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Liu Z, Yuan Y, Zhang C, Zhu Q, Xu X, Yuan M, Tan W. Hierarchical classification of early microscopic lung nodule based on cascade network. Health Inf Sci Syst 2024; 12:13. [PMID: 38404714 PMCID: PMC10891040 DOI: 10.1007/s13755-024-00273-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 01/08/2024] [Indexed: 02/27/2024] Open
Abstract
Purpose Early-stage lung cancer is typically characterized clinically by the presence of isolated lung nodules. Thousands of cases are examined each year, and one case usually contains numerous lung CT slices. Detecting and classifying early microscopic lung nodules is demanding due to their diminutive dimensions and restricted characterization capabilities. Therefore, a lung nodule classification model that performs well and is sensitive to microscopic lung nodules is needed to accurately classify lung nodules. Methods This paper uses the Resnet34 network as a basic classification model. A new cascade lung nodule classification method is proposed to classify lung nodules into 6 classes instead of the traditional 2 or 4 classes. It can effectively classify six different nodule types including ground-glass and solid nodules, benign and malignant nodules, and nodules with predominantly ground-glass or solid components. Results In this paper, the traditional multi-classification method and the cascade classification method proposed in this paper were tested using real lung nodule data collected in the clinic. The test results demonstrate that the cascade classification method in this study achieves an accuracy of 80.04% , outperforming the conventional multi-classification approach. Conclusions Different from the existing methods for categorizing the benign and malignant nature of lung nodules, the approach presented in this paper can classify lung nodules into 6 categories more accurately. At the same time, This paper proposes a rapid, precise, and dependable approach for classifying six distinct categories of lung nodules, which increases the accuracy categorization compared with the traditional multivariate categorization method.
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Affiliation(s)
- Ziang Liu
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Ye Yuan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Cui Zhang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Quan Zhu
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029 China
| | - Xinfeng Xu
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029 China
| | - Mei Yuan
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029 China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
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Ma Y, Cao H, Li J, Lin M, Gong X, Lin Y. Multi-instance learning based lung nodule system for assessment of CT quality after small-field-of-view reconstruction. Sci Rep 2024; 14:3109. [PMID: 38326410 PMCID: PMC10850475 DOI: 10.1038/s41598-024-53797-4] [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: 05/18/2023] [Accepted: 02/05/2024] [Indexed: 02/09/2024] Open
Abstract
Small-field-of-view reconstruction CT images (sFOV-CT) increase the pixel density across airway structures and reduce partial volume effects. Multi-instance learning (MIL) is proposed as a weakly supervised machine learning method, which can automatically assess the image quality. The aim of this study was to evaluate the disparities between conventional CT (c-CT) and sFOV-CT images using a lung nodule system based on MIL and assessments from radiologists. 112 patients who underwent chest CT were retrospectively enrolled in this study between July 2021 to March 2022. After undergoing c-CT examinations, sFOV-CT images with small-field-of-view were reconstructed. Two radiologists analyzed all c-CT and sFOV-CT images, including features such as location, nodule type, size, CT values, and shape signs. Then, an MIL-based lung nodule system objectively analyzed the c-CT (c-MIL) and sFOV-CT (sFOV-MIL) to explore their differences. The signal-to-noise ratio of lungs (SNR-lung) and contrast-to-noise ratio of nodules (CNR-nodule) were calculated to evaluate the quality of CT images from another perspective. The subjective evaluation by radiologists showed that feature of minimal CT value (p = 0.019) had statistical significance between c-CT and sFOV-CT. However, most features (all with p < 0.05), except for nodule type, location, volume, mean CT value, and vacuole sign (p = 0.056-1.000), had statistical differences between c-MIL and sFOV-MIL by MIL system. The SNR-lung between c-CT and sFOV-CT had no statistical significance, while the CNR-nodule showed statistical difference (p = 0.007), and the CNR of sFOV-CT was higher than that of c-CT. In detecting the difference between c-CT and sFOV-CT, features extracted by the MIL system had more statistical differences than those evaluated by radiologists. The image quality of those two CT images was different, and the CNR-nodule of sFOV-CT was higher than that of c-CT.
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Affiliation(s)
- Yanqing Ma
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China
| | - Hanbo Cao
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China
| | - Jie Li
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China
| | - Mu Lin
- Infervision Technology Co. Ltd., Beijing, 100010, China
| | - Xiangyang Gong
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China
| | - Yi Lin
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China.
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3
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Hardie RC, Trout AT, Dillman JR, Narayanan BN, Tanimoto AA. Performance Analysis in Children of Traditional and Deep Learning CT Lung Nodule Computer-Aided Detection Systems Trained on Adults. AJR Am J Roentgenol 2024; 222:e2330345. [PMID: 37991333 DOI: 10.2214/ajr.23.30345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
BACKGROUND. Although primary lung cancer is rare in children, chest CT is commonly performed to assess for lung metastases in children with cancer. Lung nodule computer-aided detection (CAD) systems have been designed and studied primarily using adult training data, and the efficacy of such systems when applied to pediatric patients is poorly understood. OBJECTIVE. The purpose of this study was to evaluate in children the diagnostic performance of traditional and deep learning CAD systems trained with adult data for the detection of lung nodules on chest CT scans and to compare the ability of such systems to generalize to children versus to other adults. METHODS. This retrospective study included pediatric and adult chest CT test sets. The pediatric test set comprised 59 CT scans in 59 patients (30 boys, 29 girls; mean age, 13.1 years; age range, 4-17 years), which were obtained from November 30, 2018, to August 31, 2020; lung nodules were annotated by fellowship-trained pediatric radiologists as the reference standard. The adult test set was the publicly available adult Lung Nodule Analysis (LUNA) 2016 subset 0, which contained 89 deidentified scans with previously annotated nodules. The test sets were processed through the traditional FlyerScan (github.com/rhardie1/FlyerScanCT) and deep learning Medical Open Network for Artificial Intelligence (MONAI; github.com/Project-MONAI/model-zoo/releases) lung nodule CAD systems, which had been trained on separate sets of CT scans in adults. Sensitivity and false-positive (FP) frequency were calculated for nodules measuring 3-30 mm; nonoverlapping 95% CIs indicated significant differences. RESULTS. Operating at two FPs per scan, on pediatric testing data FlyerScan and MONAI showed significantly lower detection sensitivities of 68.4% (197/288; 95% CI, 65.1-73.0%) and 53.1% (153/288; 95% CI, 46.7-58.4%), respectively, than on adult LUNA 2016 subset 0 testing data (83.9% [94/112; 95% CI, 79.1-88.0%] and 95.5% [107/112; 95% CI, 90.0-98.4%], respectively). Mean nodule size was smaller (p < .001) in the pediatric testing data (5.4 ± 3.1 [SD] mm) than in the adult LUNA 2016 subset 0 testing data (11.0 ± 6.2 mm). CONCLUSION. Adult-trained traditional and deep learning-based lung nodule CAD systems had significantly lower sensitivity for detection on pediatric data than on adult data at a matching FP frequency. The performance difference may relate to the smaller size of pediatric lung nodules. CLINICAL IMPACT. The results indicate a need for pediatric-specific lung nodule CAD systems trained on data specific to pediatric patients.
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Affiliation(s)
- Russell C Hardie
- Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469
| | - Andrew T Trout
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Jonathan R Dillman
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Barath N Narayanan
- Sensor and Software Systems, University of Dayton Research Institute, Dayton, OH
| | - Aki A Tanimoto
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH
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V R N, Chandra S S V. ExtRanFS: An Automated Lung Cancer Malignancy Detection System Using Extremely Randomized Feature Selector. Diagnostics (Basel) 2023; 13:2206. [PMID: 37443600 DOI: 10.3390/diagnostics13132206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/22/2023] [Accepted: 06/25/2023] [Indexed: 07/15/2023] Open
Abstract
Lung cancer is an abnormality where the body's cells multiply uncontrollably. The disease can be deadly if not detected in the initial stage. To address this issue, an automated lung cancer malignancy detection (ExtRanFS) framework is developed using transfer learning. We used the IQ-OTH/NCCD dataset gathered from the Iraq Hospital in 2019, encompassing CT scans of patients suffering from various lung cancers and healthy subjects. The annotated dataset consists of CT slices from 110 patients, of which 40 were diagnosed with malignant tumors and 15 with benign tumors. Fifty-five patients were determined to be in good health. All CT images are in DICOM format with a 1mm slice thickness, consisting of 80 to 200 slices at various sides and angles. The proposed system utilized a convolution-based pre-trained VGG16 model as the feature extractor and an Extremely Randomized Tree Classifier as the feature selector. The selected features are fed to the Multi-Layer Perceptron (MLP) Classifier for detecting whether the lung cancer is benign, malignant, or normal. The accuracy, sensitivity, and F1-Score of the proposed framework are 99.09%, 98.33%, and 98.33%, respectively. To evaluate the proposed model, a comparison is performed with other pre-trained models as feature extractors and also with the existing state-of-the-art methodologies as classifiers. From the experimental results, it is evident that the proposed framework outperformed other existing methodologies. This work would be beneficial to both the practitioners and the patients in identifying whether the tumor is benign, malignant, or normal.
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Affiliation(s)
- Nitha V R
- Department of Computer Science, University of Kerala, Thiruvananthapuram 695581, India
| | - Vinod Chandra S S
- Department of Computer Science, University of Kerala, Thiruvananthapuram 695581, India
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Tan H, Xu H, Yu N, Yu Y, Duan H, Fan Q, Zhanyu T. The value of deep learning-based computer aided diagnostic system in improving diagnostic performance of rib fractures in acute blunt trauma. BMC Med Imaging 2023; 23:55. [PMID: 37055752 PMCID: PMC10099632 DOI: 10.1186/s12880-023-01012-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 04/04/2023] [Indexed: 04/15/2023] Open
Abstract
BACKGROUND To evaluate the value of a deep learning-based computer-aided diagnostic system (DL-CAD) in improving the diagnostic performance of acute rib fractures in patients with chest trauma. MATERIALS AND METHODS CT images of 214 patients with acute blunt chest trauma were retrospectively analyzed by two interns and two attending radiologists independently firstly and then with the assistance of a DL-CAD one month later, in a blinded and randomized manner. The consensusdiagnosis of fib fracture by another two senior thoracic radiologists was regarded as reference standard. The rib fracture diagnostic sensitivity, specificity, positive predictive value, diagnostic confidence and mean reading time with and without DL-CAD were calculated and compared. RESULTS There were 680 rib fracture lesions confirmed as reference standard among all patients. The diagnostic sensitivity and positive predictive value of interns weresignificantly improved from (68.82%, 84.50%) to (91.76%, 93.17%) with the assistance of DL-CAD, respectively. Diagnostic sensitivity and positive predictive value of attendings aided by DL-CAD (94.56%, 95.67%) or not aided (86.47%, 93.83%), respectively. In addition, when radiologists were assisted by DL-CAD, the mean reading time was significantly reduced, and diagnostic confidence was significantly enhanced. CONCLUSIONS DL-CAD improves the diagnostic performance of acute rib fracture in chest trauma patients, which increases the diagnostic confidence, sensitivity, and positive predictive value for radiologists. DL-CAD can advance the diagnostic consistency of radiologists with different experiences.
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Affiliation(s)
- Hui Tan
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Hui Xu
- Peter Boris Centre for Addiction Research, McMaster University & St. Joseph's Health Care Hamilton, 100 West 5th Street, Hamilton, ON, L8P 3R2, Canada.
| | - Nan Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Yong Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Haifeng Duan
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Qiuju Fan
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China.
| | - Tian Zhanyu
- Institute of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, China
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Hadjiiski L, Cha K, Chan HP, Drukker K, Morra L, Näppi JJ, Sahiner B, Yoshida H, Chen Q, Deserno TM, Greenspan H, Huisman H, Huo Z, Mazurchuk R, Petrick N, Regge D, Samala R, Summers RM, Suzuki K, Tourassi G, Vergara D, Armato SG. AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging. Med Phys 2023; 50:e1-e24. [PMID: 36565447 DOI: 10.1002/mp.16188] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 11/13/2022] [Accepted: 11/22/2022] [Indexed: 12/25/2022] Open
Abstract
Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer-aided diagnosis (CAD) development and applications using both "traditional" machine learning methods and newer DL-based methods. We use the term CAD-AI to refer to this expanded clinical decision support environment that uses traditional and DL-based AI methods. Numerous studies have been published to date on the development of machine learning tools for computer-aided, or AI-assisted, clinical tasks. However, most of these machine learning models are not ready for clinical deployment. It is of paramount importance to ensure that a clinical decision support tool undergoes proper training and rigorous validation of its generalizability and robustness before adoption for patient care in the clinic. To address these important issues, the American Association of Physicists in Medicine (AAPM) Computer-Aided Image Analysis Subcommittee (CADSC) is charged, in part, to develop recommendations on practices and standards for the development and performance assessment of computer-aided decision support systems. The committee has previously published two opinion papers on the evaluation of CAD systems and issues associated with user training and quality assurance of these systems in the clinic. With machine learning techniques continuing to evolve and CAD applications expanding to new stages of the patient care process, the current task group report considers the broader issues common to the development of most, if not all, CAD-AI applications and their translation from the bench to the clinic. The goal is to bring attention to the proper training and validation of machine learning algorithms that may improve their generalizability and reliability and accelerate the adoption of CAD-AI systems for clinical decision support.
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Affiliation(s)
- Lubomir Hadjiiski
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kenny Cha
- U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Heang-Ping Chan
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Karen Drukker
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
| | - Lia Morra
- Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy
| | - Janne J Näppi
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Berkman Sahiner
- U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Hiroyuki Yoshida
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Quan Chen
- Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Thomas M Deserno
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
| | - Hayit Greenspan
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv, Israel & Department of Radiology, Ichan School of Medicine, Tel Aviv University, Mt Sinai, New York, New York, USA
| | - Henkjan Huisman
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Zhimin Huo
- Tencent America, Palo Alto, California, USA
| | - Richard Mazurchuk
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Daniele Regge
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.,Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Ravi Samala
- U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ronald M Summers
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Maryland, USA
| | - Kenji Suzuki
- Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan
| | | | - Daniel Vergara
- Department of Radiology, Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Samuel G Armato
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
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7
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Comparison of single- and dual-energy CT combined with artificial intelligence for the diagnosis of pulmonary nodules. Clin Radiol 2023; 78:e99-e105. [PMID: 36266099 DOI: 10.1016/j.crad.2022.09.114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 08/26/2022] [Accepted: 09/02/2022] [Indexed: 01/18/2023]
Abstract
AIM To explore the efficiency of single- and dual-energy computed tomography (CT) with artificial intelligence (AI) for the diagnosis of pulmonary nodules. MATERIALS AND METHODS In a prospective study, 682 patients undergoing a chest CT examination using a dual-energy system were divided randomly into two groups: single-energy mode (group S, n=341) and dual-energy mode (group D, n=341). CT images were first analysed automatically with the AI pulmonary nodule-detection software. CT features including nodule number, lesion size, and nodule type were then analysed by experienced radiologists to establish a reference diagnosis. Subsequently, the accuracy, sensitivity, false-positive rate, and miss rate of AI were calculated. Additionally, image quality and radiation dose were also compared between the two groups. RESULTS The contrast-to-noise ratio data suggested that the image quality of group D was superior to that of group S (0.16 ± 0.10 versus 0.00 ± 0.17), and the radiation dose of group D was lower than that of group S (0.32 ± 0.10 versus 0.62 ± 0.11 mSv.cm). Compared to group S, group D exhibited a significantly higher sensitivity and lower accuracy for nodule identification, size classification, and nodule type (all p<0.05, except for 5-10 mm and calcified nodules). CONCLUSIONS Compared with single-energy CT, dual-energy CT may significantly improve the sensitivity of AI for the diagnosis of pulmonary nodules and is practical for the screening of pulmonary nodules in a large population. In addition, dual-energy CT examination demonstrates improved image quality and is associated with reduced exposure to ionising radiation, but its accuracy is poorer.
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Chao HS, Tsai CY, Chou CW, Shiao TH, Huang HC, Chen KC, Tsai HH, Lin CY, Chen YM. Artificial Intelligence Assisted Computational Tomographic Detection of Lung Nodules for Prognostic Cancer Examination: A Large-Scale Clinical Trial. Biomedicines 2023; 11:biomedicines11010147. [PMID: 36672655 PMCID: PMC9856020 DOI: 10.3390/biomedicines11010147] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 12/23/2022] [Accepted: 12/29/2022] [Indexed: 01/11/2023] Open
Abstract
Low-dose computed tomography (LDCT) has emerged as a standard method for detecting early-stage lung cancer. However, the tedious computer tomography (CT) slide reading, patient-by-patient check, and lack of standard criteria to determine the vague but possible nodule leads to variable outcomes of CT slide interpretation. To determine the artificial intelligence (AI)-assisted CT examination, AI algorithm-assisted CT screening was embedded in the hospital picture archiving and communication system, and a 200 person-scaled clinical trial was conducted at two medical centers. With AI algorithm-assisted CT screening, the sensitivity of detecting nodules sized 4−5 mm, 6~10 mm, 11~20 mm, and >20 mm increased by 41%, 11.2%, 10.3%, and 18.7%, respectively. Remarkably, the overall sensitivity of detecting varied nodules increased by 20.7% from 67.7% to 88.4%. Furthermore, the sensitivity increased by 18.5% from 72.5% to 91% for detecting ground glass nodules (GGN), which is challenging for radiologists and physicians. The free-response operating characteristic (FROC) AI score was ≥0.4, and the AI algorithm standalone CT screening sensitivity reached >95% with an area under the localization receiver operating characteristic curve (LROC-AUC) of >0.88. Our study demonstrates that AI algorithm-embedded CT screening significantly ameliorates tedious LDCT practices for doctors.
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Affiliation(s)
- Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chiao-Yun Tsai
- Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Institute of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Chung-Wei Chou
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Tsu-Hui Shiao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Hsu-Chih Huang
- Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Institute of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Kun-Chieh Chen
- Division of Pulmonary Medicine, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Department of Applied Chemistry, National Chi Nan University, Nantou 545301, Taiwan
| | - Hao-Hung Tsai
- Institute of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- School of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Chin-Yu Lin
- Institute of New Drug Development, College of Medicine, China Medical University, Taichung 40402, Taiwan
- Tsuzuki Institute for Traditional Medicine, College of Pharmacy, China Medical University, Taichung 40402, Taiwan
- Department for Biomedical Engineering, Collage of Biomedical Engineering, China Medical University, Taichung 40402, Taiwan
| | - Yuh-Min Chen
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: ; Tel.: +886-2-28712121 (ext. 7865)
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9
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Katase S, Ichinose A, Hayashi M, Watanabe M, Chin K, Takeshita Y, Shiga H, Tateishi H, Onozawa S, Shirakawa Y, Yamashita K, Shudo J, Nakamura K, Nakanishi A, Kuroki K, Yokoyama K. Development and performance evaluation of a deep learning lung nodule detection system. BMC Med Imaging 2022; 22:203. [PMID: 36419044 PMCID: PMC9682774 DOI: 10.1186/s12880-022-00938-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 11/14/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Lung cancer is the leading cause of cancer-related deaths throughout the world. Chest computed tomography (CT) is now widely used in the screening and diagnosis of lung cancer due to its effectiveness. Radiologists must identify each small nodule shadow from 3D volume images, which is very burdensome and often results in missed nodules. To address these challenges, we developed a computer-aided detection (CAD) system that automatically detects lung nodules in CT images. METHODS A total of 1997 chest CT scans were collected for algorithm development. The algorithm was designed using deep learning technology. In addition to evaluating detection performance on various public datasets, its robustness to changes in radiation dose was assessed by a phantom study. To investigate the clinical usefulness of the CAD system, a reader study was conducted with 10 doctors, including inexperienced and expert readers. This study investigated whether the use of the CAD as a second reader could prevent nodular lesions in lungs that require follow-up examinations from being overlooked. Analysis was performed using the Jackknife Free-Response Receiver-Operating Characteristic (JAFROC). RESULTS The CAD system achieved sensitivity of 0.98/0.96 at 3.1/7.25 false positives per case on two public datasets. Sensitivity did not change within the range of practical doses for a study using a phantom. A second reader study showed that the use of this system significantly improved the detection ability of nodules that could be picked up clinically (p = 0.026). CONCLUSIONS We developed a deep learning-based CAD system that is robust to imaging conditions. Using this system as a second reader increased detection performance.
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Affiliation(s)
- Shichiro Katase
- grid.411205.30000 0000 9340 2869Department of Radiology, Faculty of Medicine, Kyorin University, 6-20-2, Shinkawa, Mitaka-shi, Tokyo, Japan
| | - Akimichi Ichinose
- grid.410862.90000 0004 1770 2279Imaging Technology Center, ICT Strategy Division, Fujifilm Corporation, 2-26-30, Nishi-Azabu, Minato-ku, Tokyo, Japan
| | - Mahiro Hayashi
- grid.411205.30000 0000 9340 2869Department of Radiology, Faculty of Medicine, Kyorin University, 6-20-2, Shinkawa, Mitaka-shi, Tokyo, Japan
| | - Masanaka Watanabe
- grid.411205.30000 0000 9340 2869Department of Radiology, Faculty of Medicine, Kyorin University, 6-20-2, Shinkawa, Mitaka-shi, Tokyo, Japan
| | - Kinka Chin
- grid.411205.30000 0000 9340 2869Department of Radiology, Faculty of Medicine, Kyorin University, 6-20-2, Shinkawa, Mitaka-shi, Tokyo, Japan
| | - Yuhei Takeshita
- grid.411205.30000 0000 9340 2869Department of Radiology, Faculty of Medicine, Kyorin University, 6-20-2, Shinkawa, Mitaka-shi, Tokyo, Japan
| | - Hisae Shiga
- grid.411205.30000 0000 9340 2869Department of Radiology, Faculty of Medicine, Kyorin University, 6-20-2, Shinkawa, Mitaka-shi, Tokyo, Japan
| | - Hidekatsu Tateishi
- grid.411205.30000 0000 9340 2869Department of Radiology, Faculty of Medicine, Kyorin University, 6-20-2, Shinkawa, Mitaka-shi, Tokyo, Japan
| | - Shiro Onozawa
- grid.411205.30000 0000 9340 2869Department of Radiology, Faculty of Medicine, Kyorin University, 6-20-2, Shinkawa, Mitaka-shi, Tokyo, Japan
| | - Yuya Shirakawa
- grid.459686.00000 0004 0386 8956Department of Radiology, Kyorin University Hospital, 6-20-2, Shinkawa, Mitaka-shi, Tokyo, Japan
| | - Koji Yamashita
- grid.459686.00000 0004 0386 8956Department of Radiology, Kyorin University Hospital, 6-20-2, Shinkawa, Mitaka-shi, Tokyo, Japan
| | - Jun Shudo
- grid.459686.00000 0004 0386 8956Department of Radiology, Kyorin University Hospital, 6-20-2, Shinkawa, Mitaka-shi, Tokyo, Japan
| | - Keigo Nakamura
- grid.410862.90000 0004 1770 2279Imaging Technology Center, ICT Strategy Division, Fujifilm Corporation, 2-26-30, Nishi-Azabu, Minato-ku, Tokyo, Japan
| | - Akihito Nakanishi
- grid.459686.00000 0004 0386 8956Department of Radiology, Kyorin University Hospital, 6-20-2, Shinkawa, Mitaka-shi, Tokyo, Japan
| | - Kazunori Kuroki
- grid.411205.30000 0000 9340 2869Department of Radiology, Faculty of Medicine, Kyorin University, 6-20-2, Shinkawa, Mitaka-shi, Tokyo, Japan
| | - Kenichi Yokoyama
- grid.411205.30000 0000 9340 2869Department of Radiology, Faculty of Medicine, Kyorin University, 6-20-2, Shinkawa, Mitaka-shi, Tokyo, Japan
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10
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Sekeroglu K, Soysal ÖM. Multi-Perspective Hierarchical Deep-Fusion Learning Framework for Lung Nodule Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:8949. [PMID: 36433541 PMCID: PMC9697252 DOI: 10.3390/s22228949] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 06/16/2023]
Abstract
Lung cancer is the leading cancer type that causes mortality in both men and women. Computer-aided detection (CAD) and diagnosis systems can play a very important role for helping physicians with cancer treatments. This study proposes a hierarchical deep-fusion learning scheme in a CAD framework for the detection of nodules from computed tomography (CT) scans. In the proposed hierarchical approach, a decision is made at each level individually employing the decisions from the previous level. Further, individual decisions are computed for several perspectives of a volume of interest. This study explores three different approaches to obtain decisions in a hierarchical fashion. The first model utilizes raw images. The second model uses a single type of feature image having salient content. The last model employs multi-type feature images. All models learn the parameters by means of supervised learning. The proposed CAD frameworks are tested using lung CT scans from the LIDC/IDRI database. The experimental results showed that the proposed multi-perspective hierarchical fusion approach significantly improves the performance of the classification. The proposed hierarchical deep-fusion learning model achieved a sensitivity of 95% with only 0.4 fp/scan.
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Affiliation(s)
- Kazim Sekeroglu
- Department of Computer Science, Southeastern Louisiana University, Hammond, LA 70402, USA
| | - Ömer Muhammet Soysal
- Department of Computer Science, Southeastern Louisiana University, Hammond, LA 70402, USA
- School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA 70803, USA
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11
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Artificial Intelligence (AI) for Lung Nodules, From the AJR Special Series on AI Applications. AJR Am J Roentgenol 2022; 219:703-712. [PMID: 35544377 DOI: 10.2214/ajr.22.27487] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Interest in artificial intelligence (AI) applications for lung nodules continues to grow among radiologists, particularly with the expanding eligibility criteria and clinical utilization of lung cancer screening CT. AI has been heavily investigated for detecting and characterizing lung nodules and for guiding prognostic assessment. AI tools have also been used for image postprocessing (e.g., rib suppression on radiography or vessel suppression on CT) and for noninterpretive aspects of reporting and workflow, including management of nodule follow-up. Despite growing interest in and rapid development of AI tools and FDA approval of AI tools for pulmonary nodule evaluation, integration into clinical practice has been limited. Challenges to clinical adoption have included concerns about generalizability, regulatory issues, technical hurdles in implementation, and human skepticism. Further validation of AI tools for clinical use and demonstration of benefit in terms of patient-oriented outcomes also are needed. This article provides an overview of potential applications of AI tools in the imaging evaluation of lung nodules and discusses the challenges faced by practices interested in clinical implementation of such tools.
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Mumuni AN, Hasford F, Udeme NI, Dada MO, Awojoyogbe BO. A SWOT analysis of artificial intelligence in diagnostic imaging in the developing world: making a case for a paradigm shift. PHYSICAL SCIENCES REVIEWS 2022. [DOI: 10.1515/psr-2022-0121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Abstract
Diagnostic imaging (DI) refers to techniques and methods of creating images of the body’s internal parts and organs with or without the use of ionizing radiation, for purposes of diagnosing, monitoring and characterizing diseases. By default, DI equipment are technology based and in recent times, there has been widespread automation of DI operations in high-income countries while low and middle-income countries (LMICs) are yet to gain traction in automated DI. Advanced DI techniques employ artificial intelligence (AI) protocols to enable imaging equipment perceive data more accurately than humans do, and yet automatically or under expert evaluation, make clinical decisions such as diagnosis and characterization of diseases. In this narrative review, SWOT analysis is used to examine the strengths, weaknesses, opportunities and threats associated with the deployment of AI-based DI protocols in LMICs. Drawing from this analysis, a case is then made to justify the need for widespread AI applications in DI in resource-poor settings. Among other strengths discussed, AI-based DI systems could enhance accuracies in diagnosis, monitoring, characterization of diseases and offer efficient image acquisition, processing, segmentation and analysis procedures, but may have weaknesses regarding the need for big data, huge initial and maintenance costs, and inadequate technical expertise of professionals. They present opportunities for synthetic modality transfer, increased access to imaging services, and protocol optimization; and threats of input training data biases, lack of regulatory frameworks and perceived fear of job losses among DI professionals. The analysis showed that successful integration of AI in DI procedures could position LMICs towards achievement of universal health coverage by 2030/2035. LMICs will however have to learn from the experiences of advanced settings, train critical staff in relevant areas of AI and proceed to develop in-house AI systems with all relevant stakeholders onboard.
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Affiliation(s)
| | - Francis Hasford
- Department of Medical Physics , University of Ghana, Ghana Atomic Energy Commission , Accra , Ghana
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13
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Lee JJB, Suh YJ, Oh C, Lee BM, Kim JS, Chang Y, Jeon YJ, Kim JY, Park SY, Chang JS. Automated Computer-aided Detection of Lung Nodules in Metastatic Colorectal Cancer Patients for the Identification of Pulmonary Oligometastatic Disease. Int J Radiat Oncol Biol Phys 2022; 114:1045-1052. [PMID: 36028066 DOI: 10.1016/j.ijrobp.2022.08.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 08/09/2022] [Accepted: 08/13/2022] [Indexed: 11/26/2022]
Abstract
PURPOSE This study aims to explore the possibility and clinical utility of existing artificial intelligence (AI)-based computer-aided detection (CAD) of lung nodules to identify pulmonary oligometastases. PATIENTS AND METHODS The chest computed tomography (CT) scans of patients with lung metastasis from colorectal cancer between March 2006 and November 2018 were analyzed. The patients were selected from a database of 1,395 patients and studied in two cohorts. The first cohort included 50 patients and the CT scans of these patients were independently evaluated for lung nodule (≥3 mm) detection by a CAD-assisted radiation oncologist (CAD-RO) as well as an expert radiologist. Inter-observer variability in additional two radiation oncologists and two thoracic surgeons was also measured. In the second cohort of 305 patients, survival outcomes were evaluated based on the number of CAD-RO-detected nodules. RESULTS In the first cohort, the sensitivity and specificity of the CAD-RO for the identification of oligometastatic disease (OMD) from varying criteria by ≤2 nodules, ≤3 nodules, ≤4 nodules, and ≤5 nodules were 71.9% and 88.9%; 82.9% and 93.3%; 97.1% and 73.3%; and 97.5% and 90.0%, respectively. The sensitivity of the CAD-RO in the nodule detection compared with the radiologist was 81.6%. The average (standard deviation) sensitivity in inter-observer variability analysis was 80.0% (3.7%). In the second cohort, the 5-year survival rates of patients with 1, 2, 3, 4, or ≥5 metastatic nodules were 75.2%, 52.9%, 45.7%, 29.1%, and 22.7%, respectively. CONCLUSIONS Proper identification of the pulmonary OMD and the correlation between the number of CAD-RO-detected nodules and survival suggest the potential practicality of AI in OMD recognition. Developing a deep learning-based model specific to the metastatic setting, which enables a quick estimation of disease burden and identification of OMD, is underway.
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Affiliation(s)
- Jason Joon Bock Lee
- Deparment of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Korea; Department of Radiation Oncology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Young Joo Suh
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Caleb Oh
- Deparment of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Korea
| | - Byung Min Lee
- Deparment of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Korea
| | - Jin Sung Kim
- Deparment of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Korea
| | | | - Yeong Jeong Jeon
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Ji Young Kim
- Deparment of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Seong Yong Park
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea; Department of Thoracic and Cardiovascular Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
| | - Jee Suk Chang
- Deparment of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Korea.
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The Usefulness of Computer-Aided Detection of Brain Metastases on Contrast-Enhanced Computed Tomography Using Single-Shot Multibox Detector: Observer Performance Study. J Comput Assist Tomogr 2022; 46:786-791. [PMID: 35819922 DOI: 10.1097/rct.0000000000001339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE This study aimed to test the usefulness of computer-aided detection (CAD) for the detection of brain metastasis (BM) on contrast-enhanced computed tomography. METHODS The test data set included whole-brain axial contrast-enhanced computed tomography images of 25 cases with 62 BMs and 5 cases without BM. Six radiologists from 3 institutions with 2 to 4 years of experience independently reviewed the cases, both in conditions with and without CAD assistance. Sensitivity, positive predictive value, number of false positives, and reading time were compared between the conditions using paired t tests. Subanalysis was also performed for groups of lesions divided according to size. A P value <0.05 was considered statistically significant. RESULTS With CAD, sensitivity significantly increased from 80.4% to 83.9% (P = 0.04), whereas positive predictive value significantly decreased from 88.7% to 84.8% (P = 0.03). Reading time with and without CAD was 112 and 107 seconds, respectively (P = 0.38), and the number of false positives was 10.5 with CAD and 7.0 without CAD (P = 0.053). Sensitivity significantly improved for 6- to 12-mm lesions, from 71.2% without CAD to 80.3% with CAD (P = 0.02). The sensitivity of the CAD (95.2%) was significantly higher than that of any reader (with CAD: P = 0.01; without CAD: P = 0.005). CONCLUSIONS Computer-aided detection significantly improved BM detection sensitivity without prolonging reading time while marginally increased the false positives.
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15
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Au C, Reeves R, Li Z, Gingold E, Halpern E, Sundaram B. Impact of multidetector computed tomography scan parameters, novel reconstruction settings, and lung nodule characteristics on nodule diameter measurements: A Phantom Study. Med Phys 2022; 49:3936-3943. [PMID: 35358333 DOI: 10.1002/mp.15639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 03/09/2022] [Accepted: 03/18/2022] [Indexed: 11/12/2022] Open
Abstract
PURPOSE Novel CT reconstruction techniques strive to maintain image quality and processing efficiency. The purpose of this study is to investigate the impact of a newer hybrid iterative reconstruction technique, Adaptive Statistical Iterative Reconstruction-V (ASIR-V) in combination with various CT scan parameters on the semi-automated quantification using various lung nodules. METHODS A chest phantom embedded with eight spherical objects was scanned using varying CT parameters such as tube current and ASIR-V levels. We calculated absolute percentage error (APE) and mean APE (MAPE) using differences between the semi-automated measured diameters and known dimensions. Predictive variables were assessed using a multivariable general linear model. The linear regression slope coefficients (β) were reported to demonstrate effect size and directionality. RESULTS The APE of the semi-automated measured diameters was higher in ground-glass than solid nodules (β = 9.000, p<0.001). APE had an inverse relationship with nodule diameter (mm; β = -3.499, p<0.001) and tube current (mA; β = -0.006, p<0.001). MAPE did not vary based on the ASIR-V level (range: 5.7-13.1%). CONCLUSION Error is dominated by nodule characteristics with a small effect of tube current. Regardless of phantom size, nodule size accuracy is not affected by tube voltage or ASIR-V level, maintaining accuracy while maximizing radiation dose reduction. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Cherry Au
- Department of Internal Medicine, Rush University Medical Center, 1620 W Harrison St, Chicago, IL, 60612
| | - Russell Reeves
- Department of Radiology, Thomas Jefferson University Hospital, 111 S 11th St, Philadelphia, PA, 19107
| | - Zhenteng Li
- Department of Radiology, Thomas Jefferson University Hospital, 111 S 11th St, Philadelphia, PA, 19107.,The Vascular Center, St. Luke's Anderson Campus - Medical Office Building, 1700 St. Luke's Boulevard, Suite 301, Easton, PA
| | - Eric Gingold
- Department of Radiology, Thomas Jefferson University Hospital, 111 S 11th St, Philadelphia, PA, 19107
| | - Ethan Halpern
- Department of Radiology, Thomas Jefferson University Hospital, 111 S 11th St, Philadelphia, PA, 19107
| | - Baskaran Sundaram
- Department of Radiology, Thomas Jefferson University Hospital, 111 S 11th St, Philadelphia, PA, 19107
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16
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Jungblut L, Blüthgen C, Polacin M, Messerli M, Schmidt B, Euler A, Alkadhi H, Frauenfelder T, Martini K. First Performance Evaluation of an Artificial Intelligence-Based Computer-Aided Detection System for Pulmonary Nodule Evaluation in Dual-Source Photon-Counting Detector CT at Different Low-Dose Levels. Invest Radiol 2022; 57:108-114. [PMID: 34324462 DOI: 10.1097/rli.0000000000000814] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The aim of this study was to evaluate the image quality (IQ) and performance of an artificial intelligence (AI)-based computer-aided detection (CAD) system in photon-counting detector computed tomography (PCD-CT) for pulmonary nodule evaluation at different low-dose levels. MATERIALS AND METHODS An anthropomorphic chest-phantom containing 14 pulmonary nodules of different sizes (range, 3-12 mm) was imaged on a PCD-CT and on a conventional energy-integrating detector CT (EID-CT). Scans were performed with each of the 3 vendor-specific scanning modes (QuantumPlus [Q+], Quantum [Q], and High Resolution [HR]) at decreasing matched radiation dose levels (volume computed tomography dose index ranging from 1.79 to 0.31 mGy) by adapting IQ levels from 30 to 5. Image noise was measured manually in the chest wall at 8 different locations. Subjective IQ was evaluated by 2 readers in consensus. Nodule detection and volumetry were performed using a commercially available AI-CAD system. RESULTS Subjective IQ was superior in PCD-CT compared with EID-CT (P < 0.001), and objective image noise was similar in the Q+ and Q-mode (P > 0.05) and superior in the HR-mode (PCD 55.8 ± 11.7 HU vs EID 74.8 ± 5.4 HU; P = 0.01). High resolution showed the lowest image noise values among PCD modes (P = 0.01). Overall, the AI-CAD system delivered comparable results for lung nodule detection and volumetry between PCD- and dose-matched EID-CT (P = 0.08-1.00), with a mean sensitivity of 95% for PCD-CT and of 86% for dose-matched EID-CT in the lowest evaluated dose level (IQ5). Q+ and Q-mode showed higher false-positive rates than EID-CT at lower-dose levels (IQ10 and IQ5). The HR-mode showed a sensitivity of 100% with a false-positive rate of 1 even at the lowest evaluated dose level (IQ5; CDTIvol, 0.41 mGy). CONCLUSIONS Photon-counting detector CT was superior to dose-matched EID-CT in subjective IQ while showing comparable to lower objective image noise. Fully automatized AI-aided nodule detection and volumetry are feasible in PCD-CT, but attention has to be paid to false-positive findings.
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Affiliation(s)
- Lisa Jungblut
- From the Institute of Diagnostic and Interventional Radiology
| | | | | | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | | | - Andre Euler
- From the Institute of Diagnostic and Interventional Radiology
| | - Hatem Alkadhi
- From the Institute of Diagnostic and Interventional Radiology
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Kapoor R. Impact of newer technologies in cancer research and its management. INTERNATIONAL JOURNAL OF NONCOMMUNICABLE DISEASES 2022. [DOI: 10.4103/jncd.jncd_95_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
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18
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Fischer G, De Silvestro A, Müller M, Frauenfelder T, Martini K. Computer-Aided Detection of Seven Chest Pathologies on Standard Posteroanterior Chest X-Rays Compared to Radiologists Reading Dual-Energy Subtracted Radiographs. Acad Radiol 2021; 29:e139-e148. [PMID: 34706849 DOI: 10.1016/j.acra.2021.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 09/06/2021] [Accepted: 09/21/2021] [Indexed: 11/01/2022]
Abstract
RATIONALE AND OBJECTIVES Retrospective performance evaluation of a computer-aided detection (CAD) system on standard posteroanterior (PA) chest radiographs (PA-CXR) in detection of pulmonary nodules, infectious consolidation, pneumothorax, pleural effusion, aortic calcification, cardiomegaly and rib fractures compared to radiologists analyzing PA-CXR including dual-energy subtraction radiography (further termed as DESR). MATERIALS AND METHODS PA-CXR/DESR images of 197 patients were included. All patients underwent chest CT (gold standard) within a short interval (mean 28 hours). All images were evaluated by three blinded readers for the presence of pulmonary nodules, infectious consolidation, pneumothorax, pleural effusion, aortic calcification, cardiomegaly, and rib fractures. Meanwhile PA-CXR were analyzed by a CAD software. CAD results were compared to the majority result of the three readers. Sensitivity and specificity were calculated. McNemar's test was applied to test for significant differences. Interobserver agreement was defined using Cohen's kappa (κ). RESULTS Sensitivity of the CAD software was significantly higher (p < 0.05) for detection of infectious consolidation and pulmonary nodules (67.9% vs 26.8% and 54% vs 35.6%, respectively; p < 0.001) compared to radiologists analyzing DESR images. For the residual evaluated pathologies no statistical significant differences could be found. Overall, mean inter observer agreement between the three radiologists was moderate (k = 0.534). The best interobserver agreement could be reached for pneumothorax (k = 0.708) and pleural effusion (k = 0.699), while the worst was obtained for rib fractures (k = 0.412). CONCLUSION The CAD system has the potential to improve the detection of infectious consolidation and pulmonary nodules on CXR images.
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Impact of Vessel Suppressed-CT on Diagnostic Accuracy in Detection of Pulmonary Metastasis and Reading Time. Acad Radiol 2021; 28:988-994. [PMID: 32037256 DOI: 10.1016/j.acra.2020.01.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 01/09/2020] [Accepted: 01/09/2020] [Indexed: 11/20/2022]
Abstract
RATIONALE AND OBJECTIVES To assess if vessel suppression (VS) improves nodule detection rate, interreader agreement, and reduces reading time in oncologic chest computed tomography (CT). MATERIAL AND METHODS One-hundred consecutive oncologic patients (65 male; median age 60y) who underwent contrast-enhanced chest CT were retrospectively included. For all exams, additional VS series (ClearRead CT, Riverrain Technologies, Miamisburg) were reconstructed. Two groups of three radiologists each with matched experience were defined. Each group evaluated the SD-CT as well as VS-CT. Each reader marked the presence, size, and position of pulmonary nodules and documented reading time. In addition, for the VS-CT the presence of false positive nodules had to be stated. Cohen's Kappa (k) was used to calculate the interreader-agreement between groups. Reading time was compared using paired t test. RESULTS Nodule detection rate was significantly higher in VS-CT compared to the SD-CT (+21%; p <0.001). Interreader-agreement was higher in the VS-CT (k = 0.431, moderate agreement) compared to SD-CT (k = 0.209, fair agreement). Almost all VS-CT series had false positive findings (97-99 out of 100). Average reading time was significantly shorter in the VS-CT compared to the SD-CT (154 ± 134vs. 194 ± 126; 21%, p<0.001). CONCLUSIONS Vessel suppression increases nodule detection rate, improves interreader agreement, and reduces reading time in chest CT of oncologic patients. Due to false positive results a consensus reading with the SD-CT is essential.
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Aoki T, Kamiya T, Lu H, Terasawa T, Ueno M, Hayashida Y, Murakami S, Korogi Y. CT temporal subtraction: techniques and clinical applications. Quant Imaging Med Surg 2021; 11:2214-2223. [PMID: 34079696 DOI: 10.21037/qims-20-1367] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Takatoshi Aoki
- Department of Radiology, University of Occupational and Environmental Health School of Medicine, Kitakyushu, Japan
| | - Tohru Kamiya
- Graduate School of Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
| | - Huimin Lu
- Graduate School of Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
| | - Takashi Terasawa
- Department of Radiology, University of Occupational and Environmental Health School of Medicine, Kitakyushu, Japan
| | - Midori Ueno
- Department of Radiology, University of Occupational and Environmental Health School of Medicine, Kitakyushu, Japan
| | - Yoshiko Hayashida
- Department of Radiology, University of Occupational and Environmental Health School of Medicine, Kitakyushu, Japan
| | - Seiichi Murakami
- Department of Radiological Science, Faculty of Health Sciences, Junshin Gakuen University, Fukuoka, Japan
| | - Yukunori Korogi
- Department of Radiology, University of Occupational and Environmental Health School of Medicine, Kitakyushu, Japan
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21
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Hsu HH, Ko KH, Chou YC, Wu YC, Chiu SH, Chang CK, Chang WC. Performance and reading time of lung nodule identification on multidetector CT with or without an artificial intelligence-powered computer-aided detection system. Clin Radiol 2021; 76:626.e23-626.e32. [PMID: 34023068 DOI: 10.1016/j.crad.2021.04.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 04/15/2021] [Indexed: 10/21/2022]
Abstract
AIM To compare the performance and reading time of different readers using automatic artificial intelligence (AI)-powered computer-aided detection (CAD) to detect lung nodules in different reading modes. MATERIALS AND METHODS One hundred and fifty multidetector computed tomography (CT) datasets containing 340 nodules ≤10 mm in diameter were collected retrospectively. A CAD with vessel-suppressed function was used to interpret the images. Three junior and three senior readers were assigned to read (1) CT images without CAD, (2) second-read using CAD in which CAD was applied only after initial unassisted assessment, and (3) a concurrent read with CAD in which CAD was applied at the start of assessment. Diagnostic performances and reading times were compared using analysis of variance. RESULTS For all readers, the mean sensitivity improved from 64% (95% confidence interval [CI]: 62%, 66%) for the without-CAD mode to 82% (95% CI: 80%, 84%) for the second-reading mode and to 80% (95% CI: 79%, 82%) for the concurrent-reading mode (p<0.001). There was no significant difference between the two modes in terms of the mean sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for both junior and senior readers and all readers (p>0.05). The reading time of all readers was significantly shorter for the concurrent-reading mode (124 ± 25 seconds) compared to without CAD (156 ± 34 seconds; p<0.001) and the second-reading mode (197 ± 46 seconds; p<0.001). CONCLUSION In CAD for lung nodules at CT, the second-reading mode and concurrent-reading mode may improve detection performance for all readers in both screening and clinical routine practice. Concurrent use of CAD is more efficient for both junior and senior readers.
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Affiliation(s)
- H-H Hsu
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
| | - K-H Ko
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Y-C Chou
- School of Public Health, National Defense Medical Center, Taipei, Taiwan
| | - Y-C Wu
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - S-H Chiu
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - C-K Chang
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - W-C Chang
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
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22
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Prospective Study of Spatial Distribution of Missed Lung Nodules by Readers in CT Lung Screening Using Computer-assisted Detection. Acad Radiol 2021; 28:647-654. [PMID: 32305166 DOI: 10.1016/j.acra.2020.03.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 02/21/2020] [Accepted: 03/09/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE To evaluate the spatial patterns of missed lung nodules in a real-life routine screening environment. MATERIALS AND METHODS In a screening institute, 4,822 consecutive adults underwent chest CT, and each image set was independently interpreted by two radiologists in three steps: (1) independently interpreted without computer-assisted detection (CAD) software, (2) independently referred to the CAD results, (3) determined by the consensus of the two radiologists. The locations of nodules and the detection performance data were semi-automatically collected using a CAD server integrated into the reporting system. Fisher's exact test was employed for evaluating findings in different lung divisions. Probability maps were drawn to illustrate the spatial distribution of radiologists' missed nodules. RESULTS Radiologists significantly tended to miss lung nodules in the bilateral hilar divisions (p < 0.01). Some radiologists had their own spatial pattern of missed lung nodules. CONCLUSION Radiologists tend to miss lung nodules present in the hilar regions significantly more often than in the rest of the lung.
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23
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Peters AA, Decasper A, Munz J, Klaus J, Loebelenz LI, Hoffner MKM, Hourscht C, Heverhagen JT, Christe A, Ebner L. Performance of an AI based CAD system in solid lung nodule detection on chest phantom radiographs compared to radiology residents and fellow radiologists. J Thorac Dis 2021; 13:2728-2737. [PMID: 34164165 PMCID: PMC8182550 DOI: 10.21037/jtd-20-3522] [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] [Indexed: 01/22/2023]
Abstract
Background Despite the decreasing relevance of chest radiography in lung cancer screening, chest radiography is still frequently applied to assess for lung nodules. The aim of the current study was to determine the accuracy of a commercial AI based CAD system for the detection of artificial lung nodules on chest radiograph phantoms and compare the performance to radiologists in training. Methods Sixty-one anthropomorphic lung phantoms were equipped with 140 randomly deployed artificial lung nodules (5, 8, 10, 12 mm). A random generator chose nodule size and distribution before a two-plane chest X-ray (CXR) of each phantom was performed. Seven blinded radiologists in training (2 fellows, 5 residents) with 2 to 5 years of experience in chest imaging read the CXRs on a PACS-workstation independently. Results of the software were recorded separately. McNemar test was used to compare each radiologist’s results to the AI-computer-aided-diagnostic (CAD) software in a per-nodule and a per-phantom approach and Fleiss-Kappa was applied for inter-rater and intra-observer agreements. Results Five out of seven readers showed a significantly higher accuracy than the AI algorithm. The pooled accuracies of the radiologists in a nodule-based and a phantom-based approach were 0.59 and 0.82 respectively, whereas the AI-CAD showed accuracies of 0.47 and 0.67, respectively. Radiologists’ average sensitivity for 10 and 12 mm nodules was 0.80 and dropped to 0.66 for 8 mm (P=0.04) and 0.14 for 5 mm nodules (P<0.001). The radiologists and the algorithm both demonstrated a significant higher sensitivity for peripheral compared to central nodules (0.66 vs. 0.48; P=0.004 and 0.64 vs. 0.094; P=0.025, respectively). Inter-rater agreements were moderate among the radiologists and between radiologists and AI-CAD software (K’=0.58±0.13 and 0.51±0.1). Intra-observer agreement was calculated for two readers and was almost perfect for the phantom-based (K’=0.85±0.05; K’=0.80±0.02); and substantial to almost perfect for the nodule-based approach (K’=0.83±0.02; K’=0.78±0.02). Conclusions The AI based CAD system as a primary reader acts inferior to radiologists regarding lung nodule detection in chest phantoms. Chest radiography has reasonable accuracy in lung nodule detection if read by a radiologist alone and may be further optimized by an AI based CAD system as a second reader.
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Affiliation(s)
- Alan A Peters
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Amanda Decasper
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Jaro Munz
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Jeremias Klaus
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Laura I Loebelenz
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Maximilian Korbinian Michael Hoffner
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Cynthia Hourscht
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Johannes T Heverhagen
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.,Department of BioMedical Research, Experimental Radiology, University of Bern, Bern, Switzerland.,Department of Radiology, The Ohio State University, Columbus, OH, USA
| | - Andreas Christe
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Lukas Ebner
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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Martini K, Blüthgen C, Walter JE, Messerli M, Nguyen-Kim TDL, Frauenfelder T. Accuracy of Conventional and Machine Learning Enhanced Chest Radiography for the Assessment of COVID-19 Pneumonia: Intra-Individual Comparison with CT. J Clin Med 2020; 9:jcm9113576. [PMID: 33171999 PMCID: PMC7694629 DOI: 10.3390/jcm9113576] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 10/20/2020] [Accepted: 11/02/2020] [Indexed: 12/11/2022] Open
Abstract
PURPOSE To evaluate diagnostic accuracy of conventional radiography (CXR) and machine learning enhanced CXR (mlCXR) for the detection and quantification of disease-extent in COVID-19 patients compared to chest-CT. METHODS Real-time polymerase chain reaction (rt-PCR)-confirmed COVID-19-patients undergoing CXR from March to April 2020 together with COVID-19 negative patients as control group were retrospectively included. Two independent readers assessed CXR and mlCXR images for presence, disease extent and type (consolidation vs. ground-glass opacities (GGOs) of COVID-19-pneumonia. Further, readers had to assign confidence levels to their diagnosis. CT obtained ≤ 36 h from acquisition of CXR served as standard of reference. Inter-reader agreement, sensitivity for detection and disease extent of COVID-19-pneumonia compared to CT was calculated. McNemar test was used to test for significant differences. RESULTS Sixty patients (21 females; median age 61 years, range 38-81 years) were included. Inter-reader agreement improved from good to excellent when mlCXR instead of CXR was used (k = 0.831 vs. k = 0.742). Sensitivity for pneumonia detection improved from 79.5% to 92.3%, however, on the cost of specificity 100% vs. 71.4% (p = 0.031). Overall, sensitivity for the detection of consolidation was higher than for GGO (37.5% vs. 70.4%; respectively). No differences could be found in disease extent estimation between mlCXR and CXR, even though the detection of GGO could be improved. Diagnostic confidence was better on mlCXR compared to CXR (p = 0.013). CONCLUSION In line with the current literature, the sensitivity for detection and quantification of COVID-19-pneumonia was moderate with CXR and could be improved when mlCXR was used for image interpretation.
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Affiliation(s)
- Katharina Martini
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (C.B.); (J.E.W.); (T.D.L.N.-K.); (T.F.)
- Correspondence: ; Tel.: +41-43-253-11-11
| | - Christian Blüthgen
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (C.B.); (J.E.W.); (T.D.L.N.-K.); (T.F.)
| | - Joan E. Walter
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (C.B.); (J.E.W.); (T.D.L.N.-K.); (T.F.)
| | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland;
| | - Thi Dan Linh Nguyen-Kim
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (C.B.); (J.E.W.); (T.D.L.N.-K.); (T.F.)
| | - Thomas Frauenfelder
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (C.B.); (J.E.W.); (T.D.L.N.-K.); (T.F.)
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Sathyakumar K, Munoz M, Singh J, Hussain N, Babu BA. Automated Lung Cancer Detection Using Artificial Intelligence (AI) Deep Convolutional Neural Networks: A Narrative Literature Review. Cureus 2020; 12:e10017. [PMID: 32989411 PMCID: PMC7518939 DOI: 10.7759/cureus.10017] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Lung cancer is the number one cause of cancer-related deaths in the United States as well as worldwide. Radiologists and physicians experience heavy daily workloads, thus are at high risk for burn-out. To alleviate this burden, this narrative literature review compares the performance of four different artificial intelligence (AI) models in lung nodule cancer detection, as well as their performance to physicians/radiologists reading accuracy. A total of 648 articles were selected by two experienced physicians with over 10 years of experience in the fields of pulmonary critical care, and hospital medicine. The data bases used to search and select the articles are PubMed/MEDLINE, EMBASE, Cochrane library, Google Scholar, Web of science, IEEEXplore, and DBLP. The articles selected range from the years between 2008 and 2019. Four out of 648 articles were selected using the following inclusion criteria: 1) 18-65 years old, 2) CT chest scans, 2) lung nodule, 3) lung cancer, 3) deep learning, 4) ensemble and 5) classic methods. The exclusion criteria used in this narrative review include: 1) age greater than 65 years old, 2) positron emission tomography (PET) hybrid scans, 3) chest X-ray (CXR) and 4) genomics. The model performance outcomes metrics are measured and evaluated in sensitivity, specificity, accuracy, receiver operator characteristic (ROC) curve, and the area under the curve (AUC). This hybrid deep-learning model is a state-of-the-art architecture, with high-performance accuracy and low false-positive results. Future studies, comparing each model accuracy at depth is key. Automated physician-assist systems as this model in this review article help preserve a quality doctor-patient relationship.
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Affiliation(s)
- Kaviya Sathyakumar
- Family Medicine, University of Florida College of Medicine, Gainesville, USA
| | - Michael Munoz
- Pediatrics, Monmouth Medical Center, Long Branch, USA
| | - Jaikaran Singh
- Internal Medicine, Saint John Regional Hospital, Saint John, CAN
| | - Nowair Hussain
- Internal Medicine, Ross University School of Medicine, Bridgetown, BRB
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26
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Liew CJY, Leong LCH, Teo LLS, Ong CC, Cheah FK, Tham WP, Salahudeen HMM, Lee CH, Kaw GJL, Tee AKH, Tsou IYY, Tay KH, Quah R, Tan BP, Chou H, Tan D, Poh ACC, Tan AGS. A practical and adaptive approach to lung cancer screening: a review of international evidence and position on CT lung cancer screening in the Singaporean population by the College of Radiologists Singapore. Singapore Med J 2020; 60:554-559. [PMID: 31781779 DOI: 10.11622/smedj.2019145] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Lung cancer is the leading cause of cancer-related death around the world, being the top cause of cancer-related deaths among men and the second most common cause of cancer-related deaths among women in Singapore. Currently, no screening programme for lung cancer exists in Singapore. Since there is mounting evidence indicating a different epidemiology of lung cancer in Asian countries, including Singapore, compared to the rest of the world, a unique and adaptive approach must be taken for a screening programme to be successful at reducing mortality while maintaining cost-effectiveness and a favourable risk-benefit ratio. This review article promotes the use of low-dose computed tomography of the chest and explores the radiological challenges and future directions.
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Affiliation(s)
| | | | - Lynette Li San Teo
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | - Ching Ching Ong
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | - Foong Koon Cheah
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore
| | - Wei Ping Tham
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore
| | | | - Chau Hung Lee
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore
| | | | - Augustine Kim Huat Tee
- Department of Respiratory and Critical Care Medicine, Changi General Hospital, Singapore
| | - Ian Yu Yan Tsou
- Department of Diagnostic Radiology, Mount Elizabeth Hospital, Singapore
| | - Kiang Hiong Tay
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore
| | - Raymond Quah
- Department of Diagnostic Radiology, Farrer Park Hospital, Singapore
| | - Bien Peng Tan
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore
| | - Hong Chou
- Department of Diagnostic Radiology, Khoo Teck Puat Hospital, Singapore
| | - Daniel Tan
- Department of Diagnostic Radiology Oncology, Farrer Park Hospital, Singapore
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27
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Walker E, Liu Y, Kim I, Biro M, Iyer SR, Ezaldein H, Scott J, Merati M, Mistur R, Zhou B, Straight B, Yim JJ, Bogyo M, Mann M, Wilson DL, Basilion JP, Popkin DL. A Protease-Activated Fluorescent Probe Allows Rapid Visualization of Keratinocyte Carcinoma during Excision. Cancer Res 2020; 80:2045-2055. [PMID: 32132111 DOI: 10.1158/0008-5472.can-19-3067] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 01/10/2020] [Accepted: 02/24/2020] [Indexed: 12/26/2022]
Abstract
Keratinocyte carcinomas, including basal and squamous cell carcinomas, are the most common human cancers worldwide. While 75% of all keratinocyte carcinoma (4 million annual cases in the United States) are treated with conventional excision, this surgical modality has much lower cure rates than Mohs micrographic surgery, likely due to the bread-loaf histopathologic assessment that visualizes <1% of the tissue margins. A quenched protease-activated fluorescent probe 6qcNIR, which produces a signal only in the protease-rich tumor microenvironment, was topically applied to 90 specimens ex vivo immediately following excision. "Puzzle-fit" analysis was used to correlate the fluorescent images with histology. Probe-dependent fluorescent images correlated with cancer determined by conventional histology. Point-of-care fluorescent detection of skin cancer had a clinically relevant sensitivity of 0.73 and corresponding specificity of 0.88. Importantly, clinicians were effectively trained to read fluorescent images within 15 minutes with reliability and confidence, resulting in sensitivities of 62%-78% and specificities of 92%-97%. Fluorescent imaging using 6qcNIR allows 100% tumor margin assessment by generating en face images that correlate with histology and may be used to overcome the limitations of conventional bread-loaf histology. The utility of 6qcNIR was validated in a busy real-world clinical setting, and clinicians were trained to effectively read fluorescent margins with a short guided instruction, highlighting clinical adaptability. When used in conventional excision, this approach may result in higher cure rates at a lower cost by allowing same-day reexcision when needed, reducing patient anxiety and improving compliance by expediting postsurgical specimen assessment. SIGNIFICANCE: A fluorescent-probe-tumor-visualization platform was developed and validated in human keratinocyte carcinoma excision specimens that may provide simple, rapid, and global assessment of margins during skin cancer excision, allowing same-day reexcision when needed.
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Affiliation(s)
- Ethan Walker
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Yiqiao Liu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - InYoung Kim
- Department of Dermatology, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, Ohio.,Department of Dermatology, Case Western Reserve University, Cleveland, Ohio
| | - Mark Biro
- Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Sukanya Raj Iyer
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Harib Ezaldein
- Department of Dermatology, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, Ohio.,Department of Dermatology, Case Western Reserve University, Cleveland, Ohio
| | - Jeffrey Scott
- Department of Dermatology, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, Ohio.,Department of Dermatology, Case Western Reserve University, Cleveland, Ohio
| | - Miesha Merati
- Department of Dermatology, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, Ohio.,Department of Dermatology, Case Western Reserve University, Cleveland, Ohio
| | - Rachel Mistur
- Department of Dermatology, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, Ohio.,Department of Dermatology, Case Western Reserve University, Cleveland, Ohio
| | - Bo Zhou
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | | | - Joshua J Yim
- Chemical and Systems Biology, Stanford University, Palo Alto, California
| | - Matthew Bogyo
- Chemical and Systems Biology, Stanford University, Palo Alto, California.,Department of Pathology, Stanford University, Palo Alto, California.,Department of Microbiology and Immunology, Stanford University, Palo Alto, California
| | - Margaret Mann
- Department of Dermatology, Case Western Reserve University, Cleveland, Ohio
| | - David L Wilson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.,Department of Radiology, Case Western Reserve University, Cleveland, Ohio
| | - James P Basilion
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio. .,Department of Radiology, Case Western Reserve University, Cleveland, Ohio.,Fellow, National Foundation for Cancer Research, Case Western Reserve University, Cleveland, Ohio
| | - Daniel L Popkin
- Department of Dermatology, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, Ohio. .,Department of Dermatology, Case Western Reserve University, Cleveland, Ohio
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28
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Li Q, Li QC, Cao RT, Wang X, Chen RT, Liu K, Fan L, Xiao Y, Zhang ZT, Fu CC, Song Q, Liu W, Fang Q, Liu SY. Detectability of pulmonary nodules by deep learning: results from a phantom study. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/s42058-019-00015-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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29
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Lee LIT, Kanthasamy S, Ayyalaraju RS, Ganatra R. The Current State of Artificial Intelligence in Medical Imaging and Nuclear Medicine. BJR Open 2019; 1:20190037. [PMID: 33178956 PMCID: PMC7592467 DOI: 10.1259/bjro.20190037] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 09/08/2019] [Accepted: 09/25/2019] [Indexed: 12/31/2022] Open
Abstract
The last decade has seen a huge surge in interest surrounding artificial intelligence (AI). AI has been around since the 1950s, although technological limitations in the early days meant performance was initially inferior compared to humans.1 With rapid progression of algorithm design, growth of vast digital datasets and development of powerful computing power, AI now has the capability to outperform humans. Consequently, the integration of AI into the modern world is skyrocketing. This review article will give an overview of the use of AI in the modern world and discuss current and potential uses in healthcare, with a particular focus on its applications and likely impact in medical imaging. We will discuss the consequences and challenges of AI integration into healthcare.
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Affiliation(s)
- Louise I T Lee
- Department of Radiology, University Hospitals of Leicester, Leicester Royal Infirmary, England, UK
| | - Senthooran Kanthasamy
- Department of Trauma and Orthopaedics, Cambridge University Hospitals, Cambridge University Hospitals, Addenbrooke's Hospital, England, UK
| | | | - Rakesh Ganatra
- Department of Nuclear Medicine, University Hospitals of Leicester, Glenfield Hospital, England, UK
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30
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Wood DE, Kazerooni EA, Baum SL, Eapen GA, Ettinger DS, Hou L, Jackman DM, Klippenstein D, Kumar R, Lackner RP, Leard LE, Lennes IT, Leung ANC, Makani SS, Massion PP, Mazzone P, Merritt RE, Meyers BF, Midthun DE, Pipavath S, Pratt C, Reddy C, Reid ME, Rotter AJ, Sachs PB, Schabath MB, Schiebler ML, Tong BC, Travis WD, Wei B, Yang SC, Gregory KM, Hughes M. Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2019; 16:412-441. [PMID: 29632061 DOI: 10.6004/jnccn.2018.0020] [Citation(s) in RCA: 383] [Impact Index Per Article: 76.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Lung cancer is the leading cause of cancer-related mortality in the United States and worldwide. Early detection of lung cancer is an important opportunity for decreasing mortality. Data support using low-dose computed tomography (LDCT) of the chest to screen select patients who are at high risk for lung cancer. Lung screening is covered under the Affordable Care Act for individuals with high-risk factors. The Centers for Medicare & Medicaid Services (CMS) covers annual screening LDCT for appropriate Medicare beneficiaries at high risk for lung cancer if they also receive counseling and participate in shared decision-making before screening. The complete version of the NCCN Guidelines for Lung Cancer Screening provides recommendations for initial and subsequent LDCT screening and provides more detail about LDCT screening. This manuscript focuses on identifying patients at high risk for lung cancer who are candidates for LDCT of the chest and on evaluating initial screening findings.
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31
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Lung Nodule: Imaging Features and Evaluation in the Age of Machine Learning. CURRENT PULMONOLOGY REPORTS 2019. [DOI: 10.1007/s13665-019-00229-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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32
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Shaukat F, Raja G, Frangi AF. Computer-aided detection of lung nodules: a review. J Med Imaging (Bellingham) 2019. [DOI: 10.1117/1.jmi.6.2.020901] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Furqan Shaukat
- University of Engineering and Technology, Department of Electrical Engineering, Taxila
| | - Gulistan Raja
- University of Engineering and Technology, Department of Electrical Engineering, Taxila
| | - Alejandro F. Frangi
- University of Leeds Woodhouse Lane, School of Computing and School of Medicine, Leeds
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33
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Li X, Guo F, Zhou Z, Zhang F, Wang Q, Peng Z, Su D, Fan Y, Wang Y. [Performance of Deep-learning-based Artificial Intelligence on Detection of Pulmonary Nodules in Chest CT]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2019; 22:336-340. [PMID: 31196366 PMCID: PMC6580084 DOI: 10.3779/j.issn.1009-3419.2019.06.02] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
背景与目的 肺结节精确检测是实现肺癌早诊的基础。基于深度学习的人工智能在肺内结节检测领域发展迅速,对其效能进行验证是促进其应用于临床的前提。本研究旨在评估基于深度学习技术的人工智能软件在胸部计算机断层扫描(computed tomography, CT)恶性及非钙化结节检出中的价值。 方法 由天津医科大学总医院自建胸部CT肺结节数据库中随机抽取200例胸部CT数据,包含病理证实的肺癌及随访结节病例,导入肺结节人工智能识别系统,记录软件自动识别结节,并与原始影像报告结果进行对比。人工智能软件及阅片者检测到的结节由2名胸部专家进行评估并记录其大小及特征。计算灵敏度、假阳性率评估人工智能软件及医师的结节检测效能,应用McNemar检验确定二者之间是否存在显著性差异。 结果 200例胸部多层螺旋CT共包含非钙化结节889枚,其中肺癌结节133枚,小于5 mm结节442枚。人工智能及放射科医师肺癌检出率皆为100%。人工智能软件结节检测灵敏度明显高于放射科医师(99.1% vs 43%, P < 0.001)。人工智能总体假阳性率为每例CT 4.9个,排除5 mm以下结节后降为1.5个。 结论 基于深度学习的人工智能软件能实现恶性肺结节的无漏诊检出,具有较医师更高的结节检出灵敏度,在排除微小结节后可降低假阳性率。
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Affiliation(s)
- Xinling Li
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Fangfang Guo
- Department of Radiology, the First Affiliated Hospital of XinXiang Medical College, Xinxiang 453100, China
| | - Zhen Zhou
- Deepwise Healthcare, Beijing 100080, China
| | | | - Qin Wang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Zhijun Peng
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Datong Su
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yaguang Fan
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Ying Wang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
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Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, Tse D, Etemadi M, Ye W, Corrado G, Naidich DP, Shetty S. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 2019; 25:954-961. [PMID: 31110349 DOI: 10.1038/s41591-019-0447-x] [Citation(s) in RCA: 809] [Impact Index Per Article: 161.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 04/05/2019] [Indexed: 11/09/2022]
Abstract
With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States1. Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20-43% and is now included in US screening guidelines1-6. Existing challenges include inter-grader variability and high false-positive and false-negative rates7-10. We propose a deep learning algorithm that uses a patient's current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the-art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases. We conducted two reader studies. When prior computed tomography imaging was not available, our model outperformed all six radiologists with absolute reductions of 11% in false positives and 5% in false negatives. Where prior computed tomography imaging was available, the model performance was on-par with the same radiologists. This creates an opportunity to optimize the screening process via computer assistance and automation. While the vast majority of patients remain unscreened, we show the potential for deep learning models to increase the accuracy, consistency and adoption of lung cancer screening worldwide.
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Affiliation(s)
| | | | | | | | - Joshua J Reicher
- Stanford Health Care and Palo Alto Veterans Affairs, Palo Alto, CA, USA
| | | | | | | | | | | | - David P Naidich
- New York University-Langone Medical Center, Center for Biological Imaging, New York City, NY, USA
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35
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Governance of automated image analysis and artificial intelligence analytics in healthcare. Clin Radiol 2019; 74:329-337. [PMID: 30898383 DOI: 10.1016/j.crad.2019.02.005] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Accepted: 02/22/2019] [Indexed: 12/29/2022]
Abstract
The hype over artificial intelligence (AI) has spawned claims that clinicians (particularly radiologists) will become redundant. It is still moot as to whether AI will replace radiologists in day-to-day clinical practice, but more AI applications are expected to be incorporated into the workflows in the foreseeable future. These applications could produce significant ethical and legal issues in healthcare if they cause abrupt disruptions to its contextual integrity and relational dynamics. Sustaining trust and trustworthiness is a key goal of governance, which is necessary to promote collaboration among all stakeholders and to ensure the responsible development and implementation of AI in radiology and other areas of clinical work. In this paper, the nature of AI governance in biomedicine is discussed along with its limitations. It is argued that radiologists must assume a more active role in propelling medicine into the digital age. In this respect, professional responsibilities include inquiring into the clinical and social value of AI, alleviating deficiencies in technical knowledge in order to facilitate ethical evaluation, supporting the recognition, and removal of biases, engaging the "black box" obstacle, and brokering a new social contract on informational use and security. In essence, a much closer integration of ethics, laws, and good practices is needed to ensure that AI governance achieves its normative goals.
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The Added Value of Computer-aided Detection of Small Pulmonary Nodules and Missed Lung Cancers. J Thorac Imaging 2019; 33:390-395. [PMID: 30239461 DOI: 10.1097/rti.0000000000000362] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Lung cancer at its earliest stage is typically manifested on computed tomography as a pulmonary nodule, which could be detected by low-dose multidetector computed tomography technology and the use of thinner collimation. Within the last 2 decades, computer-aided detection (CAD) of pulmonary nodules has been developed to meet the increasing demand for lung cancer screening computed tomography with a larger set of images per scan. This review introduced the basic techniques and then summarized the up-to-date applications of CAD systems in clinical and research programs and in the low-dose lung cancer screening trials, especially in the detection of small pulmonary nodules and missed lung cancers. Many studies have already shown that the CAD systems could increase the sensitivity and reduce the false-positive rate in the diagnosis of pulmonary nodules, especially for the small and isolated nodules. Further improvements to the current CAD schemes are needed to detect nodules accurately, particularly for subsolid nodules.
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Aissa J, Schaarschmidt BM, Below J, Bethge OT, Böven J, Sawicki LM, Hoff NP, Kröpil P, Antoch G, Boos J. Performance and clinical impact of machine learning based lung nodule detection using vessel suppression in melanoma patients. Clin Imaging 2018; 52:328-333. [PMID: 30236779 DOI: 10.1016/j.clinimag.2018.09.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 08/14/2018] [Accepted: 09/04/2018] [Indexed: 11/21/2022]
Abstract
PURPOSE To evaluate performance and the clinical impact of a novel machine learning based vessel-suppressing computer-aided detection (CAD) software in chest computed tomography (CT) of patients with malignant melanoma. MATERIALS AND METHODS We retrospectively included consecutive malignant melanoma patients with a chest CT between 01/2015 and 01/2016. Machine learning based CAD software was used to reconstruct additional vessel-suppressed axial images. Three radiologists independently reviewed a maximum of 15 lung nodules per patient. Vessel-suppressed reconstructions were reviewed independently and results were compared. Follow-up CT examinations and clinical follow-up were used to assess the outcome. Impact of additional nodules on clinical management was assessed. RESULTS In 46 patients, vessel-suppressed axial images led to the detection of additional nodules in 25/46 (54.3%) patients. CT or clinical follow up was available in 25/25 (100%) patients with additionally detected nodules. 2/25 (8%) of these patients developed new pulmonary metastases. None of the additionally detected nodules were found to be metastases. None of the lung nodules detected by the radiologists was missed by the CAD software. The mean diameter of the 92 additional nodules was 1.5 ± 0.8 mm. The additional nodules did not affect therapeutic management. However, in 14/46 (30.4%) of patients the additional nodules might have had an impact on the radiological follow-up recommendations. CONCLUSION Machine learning based vessel suppression led to the detection of significantly more lung nodules in melanoma patients. Radiological follow-up recommendations were altered in 30% of the patients. However, all lung nodules turned out to be non-malignant on follow-up.
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Affiliation(s)
- Joel Aissa
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany.
| | | | - Janina Below
- University Dusseldorf, Medical Faculty, Clinic of Dermatology, Moorenstr. 5, D-40225 Dusseldorf, Germany
| | - Oliver Th Bethge
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany
| | - Judith Böven
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany
| | - Lino Morris Sawicki
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany
| | - Norman-Philipp Hoff
- University Dusseldorf, Medical Faculty, Clinic of Dermatology, Moorenstr. 5, D-40225 Dusseldorf, Germany
| | - Patric Kröpil
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany
| | - Gerald Antoch
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany
| | - Johannes Boos
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany
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Ueno M, Aoki T, Murakami S, Kim H, Terasawa T, Fujisaki A, Hayashida Y, Korogi Y. CT temporal subtraction method for detection of sclerotic bone metastasis in the thoracolumbar spine. Eur J Radiol 2018; 107:54-59. [DOI: 10.1016/j.ejrad.2018.07.017] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 07/13/2018] [Accepted: 07/19/2018] [Indexed: 10/28/2022]
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Fazal MI, Patel ME, Tye J, Gupta Y. The past, present and future role of artificial intelligence in imaging. Eur J Radiol 2018; 105:246-250. [DOI: 10.1016/j.ejrad.2018.06.020] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Revised: 05/08/2018] [Accepted: 06/21/2018] [Indexed: 02/06/2023]
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A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies. Eur Radiol 2018; 29:144-152. [DOI: 10.1007/s00330-018-5528-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 04/27/2018] [Accepted: 05/07/2018] [Indexed: 01/04/2023]
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Vlahos I, Stefanidis K, Sheard S, Nair A, Sayer C, Moser J. Lung cancer screening: nodule identification and characterization. Transl Lung Cancer Res 2018; 7:288-303. [PMID: 30050767 DOI: 10.21037/tlcr.2018.05.02] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The accurate identification and characterization of small pulmonary nodules at low-dose CT is an essential requirement for the implementation of effective lung cancer screening. Individual reader detection performance is influenced by nodule characteristics and technical CT parameters but can be improved by training, the application of CT techniques, and by computer-aided techniques. However, the evaluation of nodule detection in lung cancer screening trials differs from the assessment of individual readers as it incorporates multiple readers, their inter-observer variability, reporting thresholds, and reflects the program accuracy in identifying lung cancer. Understanding detection and interpretation errors in screening trials aids in the implementation of lung cancer screening in clinical practice. Indeed, as CT screening moves to ever lower radiation doses, radiologists must be cognisant of new technical challenges in nodule assessment. Screen detected lung cancers demonstrate distinct morphological features from incidentally or symptomatically detected lung cancers. Hence characterization of screen detected nodules requires an awareness of emerging concepts in early lung cancer appearances and their impact on radiological assessment and malignancy prediction models. Ultimately many nodules remain indeterminate, but further imaging evaluation can be appropriate with judicious utilization of contrast enhanced CT or MRI techniques or functional evaluation by PET-CT.
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Affiliation(s)
- Ioannis Vlahos
- St George's NHS Foundation Hospitals Trust and School of Medicine, London, UK
| | | | | | - Arjun Nair
- Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
| | - Charles Sayer
- Brighton and Sussex University Hospitals Trust, Haywards Heath, UK
| | - Joanne Moser
- St George's NHS Foundation Hospitals Trust and School of Medicine, London, UK
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JOURNAL CLUB: Computer-Aided Detection of Lung Nodules on CT With a Computerized Pulmonary Vessel Suppressed Function. AJR Am J Roentgenol 2018; 210:480-488. [PMID: 29336601 DOI: 10.2214/ajr.17.18718] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
OBJECTIVE The purpose of this study is to evaluate radiologists' performance in detecting actionable nodules on chest CT when aided by a pulmonary vessel image-suppressed function and a computer-aided detection (CADe) system. MATERIALS AND METHODS A novel computerized pulmonary vessel image-suppressed function with a built-in CADe (VIS/CADe) system was developed to assist radiologists in interpreting thoracic CT images. Twelve radiologists participated in a comparative study without and with the VIS/CADe using 324 cases (involving 95 cancers and 83 benign nodules). The ratio of nodule-free cases to cases with nodules was 2:1 in the study. Localization ROC (LROC) methods were used for analysis. RESULTS In a stand-alone test, the VIS/CADe system detected 89.5% and 82.0% of malignant nodules and all nodules no smaller than 5 mm, respectively. The false-positive rate per CT study was 0.58. For the reader study, the mean area under the LROC curve (LROCAUC) for the detection of lung cancer significantly increased from 0.633 when unaided by VIS/CADe to 0.773 when aided by VIS/CADe (p < 0.01). For the detection of all clinically actionable nodules, the mean LROC-AUC significantly increased from 0.584 when unaided by VIS/CADe to 0.692 when detection was aided by VIS/CADe (p < 0.01). Radiologists detected 80.0% of cancers with VIS/CADe versus 64.45% of cancers unaided (p < 0.01); specificity decreased from 89.9% to 84.4% (p < 0.01). Radiologist interpretation time significantly decreased by 26%. CONCLUSION The VIS/CADe system significantly increased radiologists' detection of cancers and actionable nodules with somewhat lower specificity. With use of the VIS/CADe system, radiologists increased their interpretation speed by a factor of approximately one-fourth. Our study suggests that the technique has the potential to assist radiologists in the detection of additional actionable nodules on thoracic CT.
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Nair A, Screaton NJ, Holemans JA, Jones D, Clements L, Barton B, Gartland N, Duffy SW, Baldwin DR, Field JK, Hansell DM, Devaraj A. The impact of trained radiographers as concurrent readers on performance and reading time of experienced radiologists in the UK Lung Cancer Screening (UKLS) trial. Eur Radiol 2017. [PMID: 28643093 PMCID: PMC5717117 DOI: 10.1007/s00330-017-4903-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Objectives To compare radiologists’ performance reading CTs independently with their performance using radiographers as concurrent readers in lung cancer screening. Methods 369 consecutive baseline CTs performed for the UK Lung Cancer Screening (UKLS) trial were double-read by radiologists reading either independently or concurrently with a radiographer. In concurrent reading, the radiologist reviewed radiographer-identified nodules and then detected any additional nodules. Radiologists recorded their independent and concurrent reading times. For each radiologist, sensitivity, average false-positive detections (FPs) per case and mean reading times for each method were calculated. Results 694 nodules in 246/369 (66.7%) studies comprised the reference standard. Radiologists’ mean sensitivity and average FPs per case both increased with concurrent reading compared to independent reading (90.8 ± 5.6% vs. 77.5 ± 11.2%, and 0.60 ± 0.53 vs. 0.33 ± 0.20, respectively; p < 0.05 for 3/4 and 2/4 radiologists, respectively). The mean reading times per case decreased from 9.1 ± 2.3 min with independent reading to 7.2 ± 1.0 min with concurrent reading, decreasing significantly for 3/4 radiologists (p < 0.05). Conclusions The majority of radiologists demonstrated improved sensitivity, a small increase in FP detections and a statistically significantly reduced reading time using radiographers as concurrent readers. Key Points • Radiographers as concurrent readers could improve radiologists’ sensitivity in lung nodule detection. • An increase in false-positive detections with radiographer-assisted concurrent reading occurred. • The false-positive detection rate was still lower than reported for computer-aided detection. • Concurrent reading with radiographers was also faster than single reading. • The time saved per case using concurrently reading radiographers was relatively modest. Electronic supplementary material The online version of this article (doi:10.1007/s00330-017-4903-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Arjun Nair
- Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London, SE1 9RT, UK.
| | - Nicholas J Screaton
- Department of Radiology, Papworth Hospital NHS Foundation Trust, Papworth Everard, Cambridge, CB23 3RE, UK
| | - John A Holemans
- Department of Radiology, Liverpool Heart and Chest Hospital, Thomas Drive, Liverpool, Merseyside, L14 3PE, UK
| | - Diane Jones
- Department of Radiology, Liverpool Heart and Chest Hospital, Thomas Drive, Liverpool, Merseyside, L14 3PE, UK
| | - Leigh Clements
- Department of Radiology, Papworth Hospital NHS Foundation Trust, Papworth Everard, Cambridge, CB23 3RE, UK
| | - Bruce Barton
- Department of Radiology, Royal Brompton Hospital, Sydney Street, London, SW3 6NP, UK
| | - Natalie Gartland
- Department of Radiology, Royal Brompton Hospital, Sydney Street, London, SW3 6NP, UK
| | - Stephen W Duffy
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Charterhouse Square, London, EC1M 6BQ, UK
| | - David R Baldwin
- Respiratory Medicine Unit, David Evans Research Centre, Nottingham University Hospitals, Nottingham, NG5 1PB, UK
| | - John K Field
- Roy Castle Lung Cancer Research Programme, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, The University of Liverpool, The William Duncan Building, 6 West Derby Street, L7 8TX, Liverpool, UK
| | - David M Hansell
- Department of Radiology, Royal Brompton Hospital, Sydney Street, London, SW3 6NP, UK
| | - Anand Devaraj
- Department of Radiology, Royal Brompton Hospital, Sydney Street, London, SW3 6NP, UK
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Sunwoo L, Kim YJ, Choi SH, Kim KG, Kang JH, Kang Y, Bae YJ, Yoo RE, Kim J, Lee KJ, Lee SH, Choi BS, Jung C, Sohn CH, Kim JH. Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study. PLoS One 2017; 12:e0178265. [PMID: 28594923 PMCID: PMC5464563 DOI: 10.1371/journal.pone.0178265] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 05/02/2017] [Indexed: 11/19/2022] Open
Abstract
PURPOSE To assess the effect of computer-aided detection (CAD) of brain metastasis (BM) on radiologists' diagnostic performance in interpreting three-dimensional brain magnetic resonance (MR) imaging using follow-up imaging and consensus as the reference standard. MATERIALS AND METHODS The institutional review board approved this retrospective study. The study cohort consisted of 110 consecutive patients with BM and 30 patients without BM. The training data set included MR images of 80 patients with 450 BM nodules. The test set included MR images of 30 patients with 134 BM nodules and 30 patients without BM. We developed a CAD system for BM detection using template-matching and K-means clustering algorithms for candidate detection and an artificial neural network for false-positive reduction. Four reviewers (two neuroradiologists and two radiology residents) interpreted the test set images before and after the use of CAD in a sequential manner. The sensitivity, false positive (FP) per case, and reading time were analyzed. A jackknife free-response receiver operating characteristic (JAFROC) method was used to determine the improvement in the diagnostic accuracy. RESULTS The sensitivity of CAD was 87.3% with an FP per case of 302.4. CAD significantly improved the diagnostic performance of the four reviewers with a figure-of-merit (FOM) of 0.874 (without CAD) vs. 0.898 (with CAD) according to JAFROC analysis (p < 0.01). Statistically significant improvement was noted only for less-experienced reviewers (FOM without vs. with CAD, 0.834 vs. 0.877, p < 0.01). The additional time required to review the CAD results was approximately 72 sec (40% of the total review time). CONCLUSION CAD as a second reader helps radiologists improve their diagnostic performance in the detection of BM on MR imaging, particularly for less-experienced reviewers.
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Affiliation(s)
- Leonard Sunwoo
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Young Jae Kim
- Department of Biomedical Engineering, Gachon University, Incheon, Korea
- Department of Plasma Bio Display, Kwangwoon University, Seoul, Korea
| | - Seung Hong Choi
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- * E-mail: (SHC); (K-GK)
| | - Kwang-Gi Kim
- Department of Biomedical Engineering, Gachon University, Incheon, Korea
- * E-mail: (SHC); (K-GK)
| | - Ji Hee Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Yeonah Kang
- Department of Radiology, Seoul Metropolitan Government - Seoul National University Boramae Medical Center, Seoul, Korea
| | - Yun Jung Bae
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Roh-Eul Yoo
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Jihang Kim
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Kyong Joon Lee
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Seung Hyun Lee
- Department of Plasma Bio Display, Kwangwoon University, Seoul, Korea
| | - Byung Se Choi
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Cheolkyu Jung
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Chul-Ho Sohn
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Jae Hyoung Kim
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
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A review of lung cancer screening and the role of computer-aided detection. Clin Radiol 2017; 72:433-442. [DOI: 10.1016/j.crad.2017.01.002] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Revised: 12/14/2016] [Accepted: 01/04/2017] [Indexed: 12/26/2022]
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Nomura Y, Higaki T, Fujita M, Miki S, Awaya Y, Nakanishi T, Yoshikawa T, Hayashi N, Awai K. Effects of Iterative Reconstruction Algorithms on Computer-assisted Detection (CAD) Software for Lung Nodules in Ultra-low-dose CT for Lung Cancer Screening. Acad Radiol 2017; 24:124-130. [PMID: 27986507 DOI: 10.1016/j.acra.2016.09.023] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2016] [Revised: 09/21/2016] [Accepted: 09/25/2016] [Indexed: 02/06/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to evaluate the effects of iterative reconstruction (IR) algorithms on computer-assisted detection (CAD) software for lung nodules in ultra-low-dose computed tomography (ULD-CT) for lung cancer screening. MATERIALS AND METHODS We selected 85 subjects who underwent both a low-dose CT (LD-CT) scan and an additional ULD-CT scan in our lung cancer screening program for high-risk populations. The LD-CT scans were reconstructed with filtered back projection (FBP; LD-FBP). The ULD-CT scans were reconstructed with FBP (ULD-FBP), adaptive iterative dose reduction 3D (AIDR 3D; ULD-AIDR 3D), and forward projected model-based IR solution (FIRST; ULD-FIRST). CAD software for lung nodules was applied to each image dataset, and the performance of the CAD software was compared among the different IR algorithms. RESULTS The mean volume CT dose indexes were 3.02 mGy (LD-CT) and 0.30 mGy (ULD-CT). For overall nodules, the sensitivities of CAD software at 3.0 false positives per case were 78.7% (LD-FBP), 9.3% (ULD-FBP), 69.4% (ULD-AIDR 3D), and 77.8% (ULD-FIRST). Statistical analysis showed that the sensitivities of ULD-AIDR 3D and ULD-FIRST were significantly higher than that of ULD-FBP (P < .001). CONCLUSIONS The performance of CAD software in ULD-CT was improved by using IR algorithms. In particular, the performance of CAD in ULD-FIRST was almost equivalent to that in LD-FBP.
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Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets. BIOMED RESEARCH INTERNATIONAL 2016; 2016:8052436. [PMID: 27722173 PMCID: PMC5046100 DOI: 10.1155/2016/8052436] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Revised: 07/23/2016] [Accepted: 08/02/2016] [Indexed: 11/18/2022]
Abstract
In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detection algorithm is proposed based on SVM and CT image feature-level fusion with rough sets. Firstly, CT images of pulmonary nodule are analyzed, and 42-dimensional feature components are extracted, including six new 3-dimensional features proposed by this paper and others 2-dimensional and 3-dimensional features. Secondly, these features are reduced for five times with rough set based on feature-level fusion. Thirdly, a grid optimization model is used to optimize the kernel function of support vector machine (SVM), which is used as a classifier to identify pulmonary nodules. Finally, lung CT images of 70 patients with pulmonary nodules are collected as the original samples, which are used to verify the effectiveness and stability of the proposed model by four groups' comparative experiments. The experimental results show that the effectiveness and stability of the proposed model based on rough set feature-level fusion are improved in some degrees.
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Maximum-Intensity-Projection and Computer-Aided-Detection Algorithms as Stand-Alone Reader Devices in Lung Cancer Screening Using Different Dose Levels and Reconstruction Kernels. AJR Am J Roentgenol 2016; 207:282-8. [PMID: 27249174 DOI: 10.2214/ajr.15.15588] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE The objective of our study was to evaluate lung nodule detection rates on standard and microdose chest CT with two different computer-aided detection systems (SyngoCT-CAD, VA 20, Siemens Healthcare [CAD1]; Lung CAD, IntelliSpace Portal DX Server, Philips Healthcare [CAD2]) as well as maximum-intensity-projection (MIP) images. We also assessed the impact of different reconstruction kernels. MATERIALS AND METHODS Standard and microdose CT using three reconstruction kernels (i30, i50, i70) was performed with an anthropomorphic chest phantom. We placed 133 ground-glass and 133 solid nodules (diameters of 5 mm, 8 mm, 10 mm, and 12 mm) in 55 phantoms. Four blinded readers evaluated the MIP images; one recorded the results of CAD1 and CAD2. Sensitivities for CAD and MIP nodule detection on standard dose and microdose CT were calculated for each reconstruction kernel. RESULTS Dose for microdose CT was significantly less than that for standard-dose CT (0.1323 mSv vs 1.65 mSv; p < 0.0001). CAD1 delivered superior results compared with CAD2 for standard-dose and microdose CT (p < 0.0001). At microdose level, the best stand-alone sensitivity (97.6%) was comparable with CAD1 sensitivity (96.0%; p = 0.36; both with i30 reconstruction kernel). Pooled sensitivities for all nodules, doses, and reconstruction kernels on CAD1 ranged from 88.9% to 97.3% versus 49.6% to 73.9% for CAD2. The best sensitivity was achieved with standard-dose CT, i50 kernel, and CAD1 (97.3%) versus 96% with microdose CT, i30 or i50 kernel, and CAD1. MIP images and CAD1 had similar performance at both dose levels (p = 0.1313 and p = 0.48). CONCLUSION Submillisievert CT is feasible for detecting solid and ground-glass nodules that require soft-tissue kernels for MIP and CAD systems to achieve acceptable sensitivities. MIP reconstructions remain a valuable adjunct to the interpretation of chest CT for increasing sensitivity and have the advantage of significantly lower false-positive rates.
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Fehr D, Schmidtlein C, Hwang S, Deasy JO, Veeraraghavan H. AUTOMATIC DETECTION AND TRACKING OF LONGITUDINAL CHANGES OF MULTIPLE BONE METASTASES FROM DUAL ENERGY CT. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2016; 2016:168-171. [PMID: 31723375 PMCID: PMC6853027 DOI: 10.1109/isbi.2016.7493236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Early detection and the assessment of changes in bone metastatic cancers can enable clinicians to monitor disease progression and modify treatment to help achieve improved results for patients. However, poor contrast makes detection difficult, and multiple disease sites make tracking of their changes over time difficult. We present a method for automatically detecting and tracking the longitudinal changes in multiple sclerotic bone metastases from Dual Energy Computed Tomography (DECT) images. We employ a multi-stage approach involving (i) bone and marrow extraction, (ii) slice-wise lesion candidate detection and volumetric segmentation, and (iii) aggregation of these 3D candidates. The algorithm achieved 78% agreement with radiologist identified lesions from 10 patients. Longitudinal consistency in the lesion detection computed over 26 scans using Williams' index was 1.02 ± 0.23 using DICE and 1.03±0.30 using Hausdorff metrics. We also present preliminary results for analyzing lesion material composition changes by using a novel representation computed from the DECT images, where clear differences between bone metastases and normal marrow can be seen.
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Affiliation(s)
- Duc Fehr
- Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - C.Ross Schmidtlein
- Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Sinchun Hwang
- Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Joseph O. Deasy
- Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Harini Veeraraghavan
- Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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