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扩散峰度成像和F-FDG PET在评估非小细胞肺癌的亚型、分期及增殖状态中的应用

Application of diffusion kurtosis imaging and F-FDG PET in evaluating the subtype, stage and proliferation status of non-small cell lung cancer.

作者信息

Feng Pengyang, Shao Zehua, Dong Bai, Fang Ting, Huang Zhun, Li Ziqiang, Fu Fangfang, Wu Yaping, Wei Wei, Yuan Jianmin, Yang Yang, Wang Zhe, Wang Meiyun

机构信息

Department of Medical Imaging, Henan University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China.

Heart Center of Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, China.

出版信息

Front Oncol. 2022 Sep 30;12:989131. doi: 10.3389/fonc.2022.989131. eCollection 2022.

DOI:10.3389/fonc.2022.989131
PMID:36248958
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9562703/
Abstract

BACKGROUND

Lung cancer has become one of the deadliest tumors in the world. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for approximately 80%-85% of all lung cancer cases. This study aimed to investigate the value of diffusion kurtosis imaging (DKI), diffusion-weighted imaging (DWI) and 2-[F]-fluoro-2-deoxy-D-glucose positron emission tomography (F-FDG PET) in differentiating squamous cell carcinoma (SCC) and adenocarcinoma (AC) and to evaluate the correlation of each parameter with stage and proliferative status Ki-67.

METHODS

Seventy-seven patients with lung lesions were prospectively scanned by hybrid 3.0-T chest F-FDG PET/MR. Mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), maximum standard uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were measured. The independent samples t test or Mann-Whitney U test was used to compare and analyze the differences in each parameter of SCC and AC. The diagnostic efficacy was evaluated by receiver operating characteristic (ROC) curve analysis and compared with the DeLong test. A logistic regression analysis was used for the evaluation of independent predictors. Bootstrapping (1000 samples) was performed to establish a control model, and calibration curves and ROC curves were used to validate its performance. Pearson's correlation coefficient and Spearman's correlation coefficient were calculated for correlation analysis.

RESULTS

The MK and ADC values of the AC group were significantly higher than those of the SCC group (all P< 0.05), and the SUVmax, MTV, and TLG values of the SCC group were significantly higher than those of the AC group (all P<0.05). There was no significant difference in the MD value between the two groups. Moreover, MK, SUVmax, TLG and MTV were independent predictors of the NSCLC subtype, and the combination of these parameters had an optimal diagnostic efficacy (AUC, 0.876; sensitivity, 86.27%; specificity, 80.77%), which was significantly better than that of MK (AUC = 0.758, z = 2.554, P = 0.011), ADC (AUC = 0.679, z = 2.322, P = 0.020), SUVmax (AUC = 0.740, z = 2.584, P = 0.010), MTV (AUC = 0.715, z = 2.530, P = 0.011) or TLG (AUC = 0.716, z = 2.799, P = 0.005). The ROC curve showed that the validation model had high accuracy in identifying AC and SCC (AUC, 0.844; 95% CI, 0.785-0.885);. The SUVmax value was weakly positively correlated with the Ki-67 index (r = 0.340, P< 0.05), the ADC and MD values were weakly negatively correlated with the Ki-67 index (r = -0.256, -0.282, P< 0.05), and the MTV and TLG values were weakly positively correlated with NSCLC stage (r = 0.342, 0.337, P< 0.05).

CONCLUSION

DKI, DWI and F-FDG PET are all effective methods for assessing the NSCLC subtype, and some parameters are correlated with stage and proliferation status.

摘要

背景

肺癌已成为全球最致命的肿瘤之一。非小细胞肺癌(NSCLC)是最常见的肺癌类型,约占所有肺癌病例的80%-85%。本研究旨在探讨扩散峰度成像(DKI)、扩散加权成像(DWI)和2-[F]-氟-2-脱氧-D-葡萄糖正电子发射断层扫描(F-FDG PET)在鉴别鳞状细胞癌(SCC)和腺癌(AC)中的价值,并评估各参数与分期及增殖状态Ki-67的相关性。

方法

对77例肺部病变患者进行前瞻性3.0-T胸部F-FDG PET/MR联合扫描。测量平均峰度(MK)、平均扩散率(MD)、表观扩散系数(ADC)、最大标准摄取值(SUVmax)、代谢肿瘤体积(MTV)和总病变糖酵解(TLG)。采用独立样本t检验或Mann-Whitney U检验比较分析SCC和AC各参数的差异。通过受试者操作特征(ROC)曲线分析评估诊断效能,并与DeLong检验进行比较。采用逻辑回归分析评估独立预测因子。进行自抽样(1000个样本)建立对照模型,并用校准曲线和ROC曲线验证其性能。计算Pearson相关系数和Spearman相关系数进行相关性分析。

结果

AC组的MK和ADC值显著高于SCC组(均P<0.05),SCC组的SUVmax、MTV和TLG值显著高于AC组(均P<0.05)。两组间MD值无显著差异。此外,MK、SUVmax、TLG和MTV是NSCLC亚型的独立预测因子,这些参数的组合具有最佳诊断效能(AUC,0.876;灵敏度,86.27%;特异性,80.77%),显著优于MK(AUC = 0.758,z = 2.554,P = 0.011)、ADC(AUC = 0.679,z = 2.322,P = 0.020)、SUVmax(AUC = 0.740,z = 2.584,P = 0.010)、MTV(AUC = 0.715,z = 2.530,P = 0.011)或TLG(AUC = 0.716,z = 2.799,P = 0.005)。ROC曲线显示,验证模型在鉴别AC和SCC方面具有较高准确性(AUC,0.844;95%CI,0.785-0.885)。SUVmax值与Ki-67指数呈弱正相关(r = 0.340,P<0.05),ADC和MD值与Ki-67指数呈弱负相关(r = -0.256,-0.282,P<0.05),MTV和TLG值与NSCLC分期呈弱正相关(r = 0.342,0.337,P<0.05)。

结论

DKI、DWI和F-FDG PET都是评估NSCLC亚型的有效方法,且一些参数与分期和增殖状态相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d94/9562703/f0177e497875/fonc-12-989131-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d94/9562703/af1c2cc6792a/fonc-12-989131-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d94/9562703/af1c2cc6792a/fonc-12-989131-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d94/9562703/461ff961b18b/fonc-12-989131-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d94/9562703/c0494a83d144/fonc-12-989131-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d94/9562703/f0177e497875/fonc-12-989131-g004.jpg

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