Department of Radiology, New York University Langone Medical Center, 560 1st Ave, New York, NY 10016, USA.
Radiology. 2012 Feb;262(2):662-71. doi: 10.1148/radiol.11100878. Epub 2011 Dec 9.
To determine the precision of a three-dimensional (3D) method for measuring the growth rate of solid and subsolid nodules and its ability to detect abnormal growth rates.
This study was approved by the Institutional Research Board and was HIPAA compliant. Informed consent was waived. The growth rates of 123 lung nodules in 59 patients who had undergone lung cancer screening computed tomography (CT) were measured by using a 3D semiautomated computer-assisted volume method. Clinical stability was established with long-term CT follow-up (mean, 6.4 years±1.9 [standard deviation]; range, 2.0-8.5 years). A mean of 4.1 CT examinations per patient±1.2 (range, two to seven CT examinations per patient) was analyzed during 2.4 years±0.5 after baseline CT. Nodule morphology, attenuation, and location were characterized. The analysis of standard deviation of growth rate in relation to time between scans yielded a normative model for detecting abnormal growth.
Growth rate precision increased with greater time between scans. Overall estimate for standard deviation of growth rate, on the basis of 939 growth rate determinations in clinically stable nodules, was 36.5% per year. Peripheral location (P=.01; 37.1% per year vs 25.6% per year) and adjacency to pleural surface (P=.05; 38.9% per year vs 34.0% per year) significantly increased standard deviation of growth rate. All eight malignant nodules had an abnormally high growth rate detected. By using 3D volumetry, growth rate-based diagnosis of malignancy was made at a mean of 183 days±158, compared with radiologic or clinical diagnosis at 344 days±284.
A normative model derived from the variability of growth rates of nodules that were stable for an average of 6.4 years may enable identification of lung cancer.
确定一种用于测量实性和亚实性结节生长率的三维（3D）方法的精确性及其检测异常生长率的能力。
本研究获得了机构研究委员会的批准，并符合 HIPAA 规定。豁免了知情同意。通过使用 3D 半自动计算机辅助体积法测量了 59 名接受肺癌筛查 CT 的患者的 123 个肺结节的生长率。通过长期 CT 随访（平均 6.4 年±1.9[标准差]；范围 2.0-8.5 年）确定临床稳定性。在基线 CT 后 2.4 年±0.5 的时间内，对每位患者平均进行 4.1 次 CT 检查±1.2（范围为每位患者 2 至 7 次 CT 检查）。对结节形态、衰减和位置进行了特征描述。分析两次扫描之间生长率标准差与时间的关系，得出了用于检测异常生长的规范模型。
生长率精度随扫描时间间隔的增加而提高。在临床上稳定的结节的 939 个生长率测定值的基础上，总体估计生长率标准差为每年 36.5%。周围位置（P=.01；每年 37.1%比每年 25.6%）和靠近胸膜表面（P=.05；每年 38.9%比每年 34.0%）显著增加了生长率标准差。所有 8 个恶性结节的生长率均异常升高。通过使用 3D 体绘制，基于生长率的恶性肿瘤诊断在平均 183 天±158时做出，而放射学或临床诊断在 344 天±284时做出。
从平均稳定 6.4 年的结节的生长率变化中得出的规范模型，可能能够识别肺癌。
