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基于平均 CT 值预测非小细胞肺癌的复发。

Predicting recurrence of non-small cell lung cancer based on mean computed tomography value.

机构信息

Department of Thoracic Surgery, Kanazawa University School of Medicine, Takara-machi 13-1, Kanazawa, 920-8640, Japan.

出版信息

J Cardiothorac Surg. 2021 May 12;16(1):128. doi: 10.1186/s13019-021-01476-0.

Abstract

BACKGROUND

The aim of this study was to assess the ability of using mean computed tomography (mCT) values to predict non-small cell lung cancer (NSCLC) tumor recurrence.

METHODS

A retrospective study was conducted on 494 patients with stage IA NSCLC. Receiver operating characteristics analysis was used to assess the ability to use mCT value, C/T ratio, tumor size, and SUV to predict tumor recurrence. Multiple logistic regression analyses were performed to determine the independent variables for the prediction of tumor recurrence.

RESULTS

The m-CT values were - 213.7 ± 10.2 Hounsfield Units (HU) for the recurrence group and - 594.1 ± 11.6 HU for the non-recurrence group (p < 0.0001). Recurrence occurred in 45 patients (9.1%). The tumor recurrence group was strongly associated with a high CT attenuation value, high C/T ratio, large solid tumor size, and SUV. The diagnostic value of mCT value was more accurate than the C/T ratio, excluding the pure ground-glass opacity and pure solid (0 < C/T ratio < 100) groups. The SUV and mCT are independent predictive factors of tumor recurrence.

CONCLUSIONS

The evaluation of mCT values was useful for predicting recurrence after the limited resection of small-sized NSCLC, and may potentially contribute to the selection of suitable treatment strategies.

摘要

背景

本研究旨在评估使用平均计算机断层扫描(mCT)值预测非小细胞肺癌(NSCLC)肿瘤复发的能力。

方法

对 494 例 I 期 NSCLC 患者进行回顾性研究。采用受试者工作特征分析评估 mCT 值、C/T 比值、肿瘤大小和 SUV 值预测肿瘤复发的能力。采用多因素逻辑回归分析确定预测肿瘤复发的独立变量。

结果

复发组的 m-CT 值为-213.7±10.2 亨氏单位(HU),无复发组为-594.1±11.6 HU(p<0.0001)。45 例患者(9.1%)出现复发。肿瘤复发组与 CT 衰减值高、C/T 比值高、实性肿瘤大、SUV 高密切相关。mCT 值的诊断价值优于 C/T 比值,排除纯磨玻璃密度和纯实性(0<C/T 比值<100)组。SUV 和 mCT 是肿瘤复发的独立预测因素。

结论

评估 mCT 值有助于预测局限性切除小尺寸 NSCLC 后的复发,可能有助于选择合适的治疗策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63d5/8117299/da89f4fe23ba/13019_2021_1476_Fig1_HTML.jpg

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