, Seoul, Korea (S.P.)
| | - Eui Jin Hwang
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., H.K., E.J.H., J.M.G., C.M.P.); Department of Radiology, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea (H.Y.S.); and Lunit Inc, Seoul, Korea (S.P.)
| | - Jin Mo Goo
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., H.K., E.J.H., J.M.G., C.M.P.); Department of Radiology, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea (H.Y.S.); and Lunit Inc, Seoul, Korea (S.P.)
| | - Chang Min Park
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., H.K., E.J.H., J.M.G., C.M.P.); Department of Radiology, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea (H.Y.S.); and Lunit Inc, Seoul, Korea (S.P.)
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Yoo H, Kim KH, Singh R, Digumarthy SR, Kalra MK. Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs. JAMA Netw Open 2020; 3:e2017135. [PMID: 32970157 PMCID: PMC7516603 DOI: 10.1001/jamanetworkopen.2020.17135] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
IMPORTANCE The improvement of pulmonary nodule detection, which is a challenging task when using chest radiographs, may help to elevate the role of chest radiographs for the diagnosis of lung cancer. OBJECTIVE To assess the performance of a deep learning-based nodule detection algorithm for the detection of lung cancer on chest radiographs from participants in the National Lung Screening Trial (NLST). DESIGN, SETTING, AND PARTICIPANTS This diagnostic study used data from participants in the NLST ro assess the performance of a deep learning-based artificial intelligence (AI) algorithm for the detection of pulmonary nodules and lung cancer on chest radiographs using separate training (in-house) and validation (NLST) data sets. Baseline (T0) posteroanterior chest radiographs from 5485 participants (full T0 data set) were used to assess lung cancer detection performance, and a subset of 577 of these images (nodule data set) were used to assess nodule detection performance. Participants aged 55 to 74 years who currently or formerly (ie, quit within the past 15 years) smoked cigarettes for 30 pack-years or more were enrolled in the NLST at 23 US centers between August 2002 and April 2004. Information on lung cancer diagnoses was collected through December 31, 2009. Analyses were performed between August 20, 2019, and February 14, 2020. EXPOSURES Abnormality scores produced by the AI algorithm. MAIN OUTCOMES AND MEASURES The performance of an AI algorithm for the detection of lung nodules and lung cancer on radiographs, with lung cancer incidence and mortality as primary end points. RESULTS A total of 5485 participants (mean [SD] age, 61.7 [5.0] years; 3030 men [55.2%]) were included, with a median follow-up duration of 6.5 years (interquartile range, 6.1-6.9 years). For the nodule data set, the sensitivity and specificity of the AI algorithm for the detection of pulmonary nodules were 86.2% (95% CI, 77.8%-94.6%) and 85.0% (95% CI, 81.9%-88.1%), respectively. For the detection of all cancers, the sensitivity was 75.0% (95% CI, 62.8%-87.2%), the specificity was 83.3% (95% CI, 82.3%-84.3%), the positive predictive value was 3.8% (95% CI, 2.6%-5.0%), and the negative predictive value was 99.8% (95% CI, 99.6%-99.9%). For the detection of malignant pulmonary nodules in all images of the full T0 data set, the sensitivity was 94.1% (95% CI, 86.2%-100.0%), the specificity was 83.3% (95% CI, 82.3%-84.3%), the positive predictive value was 3.4% (95% CI, 2.2%-4.5%), and the negative predictive value was 100.0% (95% CI, 99.9%-100.0%). In digital radiographs of the nodule data set, the AI algorithm had higher sensitivity (96.0% [95% CI, 88.3%-100.0%] vs 88.0% [95% CI, 75.3%-100.0%]; P = .32) and higher specificity (93.2% [95% CI, 89.9%-96.5%] vs 82.8% [95% CI, 77.8%-87.8%]; P = .001) for nodule detection compared with the NLST radiologists. For malignant pulmonary nodule detection on digital radiographs of the full T0 data set, the sensitivity of the AI algorithm was higher (100.0% [95% CI, 100.0%-100.0%] vs 94.1% [95% CI, 82.9%-100.0%]; P = .32) compared with the NLST radiologists, and the specificity (90.9% [95% CI, 89.6%-92.1%] vs 91.0% [95% CI, 89.7%-92.2%]; P = .91), positive predictive value (8.2% [95% CI, 4.4%-11.9%] vs 7.8% [95% CI, 4.1%-11.5%]; P = .65), and negative predictive value (100.0% [95% CI, 100.0%-100.0%] vs 99.9% [95% CI, 99.8%-100.0%]; P = .32) were similar to those of NLST radiologists. CONCLUSIONS AND RELEVANCE In this study, the AI algorithm performed better than NLST radiologists for the detection of pulmonary nodules on digital radiographs. When used as a second reader, the AI algorithm may help to detect lung cancer.
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Affiliation(s)
| | | | - Ramandeep Singh
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - Subba R. Digumarthy
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - Mannudeep K. Kalra
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
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Hwang EJ, Park CM. Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges. Korean J Radiol 2020; 21:511-525. [PMID: 32323497 PMCID: PMC7183830 DOI: 10.3348/kjr.2019.0821] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 01/31/2020] [Indexed: 12/25/2022] Open
Abstract
Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic radiology, are under active investigation with deep learning technology, which has shown promising performance in various tasks, including detection, classification, segmentation, and image synthesis, outperforming conventional methods and suggesting its potential for clinical implementation. However, the implementation of deep learning in daily clinical practice is in its infancy and facing several challenges, such as its limited ability to explain the output results, uncertain benefits regarding patient outcomes, and incomplete integration in daily workflow. In this review article, we will introduce the potential clinical applications of deep learning technology in thoracic radiology and discuss several challenges for its implementation in daily clinical practice.
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Affiliation(s)
- Eui Jin Hwang
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Chang Min Park
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
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Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings. Eur Radiol 2019; 30:1359-1368. [DOI: 10.1007/s00330-019-06532-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 09/18/2019] [Accepted: 10/18/2019] [Indexed: 01/17/2023]
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Hao Z, Pan Y, Huang C, Wang Z, Zhao X. Sensitive detection of lung cancer biomarkers using an aptameric graphene-based nanosensor with enhanced stability. Biomed Microdevices 2019; 21:65. [PMID: 31273548 DOI: 10.1007/s10544-019-0409-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We present an electrolyte-gated graphene field effect transistor (GFET) nanosensor using aptamer for rapid, highly sensitive and specific detection of a lung cancer biomarker interleukin-6 (IL-6) with enhanced stability. The negatively charged aptamer folds into a compact secondary conformation upon binding with IL-6, thus altering the carrier concentration of graphene and yielding a detectable change in the drain-source current Ids. Aptamer has smaller size than other receptors (e.g. antibodies), making it possible to bring the charged IL-6 more closely to the graphene surface upon affinity binding, thereby enhancing the sensitivity of the detection. Thanks to the higher stability of aptamer over antibodies, which degrade easily with increasing storage time, consistent sensing performance was obtained by our nanosensor over extended-time (>24 h) storage at 25 °C. Additionally, due to the GFET-enabled rapid transduction of the affinity recognition to IL-6, detection of IL-6 can be achieved in several minutes (<10 min). Experimental results indicate that this nanosensor can rapidly and specifically respond to the change in IL-6 levels with high consistency after extended-time storage and a detection limit (DL) down to 139 fM. Therefore, our nanosensor holds great potential for lung cancer diagnosis at its early stage.
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Affiliation(s)
- Zhuang Hao
- Key Laboratory of Micro-systems and Micro-structures Manufacturing, Ministry of Education and School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Yunlu Pan
- Key Laboratory of Micro-systems and Micro-structures Manufacturing, Ministry of Education and School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China.
| | - Cong Huang
- Key Laboratory of Micro-systems and Micro-structures Manufacturing, Ministry of Education and School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Ziran Wang
- Key Laboratory of Micro-systems and Micro-structures Manufacturing, Ministry of Education and School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Xuezeng Zhao
- Key Laboratory of Micro-systems and Micro-structures Manufacturing, Ministry of Education and School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
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21
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Nam JG, Park S, Hwang EJ, Lee JH, Jin KN, Lim KY, Vu TH, Sohn JH, Hwang S, Goo JM, Park CM. Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs. Radiology 2018; 290:218-228. [PMID: 30251934 DOI: 10.1148/radiol.2018180237] [Citation(s) in RCA: 305] [Impact Index Per Article: 50.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Purpose To develop and validate a deep learning-based automatic detection algorithm (DLAD) for malignant pulmonary nodules on chest radiographs and to compare its performance with physicians including thoracic radiologists. Materials and Methods For this retrospective study, DLAD was developed by using 43 292 chest radiographs (normal radiograph-to-nodule radiograph ratio, 34 067:9225) in 34 676 patients (healthy-to-nodule ratio, 30 784:3892; 19 230 men [mean age, 52.8 years; age range, 18-99 years]; 15 446 women [mean age, 52.3 years; age range, 18-98 years]) obtained between 2010 and 2015, which were labeled and partially annotated by 13 board-certified radiologists, in a convolutional neural network. Radiograph classification and nodule detection performances of DLAD were validated by using one internal and four external data sets from three South Korean hospitals and one U.S. hospital. For internal and external validation, radiograph classification and nodule detection performances of DLAD were evaluated by using the area under the receiver operating characteristic curve (AUROC) and jackknife alternative free-response receiver-operating characteristic (JAFROC) figure of merit (FOM), respectively. An observer performance test involving 18 physicians, including nine board-certified radiologists, was conducted by using one of the four external validation data sets. Performances of DLAD, physicians, and physicians assisted with DLAD were evaluated and compared. Results According to one internal and four external validation data sets, radiograph classification and nodule detection performances of DLAD were a range of 0.92-0.99 (AUROC) and 0.831-0.924 (JAFROC FOM), respectively. DLAD showed a higher AUROC and JAFROC FOM at the observer performance test than 17 of 18 and 15 of 18 physicians, respectively (P < .05), and all physicians showed improved nodule detection performances with DLAD (mean JAFROC FOM improvement, 0.043; range, 0.006-0.190; P < .05). Conclusion This deep learning-based automatic detection algorithm outperformed physicians in radiograph classification and nodule detection performance for malignant pulmonary nodules on chest radiographs, and it enhanced physicians' performances when used as a second reader. © RSNA, 2018 Online supplemental material is available for this article.
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Affiliation(s)
- Ju Gang Nam
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.)
| | - Sunggyun Park
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.)
| | - Eui Jin Hwang
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.)
| | - Jong Hyuk Lee
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.)
| | - Kwang-Nam Jin
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.)
| | - Kun Young Lim
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.)
| | - Thienkai Huy Vu
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.)
| | - Jae Ho Sohn
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.)
| | - Sangheum Hwang
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.)
| | - Jin Mo Goo
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.)
| | - Chang Min Park
- From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.)
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A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer. SENSORS 2018; 18:s18092845. [PMID: 30154385 PMCID: PMC6164114 DOI: 10.3390/s18092845] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 08/22/2018] [Accepted: 08/23/2018] [Indexed: 01/23/2023]
Abstract
Lung cancer is the leading cause of cancer death around the world, and lung cancer screening remains challenging. This study aimed to develop a breath test for the detection of lung cancer using a chemical sensor array and a machine learning technique. We conducted a prospective study to enroll lung cancer cases and non-tumour controls between 2016 and 2018 and analysed alveolar air samples using carbon nanotube sensor arrays. A total of 117 cases and 199 controls were enrolled in the study of which 72 subjects were excluded due to having cancer at another site, benign lung tumours, metastatic lung cancer, carcinoma in situ, minimally invasive adenocarcinoma, received chemotherapy or other diseases. Subjects enrolled in 2016 and 2017 were used for the model derivation and internal validation. The model was externally validated in subjects recruited in 2018. The diagnostic accuracy was assessed using the pathological reports as the reference standard. In the external validation, the areas under the receiver operating characteristic curve (AUCs) were 0.91 (95% CI = 0.79–1.00) by linear discriminant analysis and 0.90 (95% CI = 0.80–0.99) by the supportive vector machine technique. The combination of the sensor array technique and machine learning can detect lung cancer with high accuracy.
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Arab A, Karimipoor M, Irani S, Kiani A, Zeinali S, Tafsiri E, Sheikhy K. Potential circulating miRNA signature for early detection of NSCLC. Cancer Genet 2017; 216-217:150-158. [PMID: 29025589 DOI: 10.1016/j.cancergen.2017.07.006] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2017] [Revised: 07/14/2017] [Accepted: 07/19/2017] [Indexed: 12/01/2022]
Abstract
Circulating microRNAs (c-miRNAs) are promising biomarkers for screening, early detection and prognosis of cancer. The purpose of this investigation was to identify a panel of c-miRNAs in plasma that could contribute to early detection of non-small cell lung cancer (NSCLC). We profiled the expression of 44 unique plasma miRNAs in training set of 34 NSCLC patients and 20 matched healthy individuals by miRCURY LNA™ Universal RT microRNA PCR Panel and calculated dysregulation fold changes using the 2-ΔΔCt equation. Selected plasma miRNAs were then validated by SYBR green q-RT PCR using an independent validation set of plasma samples from NSCLC patients (n: 72) and NC (n: 50). In the validation set, the receiver operating characteristic (ROC) curves were generated for four miRNAs. In the training set, 17 miRNAs were significantly up-regulated and nine were down-regulated in the plasma from NSCLC patients versus matched normal controls. Four miRNAs (miR-21, miR-328, miR-375 and miR-141) were selected for validating their diagnostic value in the testing set. ROC plot analysis showed that a high specificity (98%) and sensitivity (82.7%) in miR-141 in comparing early NSCLC patient and controls. So among these four plasma miRNAs only miR-141 could be promising biomarkers for early detection of NSCLC. In addition to, we found a significant positive correlation between stage and miR-21 expression level (95% CI: 0.687-0.863; p-value < 0.0001). Considering the accessibility and stability of circulating miRNAs, plasma miR-141 is a useful biomarker early detection of NSCLC as a supplement in future screening studies.
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Affiliation(s)
- Ayda Arab
- Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Morteza Karimipoor
- Department of Molecular Medicine, Biotechnology Research Center, Pasture Institute of Iran, Tehran, Iran.
| | - Shiva Irani
- Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Arda Kiani
- Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sirous Zeinali
- Department of Molecular Medicine, Biotechnology Research Center, Pasture Institute of Iran, Tehran, Iran
| | - Elham Tafsiri
- Department of Molecular Medicine, Biotechnology Research Center, Pasture Institute of Iran, Tehran, Iran
| | - Kambiz Sheikhy
- Lung Transplantation Research Center, NRITLD, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Li X, An Z, Li P, Liu H. A prognostic model for lung adenocarcinoma patient survival with a focus on four miRNAs. Oncol Lett 2017; 14:2991-2995. [PMID: 28927049 PMCID: PMC5588086 DOI: 10.3892/ol.2017.6481] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 06/15/2017] [Indexed: 12/15/2022] Open
Abstract
There is currently no effective biomarker for determining the survival of patients with lung adenocarcinoma. The purpose of the present study was to construct a prognostic survival model using microRNA (miRNA) expression data from patients with lung adenocarcinoma. miRNA data were obtained from The Cancer Genome Atlas, and patients with lung adenocarcinoma were divided into either the training or validation set based on the random allocation principle. The prognostic model focusing on miRNA was constructed, and patients were divided into high-risk or low-risk groups as per the scores, to assess their survival time. The 5-year survival rate from the subgroups within the high- and low-risk groups was assessed. P-values of the prognostic model in the total population, the training set and validation set were 0.0017, 0.01986 and 0.02773, respectively, indicating that the survival time of the lung adenocarcinoma high-risk group was less than that of the low-risk group. Thus, the model had a good assessment effectiveness for the untreated group (P=0.00088) and the Caucasian patient group (P=0.00043). In addition, the model had the best prediction effect for the 5-year survival rate of the Caucasian patient group (AUC=0.629). In conclusion, the prognostic model developed in the present study can be used as an independent prognostic model for patients with lung adenocarcinoma.
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Affiliation(s)
- Xianqiu Li
- Department of Pharmacy, Weifang People's Hospital, Weifang, Shandong 261041, P.R. China
| | - Zhaoling An
- Department of Pharmacy, Weifang People's Hospital, Weifang, Shandong 261041, P.R. China
| | - Peihui Li
- Department of Pharmacy, Weifang People's Hospital, Weifang, Shandong 261041, P.R. China
| | - Haihua Liu
- Department of Pharmacy, Weifang People's Hospital, Weifang, Shandong 261041, P.R. China
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Simmons VN, Gray JE, Schabath MB, Wilson LE, Quinn GP. High-risk community and primary care providers knowledge about and barriers to low-dose computed topography lung cancer screening. Lung Cancer 2017; 106:42-49. [DOI: 10.1016/j.lungcan.2017.01.012] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 01/12/2017] [Accepted: 01/21/2017] [Indexed: 11/30/2022]
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Pertile P, Poli A, Dominioni L, Rotolo N, Nardecchia E, Castiglioni M, Paolucci M, Mantovani W, Imperatori A. Is chest X-ray screening for lung cancer in smokers cost-effective? Evidence from a population-based study in Italy. COST EFFECTIVENESS AND RESOURCE ALLOCATION 2015; 13:15. [PMID: 26366122 PMCID: PMC4567810 DOI: 10.1186/s12962-015-0041-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2015] [Accepted: 09/04/2015] [Indexed: 12/18/2022] Open
Abstract
Background After implementation of the PREDICA annual chest X-ray (CXR) screening program in smokers in the general practice setting of Varese-Italy a significant reduction in lung cancer-specific mortality (18 %) was observed. The objective of this study covering July 1997 through December 2006 was to estimate the cost-effectiveness of this intervention. Methods We examined detailed information on lung cancer (LC) cases that occurred among smokers invited to be screened in the PREDICA study (Invitation-to-screening Group, n = 5815 subjects) to estimate costs and quality-adjusted life-years (QALYs) from LC diagnosis until death. The control group consisted of 156 screening-eligible smokers from the same area, uninvited and unscreened, who developed LC and were treated by usual care. We calculated the incremental net monetary benefit (INMB) by comparing LC management in screening participants (n = 1244 subjects) and in the Invitation-to-screening group versus control group. Results The average number of QALYs since LC diagnosis was 1.7, 1.49 and 1.07, respectively, in screening participants, the invitation-to-screening group, and the control group. The average total cost (screening + management) per LC case was higher in screening participants (€17,516) and the Invitation-to-screening Group (€16,167) than in the control group (€15,503). Assuming a maximum willingness to pay of €30,000/QALY, we found that the intervention was cost-effective with high probability: 79 % for screening participation (screening participants vs. control group) and 95 % for invitation-to-screening (invitation-to-screening group vs. control group). Conclusions Based on the PREDICA study, annual CXR screening of high-risk smokers in a general practice setting has high probability of being cost-effective with a maximum willingness to pay of €30,000/QALY.
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Affiliation(s)
- Paolo Pertile
- Department of Economics, University of Verona, Via dell'Artigliere 19, 37129 Verona, Italy
| | - Albino Poli
- Department of Public Health and Community Medicine, University of Verona, Verona, Italy
| | - Lorenzo Dominioni
- Center for Thoracic Surgery, University of Insubria, Ospedale di Circolo, Varese, Italy
| | - Nicola Rotolo
- Center for Thoracic Surgery, University of Insubria, Ospedale di Circolo, Varese, Italy
| | - Elisa Nardecchia
- Center for Thoracic Surgery, University of Insubria, Ospedale di Circolo, Varese, Italy
| | - Massimo Castiglioni
- Center for Thoracic Surgery, University of Insubria, Ospedale di Circolo, Varese, Italy
| | - Massimo Paolucci
- Department of Radiology, Ospedale S. Antonio Abate, Gallarate, Italy
| | - William Mantovani
- Department of Public Health and Community Medicine, University of Verona, Verona, Italy ; Department of Prevention, Public Health Trust, Trento, Italy
| | - Andrea Imperatori
- Center for Thoracic Surgery, University of Insubria, Ospedale di Circolo, Varese, Italy
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Evidenced-based management of haemoptysis by otolaryngologists. The Journal of Laryngology & Otology 2015; 129:807-11. [PMID: 26044458 DOI: 10.1017/s0022215115001310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Haemoptysis is an uncommon presenting symptom to the ENT clinic and ward, but has potentially sinister aetiology. This article aims to provide a systematic and evidence-based method of managing patients with haemoptysis. METHODS The data in this article are based on a literature search performed using PubMed in August 2013. The keywords used included 'haemoptysis' in combination with 'otolaryngology', 'ENT', 'head & neck', 'diagnosis', 'management', 'investigations' and 'treatment'. RESULTS The majority of published literature on the subject is level IV evidence. However, this can guide ENT specialists in assessing, investigating and managing presentations of haemoptysis. CONCLUSION Understanding the different causes of haemoptysis is important for the otolaryngologist. The main concern is the detection of a malignant lesion in the upper aerodigestive tract or tracheobronchial tree. A thorough history and systematic examination can aid diagnosis.
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Wang X, Ling L, Su H, Cheng J, Jin L. Aberrant methylation of genes in sputum samples as diagnostic biomarkers for non-small cell lung cancer: a meta-analysis. Asian Pac J Cancer Prev 2015; 15:4467-74. [PMID: 24969871 DOI: 10.7314/apjcp.2014.15.11.4467] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND We aimed to comprehensively review the evidence for using sputum DNA to detect non-small cell lung cancer (NSCLC). MATERIALS AND METHODS We searched PubMed, Science Direct, Web of Science, Chinese Biological Medicine (CBM), Chinese National Knowledge Infrastructure (CNKI), Wanfang, Vip Databases and Google Scholar from 2003 to 2013. The meta-analysis was carried out using a random-effect model with sensitivity, specificity, diagnostic odd ratios (DOR), summary receiver operating characteristic curves (ROC curves), area under the curve (AUC), and 95% confidence intervals (CI) as effect measurements. RESULTS There were twenty-two studies meeting the inclusion criteria for the meta-analysis. Combined sensitivity and specificity were 0.62 (95%CI: 0.59-0.65) and 0.73 (95%CI: 0.70-0.75), respectively. The DOR was 10.3 (95%CI: 5.88-18.1) and the AUC was 0.78. CONCLUSIONS The overall accuracy of the test was currently not strong enough for the detection of NSCLC for clinical application. Discovery and evaluation of additional biomarkers with improved sensitivity and specificity from studies rated high quality deserve further attention.
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Affiliation(s)
- Xu Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, Anhui Province, China E-mail :
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González-Santiago AE, Mendoza-Topete LA, Sánchez-Llamas F, Troyo-Sanromán R, Gurrola-Díaz CM. TGF-β1 serum concentration as a complementary diagnostic biomarker of lung cancer: establishment of a cut-point value. J Clin Lab Anal 2012; 25:238-43. [PMID: 21786325 DOI: 10.1002/jcla.20465] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
UNLABELLED Lung cancer is a malignant disease with increasing mortality rates. Cytokines play a role in normal cell growth regulation and differentiation and are also implicated in malignant disease. Among these cytokines, Transforming Growth Factor β type 1 (TGF-β1) acts as a tumor promoter in malignant cells. Several clinical studies have found high levels of TGF-β1 in various cancer types. The aim of this study was to establish a TGF-β1 cut-off point as a complementary diagnostic tool in lung cancer detection. Therefore, 72 clinically well-characterized individuals were studied, 41 lung cancer patients and 31 healthy subjects. Serum TGF-β1 concentration was measured by an enzyme-linked immunosorbent assay (ELISA). We compared statistically the serum TGF-β1 concentration between both groups with analysis of variance, linear regression and receiver operating curve analysis. We observed that lung cancer patients produced higher TGF-β1 levels than healthy individuals (37,225±9,436 vs. 28,416±9,324 pg/ml, P<0.001). The cut-point diagnostic value was 30,500 pg/ml with 80.5% sensitivity, 64.5% specificity and odds ratio: 7.5, 95% CI: 2.6-21.8. CONCLUSIONS We found significantly higher TGF-β1 levels in lung cancer patients than in healthy individuals. We propose the measurement of serum TGF-β1 levels as a complementary diagnostic test in lung cancer detection.
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Affiliation(s)
- Ana E González-Santiago
- Instituto de Enfermedades Crónico-Degenerativas, Departamento de Biología Molecular y Genómica, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara, Jalisco, México
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de Hoop B, De Boo DW, Gietema HA, van Hoorn F, Mearadji B, Schijf L, van Ginneken B, Prokop M, Schaefer-Prokop C. Computer-aided Detection of Lung Cancer on Chest Radiographs: Effect on Observer Performance. Radiology 2010; 257:532-40. [PMID: 20807851 DOI: 10.1148/radiol.10092437] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Bartjan de Hoop
- Department of Radiology and Image Sciences Institute, University Medical Center, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands.
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de Hoop B, Schaefer-Prokop C, Gietema HA, de Jong PA, van Ginneken B, van Klaveren RJ, Prokop M. Screening for Lung Cancer with Digital Chest Radiography: Sensitivity and Number of Secondary Work-up CT Examinations. Radiology 2010; 255:629-37. [PMID: 20413773 DOI: 10.1148/radiol.09091308] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Bartjan de Hoop
- Department of Radiology, University Medical Center, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands.
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Anglim PP, Alonzo TA, Laird-Offringa IA. DNA methylation-based biomarkers for early detection of non-small cell lung cancer: an update. Mol Cancer 2008; 7:81. [PMID: 18947422 PMCID: PMC2585582 DOI: 10.1186/1476-4598-7-81] [Citation(s) in RCA: 114] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2008] [Accepted: 10/23/2008] [Indexed: 12/19/2022] Open
Abstract
Lung cancer is the number one cancer killer in the United States. This disease is clinically divided into two sub-types, small cell lung cancer, (10–15% of lung cancer cases), and non-small cell lung cancer (NSCLC; 85–90% of cases). Early detection of NSCLC, which is the more common and less aggressive of the two sub-types, has the highest potential for saving lives. As yet, no routine screening method that enables early detection exists, and this is a key factor in the high mortality rate of this disease. Imaging and cytology-based screening strategies have been employed for early detection, and while some are sensitive, none have been demonstrated to reduce lung cancer mortality. However, mortality might be reduced by developing specific molecular markers that can complement imaging techniques. DNA methylation has emerged as a highly promising biomarker and is being actively studied in multiple cancers. The analysis of DNA methylation-based biomarkers is rapidly advancing, and a large number of potential biomarkers have been identified. Here we present a detailed review of the literature, focusing on DNA methylation-based markers developed using primary NSCLC tissue. Viable markers for clinical diagnosis must be detectable in 'remote media' such as blood, sputum, bronchoalveolar lavage, or even exhaled breath condensate. We discuss progress on their detection in such media and the sensitivity and specificity of the molecular marker panels identified to date. Lastly, we look to future advancements that will be made possible with the interrogation of the epigenome.
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Affiliation(s)
- Paul P Anglim
- Departments of Surgery and of Biochemistry and Molecular Biology, Keck School of Medicine, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089-9176, USA.
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Anglim PP, Galler JS, Koss MN, Hagen JA, Turla S, Campan M, Weisenberger DJ, Laird PW, Siegmund KD, Laird-Offringa IA. Identification of a panel of sensitive and specific DNA methylation markers for squamous cell lung cancer. Mol Cancer 2008; 7:62. [PMID: 18616821 PMCID: PMC2483990 DOI: 10.1186/1476-4598-7-62] [Citation(s) in RCA: 93] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2008] [Accepted: 07/10/2008] [Indexed: 02/06/2023] Open
Abstract
Background Lung cancer is the leading cause of cancer death in men and women in the United States and Western Europe. Over 160,000 Americans die of this disease every year. The five-year survival rate is 15% – significantly lower than that of other major cancers. Early detection is a key factor in increasing lung cancer patient survival. DNA hypermethylation is recognized as an important mechanism for tumor suppressor gene inactivation in cancer and could yield powerful biomarkers for early detection of lung cancer. Here we focused on developing DNA methylation markers for squamous cell carcinoma of the lung. Using the sensitive, high-throughput DNA methylation analysis technique MethyLight, we examined the methylation profile of 42 loci in a collection of 45 squamous cell lung cancer samples and adjacent non-tumor lung tissues from the same patients. Results We identified 22 loci showing significantly higher DNA methylation levels in tumor tissue than adjacent non-tumor lung. Of these, eight showed highly significant hypermethylation in tumor tissue (p < 0.0001): GDNF, MTHFR, OPCML, TNFRSF25, TCF21, PAX8, PTPRN2 and PITX2. Used in combination on our specimen collection, this eight-locus panel showed 95.6% sensitivity and specificity. Conclusion We have identified 22 DNA methylation markers for squamous cell lung cancer, several of which have not previously been reported to be methylated in any type of human cancer. The top eight markers show great promise as a sensitive and specific DNA methylation marker panel for squamous cell lung cancer.
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Affiliation(s)
- Paul P Anglim
- Department of Surgery, Norris Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089-9176, USA.
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Chien CR, Chen THH. Mean sojourn time and effectiveness of mortality reduction for lung cancer screening with computed tomography. Int J Cancer 2008; 122:2594-9. [PMID: 18302157 DOI: 10.1002/ijc.23413] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
This study aimed to estimate the mean sojourn time (MST) and sensitivity of asymptomatic lung cancer (ALC) detected by computed tomography (CT) or chest X-ray (CXR). Translation of early diagnosis into mortality reduction by 2 detection modalities and inter-screening interval was projected using a Markov model. On the basis of systematic literature review, data from 6 prospective CT screening studies were retrieved. The MST in association with the natural history of lung cancer depicted by a 3-state Markov model was estimated with a Bayesian approach. To project mortality reduction attributed to screening, the model was further extended to 5 health states for the inclusion of prognostic part. The analysis was run with a 10-year time horizon of follow-up, mimicking the Dutch-Belgian randomized lung cancer screening trial (NELSON). Screening for lung cancer with CT had high sensitivity (median: 97%) and may advance 1 year earlier than CXR in detecting ALC. By simulating the scenario similar to NELSON study, CT screen may gain an extra of 0.019 year of life expectancy per person, yields 15% mortality reduction (relative risk (RR): 0.85, 95% confidence interval [95%CI: (0.58-1.01)]. Approximate 23% [RR: 0.77, 95%CI: (0.43-0.98)] mortality reduction would be achieved by annual CT screening program. The mortality findings in conjunction with higher sensitivity and shorter MST estimate given data on prevalent and incident (2nd) screen may provide a tentative evidence, suggesting that annual CT screening may be required in order to be effective in reducing mortality before the results of randomized controlled studies available.
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Affiliation(s)
- Chun-Ru Chien
- Institute of Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
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Shkumat NA, Siewerdsen JH, Dhanantwari AC, Williams DB, Richard S, Paul NS, Yorkston J, Van Metter R. Optimization of image acquisition techniques for dual-energy imaging of the chest. Med Phys 2007; 34:3904-15. [PMID: 17985636 DOI: 10.1118/1.2777278] [Citation(s) in RCA: 47] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Experimental and theoretical studies were conducted to determine optimal acquisition techniques for a prototype dual-energy (DE) chest imaging system. Technique factors investigated included the selection of added x-ray filtration, kVp pair, and the allocation of dose between low- and high-energy projections, with total dose equal to or less than that of a conventional chest radiograph. Optima were computed to maximize lung nodule detectability as characterized by the signal-difference-to-noise ratio (SDNR) in DE chest images. Optimal beam filtration was determined by cascaded systems analysis of DE image SDNR for filter selections across the periodic table (Z(filter) = 1-92), demonstrating the importance of differential filtration between low- and high-kVp projections and suggesting optimal high-kVp filters in the range Z(filter) = 25-50. For example, added filtration of approximately 2.1 mm Cu, approximately 1.2 mm Zr, approximately 0.7 mm Mo, and approximately 0.6 mm Ag to the high-kVp beam provided optimal (and nearly equivalent) soft-tissue SDNR. Optimal kVp pair and dose allocation were investigated using a chest phantom presenting simulated lung nodules and ribs for thin, average, and thick body habitus. Low- and high-energy techniques ranged from 60-90 kVp and 120-150 kVp, respectively, with peak soft-tissue SDNR achieved at [60/120] kVp for all patient thicknesses and all levels of imaging dose. A strong dependence on the kVp of the low-energy projection was observed. Optimal allocation of dose between low- and high-energy projections was such that approximately 30% of the total dose was delivered by the low-kVp projection, exhibiting a fairly weak dependence on kVp pair and dose. The results have guided the implementation of a prototype DE imaging system for imaging trials in early-stage lung nodule detection and diagnosis.
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Affiliation(s)
- N A Shkumat
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada M5G 2M9
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Shi H, Lyons-Weiler J. Clinical decision modeling system. BMC Med Inform Decis Mak 2007; 7:23. [PMID: 17697328 PMCID: PMC2131745 DOI: 10.1186/1472-6947-7-23] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2007] [Accepted: 08/13/2007] [Indexed: 01/31/2023] Open
Abstract
Background Decision analysis techniques can be applied in complex situations involving uncertainty and the consideration of multiple objectives. Classical decision modeling techniques require elicitation of too many parameter estimates and their conditional (joint) probabilities, and have not therefore been applied to the problem of identifying high-performance, cost-effective combinations of clinical options for diagnosis or treatments where many of the objectives are unknown or even unspecified. Methods We designed a Java-based software resource, the Clinical Decision Modeling System (CDMS), to implement Naïve Decision Modeling, and provide a use case based on published performance evaluation measures of various strategies for breast and lung cancer detection. Because cost estimates for many of the newer methods are not yet available, we assume equal cost. Our use case reveals numerous potentially high-performance combinations of clinical options for the detection of breast and lung cancer. Results Naïve Decision Modeling is a highly practical applied strategy which guides investigators through the process of establishing evidence-based integrative translational clinical research priorities. CDMS is not designed for clinical decision support. Inputs include performance evaluation measures and costs of various clinical options. The software finds trees with expected emergent performance characteristics and average cost per patient that meet stated filtering criteria. Key to the utility of the software is sophisticated graphical elements, including a tree browser, a receiver-operator characteristic surface plot, and a histogram of expected average cost per patient. The analysis pinpoints the potentially most relevant pairs of clinical options ('critical pairs') for which empirical estimates of conditional dependence may be critical. The assumption of independence can be tested with retrospective studies prior to the initiation of clinical trials designed to estimate clinical impact. High-performance combinations of clinical options may exist for breast and lung cancer detection. Conclusion The software could be found useful in simplifying the objective-driven planning of complex integrative clinical studies without requiring a multi-attribute utility function, and it could lead to efficient integrative translational clinical study designs that move beyond simple pair wise competitive studies. Collaborators, who traditionally might compete to prioritize their own individual clinical options, can use the software as a common framework and guide to work together to produce increased understanding on the benefits of using alternative clinical combinations to affect strategic and cost-effective clinical workflows.
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Affiliation(s)
- Haiwen Shi
- Bioinformatics Analysis Core, Genomics and Proteomics Core Laboratories, 3343 Forbes Avenue, Pittsburgh, PA 15260 USA
| | - James Lyons-Weiler
- Bioinformatics Analysis Core, Genomics and Proteomics Core Laboratories, 3343 Forbes Avenue, Pittsburgh, PA 15260 USA
- Department of Biomedical Informatics, University of Pittsburgh Medical School and University of Pittsburgh Graduate School of Public Health, Parkvale Building M-183, 200 Meyran Avenue, Pittsburgh, PA 15260 USA
- Department of Pathology, University of Pittsburgh, School of Medicine, S-417 BST, 200 Lothrop Street, Pittsburgh, PA 15261 USA
- Clinical Genomics Facility and Clinical Proteomics Facility, University of Pittsburgh Cancer Institute, Hillman Cancer Center, UPCI Research Pavilion, Suite 2.26d, 5177 Centre Ave., Pittsburgh, PA 15213-1863, USA
- Interdisciplinary Biomedical Graduate Program, University of Pittsburgh, School of Medicine Graduate Office, 524 Scaife Hall, Pittsburgh, PA 15261-0001 USA
- University of Pittsburgh Cancer Institute, 5150 Centre Ave, Pittsburgh, PA 15232, USA
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Chang R, Stetter M. Quantitative Bayesian Inference by Qualitative Knowledge Modeling. ACTA ACUST UNITED AC 2007. [DOI: 10.1109/ijcnn.2007.4371362] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Richard S, Siewerdsen JH. Optimization of dual-energy imaging systems using generalized NEQ and imaging task. Med Phys 2007; 34:127-39. [PMID: 17278498 DOI: 10.1118/1.2400620] [Citation(s) in RCA: 80] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Dual-energy (DE) imaging is a promising advanced application of flat-panel detectors (FPDs) with a potential host of applications ranging from thoracic and cardiac imaging to interventional procedures. The performance of FPD-based DE imaging systems is investigated in this work by incorporating the noise-power spectrum associated with overlying anatomical structures ("anatomical noise" modeled according to a 1/f characteristic) into descriptions of noise-equivalent quanta (NEQ) to yield the generalized NEQ (GNEQ). Signal and noise propagation in the DE imaging chain is modeled by cascaded systems analysis. A Fourier-based description of the imaging task is integrated with the GNEQ to yield a detectability index used as an objective function for optimizing DE image reconstruction, allocation of dose between low- and high-energy images, and selection of low- and high-kVp. Optimal reconstruction and acquisition parameters were found to depend on dose; for example, optimal kVp varied from [60/150] kVp at typical radiographic dose levels (approximately 0.5 mGy entrance surface dose, ESD) but increased to [90/150] kVp at high dose (ESD approximately 5.0 mGy). At very low dose (ESD approximately 0.05 mGy), detectability index indicates an optimal low-energy technique of 60 kVp but was largely insensitive to the choice of high-kVp in the range 120-150 kVp. Similarly, optimal dose allocation, defined as the ratio of low-energy ESD and the total ESD, varied from 0.2 to 0.4 over the range ESD=(0.05-5.0) mGy. Furthermore, two applications of the theoretical framework were explored: (i) the increase in detectability for DE imaging compared to conventional radiography; and (ii) the performance of single-shot vs double-shot DE imaging, wherein the latter is found to have a DQE approximately twice that of the former. Experimental and theoretical analysis of GNEQ and task-based detectability index provides a fundamental understanding of the factors governing DE imaging performance and offers a framework for system design and optimization.
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Affiliation(s)
- S Richard
- Department of Medical Biophysics, University of Toronto, Ontario, M5G 2M9, Canada
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Richard S, Siewerdsen JH, Jaffray DA, Moseley DJ, Bakhtiar B. Generalized DQE analysis of radiographic and dual-energy imaging using flat-panel detectors. Med Phys 2005; 32:1397-413. [PMID: 15984691 DOI: 10.1118/1.1901203] [Citation(s) in RCA: 91] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Analysis of detective quantum efficiency (DQE) is an important component of the investigation of imaging performance for flat-panel detectors (FPDs). Conventional descriptions of DQE are limited, however, in that they take no account of anatomical noise (i.e., image fluctuations caused by overlying anatomy), even though such noise can be the most significant limitation to detectability, often outweighing quantum or electronic noise. We incorporate anatomical noise in experimental and theoretical descriptions of the "generalized DQE" by including a spatial-frequency-dependent noise-power term, S(B), corresponding to background anatomical fluctuations. Cascaded systems analysis (CSA) of the generalized DQE reveals tradeoffs between anatomical noise and the factors that govern quantum noise. We extend such analysis to dual-energy (DE) imaging, in which the overlying anatomical structure is selectively removed in image reconstructions by combining projections acquired at low and high kVp. The effectiveness of DE imaging in removing anatomical noise is quantified by measurement of S(B) in an anthropomorphic phantom. Combining the generalized DQE with an idealized task function to yield the detectability index, we show that anatomical noise dramatically influences task-based performance, system design, and optimization. For the case of radiography, the analysis resolves a fundamental and illustrative quandary: The effect of kVp on imaging performance, which is poorly described by conventional DQE analysis but is clarified by consideration of the generalized DQE. For the case of DE imaging, extension of a generalized CSA methodology reveals a potentially powerful guide to system optimization through the optimal selection of the tissue cancellation parameter. Generalized task-based analysis for DE imaging shows an improvement in the detectability index by more than a factor of 2 compared to conventional radiography for idealized detection tasks.
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Affiliation(s)
- S Richard
- Department of Medical Biophysics, University of Toronto, Ontario, Canada M5G 2M9
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Vogt FM, Herborn CU, Hunold P, Lauenstein TC, Schröder T, Debatin JF, Barkhausen J. HASTE MRI Versus Chest Radiography in the Detection of Pulmonary Nodules: Comparison with MDCT. AJR Am J Roentgenol 2004; 183:71-8. [PMID: 15208113 DOI: 10.2214/ajr.183.1.1830071] [Citation(s) in RCA: 65] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE The purpose of our study was to compare the diagnostic accuracy of an ultrafast ECG-triggered black blood-prepared HASTE sequence with chest radiography for the detection of pulmonary nodules. SUBJECTS AND METHODS. Sixty-four patients with various primary malignancies who had undergone radiography and MDCT of the chest also underwent ECG-triggered black blood-prepared HASTE MRI of the lung. MR images and radiographs were interpreted separately. The number, location, and size of detected lesions were recorded, and each hemithorax was classified as affected or not affected on the basis of a grade reflecting the conspicuity of nodular involvement. Sensitivity, specificity, and positive and negative predictive values for the detection of pulmonary nodules with diameters of 5 mm or larger were determined, using MDCT findings as the standard of reference. Lesions with diameters smaller than 5 mm were not evaluated. Additional lesion-by-lesion comparisons between MDCT and MRI findings were performed. RESULTS MDCT confirmed pulmonary lesions in 32 patients, whereas HASTE MRI revealed lesions in 30 patients and chest radiography, in 19 patients. MDCT revealed 226 nodules in 32 patients, whereas MRI HASTE revealed 227 lesions in 30 patients. Conspicuity scale-based sensitivity and specificity for chest radiography were 55.8% and 92.4%, respectively, whereas HASTE MRI had a sensitivity of 93.0% and a specificity of 96.2%. Positive and negative predictive values for chest radiography were 80% and 79.3%, respectively, and for HASTE MRI, 93.0% and 96.2%, respectively. The sensitivity of HASTE MRI increased with lesion size, ranging from 94.9% for nodules between 5 and 10 mm in diameter to 100% for lesions exceeding 3 cm in diameter. CONCLUSION ECG-triggered black blood-prepared HASTE MRI is reliable for detecting pulmonary nodules exceeding 5 mm and has proven significantly more accurate than conventional chest radiography. The technique appears useful as an adjunct to MRI of the heart, great vessels, or chest, potentially increasing the diagnostic yield of MRI examinations.
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Affiliation(s)
- Florian M Vogt
- Department of Diagnostic and Interventional Radiology, University Hospital Essen, Hufelandstrasse 55, Essen 45122, Germany
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Anyanwu AC, Townsend ER, Banner NR, Burke M, Khaghani A, Yacoub MH. Primary lung carcinoma after heart or lung transplantation: management and outcome. J Thorac Cardiovasc Surg 2002; 124:1190-7. [PMID: 12447186 DOI: 10.1067/mtc.2002.124885] [Citation(s) in RCA: 41] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
OBJECTIVE We sought to examine our management and outcome of lung carcinoma occurring after thoracic organ transplantation. METHODS We performed a retrospective review of cases of primary lung carcinoma diagnosed between 1990 and 2000 in patients who have previously undergone thoracic transplantation at our institution. RESULTS Seventeen patients were identified (1 lung and 16 heart transplants). Median time from transplantation to diagnosis of lung carcinoma was 89 months (range, 46-138 months). Predominant presentation was as an incidental finding at chest radiography (13/17). All patients had smoked cigarettes before transplantation, with 5 continuing to smoke after transplantation. Histologic types were squamous (n = 11), adenocarcinoma (n = 3), small cell (n = 2), and undifferentiated (n = 1). Revised International Union Against Cancer (UICC) clinical stage at the time of diagnosis was stage I or II in 11 of 17 patients. Of these, 9 underwent surgical resection; 2 patients unfit for surgical intervention had radiotherapy. Surgical procedures were lobectomy (n = 5), wedge excision (n = 3), and no resection (n = 1). Median survival after diagnosis was 12 months for all patients and 24 months if the tumor was resected. Six patients who had surgical resection subsequently died (survival of 2, 9, 21, 21, 36, and 67 months); 2 remain alive after 12 and 54 months, respectively. CONCLUSIONS When possible, surgical intervention should be undertaken for early stage lung cancer occurring after thoracic transplantation because medium-term survival is achievable. Sublobar excisions and definitive radiotherapy should be considered if comorbidity prevents optimal surgical treatment.
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Affiliation(s)
- A C Anyanwu
- Thoracic Surgery and Transplant Unit, Royal Brompton and Harefield NHS Trust, Harefield Hospital, Uxbridge, Middlesex, United Kingdom
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