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Evaluating Lung Cancer with Tumor Markers: CEA, CA 19-9 and CA 125. JOURNAL OF CONTEMPORARY MEDICINE 2021. [DOI: 10.16899/jcm.840949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Hong GS, Do KH, Son AY, Jo KW, Kim KP, Yun J, Lee CW. Value of bone suppression software in chest radiographs for improving image quality and reducing radiation dose. Eur Radiol 2021; 31:5160-5171. [PMID: 33439320 DOI: 10.1007/s00330-020-07596-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 11/07/2020] [Accepted: 12/03/2020] [Indexed: 11/24/2022]
Abstract
OBJECTIVES To compare image quality and radiation dose between dual-energy subtraction (DES)-based bone suppression images (D-BSIs) and software-based bone suppression images (S-BSIs). METHODS Chest radiographs (CXRs) of forty adult patients were obtained with the two X-ray devices, one with DES and one with bone suppression software. Three image quality metrics (relative mean absolute error (RMAE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM)) between original CXR and BSI for each of D-BSI and S-SBI groups were calculated for each bone and soft tissue areas. Two readers rated the visual image quality for original CXR and BSI for each of D-BSI and S-SBI groups. The dose area product (DAP) values were recorded. Paired t test was used to compare the image quality and DAP values between D-BSI and S-BSI groups. RESULTS In bone areas, S-BSIs had better SSIM values than D-BSI (94.57 vs. 87.77) but worse RMAE and PSNR values (0.50 vs. 0.20; 20.93 vs. 34.37) (all p < 0.001). In soft tissue areas, S-BSIs had better SSIM values than D-BSI (97.56 vs. 91.42) but similar RMAE and PSNR values (0.29 vs. 0.27; 31.35 vs. 29.87) (all p < 0.001). Both readers gave S-BSIs significantly higher image quality scores than D-BSI (p < 0.001). The mean DAP in software-related images (0.98 dGy·cm2) was significantly lower than that in the DES-related images (1.48 dGy·cm2) (p < 0.001). CONCLUSION Bone suppression software significantly improved the image quality of bone suppression images with a relatively lower radiation dose, compared with dual-energy subtraction technique. KEY POINTS • Bone suppression software preserves structure similarity of soft tissues better than dual-energy subtraction technique in bone suppression images. • Bone suppression software achieves superior image quality for lung lesions than dual-energy subtraction technique in bone suppression images. • Bone suppression software can decrease the radiation dose over the hardware-based image processing technique.
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Affiliation(s)
- Gil-Sun Hong
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea
| | - Kyung-Hyun Do
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea.
| | - A-Yeon Son
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea
| | - Kyung-Wook Jo
- Division of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Kwang Pyo Kim
- Department of Nuclear Engineering, Kyung Hee University, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea
| | - Choong Wook Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea
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Kim TJ, Kim CH, Lee HY, Chung MJ, Shin SH, Lee KJ, Lee KS. Management of incidental pulmonary nodules: current strategies and future perspectives. Expert Rev Respir Med 2019; 14:173-194. [PMID: 31762330 DOI: 10.1080/17476348.2020.1697853] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Introduction: Detection and characterization of pulmonary nodules is an important issue, because the process is the first step in the management of lung cancers.Areas covered: Literature review was performed on May 15 2019 by using the PubMed, US National Library of Medicine National Institutes of Health, and the National Center for Biotechnology information. CT features helping identify the druggable mutations and predict the prognosis of malignant nodules were presented. Technical advancements in MRI and PET/CT were introduced for providing functional information about malignant nodules. Advances in various tissue biopsy techniques enabling molecular analysis and histologic diagnosis of indeterminate nodules were also presented. New techniques such as radiomics, deep learning (DL) technology, and artificial intelligence showing promise in differentiating between malignant and benign nodules were summarized. Recently, updated management guidelines for solid and subsolid nodules incidentally detected on CT were described. Risk stratification and prediction models for indeterminate nodules under active investigation were briefly summarized.Expert opinion: Advancement in CT knowledge has led to a better correlation between CT features and genomic alterations or tumor histology. Recent advances like PET/CT, MRI, radiomics, and DL-based approach have shown promising results in the characterization and prognostication of pulmonary nodules.
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Affiliation(s)
- Tae Jung Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Cho Hee Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Ho Yun Lee
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Myung Jin Chung
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Sun Hye Shin
- Respiratory and Critical Care Division of Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Kyung Jong Lee
- Respiratory and Critical Care Division of Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
| | - Kyung Soo Lee
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea
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Li X, Shen L, Xie X, Huang S, Xie Z, Hong X, Yu J. Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection. Artif Intell Med 2019; 103:101744. [PMID: 31732411 DOI: 10.1016/j.artmed.2019.101744] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 10/23/2019] [Accepted: 10/23/2019] [Indexed: 10/25/2022]
Abstract
Lung cancer is the leading cause of cancer death worldwide. Early detection of lung cancer is helpful to provide the best possible clinical treatment for patients. Due to the limited number of radiologist and the huge number of chest x-ray radiographs (CXR) available for observation, a computer-aided detection scheme should be developed to assist radiologists in decision-making. While deep learning showed state-of-the-art performance in several computer vision applications, it has not been used for lung nodule detection on CXR. In this paper, a deep learning-based lung nodule detection method was proposed. We employed patch-based multi-resolution convolutional networks to extract the features and employed four different fusion methods for classification. The proposed method shows much better performance and is much more robust than those previously reported researches. For publicly available Japanese Society of Radiological Technology (JSRT) database, more than 99% of lung nodules can be detected when the false positives per image (FPs/image) was 0.2. The FAUC and R-CPM of the proposed method were 0.982 and 0.987, respectively. The proposed approach has the potential of applications in clinical practice.
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Affiliation(s)
- Xuechen Li
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong province, PR China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, PR China; Guangdong Key Laboratory of Itelligent Information Processing, Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, PR China
| | - Linlin Shen
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong province, PR China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, PR China; Guangdong Key Laboratory of Itelligent Information Processing, Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, PR China.
| | - Xinpeng Xie
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong province, PR China
| | - Shiyun Huang
- Sun Yat-Sen University Public Health Insititue, Guangzhou, Guangdong province, PR China.
| | - Zhien Xie
- GuangzhHou Thoracic Hospital, Guangzhou, Guangdong province, PR China.
| | - Xian Hong
- GuangzhHou Thoracic Hospital, Guangzhou, Guangdong province, PR China
| | - Juan Yu
- Imaging Department of Shenzhen University Health Science Center, Shenzhen University School of Medicine, Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, PR China.
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Hong GS, Do KH, Lee CW. Added Value of Bone Suppression Image in the Detection of Subtle Lung Lesions on Chest Radiographs with Regard to Reader's Expertise. J Korean Med Sci 2019; 34:e250. [PMID: 31583870 PMCID: PMC6776835 DOI: 10.3346/jkms.2019.34.e250] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Accepted: 08/19/2019] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND Chest radiographs (CXR) are the most commonly used imaging techniques by various clinicians and radiologists. However, detecting lung lesions on CXR depends largely on the reader's experience level, so there have been several trials to overcome this problem using post-processing of CXR. We investigated the added value of bone suppression image (BSI) in detecting various subtle lung lesions on CXR with regard to reader's expertise. METHODS We applied a software program to generate BSI in 1,600 patients in the emergency department. Of them, 80 patients with subtle lung lesions and 80 patients with negative finding on CXR were retrospectively selected based on the subtlety scores on CXR and CT findings. Ten readers independently rated their confidence in deciding the presence or absence of a lung lesion at each of 960 lung regions on the two separated imaging sessions: CXR alone vs. CXR with BSI. RESULTS The additional use of BSI for all readers significantly increased the mean area under the curve (AUC) in detecting subtle lung lesions (0.663 vs. 0.706; P < 0.001). The less experienced readers were, the more AUC differences increased: 0.067 (P < 0.001) for junior radiology residents; 0.064 (P < 0.001) for non-radiology clinicians; 0.044 (P < 0.001) for senior radiology residents; and 0.019 (P = 0.041) for chest radiologists. The additional use of BSI significantly increased the mean confidence regarding the presence or absence of lung lesions for 213 positive lung regions (2.083 vs. 2.357; P < 0.001) and for 747 negative regions (1.217 vs. 1.195; P = 0.008). CONCLUSION The use of BSI increases diagnostic performance and confidence, regardless of reader's expertise, reduces the impact of reader's expertise and can be helpful for less experienced clinicians and residents in the detection of subtle lung lesions.
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Affiliation(s)
- Gil Sun Hong
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Kyung Hyun Do
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| | - Choong Wook Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Shankar A, Saini D, Dubey A, Roy S, Bharati SJ, Singh N, Khanna M, Prasad CP, Singh M, Kumar S, Sirohi B, Seth T, Rinki M, Mohan A, Guleria R, Rath GK. Feasibility of lung cancer screening in developing countries: challenges, opportunities and way forward. Transl Lung Cancer Res 2019; 8:S106-S121. [PMID: 31211111 DOI: 10.21037/tlcr.2019.03.03] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Lung cancer is the leading cause of all cancer deaths worldwide, comprising 18.4% of all cancer deaths. Low-dose computed tomography (LDCT) has shown mortality benefit in various trials and now a standard tool for lung cancer screening. Most researches have been carried out in developed countries where lung cancer incidence and mortality is very high. There is an increasing trend in lung cancer incidence in developing countries attributed to tobacco smoking and various environmental and occupational risk factors. Implementation of lung cancer screening is challenging, so organised lung cancer screening is practically non-existent. There are numerous challenges in implementing such programs ranging from infrastructure, trained human resources, referral algorithm to cost and psychological trauma due to over-diagnosis. Pulmonary tuberculosis and other chest infections are important issues to be addressed while planning for lung cancer screening in developing countries. Burden of these diseases is very high and can lead to over-diagnosis in view of cut off of lung nodule size in various studies. Assessment of high risk cases for lung cancer is difficult as various forms of smoking make quantification non-uniform and difficult. Lung cancer screening targets only high risk population unlike screening programs for other cancers where entire population is targeted. There is a need of lung cancer screening for high risk cases as it saves life. Tobacco control and smoking cessation remain the most important long term intervention to decrease morbidity and mortality from lung cancer in developing countries. There is no sufficient evidence supporting the introduction of population-based screening for lung cancer in public health services.
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Affiliation(s)
- Abhishek Shankar
- Preventive Oncology, Dr BR Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, Delhi, India
| | - Deepak Saini
- Indian Society of Clinical Oncology, Delhi, India
| | - Anusha Dubey
- Indian Society of Clinical Oncology, Delhi, India
| | - Shubham Roy
- Indian Society of Clinical Oncology, Delhi, India
| | - Sachidanand Jee Bharati
- Oncoanaesthesia and Palliative Medicine, Dr BR Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, Delhi, India
| | - Navneet Singh
- Pulmonary Medicine, Post Graduate Institute of Medical Education & Research, Chandigarh, India
| | | | - Chandra Prakash Prasad
- Medical Oncology (Lab), Dr BR Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, Delhi, India
| | - Mayank Singh
- Medical Oncology (Lab), Dr BR Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, Delhi, India
| | - Sunil Kumar
- Surgical Oncology, Dr BR Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, Delhi, India
| | - Bhawna Sirohi
- Medical Oncology, Max Institute of Cancer Care, Delhi, India
| | - Tulika Seth
- Clinical Hematology, All India Institute of Medical Sciences, Delhi, India
| | - Minakshi Rinki
- Biotechnology, Swami Shraddhanand College, Delhi University, Delhi, India
| | - Anant Mohan
- Pulmonary Medicine & Sleep Disorders, All India Institute of Medical Sciences, Delhi, India
| | - Randeep Guleria
- Pulmonary Medicine & Sleep Disorders, All India Institute of Medical Sciences, Delhi, India
| | - Goura Kishor Rath
- Radiation Oncology, Dr BR Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, Delhi, India
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Charles Edgar Metz, Ph.D. (1942–2012): pioneer in receiver operating characteristic (ROC) analysis. Radiol Phys Technol 2019; 12:1-5. [DOI: 10.1007/s12194-018-0483-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Li X, Shen L, Luo S. A Solitary Feature-Based Lung Nodule Detection Approach for Chest X-Ray Radiographs. IEEE J Biomed Health Inform 2018; 22:516-524. [DOI: 10.1109/jbhi.2017.2661805] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Wang C, Elazab A, Wu J, Hu Q. Lung nodule classification using deep feature fusion in chest radiography. Comput Med Imaging Graph 2017; 57:10-18. [DOI: 10.1016/j.compmedimag.2016.11.004] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 11/08/2016] [Accepted: 11/10/2016] [Indexed: 11/28/2022]
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Anconina R, Zur D, Kesler A, Lublinsky S, Toledano R, Novack V, Benkobich E, Novoa R, Novic EF, Shelef I. Creating normograms of dural sinuses in healthy persons using computer-assisted detection for analysis and comparison of cross-section dural sinuses in the brain. J Clin Neurosci 2017; 40:190-194. [PMID: 28286027 DOI: 10.1016/j.jocn.2017.02.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2016] [Revised: 11/14/2016] [Accepted: 02/07/2017] [Indexed: 11/18/2022]
Abstract
Dural sinuses vary in size and shape in many pathological conditions with abnormal intracranial pressure. Size and shape normograms of dural brain sinuses are not available. The creation of such normograms may enable computer-assisted comparison to pathologic exams and facilitate diagnoses. The purpose of this study was to quantitatively evaluate normal magnetic resonance venography (MRV) studies in order to create normograms of dural sinuses using a computerized algorithm for vessel cross-sectional analysis. This was a retrospective analysis of MRV studies of 30 healthy persons. Data were analyzed using a specially developed Matlab algorithm for vessel cross-sectional analysis. The cross-sectional area and shape measurements were evaluated to create normograms. Mean cross-sectional size was 53.27±13.31 for the right transverse sinus (TS), 46.87+12.57 for the left TS (p=0.089) and 36.65+12.38 for the superior sagittal sinus. Normograms were created. The distribution of cross-sectional areas along the vessels showed distinct patterns and a parallel course for the median, 25th, 50th and 75th percentiles. In conclusion, using a novel computerized method for vessel cross-sectional analysis we were able to quantitatively characterize dural sinuses of healthy persons and create normograms.
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Affiliation(s)
- Reut Anconina
- Radiology Institute, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Dinah Zur
- Ophthalmology Division, Sourasky Medical Center, Tel Aviv University, Tel-Aviv, Israel.
| | - Anat Kesler
- Ophthalmology Division, Sourasky Medical Center, Tel Aviv University, Tel-Aviv, Israel.
| | - Svetlana Lublinsky
- Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Ronen Toledano
- Clinical Research Center, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Victor Novack
- Clinical Research Center, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Elya Benkobich
- Radiology Institute, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Rosa Novoa
- Radiology Institute, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Evelyne Farkash Novic
- Radiology Institute, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Ilan Shelef
- Radiology Institute, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
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Zur D, Anconina R, Kesler A, Lublinsky S, Toledano R, Shelef I. Quantitative imaging biomarkers for dural sinus patterns in idiopathic intracranial hypertension. Brain Behav 2017; 7:e00613. [PMID: 28239523 PMCID: PMC5318366 DOI: 10.1002/brb3.613] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Revised: 09/06/2016] [Accepted: 10/17/2016] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE To quantitatively characterize transverse dural sinuses (TS) on magnetic resonance venography (MRV) in patients with idiopathic intracranial hypertension (IIH), compared to healthy controls, using a computer assisted detection (CAD) method. MATERIALS AND METHODS We retrospectively analyzed MRV studies of 38 IIH patients and 30 controls, matched by age and gender. Data analysis was performed using a specially developed Matlab algorithm for vessel cross-sectional analysis. The cross-sectional area and shape measurements were evaluated in patients and controls. RESULTS Mean, minimal, and maximal cross-sectional areas as well as volumetric parameters of the right and left transverse sinuses were significantly smaller in IIH patients than in controls (p < .005 for all). Idiopathic intracranial hypertension patients showed a narrowed segment in both TS, clustering near the junction with the sigmoid sinus. In 36% (right TS) and 43% (left TS), the stenosis extended to >50% of the entire length of the TS, i.e. the TS was hypoplastic. Narrower vessels tended to have a more triangular shape than did wider vessels. CONCLUSION Using CAD we precisely quantified TS stenosis and its severity in IIH patients by cross-sectional and volumetric analysis. This method can be used as an exact tool for investigating mechanisms of IIH development and response to treatment.
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Affiliation(s)
- Dinah Zur
- Division of Ophthalmology Sackler Faculty of Medicine Tel Aviv Sourasky Medical Center Tel Aviv University Tel Aviv Israel
| | - Reut Anconina
- Diagnostic Imaging Department Soroka University Medical Center Ben-Gurion University of the Negev Beer-Sheva Israel
| | - Anat Kesler
- Division of Ophthalmology Sackler Faculty of Medicine Tel Aviv Sourasky Medical Center Tel Aviv University Tel Aviv Israel
| | - Svetlana Lublinsky
- Zolotowsky Neuroscience Center Ben-Gurion University of the Negev Beer-Sheva Israel
| | - Ronen Toledano
- Clinical Research Center Soroka University Medical Center Ben-Gurion University of the Negev Beer-Sheva Israel
| | - Ilan Shelef
- Diagnostic Imaging Department Soroka University Medical Center Ben-Gurion University of the Negev Beer-Sheva Israel
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Chen S, Yao L, Chen B. A parameterized logarithmic image processing method with Laplacian of Gaussian filtering for lung nodule enhancement in chest radiographs. Med Biol Eng Comput 2016; 54:1793-1806. [DOI: 10.1007/s11517-016-1469-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Accepted: 02/15/2016] [Indexed: 12/17/2022]
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Kao EF, Liu GC, Lee LY, Tsai HY, Jaw TS. Computer-aided detection system for chest radiography: reducing report turnaround times of examinations with abnormalities. Acta Radiol 2015; 56:696-701. [PMID: 24948788 DOI: 10.1177/0284185114538017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2014] [Accepted: 05/12/2014] [Indexed: 12/25/2022]
Abstract
BACKGROUND The ability to give high priority to examinations with pathological findings could be very useful to radiologists with large work lists who wish to first evaluate the most critical studies. A computer-aided detection (CAD) system for identifying chest examinations with abnormalities has therefore been developed. PURPOSE To evaluate the effectiveness of a CAD system on report turnaround times of chest examinations with abnormalities. MATERIAL AND METHODS The CAD system was designed to automatically mark chest examinations with possible abnormalities in the work list of radiologists interpreting chest examinations. The system evaluation was performed in two phases: two radiologists interpreted the chest examinations without CAD in phase 1 and with CAD in phase 2. The time information recorded by the radiology information system was then used to calculate the turnaround times. All chest examinations were reviewed by two other radiologists and were divided into normal and abnormal groups. The turnaround times for the examinations with pathological findings with and without the CAD system assistance were compared. RESULTS The sensitivity and specificity of the CAD for chest abnormalities were 0.790 and 0.697, respectively, and use of the CAD system decreased the turnaround time for chest examinations with abnormalities by 44%. CONCLUSION The turnaround times required for radiologists to identify chest examinations with abnormalities could be reduced by using the CAD system. This system could be useful for radiologists with large work lists who wish to first evaluate the most critical studies.
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Affiliation(s)
- E-Fong Kao
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Gin-Chung Liu
- Department of Medical Imaging, Kaohsiung Medical University Chung-Ho Memorial Hospital, Kaohsiung, Taiwan
| | - Lo-Yeh Lee
- Department of Medical Imaging, Kaohsiung Medical University Chung-Ho Memorial Hospital, Kaohsiung, Taiwan
| | - Huei-Yi Tsai
- Department of Radiology, St. Joseph Hospital, Kaohsiung, Taiwan
| | - Twei-Shiun Jaw
- Department of Medical Imaging, Kaohsiung Medical University Chung-Ho Memorial Hospital, Kaohsiung, Taiwan
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Li F. Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologist's role and successful observer study. Radiol Phys Technol 2015; 8:161-73. [PMID: 25981309 DOI: 10.1007/s12194-015-0319-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Accepted: 05/06/2015] [Indexed: 11/29/2022]
Abstract
This review paper is based on our research experience in the past 30 years. The importance of radiologists' role is discussed in the development or evaluation of new medical images and of computer-aided detection (CAD) schemes in chest radiology. The four main topics include (1) introducing what diseases can be included in a research database for different imaging techniques or CAD systems and what imaging database can be built by radiologists, (2) understanding how radiologists' subjective judgment can be combined with technical objective features to improve CAD performance, (3) sharing our experience in the design of successful observer performance studies, and (4) finally, discussing whether the new images and CAD systems can improve radiologists' diagnostic ability in chest radiology. In conclusion, advanced imaging techniques and detection/classification of CAD systems have a potential clinical impact on improvement of radiologists' diagnostic ability, for both the detection and the differential diagnosis of various lung diseases, in chest radiology.
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Affiliation(s)
- Feng Li
- Department of Radiology, MC 2026, The University of Chicago, 5841 S. Maryland Avenue, Chicago, IL, 60637, USA,
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Li F, Engelmann R, Armato SG, MacMahon H. Computer-aided nodule detection system: results in an unselected series of consecutive chest radiographs. Acad Radiol 2015; 22:475-80. [PMID: 25592026 DOI: 10.1016/j.acra.2014.11.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2014] [Revised: 11/11/2014] [Accepted: 11/15/2014] [Indexed: 10/24/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate the performance of a computer-aided detection (CAD) system with bone suppression imaging when applied to unselected consecutive chest radiographs (CXRs) with computed tomography (CT) correlation. MATERIALS AND METHODS This study included 586 consecutive patients with standard or portable CXRs who had a chest CT scan on the same day. Among the 586 CXRs, 438 had various abnormalities, including 46 CXRs with 66 lung nodules, and 148 CXRs had no significant abnormalities. A commercially available CAD system was applied to all 586 CXRs. True nodules and false positives (FPs) marked on CXRs by the CAD system were evaluated based on the corresponding chest CT findings. RESULTS The CAD system marked 47 of 66 (71%) lung nodules in this consecutive series of CXRs. The mean FP rate per image was 1.3 across all 586 CXRs, with 1.5 FPs per image on the 438 abnormal CXRs and 0.8 FPs per image on the 148 normal CXRs. A total of 41% of the 752 FP marks were related to non-nodule pathologic findings. CONCLUSIONS A currently available CAD system marked 71% of radiologist-identified lung nodules in a large consecutive series of CXRs, and 41% of "false" marks were caused by pathologic findings.
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Sayyouh M, Vummidi DR, Kazerooni EA. Evaluation and management of pulmonary nodules: state-of-the-art and future perspectives. ACTA ACUST UNITED AC 2014; 7:629-44. [PMID: 24175679 DOI: 10.1517/17530059.2013.858117] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
INTRODUCTION The imaging evaluation of pulmonary nodules, often incidentally detected on imaging examinations performed for other clinical reasons, is a frequently encountered clinical circumstance. With advances in imaging modalities, both the detection and characterization of pulmonary nodules continue to evolve and improve. AREAS COVERED This article will review the imaging modalities used to detect and diagnose benign and malignant pulmonary nodules, with a focus on computed tomography (CT), which continues to be the mainstay for evaluation. The authors discuss recent advances in the lung nodule management, and an algorithm for the management of indeterminate pulmonary nodules. EXPERT OPINION There are set of criteria that define a benign nodule, the most important of which are the lack of temporal change for 2 years or more, and certain benign imaging criteria, including specific patterns of calcification or the presence of fat. Although some indeterminate pulmonary nodules are immediately actionable, generally those approaching 1 cm or larger in diameter, at which size the diagnostic accuracy of tools such as positron emission tomography (PET)/CT, single photon emission CT (SPECT) and biopsy techniques are sufficient to warrant their use. The majority of indeterminate pulmonary nodules are under 1 cm, for which serial CT examinations through at least 2 years for solid nodules and 3 years for ground-glass nodules, are used to demonstrate either benign biologic behavior or otherwise. The management of incidental pulmonary nodules involves a multidisciplinary approach in which radiology plays a pivotal role. Newer imaging and postprocessing techniques have made this a more accurate technique eliminating ambiguity and unnecessary follow-up.
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Affiliation(s)
- Mohamed Sayyouh
- University of Michigan Health System, Division of Cardiothoracic Radiology, Department of Radiology , Ann Arbor, MI , USA
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Jo HH, Hong H, Goo JM. Pulmonary nodule registration in serial CT scans using global rib matching and nodule template matching. Comput Biol Med 2013; 45:87-97. [PMID: 24480168 DOI: 10.1016/j.compbiomed.2013.10.028] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2013] [Revised: 10/28/2013] [Accepted: 10/30/2013] [Indexed: 10/26/2022]
Abstract
We propose an automatic nodule registration method between baseline and follow-up chest CT scans. Initial alignment using the center of the lung volume corrects the gross translational mismatch, and rigid registration using coronal and sagittal maximum intensity projection images effectively refines the rigid motion of the lungs. Nodule correspondences are established by finding the most similar region in terms of density as well as the geometrical constraint. The proposed nodule registration method increased the nodule hit rate (the ratio of the number of successfully matched nodules to total nodule number) from 26% to 100%.
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Affiliation(s)
- Hyun Hee Jo
- Department of Multimedia Engineering, College of Information and Media, Seoul Women's University, 126 Gongreung-dong, Nowon-gu, Seoul 139-774, Republic of Korea.
| | - Helen Hong
- Department of Multimedia Engineering, College of Information and Media, Seoul Women's University, 126 Gongreung-dong, Nowon-gu, Seoul 139-774, Republic of Korea.
| | - Jin Mo Goo
- Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul 110-744, Republic of Korea.
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Diagnostic Role of Tumour Markers CEA, CA15-3, CA19-9 and CA125 in Lung Cancer. Indian J Clin Biochem 2012; 28:24-9. [PMID: 24381417 DOI: 10.1007/s12291-012-0257-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2012] [Accepted: 08/27/2012] [Indexed: 12/30/2022]
Abstract
The aim of this study was to assess the diagnostic yield of the tumour markers carcinoembryonic antigen, carbohydrate antigen 15-3, carbohydrate antigen 19-9 and carbohydrate antigen 125, in serum and bronchoalveolar lavage fluid in a group of patients with bronchogenic carcinoma. Serum and bronchoalveolar lavage fluid samples were collected in a group of 90 patients with benign or malignant pulmonary diseases. After appropriate processing, tumour markers were determined by enzyme immunoassay. The diagnostic yields (sensitivity, specificity and predictive values) in each environment (serum and bronchoalveolar lavage fluid) were obtained by using "Receivers operating characteristic" curve. Determined individually, carcinoembryonic antigen, carbohydrate antigen 19-9 and carbohydrate antigen 125, showed the greatest diagnostic accuracy in bronchoalveolar lavage fluid. Carbohydrate antigen 15-3 did so in serum. Carcinoembryonic antigen was the most relevant marker in bronchoalveolar lavage fluid. For the factors evaluated in this study, determination of carcinoembryonic antigen, carbohydrate antigen 19-9 and carbohydrate antigen 125 in bronchoalveolar lavage fluid were clinically more useful markers in comparison with serum, although the latter may also be helpful in certain situations. Although there is no specific tumour marker for lung cancer, the combination of several can be used to diagnose most patients with lung cancer and also to rule out false positive and negative cases.
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Consensus versus disagreement in imaging research: a case study using the LIDC database. J Digit Imaging 2012; 25:423-36. [PMID: 22193755 DOI: 10.1007/s10278-011-9445-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022] Open
Abstract
Traditionally, image studies evaluating the effectiveness of computer-aided diagnosis (CAD) use a single label from a medical expert compared with a single label produced by CAD. The purpose of this research is to present a CAD system based on Belief Decision Tree classification algorithm, capable of learning from probabilistic input (based on intra-reader variability) and providing probabilistic output. We compared our approach against a traditional decision tree approach with respect to a traditional performance metric (accuracy) and a probabilistic one (area under the distance-threshold curve-AuC(dt)). The probabilistic classification technique showed notable performance improvement in comparison with the traditional one with respect to both evaluation metrics. Specifically, when applying cross-validation technique on the training subset of instances, boosts of 28.26% and 30.28% were noted for the probabilistic approach with respect to accuracy and AuC(dt), respectively. Furthermore, on the validation subset of instances, boosts of 20.64% and 23.21% were noted again for the probabilistic approach with respect to the same two metrics. In addition, we compared our CAD system results with diagnostic data available for a small subset of the Lung Image Database Consortium database. We discovered that when our CAD system errs, it generally does so with low confidence. Predictions produced by the system also agree with diagnoses of truly benign nodules more often than radiologists, offering the possibility of reducing the false positives.
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Hodnett PA, Ko JP. Evaluation and Management of Indeterminate Pulmonary Nodules. Radiol Clin North Am 2012; 50:895-914. [DOI: 10.1016/j.rcl.2012.06.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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De Boo DW, Uffmann M, Weber M, Bipat S, Boorsma EF, Scheerder MJ, Freling NJ, Schaefer-Prokop CM. Computer-aided detection of small pulmonary nodules in chest radiographs: an observer study. Acad Radiol 2011; 18:1507-14. [PMID: 21963532 DOI: 10.1016/j.acra.2011.08.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2011] [Revised: 07/26/2011] [Accepted: 07/29/2011] [Indexed: 12/25/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate the impact of computer-aided detection (CAD, IQQA-Chest; EDDA Technology, Princeton Junction, NJ) used as second reader on the detection of small pulmonary nodules in chest radiography (CXR). MATERIALS AND METHODS A total of 113 patients (mean age 62 years) with CT and CXR within 6 weeks were selected. Fifty-nine patients showed 101 pulmonary nodules (diameter 5-15mm); the remaining 54 patients served as negative controls. Six readers of varying experience individually evaluated the CXR without and with CAD as second reader in two separate reading sessions. The sensitivity per lesion, figure of merit (FOM), and mean false positive per image (mFP) were calculated. Institutional review board approval was waived. RESULTS With CAD, the sensitivity increased for inexperienced readers (39% vs. 45%, P < .05) and remained unchanged for experienced readers (50% vs. 51%). The mFP nonsignificantly increased for both inexperienced and experienced readers (0.27 vs. 0.34 and 0.16 vs. 0.21). The mean FOM did not significantly differ for readings without and with CAD irrespective of reader experience (0.71 vs. 0.71 and 0.84 vs. 0.87). All readers together dismissed 33% of true-positive CAD candidates. False-positive candidates by CAD provoked 40% of all false-positive marks made by the readers. CONCLUSION CAD improves the sensitivity of inexperienced readers for the detection of small nodules at the expense of loss of specificity. Overall performance by means of FOM was therefore not affected. To use CAD more beneficial, readers need to improve their ability to differentiate true from false-positive CAD candidates.
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Obuchowski NA. Predicting readers' diagnostic accuracy with a new CAD algorithm. Acad Radiol 2011; 18:1412-9. [PMID: 21917487 DOI: 10.1016/j.acra.2011.07.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2011] [Revised: 07/15/2011] [Accepted: 07/23/2011] [Indexed: 12/25/2022]
Abstract
RATIONALE AND OBJECTIVES Before computer-aided detection (CAD) algorithms can be used in clinical practice, they must be shown to improve readers' diagnostic accuracy over their unaided performance. This is usually accomplished through a large multireader, multicase (MRMC) clinical trial. It is burdensome, however, for an MRMC study to be performed with each new release of a CAD algorithm. The aim of this report is to present an approach for building models to predict readers' accuracy with a new CAD algorithm. MATERIALS AND METHODS A modeling approach for predicting readers' results with a new CAD algorithm is described. Multiple-variable logistic regression was used to build models for readers' sensitivity and false-positive rate, given the results of an MRMC study with an older CAD algorithm and the stand-alone performance results of a new CAD algorithm. Data from a large lung MRMC CAD trial are used to illustrate the modeling approach and test the ability of the models to predict readers' accuracy with the new CAD algorithm. RESULTS The model overestimated the readers' actual sensitivity with the new CAD algorithm, but this did not reach statistical significance (0.621 vs 0.603, P = .147). The observed and predicted false-positive rates also did not differ significantly (0.275 vs 0.285, P = .250). CONCLUSIONS Using one clinical study as a test case, it is shown that the modeling approach is feasible. More testing of the approach is needed to determine if and under what circumstances it can be used as an alternative to a full-scale MRMC study. Meanwhile, the approach can be used to determine if a new CAD algorithm is likely to improve readers' accuracy before embarking on a full-scale MRMC study.
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Affiliation(s)
- Nancy A Obuchowski
- Cleveland Clinic Foundation, Department of Quantitative Health Sciences, Cleveland, OH 44195, USA.
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Li F, Engelmann R, Pesce LL, Doi K, Metz CE, Macmahon H. Small lung cancers: improved detection by use of bone suppression imaging--comparison with dual-energy subtraction chest radiography. Radiology 2011; 261:937-49. [PMID: 21946054 DOI: 10.1148/radiol.11110192] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE To determine whether use of bone suppression (BS) imaging, used together with a standard radiograph, could improve radiologists' performance for detection of small lung cancers compared with use of standard chest radiographs alone and whether BS imaging would provide accuracy equivalent to that of dual-energy subtraction (DES) radiography. MATERIALS AND METHODS Institutional review board approval was obtained. The requirement for informed consent was waived. The study was HIPAA compliant. Standard and DES chest radiographs of 50 patients with 55 confirmed primary nodular cancers (mean diameter, 20 mm) as well as 30 patients without cancers were included in the observer study. A new BS imaging processing system that can suppress the conspicuity of bones was applied to the standard radiographs to create corresponding BS images. Ten observers, including six experienced radiologists and four radiology residents, indicated their confidence levels regarding the presence or absence of a lung cancer for each lung, first by using a standard image, then a BS image, and finally DES soft-tissue and bone images. Receiver operating characteristic (ROC) analysis was used to evaluate observer performance. RESULTS The average area under the ROC curve (AUC) for all observers was significantly improved from 0.807 to 0.867 with BS imaging and to 0.916 with DES (both P < .001). The average AUC for the six experienced radiologists was significantly improved from 0.846 with standard images to 0.894 with BS images (P < .001) and from 0.894 to 0.945 with DES images (P = .001). CONCLUSION Use of BS imaging together with a standard radiograph can improve radiologists' accuracy for detection of small lung cancers on chest radiographs. Further improvements can be achieved by use of DES radiography but with the requirement for special equipment and a potential small increase in radiation dose.
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Affiliation(s)
- Feng Li
- Department of Radiology, MC-2026, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637, USA. feng@uchicago .edu
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Bağcı U, Bray M, Caban J, Yao J, Mollura DJ. Computer-assisted detection of infectious lung diseases: a review. Comput Med Imaging Graph 2011; 36:72-84. [PMID: 21723090 DOI: 10.1016/j.compmedimag.2011.06.002] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2011] [Revised: 05/11/2011] [Accepted: 06/01/2011] [Indexed: 02/05/2023]
Abstract
Respiratory tract infections are a leading cause of death and disability worldwide. Although radiology serves as a primary diagnostic method for assessing respiratory tract infections, visual analysis of chest radiographs and computed tomography (CT) scans is restricted by low specificity for causal infectious organisms and a limited capacity to assess severity and predict patient outcomes. These limitations suggest that computer-assisted detection (CAD) could make a valuable contribution to the management of respiratory tract infections by assisting in the early recognition of pulmonary parenchymal lesions, providing quantitative measures of disease severity and assessing the response to therapy. In this paper, we review the most common radiographic and CT features of respiratory tract infections, discuss the challenges of defining and measuring these disorders with CAD, and propose some strategies to address these challenges.
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Affiliation(s)
- Ulaş Bağcı
- Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health (NIH), Bethesda, MD 20892, USA.
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A comparison of follow-up recommendations by chest radiologists, general radiologists, and pulmonologists using computer-aided detection to assess radiographs for actionable pulmonary nodules. AJR Am J Roentgenol 2011; 196:W542-9. [PMID: 21512043 DOI: 10.2214/ajr.10.5048] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE The primary objective of our study was to compare the effect of a chest radiography computer-aided detection (CAD) system on the follow-up recommendations of chest radiologists, general radiologists, and pulmonologists. MATERIALS AND METHODS A chest radiography CAD system (RapidScreen 1.1) that has been approved by the U.S. Food and Drug Administration (FDA) and a second-generation version of the system (OnGuard 3.0) not yet approved by the FDA were applied to single frontal radiographs of 200 patients at high risk for lung cancer. One hundred patients had actionable nodules (mean size, 16.9 mm) and 100 patients did not. Six chest radiologists, six general radiologists, and six pulmonologists independently interpreted each image first without CAD and then with CAD during blinded reading sessions. The frequency with which readers correctly referred patients for follow-up tests was measured. Differential effects based on nodule size, shape, location, density, and subtlety were tested with multiplevariable logistic regression. RESULTS For patients without actionable lesions, pulmonologists showed an increase in their recommendations for follow-up from 0.46 unaided to 0.52 with CAD (p = 0.001), whereas chest and general radiologists had much lower average rates and were not affected by CAD's false marks (0.26 without CAD vs 0.25 with RapidScreen 1.1 and 0.26 with OnGuard 3.0, p ≥ 0.734). CAD improved all readers' detection of moderately subtle lesions (p = 0.013) but did not significantly increase follow-up rates overall for patients with actionable nodules (0.63 unaided vs 0.63 with RapidScreen 1.1, p = 0.795; and 0.63 unaided vs 0.64 with OnGuard 3.0, p = 0.187). CONCLUSION The effect of CAD on readers' clinical decisions varies depending on the training of the reader. CAD did not improve the performance of chest or general radiologists. Nonradiologists are particularly vulnerable to CAD's false-positive marks.
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Chen S, Suzuki K, MacMahon H. Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule enhancement with support vector classification. Med Phys 2011; 38:1844-58. [PMID: 21626918 DOI: 10.1118/1.3561504] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
PURPOSE To develop a computer-aided detection (CADe) scheme for nodules in chest radiographs (CXRs) with a high sensitivity and a low false-positive (FP) rate. METHODS The authors developed a CADe scheme consisting of five major steps, which were developed for improving the overall performance of CADe schemes. First, to segment the lung fields accurately, the authors developed a multisegment active shape model. Then, a two-stage nodule-enhancement technique was developed for improving the conspicuity of nodules. Initial nodule candidates were detected and segmented by using the clustering watershed algorithm. Thirty-one shape-, gray-level-, surface-, and gradient-based features were extracted from each segmented candidate for determining the feature space, including one of the new features based on the Canny edge detector to eliminate a major FP source caused by rib crossings. Finally, a nonlinear support vector machine (SVM) with a Gaussian kernel was employed for classification of the nodule candidates. RESULTS To evaluate and compare the scheme to other published CADe schemes, the authors used a publicly available database containing 140 nodules in 140 CXRs and 93 normal CXRs. The CADe scheme based on the SVM classifier achieved sensitivities of 78.6% (110/140) and 71.4% (100/140) with averages of 5.0 (1165/233) FPs/image and 2.0 (466/233) FPs/image, respectively, in a leave-one-out cross-validation test, whereas the CADe scheme based on a linear discriminant analysis classifier had a sensitivity of 60.7% (85/140) at an FP rate of 5.0 FPs/image. For nodules classified as "very subtle" and "extremely subtle," a sensitivity of 57.1% (24/42) was achieved at an FP rate of 5.0 FPs/image. When the authors used a database developed at the University of Chicago, the sensitivities was 83.3% (40/48) and 77.1% (37/48) at an FP rate of 5.0 (240/48) FPs/image and 2.0 (96/48) FPs/image, respectively. CONCLUSIONS These results compare favorably to those described for other commercial and non-commercial CADe nodule detection systems.
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Affiliation(s)
- Sheng Chen
- Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, Illinois 60637, USA.
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Efficacy of computer-aided diagnosis in lung cancer screening with low-dose spiral computed tomography: receiver operating characteristic analysis of radiologists' performance. Jpn J Radiol 2010; 28:649-55. [PMID: 21113748 DOI: 10.1007/s11604-010-0486-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2010] [Accepted: 07/05/2010] [Indexed: 01/06/2023]
Abstract
PURPOSE The aim of this study was to evaluate the efficacy of a computer-aided diagnosis (CAD) system we developed that can also respond to subsolid nodules, for lung cancer screening using low-dose spiral computed tomography (LDCT). MATERIALS AND METHODS The institutional review board approved this study. A total of 30 positive cases (including 15 lung cancer cases) that needed further examination and 30 negative cases were used for the observer performance study. Three thoracic radiologists, five general radiologists, and three residents participated in this study in which they first read the original CT image on its own and then reassessed the same image with the assistance of CAD. Radiologists' performance was evaluated using receiver operating characteristic (ROC) analysis. RESULTS The Az values without and with CAD were 0.872 and 0.910 for the thoracic radiologists, 0.864 and 0.924 for general radiologists, and 0.875 and 0.837 for residents, respectively. The detection accuracy improved significantly for the thoracic and general radiologists with our CAD system; however, no statistically significant difference between without or with CAD was seen for residents. CONCLUSION This CAD system is beneficial in the detection of pulmonary nodules on LDCT when used by experienced radiologists.
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Sugimoto K, Shiraishi J, Moriyasu F, Ichimura S, Metoki R, Doi K. Analysis of intrahepatic vascular morphological changes of chronic liver disease for assessment of liver fibrosis stages by micro-flow imaging with contrast-enhanced ultrasound: preliminary experience. Eur Radiol 2010; 20:2749-57. [PMID: 20571803 DOI: 10.1007/s00330-010-1852-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2010] [Revised: 05/10/2010] [Accepted: 05/24/2010] [Indexed: 02/06/2023]
Abstract
OBJECTIVE To assess morphological vascular changes due to an increase in liver fibrosis by using micro-flow imaging (MFI) of contrast-enhanced ultrasound. METHODS MFI was performed in 47 patients who underwent liver biopsy, and in 10 normal cases. For 27/57 cases, we performed MFI twice in order to assess the reproducibility of the examination, thus yielding a total of 84 examinations. Seven physicians interpreted each case individually by assigning confidence levels for the presence or absence of three imaging features that were related to alteration of portal vein morphology: angle widening, tapering/interruption and tortuosity. RESULTS Pearson's correlation coefficient between the average rating scores based on tortuosity and the histological fibrosis stage was 0.806 (p < 0.001). The diagnostic accuracy of the average area under the ROC curve, which was estimated by use of the confidence levels of tapering/interruption, tortuosity and angle widening, was 0.964 for F1 vs. F2-4, 0.968 for F1-2 vs. F3-4 and 0.910 for F1-3 vs. F4. The average correlation coefficient between the ratings on different images from the same patients was 0.838. CONCLUSION Assessment of morphological intrahepatic vascular changes on MFI may be useful for grading liver fibrosis.
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Affiliation(s)
- Katsutoshi Sugimoto
- Department of Gastroenterology and Hepatology, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.
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Szucs-Farkas Z, Patak MA, Yuksel-Hatz S, Ruder T, Vock P. Improved detection of pulmonary nodules on energy-subtracted chest radiographs with a commercial computer-aided diagnosis software: comparison with human observers. Eur Radiol 2009; 20:1289-96. [PMID: 19936752 DOI: 10.1007/s00330-009-1667-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2009] [Revised: 09/01/2009] [Accepted: 09/11/2009] [Indexed: 12/21/2022]
Abstract
OBJECTIVE To retrospectively analyze the performance of a commercial computer-aided diagnosis (CAD) software in the detection of pulmonary nodules in original and energy-subtracted (ES) chest radiographs. METHODS Original and ES chest radiographs of 58 patients with 105 pulmonary nodules measuring 5-30 mm and images of 25 control subjects with no nodules were randomized. Five blinded readers evaluated firstly the original postero-anterior images alone and then together with the subtracted radiographs. In a second phase, original and ES images were analyzed by a commercial CAD program. CT was used as reference standard. CAD results were compared to the readers' findings. True-positive (TP) and false-positive (FP) findings with CAD on subtracted and non-subtracted images were compared. RESULTS Depending on the reader's experience, CAD detected between 11 and 21 nodules missed by readers. Human observers found three to 16 lesions missed by the CAD software. CAD used with ES images produced significantly fewer FPs than with non-subtracted images: 1.75 and 2.14 FPs per image, respectively (p = 0.029). The difference for the TP nodules was not significant (40 nodules on ES images and 34 lesions in non-subtracted radiographs, p = 0.142). CONCLUSION CAD can improve lesion detection both on energy subtracted and non-subtracted chest images, especially for less experienced readers. The CAD program marked less FPs on energy-subtracted images than on original chest radiographs.
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Affiliation(s)
- Zsolt Szucs-Farkas
- Department of Diagnostic, Interventional and Pediatric Radiology, University Hospital of Berne, Freiburgstrasse 4, Berne 3010, Switzerland.
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De Boo DW, Prokop M, Uffmann M, van Ginneken B, Schaefer-Prokop CM. Computer-aided detection (CAD) of lung nodules and small tumours on chest radiographs. Eur J Radiol 2009; 72:218-25. [PMID: 19747791 DOI: 10.1016/j.ejrad.2009.05.062] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2009] [Accepted: 05/07/2009] [Indexed: 11/28/2022]
Abstract
Detection of focal pulmonary lesions is limited by quantum and anatomic noise and highly influenced by variable perception capacity of the reader. Multiple studies have proven that lesions - missed at time of primary interpretation - were visible on the chest radiographs in retrospect. Computer-aided diagnosis (CAD) schemes do not alter the anatomic noise but aim at decreasing the intrinsic limitations and variations of human perception by alerting the reader to suspicious areas in a chest radiograph when used as a 'second reader'. Multiple studies have shown that the detection performance can be improved using CAD especially for less experienced readers at a variable amount of decreased specificity. There seem to be a substantial learning process for both, experienced and inexperienced readers, to be able to optimally differentiate between false positive and true positive lesions and to build up sufficient trust in the capabilities of these systems to be able to use them at their full advantage. Studies so far focussed on stand-alone performance of the CAD schemes to reveal the magnitude of potential impact or on retrospective evaluation of CAD as a second reader for selected study groups. Further research is needed to assess the performance of these systems in clinical routine and to determine the trade-off between performance increase in terms of increased sensitivity and decreased inter-reader variability and loss of specificity and secondary indicated follow-up examinations for further diagnostic workup.
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Affiliation(s)
- D W De Boo
- Dept. of Radiology, Academic Medical Center, Meibergdreef 9, Amsterdam, Netherlands.
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White CS, Flukinger T, Jeudy J, Chen JJ. Use of a computer-aided detection system to detect missed lung cancer at chest radiography. Radiology 2009; 252:273-81. [PMID: 19561261 DOI: 10.1148/radiol.2522081319] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
PURPOSE To study the ability of a computer-aided detection (CAD) system to detect lung cancer overlooked at initial interpretation by the radiologist. MATERIALS AND METHODS Institutional review board approval was given for this study. Patient consent was not required; a HIPAA waiver was granted because of the retrospective nature of the data collection. In patients with lung cancer diagnosed from 1995 to 2006 at two institutions, each chest radiograph obtained prior to tumor discovery was evaluated by two radiologists for an overlooked lesion. The size and location of the nodules were documented and graded for subtlety (grades 1-4, 1 = very subtle). Each radiograph with a missed lesion was analyzed by a commercial CAD system, as was the follow-up image at diagnosis. An age- and sex-matched control group was used to assess CAD false-positive rates. RESULTS Missed lung cancer was found in 89 patients (age range, 51-86 years; mean age, 65 years; 80 men, nine women) on 114 radiographs. Lesion size ranged from 0.4 to 5.5 cm (mean, 1.8 cm). Lesions were most commonly peripheral (n = 63, 71%) and in upper lobes (n = 67, 75%). Lesion subtlety score was 1, 2, 3, or 4 on 43, 49, 17, and five radiographs, respectively. CAD identified 53 (47%) and 46 (52%) undetected lesions on a per-image and per-patient basis, respectively. The average size of lesions detected with CAD was 1.73 cm compared with 1.85 cm for lesions that were undetected (P = .47). A significant difference (P = .017) was found in the average subtlety score between detected lesions (score, 2.06) and undetected lesions (score, 1.68). An average of 3.9 false-positive results occurred per radiograph; an average of 2.4 false-positive results occurred per radiograph for the control group. CONCLUSION CAD has the potential to detect approximately half of the lesions overlooked by human readers at chest radiography.
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Affiliation(s)
- Charles S White
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201, USA.
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Sugimoto K, Shiraishi J, Moriyasu F, Saito K, Doi K. Improved detection of hepatic metastases with contrast-enhanced low mechanical-index pulse inversion ultrasonography during the liver-specific phase of sonazoid: observer performance study with JAFROC analysis. Acad Radiol 2009; 16:798-809. [PMID: 19394876 DOI: 10.1016/j.acra.2008.12.025] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2008] [Revised: 12/22/2008] [Accepted: 12/24/2008] [Indexed: 12/27/2022]
Abstract
RATIONALE AND OBJECTIVES To compare B-mode ultrasonography (US) alone with the combination of B-mode and contrast-enhanced (Sonazoid) late-phase pulse-inversion US for the detection of hepatic metastases by use of jackknife free-response receiver-operating characteristic (JAFROC) analysis. MATERIALS AND METHODS Twenty-seven patients with 57 hepatic metastases and 6 patients without hepatic metastases underwent B-mode and contrast-enhanced US. We used the diagnoses established by contrast-enhanced computed tomography and contrast-enhanced US as the standard of reference. All ultrasonographic scanning was performed by an experienced radiologist with a routine clinical procedure. All scanning data were archived with digital cine clips. A review system, which can display pairs of cine clips for B-mode and contrast-enhanced US side by side, was developed for off-site observer study. Seven radiologists interpreted each case individually first by B-mode US only, and then by the combination with contrast-enhanced US by identifying locations of possible candidates for hepatic metastasis with their confidence ratings. The figure-of-merit (FOM) values, sensitivity, and false-positives per case were estimated for B-mode US alone, and for the combination of B-mode and contrast-enhanced US. RESULTS The sensitivities of the combined ultrasonographic imaging (mean, 72.2%) were clearly improved from that of B-mode US alone (mean, 41.6%) while reducing the average number of false positives from 1.1 to 0.5 per case. In the jackknife analysis, there was a statistically significant difference between mean FOM values for the combined imaging (0.76) and for B-mode US alone (0.44, P < .00001). CONCLUSION Evaluating cine clips of contrast-enhanced liver US together with B-mode US could improve physicians' accuracy for detection of hepatic metastases.
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Affiliation(s)
- Katsutoshi Sugimoto
- Kurt Rossmann Laboratories for Radiologic Imaging Research, Department of Radiology, The University of Chicago, 5841 S. Maryland Ave., MC 2026, Chicago, IL 60637, USA.
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Kitajima M, Hirai T, Katsuragawa S, Okuda T, Fukuoka H, Sasao A, Akter M, Awai K, Nakayama Y, Ikeda R, Yamashita Y, Yano S, Kuratsu JI, Doi K. Differentiation of common large sellar-suprasellar masses effect of artificial neural network on radiologists' diagnosis performance. Acad Radiol 2009; 16:313-20. [PMID: 19201360 DOI: 10.1016/j.acra.2008.09.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2008] [Revised: 09/14/2008] [Accepted: 09/14/2008] [Indexed: 10/21/2022]
Abstract
RATIONALE AND OBJECTIVES When pituitary adenoma, craniopharyngioma, and Rathke's cleft cyst grow in the sellar and suprasellar region, it is often difficult to differentiate among these three lesions on magnetic resonance (MR) images. The purpose of this study was to apply an artificial neural network (ANN) for differential diagnosis among these three lesions with MR images and retrospectively evaluate the effect of ANN output on radiologists' performance. MATERIALS AND METHODS Forty-three patients with sellar-suprasellar masses were studied. The ANN was designed to differentiate among pituitary adenoma, craniopharyngioma, and Rathke's cleft cyst by using patients' ages and nine MR image findings obtained by three neuroradiologists using a subjective rating scale. In the observer performance test, MR images were viewed by nine radiologists, including four neuroradiologists and five general radiologists, first without and then with ANN output. The radiologists' performance was evaluated using receiver-operating characteristic analysis with a continuous rating scale. RESULTS The ANN showed high performance in differentiation among the three lesions (area under the receiver-operating characteristic curve, 0.990). The average area under the curve for all radiologists for differentiation among the three lesions increased significantly from 0.910 to 0.985 (P = .0024) when they used the computer output. Areas under the curves for the general radiologists and neuroradiologists increased from 0.876 to 0.983 (P = .0083) and from 0.952 to 0.989 (P = .038), respectively. CONCLUSION In diagnostic performance for differentiation among pituitary macroadenoma, craniopharyngioma, and Rathke's cleft cyst with MR imaging, the ANN resulted in parity between neuroradiologists and general radiologists.
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Giger ML, Chan HP, Boone J. Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. Med Phys 2009; 35:5799-820. [PMID: 19175137 PMCID: PMC2673617 DOI: 10.1118/1.3013555] [Citation(s) in RCA: 165] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists' goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities that are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists-as opposed to a completely automatic computer interpretation-focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous-from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects-collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more-from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis.
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Affiliation(s)
- Maryellen L Giger
- Department of Radiology, University of Chicago, Chicago, Illinois 60637, USA.
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Comparison of radiologist and CAD performance in the detection of CT-confirmed subtle pulmonary nodules on digital chest radiographs. Invest Radiol 2008; 43:343-8. [PMID: 18496038 DOI: 10.1097/rli.0b013e318168f705] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
OBJECTIVES Detection of subtle pulmonary nodules on digital radiography is a challenging task for radiologists. The aim of this study was to evaluate the performance of a newly approved computer aided detection (CAD) system. MATERIALS AND METHODS The sensitivity of 3 radiologists and of a CAD system for the detection of pulmonary nodules from 5 to 15 mm in size on digital chest radiography of 117 patients was compared. The reference standard was established by consensus reading of computed tomography scans by 2 experienced radiologists. Computed tomography scans and chest radiographs were performed within 4 weeks. Sixty-six pulmonary nodules from 42 patients, with a mean nodule diameter of 7.5 mm (standard deviation: 2.2 mm), were included in the statistical analysis. Seventy-five of the 117 patients did not have nodules from 5 to 15 mm of size. RESULTS Two hundred and eighty-eight false-positive detections of the CAD system were found with an average of 2.5 false-positives per image. Sensitivity of the CAD system was 39.4% (95% confidence interval: 11.8%), when compared with 18.2% to 30.3% (95% confidence interval 9.3% to 11.1%) of the 3 radiologists. Substantial agreement for nodule detection ([kappa]N: 0.64-0.73) was found among the 3 radiologists, whereas only moderate agreement was found between the radiologists and the CAD performance ([kappa]N: 0.45-0.52). CONCLUSIONS The CAD system's diagnostic sensitivity in detecting pulmonary nodules of 5 to 15 mm of size was superior to the 1 of radiologists. The CAD system may be used for assisting the radiologist in the detection of lung nodules on digital chest radiographs.
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Improved detection of small lung cancers with dual-energy subtraction chest radiography. AJR Am J Roentgenol 2008; 190:886-91. [PMID: 18356433 DOI: 10.2214/ajr.07.2875] [Citation(s) in RCA: 42] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE The objective of our study was to retrospectively evaluate whether the use of dual-energy subtraction chest radiographs can improve radiologists' performance for the detection of small previously missed lung cancers. MATERIALS AND METHODS Dual-energy subtraction chest radiographs of 19 patients with previously missed nodular cancers, in which the radiology report did not mention a nodule that was visible in retrospect, were selected. Dual-energy subtraction radiographs of 19 patients with cancer and 16 patients without cancer were used for an observer study. Six radiologists indicated their confidence level regarding the presence of a lung cancer and, if they thought a cancer was present, also marked the most likely position for each lung, first using standard posteroanterior and lateral chest radiographs and then using both soft-tissue and bone dual-energy subtraction images along with standard radiographs. Receiver operating characteristic (ROC) curves were used to evaluate the observers' performance. The indicated locations of cancers and false-positives were also analyzed. RESULTS The average area under the ROC curve (A(z)) value for the six radiologists was improved from 0.718 to 0.816, a statistically significant amount (p = 0.004), and the average sensitivity (correct localizations) for 19 previously missed cancers was also significantly improved from 40% to 59% (p = 0.008) with the aid of dual-energy subtraction images. The average number of false-positive (incorrect) localizations on 70 lungs was 10 without and nine with dual-energy subtraction images (p = 0.785). CONCLUSION Dual-energy subtraction chest radiography has the potential to improve radiologists' performance for the detection of small missed lung cancers.
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van Beek EJ, Mullan B, Thompson B. Evaluation of a real-time interactive pulmonary nodule analysis system on chest digital radiographic images: a prospective study. Acad Radiol 2008; 15:571-5. [PMID: 18423313 DOI: 10.1016/j.acra.2008.01.018] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2007] [Revised: 12/15/2007] [Accepted: 12/15/2007] [Indexed: 01/13/2023]
Abstract
RATIONALE AND OBJECTIVES We sought to assess the performance of a real-time interactive pulmonary nodule analysis system for evaluation of chest digital radiographic (DR) images in a routine clinical environment. MATERIALS AND METHODS A real-time interactive pulmonary nodule analysis system for chest DR image softcopy reading (IQQA-Chest; EDDA Technology, Princeton Junction, NJ) was used in daily practice with a Picture Archiving and Communication System in a National Cancer Institute-designated cancer teaching hospital. Patients referred for follow-up of known cancer underwent digital chest radiography. Posteroanterior and lateral DR images were first read by resident radiologists along with experienced chest radiologists using a Picture Archiving and Communication System work station. The computer-assisted detection (CAD) program was subsequently applied to the posteroanterior DR images, and changes (if any) in diagnosis were recorded. For reference standard, a follow-up chest radiograph at least 6 months following the initial examination or a follow-up computed tomographic scan of the chest within 3 months was used to establish diagnostic accuracy. RESULTS Of 324 DR examinations, follow-up imaging according to our parameters was available for 214 patients (67%). Lung nodules were found and subsequently confirmed in 35 patients (10%) without CAD. Using CAD, nodules were found and subsequently confirmed in 51 patients (15%), improving sensitivity from 63.8% (95% confidence interval [CI], 0.49%-0.76%) to 92.7% (95% CI, 0.82%-0.98%) (P < .0001, McNemar). Nodules were subsequently proved to be malignant in five of the 16 additional cases (31%). False-positive readings increased from three to six cases; specificity decreased from 98.1% (95% CI, 0.95%-0.99%) to 96.2% (95% CI, 0.92%-0.98%) (not significant). There were 153 true-negative cases (71.4%). CONCLUSIONS This study suggests that the interpretation of chest radiographs for lung nodules can be improved using an automated CAD nodule detection system. This improvement in reader performance comes with a minimal number of false-positive interpretations.
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Chan HP, Hadjiiski L, Zhou C, Sahiner B. Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography-a review. Acad Radiol 2008; 15:535-55. [PMID: 18423310 PMCID: PMC2800985 DOI: 10.1016/j.acra.2008.01.014] [Citation(s) in RCA: 58] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2007] [Revised: 01/01/2008] [Accepted: 01/17/2008] [Indexed: 02/08/2023]
Abstract
Computer-aided detection (CADe) and computer-aided diagnosis (CADx) have been important areas of research in the last two decades. Significant progress has been made in the area of breast cancer detection, and CAD techniques are being developed in many other areas. Recent advances in multidetector row computed tomography have made it an increasingly common modality for imaging of lung diseases. A thoracic examination using thin-section computed tomography contains hundreds of images. Detection of lung cancer and pulmonary embolism on computed tomographic (CT) examinations are demanding tasks for radiologists because they have to search for abnormalities in a large number of images, and the lesions can be subtle. If successfully developed, CAD can be a useful second opinion to radiologists in thoracic CT interpretation. In this review, we summarize the studies that have been reported in these areas, discuss some challenges in the development of CAD, and identify areas that deserve particular attention in future research.
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Affiliation(s)
- Heang-Ping Chan
- Department of Radiology, Med Inn Building C477, 1500 East Medical Center Drive, The University of Michigan, Ann Arbor, MI 48109-5842, USA.
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Li F, Engelmann R, Metz CE, Doi K, MacMahon H. Lung cancers missed on chest radiographs: results obtained with a commercial computer-aided detection program. Radiology 2008; 246:273-80. [PMID: 18096539 DOI: 10.1148/radiol.2461061848] [Citation(s) in RCA: 56] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE To retrospectively determine the sensitivity of and number of false-positive marks made by a commercially available computer-aided detection (CAD) system for identifying lung cancers previously missed on chest radiographs by radiologists, with histopathologic results as the reference standard. MATERIALS AND METHODS Institutional review board approval was obtained for this HIPAA-compliant study; the requirement for informed patient consent was waived. A CAD nodule detection program was applied to 34 posteroanterior digital chest radiographs obtained in 34 patients (21 men, 13 women; mean age, 69 years). All 34 radiographs showed a nodular lung cancer that was apparent in retrospect but had not been mentioned in the report. Two radiologists identified these radiologist-missed cancers on the chest radiographs and graded them for visibility, location, subtlety (extremely subtle to extremely obvious on a 10-point scale), and actionability (actionable or not actionable according to whether the radiologists probably would have recommended follow-up if the nodule had been detected). The CAD results were analyzed to determine the numbers of cancers and false-positive nodules marked and to correlate the CAD results with the nodule grades for subtlety and actionability. The chi2 test or Fisher exact test for independence was used to compare CAD sensitivity between the very subtle (grade 1-3) and relatively obvious (grade > 3) cancers and between the actionable and not actionable cancers. RESULTS The CAD program had an overall sensitivity of 35% (12 of 34 cancers), identifying seven (30%) of 23 very subtle and five (45%) of 11 relatively obvious radiologist-missed cancers (P = .21) and detecting two (25%) of eight missed not actionable and ten (38%) of 26 missed actionable cancers (P = .33). The CAD program made an average of 5.9 false-positive marks per radiograph. CONCLUSION The described CAD system can mark a substantial proportion of visually subtle lung cancers that are likely to be missed by radiologists.
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Affiliation(s)
- Feng Li
- Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, MC-2026, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637, USA.
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Cronin P. 2D or not 2D that is the question, but 3D is the answer. Acad Radiol 2007; 14:769-71. [PMID: 17574127 DOI: 10.1016/j.acra.2007.05.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2007] [Revised: 05/09/2007] [Accepted: 05/09/2007] [Indexed: 11/22/2022]
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Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 2007; 31:198-211. [PMID: 17349778 PMCID: PMC1955762 DOI: 10.1016/j.compmedimag.2007.02.002] [Citation(s) in RCA: 693] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. In this article, the motivation and philosophy for early development of CAD schemes are presented together with the current status and future potential of CAD in a PACS environment. With CAD, radiologists use the computer output as a "second opinion" and make the final decisions. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms. A CAD scheme that makes use of lateral chest images has the potential to improve the overall performance in the detection of lung nodules when combined with another CAD scheme for PA chest images. Because vertebral fractures can be detected reliably by computer on lateral chest radiographs, radiologists' accuracy in the detection of vertebral fractures would be improved by the use of CAD, and thus early diagnosis of osteoporosis would become possible. In MRA, a CAD system has been developed for assisting radiologists in the detection of intracranial aneurysms. On successive bone scan images, a CAD scheme for detection of interval changes has been developed by use of temporal subtraction images. In the future, many CAD schemes could be assembled as packages and implemented as a part of PACS. For example, the package for chest CAD may include the computerized detection of lung nodules, interstitial opacities, cardiomegaly, vertebral fractures, and interval changes in chest radiographs as well as the computerized classification of benign and malignant nodules and the differential diagnosis of interstitial lung diseases. In order to assist in the differential diagnosis, it would be possible to search for and retrieve images (or lesions) with known pathology, which would be very similar to a new unknown case, from PACS when a reliable and useful method has been developed for quantifying the similarity of a pair of images for visual comparison by radiologists.
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Affiliation(s)
- Kunio Doi
- Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA.
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Campadelli P, Casiraghi E, Artioli D. A fully automated method for lung nodule detection from postero-anterior chest radiographs. IEEE TRANSACTIONS ON MEDICAL IMAGING 2006; 25:1588-603. [PMID: 17167994 DOI: 10.1109/tmi.2006.884198] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
In the past decades, a great deal of research work has been devoted to the development of systems that could improve radiologists' accuracy in detecting lung nodules. Despite the great efforts, the problem is still open. In this paper, we present a fully automated system processing digital postero-anterior (PA) chest radiographs, that starts by producing an accurate segmentation of the lung field area. The segmented lung area includes even those parts of the lungs hidden behind the heart, the spine, and the diaphragm, which are usually excluded from the methods presented in the literature. This decision is motivated by the fact that lung nodules may be found also in these areas. The segmented area is processed with a simple multiscale method that enhances the visibility of the nodules, and an extraction scheme is then applied to select potential nodules. To reduce the high number of false positives extracted, cost-sensitive support vector machines (SVMs) are trained to recognize the true nodules. Different learning experiments were performed on two different data sets, created by means of feature selection, and employing Gaussian and polynomial SVMs trained with different parameters; the results are reported and compared. With the best SVM models, we obtain about 1.5 false positives per image (fp/image) when sensitivity is approximately equal to 0.71; this number increases to about 2.5 and 4 fp/image when sensitivity is = 0.78 and = 0.85, respectively. For the highest sensitivity (= 0.92 and 1.0), we get 7 or 8 fp/image.
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Affiliation(s)
- Paola Campadelli
- Department of Computer Science, Universita degli Studi di Milano, Milan 20135, Italy.
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Abstract
There have been many remarkable advances in conventional thoracic imaging over the past decade. Perhaps the most remarkable is the rapid conversion from film-based to digital radiographic systems. Computed radiography is now the preferred imaging modality for bedside chest imaging. Direct radiography is rapidly replacing film-based chest units for in-department posteroanterior and lateral examinations. An exciting aspect of the conversion to digital radiography is the ability to enhance the diagnostic capabilities and influence of chest radiography. Opportunities for direct computer-aided detection of various lesions may enhance the radiologist's accuracy and improve efficiency. Newer techniques such as dual-energy and temporal subtraction radiography show promise for improved detection of subtle and often obscured or overlooked lung lesions. Digital tomosynthesis is a particularly promising technique that allows reconstruction of multisection images from a short acquisition at very low patient dose. Preliminary data suggest that, compared with conventional radiography, tomosynthesis may also improve detection of subtle lung lesions. The ultimate influence of these new technologies will, of course, depend on the outcome of rigorous scientific validation.
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Affiliation(s)
- H Page McAdams
- Department of Radiology, Duke Advanced Imaging Laboratories, Duke University Medical Center, Box 3808, Durham, NC 27710, USA.
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Ober CP, Barber D. COMPARISON OF TWO- VS. THREE-VIEW THORACIC RADIOGRAPHIC STUDIES ON CONSPICUITY OF STRUCTURED INTERSTITIAL PATTERNS IN DOGS. Vet Radiol Ultrasound 2006; 47:542-5. [PMID: 17153062 DOI: 10.1111/j.1740-8261.2006.00183.x] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Three-view thoracic radiography is often used to evaluate patients for pulmonary metastatic disease. Although use of three views has been reported to be more sensitive than two views for focal lung disease, it also requires increased time, effort, and radiographic exposure of patients and personnel. This study was performed to evaluate the conspicuity of lesions on two-view vs. three-view radiographic procedures to determine the proportion of diagnoses that would change. One hundred three-view radiographic studies of the canine thorax were randomized, and four protocols were reviewed for each study: right lateral and ventrodorsal views, left lateral and ventrodorsal views, both lateral views, and all three views. Radiographs were interpreted as either positive or negative for structured interstitial pulmonary disease, and the certainty of the reading was recorded using a visual analog scale. There was 85-88% agreement between each two-view group and the three-view group, with the kapp statistic ranging from 0.698 to 0.758. There were no differences in certainty of diagnosis among the groups, though within each group there was more certainty for positive diagnoses than negative diagnoses. These findings indicate that three-view studies should be continued when evaluating for possible structured interstitial pulmonary disease, including metastatic disease, as eliminating one view from a three-view study would change the diagnosis in 12-15% of patients.
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Affiliation(s)
- Christopher P Ober
- Department of Small Animal Clinical Sciences, Virginia-Maryland Regional College of Veterinary Medicine, Mailcode 0442, Blacksburg, VA 24061, USA.
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Li F, Li Q, Engelmann R, Aoyama M, Sone S, MacMahon H, Doi K. Improving radiologists' recommendations with computer-aided diagnosis for management of small nodules detected by CT. Acad Radiol 2006; 13:943-50. [PMID: 16843846 DOI: 10.1016/j.acra.2006.04.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2005] [Revised: 04/07/2006] [Accepted: 04/17/2006] [Indexed: 12/21/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate how computer-aided diagnosis (CAD) can improve radiologists' recommendations for management of possible early lung cancers on CT. MATERIALS AND METHODS Twenty-eight lung cancers and 28 benign lesions were employed. Each group of 28 lesions was classified into subgroups of two sizes (9 between 6 and 10 mm and 19 between 11 and 20 mm) and three patterns (8 with pure ground glass opacity [GGO], 12 with mixed GGO and 8 solid lesions). Sixteen radiologists participated in the observer study, first without and then with CAD. Radiologists' recommendations, including (1) follow-up in 12 months, (2) in 6 months, (3) in 3 months, or (4) biopsy, were compared at three levels of their malignancy probability ratings (low: 1%-33%; medium: 34%-66%; high: 67%-99%) for 896 observations (56 lesions by the 16 radiologists) in the two size subgroups and three patterns. RESULTS The number of recommendations changed by radiologists by use of CAD was 163 (18%) among all 896 observations. Among these changed recommendations, the fraction showing a beneficial effect from CAD was 68% (111/163), and the fraction showing a beneficial effect regarding biopsy recommendations was 69% (48/70). With CAD, the radiologists' performance regarding biopsy recommendations was significantly improved for 43 lung cancers (31 changed to biopsy versus 12 changed away from biopsy; P = .003) and was also improved for 27 benign lesions (10 changed to biopsy versus 17 changed away from biopsy; P = .18). Most of the cancers with improved recommendations were solid lesions or mixed GGO and relatively large. CONCLUSION CAD has the potential to improve the appropriateness of radiologists' recommendations for small malignant and benign lesions on CT scans.
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Affiliation(s)
- Feng Li
- Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, Chicago, IL, USA.
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Matsumoto S, Kundel HL, Gee JC, Gefter WB, Hatabu H. Pulmonary nodule detection in CT images with quantized convergence index filter. Med Image Anal 2006; 10:343-52. [PMID: 16542867 DOI: 10.1016/j.media.2005.07.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2003] [Revised: 01/08/2004] [Accepted: 07/07/2005] [Indexed: 01/15/2023]
Abstract
A novel filter termed quantized convergence index filter (QCI filter) that is capable of enhancing the conspicuity of rounded lesions is proposed as part of a CAD (computer-aided diagnosis) scheme for detecting pulmonary nodules in computed tomography (CT) images. In this filter and its predecessor, the convergence index filter (CI filter), the output at a pixel represents the degree of convergence toward the pixel shown by the directions of gray-level gradients at surrounding pixels. The QCI filter and the CAD scheme were evaluated using five clinical datasets containing 50 nodules. With the support region of 9 x 9 pixels, the QCI filter showed more selective response to the nodules than the CI filter. In the CAD scheme, intermediate nodule candidates are generated based on the QCI filter output and then classified using linear discriminant analysis of eight features that are attributed to each intermediate nodule candidate. The QCI filter output level itself was used as one of the features. The scheme achieved a sensitivity of 90% with 1.67 false positives per slice. The QCI filter output level was most effective among the features in correctly classifying intermediate nodule candidates. The QCI filter is promising as a tool of preprocessing for automated pulmonary nodule detection in CT images.
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Affiliation(s)
- Sumiaki Matsumoto
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan.
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Li F, Arimura H, Suzuki K, Shiraishi J, Li Q, Abe H, Engelmann R, Sone S, MacMahon H, Doi K. Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. Radiology 2005; 237:684-90. [PMID: 16244277 DOI: 10.1148/radiol.2372041555] [Citation(s) in RCA: 96] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE To retrospectively evaluate whether a difference-image computer-aided detection (CAD) scheme can help radiologists detect peripheral lung cancers missed at low-dose computed tomography (CT). MATERIALS AND METHODS Institutional review board approval and informed patient and observer consent were obtained. Seventeen patients (eight men and nine women; mean age, 60 years) with a missed peripheral lung cancer and 10 control subjects (five men and five women; mean age, 63 years) without cancer at low-dose CT were included in an observer study. Fourteen radiologists were divided into two groups on the basis of different image display formats: Six radiologists (group 1) reviewed CT scans with a multiformat display, and eight radiologists (group 2) reviewed images with a "stacked" cine-mode display. The radiologists, first without and then with the CAD scheme, indicated their confidence level regarding the presence (or absence) of cancer and the most likely position of a lesion on each CT scan. Receiver operating characteristic (ROC) curves were calculated without and with localization to evaluate the observers' performance. RESULTS With the CAD scheme, the average area under the ROC curve improved from 0.763 to 0.854 for all radiologists (P = .002), from 0.757 to 0.862 for group 1 (P = .04), and from 0.768 to 0.848 for group 2 (P = .01). The average sensitivity in the detection of 17 cancers improved from 52% (124 of 238 observations) to 68% (163 of 238 observations) for all radiologists (P < .001), from 49% (50 of 102 observations) to 71% (72 of 102 observations) for group 1 (P = .02), and from 54% (74 of 136 observations) to 67% (91 of 136 observations) for group 2 (P = .006). The localization ROC curve also improved. CONCLUSION Lung cancers missed at low-dose CT were very difficult to detect, even in an observer study. The use of CAD, however, can improve radiologists' performance in the detection of these subtle cancers.
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Affiliation(s)
- Feng Li
- Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, IL 60637, USA.
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Peldschus K, Herzog P, Wood SA, Cheema JI, Costello P, Schoepf UJ. Computer-aided diagnosis as a second reader: spectrum of findings in CT studies of the chest interpreted as normal. Chest 2005; 128:1517-23. [PMID: 16162752 DOI: 10.1378/chest.128.3.1517] [Citation(s) in RCA: 49] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
Abstract
STUDY OBJECTIVES To assess the performance of an automated computer-aided detection (CAD) system as a second reader on chest CT studies interpreted as normal at routine clinical interpretation. DESIGN Chest CT studies were processed using a prototype CAD system for automated detection of lung lesions. Three experienced radiologists analyzed each CAD finding and confirmed or dismissed the marked image features as lung lesions. Noncalcified, focal lung lesions were classified according to size as being of high (> or = 10 mm), intermediate (5 to 9 mm), or low (< or = 4 mm) significance. SETTING Two sub-specialized academic tertiary referral centers in the United States and Germany. PATIENTS Chest CT studies were performed in 100 patients, with results initially reported as normal at clinical double reading. Indications for chest CT were suspected pulmonary embolism (PE) [n = 33], lung cancer screening in a high-risk population (n = 28), or follow-up for a cancer history (n = 39). INTERVENTIONS Reevaluation of all chest CT studies for focal lung lesions with the CAD system as a second reader. MEASUREMENTS Prevalence and spectrum of lung lesions missed at routine clinical interpretation but found by the CAD system. RESULTS In 33% (33 of 100 patients), CAD detected significant lung lesions that were not previously reported. Fifty-three significant lesions were detected (mean, 1.6 lesions per case), of which 5 lesions (9.4%) were of high significance, 21 lesions (39.6%) were of intermediate significance, and 27 lesions (50.9%) were of low significance. In the PE group, the lung cancer screening group, and the group with a cancer history, four patients (12.1%), six patients (21.4%), and nine patients (23.1%), respectively, had focal lung lesions of high and/or intermediate significance. The false-positive rate of the CAD system was an average of 1.25 per case (range, 0 to 11). CONCLUSIONS Significant lung lesions are frequently missed at routine clinical interpretation of chest CT studies but may be detected if CAD is used as an additional reader.
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Affiliation(s)
- Kersten Peldschus
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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