Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification

Lancaster, Harriet L.; Zheng, Sunyi; Aleshina, Olga; Yu, Donghoon; Chernina, Valeria; Heuvelmans, Marjolein A; Bock, Geertruida H. de; Dorrius, Monique D.; Gratama, Jan W.; Морозов, С. П.; Gombolevskiy, Victor A.; Silva, Mario; Yi, Jaeyoun; Oudkerk, Matthijs

doi:10.1016/j.lungcan.2022.01.002

Cited by 26 publications

(17 citation statements)

References 26 publications

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“…Google's AI algorithm has shown potential in predicting lung cancer risk based on LDCT images, with the authors reporting performance levels similar to or better than those of radiologists (Ardila et al, 2019). Using AI in ultra-LDCT lung cancer screening may significantly reduce the workload of radiologists (Lancaster et al, 2022).…”

Section: Ai For Ct Images Analysis In Lung Cancer Screeningmentioning

confidence: 99%

Digital public health technologies: rationale for a multidisciplinary research laboratory

Andreychenko¹,

Gorbacheva²,

Denisova³

et al. 2022

Preprint

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An expansion of digital technologies in healthcare includes various artificial intelligence (AI) approaches to diagnostic, patient care, and research. This state-of-art review aims to critically assess the current role of AI and digital technologies in public health. Furthermore, it explores the overall progress and determines possible direction and expediency for public health research projects in the areas that would benefit from AI. We paid particular attention to describing biomedical data sources for AI-based analysis and digital technology advances in radiology, pathology, drug development and disease screening. In addition, we focused on the ethical, legal and technical issues linked to AI technologies in the context of public health successfully. In conclusion, we consider several promising AI applications in public health: computer tomography analysis in lung cancer screening, digital fundus camera images for eye disease tracking, echocardiogram analysis for heart disease diagnosis, and reading mammograms and magnetic resonance imaging in breast cancer screening.

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Section: Ai For Ct Images Analysis In Lung Cancer Screeningmentioning

confidence: 99%

Digital public health technologies: rationale for a multidisciplinary research laboratory

Andreychenko¹,

Gorbacheva²,

Denisova³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Alternatively, in place of AI differentiating between benign and malignant lung nodules, workload reduction can be achieved by correctly classifying nodules by size. A recent study on the performance of AI for categorization of lung nodules based on volumetric size measurement showed that AI could outperform four experienced radiologists when looking at negative misclassifications, resulting in a possible workload reduction of up to 86.7% [72].…”

Section: Artificial Intelligence In Lcsmentioning

confidence: 99%

Low‐dose computed tomography lung cancer screening: Clinical evidence and implementation research

2022

Self Cite

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Lung cancer causes more deaths than breast, cervical, and colorectal cancer combined. Nevertheless, population-based lung cancer screening is still not considered standard practice in most countries worldwide. Early lung cancer detection leads to better survival outcomes: patients diagnosed with stage 1A lung cancer have a >75% 5-year survival rate, compared to <5% at stage 4. Low-dose computed tomography (LDCT) thorax imaging for the secondary prevention of lung cancer has been studied at length, and has been shown to significantly reduce lung cancer mortality in high-risk populations. The US National Lung Screening Trial reported a 20% overall reduction in lung cancer mortality when comparing LDCT to chest X-ray, and the Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) trial more recently reported a 24% reduction when comparing LDCT to no screening. Hence, the focus has now shifted to implementation research. Consequently, the 4-IN-THE-LUNG-RUN consortium based in five European countries, has set up a large-scale multicenter implementation trial. Suc-cessful implementation of and accessibility to LDCT lung cancer screening are dependent on many factors, not limited to population selection, recruitment strategy, computed tomography screening frequency, lung-nodule management, participant compliance, and cost effectiveness. This review provides an overview of current evidence for LDCT lung cancer screening, and draws attention to major factors that need to be addressed to successfully implement standardized, effective, and accessible screening throughout Europe. Evidence shows that through the appropriate use of risk-prediction models and a more personalized approach to screening, efficacy could be improved. Furthermore, extending the screening interval for low-risk individuals to reduce costs and associated harms is a possibility, and through the use of volumetric-based measurement and follow-up, false positive results can be greatly reduced. Finally, smoking cessation programs could be a valuable addition to screening programs and artificial intelligence could offer a solution to the added workload pressures radiologists are facing.

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“…4). Lancaster et al recently evaluated this type of deep-learning tool in 283 participants from the Moscow lung-cancer screening program who had at least one solid lung nodule [41]. CT examinations were analyzed independently by five experienced thoracic radiologists.…”

Section: Lung Nodule Segmentation and Characterizationmentioning

confidence: 99%

Artificial intelligence in lung cancer: current applications and perspectives

et al. 2022

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Artificial intelligence (AI) has been a very active research topic over the last years and thoracic imaging has particularly benefited from the development of AI and in particular deep learning. We have now entered a phase of adopting AI into clinical practice. The objective of this article was to review the current applications and perspectives of AI in thoracic oncology. For pulmonary nodule detection, computer-aided detection (CADe) tools have been commercially available since the early 2000s. The more recent rise of deep learning and the availability of large annotated lung nodule datasets have allowed the development of new CADe tools with fewer false-positive results per examination. Classical machine learning and deeplearning methods were also used for pulmonary nodule segmentation allowing nodule volumetry and pulmonary nodule characterization. For pulmonary nodule characterization, radiomics and deep-learning approaches were used. Data from the National Lung Cancer Screening Trial (NLST) allowed the development of several computer-aided diagnostic (CADx) tools for diagnosing lung cancer on chest computed tomography. Finally, AI has been used as a means to perform virtual biopsies and to predict response to treatment or survival. Thus, many detection, characterization and stratification tools have been proposed, some of which are commercially available.

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Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification

Cited by 26 publications

References 26 publications

Digital public health technologies: rationale for a multidisciplinary research laboratory

Digital public health technologies: rationale for a multidisciplinary research laboratory

Low‐dose computed tomography lung cancer screening: Clinical evidence and implementation research

Artificial intelligence in lung cancer: current applications and perspectives

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