Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?

Schreuder, Anton; Scholten, Ernst Th.; Ginneken, Bram van; Jacobs, Colin

doi:10.21037/tlcr-2020-lcs-06

Cited by 44 publications

(32 citation statements)

References 68 publications

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“…Though the development of AI-based models seemed not statistically different, the trend can also be validated by studies in the same data set. 59 Nevertheless, this might indicate that the performance of well-trained AI models might exceed that of the current methods in PN evaluation in the future. External validation is still needed for the AI-based models.…”

Section: Resultsmentioning

confidence: 99%

Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules

Zhang

Wei

Nie

et al. 2022

JTO Clinical and Research Reports

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Section: Resultsmentioning

confidence: 99%

Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules

Zhang

Wei

Nie

et al. 2022

JTO Clinical and Research Reports

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“…AI systems that can be used in LCS have shown significant improvement over the decades. Different methods of using an AI system to assist a human reader have been investigated-as a first reader, second reader, or a concurrent reader [58]. AI as a first reader is the optimal strategy when looking to reduce the radiologists' workload, as the human reader only reviews the nodules deemed clinically significant by the AI system.…”

Section: Ai and Lung-nodule Detectionmentioning

confidence: 99%

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

2022

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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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“…These trials boosted the demand for image reading. The application of AI in LDCT reading can help radiologists reduce laborious work, minimize reader variability, and improve screening efficiency [99,100]. The main task for AI application in LDCT reading is the same as in CXR: nodule detection and classification/malignancy prediction.…”

Section: Chest Ctmentioning

confidence: 99%

Application of Artificial Intelligence in Lung Cancer

Chiu

Chao

Chen

2022

Cancers

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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.

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Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?

Cited by 44 publications

References 68 publications

Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules

Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules

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

Application of Artificial Intelligence in Lung Cancer

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