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Decoding pulmonary nodules: can machine learning enhance malignancy risk stratification?
Thorax ( IF 10 ) Pub Date : 2024-04-01 , DOI: 10.1136/thorax-2023-221300
Colin Jacobs

Randomised controlled trials, with the National Lung Screening Trial and Dutch-Belgian NELSON trial being the two largest, have demonstrated that lung cancer screening of high-risk individuals using low-dose CT reduces lung cancer mortality compared with no screening or screening with chest X-ray. Fuelled by the positive results of these landmark trials, low-dose CT-based lung cancer screening of high-risk individuals is being implemented at national or regional scale in an increasing number of countries worldwide. A comprehensive overview of the current status of implementation of lung cancer screening worldwide can be observed in the interactive lung cancer screening overview maintained by the Lung Cancer Policy Network.1 One of the challenges in the radiological interpretation of screening CT scans is the management of screen-detected pulmonary nodules. The prevalence of nodules in screening CT scans of the eligible population is high and ranges from 22% to 74%, depending on the inclusion criteria, CT parameters and minimal size cut-off. The vast majority of these pulmonary nodules are benign. This illustrates the importance of accurate risk estimators and management guidelines for screen-detected nodules; they will be crucial to maintain a good sensitivity for malignant nodules, …

中文翻译:

解码肺结节:机器学习能否增强恶性肿瘤风险分层?

随机对照试验(其中规模最大的两项试验是国家肺部筛查试验和荷兰-比利时 NELSON 试验)已证明,与不筛查或胸部 X 线筛查相比,使用低剂量 CT 对高危人群进行肺癌筛查可降低肺癌死亡率-射线。在这些具有里程碑意义的试验积极结果的推动下,全球越来越多的国家正在国家或区域范围内实施基于低剂量 CT 的高危人群肺癌筛查。在肺癌政策网络维护的交互式肺癌筛查概述中可以观察到全球肺癌筛查实施现状的全面概述。1 筛查 CT 扫描的放射学解释面临的挑战之一是筛查的管理- 检测到肺部结节。在合格人群的 CT 扫描筛查中,结节的患病率很高,范围为 22% 至 74%,具体取决于纳入标准、CT 参数和最小尺寸截止值。这些肺部结节绝大多数是良性的。这说明了准确的风险评估器和筛查检测结节管理指南的重要性;它们对于保持对恶性结节的良好敏感性至关重要,……
更新日期:2024-03-15
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