人们认为在癌症筛查中观察到的肺结节呈指数增长，并且已提出将其相关的体积倍增时间（VDT）用于结节分类。这项回顾性研究旨在阐明肺结节的生长动态，并确定良性或恶性的最佳分类。数据分析来自参加 I-ELCAP 筛查计划的 180 名参与者（73.7% 男性）（140 名原发性肺癌和 40 名良性肺癌） ）在切除前每年进行三次或多次 CT 检查。作为分类方法对衰减、体积、质量和生长模式（减速、线性、次指数、指数和加速）进行评估和比较。大多数肺癌（83/140）和少数良性结节（11/40）表现出加速，比指数增长模式。一半（50%）的良性结节和 26.4% 的恶性结节显示出生长减慢。生长模式的差异允许对结节恶性肿瘤进行分类，最有效的个体变量是两年间隔扫描之间体积的增加（ROC-AUC = 0.871）。前两次随访的相同指标得出的 AUC 值为 0.769。进一步分类为实体、部分实体或非实体，结果得到改善（第一年 ROC-AUC 为 0.813，第二年为 0.897）。在我们的数据集中，大多数肺癌与良性肺癌相比表现出加速生长。体积生长的测量可以区分良性和恶性结节。当添加有关结节致密性的信息时，其分类能力会增加。这两个有意义且容易获得的变量的组合可用于评估肺癌结节的恶性程度。© 2024。作者。

Lung nodules observed in cancer screening are believed to grow exponentially, and their associated volume doubling time (VDT) has been proposed for nodule classification. This retrospective study aimed to elucidate the growth dynamics of lung nodules and determine the best classification as either benign or malignant.Data were analyzed from 180 participants (73.7% male) enrolled in the I-ELCAP screening program (140 primary lung cancer and 40 benign) with three or more annual CT examinations before resection. Attenuation, volume, mass and growth patterns (decelerated, linear, subexponential, exponential and accelerated) were assessed and compared as classification methods.Most lung cancers (83/140) and few benign nodules (11/40) exhibited an accelerated, faster than exponential, growth pattern. Half (50%) of the benign nodules versus 26.4% of the malignant ones displayed decelerated growth. Differences in growth patterns allowed nodule malignancy to be classified, the most effective individual variable being the increase in volume between two-year-interval scans (ROC-AUC = 0.871). The same metric on the first two follow-ups yielded an AUC value of 0.769. Further classification into solid, part-solid or non-solid, improved results (ROC-AUC of 0.813 in the first year and 0.897 in the second year).In our dataset, most lung cancers exhibited accelerated growth in contrast to their benign counterparts. A measure of volumetric growth allowed discrimination between benign and malignant nodules. Its classification power increased when adding information on nodule compactness. The combination of these two meaningful and easily obtained variables could be used to assess malignancy of lung cancer nodules.© 2024. The Author(s).