Citation: | GAO N L, WANG X W, XUE L Y, et al. Correlation between Dual-Phase Quantitative Parameters from Dual-Layer Spectral Detector Computed Tomography and Ki-67 Expression in Non-Small Cell Lung Cancer[J]. CT Theory and Applications, xxxx, x(x): 1-8. DOI: 10.15953/j.ctta.2024.299. (in Chinese). |
Objective: We investigated the predictive value of dual-phase quantitative parameters of dual-layer spectral detector computed tomography (DLSCT) combined with Ki-67 expression in non-small-cell lung cancer (NSCLC). Methods: Seventy-seven patients with pathologically confirmed non-small cell lung cancer who underwent dual-phase enhanced scanning at our hospital between August 2022 and December 2024 were retrospectively analyzed. According to immunohistochemical results, they were divided into low (Ki-67≤30%) and high (Ki-67>30%) Ki-67 expression groups. Spectral CT viewer software was used to measure, calculate, and analyze the quantitative parameters obtained with dual-layer spectral CT in the arterial and venous phases in both groups, including iodine density (IC), standardized iodine density (NIC), effective atomic number (Zeff), and energy spectrum curve slope (K, P < 0.05) (referred to as K and MonoE [monochromatic energy spectroscopy]) results, and CT40 keV-CT100 keV at 10 keV intervals. An independent samples t-test was used to compare differences between groups. Spearman’s correlation analysis was used to evaluate the correlation between the quantitative parameters of DLSCT and Ki-67 expression. A receiver-operating characteristic (ROC) curve was constructed to obtain the area under the curve (AUC). Youden index, sensitivity, and specificity were used to measure the efficacy of each quantitative parameter of DLSCT in predicting Ki-67 expression. Results: IC, NIC, Zeff, K40-100 keV, CT40 keV-CT70 keV (interval 10 keV) were higher in the low expression group than in the high expression group in venous phase, and K40-100 keV and CT40 keV were higher in the low expression group than in the high expression group in arterial phase. The differences were statistically significant (P<0.05). IC, NIC, Zeff, K40-100 keV, CT40 keV-CT60 keV (interval 10 keV) in venous phase, and K40-100 keV, CT40 keV in arterial phase correlated negatively with Ki-67 expression level (|r| < 0.40,P < 0.05). The ROC curve showed that K40-100 keV in venous phase was the best parameter for predicting Ki-67 expression in NSCLC (AUC=0.750). Conclusion: Dual-phase quantitative parameters of DLSCT are effective tools for predicting Ki-67 expression in non-small cell lung cancer, and research evidence shows that the slope (K) of the spectral curve in the venous phase is the most valuable index.
[1] |
KIRI S, RYBA T. Cancer, metastasis, and the epigenome[J]. Molecular Cancer, 2024, 23(1): 154. DOI: 10.1186/s12943-024-02069-w.
|
[2] |
SIEGEL R L, GIAQUINTO A N, JEMAL A. Cancer statistics, 2024[J]. CA: a cancer journal for clinicians, 2024, 74(1). DOI: 10.3322/caac.21820
|
[3] |
LEITER A, VELUSWAMY R R, WISNIVESKY J P. The global burden of lung cancer: current status and future trends[J]. Nature reviews Clinical oncology, 2023, 20(9): 624-639. DOI: 10.1038/s41571-023-00798-3.
|
[4] |
MROUJ K, ANDRéS-SáNCHEZ N, DUBRA G, et al. Ki-67 regulates global gene expression and promotes sequential stages of carcinogenesis[J]. Proceedings of the National Academy of Sciences, 2021, 118(10): e2026507118. DOI: 10.1073/pnas.2026507118.
|
[5] |
LUO X, ZHENG R, ZHANG J, et al. CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis[J]. Frontiers in Oncology, 2024, 14: 1329801. DOI: 10.3389/fonc.2024.1329801.
|
[6] |
DENG L, YANG J, ZHANG M, et al. Whole-lesion iodine map histogram analysis versus single-slice spectral CT parameters for determining novel International Association for the Study of Lung Cancer grade of invasive non-mucinous pulmonary adenocarcinomas[J]. Diagnostic and Interventional Imaging, 2024, 105(5): 165-173. DOI: 10.1016/j.diii.2023.12.001.
|
[7] |
MA Y, LI S, HUANG G, et al. Role of iodine density value on dual-energy CT for detection of high tumor cell proportion region in lung cancer during CT-guided transthoracic biopsy[J]. European Journal of Radiology, 2023, 160: 110689. DOI: 10.1016/j.ejrad.2023.110689.
|
[8] |
LIN L, CHENG J, TANG D, et al. The associations among quantitative spectral CT parameters, Ki-67 expression levels and EGFR mutation status in NSCLC.[J]. Sci Rep, 2020, 1: 3436. DOI: 10.1038/s41598-020-60445-0.
|
[9] |
ZHU T, XIE K, WANG C, et al. Diagnostic Effectiveness of Dual Source Dual Energy Computed Tomography for Benign and Malignant Thyroid Nodules[J]. Evidence‐Based Complementary and Alternative Medicine, 2022, 2022(1): 2257304. DOI: 10.1155/2022/2257304.
|
[10] |
中华放射学杂志双层探测器光谱CT临床应用协作组. 双层探测器光谱CT临床应用中国专家共识(第一版)[J]. 中华放射学杂志, 2020, 54(7): 635-643. DOI: 10.3760/cma.j.cn112149-20200513-00679.
CHINESE JOURNAL OF RADIOLOGY DUAL-LAYER SPECTRAL DETECTOR CT CLINICAL APPLICATION COLLABORATIVE GROUP. Chinese expert consensus on clinical application of dual-layer spectral detector CT (first edition)[J]. Chinese Journal of Radiology, 2020, 54(7): 635-643. DOI: 10.3760/cma.j.cn112149-20200513-00679.
|
[11] |
FULTON N, RAJIAH P. Abdominal applications of a novel detector-based spectral CT[J]. Current Problems in Diagnostic Radiology, 2018, 47(2): 110-118. DOI: 10.1067/j.cpradiol.2017.05.001.
|
[12] |
ZHANG Z, ZOU H, YUAN A, et al. A Single Enhanced Dual-Energy CT Scan May Distinguish Lung Squamous Cell Carcinoma From Adenocarcinoma During the Venous phase.[J]. Acad Radiol, 2020, 5: 624-629. DOI: 10.1016/j.acra.2019.07.018.
|
[13] |
薛莉雅, 赵卫东, 苏琳, 等. 双层探测器光谱CT多参数成像在不同病理类型肺癌中的应用[J]. 中国CT和MRI杂志, 2023, 21(12): 52-55. DOI: 10.3969/j.issn.1672-5131.2023.12.016.
XUE L Y, ZHAO W D, SU L, et al. Application of multi-parameter imaging of dual-layer spectral detector CT in different pathological types of lung cancer[J]. Chinese journal of CT and MRI, 2023, 21(12): 52-55. DOI: 10.3969/j.issn.1672-5131.2023.12.016.
|
[14] |
刘秀丽, 张戟风, 刘景旺, 等. 能谱CT在中央型肺癌伴阻塞性肺不张诊断及放疗定位中应用价值[J]. CT理论与应用研究, 2023, 32(4): 509-514. DOI: 10.15953/j.ctta.2022.164.
LIU X L, ZHANG J F, LIU J W, et al. The value of Spectral CT in differential diagnosis and ra-diotherapy localiation of central lung cancer with obstructive atelectasis[J]. CT Theory and App-lications, 2023, 32(4): 509-514. DOI: 10.15953/j.ctta.2022.164.
|
[15] |
WU J, LV Y, WANG N, et al. The value of single-source dual-energy CT imaging for discriminating microsatellite instability from microsatellite stability human colorectal cancer.[J]. Eur Radiol, 2019, 7: 3782-3790. DOI: 10.1007/s00330-019-06144-5.
|
[16] |
田双凤, 杨萌, 夏建国, 等. 实性肺癌能谱CT参数与Ki-67表达水平的相关性研究[J]. 影像诊断与介入放射学, 2021, 30(1): 20-24. DOI: 10.3969/j.issn.1005-8001.2021.01.004.
TIAN S F, YANG M, XIA J G, et al. Correlation between spectral CT parameters and Ki-67 expression in solid lung cancer [J]. The imaging diagnosis and interventional radiology, 2021, 30 (1) : 20 to 24. DOI: 10.3969 / j.i SSN. 1005-8001.2021.01.004.
|
[17] |
周潋滟, 张浩荡, 殷世武. 双层光谱CT评估非小细胞肺癌Ki-67表达水平的可行性[J]. 中国介入影像与治疗学, 2023, 20(2): 107-111. DOI: 10.13929/j.issn.1672-8475.2023.02.011.
ZHOU L Y, ZHANG H D, YIN S W. Feasibility of assessing Ki-67 expression level in non-small cell lung cancer using dual-layer spectral CT[J]. Chinese interventional imaging and therapy, 2023, 20(2): 107-111. DOI: 10.13929/j.issn.1672-8475.2023.02.011.
|
[18] |
MAO L T, CHEN W C, LU J Y, et al. Quantitative parameters in novel spectral computed tomography: Assessment of Ki-67 expression in patients with gastric adenocarcinoma[J]. World Journal of Gastroenterology, 2023, 29(10): 1602. DOI: 10.3748/wjg.v29.i10.1602.
|
[19] |
ZEGADŁO A, ŻABICKA M, RóŻYK A, et al. A new outlook on the ability to accumulate an iodine contrast agent in solid lung tumors based on virtual monochromatic images in dual energy computed tomography (DECT): Analysis in two phases of contrast enhancement[J]. Journal of Clinical Medicine, 2021, 10(9): 1870. DOI: 10.3390/jcm10091870.
|
[20] |
WU Y, LI J, DING L, et al. Differentiation of pathological subtypes and Ki-67 and TTF-1 expression by dual-energy CT (DECT) volumetric quantitative analysis in non-small cell lung cancer[J]. Cancer Imaging, 2024, 24(1): 146. DOI: 10.1186/s40644-024-00793-6.
|
[21] |
DOU P, LIU Z, XIE L, et al. The predictive value of energy spectral CT parameters for assessing Ki-67 expression of lung cancer[J]. Translational Cancer Research, 2020, 9(7): 4267. DOI: 10.21037/tcr-19-2769a.
|
[22] |
窦沛沛, 赵恒亮, 曹爱红. 能谱CT联合肿瘤标志物预测肺腺癌Ki-67表达[J]. CT理论与应用研究, 2023, 32(6): 753-760. DOI: 10.15953/j.ctta.2022.172.
DOU P P, ZHAO H L, CAO A H. Spectral CT combined with tumor markers to predict Ki-67 expression in lung adenocarcinoma[J]. CT Theory and Applications, 2023, 32(6): 753-760. DOI: 10.15953 / j.carol carroll tta. 2022.172. DOI: 10.15953/j.ctta.2022.172.
|
[23] |
YU J, LIN S, LU H, et al. Optimize scan timing in abdominal multiphase CT: Bolus tracking with an individualized post-trigger delay. [J]. Eur J Radiol, 2022: 110139. DOI: 10.1016/j.ejrad.2021.110139
|
[24] |
QI K, LI L, YUAN D, et al. Optimized contrast enhancement and homogeneity in aortic CT angiography: bolus tracking with personalized post-trigger delay[J]. Quantitative Imaging in Medicine and Surgery, 2024, 15(1): 709. DOI: 10.21037/qims-24-624.
|
[25] |
YUAN D, LI L, ZHANG Y, et al. Image quality improvement in head and neck CT angiography: Individualized post-trigger delay versus fixed delay. [J]. Eur J Radiol, 2023, 111142. DOI: 10.1016/j.ejrad.2023.111142
|
[1] | CHEN Qian, YU Baodi, QIN Yanwei, WANG Sunyang, SU Xiaohui, JIN Xin, MENG Fanyong. Deep-learning Enhanced CT Reconstruction Algorithm for Multiphase-flow Measurement[J]. CT Theory and Applications, 2025, 34(3): 419-426. DOI: 10.15953/j.ctta.2025.097 |
[2] | LIU Dandan, LI Wei, ZHANG Yongxian, ZHAO Bo, CUI Ying, KANG Tianliang, MA Zixuan, LIU Yuhang, NIU Yantao. Preliminary exploration of non-gated high-pitch chest CT combined with artificial intelligence in assessing coronary artery calcium score[J]. CT Theory and Applications. DOI: 10.15953/j.ctta.2024.085 |
[3] | ZHANG Mingxia, LI Ling, SUN Ying, GUO Jia, DU Changyue, LI Xingpeng, ZHANG Yan, HAO Qi, DUAN Shuhong, LIU Xiaoyan, SUN Lei, HUO Meng, ZHANG Chunyan, WANG Rengui. Comparative Analysis of Clinical and Computed Tomography Imaging Features of COVID-19 with Different Disease Courses[J]. CT Theory and Applications, 2023, 32(3): 380-386. DOI: 10.15953/j.ctta.2023.021 |
[4] | LI Ling, ZHANG Mingxia, SUN Ying, DUAN Shuhong, GUO Jia, DU Changyue, LIU Mengke, ZHANG Yimeng, SUN Lei, HUO Meng, WANG Rengui. Imaging Study of COVID-19 Patients with Diabetes Mellitus by Computed Tomograpgh Quantitative Indicators Based on Deep Learning[J]. CT Theory and Applications, 2023, 32(3): 373-379. DOI: 10.15953/j.ctta.2023.020 |
[5] | WEN Deying, YANG Jieyin, WANG Qin, LI Zhen, WANG Hanxiao, WANG Aijie, DENG Qiao, TANG Lu, WU Xi, YAO Jin, LU Chunyan, SUN Jiayu. Application of Deep Learning Reconstruction Algorithm in Upper Abdomen CT[J]. CT Theory and Applications, 2022, 31(3): 329-336. DOI: 10.15953/j.ctta.2021.005 |
[6] | WU Tenghui, ZHA Yunfei, YANG Feng. The Study of Application of Different Pitch Combined with ASIR in Low-dose Chest CT Screening on COVID-19[J]. CT Theory and Applications, 2022, 31(2): 186-194. DOI: 10.15953/j.1004-4140.2022.31.02.05 |
[7] | HAN Zefang, SHANGGUAN Hong, ZHANG Xiong, HAN Xinglong, GUI Zhiguo, CUI Xueying, ZHANG Pengcheng. Advances in Research on Low-dose CT Imaging Algorithm Based on Deep Learning[J]. CT Theory and Applications, 2022, 31(1): 117-134. DOI: 10.15953/j.1004-4140.2022.31.01.14 |
[8] | SHEN Jing, YU Jing, YAN Yingnan, SANG Yarong, JU Ronghui, PAN Long, LI Guize, LI Xin, WU Jianlin. Chest CT Features of COVID-19 and Its Evolution[J]. CT Theory and Applications, 2021, 30(2): 199-207. DOI: 10.15953/j.1004-4140.2021.30.02.07 |
[9] | HUANG Guo, JIANG Beibei, JIE Xueqian, LU Huiliang, GAO Xiaolong. Establishment of a Diagnostic Model for Lung Adenocarcinoma with Invasive Tendency by CT and Laboratory Indexes[J]. CT Theory and Applications, 2021, 30(1): 81-90. DOI: 10.15953/j.1004-4140.2021.30.01.08 |
[10] | YAO Yonggang, DU Jingbo, LIAO Jianyong, GOU Zhenheng, FU Shunbin, JIN Erhu. Study of Chest CT Features of COVID-19[J]. CT Theory and Applications, 2020, 29(2): 169-176. DOI: 10.15953/j.1004-4140.2020.29.02.07 |
1. |
张科,张春晓. 基于深度残差网络的儿科肺炎辅助诊断算法. 中国医疗设备. 2022(09): 42-46+56 .
![]() | |
2. |
周丽媛,赵启军,高定国. 基于注意力引导深度纹理特征学习的复杂背景藏药材切片图像识别. 世界科学技术-中医药现代化. 2022(12): 4825-4832 .
![]() |