European radiology · 2026
Commercial AI for CT lung cancer screening: product capabilities, coverage of nodule management tasks and supporting evidence.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Antonissen Noa, Schalekamp Steven, Hahn Horst K, van Leeuwen Kicky G, Jacobs Colin
Аннотация
OBJECTIVES: To characterize the capabilities of CE-marked AI products for lung nodule analysis in lung cancer screening (LCS), quantify their coverage of tasks defined in nodule management recommendations, and assess their peer-reviewed evidence.
MATERIALS AND METHODS: Six core tasks in LCS (nodule detection, classification, measurement, growth assessment, malignancy risk estimation, and structured management) were derived from 4 nodule management recommendations: Lung-RADS 2022, British Thoracic Society (BTS) guidelines, European Union Position Statement (EUPS), and European Society of Thoracic Imaging (ESTI). Products were identified through www.healthairegister.com . Vendors confirmed capabilities using a standardized questionnaire; public documentation supplemented non-responders. Task coverage was calculated as the percentage of functional overlap (0-100%) per recommendation. Peer-reviewed evidence was evaluated using a six-level efficacy framework and assessed for study characteristics.
RESULTS: In total, 16 products from 16 vendors were included; 10 vendors completed questionnaires. Analysis showed that 14 products detect and measure solid and subsolid nodules, 12 support growth assessment, and 9 provide malignancy risk estimation (PanCan in 5, AI-based scores in 4). No product provides support for endobronchial or cystic lesions. High task coverage (> 75%) was observed in 10 products for EUPS and 4 for BTS, whereas no product achieved high coverage for Lung-RADS or ESTI. Overall, 60 peer-reviewed studies were identified; 7% were prospective and evidence clustered at lower efficacy levels: 70% assessed diagnostic accuracy, while none reported patient outcomes or societal impact.
CONCLUSION: Numerous CE-certified AI products could support CT-based lung cancer screening, but gaps in task coverage and predominantly lower-level evidence necessitate cautious, monitored implementation.
KEY POINTS: Question Do commercially available AI products for lung nodule analysis functionally cover international nodule management recommendation-defined tasks, and what peer-reviewed clinical evidence supports them? Findings AI products support standard nodule detection and measurement in line with management recommendations but lack support for endobronchial or cystic lesions and high-level clinical evidence. Clinical relevance CE-marked AI products can assist radiologists with core lung cancer screening tasks, but capability gaps exist. Limited high-level clinical evidence complicates integrating AI into guidelines, securing reimbursement, and formulating recommendations for its use in lung cancer screening programs.
MATERIALS AND METHODS: Six core tasks in LCS (nodule detection, classification, measurement, growth assessment, malignancy risk estimation, and structured management) were derived from 4 nodule management recommendations: Lung-RADS 2022, British Thoracic Society (BTS) guidelines, European Union Position Statement (EUPS), and European Society of Thoracic Imaging (ESTI). Products were identified through www.healthairegister.com . Vendors confirmed capabilities using a standardized questionnaire; public documentation supplemented non-responders. Task coverage was calculated as the percentage of functional overlap (0-100%) per recommendation. Peer-reviewed evidence was evaluated using a six-level efficacy framework and assessed for study characteristics.
RESULTS: In total, 16 products from 16 vendors were included; 10 vendors completed questionnaires. Analysis showed that 14 products detect and measure solid and subsolid nodules, 12 support growth assessment, and 9 provide malignancy risk estimation (PanCan in 5, AI-based scores in 4). No product provides support for endobronchial or cystic lesions. High task coverage (> 75%) was observed in 10 products for EUPS and 4 for BTS, whereas no product achieved high coverage for Lung-RADS or ESTI. Overall, 60 peer-reviewed studies were identified; 7% were prospective and evidence clustered at lower efficacy levels: 70% assessed diagnostic accuracy, while none reported patient outcomes or societal impact.
CONCLUSION: Numerous CE-certified AI products could support CT-based lung cancer screening, but gaps in task coverage and predominantly lower-level evidence necessitate cautious, monitored implementation.
KEY POINTS: Question Do commercially available AI products for lung nodule analysis functionally cover international nodule management recommendation-defined tasks, and what peer-reviewed clinical evidence supports them? Findings AI products support standard nodule detection and measurement in line with management recommendations but lack support for endobronchial or cystic lesions and high-level clinical evidence. Clinical relevance CE-marked AI products can assist radiologists with core lung cancer screening tasks, but capability gaps exist. Limited high-level clinical evidence complicates integrating AI into guidelines, securing reimbursement, and formulating recommendations for its use in lung cancer screening programs.