能谱和灌注CT多参数定量分析鉴别肺癌病理分型的应用

doi:10.3969/j.issn.1006-5725.2025.19.021

Abstract

Abstract:

Objective This study aims to explore the application value of spectral CT and perfusion CT parameters in the pathological classification and prognostic assessment of lung cancer. Methods A total of 94 lung cancer patients confirmed by pathology at Shanghai Chest Hospital from January 2023 to November 2024 were included in the study， including 49 cases of lung adenocarcinoma （LUAD）， 30 cases of lung squamous cell carcinoma （LUSC）， and 15 cases of small cell lung cancer （SCLC）. All patients underwent spectral CT combined with perfusion scanning using a 256-slice Revolution Apex from GE. Two radiologists independently measured the spectral and perfusion parameters of the three groups of images， including spectral curve slope （K）， iodine concentration in the lesion area （ICL）， effective atomic number （Zeff）， surface permeability （PS）， and perfusion index （PI）， and established a lung cancer pathological subtype discrimination prediction model based on spectral CT radiomics features. All subjects were randomly divided into a training group and a validation group at a ratio of 3∶1. The discrimination efficacy of the spectral discrimination model between different pathological subtypes and the discrimination efficacy of arterial and venous phase images were compared in multiple dimensions. The performance of the model was evaluated using the receiver operating characteristic （ROC） curve. Results Statistical analysis showed that the spectral curve slope， ICL， NIC， and Zeff of LUAD patients were significantly higher than those of LUSC and SCLC patients （P < 0.05）， while there were no significant differences in these parameters between LUSC and SCLC patients （P > 0.05）. Among the perfusion CT parameters， surface permeability （PS） showed significant differences among the three groups （P < 0.05）， while blood volume （BV）， blood flow （BF）， perfusion index （PI）， time to peak （TTP）， and mean transit time （MTT） did not show statistical differences. The multi-factor logistic regression model based on spectral parameters showed strong discriminatory performance： the area under the curve （AUC） of the LUAD and LUSC discrimination model was 0.806/0.77 （training group/test group） in the arterial phase and 0.867/0.9 （training group/test group） in the venous phase； the AUC of the LUAD and SCLC discrimination model was 0.885/0.883 （training group/test group） in the arterial phase and 0.851/0.776 （training group/test group） in the venous phase. Conclusion This study indicates that the multi-dimensional functional metabolic analysis indicators of spectral and perfusion CT imaging have significant value in the differential diagnosis of lung cancer pathological subtypes. The diagnostic model constructed by combining multiple spectral parameters can significantly improve the discrimination efficacy of lung adenocarcinoma， squamous cell carcinoma， and small cell lung cancer， providing precise imaging evidence for the formulation of individualized treatment plans.

Key words: spectral CT, perfusion, small cell carcinoma, non-small cell lung cancer, diagnosis

CLC Number:

R734.2

Xiaokun GAO,Ziming XIE,Guangyu TAO,Yanbing SUN,Hua REN,Jiahui YU,Lin ZHU,Hong YU,Qiming. NI. The application value of multi⁃parameter quantitative analysis of spectral and perfusion CT in differentiating pathological types of lung cancer[J]. The Journal of Practical Medicine, 2025, 41(19): 3096-3105.

Figures/Tables 8

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References 30

[1]	SUNG H， FERLAY J， SIEGEL R L， et al. Global Cancer Statistics 2020： GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries ［J］. CA Cancer J Clin， 2021， 71（3）： 209-249. doi:10.3322/caac.21660 doi: 10.3322/caac.21660
[2]	RUIZ-CORDERO R， DEVINE W P. Targeted Therapy and Checkpoint Immunotherapy in Lung Cancer ［J］. Surgical Pathology Clinics， 2020， 13（1）： 17-33. doi:10.1016/j.path.2019.11.002 doi: 10.1016/j.path.2019.11.002
[3]	熊丙万，柯文飞，江文洋. 分子靶向治疗在EGFR突变肺鳞癌中的研究进展［J］. 中国肺癌杂志， 2024，（4）： 283-290.
[4]	MOLINA J R， YANG P， CASSIVI S D， et al. Non-small cell lung cancer： Epidemiology， risk factors， treatment， and survivorship ［J］. Mayo Clin Proc， 2008， 83（5）： 584-594. doi:10.1016/s0025-6196(11)60735-0 doi: 10.1016/s0025-6196(11)60735-0
[5]	KELLY K， CROWLEY J， BUNN P A， JR.， et al. Randomized phase Ⅲ trial of paclitaxel plus carboplatin versus vinorelbine plus cisplatin in the treatment of patients with advanced non--small-cell lung cancer： A Southwest Oncology Group trial ［J］. J Clin Oncol， 2001， 19（13）： 3210-3218. doi:10.1200/jco.2001.19.13.3210 doi: 10.1200/jco.2001.19.13.3210
[6]	NGUYEN K T， SAKTHIVEL G， MILANO M T， et al. Oligoprogression in non-small cell lung cancer： A narrative review ［J］. J Thorac Dis， 2022， 14（12）： 4998-5011. doi:10.21037/jtd-22-536 doi: 10.21037/jtd-22-536
[7]	GONG L， XU L， YUAN Z， et al. Clinical outcome for small cell lung cancer patients with bone metastases at the time of diagnosis ［J］. J Bone Oncol， 2019， 19： 100265. doi:10.1016/j.jbo.2019.100265 doi: 10.1016/j.jbo.2019.100265
[8]	DENG L， ZHANG G， LIN X， et al. Comparison of Spectral and Perfusion Computed Tomography Imaging in the Differential Diagnosis of Peripheral Lung Cancer and Focal Organizing Pneumonia ［J］. Front Oncol， 2021， 11： 690254. doi:10.3389/fonc.2021.690254 doi: 10.3389/fonc.2021.690254
[9]	韩冬，周洁丽，于勇，等. 基于能谱曲线斜率鉴别肾透明细胞癌WHO/ISUP简化分级［J］. 临床放射学杂志， 2020， 39（6）： 1133-1138.
[10]	邓靓娜，张国晋，林晓强，等. 能谱及灌注CT成像鉴别诊断周围型肺癌和局灶性机化性肺炎的对比研究［J］. 中国医学影像学杂志， 2021， 29（12）： 1206-1211.
[11]	WANG Z， LI M， HUANG Y， et al. Clinical and radiological characteristics of central pulmonary adenocarcinoma： A comparison with central squamous cell carcinoma and small cell lung cancer and the impact on treatment response ［J］. Onco Targets Ther， 2018， 11： 2509-2517. doi:10.2147/ott.s154385 doi: 10.2147/ott.s154385
[12]	KOENIGKAM SANTOS M， MULEY T， WARTH A， et al. Morphological computed tomography features of surgically resectable pulmonary squamous cell carcinomas： Impact on prognosis and comparison with adenocarcinomas ［J］. Eur J Radiol， 2014， 83（7）： 1275-1281. doi:10.1016/j.ejrad.2014.04.019 doi: 10.1016/j.ejrad.2014.04.019
[13]	JIA Y， XIAO X， SUN Q， et al. CT spectral parameters and serum tumour markers to differentiate histological types of cancer histology ［J］. Clin Radiol， 2018， 73（12）： 1033-1040. doi:10.1016/j.crad.2018.07.104 doi: 10.1016/j.crad.2018.07.104
[14]	XU X， SUI X， ZHONG W， et al. Clinical utility of quantitative dual-energy CT iodine maps and CT morphological features in distinguishing small-cell from non-small-cell lung cancer ［J］. Clin Radiol， 2019， 74（4）： 268-277. doi:10.1016/j.crad.2018.10.012 doi: 10.1016/j.crad.2018.10.012
[15]	LI J， XU Q， MAO C， et al. Correlation between Imaging Features and Pathological Stages of Primary Lung Tumors Based on Nanocontrast Agents ［J］. Comput Math Methods Med， 2021， 2021： 2343299. doi:10.1155/2021/2343299 doi: 10.1155/2021/2343299
[16]	LIN L Y， ZHANG Y， SUO S T， et al. Correlation between dual-energy spectral CT imaging parameters and pathological grades of non-small cell lung cancer ［J］. Clin Radiol， 2018， 73（4）： 412.e1-e7. doi:10.1016/j.crad.2017.11.004 doi: 10.1016/j.crad.2017.11.004
[17]	WANG S， LIU G， FU Z， et al. Predicting Pathological Invasiveness of Lung Adenocarcinoma Manifesting as GGO-Predominant Nodules： A Combined Prediction Model Generated From DECT ［J］. Acad Radiol， 2021， 28（4）： 509-516. doi:10.1016/j.acra.2020.03.007 doi: 10.1016/j.acra.2020.03.007
[18]	LI Q， LI X， LI X Y， et al. Histological subtypes of solid-dominant invasive lung adenocarcinoma： Differentiation using dual-energy spectral CT ［J］. Clin Radiol， 2021， 76（1）： 77.e1-e7. doi:10.1016/j.crad.2020.08.034 doi: 10.1016/j.crad.2020.08.034
[19]	YUAN A， YU C J， KUO S H， et al. Vascular endothelial growth factor 189 mRNA isoform expression specifically correlates with tumor angiogenesis， patient survival， and postoperative relapse in non-small-cell lung cancer ［J］. J Clin Oncol， 2001， 19（2）： 432-441. doi:10.1200/jco.2001.19.2.432 doi: 10.1200/jco.2001.19.2.432
[20]	HONG S R， HUR J， MOON Y W， et al. Predictive factors for treatment response using dual-energy computed tomography in patients with advanced lung adenocarcinoma ［J］. Eur J Radiol， 2018， 101： 118-123. doi:10.1016/j.ejrad.2018.02.019 doi: 10.1016/j.ejrad.2018.02.019
[21]	廖淑婷，于向荣. 能谱CT和人工智能在甲状腺癌诊断中的应用［J］. 实用医学杂志， 2022，（2）： 129-133.
[22]	梁百晖，杨文，刘岘，等. 双层探测器光谱CT对直肠腺癌转移淋巴结的诊断价值［J］. 实用医学杂志， 2023， 39（3）： 374-380. doi:10.3969/j.issn.1006-5725.2023.03.020 doi: 10.3969/j.issn.1006-5725.2023.03.020
[23]	ZHONG L J， YU N， ZHOU X J， et al. Differentiating between pulmonary adenocarcinoma and squamous cell carcinoma by spectral CT volumetric quantitative analysis： A comparative study with conventional spectral analysis ［J］. J Thorac Dis， 2023， 15（2）： 679-689. doi:10.21037/jtd-23-115 doi: 10.21037/jtd-23-115
[24]	MU R， MENG Z， GUO Z， et al. Diagnostic value of dual-layer spectral detector CT in differentiating lung adenocarcinoma from squamous cell carcinoma ［J］. Front Oncol， 2022， 12： 868216. doi:10.3389/fonc.2022.868216 doi: 10.3389/fonc.2022.868216
[25]	NASRULLAH N， SANG J， ALAM M S， et al. Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies ［J］. Sensors （Basel）， 2019， 19（17）：3722. doi:10.3390/s19173722 doi: 10.3390/s19173722
[26]	CHU L L， KNEBEL R J， SHAY A D， et al. CT perfusion imaging of lung cancer： Benefit of motion correction for blood flow estimates ［J］. Eur Radiol， 2018， 28（12）： 5069-5075. doi:10.1007/s00330-018-5492-1 doi: 10.1007/s00330-018-5492-1
[27]	ZIELIŃSKI K W， KULIG A， ZIELIŃSKI J. Morphology of the microvascular bed in primary human carcinomas of lung. Part II. Morphometric investigations of microvascular bed of lung tumors ［J］. Pathol Res Pract， 1984， 178（4）： 369-377. doi:10.1016/s0344-0338(84)80029-1 doi: 10.1016/s0344-0338(84)80029-1
[28]	LUCCHI M， MUSSI A， FONTANINI G， et al. Small cell lung carcinoma （SCLC）： The angiogenic phenomenon ［J］. Eur J Cardiothorac Surg， 2002， 21（6）： 1105-1110. doi:10.1016/s1010-7940(02)00112-4 doi: 10.1016/s1010-7940(02)00112-4
[29]	CHEN M L， WEI Y Y， LI X T， et al. Low-dose spectral CT perfusion imaging of lung cancer quantitative analysis in different pathological subtypes ［J］. Transl Cancer Res， 2021， 10（6）： 2841-2848. doi:10.21037/tcr-20-3479 doi: 10.21037/tcr-20-3479
[30]	胡蓉，徐耀，侯金鹏，等. 能谱CT多参数定量分析对鉴别肺癌病理类型的价值［J］. 实用放射学杂志， 2019， 35（3）： 5.

项目	LUAD组（n = 49）	LUSC组（n = 30）	SCLC组（n = 15）	P值
项目	LUAD组（n = 49）	LUSC组（n = 30）	SCLC组（n = 15）	三组间P值	LUAD组 vs. LUSC组	LUAD组 vs. SCLC组	LUSC组 vs. SCLC组
性别				< 0.001	< 0.001	0.078	0.115
男	24（48.98）	29（96.67）	11（73.33）
女	25（51.02）	1（3.33）	4（26.67）
影像征象
分叶	47（95.92）	25（83.33）	15（100.00）	0.059
毛刺	40（81.63）	23（76.67）	14（93.33）	0.394
胸膜牵拉	38（77.55）	8（26.67）	6（40.00）	< 0.001	< 0.001	0.011	0.399
肿瘤标志物
癌胚抗原（+）	29（59.18）	7（23.33）	6（40.00）	0.008	0.002	0.193	0.292
鳞癌特异性抗原（+）	6（12.24）	17（56.67）	2（13.33）	< 0.001	< 0.001	0.934	0.002
神经元特异性烯醇化酶（+）	31（63.27）	14（46.67）	12（80.00）	0.086
细胞角蛋白19片段（+）	18（36.73）	23（76.67）	6（40.00）	0.002	0.001	0.826	0.021
糖类抗原125（+）	16（32.65）	8（26.67）	4（26.67）	0.820

能谱参数	LUAD组（n = 49）	LUSC组（n = 30）	SCLC组（n = 15）	P值
能谱参数	LUAD组（n = 49）	LUSC组（n = 30）	SCLC组（n = 15）	三组间P值	LUAD组 vs. LUSC组	LUAD组 vs. SCLC组	LUSC组 vs. SCLC组
动脉期
K1	4.495（2.630，6.190）	3.050（2.610，4.188）	2.825（1.712，4.263）	0.011	0.009	0.028	0.894
K2	0.760（0.445，1.055）	0.520（0.446，0.712）	0.480（0.292，0.722）	0.011	0.009	0.03	0.903
K3	0.215（0.135，0.295）	0.152（0.131，0.712）	0.145（0.087，0.208）	0.01	0.008	0.025	0.879
K	1.518（0.889，2.090）	1.031（0.882，1.416）	0.956（0.577，1.441）	0.011	0.009	0.027	0.886
ICL（x ± s）	19.296 ± 7.474	13.962 ± 5.627	13.715 ± 7.516	0.017	0.015	0.032	0.83
NIC（x ± s）	0.153 ± 0.088	0.121 ± 0.078	0.110 ± 0.073	0.027	0.04	0.024	0.544
WC/（mg/cm3）	1 014（995，1 029）	1 032（1 026，1 035）	1 028（1 022，1 032）	< 0.001	< 0.001	0.04	0.287
Zeff（x ± s）	8.772 ± 0.564	8.407 ± 0.342	8.409 ± 0.427	0.006	0.004	0.022	0.968
静脉期
K1	4.485（3.390，5.565）	3.370（2.337，3.904）	3.160（2.288，4.537）	0.002	0.001	0.032	0.702
K2	0.755（0.580，0.950）	0.577（0.401，0.664）	0.540（0.390，0.765）	0.003	0.001	0.034	0.686
K3	0.205（0.165，0.260）	0.168（0.120，0.189）	0.155（0.120，0.217）	0.004	0.002	0.051	0.644
K	1.516（1.147，1.906）	1.140（0.789，1.320）	1.068（0.773，1.534）	0.002	0.001	0.031	0.701
ICL（x ± s）	19.703 ± 10.212	15.484 ± 12.546	15.183 ± 6.400	0.003	0.001	0.034	0.688
NIC（x ± s）	0.367 ± 0.124	0.291 ± 0.119	0.304 ± 0.130	0.001	0.001	0.025	0.674
WC/（mg/cm3）	1 019（1 002，1 026）	1 030（1 023，1 037）	1 030（1 023，1 032）	< 0.001	< 0.001	0.002	0.703
Zeff（x ± s）	8.739 ± 0.401	8.428 ± 0.311	8.497 ± 0.342	0.001	0.001	0.025	0.674

灌注参数	LUAD组（n = 49）	LUSC组（n = 30）	SCLC组（n = 15）	P值
灌注参数	LUAD组（n = 49）	LUSC组（n = 30）	SCLC组（n = 15）	三组间P值	LUAD组 vs. LUSC组	LUAD组 vs. SCLC组	LUSC组 vs. SCLC组
PI	0.63 ± 3.31	0.16 ± 0.11	0.13 ± 0.09	0.498
TTP/s	24.34 ± 7.79	24.29 ± 7.67	25.2 ± 7.84	0.399
BV/（mL/100 g）	6.29 ± 5.31	7.43 ± 5.68	5.48 ± 4.38	0.177
BF/［mL/（100 g·min）］	120.48 ± 14	128.27 ± 13	95.64 ± 12	0.603
PS/［mL/（100 g·min）］	22.2 ± 4.93	19.1 ± 4.23	16.44 ± 3.6	0.014	0.001	0.032	0.164
MTT/s	6.86 ± 5.16	6.58 ± 3.82	7.40 ± 4.94	0.881

参数类别	AUC		约登指数		敏感度/%		特异度/%
参数类别	训练组	测试组	训练组	测试组	训练组	测试组	训练组	测试组
诊断模型1	0.81	0.77	0.55	0.72	100.0	100.0	54.8	72.4
诊断模型2	0.87	0.90	0.68	0.62	93.3	100.0	74.2	62.1
诊断模型3	0.88	0.88	0.72	0.68	100.0	83.3	72.4	85.0
诊断模型4	0.85	0.78	0.62	0.51	100.0	66.7	62.1	85.0

The application value of multi⁃parameter quantitative analysis of spectral and perfusion CT in differentiating pathological types of lung cancer

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