1
|
Paramasamy J, Mandal S, Blomjous M, Mulders T, Bos D, Aerts JGJV, Vanapalli P, Challa V, Sathyamurthy S, Devi R, Jain R, Visser JJ. Validation of a commercially available CAD-system for lung nodule detection and characterization using CT-scans. Eur Radiol 2024:10.1007/s00330-024-10969-0. [PMID: 39042303 DOI: 10.1007/s00330-024-10969-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/27/2024] [Accepted: 06/30/2024] [Indexed: 07/24/2024]
Abstract
OBJECTIVES This study aims to externally validate a commercially available Computer-Aided Detection (CAD)-system for the automatic detection and characterization of solid, part-solid, and ground-glass lung nodules (LN) on CT scans. METHODS This retrospective study encompasses 263 chest CT scans performed between January 2020 and December 2021 at a Dutch university hospital. All scans were read by a radiologist (R1) and compared with the initial radiology report. Conflicting scans were assessed by an adjudicating radiologist (R2). All scans were also processed by CAD. The standalone performance of CAD in terms of sensitivity and false-positive (FP)-rate for detection was calculated together with the sensitivity for characterization, including texture, calcification, speculation, and location. The R1's detection sensitivity was also assessed. RESULTS A total of 183 true nodules were identified in 121 nodule-containing scans (142 non-nodule-containing scans), of which R1 identified 165/183 (90.2%). CAD detected 149 nodules, of which 12 were not identified by R1, achieving a sensitivity of 149/183 (81.4%) with an FP-rate of 49/121 (0.405). CAD's detection sensitivity for solid, part-solid, and ground-glass LNs was 82/94 (87.2%), 42/47 (89.4%), and 25/42 (59.5%), respectively. The classification accuracy for solid, part-solid, and ground-glass LNs was 81/82 (98.8%), 16/42 (38.1%), and 18/25 (72.0%), respectively. Additionally, CAD demonstrated overall classification accuracies of 137/149 (91.9%), 123/149 (82.6%), and 141/149 (94.6%) for calcification, spiculation, and location, respectively. CONCLUSIONS Although the overall detection rate of this system slightly lags behind that of a radiologist, CAD is capable of detecting different LNs and thereby has the potential to enhance a reader's detection rate. While promising characterization performances are obtained, the tool's performance in terms of texture classification remains a subject of concern. CLINICAL RELEVANCE STATEMENT Numerous lung nodule computer-aided detection-systems are commercially available, with some of them solely being externally validated based on their detection performance on solid nodules. We encourage researchers to assess performances by incorporating all relevant characteristics, including part-solid and ground-glass nodules. KEY POINTS Few computer-aided detection (CAD) systems are externally validated for automatic detection and characterization of lung nodules. A detection sensitivity of 81.4% and an overall texture classification sensitivity of 77.2% were measured utilizing CAD. CAD has the potential to increase single reader detection rate, however, improvement in texture classification is required.
Collapse
Affiliation(s)
- Jasika Paramasamy
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Souvik Mandal
- Qure.ai, Level 7, Oberoi Commerz II, Goregaon East, Mumbai, 400063, India
| | - Maurits Blomjous
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Ties Mulders
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Daniel Bos
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Joachim G J V Aerts
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Prakash Vanapalli
- Qure.ai, Level 7, Oberoi Commerz II, Goregaon East, Mumbai, 400063, India
| | - Vikash Challa
- Qure.ai, Level 7, Oberoi Commerz II, Goregaon East, Mumbai, 400063, India
| | | | - Ranjana Devi
- Qure.ai, Level 7, Oberoi Commerz II, Goregaon East, Mumbai, 400063, India
| | - Ritvik Jain
- Qure.ai, Level 7, Oberoi Commerz II, Goregaon East, Mumbai, 400063, India
| | - Jacob J Visser
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.
| |
Collapse
|
2
|
Okumura E, Kato H, Honmoto T, Suzuki N, Okumura E, Higashigawa T, Kitamura S, Ando J, Ishida T. [Segmentation of Mass in Mammogram Using Gaze Search Patterns]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2024; 80:487-498. [PMID: 38479883 DOI: 10.6009/jjrt.2024-1438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/21/2024]
Abstract
PURPOSE It is very difficult for a radiologist to correctly detect small lesions and lesions hidden on dense breast tissue on a mammogram. Therefore, recently, computer-aided detection (CAD) systems have been widely used to assist radiologists in interpreting images. Thus, in this study, we aimed to segment mass on the mammogram with high accuracy by using focus images obtained from an eye-tracking device. METHODS We obtained focus images for two mammography expert radiologists and 19 mammography technologists on 8 abnormal and 8 normal mammograms published by the DDSM. Next, the auto-encoder, Pix2Pix, and UNIT learned the relationship between the actual mammogram and the focus image, and generated the focus image for the unknown mammogram. Finally, we segmented regions of mass on mammogram using the U-Net for each focus image generated by the auto-encoder, Pix2Pix, and UNIT. RESULTS The dice coefficient in the UNIT was 0.64±0.14. The dice coefficient in the UNIT was higher than that in the auto-encoder and Pix2Pix, and there was a statistically significant difference (p<0.05). The dice coefficient of the proposed method, which combines the focus images generated by the UNIT and the original mammogram, was 0.66±0.15, which is equivalent to the method using the original mammogram. CONCLUSION In the future, it will be necessary to increase the number of cases and further improve the segmentation.
Collapse
Affiliation(s)
- Eiichiro Okumura
- Department of Radiological Technology, Faculty of Health Sciences, Tsukuba International University
| | - Hideki Kato
- Department of Radiological Science, Faculty of Health Science, Gunma Paz University
| | - Tsuyoshi Honmoto
- Department of Radiological Technology, Ibaraki Children's Hospital
| | | | - Erika Okumura
- Department of Radiological Technology, Faculty of Health Sciences, Tsukuba International University
| | - Takuji Higashigawa
- Group of Visual Measurement, Department of Technology, Nac Image Technology
| | - Shigemi Kitamura
- Department of Radiological Technology, Faculty of Health Sciences, Tsukuba International University
| | - Jiro Ando
- Hospital Director, Tochigi Cancer Center
| | - Takayuki Ishida
- Division of Health Sciences, Graduate School of Medicine, Osaka University
| |
Collapse
|
3
|
Bharadwaj P, Nicola L, Breau-Brunel M, Sensini F, Tanova-Yotova N, Atanasov P, Lobig F, Blankenburg M. Unlocking the Value: Quantifying the Return on Investment of Hospital Artificial Intelligence. J Am Coll Radiol 2024:S1546-1440(24)00292-8. [PMID: 38499053 DOI: 10.1016/j.jacr.2024.02.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/23/2024] [Accepted: 02/28/2024] [Indexed: 03/20/2024]
Abstract
PURPOSE A comprehensive return on investment (ROI) calculator was developed to evaluate the monetary and nonmonetary benefits of an artificial intelligence (AI)-powered radiology diagnostic imaging platform to inform decision makers interested in adopting AI. METHODS A calculator was constructed to calculate comparative costs, estimated revenues, and quantify the clinical value of using an AI platform compared with no use of AI in radiology workflows of a US hospital over a 5-year time horizon. Parameters were determined on the basis of expert interviews and a literature review. Scenario and deterministic sensitivity analyses were conducted to evaluate calculator drivers. RESULTS In the calculator, the introduction of an AI platform into the hospital radiology workflow resulted in labor time reductions and delivery of an ROI of 451% over a 5-year period. The ROI was increased to 791% when radiologist time savings were considered. Time savings for radiologists included more than 15 8-hour working days of waiting time, 78 days in triage time, 10 days in reading time, and 41 days in reporting time. Using the platform also provided revenue benefits for the hospital in bringing in patients for clinically beneficial follow-up scans, hospitalizations, and treatment procedures. Results were sensitive to the time horizon, health center setting, and number of scans performed. Among those, the most influential outcome was the number of additional necessary treatments performed because of AI identification of patients. CONCLUSIONS The authors demonstrate a substantial 5-year ROI of implementing an AI platform in a stroke management-accredited hospital. The ROI calculator may be useful for decision makers evaluating AI-powered radiology platforms.
Collapse
Affiliation(s)
| | - Lauren Nicola
- CEO/Partner, Triad Radiology Associates; Chair, Ultrasound Commission, ACR; Chair, Reimbursement Committee, ACR
| | | | | | | | - Petar Atanasov
- Principal Consultant, Amaris Consulting, London, United Kingdom
| | | | | |
Collapse
|
4
|
Du Y, Greuter MJW, Prokop MW, de Bock GH. Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening. Insights Imaging 2023; 14:208. [PMID: 38010436 PMCID: PMC10682324 DOI: 10.1186/s13244-023-01561-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 10/28/2023] [Indexed: 11/29/2023] Open
Abstract
OBJECTIVE An increasing number of commercial deep learning computer-aided detection (DL-CAD) systems are available but their cost-saving potential is largely unknown. This study aimed to gain insight into appropriate pricing for DL-CAD in different reading modes to be cost-saving and to determine the potentially most cost-effective reading mode for lung cancer screening. METHODS In three representative settings, DL-CAD was evaluated as a concurrent, pre-screening, and second reader. Scoping review was performed to estimate radiologist reading time with and without DL-CAD. Hourly cost of radiologist time was collected for the USA (€196), UK (€127), and Poland (€45), and monetary equivalence of saved time was calculated. The minimum number of screening CTs to reach break-even was calculated for one-time investment of €51,616 for DL-CAD. RESULTS Mean reading time was 162 (95% CI: 111-212) seconds per case without DL-CAD, which decreased by 77 (95% CI: 47-107) and 104 (95% CI: 71-136) seconds for DL-CAD as concurrent and pre-screening reader, respectively, and increased by 33-41 s for DL-CAD as second reader. This translates into €1.0-4.3 per-case cost for concurrent reading and €0.8-5.7 for pre-screening reading in the USA, UK, and Poland. To achieve break-even with a one-time investment, the minimum number of CT scans was 12,300-53,600 for concurrent reader, and 9400-65,000 for pre-screening reader in the three countries. CONCLUSIONS Given current pricing, DL-CAD must be priced substantially below €6 in a pay-per-case setting or used in a high-workload environment to reach break-even in lung cancer screening. DL-CAD as pre-screening reader shows the largest potential to be cost-saving. CRITICAL RELEVANCE STATEMENT Deep-learning computer-aided lung nodule detection (DL-CAD) software must be priced substantially below 6 euro in a pay-per-case setting or must be used in high-workload environments with one-time investment in order to achieve break-even. DL-CAD as a pre-screening reader has the greatest cost savings potential. KEY POINTS • DL-CAD must be substantially below €6 in a pay-per-case setting to reach break-even. • DL-CAD must be used in a high-workload screening environment to achieve break-even. • DL-CAD as a pre-screening reader shows the largest potential to be cost-saving.
Collapse
Affiliation(s)
- Yihui Du
- Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Normal University, Hangzhou, China
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Marcel J W Greuter
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Mathias W Prokop
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Geertruida H de Bock
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
| |
Collapse
|
5
|
Lafata KJ, Read C, Tong BC, Akinyemiju T, Wang C, Cerullo M, Tailor TD. Lung Cancer Screening in Clinical Practice: A 5-Year Review of Frequency and Predictors of Lung Cancer in the Screened Population. J Am Coll Radiol 2023:S1546-1440(23)00861-X. [PMID: 37952807 DOI: 10.1016/j.jacr.2023.05.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 05/05/2023] [Accepted: 05/16/2023] [Indexed: 11/14/2023]
Abstract
PURPOSE The aims of this study were to evaluate (1) frequency, type, and lung cancer stage in a clinical lung cancer screening (LCS) population and (2) the association between patient characteristics and Lung CT Screening Reporting & Data System (Lung-RADS®) with lung cancer diagnosis. METHODS This retrospective study enrolled individuals undergoing LCS between January 1, 2015, and June 30, 2020. Individuals' sociodemographic characteristics, Lung-RADS scores, pathology-proven lung cancers, and tumor characteristics were determined via electronic health record and the health system's tumor registry. Associations between the outcome of lung cancer diagnosis within 1 year after LCS and covariates of sociodemographic characteristics and Lung-RADS score were determined using logistic regression. RESULTS Of 3,326 individuals undergoing 5,150 LCS examinations, 102 (3.1%) were diagnosed with lung cancer within 1 year of LCS; most of these cancers were screen detected (97 of 102 [95.1%]). Over the study period, there were 118 total LCS-detected cancers in 113 individuals (3.4%). Most LCS-detected cancers were adenocarcinomas (62 of 118 [52%]), 55.9% (65 of 118) were stage I, and 16.1% (19 of 118) were stage IV. The sensitivity, specificity, positive predictive value, and negative predictive value of Lung-RADS in diagnosing lung cancer within 1 year of LCS were 93.1%, 83.8%, 10.6%, and 99.8%, respectively. On multivariable analysis controlling for sociodemographic characteristics, only Lung-RADS score was associated with lung cancer (odds ratio for a one-unit increase in Lung-RADS score, 4.68; 95% confidence interval, 3.87-5.78). CONCLUSIONS The frequency of LCS-detected lung cancer and stage IV cancers was higher than reported in the National Lung Screening Trial. Although Lung-RADS was a significant predictor of lung cancer, the positive predictive value of Lung-RADS is relatively low, implying opportunity for improved nodule classification.
Collapse
Affiliation(s)
- Kyle J Lafata
- Department of Radiology, Duke University Medical Center, Durham, North Carolina; Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina; Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina; Department of Medical Physics Graduate Program, Duke University, Durham, North Carolina
| | - Charlotte Read
- Department of Medical Physics Graduate Program, Duke University, Durham, North Carolina
| | - Betty C Tong
- Department of Surgery, Duke University Medical Center, Durham, North Carolina; Duke Cancer Institute, Durham, North Carolina; Clinical Director, Duke Lung Cancer Screening Program
| | - Tomi Akinyemiju
- Vice Chair, Diversity and Inclusion, Department of Population Health Sciences, Duke University Medical Center, Durham, North Carolina; Associate Director, Community Outreach, Engagement, and Equity, Duke Cancer Institute, Durham, North Carolina
| | - Chunhao Wang
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina
| | - Marcelo Cerullo
- Department of Surgery, Duke University Medical Center, Durham, North Carolina
| | - Tina D Tailor
- Department of Radiology, Duke University Medical Center, Durham, North Carolina; Research Director, Duke Lung Cancer Screening Program, and Cardiothoracic Radiology Fellowship Director.
| |
Collapse
|
6
|
Zhou J, Yu B, Guo P, Wang S. The predictive value of the change of the number of pixels under different CT value intervals in the CT-occult central lung squamous cell carcinoma and squamous epithelial precancerous lesions. BMC Pulm Med 2023; 23:426. [PMID: 37924039 PMCID: PMC10623708 DOI: 10.1186/s12890-023-02732-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 10/24/2023] [Indexed: 11/06/2023] Open
Abstract
BACKGROUND Due to the fact that the CT-occult central lung squamous cell carcinoma and squamous epithelial precancerous lesions. (CT-occult CLSCC and SEPL) cannot be detected by lung CT screening, early and timely diagnosis of central lung cancer becomes very difficult, which directly affects the prognosis of patients. METHODS We retrospectively review medical records of patients at the Zhejiang Cancer Hospital and enrolled 41 patients with the CT-occult CLSCC and SEPL and 48 patients without the CT-occult CLSCC and SEPL. We compare the clinical characteristics, imaging features and Changes in the number of pixels under different CT value intervals of patients with and without the CT-occult CLSCC and SEPL and we perform univariate and multivariate logistic regression analysis to explore independent factors for the CT-occult CLSCC and SEPL in the patients. RESULTS We demonstrate that pack-years ≥ 20 (OR: 3.848, 95% CI: 1.086 ~ 13.633), the number of pixels change of CT value in interval [-850 ~ -750HU] (OR: 5.302, 95% CI: 1.122 ~ 25.057) and in interval [-900 ~ -850HU] (OR: 3.478, 95% CI: 1.167 ~ 10.365) are independently associated with the CT-occult CLSCC and SEPL in the patients. Ultimately, the logistic model obtained is statistically significant (p < 0.05) and an area under the ROC curve is 0.776 (95% CI: 0.682-0.870). The sensitivity of this model is 90.2% and the specificity is 52.1%. CONCLUSION The results of this study indicate that in the CT value range [-950 ~ -750HU], when the total number of lung pixels tend to increase towards the region with high CT value, the probability of the occurrence of CT-occult CLSCC and SEPL lesions also increases. Meanwhile, these results have guiding significance for the further study of radiomic.
Collapse
Affiliation(s)
- Jiaming Zhou
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China
| | - Bijun Yu
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China
| | - Peng Guo
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China
| | - Shi Wang
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China.
| |
Collapse
|
7
|
Adams SJ, Mikhael P, Wohlwend J, Barzilay R, Sequist LV, Fintelmann FJ. Artificial Intelligence and Machine Learning in Lung Cancer Screening. Thorac Surg Clin 2023; 33:401-409. [PMID: 37806742 DOI: 10.1016/j.thorsurg.2023.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Recent advances in artificial intelligence and machine learning (AI/ML) hold substantial promise to address some of the current challenges in lung cancer screening and improve health equity. This article reviews the status and future directions of AI/ML tools in the lung cancer screening workflow, focusing on determining screening eligibility, radiation dose reduction and image denoising for low-dose chest computed tomography (CT), lung nodule detection, lung nodule classification, and determining optimal screening intervals. AI/ML tools can assess for chronic diseases on CT, which creates opportunities to improve population health through opportunistic screening.
Collapse
Affiliation(s)
- Scott J Adams
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Peter Mikhael
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeremy Wohlwend
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Regina Barzilay
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lecia V Sequist
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA; Harvard Medical School, Boston, MA, USA.
| | - Florian J Fintelmann
- Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.
| |
Collapse
|
8
|
Martin MD, Henry TS, Berry MF, Johnson GB, Kelly AM, Ko JP, Kuzniewski CT, Lee E, Maldonado F, Morris MF, Munden RF, Raptis CA, Shim K, Sirajuddin A, Small W, Tong BC, Wu CC, Donnelly EF. ACR Appropriateness Criteria® Incidentally Detected Indeterminate Pulmonary Nodule. J Am Coll Radiol 2023; 20:S455-S470. [PMID: 38040464 DOI: 10.1016/j.jacr.2023.08.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 08/22/2023] [Indexed: 12/03/2023]
Abstract
Incidental pulmonary nodules are common. Although the majority are benign, most are indeterminate for malignancy when first encountered making their management challenging. CT remains the primary imaging modality to first characterize and follow-up incidental lung nodules. This document reviews available literature on various imaging modalities and summarizes management of indeterminate pulmonary nodules detected incidentally. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.
Collapse
Affiliation(s)
- Maria D Martin
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
| | | | - Mark F Berry
- Stanford University Medical Center, Stanford, California; Society of Thoracic Surgeons
| | - Geoffrey B Johnson
- Mayo Clinic, Rochester, Minnesota; Commission on Nuclear Medicine and Molecular Imaging
| | | | - Jane P Ko
- New York University Langone Health, New York, New York; IF Committee
| | | | - Elizabeth Lee
- University of Michigan Health System, Ann Arbor, Michigan
| | - Fabien Maldonado
- Vanderbilt University Medical Center, Nashville, Tennessee; American College of Chest Physicians
| | | | - Reginald F Munden
- Medical University of South Carolina, Charleston, South Carolina; IF Committee
| | | | - Kyungran Shim
- John H. Stroger, Jr. Hospital of Cook County, Chicago, Illinois; American College of Physicians
| | | | - William Small
- Loyola University Chicago, Stritch School of Medicine, Department of Radiation Oncology, Cardinal Bernardin Cancer Center, Maywood, Illinois; Commission on Radiation Oncology
| | - Betty C Tong
- Duke University School of Medicine, Durham, North Carolina; Society of Thoracic Surgeons
| | - Carol C Wu
- The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Edwin F Donnelly
- Specialty Chair, Ohio State University Wexner Medical Center, Columbus, Ohio
| |
Collapse
|
9
|
Ng CKC. Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review. CHILDREN (BASEL, SWITZERLAND) 2023; 10:1372. [PMID: 37628371 PMCID: PMC10453402 DOI: 10.3390/children10081372] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023]
Abstract
Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1-158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely.
Collapse
Affiliation(s)
- Curtise K. C. Ng
- Curtin Medical School, Curtin University, GPO Box U1987, Perth, WA 6845, Australia; or ; Tel.: +61-8-9266-7314; Fax: +61-8-9266-2377
- Curtin Health Innovation Research Institute (CHIRI), Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
| |
Collapse
|
10
|
Ewals LJS, van der Wulp K, van den Borne BEEM, Pluyter JR, Jacobs I, Mavroeidis D, van der Sommen F, Nederend J. The Effects of Artificial Intelligence Assistance on the Radiologists' Assessment of Lung Nodules on CT Scans: A Systematic Review. J Clin Med 2023; 12:jcm12103536. [PMID: 37240643 DOI: 10.3390/jcm12103536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/19/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
To reduce the number of missed or misdiagnosed lung nodules on CT scans by radiologists, many Artificial Intelligence (AI) algorithms have been developed. Some algorithms are currently being implemented in clinical practice, but the question is whether radiologists and patients really benefit from the use of these novel tools. This study aimed to review how AI assistance for lung nodule assessment on CT scans affects the performances of radiologists. We searched for studies that evaluated radiologists' performances in the detection or malignancy prediction of lung nodules with and without AI assistance. Concerning detection, radiologists achieved with AI assistance a higher sensitivity and AUC, while the specificity was slightly lower. Concerning malignancy prediction, radiologists achieved with AI assistance generally a higher sensitivity, specificity and AUC. The radiologists' workflows of using the AI assistance were often only described in limited detail in the papers. As recent studies showed improved performances of radiologists with AI assistance, AI assistance for lung nodule assessment holds great promise. To achieve added value of AI tools for lung nodule assessment in clinical practice, more research is required on the clinical validation of AI tools, impact on follow-up recommendations and ways of using AI tools.
Collapse
Affiliation(s)
- Lotte J S Ewals
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Kasper van der Wulp
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Ben E E M van den Borne
- Department of Pulmonology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Jon R Pluyter
- Department of Experience Design, Royal Philips, 5656 AE Eindhoven, The Netherlands
| | - Igor Jacobs
- Department of Hospital Services and Informatics, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Dimitrios Mavroeidis
- Department of Data Science, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Joost Nederend
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| |
Collapse
|
11
|
Cao Z, Li R, Yang X, Fang L, Li Z, Li J. Multi-scale detection of pulmonary nodules by integrating attention mechanism. Sci Rep 2023; 13:5517. [PMID: 37015969 PMCID: PMC10073202 DOI: 10.1038/s41598-023-32312-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 03/25/2023] [Indexed: 04/06/2023] Open
Abstract
The detection of pulmonary nodules has a low accuracy due to the various shapes and sizes of pulmonary nodules. In this paper, a multi-scale detection network for pulmonary nodules based on the attention mechanism is proposed to accurately predict pulmonary nodules. During data processing, the pseudo-color processing strategy is designed to enhance the gray image and introduce more contextual semantic information. In the feature extraction network section, this paper designs a basic module of ResSCBlock integrating attention mechanism for feature extraction. At the same time, the feature pyramid structure is used for feature fusion in the network, and the problem of the detection of small-size nodules which are easily lost is solved by multi-scale prediction method. The proposed method is tested on the LUNA16 data set, with an 83% mAP value. Compared with other detection networks, the proposed method achieves an improvement in detecting pulmonary nodules.
Collapse
Affiliation(s)
- Zhenguan Cao
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
| | - Rui Li
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China.
| | - Xun Yang
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
| | - Liao Fang
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
| | - Zhuoqin Li
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
| | - Jinbiao Li
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
| |
Collapse
|
12
|
Luo X, Yang Y, Yin S, Li H, Zhang W, Xu G, Fan W, Zheng D, Li J, Shen D, Gao Y, Shao Y, Ban X, Li J, Lian S, Zhang C, Ma L, Lin C, Luo Y, Zhou F, Wang S, Sun Y, Zhang R, Xie C. False-negative and false-positive outcomes of computer-aided detection on brain metastasis: Secondary analysis of a multicenter, multireader study. Neuro Oncol 2023; 25:544-556. [PMID: 35943350 PMCID: PMC10013637 DOI: 10.1093/neuonc/noac192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Errors have seldom been evaluated in computer-aided detection on brain metastases. This study aimed to analyze false negatives (FNs) and false positives (FPs) generated by a brain metastasis detection system (BMDS) and by readers. METHODS A deep learning-based BMDS was developed and prospectively validated in a multicenter, multireader study. Ad hoc secondary analysis was restricted to the prospective participants (148 with 1,066 brain metastases and 152 normal controls). Three trainees and 3 experienced radiologists read the MRI images without and with the BMDS. The number of FNs and FPs per patient, jackknife alternative free-response receiver operating characteristic figure of merit (FOM), and lesion features associated with FNs were analyzed for the BMDS and readers using binary logistic regression. RESULTS The FNs, FPs, and the FOM of the stand-alone BMDS were 0.49, 0.38, and 0.97, respectively. Compared with independent reading, BMDS-assisted reading generated 79% fewer FNs (1.98 vs 0.42, P < .001); 41% more FPs (0.17 vs 0.24, P < .001) but 125% more FPs for trainees (P < .001); and higher FOM (0.87 vs 0.98, P < .001). Lesions with small size, greater number, irregular shape, lower signal intensity, and located on nonbrain surface were associated with FNs for readers. Small, irregular, and necrotic lesions were more frequently found in FNs for BMDS. The FPs mainly resulted from small blood vessels for the BMDS and the readers. CONCLUSIONS Despite the improvement in detection performance, attention should be paid to FPs and small lesions with lower enhancement for radiologists, especially for less-experienced radiologists.
Collapse
Affiliation(s)
- Xiao Luo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yadi Yang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Shaohan Yin
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Hui Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Weijing Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Guixiao Xu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Weixiong Fan
- Department of Radiology, Meizhou People's Hospital, Meizhou, China
| | - Dechun Zheng
- Department of Radiology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, Fujian Province, China
| | - Jianpeng Li
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Guangzhou, China
| | - Dinggang Shen
- R&D Department, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.,School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Yaozong Gao
- R&D Department, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Ying Shao
- R&D Department, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.,Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Xiaohua Ban
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Jing Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Shanshan Lian
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Cheng Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Lidi Ma
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Cuiping Lin
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yingwei Luo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Fan Zhou
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Shiyuan Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Ying Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Rong Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Chuanmiao Xie
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| |
Collapse
|
13
|
Computer-Aided Detection of Subsolid Nodules on Chest Computed Tomography: Assessment of Visualization on Vessel-Suppressed Images. J Comput Assist Tomogr 2023; 47:412-417. [PMID: 36877791 DOI: 10.1097/rct.0000000000001444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
OBJECTIVES This study aimed to clarify the performance of automatic detection of subsolid nodules by commercially available software on computed tomography (CT) images of various slice thicknesses and compare it with visualization on the accompanying vessel-suppression CT (VS-CT) images. METHODS A total of 95 subsolid nodules from 84 CT examinations of 84 patients were included. The reconstructed CT image series of each case with 3-, 2-, and 1-mm slice thicknesses were loaded into a commercially available software application (ClearRead CT) for automatic detection of subsolid nodules and generation of VS-CT images. Automatic nodule detection sensitivity was assessed for 95 nodules on each series of images acquired at 3 slice thicknesses. Four radiologists subjectively evaluated visual assessment of the nodules on VS-CT. RESULTS ClearRead CT automatically detected 69.5% (66/95 nodules), 68.4% (65/95 nodules), and 70.5% (67/95 nodules) of all subsolid nodules in 3-, 2-, and 1-mm slices, respectively. The detection rate was higher for part-solid nodules than for pure ground-glass nodules at all slice thicknesses. In the visualization assessment on VS-CT, 3 nodules at each slice thickness (3.2%) were judged as invisible, while 26 of 29 (89.7%), 27 of 30 (90.0%), and 25 of 28 (89.3%) nodules, which were missed by computer-aided detection, were judged as visible in 3-, 2-, and 1-mm slices, respectively. CONCLUSIONS The automatic detection rate of subsolid nodules by ClearRead CT was approximately 70% at all slice thicknesses. More than 95% of subsolid nodules were visualized on VS-CT, including nodules undetected by the automated software. Computed tomography acquisition at slices thinner than 3 mm did not confer any benefits.
Collapse
|
14
|
Ladbury C, Amini A, Govindarajan A, Mambetsariev I, Raz DJ, Massarelli E, Williams T, Rodin A, Salgia R. Integration of artificial intelligence in lung cancer: Rise of the machine. Cell Rep Med 2023; 4:100933. [PMID: 36738739 PMCID: PMC9975283 DOI: 10.1016/j.xcrm.2023.100933] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 11/14/2022] [Accepted: 01/17/2023] [Indexed: 02/05/2023]
Abstract
The goal of oncology is to provide the longest possible survival outcomes with the therapeutics that are currently available without sacrificing patients' quality of life. In lung cancer, several data points over a patient's diagnostic and treatment course are relevant to optimizing outcomes in the form of precision medicine, and artificial intelligence (AI) provides the opportunity to use available data from molecular information to radiomics, in combination with patient and tumor characteristics, to help clinicians provide individualized care. In doing so, AI can help create models to identify cancer early in diagnosis and deliver tailored therapy on the basis of available information, both at the time of diagnosis and in real time as they are undergoing treatment. The purpose of this review is to summarize the current literature in AI specific to lung cancer and how it applies to the multidisciplinary team taking care of these complex patients.
Collapse
Affiliation(s)
- Colton Ladbury
- Department of Radiation Oncology, City of Hope National Medical Center, 1500 E Duarte Road, Duarte, CA 91010, USA
| | - Arya Amini
- Department of Radiation Oncology, City of Hope National Medical Center, 1500 E Duarte Road, Duarte, CA 91010, USA.
| | - Ameish Govindarajan
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Isa Mambetsariev
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Dan J Raz
- Department of Surgery, City of Hope National Medical Center, Duarte, CA, USA
| | - Erminia Massarelli
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Terence Williams
- Department of Radiation Oncology, City of Hope National Medical Center, 1500 E Duarte Road, Duarte, CA 91010, USA
| | - Andrei Rodin
- Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Ravi Salgia
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| |
Collapse
|
15
|
Milam ME, Koo CW. The current status and future of FDA-approved artificial intelligence tools in chest radiology in the United States. Clin Radiol 2023; 78:115-122. [PMID: 36180271 DOI: 10.1016/j.crad.2022.08.135] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/19/2022] [Indexed: 01/18/2023]
Abstract
Artificial intelligence (AI) is becoming more widespread within radiology. Capabilities that AI algorithms currently provide include detection, segmentation, classification, and quantification of pathological findings. Artificial intelligence software have created challenges for the traditional United States Food and Drug Administration (FDA) approval process for medical devices given their abilities to evolve over time with incremental data input. Currently, there are 190 FDA-approved radiology AI-based software devices, 42 of which pertain specifically to thoracic radiology. The majority of these algorithms are approved for the detection and/or analysis of pulmonary nodules, for monitoring placement of endotracheal tubes and indwelling catheters, for detection of emergent findings, and for assessment of pulmonary parenchyma; however, as technology evolves, there are many other potential applications that can be explored. For example, evaluation of non-idiopathic pulmonary fibrosis interstitial lung diseases, synthesis of imaging, clinical and/or laboratory data to yield comprehensive diagnoses, and survival or prognosis prediction of certain pathologies. With increasing physician and developer engagement, transparency and frequent communication between developers and regulatory agencies, such as the FDA, AI medical devices will be able to provide a critical supplement to patient management and ultimately enhance physicians' ability to improve patient care.
Collapse
Affiliation(s)
- M E Milam
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - C W Koo
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
| |
Collapse
|
16
|
Chao HS, Tsai CY, Chou CW, Shiao TH, Huang HC, Chen KC, Tsai HH, Lin CY, Chen YM. Artificial Intelligence Assisted Computational Tomographic Detection of Lung Nodules for Prognostic Cancer Examination: A Large-Scale Clinical Trial. Biomedicines 2023; 11:biomedicines11010147. [PMID: 36672655 PMCID: PMC9856020 DOI: 10.3390/biomedicines11010147] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 12/23/2022] [Accepted: 12/29/2022] [Indexed: 01/11/2023] Open
Abstract
Low-dose computed tomography (LDCT) has emerged as a standard method for detecting early-stage lung cancer. However, the tedious computer tomography (CT) slide reading, patient-by-patient check, and lack of standard criteria to determine the vague but possible nodule leads to variable outcomes of CT slide interpretation. To determine the artificial intelligence (AI)-assisted CT examination, AI algorithm-assisted CT screening was embedded in the hospital picture archiving and communication system, and a 200 person-scaled clinical trial was conducted at two medical centers. With AI algorithm-assisted CT screening, the sensitivity of detecting nodules sized 4−5 mm, 6~10 mm, 11~20 mm, and >20 mm increased by 41%, 11.2%, 10.3%, and 18.7%, respectively. Remarkably, the overall sensitivity of detecting varied nodules increased by 20.7% from 67.7% to 88.4%. Furthermore, the sensitivity increased by 18.5% from 72.5% to 91% for detecting ground glass nodules (GGN), which is challenging for radiologists and physicians. The free-response operating characteristic (FROC) AI score was ≥0.4, and the AI algorithm standalone CT screening sensitivity reached >95% with an area under the localization receiver operating characteristic curve (LROC-AUC) of >0.88. Our study demonstrates that AI algorithm-embedded CT screening significantly ameliorates tedious LDCT practices for doctors.
Collapse
Affiliation(s)
- Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chiao-Yun Tsai
- Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Institute of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Chung-Wei Chou
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Tsu-Hui Shiao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Hsu-Chih Huang
- Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Institute of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Kun-Chieh Chen
- Division of Pulmonary Medicine, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Department of Applied Chemistry, National Chi Nan University, Nantou 545301, Taiwan
| | - Hao-Hung Tsai
- Institute of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- School of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Chin-Yu Lin
- Institute of New Drug Development, College of Medicine, China Medical University, Taichung 40402, Taiwan
- Tsuzuki Institute for Traditional Medicine, College of Pharmacy, China Medical University, Taichung 40402, Taiwan
- Department for Biomedical Engineering, Collage of Biomedical Engineering, China Medical University, Taichung 40402, Taiwan
| | - Yuh-Min Chen
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: ; Tel.: +886-2-28712121 (ext. 7865)
| |
Collapse
|
17
|
Kato S, Amemiya S, Takao H, Yamashita H, Sakamoto N, Miki S, Watanabe Y, Suzuki F, Fujimoto K, Mizuki M, Abe O. Computer-aided detection improves brain metastasis identification on non-enhanced CT in less experienced radiologists. Acta Radiol 2022; 64:1958-1965. [PMID: 36426577 DOI: 10.1177/02841851221139124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Background Brain metastases (BMs) are the most common intracranial tumors causing neurological complications associated with significant morbidity and mortality. Purpose To evaluate the effect of computer-aided detection (CAD) on the performance of observers in detecting BMs on non-enhanced computed tomography (NECT). Material and Methods Three less experienced and three experienced radiologists interpreted 30 NECT scans with 89 BMs in 25 cases to detect BMs with and without the assistance of CAD. The observers’ sensitivity, number of false positives (FPs), positive predictive value (PPV), and reading time with and without CAD were compared using paired t-tests. The sensitivity of CAD and the observers were compared using a one-sample t-test Results With CAD, less experienced radiologists’ sensitivity significantly increased from 27.7% ± 4.6% to 32.6% ± 4.8% ( P = 0.007), while the experienced radiologists’ sensitivity did not show a significant difference (from 33.3% ± 3.5% to 31.9% ± 3.7%; P = 0.54). There was no significant difference between conditions with CAD and without CAD for FPs (less experienced radiologists: 23.0 ± 10.4 and 25.0 ± 9.3; P = 0.32; experienced radiologists: 18.3 ± 7.4 and 17.3 ± 6.7; P = 0.76) and PPVs (less experienced radiologists: 57.9% ± 8.3% and 50.9% ± 7.0%; P = 0.14; experienced radiologists: 61.8% ± 12.7% and 64.0% ± 12.1%; P = 0.69). There were no significant differences in reading time with and without CAD (85.0 ± 45.6 s and 73.7 ± 36.7 s; P = 0.09). The sensitivity of CAD was 47.2% (with a PPV of 8.9%), which was significantly higher than that of any radiologist ( P < 0.001). Conclusion CAD improved BM detection sensitivity on NECT without increasing FPs or reading time among less experienced radiologists, but this was not the case among experienced radiologists.
Collapse
Affiliation(s)
- Shimpei Kato
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Shiori Amemiya
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Hidemasa Takao
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Hiroshi Yamashita
- Department of Radiology, Teikyo University Hospital, Kawasaki, Kanagawa, Japan
| | - Naoya Sakamoto
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Soichiro Miki
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Yusuke Watanabe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Fumio Suzuki
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Kotaro Fujimoto
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Masumi Mizuki
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| |
Collapse
|
18
|
Ko JP, Bagga B, Gozansky E, Moore WH. Solitary Pulmonary Nodule Evaluation: Pearls and Pitfalls. Semin Ultrasound CT MR 2022; 43:230-245. [PMID: 35688534 DOI: 10.1053/j.sult.2022.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Lung nodules are frequently encountered while interpreting chest CTs and are challenging to detect, characterize, and manage given they can represent both benign or malignant etiologies. An understanding of features associated with malignancy and causes of interpretive pitfalls is helpful to avoid misdiagnoses. This review addresses pertinent topics related to the etiologies for missed lung nodules on radiography and CT. Additionally, CT imaging technical pitfalls and challenges in addition to issues in the evaluation of nodule morphology, attenuation, and size will be discussed. Nodule management guidelines will be addressed as well as recent investigations that further our understanding of lung nodules.
Collapse
Affiliation(s)
- Jane P Ko
- Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY.
| | - Barun Bagga
- Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY
| | - Elliott Gozansky
- Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY
| | - William H Moore
- Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY
| |
Collapse
|
19
|
Murchison JT, Ritchie G, Senyszak D, Nijwening JH, van Veenendaal G, Wakkie J, van Beek EJR. Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population. PLoS One 2022; 17:e0266799. [PMID: 35511758 PMCID: PMC9070877 DOI: 10.1371/journal.pone.0266799] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 03/28/2022] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE In this study, we evaluated a commercially available computer assisted diagnosis system (CAD). The deep learning algorithm of the CAD was trained with a lung cancer screening cohort and developed for detection, classification, quantification, and growth of actionable pulmonary nodules on chest CT scans. Here, we evaluated the CAD in a retrospective cohort of a routine clinical population. MATERIALS AND METHODS In total, a number of 337 scans of 314 different subjects with reported nodules of 3-30 mm in size were included into the evaluation. Two independent thoracic radiologists alternately reviewed scans with or without CAD assistance to detect, classify, segment, and register pulmonary nodules. A third, more experienced, radiologist served as an adjudicator. In addition, the cohort was analyzed by the CAD alone. The study cohort was divided into five different groups: 1) 178 CT studies without reported pulmonary nodules, 2) 95 studies with 1-10 pulmonary nodules, 23 studies from the same patients with 3) baseline and 4) follow-up studies, and 5) 18 CT studies with subsolid nodules. A reference standard for nodules was based on majority consensus with the third thoracic radiologist as required. Sensitivity, false positive (FP) rate and Dice inter-reader coefficient were calculated. RESULTS After analysis of 470 pulmonary nodules, the sensitivity readings for radiologists without CAD and radiologist with CAD, were 71.9% (95% CI: 66.0%, 77.0%) and 80.3% (95% CI: 75.2%, 85.0%) (p < 0.01), with average FP rate of 0.11 and 0.16 per CT scan, respectively. Accuracy and kappa of CAD for classifying solid vs sub-solid nodules was 94.2% and 0.77, respectively. Average inter-reader Dice coefficient for nodule segmentation was 0.83 (95% CI: 0.39, 0.96) and 0.86 (95% CI: 0.51, 0.95) for CAD versus readers. Mean growth percentage discrepancy of readers and CAD alone was 1.30 (95% CI: 1.02, 2.21) and 1.35 (95% CI: 1.01, 4.99), respectively. CONCLUSION The applied CAD significantly increased radiologist's detection of actionable nodules yet also minimally increasing the false positive rate. The CAD can automatically classify and quantify nodules and calculate nodule growth rate in a cohort of a routine clinical population. Results suggest this Deep Learning software has the potential to assist chest radiologists in the tasks of pulmonary nodule detection and management within their routine clinical practice.
Collapse
Affiliation(s)
- John T. Murchison
- Department of Radiology, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
- * E-mail: (JTM); (JHN)
| | - Gillian Ritchie
- Department of Radiology, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
| | - David Senyszak
- Edinburgh Imaging facility QMRI, University of Edinburgh, Edinburgh, United Kingdom
| | | | | | | | - Edwin J. R. van Beek
- Department of Radiology, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging facility QMRI, University of Edinburgh, Edinburgh, United Kingdom
| |
Collapse
|
20
|
Chiu HY, Chao HS, Chen YM. Application of Artificial Intelligence in Lung Cancer. Cancers (Basel) 2022; 14:cancers14061370. [PMID: 35326521 PMCID: PMC8946647 DOI: 10.3390/cancers14061370] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 03/07/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Lung cancer is the leading cause of malignancy-related mortality worldwide. AI has the potential to help to treat lung cancer from detection, diagnosis and decision making to prognosis prediction. AI could reduce the labor work of LDCT, CXR, and pathology slides reading. AI as a second reader in LDCT and CXR reading reduces the effort of radiologists and increases the accuracy of nodule detection. Introducing AI to WSI in digital pathology increases the Kappa value of the pathologist and help to predict molecular phenotypes with radiomics and H&E staining. By extracting radiomics from image data and WSI from the histopathology field, clinicians could use AI to predict tumor properties such as gene mutation and PD-L1 expression. Furthermore, AI could help clinicians in decision-making by predicting treatment response, side effects, and prognosis prediction in medical treatment, surgery, and radiotherapy. Integrating AI in the future clinical workflow would be promising. Abstract Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.
Collapse
Affiliation(s)
- Hwa-Yen Chiu
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (H.-Y.C.); (Y.-M.C.)
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Division of Internal Medicine, Hsinchu Branch, Taipei Veterans General Hospital, Hsinchu 310, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (H.-Y.C.); (Y.-M.C.)
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: ; Tel.: +886-2-28712121
| | - Yuh-Min Chen
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (H.-Y.C.); (Y.-M.C.)
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| |
Collapse
|
21
|
Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective. Pediatr Radiol 2022; 52:2120-2130. [PMID: 34471961 PMCID: PMC8409695 DOI: 10.1007/s00247-021-05146-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/22/2021] [Accepted: 06/28/2021] [Indexed: 12/19/2022]
Abstract
Artificial intelligence (AI) applications for chest radiography and chest CT are among the most developed applications in radiology. More than 40 certified AI products are available for chest radiography or chest CT. These AI products cover a wide range of abnormalities, including pneumonia, pneumothorax and lung cancer. Most applications are aimed at detecting disease, complemented by products that characterize or quantify tissue. At present, none of the thoracic AI products is specifically designed for the pediatric population. However, some products developed to detect tuberculosis in adults are also applicable to children. Software is under development to detect early changes of cystic fibrosis on chest CT, which could be an interesting application for pediatric radiology. In this review, we give an overview of current AI products in thoracic radiology and cover recent literature about AI in chest radiography, with a focus on pediatric radiology. We also discuss possible pediatric applications.
Collapse
|
22
|
[What can machines do?]. ACTA ACUST UNITED AC 2021; 28:124-129. [PMID: 34729002 PMCID: PMC8554512 DOI: 10.1007/s41972-021-00148-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/06/2021] [Indexed: 11/08/2022]
Abstract
Mit zunehmender Rechen- und Speicherkapazität hält in der Medizin fast unaufhaltsam die Anwendung von Big Data und künstlicher Intelligenz Einzug. Haupteinsatzgebiete sind bisher v. a. in der Radiologie und Pathologie, jedoch auch in weiteren Bereichen wie der Genomsequenzierung bis hin zur Psychiatrie zu finden. Durch die Auswertung von immer größeren Datenmengen wird nach Mustern gesucht, die die Arbeit der Ärzte erleichtern können, indem sie eine Vorselektion betreiben. Darüber hinaus bestehen auch Bemühungen bisher unerkannte Zusammenhänge aufzudecken. Große Datenmengen werden von den Patienten selbst über sog. Wearables erfasst. Wie können diese Daten sinnvoll genutzt werden? Welche Gefahren bestehen? Auch in der Urologie gibt es Bestrebungen, eine Verbesserung der Behandlung von z. B. Harnwegsinfektionen oder Nierensteinen durch den Einsatz von vernetzten Computersystemen zu erreichen. Auf dem Gebiet von Big Data und Künstlicher Intelligenz sind in den nächsten Jahren große Fortschritte zu erwarten. Es wird auch zu einer erheblichen Erweiterung der Einsatzgebiete kommen, und wir sind somit erst am Anfang der Entwicklung.
Collapse
|
23
|
Girard P, Gossot D, Mariolo A, Caliandro R, Seguin-Givelet A, Girard N. Oligometastases for Clinicians: Size Matters. J Clin Oncol 2021; 39:2643-2646. [PMID: 34133197 DOI: 10.1200/jco.21.00445] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
|
24
|
Hsu HH, Ko KH, Chou YC, Wu YC, Chiu SH, Chang CK, Chang WC. Performance and reading time of lung nodule identification on multidetector CT with or without an artificial intelligence-powered computer-aided detection system. Clin Radiol 2021; 76:626.e23-626.e32. [PMID: 34023068 DOI: 10.1016/j.crad.2021.04.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 04/15/2021] [Indexed: 10/21/2022]
Abstract
AIM To compare the performance and reading time of different readers using automatic artificial intelligence (AI)-powered computer-aided detection (CAD) to detect lung nodules in different reading modes. MATERIALS AND METHODS One hundred and fifty multidetector computed tomography (CT) datasets containing 340 nodules ≤10 mm in diameter were collected retrospectively. A CAD with vessel-suppressed function was used to interpret the images. Three junior and three senior readers were assigned to read (1) CT images without CAD, (2) second-read using CAD in which CAD was applied only after initial unassisted assessment, and (3) a concurrent read with CAD in which CAD was applied at the start of assessment. Diagnostic performances and reading times were compared using analysis of variance. RESULTS For all readers, the mean sensitivity improved from 64% (95% confidence interval [CI]: 62%, 66%) for the without-CAD mode to 82% (95% CI: 80%, 84%) for the second-reading mode and to 80% (95% CI: 79%, 82%) for the concurrent-reading mode (p<0.001). There was no significant difference between the two modes in terms of the mean sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for both junior and senior readers and all readers (p>0.05). The reading time of all readers was significantly shorter for the concurrent-reading mode (124 ± 25 seconds) compared to without CAD (156 ± 34 seconds; p<0.001) and the second-reading mode (197 ± 46 seconds; p<0.001). CONCLUSION In CAD for lung nodules at CT, the second-reading mode and concurrent-reading mode may improve detection performance for all readers in both screening and clinical routine practice. Concurrent use of CAD is more efficient for both junior and senior readers.
Collapse
Affiliation(s)
- H-H Hsu
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
| | - K-H Ko
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Y-C Chou
- School of Public Health, National Defense Medical Center, Taipei, Taiwan
| | - Y-C Wu
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - S-H Chiu
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - C-K Chang
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - W-C Chang
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| |
Collapse
|
25
|
Schreuder A, Scholten ET, van Ginneken B, Jacobs C. Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice? Transl Lung Cancer Res 2021; 10:2378-2388. [PMID: 34164285 PMCID: PMC8182724 DOI: 10.21037/tlcr-2020-lcs-06] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Lung cancer computed tomography (CT) screening trials using low-dose CT have repeatedly demonstrated a reduction in the number of lung cancer deaths in the screening group compared to a control group. With various countries currently considering the implementation of lung cancer screening, recurring discussion points are, among others, the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of lung cancer screening. We discuss the performance levels of AI algorithms for various tasks related to the interpretation of lung screening CT scans, how they compare to human experts, and how AI and humans may complement each other. We discuss how AI may be used in the lung cancer CT screening workflow according to the current evidence and describe the additional research that will be required before AI can take a more prominent role in the analysis of lung screening CT scans.
Collapse
Affiliation(s)
- Anton Schreuder
- Department of Radiology, Nuclear Medicine, and Anatomy, Radboudumc, Nijmegen, The Netherlands
| | - Ernst T Scholten
- Department of Radiology, Nuclear Medicine, and Anatomy, Radboudumc, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Department of Radiology, Nuclear Medicine, and Anatomy, Radboudumc, Nijmegen, The Netherlands.,Fraunhofer MEVIS, Bremen, Germany
| | - Colin Jacobs
- Department of Radiology, Nuclear Medicine, and Anatomy, Radboudumc, Nijmegen, The Netherlands
| |
Collapse
|
26
|
Singh R, Kalra MK, Homayounieh F, Nitiwarangkul C, McDermott S, Little BP, Lennes IT, Shepard JAO, Digumarthy SR. Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography. Quant Imaging Med Surg 2021; 11:1134-1143. [PMID: 33816155 DOI: 10.21037/qims-20-630] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Lung cancer screening (LCS) with low-dose computed tomography (LDCT) helps early lung cancer detection, commonly presenting as small pulmonary nodules. Artificial intelligence (AI)-based vessel suppression (AI-VS) and automatic detection (AI-AD) algorithm can improve detection of subsolid nodules (SSNs) on LDCT. We assessed the impact of AI-VS and AI-AD in detection and classification of SSNs [ground-glass nodules (GGNs) and part-solid nodules (PSNs)], on LDCT performed for LCS. Methods Following regulatory approval, 123 LDCT examinations with sub-solid pulmonary nodules (average diameter ≥6 mm) were processed to generate three image series for each examination-unprocessed, AI-VS, and AI-AD series with annotated lung nodules. Two thoracic radiologists in consensus formed the standard of reference (SOR) for this study. Two other thoracic radiologists (R1 and R2; 5 and 10 years of experience in thoracic CT image interpretation) independently assessed the unprocessed images alone, then together with AI-VS series, and finally with AI-AD for detecting all ≥6 mm GGN and PSN. We performed receiver operator characteristics (ROC) and Cohen's Kappa analyses for statistical analyses. Results On unprocessed images, R1 and R2 detected 232/310 nodules (R1: 114 GGN, 118 PSN) and 255/310 nodules (R2: 122 GGN, 133 PSN), respectively (P>0.05). On AI-VS images, they detected 249/310 nodules (119 GGN, 130 PSN) and 277/310 nodules (128 GGN, 149 PSN), respectively (P≥0.12). When compared to the SOR, accuracy (AUC) for detection of PSN on the AI-VS images (AUC 0.80-0.81) was greater than on the unprocessed images (AUC 0.70-0.76). AI-VS images enabled detection of solid components in five nodules deemed as GGN on the unprocessed images. Accuracy of AI-AD was lower than both the radiologists (AUC 0.60-0.72). Conclusions AI-VS improved the detection and classification of SSN into GGN and PSN on LDCT of the chest for the two radiologist (R1 and R2) readers.
Collapse
Affiliation(s)
- Ramandeep Singh
- Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Mannudeep K Kalra
- Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Fatemeh Homayounieh
- Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Chayanin Nitiwarangkul
- Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Division of Diagnostic Radiology, Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Ratchathewi, Bangkok, Thailand
| | - Shaunagh McDermott
- Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Brent P Little
- Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Inga T Lennes
- Harvard Medical School, Boston, MA, USA.,Massachusetts General Hospital Cancer Center, Division of Thoracic Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Jo-Anne O Shepard
- Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Subba R Digumarthy
- Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| |
Collapse
|
27
|
Mao Q, Zhao S, Tong D, Su S, Li Z, Cheng X. Hessian-MRLoG: Hessian information and multi-scale reverse LoG filter for pulmonary nodule detection. Comput Biol Med 2021; 131:104272. [PMID: 33636420 DOI: 10.1016/j.compbiomed.2021.104272] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 02/02/2021] [Accepted: 02/06/2021] [Indexed: 12/29/2022]
Abstract
Computer-aided detection (CADe) of pulmonary nodules is an effective approach for early detection of lung cancer. However, due to the low contrast of lung computed tomography (CT) images, the interference of blood vessels and classifications, CADe has the problems of low detection rate and high false-positive rate (FPR). To solve these problems, a novel method using Hessian information and multi-scale reverse Laplacian of Gaussian (LoG) (Hessian-MRLoG) is proposed and developed in this work. Also, since the intensity distribution of the LoG operator and the lung nodule in CT images are inconsistent, and their shapes are mismatched, a multi-scale reverse Laplacian of Gaussian (MRLoG) is constructed. In addition, in order to enhance the effectiveness of target detection, the second-order partial derivatives of MRLoG are partially adjusted by introducing an adjustment factor. On this basis, the Hessian-MRLoG model is developed, and a novel elliptic filter is designed. Ultimately, in this study, the method of Hessian-MRLoG filtering is proposed and developed for pulmonary nodule detection. To verify its effectiveness and accuracy, the proposed method was used to analyze the LUNA16 dataset. The experimental results revealed that the proposed method had an accuracy of 93.6% and produced 1.0 false positives per scan (FPs/scan), indicating that the proposed method can improve the detection rate and significantly reduce the FPR. Therefore, the proposed method has the potential for application in the detection, localization and labeling of other lesion areas.
Collapse
Affiliation(s)
- Qi Mao
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China; College of Information Science and Technology, Donghua University, Shanghai, 201620, China.
| | - Shuguang Zhao
- College of Information Science and Technology, Donghua University, Shanghai, 201620, China
| | - Dongbing Tong
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China
| | - Shengchao Su
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China
| | - Zhiwei Li
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China; College of Information Science and Technology, Donghua University, Shanghai, 201620, China
| | - Xiang Cheng
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, 333403, China
| |
Collapse
|
28
|
Abstract
BACKGROUND Artificial intelligence (AI) has the potential to fundamentally change medicine within the coming decades. Radiological imaging is one of the primary fields of its clinical application. OBJECTIVES In this article, we summarize previous AI developments with a focus on oncological radiology. Based on selected examples, we derive scenarios for developments in the next 10 years. MATERIALS AND METHODS This work is based on a review of various literature and product databases, publications by regulatory authorities, reports, and press releases. CONCLUSIONS The clinical use of AI applications is still in an early stage of development. The large number of research publications shows the potential of the field. Several certified products have already become available to users. However, for a widespread adoption of AI applications in clinical routine, several fundamental prerequisites are still awaited. These include the generation of evidence justifying the use of algorithms through representative clinical studies, adjustments to the framework for approval processes and dedicated education and teaching resources for its users. It is expected that use of AI methods will increase, thus, creating new opportunities for improved diagnostics, therapy, and more efficient workflows.
Collapse
Affiliation(s)
- Andreas M Bucher
- Institut für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Frankfurt am Main, Theodor-Stern Kai 7, 60590, Frankfurt am Main, Deutschland.
| | - Jens Kleesiek
- Translationale bildgestützte Onkologie, Institut für KI in der Medizin (IKIM), Universitätsmedizin Essen, Essen, Deutschland
| |
Collapse
|
29
|
Mun SK, Wong KH, Lo SCB, Li Y, Bayarsaikhan S. Artificial Intelligence for the Future Radiology Diagnostic Service. Front Mol Biosci 2021; 7:614258. [PMID: 33585563 PMCID: PMC7875875 DOI: 10.3389/fmolb.2020.614258] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 12/29/2020] [Indexed: 12/18/2022] Open
Abstract
Radiology historically has been a leader of digital transformation in healthcare. The introduction of digital imaging systems, picture archiving and communication systems (PACS), and teleradiology transformed radiology services over the past 30 years. Radiology is again at the crossroad for the next generation of transformation, possibly evolving as a one-stop integrated diagnostic service. Artificial intelligence and machine learning promise to offer radiology new powerful new digital tools to facilitate the next transformation. The radiology community has been developing computer-aided diagnosis (CAD) tools based on machine learning (ML) over the past 20 years. Among various AI techniques, deep-learning convolutional neural networks (CNN) and its variants have been widely used in medical image pattern recognition. Since the 1990s, many CAD tools and products have been developed. However, clinical adoption has been slow due to a lack of substantial clinical advantages, difficulties integrating into existing workflow, and uncertain business models. This paper proposes three pathways for AI's role in radiology beyond current CNN based capabilities 1) improve the performance of CAD, 2) improve the productivity of radiology service by AI-assisted workflow, and 3) develop radiomics that integrate the data from radiology, pathology, and genomics to facilitate the emergence of a new integrated diagnostic service.
Collapse
Affiliation(s)
- Seong K. Mun
- Arlington Innovation Center:Health Research, Virginia Tech-Washington DC Area, Arlington, VA, United States
| | | | | | | | | |
Collapse
|
30
|
Dyer SC, Bartholmai BJ, Koo CW. Implications of the updated Lung CT Screening Reporting and Data System (Lung-RADS version 1.1) for lung cancer screening. J Thorac Dis 2020; 12:6966-6977. [PMID: 33282402 PMCID: PMC7711402 DOI: 10.21037/jtd-2019-cptn-02] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Lung cancer remains the leading cause of cancer death in the United States. Screening with low-dose computed tomography (LDCT) has been proven to aid in early detection of lung cancer and reduce disease specific mortality. In 2014, the American College of Radiology (ACR) released version 1.0 of the Lung CT Screening Reporting and Data System (Lung-RADS) as a quality tool to standardize the reporting of lung cancer screening LDCT. In 2019, 5 years after the implementation of Lung-RADS version 1.0 the ACR released the updated Lung-RADS version 1.1 which incorporates initial experience with lung cancer screening. In this review, we outline the implications of the changes and additions in Lung-RADS version 1.1 and examine relevant literature for many of the updates. We also highlight several challenges and opportunities as Lung-RADS version 1.1 is implemented in lung cancer screening programs.
Collapse
Affiliation(s)
- Spencer C Dyer
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Chi Wan Koo
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| |
Collapse
|
31
|
Hwang HJ, Seo JB, Lee SM, Kim EY, Park B, Bae HJ, Kim N. Content-Based Image Retrieval of Chest CT with Convolutional Neural Network for Diffuse Interstitial Lung Disease: Performance Assessment in Three Major Idiopathic Interstitial Pneumonias. Korean J Radiol 2020; 22:281-290. [PMID: 33169547 PMCID: PMC7817627 DOI: 10.3348/kjr.2020.0603] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 05/08/2020] [Accepted: 06/03/2020] [Indexed: 12/02/2022] Open
Abstract
Objective To assess the performance of content-based image retrieval (CBIR) of chest CT for diffuse interstitial lung disease (DILD). Materials and Methods The database was comprised by 246 pairs of chest CTs (initial and follow-up CTs within two years) from 246 patients with usual interstitial pneumonia (UIP, n = 100), nonspecific interstitial pneumonia (NSIP, n = 101), and cryptogenic organic pneumonia (COP, n = 45). Sixty cases (30-UIP, 20-NSIP, and 10-COP) were selected as the queries. The CBIR retrieved five similar CTs as a query from the database by comparing six image patterns (honeycombing, reticular opacity, emphysema, ground-glass opacity, consolidation and normal lung) of DILD, which were automatically quantified and classified by a convolutional neural network. We assessed the rates of retrieving the same pairs of query CTs, and the number of CTs with the same disease class as query CTs in top 1–5 retrievals. Chest radiologists evaluated the similarity between retrieved CTs and queries using a 5-scale grading system (5-almost identical; 4-same disease; 3-likelihood of same disease is half; 2-likely different; and 1-different disease). Results The rate of retrieving the same pairs of query CTs in top 1 retrieval was 61.7% (37/60) and in top 1–5 retrievals was 81.7% (49/60). The CBIR retrieved the same pairs of query CTs more in UIP compared to NSIP and COP (p = 0.008 and 0.002). On average, it retrieved 4.17 of five similar CTs from the same disease class. Radiologists rated 71.3% to 73.0% of the retrieved CTs with a similarity score of 4 or 5. Conclusion The proposed CBIR system showed good performance for retrieving chest CTs showing similar patterns for DILD.
Collapse
Affiliation(s)
- Hye Jeon Hwang
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Eun Young Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Beomhee Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hyun Jin Bae
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| |
Collapse
|
32
|
Takaishi T, Ozawa Y, Bando Y, Yamamoto A, Okochi S, Suzuki H, Shibamoto Y. Incorporation of a computer-aided vessel-suppression system to detect lung nodules in CT images: effect on sensitivity and reading time in routine clinical settings. Jpn J Radiol 2020; 39:159-164. [PMID: 32940850 DOI: 10.1007/s11604-020-01043-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 09/10/2020] [Indexed: 11/24/2022]
Abstract
PURPOSE To evaluate whether a computer-aided vessel-suppression system improves lung nodule detection in routine clinical settings. MATERIALS AND METHODS We used computer software that automatically suppresses pulmonary vessels on chest CT while preserving pulmonary nodules. Sixty-one chest CT images were included in our study. Three radiologists independently read either standard CT images alone or both computer-aided CT and standard CT images randomly to detect a pulmonary nodule ≥ 4 mm in diameter. After an interval of at least 15 days to avoid recall bias, the three radiologists interpreted the counterpart images of the same patients. The reference standard was decided by an expert panel. The primary endpoint was sensitivity. The secondary endpoint was interpretation time. RESULTS The average sensitivity improved with computer-aided CT (72% for standard CT vs. 84% for computer-aided CT, p = 0.02). There was no difference in the false-positive rate (21% for both standard CT and computer-aided CT, p = 0.98). Although the average reading time was 9.5% longer for computer-aided plus standard CT compared with standard CT alone, the difference was not significant (p = 0.11). CONCLUSION Vessel-suppressed CT images helped radiologists to improve the sensitivity of pulmonary nodule detection without compromising the false-positive rate.
Collapse
Affiliation(s)
- Taku Takaishi
- Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan.
| | - Yoshiyuki Ozawa
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Yuya Bando
- Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan
| | - Akiko Yamamoto
- Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan
| | - Sachiko Okochi
- Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan
| | - Hirochika Suzuki
- Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan
| | - Yuta Shibamoto
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| |
Collapse
|
33
|
Wang L, Wang H, Xia C, Wang Y, Tang Q, Li J, Zhou XH. Toward standardized premarket evaluation of computer aided diagnosis/detection products: insights from FDA-approved products. Expert Rev Med Devices 2020; 17:899-918. [PMID: 32842797 DOI: 10.1080/17434440.2020.1813566] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
INTRODUCTION Computer aided detection and diagnosis (CADe and CADx) products are an emerging branch of medical device industry. However, limited technical standard has been developed for product verification and validation. It will be helpful to investigate the current practice of preclinical and clinical evaluation of approved products and provide insights for future standardization. AREAS COVERED Document review was conducted on 56 products approved by the United States Food and Drug Administration, including Summary of Safety and Effectiveness Data, 510(k) decision and de novo decision summaries. Key parameters describing product characteristics, preclinical studies and clinical studies were collected. Evaluation strategies for CADe/CADx products were analyzed and assessed. EXPERT OPINION Preclinical studies were widely adopted in the verification of CADe/CADx products. Standalone performance testing was a common procedure, but the selection of testing dataset and performance metrics showed significant variability and flexibility among manufacturers. Clinical studies were reported by all class III products and some class II products, and Multi-Reader Multi-Case design was commonly used. However, statistical analysis and presentation/interpretation of results was oftentimes incomplete. To resolve above issues, systematic development of standards of CADe/CADx is encouraged, which can be implemented at different aspects through the product lifecycle.
Collapse
Affiliation(s)
- Lu Wang
- Beijing International Center for Mathematical Research, Peking University , Beijing, China
| | - Hao Wang
- Institute for Medical Device Control, National Institutes for Food and Drug Control , Beijing, China
| | - Chen Xia
- Institute of Advanced Research, Beijing Infervision Technology Limited Liability Company , Beijing, China
| | - Yao Wang
- Department of Biosciences, University of Chicago , Chicago, Illinois, USA
| | - Qiaohong Tang
- Institute for Medical Device Control, National Institutes for Food and Drug Control , Beijing, China
| | - Jiage Li
- Institute for Medical Device Control, National Institutes for Food and Drug Control , Beijing, China
| | - Xiao-Hua Zhou
- Beijing International Center for Mathematical Research, Peking University , Beijing, China.,Department of Biostatistics, School of Public Health, Peking University , Beijing, China
| |
Collapse
|
34
|
The Use of Artificial Intelligence in the Differentiation of Malignant and Benign Lung Nodules on Computed Tomograms Proven by Surgical Pathology. Cancers (Basel) 2020; 12:cancers12082211. [PMID: 32784681 PMCID: PMC7464412 DOI: 10.3390/cancers12082211] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 08/04/2020] [Accepted: 08/05/2020] [Indexed: 01/23/2023] Open
Abstract
The purpose of this work was to evaluate the performance of an existing commercially available artificial intelligence (AI) software system in differentiating malignant and benign lung nodules. The AI tool consisted of a vessel-suppression function and a deep-learning-based computer-aided-detection (VS-CAD) analyzer. Fifty patients (32 females, mean age 52 years) with 75 lung nodules (47 malignant and 28 benign) underwent low-dose computed tomography (LDCT) followed by surgical excision and the pathological analysis of their 75 nodules within a 3 month time frame. All 50 cases were then processed by the AI software to generate corresponding VS images and CAD outcomes. All 75 pathologically proven lung nodules were well delineated by vessel-suppressed images. Three (6.4%) of the 47 lung cancer cases, and 11 (39.3%) of the 28 benign nodules were ignored and not detected by the AI without showing a CAD analysis summary. The AI system/radiologists produced a sensitivity and specificity (shown in %) of 93.6/89.4 and 39.3/82.1 in distinguishing malignant from benign nodules, respectively. AI sensitivity was higher than that of radiologists, though not statistically significant (p = 0.712). Specificity obtained by the radiologists was significantly higher than that of the VS-CAD AI (p = 0.003). There was no significant difference between the malignant and benign lesions with respect to age, gender, pure ground-glass pattern, the diameter and location of the nodules, or nodules <6 vs. ≥6 mm. However, more part-solid nodules were proven to be malignant than benign (90.9% vs. 9.1%), and more solid nodules were proven to be benign than malignant (86.7% vs. 13.3%) with statistical significance (p = 0.001 and <0.001, respectively). A larger cohort and prospective study are required to validate the AI performance.
Collapse
|
35
|
Adams SJ, Tang R, Babyn P. Patient Perspectives and Priorities Regarding Artificial Intelligence in Radiology: Opportunities for Patient-Centered Radiology. J Am Coll Radiol 2020; 17:1034-1036. [DOI: 10.1016/j.jacr.2020.01.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 01/14/2020] [Accepted: 01/14/2020] [Indexed: 12/14/2022]
|
36
|
Wang W, Tian J, Zhang C, Luo Y, Wang X, Li J. An improved deep learning approach and its applications on colonic polyp images detection. BMC Med Imaging 2020; 20:83. [PMID: 32698839 PMCID: PMC7374886 DOI: 10.1186/s12880-020-00482-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 07/08/2020] [Indexed: 12/22/2022] Open
Abstract
Background Colonic polyps are more likely to be cancerous, especially those with large diameter, large number and atypical hyperplasia. If colonic polyps cannot be treated in early stage, they are likely to develop into colon cancer. Colonoscopy is easily limited by the operator’s experience, and factors such as inexperience and visual fatigue will directly affect the accuracy of diagnosis. Cooperating with Hunan children’s hospital, we proposed and improved a deep learning approach with global average pooling (GAP) in colonoscopy for assisted diagnosis. Our approach for assisted diagnosis in colonoscopy can prompt endoscopists to pay attention to polyps that may be ignored in real time, improve the detection rate, reduce missed diagnosis, and improve the efficiency of medical diagnosis. Methods We selected colonoscopy images from the gastrointestinal endoscopy room of Hunan children’s hospital to form the colonic polyp datasets. And we applied the image classification method based on Deep Learning to the classification of Colonic Polyps. The classic networks we used are VGGNets and ResNets. By using global average pooling, we proposed the improved approaches: VGGNets-GAP and ResNets-GAP. Results The accuracies of all models in datasets exceed 98%. The TPR and TNR are above 96 and 98% respectively. In addition, VGGNets-GAP networks not only have high classification accuracies, but also have much fewer parameters than those of VGGNets. Conclusions The experimental results show that the proposed approach has good effect on the automatic detection of colonic polyps. The innovations of our method are in two aspects: (1) the detection accuracy of colonic polyps has been improved. (2) our approach reduces the memory consumption and makes the model lightweight. Compared with the original VGG networks, the parameters of our VGG19-GAP networks are greatly reduced.
Collapse
Affiliation(s)
- Wei Wang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China.
| | - Jinge Tian
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China
| | - Chengwen Zhang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China
| | - Yanhong Luo
- Hunan Children's Hospital, Changsha, 410000, China.
| | - Xin Wang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China.
| | - Ji Li
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China
| |
Collapse
|
37
|
Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography. Jpn J Radiol 2020; 38:1052-1061. [PMID: 32592003 DOI: 10.1007/s11604-020-01009-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 06/18/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE To evaluate the performance of a deep learning-based computer-aided diagnosis (CAD) system at detecting pulmonary nodules on CT by comparing radiologists' readings with and without CAD. MATERIALS AND METHODS A total of 120 chest CT images were randomly selected from patients with suspected lung cancer. The gold standard of nodules ≥ 3 mm was established by a panel of three expert radiologists. Two less experienced radiologists read the images without and afterward with CAD system. Their reading times were recorded. RESULTS The radiologists' sensitivity increased from 20.9% to 38.0% with the introduction of CAD. The positive predictive value (PPV) decreased from 70.5% to 61.8%, and the F1-score increased from 32.2% to 47.0%. The sensitivity significantly increased from 13.7% to 32.4% for small nodules (3-6 mm) and from 33.3% to 47.6% for medium nodules (6-10 mm). CAD alone showed a sensitivity of 70.3%, a PPV of 57.9%, and an F1-score of 63.5%. Reading time decreased by 11.3% with the use of CAD. CONCLUSION CAD improved the less experienced radiologists' sensitivity in detecting pulmonary nodules of all sizes, especially including a significant improvement in the detection of clinically important-sized medium nodules (6-10 mm) as well as small nodules (3-6 mm) and reduced their reading time.
Collapse
|
38
|
Wataya T, Nakanishi K, Suzuki Y, Kido S, Tomiyama N. Introduction to deep learning: minimum essence required to launch a research. Jpn J Radiol 2020; 38:907-921. [DOI: 10.1007/s11604-020-00998-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 06/02/2020] [Indexed: 02/08/2023]
|
39
|
Wang Y, Wu B, Zhang N, Liu J, Ren F, Zhao L. Research progress of computer aided diagnosis system for pulmonary nodules in CT images. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2020; 28:1-16. [PMID: 31815727 DOI: 10.3233/xst-190581] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Since CAD (Computer Aided Diagnosis) system can make it easier and more efficient to interpret CT (Computer Tomography) images, it has gained much attention and developed rapidly in recent years. This article reviews recent CAD techniques for pulmonary nodule detection and diagnosis in CT Images. METHODS CAD systems can be classified into computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems. This review reports recent researches of both systems, including the database, technique, innovation and experimental results of each work. Multi-task CAD systems, which can handle segmentation, false positive reduction, malignancy prediction and other tasks at the same time. The commercial CAD systems are also briefly introduced. RESULTS We have found that deep learning based CAD is the mainstream of current research. The reported sensitivity of deep learning based CADe systems ranged between 80.06% and 94.1% with an average 4.3 false-positive (FP) per scan when using LIDC-IDRI dataset, and between 94.4% and 97.9% with an average 4 FP/scan when using LUNA16 dataset, respectively. The overall accuracy of deep learning based CADx systems ranged between 86.84% and 92.3% with an average AUC of 0.956 reported when using LIDC-IDRI dataset. CONCLUSIONS We summarized the current tendency and limitations as well as future challenges in this field. The development of CAD needs to meet the rigid clinical requirements, such as high accuracy, strong robustness, high efficiency, fine-grained analysis and classification, and to provide practical clinical functions. This review provides helpful information for both engineering researchers and radiologists to learn the latest development of CAD systems.
Collapse
Affiliation(s)
- Yu Wang
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Bo Wu
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Nan Zhang
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Jiabao Liu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Fei Ren
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Liqin Zhao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
40
|
Gu X, Wang J, Zhao J, Li Q. Segmentation and suppression of pulmonary vessels in low-dose chest CT scans. Med Phys 2019; 46:3603-3614. [PMID: 31240721 DOI: 10.1002/mp.13648] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 04/29/2019] [Accepted: 05/24/2019] [Indexed: 12/12/2022] Open
Abstract
PURPOSE The suppression of pulmonary vessels in chest computed tomography (CT) images can enhance the conspicuity of lung nodules, thereby improving the detection rate of early lung cancer. This study aimed to develop two key techniques in vessel suppression, that is, segmentation and removal of pulmonary vessels while preserving the nodules. METHODS Pulmonary vessel segmentation and removal methods in CT images were developed. The vessel segmentation method used a framework of two cascaded convolutional neural networks (CNNs). A bi-class segmentation network was utilized in the first step to extract high-intensity structures, including both vessels and nonvascular tissues such as nodules. A tri-class segmentation network was employed in the second step to distinguish the vessels from nonvascular tissues (mainly nodules) and the lung parenchyma. In the vessel removal method, the voxels in the segmented vessels were replaced with randomly selected voxels from the surrounding lung parenchyma. The dataset in this study comprised 50 three-dimensional (3D) low-dose chest CT images. The labels for vessel and nodule segmentation were annotated with a semi automatic approach. The two cascaded networks for pulmonary vessel segmentation were trained with CT images of 40 cases and tested with CT images of ten cases. Pulmonary vessels were removed from the ten testing scans based on the predicted segmentation results. In addition to qualitative evaluation to the effects of segmentation and removal, the segmentation results were quantitatively evaluated using Dice coefficient (DICE), Jaccard index (JAC), and volumetric similarity (VS) and the removal results were evaluated using contrast-to-noise ratio (CNR). RESULTS In the first step of vessel segmentation, the mean DICE, JAC, and VS for high-intensity tissues, including both vessels and nodules, were 0.943, 0.893, and 0.991, respectively. In the second step, all the nodules were separated from the vessels, and the mean DICE, JAC, and VS for the vessels were 0.941, 0.890, and 0.991, respectively. After vessel removal, the mean CNR for nodules was improved from 4.23 (6.26 dB) to 6.95 (8.42 dB). CONCLUSIONS Quantitative and qualitative evaluations demonstrated that the proposed method achieved a high accuracy for pulmonary vessel segmentation and a good effect on pulmonary vessel suppression.
Collapse
Affiliation(s)
- Xiaomeng Gu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.,Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201807, China
| | - Jiyong Wang
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201807, China
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Qiang Li
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201807, China.,Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China
| |
Collapse
|
41
|
Retson TA, Besser AH, Sall S, Golden D, Hsiao A. Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging. J Thorac Imaging 2019; 34:192-201. [PMID: 31009397 PMCID: PMC7962152 DOI: 10.1097/rti.0000000000000385] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Advances in technology have always had the potential and opportunity to shape the practice of medicine, and in no medical specialty has technology been more rapidly embraced and adopted than radiology. Machine learning and deep neural networks promise to transform the practice of medicine, and, in particular, the practice of diagnostic radiology. These technologies are evolving at a rapid pace due to innovations in computational hardware and novel neural network architectures. Several cutting-edge postprocessing analysis applications are actively being developed in the fields of thoracic and cardiovascular imaging, including applications for lesion detection and characterization, lung parenchymal characterization, coronary artery assessment, cardiac volumetry and function, and anatomic localization. Cardiothoracic and cardiovascular imaging lies at the technological forefront of radiology due to a confluence of technical advances. Enhanced equipment has enabled computed tomography and magnetic resonance imaging scanners that can safely capture images that freeze the motion of the heart to exquisitely delineate fine anatomic structures. Computing hardware developments have enabled an explosion in computational capabilities and in data storage. Progress in software and fluid mechanical models is enabling complex 3D and 4D reconstructions to not only visualize and assess the dynamic motion of the heart, but also quantify its blood flow and hemodynamics. And now, innovations in machine learning, particularly in the form of deep neural networks, are enabling us to leverage the increasingly massive data repositories that are prevalent in the field. Here, we discuss developments in machine learning techniques and deep neural networks to highlight their likely role in future radiologic practice, both in and outside of image interpretation and analysis. We discuss the concepts of validation, generalizability, and clinical utility, as they pertain to this and other new technologies, and we reflect upon the opportunities and challenges of bringing these into daily use.
Collapse
Affiliation(s)
- Tara A Retson
- Department of Radiology, University of California San Diego
| | | | | | | | - Albert Hsiao
- Department of Radiology, University of California San Diego
| |
Collapse
|
42
|
Wagner AK, Hapich A, Psychogios MN, Teichgräber U, Malich A, Papageorgiou I. Computer-Aided Detection of Pulmonary Nodules in Computed Tomography Using ClearReadCT. J Med Syst 2019; 43:58. [PMID: 30706143 DOI: 10.1007/s10916-019-1180-1] [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] [Received: 10/21/2018] [Accepted: 01/22/2019] [Indexed: 12/19/2022]
Abstract
This study evaluates the accuracy of a computer-aided detection (CAD) application for pulmonary nodular lesions (PNL) in computed tomography (CT) scans, the ClearReadCT (Riverain Technologies). The study was retrospective for 106 biopsied PNLs from 100 patients. Seventy-five scans were Contrast-Enhanced (CECT) and 25 received no enhancer (NECT). Axial reconstructions in soft-tissue and lung kernel were applied at three different slice thicknesses, 0.75 mm (CECT/NECT n = 25/6), 1.5 mm (n = 18/9) and 3.0 mm (n = 43/18). We questioned the effect of (1) enhancer, (2) kernel and (3) slice thickness on the CAD performance. Our main findings are: (1) Vessel suppression is effective and specific in both NECT and CECT. (2) Contrast enhancement significantly increased the CAD sensitivity from 60% in NECT to 80% in CECT, P = 0.025 Fischer's exact test. (3) The CAD sensitivity was 84% in 3 mm slices compared to 68% in 0.75 mm slices, P > 0.2 Fischer's exact test. (4) Small lesions of low attenuation were detected with higher sensitivity. (5) Lung kernel reconstructions increased the false positive rate without affecting the sensitivity (P > 0.05 McNemar's test). In conclusion, ClearReadCT showed an optimized sensitivity of 84% and a positive predictive value of 67% in enhanced lung scans with thick, soft kernel reconstructions. NECT, thin slices and lung kernel reconstruction were associated with inferior performance.
Collapse
Affiliation(s)
- Anne-Kathrin Wagner
- Institute of Diagnostic and Interventional Radiology, University Hospital Jena, Am Klinikum 1, 07747, Jena, Germany.,Institute of Radiology, Südharz Hospital Nordhausen, Dr.-Robert-Koch street 39, 99734, Nordhausen, Germany
| | - Arno Hapich
- Department of Thoracic Surgery, Südharz Hospital Nordhausen, Dr.-Robert-Koch street 39, 99734, Nordhausen, Germany
| | - Marios Nikos Psychogios
- Institute of Diagnostic and Interventional Neuroradiology, University Medicine Göttingen, Robert Koch street 40, 37075, Göttingen, Germany
| | - Ulf Teichgräber
- Institute of Diagnostic and Interventional Radiology, University Hospital Jena, Am Klinikum 1, 07747, Jena, Germany
| | - Ansgar Malich
- Institute of Radiology, Südharz Hospital Nordhausen, Dr.-Robert-Koch street 39, 99734, Nordhausen, Germany
| | - Ismini Papageorgiou
- Institute of Diagnostic and Interventional Radiology, University Hospital Jena, Am Klinikum 1, 07747, Jena, Germany. .,Institute of Radiology, Südharz Hospital Nordhausen, Dr.-Robert-Koch street 39, 99734, Nordhausen, Germany.
| |
Collapse
|
43
|
van Beek EJR, Murchison JT. Artificial Intelligence and Computer-Assisted Evaluation of Chest Pathology. Artif Intell Med Imaging 2019. [DOI: 10.1007/978-3-319-94878-2_12] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
|