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Artificial intelligence propels lung cancer screening:innovations and the challenges of explainability and reproducibility
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作者 Mario Mascalchi Chiara Marzi Stefano Diciotti 《Signal Transduction and Targeted Therapy》 2025年第2期492-494,共3页
In a recent study published in Nature Medicine,Wang,Shao,and colleagues successfully addressed two critical issues of lung cancer(LC)screening with low-dose computed tomography(LDCT)whose widespread implementation,des... In a recent study published in Nature Medicine,Wang,Shao,and colleagues successfully addressed two critical issues of lung cancer(LC)screening with low-dose computed tomography(LDCT)whose widespread implementation,despite its capacity to decrease LC mortality,remains challenging:(1)the difficulty in accurately distinguishing malignant nodules from the far more common benign nodules detected on LDCT,and(2)the insufficient coverage of LC screening in resource-limited areas.1 To perform nodule risk stratification,Wang et al.developed and validated a multi-step,multidimensional artificial intelligence(AI)-based system(Fig.1)and introduced a data-driven Chinese Lung Nodules Reporting and Data System(C-Lung-RADS).1 A Lung-RADS system was developed in the US to stratify lung nodules into categories of increasing risk of LC and to provide corresponding management recommendations. 展开更多
关键词 SCREENING low dosecomputedtomography explainability REPRODUCIBILITY nodule risk stratificatio noduleriskstratification malignant nodules ARTIFICIALINTELLIGENCE
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