目的: 通过分析CT纹理特征改变,依据肿瘤缺氧生长分子基因机制,明确酸化与CT纹理关系,提高酸化肿瘤穿刺精确性。方法: 选择62例非小细胞肺癌(non-small cell lung cancer, NSCLC)患者,其中基因野生型31例,突变型31例,从CT增强图像中选取感兴趣区(region of interest, ROI)测量同一病例病灶整体CT值,3个低密度区CT均值,使用公式计算相对CT值,判断乳酸化、磷酸化CT值区间范围。利用CT肺肿块高级多功能软件,重建出肿块三维立体内部解剖结构图像,明确CT形态学表现与肿瘤缺氧状态下酸化的关系。结果: 野生型整体区域CT值为(57.8±25.1)HU,ROI区域CT值为(27.3±5.4)HU;突变型整体区域CT值为(49.4±24.6)HU,ROI区域CT值为(17.5±5.4)HU。野生型组相对CT值＜0.25,突变型组相对CT值0.25~0.5。野生型组出现湿地征比例为81%,突变型组出现沼泽征比例为84%。结论: CT纹理特征对NSCLC缺氧状态下酸化有明显的提示效应,可以提高酸化肿瘤穿刺精确性,对基因突变具有一定预测价值。
Objective: To explore the relationship between acidification and CT texture by analyzing the change of CT textural features based on the molecular mechanism of tumor hypoxia, so to improve the accuracy of needle biopsy of tumor. Methods: A total of 62 patients with non-small cell lung cancer (NSCLC) were selected and analyzed, including 31 cases of wild type (WT) gene and 31 cases of mutant type gene. The CT value of the lesion of the same case was measured from the region of interest (ROI) in CT enhancement image. The average CT value of the three low-density areas was measured, and the relative CT value was calculated with the formula. The range of CT values in lactylation and phosphorylation was determined. The 3D anatomical structure image of the mass was reconstructed using the advanced multi-functional software for CT lung mass, and the relationship between CT morphological features and hypoxia-induced acidification was evaluated. Results: CT value of the wild-type gene region was (57.8±25.1)HU, with the CT value of (27.3±5.4)HU in the ROI region. The CT value mutant gene region was (49.4±24.6)HU, with the CT value of (17.5±5.4)HU in the ROI region. The relative CT value in the wild-type gene group was ＜0.25, and the mutant gene group had a relative CT value of 0.25 to 0.5. The proportion of wetland signs occurred in the wild-type gene group was 81% and 84% in the mutant gene group. Conclusion: The CT textural features could effectively suggest acidification of NSCLC under hypoxic state, which could improve the accuracy of needle biopsy of tumor and predict gene mutation.
[1] 张明敏, 张强. 兔VX2胸膜肿瘤白介素-2治疗后的MR灌注成像研究[J]. 包头医学院学报, 2020, 36(11): 75-78+82.
[2] 张明敏, 张强, 刘照信, 等. 兔VX2胸膜肿瘤MR灌注成像研究[J]. 智慧健康, 2018, 4(11): 7-8+13.
[3] 赵欣, 张明敏. 凝血四项指标及肿瘤标志物诊断乙型肝炎相关肝癌的检验分析[J]. 智慧健康, 2021, 7(5): 1-2.
[4] 赵欣, 张明敏. 兔VX2胸膜腔种植瘤生长过程中血清CRP与DWI-MRI变化及临床意义[J]. 智慧健康, 2020, 6(32): 185-187.
[5] Yamamoto S, Korn RL, Oklu R, et al. 非小细胞肺癌的间变性淋巴瘤激酶分子表型: CT放射基因组学特征[J]. 国际医学放射学杂志, 2014, 37(5): 488.
[6] Rizzo S, Petrella F, Buscarino V, et al. 非小细胞肺癌中EGFR、K-RAS和ALK基因突变的CT放射基因学特征[J]. 国际医学放射学杂志, 2016, 39(2): 200.
[7] Zhang Q, Ba CX, Zhang MM, et al. Tumor growth assessment by computed tomography perfusion imaging (CTPI), perfusion-weighted imaging (PWI), and diffusion-weighted imaging (DWI) in a rabbit pleural squamous cell carcinoma VX2-implanted model[J]. Medical Science Monitor: International Medical Journal of Experimental and Clinical Research, 2018, 24: 6756-6764.
[8] Zhang D, Tang ZY, Huang H, et al. Metabolic regulation of gene expression by histone lactylation[J]. Nature, 2019, 574(7779): 575-580.
[9] 陈璐, 高威. 肿瘤酸性微环境的形成机制及其对肿瘤进展的影响[J]. 肿瘤, 2019, 39(2): 140-145.
[10] 王晓莉, 葛晓松, 刘彦魁, 等. EGFR/ALK/ROS1三联检测在非小细胞肺癌中的临床意义[J]. 临床与实验病理学杂志, 2018, 34(10): 1135-1137.
[11] 张宝会, 王晨桐, 郭淼, 等. 基于亲和色谱的肺癌细胞磷酸化蛋白质组研究及其应用[J]. 色谱, 2021, 39(1): 77-86.
[12] 张明敏, 张强. 可疑感染COVID-19发热待查肿瘤患者合并间质性肺炎CT影像学分析[J]. 包头医学院学报, 2022, 38(3): 43-47.
[13] Bailey KM, Wojtkowiak JW, Hashim AI, et al. Targeting the metabolic microenvironment of tumors[J]. Advances in Pharmacology, 2012, 65: 63-107.
[14] Taylor S, Spugnini EP, Assaraf YG, et al. Microenvironment acidity as a major determinant of tumor chemoresistance: proton pump inhibitors (PPIs) as a novel therapeutic approach[J]. Drug Resistance Updates, 2015, 23: 69-78.
[15] Peppicelli S, Bianchini F, Calorini L. Extracellular acidity, a "reappreciated" trait of tumor environment driving malignancy: perspectives in diagnosis and therapy[J]. Cancer Metastasis Reviews, 2014, 33(2-3): 823-832.
[16] Mookerjee SA, Goncalves RLS, Gerencser AA, et al. The contributions of respiration and glycolysis to extracellular acid production[J]. BBA - Bioenergetics, 2015, 1847(2) : 171-118.
[17] Liu XJ, Cooper DE, Cluntun AA, et al. Acetate production from glucose and coupling to mitochondrial metabolism in mammals[J]. Cell, 2018, 175(2): 502-513.
[18] Allis CD, Jenuwein T. The molecular hallmarks of epigenetic control[J]. Nature reviews Genetics, 2016, 17(8): 487-500.
[19] Perez-Riverol Y, Csordas A, Bai JW, et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data[J]. Nucleic Acids Research, 2019, 47(D1): D442-D450.
[20] Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements[J]. Nature Methods, 2015, 12(4): 357-360.
[21] Walenta S, Wetterling M, Lehrke M, et al. High lactate levels predict likelihood of metastases, tumor recurrence, and restricted patient survival in human cervical cancers[J]. Cancer Research, 2000, 60(4) : 916-921.
