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通过体积倍增时间预测早期肺腺癌的分期转移生长

Prediction of the stage shift growth of early-stage lung adenocarcinomas by volume-doubling time.

作者信息

Tang En-Kuei, Wu Yun-Ju, Chen Chi-Shen, Wu Fu-Zong

机构信息

Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung.

Department of Medical Imaging and Radiology, Shu-Zen Junior College of Medicine and Management, Kaohsiung.

出版信息

Quant Imaging Med Surg. 2024 Jun 1;14(6):3983-3996. doi: 10.21037/qims-23-1759. Epub 2024 May 24.

Abstract

BACKGROUND

Prediction of subsolid nodule (SSN) interval growth is crucial for clinical management and decision making in lung cancer screening program. To the best of our knowledge, no study has investigated whether volume doubling time (VDT) is an independent factor for predicting SSN interval growth, or whether its predictive power is better than that of traditional semantic methods, such as nodular diameter or type. This study aimed to investigate whether VDT could provide added value in predicting the long-term natural course of SSNs (<3 cm) regarding stage shift.

METHODS

This retrospective study enrolled 132 patients with spectrum lesions of lung adenocarcinoma who underwent two consecutive computed tomography (CT) examinations before surgical tissue proofing between 2012 and 2021 in Kaohsiung Veterans General Hospital. The VDTs were manually calculated from the volumetric segmentation using Schwartz's approximation formula. We utilized logistic regression to identify predictors associated with stage shift progression based on the VDT parameter.

RESULTS

The average duration of follow-up period was 3.629 years. A VDT-based nomogram model (model 2) based on CT semantic features, clinical characteristics, and the VDT parameter yielded an area under the curve (AUC) of 0.877 [95% confidence interval (CI): 0.807-0.928]. Compared with model 1 (CT semantic features and clinical characteristics), model 2 exhibited the better predictive performance for stage shift (AUC model 1: 0.833 versus AUC model 2: 0.877, P=0.047). In model 2, significant predictors of stage shift growth included initial nodule size [odds ratio (OR) =4.074, 95% CI: 1.368-12.135; P=0.012], SSN classification (OR =0.042; 95% CI: 0.006-0.288; P=0.001), follow-up period (OR =1.692, 95% CI: 1.337-2.140; P<0.001), and VDT classification (OR =2.327, 95% CI: 1.368-3.958; P=0.002). For the stage shift, the mean progression time for the VDT (>400 d) group was 7.595 years, and median progression time was 7.430 years. Additionally, a VDT ≤400 d is an important prognostic factor associated with aggressive growth behavior with a stage shift.

CONCLUSIONS

VDT is crucial for predicting SSN stage shift growth irrespective of clinical and CT semantic features. This highlights its significance in informing follow-up protocols and surgical planning, emphasizing its prognostic value in predicting SSN growth.

摘要

背景

亚实性结节(SSN)的间隔期生长预测对于肺癌筛查项目中的临床管理和决策至关重要。据我们所知,尚无研究调查体积倍增时间(VDT)是否为预测SSN间隔期生长的独立因素,或者其预测能力是否优于传统的语义学方法,如结节直径或类型。本研究旨在探讨VDT在预测直径<3 cm的SSN的长期自然病程分期变化方面是否能提供附加价值。

方法

本回顾性研究纳入了132例肺腺癌谱系病变患者,这些患者于2012年至2021年在高雄荣民总医院接受手术组织病理检查前连续进行了两次计算机断层扫描(CT)检查。使用施瓦茨近似公式从体积分割中手动计算VDT。我们利用逻辑回归基于VDT参数识别与分期进展相关的预测因素。

结果

平均随访期为3.629年。基于CT语义特征、临床特征和VDT参数的基于VDT的列线图模型(模型2)的曲线下面积(AUC)为0.877 [95%置信区间(CI):0.807 - 0.928]。与模型1(CT语义特征和临床特征)相比,模型2在分期变化方面表现出更好的预测性能(AUC模型1:0.833,AUC模型2:0.877,P = 0.047)。在模型2中,分期变化生长的显著预测因素包括初始结节大小[比值比(OR)= 4.074,95% CI:1.368 - 12.135;P = 0.012]、SSN分类(OR = 0.042;95% CI:0.006 - 0.288;P = 0.001)、随访期(OR = 1.692,95% CI:1.337 - 2.140;P < 0.001)和VDT分类(OR = 2.327,95% CI:1.368 - 3.958;P = 0.002)。对于分期变化,VDT(>400天)组的平均进展时间为7.595年,中位进展时间为7.430年。此外,VDT≤400天是与具有分期变化的侵袭性生长行为相关的重要预后因素。

结论

无论临床和CT语义特征如何,VDT对于预测SSN分期变化生长至关重要。这突出了其在指导随访方案和手术规划方面的重要性,强调了其在预测SSN生长方面的预后价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/86f5/11151246/580e3c366504/qims-14-06-3983-f1.jpg

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