Registered Clinical Trials for Artificial Intelligence in Lung Disease: A Scoping Review on ClinicalTrials.gov
Abstract
:1. Introduction
2. Materials and Methods
2.1. Search Technique
2.2. Selection Standards
2.3. Data Extraction and Trial Screening
2.4. Data Evaluation
3. Results
3.1. The Features of the Tests Included
3.2. Features of the Research Design
3.3. Overview of Diagnostic Clinical Trials
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Bi, W.L.; Hosny, A.; Schabath, M.B.; Giger, M.L.; Birkbak, N.; Mehrtash, A.; Allison, T.; Arnaout, O.; Abbosh, C.; Dunn, I.F.; et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J. Clin. 2019, 69, 127–157. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Machine learning in cancer diagnostics. eBioMedicine 2019, 45, 1–2. Available online: https://pubmed.ncbi.nlm.nih.gov/31326086/ (accessed on 16 August 2022). [CrossRef] [PubMed] [Green Version]
- Avanzo, M.; Stancanello, J.; Pirrone, G.; Sartor, G. Radiomics and deep learning in lung cancer. Strahlenther. Onkol. 2020, 196, 879–887. [Google Scholar] [CrossRef] [PubMed]
- Christie, J.R.; Lang, P.; Zelko, L.M.; Palma, D.A.; Abdelrazek, M.; Mattonen, S.A. Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making. Can. Assoc. Radiol. J. 2021, 72, 86–97. [Google Scholar] [CrossRef] [PubMed]
- Agarwal, M.; Agarwal, S.; Saba, L.; Chabert, G.L.; Gupta, S.; Carriero, A.; Pasche, A.; Danna, P.; Mehmedovic, A.; Faa, G.; et al. Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0. Comput. Biol. Med. 2022, 146. [Google Scholar] [CrossRef] [PubMed]
- Joy Mathew, C.; David, A.M.; Mathew, C.M.J. Artificial Intelligence and its future potential in lung cancer screening. EXCLI J. 2020, 19, 1552–1562. [Google Scholar] [PubMed]
- Li, L.; Liu, Z.; Huang, H.; Lin, M.; Luo, D. Evaluating the performance of a deep learning-based computer-aided diagnosis (DL-CAD) system for detecting and characterizing lung nodules: Comparison with the performance of double reading by radiologists. Thorac. Cancer 2019, 10, 183–192. [Google Scholar] [CrossRef] [PubMed]
- Heuvelmans, M.A.; van Ooijen, P.M.; Ather, S.; Silva, C.F.; Han, D.; Heussel, C.P.; Hickes, W.; Kauczor, H.-U.; Novotny, P.; Peschl, H.; et al. Lung cancer prediction by Deep Learning to identify benign lung nodules. Lung Cancer 2021, 154, 1–4. [Google Scholar] [CrossRef] [PubMed]
- Tsao, M.S.; Tsao, M.S.; Marguet, S.; Le Teuff, G.; Lantuejoul, S.; Shepherd, F.A.; Seymour, L.; Kratzke, R.; Graziano, S.L.; Popper, H.H.; et al. Subtype Classification of Lung Adenocarcinoma Predicts Benefit from Adjuvant Chemotherapy in Patients Undergoing Complete Resection. J. Clin. Oncol. 2015, 33, 3439–3446. [Google Scholar] [CrossRef]
- Song, Y.; Zheng, S.; Li, L.; Zhang, X.; Zhang, X.; Huang, Z.; Chen, J.; Zhao, H.; Jie, Y.; Wang, R. Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images. IEEE/ACM Trans. Comput. Biol. Bioinform. 2020, 18, 2775–2780. [Google Scholar] [CrossRef]
- Chassagnon, G.; Vakalopoulou, M.; Paragios, N.; Revel, M.-P. Artificial intelligence applications for thoracic imaging. Eur. J. Radiol. 2020, 123, 108774. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chaunzwa, T.L.; Hosny, A.; Xu, Y.; Shafer, A.; Diao, N.; Lanuti, M.; Christiani, D.C.; Mak, R.H.; Aerts, H.J.W.L. Deep learning classification of lung cancer histology using CT images. Sci. Rep. 2021, 11, 5471. [Google Scholar] [CrossRef] [PubMed]
- Dong, J.; Geng, Y.; Lu, D.; Li, B.; Tian, L.; Lin, D.; Zhang, Y. Clinical Trials for Artificial Intelligence in Cancer Diagnosis: A Cross-Sectional Study of Registered Trials in ClinicalTrials.gov. Front. Oncol. 2020, 10, 1629. [Google Scholar] [CrossRef] [PubMed]
- Kogilavani, S.V.; Prabhu, J.; Sandhiya, R.; Kumar, M.S.; Subramaniam, U.; Karthick, A.; Muhibbullah, M.; Imam, S.B.S. COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques. Comput. Math. Methods Med. 2022, 2022, 7672196. [Google Scholar] [CrossRef]
- Saood, A.; Hatem, I. COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Med Imaging 2021, 21, 19. [Google Scholar] [CrossRef]
- Wang, X.; Deng, X.; Fu, Q.; Zhou, Q.; Feng, J.; Ma, H.; Liu, W.; Zheng, C. A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT. IEEE Trans. Med Imaging 2020, 39, 2615–2625. [Google Scholar] [CrossRef]
- Glangetas, A.; Hartley, M.-A.; Cantais, A.; Courvoisier, D.S.; Rivollet, D.; Shama, D.M.; Perez, A.; Spechbach, H.; Trombert, V.; Bourquin, S.; et al. Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: Clinical protocol for a case–control and prospective cohort study. BMC Pulm. Med. 2021, 21, 103. [Google Scholar] [CrossRef]
- Wang, H.; Wang, L.; Lee, E.H.; Zheng, J.; Zhang, W.; Halabi, S.; Liu, C.; Deng, K.; Song, J.; Yeom, K.W. Decoding COVID-19 pneumonia: Comparison of deep learning and radiomics CT image signatures. Eur. J. Nucl. Med. Mol. Imaging 2020, 48, 1478–1486. [Google Scholar] [CrossRef] [PubMed]
- Lu, M.T.; Ivanov, A.; Mayrhofer, T.; Hosny, A.; Aerts, H.J.W.L.; Hoffmann, U. Deep Learning to Assess Long-term Mortality from Chest Radiographs. JAMA Netw. Open 2019, 2, e197416. [Google Scholar] [CrossRef] [Green Version]
- Chen, L.W.; Yang, S.M.; Chuang, C.C.; Wang, H.J.; Chen, Y.C.; Lin, M.W.; Hsieh, M.S.; Antonoff, M.B.; Chang, Y.C.; Wu, C.C.; et al. Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Patterns in Lung Adenocarcinomas on Computed Tomography. Ann. Surg. Oncol. 2022, 29, 7473–7482. [Google Scholar] [CrossRef]
- Hyun, S.H.; Ahn, M.S.; Koh, Y.W.; Lee, S.J. A Machine-Learning Approach Using PET-Based Radiomics to Predict the Histological Subtypes of Lung Cancer. Clin. Nucl. Med. 2019, 44, 956–960. [Google Scholar] [CrossRef] [PubMed]
- Song, S.H.; Park, H.; Lee, G.; Lee, H.Y.; Sohn, I.; Kim, H.S.; Lee, S.H.; Jeong, J.Y.; Kim, J.; Lee, K.S.; et al. Imaging Phenotyping Using Radiomics to Predict Micropapillary Pattern within Lung Adenocarcinoma. J. Thorac. Oncol. 2017, 12, 624–632. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jacobs, C.; van Ginneken, B. Google’s lung cancer AI: A promising tool that needs further validation. Nat. Rev. Clin. Oncol. 2019, 16, 532–533. [Google Scholar] [CrossRef]
- Luo, X.; Zang, X.; Yang, L.; Huang, J.; Liang, F.; Rodriguez-Canales, J.; Wistuba, I.I.; Gazdar, A.; Xie, Y.; Xiao, G. Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis. J. Thorac. Oncol. 2017, 12, 501–509. [Google Scholar] [CrossRef] [Green Version]
- Yu, K.-H.; Zhang, C.; Berry, G.J.; Altman, R.B.; Ré, C.; Rubin, D.L.; Snyder, M. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat. Commun. 2016, 7, 12474. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gu, J.; Lu, C.; Guo, J.; Chen, L.; Chu, Y.; Ji, Y.; Ge, D. Prognostic significance of the IASLC/ATS/ERS classification in Chinese patients-A single institution retrospective study of 292 lung adenocarcinoma. J. Surg. Oncol. 2013, 107, 474–480. [Google Scholar] [CrossRef] [PubMed]
- Krarup, M.M.K.; Krokos, G.; Subesinghe, M.; Nair, A.; Fischer, B.M. Artificial Intelligence for the Characterization of Pulmonary Nodules, Lung Tumors and Mediastinal Nodes on PET/CT. Semin. Nucl. Med. 2021, 51, 143–156. [Google Scholar] [CrossRef]
- Xu, Y.; Hosny, A.; Zeleznik, R.; Parmar, C.; Coroller, T.; Franco, I.; Mak, R.H.; Aerts, H.J. Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging. Clin. Cancer Res. 2019, 25, 3266–3275. [Google Scholar] [CrossRef] [Green Version]
- Niu, Y.; Wang, L.; Zhang, X.; Han, Y.; Yang, C.; Bai, H.; Huang, K.; Ren, C.; Tian, G.; Yin, S.; et al. Predicting Tumor Mutational Burden from Lung Adenocarcinoma Histopathological Images Using Deep Learning. Front. Oncol. 2022, 12, 927426. [Google Scholar] [CrossRef]
- Wu, J.; Pan, J.; Teng, D.; Xu, X.; Feng, J.; Chen, Y.-C. Interpretation of CT signs of 2019 novel coronavirus (COVID-19) pneumonia. Eur. Radiol. 2020, 30, 5455–5462. [Google Scholar] [CrossRef]
- Niu, R.; Ye, S.; Li, Y.; Ma, H.; Xie, X.; Hu, S.; Huang, X.; Ou, Y.; Chen, J. Chest CT features associated with the clinical characteristics of patients with COVID-19 pneumonia. Ann. Med. 2021, 53, 169–180. [Google Scholar] [CrossRef] [PubMed]
- Cai, W.; Liu, T.; Xue, X.; Luo, G.; Wang, X.; Shen, Y.; Fang, Q.; Sheng, J.; Chen, F.; Liang, T. CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients. Acad. Radiol. 2020, 27, 1665–1678. [Google Scholar] [CrossRef] [PubMed]
Characteristics | Number | Percentage (%) | |
---|---|---|---|
Study type | |||
Interventional | 7 | 13.2 | |
Observational | 46 | 86.8 | |
Year | |||
2015–2019 | 16 | 30.2 | |
2019–2020 | 17 | 32.1 | |
2020–2021 | 12 | 22.6 | |
2021–2022 | 8 | 15.1 | |
Status | |||
Completed | 8 | 15.1 | |
Recruiting | 30 | 56.6 | |
Active, not recruiting | 6 | 11.3 | |
Not yet recruiting | 5 | 9.4 | |
Unknown status | 4 | 7.5 | |
Withdrawn | 0 | 0 | |
Study results | |||
Has available results | 0 | 0 | |
No available results | 53 | 100 | |
Participant age (y) | |||
1 to older | 50 | 94.3 | |
Older than 18 | 3 | 5.7 | |
Enrollment | |||
<100 | 7 | 13.2 | |
100–1000 | 24 | 45.3 | |
1001–2000 | 14 | 26.4 | |
>2000 | 8 | 15.1 | |
Sponsor | |||
University | 15 | 28.3 | |
Hospital | 19 | 35.8 | |
Company | 19 | 35.8 | |
Location | |||
Europe | 27 | 50.9 | |
North America | 11 | 20.8 | |
Asia | 11 | 20.8 | |
Australia | 4 | 7.5 |
Characteristics | Number | Percentage | |
---|---|---|---|
Primary purpose | |||
Diagnostic and treatment | 1 | 14.3 | |
Diagnostic | 6 | 85.7 | |
Screening | 0 | 0 | |
Phase | |||
Phase 1/2 | 2 | 28.6 | |
Phase 3/4 | 0 | 0 | |
Not applicable | 5 | 71.4 | |
Allocation | |||
Randomized | 1 | 14.3 | |
Non-randomized | 2 | 28.6 | |
Missing value | 4 | 57.1 | |
Intervention model | |||
Parallel assignment | 2 | 28.6 | |
Sequential assignment | 1 | 14.3 | |
Crossover assignment | 0 | 0 | |
Single group assignment | 4 | 57.1 | |
Masking | |||
Single | 1 | 14.3 | |
Double | 1 | 14.3 | |
Triple | 0 | 0 | |
Without | 5 | 71.4 |
Characteristics | Number | Percentage (%) | |
---|---|---|---|
Observational model | |||
Case-only Case-control Case-crossover | 6 3 1 | 13.0 6.5 2.2 | |
Cohort | 28 | 60.9 | |
Ecological or community | 1 | 2.2 | |
Other | 7 | 15.2 | |
Time perspective | |||
Prospective | 22 | 47.8 | |
Retrospective | 20 | 43.5 | |
Cross-sectional | 1 | 2.2 | |
Other | 3 | 6.5 |
Characteristics | Number | Percentage (%) | |
---|---|---|---|
Disease | |||
Lung cancer | 18 | 34.0 | |
COVID-19 and other infectious diseases | 16 | 30.2 | |
COPD/ILD | 5 | 9.4 | |
IIP/IPF/NSIF Asthma Pneumoconiosis | 1 1 1 | 1.9 1.9 1.9 | |
Osteosarcoma | 1 | 1.9 | |
Pulmonary embolism | 1 | 1.9 | |
Pulmonary hypertension | 1 | 1.9 | |
Non-specific | 8 | 15.1 | |
Application method | |||
Device | 6 | 11.3 | |
Endoscopy | 1 | 1.9 | |
Imaging | 17 | 32.1 | |
Pathology | 4 | 7.5 | |
Biomarker | 5 | 9.4 | |
Other | 20 | 37.7 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, B.; Jiang, L.; Lin, D.; Dong, J. Registered Clinical Trials for Artificial Intelligence in Lung Disease: A Scoping Review on ClinicalTrials.gov. Diagnostics 2022, 12, 3046. https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12123046
Li B, Jiang L, Lin D, Dong J. Registered Clinical Trials for Artificial Intelligence in Lung Disease: A Scoping Review on ClinicalTrials.gov. Diagnostics. 2022; 12(12):3046. https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12123046
Chicago/Turabian StyleLi, Bingjie, Lisha Jiang, Dan Lin, and Jingsi Dong. 2022. "Registered Clinical Trials for Artificial Intelligence in Lung Disease: A Scoping Review on ClinicalTrials.gov" Diagnostics 12, no. 12: 3046. https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12123046