Thoracic cancer · 2026
Artificial Intelligence-Assisted Pulmonary Nodule Diagnosis by Thoracic Surgeons: A Comparative Study on Clinical Effectiveness.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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Jeon Yeong Jeong, Jeong Jonghun, Park Doohyun, Kang Jung-Hyun, Hong Eun Kyoung, Chung Younjoon, Park Boram, Park Seong Yong
Аннотация
MAIN PROBLEM: Clinician experience variability affects lung cancer detection using computed tomography. AI-based computer-aided diagnosis (CAD) systems can improve diagnostic accuracy, but their influence on thoracic surgeons with varying experience is unclear. This study aimed to evaluate the impact of CAD system on thoracic surgeons' assessment of pulmonary nodule malignancy.
METHODS: A multireader, crossover study included 20 thoracic surgeons (8 junior, 12 senior). Each reader interpreted 100 anonymized pulmonary nodules twice-once with CAD and once without-following a washout period. Nodules were assessed using a 10-point likelihood of malignancy (LOM) scale and a binary (benign/malignant) assessment.
RESULTS: The CAD system demonstrated high malignancy prediction (AUROC = 0.929). CAD assistance significantly improved diagnostic accuracy across all readers (AUROC: 0.770-0.804, p = 0.0014 [BINARY]; 0.833-0.879, p < 0.001 [LOM]). Junior readers showed greater improvement (BINARY: 0.780-0.848, p < 0.001; LOM: 0.838-0.904, p < 0.001) compared with senior readers (BINARY: 0.763-0.775, p = 0.344; LOM: 0.831-0.862, p = 0.026). Specificity significantly increased with CAD (p = 0.008 overall, p < 0.001 junior readers), while sensitivity remained unchanged. Reading time slightly increased with CAD for benign cases (p < 0.05), but remained stable for malignant cases.
CONCLUSIONS: The CAD system improved thoracic surgeons' diagnostic performance, especially junior readers, by enhancing specificity and overall accuracy. AI-based CAD systems are beneficial for reducing diagnostic variability and supporting lung nodule malignancy assessment by inexperienced thoracic surgeons.
METHODS: A multireader, crossover study included 20 thoracic surgeons (8 junior, 12 senior). Each reader interpreted 100 anonymized pulmonary nodules twice-once with CAD and once without-following a washout period. Nodules were assessed using a 10-point likelihood of malignancy (LOM) scale and a binary (benign/malignant) assessment.
RESULTS: The CAD system demonstrated high malignancy prediction (AUROC = 0.929). CAD assistance significantly improved diagnostic accuracy across all readers (AUROC: 0.770-0.804, p = 0.0014 [BINARY]; 0.833-0.879, p < 0.001 [LOM]). Junior readers showed greater improvement (BINARY: 0.780-0.848, p < 0.001; LOM: 0.838-0.904, p < 0.001) compared with senior readers (BINARY: 0.763-0.775, p = 0.344; LOM: 0.831-0.862, p = 0.026). Specificity significantly increased with CAD (p = 0.008 overall, p < 0.001 junior readers), while sensitivity remained unchanged. Reading time slightly increased with CAD for benign cases (p < 0.05), but remained stable for malignant cases.
CONCLUSIONS: The CAD system improved thoracic surgeons' diagnostic performance, especially junior readers, by enhancing specificity and overall accuracy. AI-based CAD systems are beneficial for reducing diagnostic variability and supporting lung nodule malignancy assessment by inexperienced thoracic surgeons.