基于临床检验指标建立肺腺癌患者浸润程度判别模型

doi:10.3969/j.issn.1674-8115.2024.01.011

上海交通大学学报（医学版） ›› 2024, Vol. 44 ›› Issue (1): 98-107.doi: 10.3969/j.issn.1674-8115.2024.01.011

基于临床检验指标建立肺腺癌患者浸润程度判别模型

王梦菲¹^,²^,³(), 杨守志⁴, 乔永霞¹(), 黄琳²^,³()

^1.上海交通大学公共卫生学院，上海 200025
^2.上海交通大学医学院附属胸科医院检验科，上海 200030
^3.上海市胸部肿瘤研究所，上海交通大学医学院附属胸科医院，上海 200030
^4.上海交通大学生物医学工程学院，上海 200030

收稿日期:2023-07-06 接受日期:2023-09-13 出版日期:2024-01-28 发布日期:2024-02-28
通讯作者: 乔永霞,黄琳 E-mail:effie_wang@sjtu.edu.cn;yongxia.qiao@shsmu.edu.cn;linhuang@shusmu.edu.cn
作者简介:王梦菲（1998—），女，硕士生，电子信箱：effie_wang@sjtu.edu.cn。
基金资助:
国家自然科学基金(82001985);上海交通大学医学院“双百人”项目(20221714)

Establishment of discriminative models for predicting the infiltration degree of patients with lung adenocarcinoma based on clinical laboratory indicators

WANG Mengfei¹^,²^,³(), YANG Shouzhi⁴, QIAO Yongxia¹(), HUANG Lin²^,³()

^1.School of Public Health, Shanghai Jiao Tong University, Shanghai 200025, China
^2.Department of Clinical Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
^3.Shanghai Chest Cancer Research Institute, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
^4.School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China

Received:2023-07-06 Accepted:2023-09-13 Online:2024-01-28 Published:2024-02-28
Contact: QIAO Yongxia,HUANG Lin E-mail:effie_wang@sjtu.edu.cn;yongxia.qiao@shsmu.edu.cn;linhuang@shusmu.edu.cn
Supported by:
National Natural Science Foundation of China(82001985);"Two-hundred Talents" Program of Shanghai Jiao Tong University School of Medicine(20221714)

摘要/Abstract

摘要：

目的·利用肿瘤标志物、凝血功能指标、血常规指标与生化指标等临床易得检验指标，建立非小细胞肺腺癌患者浸润程度的多因素判别模型。方法·回顾性选取2022年上海交通大学医学院附属胸科医院收治的肺腺癌患者，通过多因素Logistic回归分析筛选预测肺腺癌患者浸润程度的独立影响因素并建立回归模型，同时引入人工智能算法构建判别模型，采用受试者工作特征曲线下面积（area under curve，AUC）评价模型对肺腺癌患者浸润程度的判别能力。结果·共纳入肺腺癌患者202例，分为浸润前病变组（59例）以及浸润性病变组（143例）。多因素Logistic回归分析结果显示，尿素、嗜碱性粒细胞百分比、白蛋白浓度是预测肺腺癌患者浸润程度的独立影响因素（均P<0.05）。预测模型表达式为P=e ^X /（1+e ^X ），其中X=（0.534×尿素浓度）+（1.527×嗜碱性粒细胞百分比）-（1.916×白蛋白浓度）+6.373。机器学习结果显示，纳入尿素、纤维蛋白原、白蛋白浓度、嗜碱性粒细胞百分比、前白蛋白、癌胚抗原（carcino embryonic antigen，CEA）6个指标时模型判别性能最佳。通过DeLong检验比较8种机器学习算法（分别基于岭回归、最小绝对收缩和选择算子、神经网络、随机森林、k近邻、支持向量机、决策树和自适应增强算法）的判别性能，选择AUC最高的岭回归算法，预测模型AUC为0.744（95%CI 0.656~0.832），敏感度为70.8%，特异度为70.2%。结论·使用尿素、纤维蛋白原、白蛋白浓度、嗜碱性粒细胞百分比、前白蛋白、CEA这6个指标建立综合判别模型，可有效预测肺腺癌患者肿瘤浸润程度，有望为临床肺腺癌分级判别和辅助治疗提供更精确的指导。

关键词: 肺腺癌, 肿瘤标志物, 凝血功能指标, 临床生化指标, 预测判别模型

Abstract:

Objective ·To establish a multifactorial discriminative model for predicting the degree of infiltration in patients with non-small cell lung adenocarcinoma based on clinically accessible laboratory indicators, such as tumor markers, coagulation function indicators, routine blood count indicators, and biochemical indicators. Methods ·A retrospective study was conducted on 202 patients with lung adenocarcinoma admitted to Shanghai Chest Hospital in 2022. Multifactorial Logistic regression analysis was applied to screen independent factors that influenced the predictive infiltration degree of lung adenocarcinoma and to establish a regression model. In addition, machine learning was used to construct a discriminative model, and the area under the receiver operating characteristic curve (AUC) was used to evaluate the discriminative ability of the model to discriminate the degree of infiltration in lung adenocarcinoma patients. Results ·A total of 202 patients with lung adenocarcinoma were included in the study, and divided into pre-invasive lesion group (n=59) and invasive lesion group (n=143). Multifactorial Logistic regression analysis revealed that urea, percentage of basophilic granulocytes, and albumin were independent factors for predicting the degree of infiltration of lung adenocarcinoma (all P<0.05). The predictive model expression was P = e^X / (1 + e^X ), where X = (0.534×urea) + (1.527×percentage of basophilic granulocytes) - (1.916×albumin) + 6.373. Machine learning results showed that the model performed best when urea, fibrinogen, albumin, percentage of basophilic granulocytes, prealbumin and carcino embryonic antigen (CEA) were included. After comparing the performance of 8 machine learning algorithms (based on ridge regression, least absolute shrinkage and selection operator, neural network, random forest, k-nearest neighbors, support vector machine, decision tree, and adaptive boosting algorithms) using the DeLong test, the ridge regression algorithm with the highest AUC was selected. The AUC of the predictive model was calculated to be 0.744 (95% CI 0.656-0.832), with a sensitivity of 70.8% and a specificity of 70.2%. Conclusion ·A comprehensive differentiation model constructed by urea, fibrinogen, albumin, percentage of basophilic granulocytes, prealbumin and CEA can effectively predict the infiltration degree of the enrolled lung adenocarcinoma patients, holding the potential to provide more precise guidance for the clinical grading and adjunctive treatment of lung adenocarcinoma.

Key words: lung adenocarcinoma, tumor markers, coagulation function indicators, clinical biochemical indicators, predictive discriminative model

中图分类号:

R734.2

王梦菲, 杨守志, 乔永霞, 黄琳. 基于临床检验指标建立肺腺癌患者浸润程度判别模型[J]. 上海交通大学学报（医学版）, 2024, 44(1): 98-107.

WANG Mengfei, YANG Shouzhi, QIAO Yongxia, HUANG Lin. Establishment of discriminative models for predicting the infiltration degree of patients with lung adenocarcinoma based on clinical laboratory indicators[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024, 44(1): 98-107.

图/表 7

参考文献 42

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.
2	《中国肿瘤临床与康复》编辑部. 2017年中国最新癌症数据[J]. 中国肿瘤临床与康复, 2017, 24(6): 760.
	Editorial Board of Chinese Journal of Clinical Oncology and Rehabilitation. The latest cancer data in China in 2017[J]. Chinese Journal of Clinical Oncology and Rehabilitation, 2017, 24(6): 760.
3	李翔, 高申. 1990—2019年中国居民肺癌发病、患病和死亡趋势分析[J]. 中国慢性病预防与控制, 2021, 29(11): 821-826.
	LI X, GAO S. Trend analysis of the incidence, morbidity and mortality of lung cancer in China from 1990 to 2019[J]. Chinese Journal of Prevention and Control of Chronic Diseases, 2021, 29(11): 821-826.
4	LI M Y, LIU L Z, DONG M. Progress on pivotal role and application of exosome in lung cancer carcinogenesis, diagnosis, therapy and prognosis[J]. Mol Cancer, 2021, 20(1): 22.
5	SUCCONY L, RASSL D M, BARKER A P, et al. Adenocarcinoma spectrum lesions of the lung: detection, pathology and treatment strategies[J]. Cancer Treat Rev, 2021, 99: 102237.
6	姜格宁, 陈昶, 朱余明, 等. 上海市肺科医院磨玻璃结节早期肺腺癌的诊疗共识(第一版)[J]. 中国肺癌杂志, 2018, 21(3): 147-159.
	JIANG G N, CHEN C, ZHU Y M, et al. Shanghai pulmonary hospital experts consensus on the management of groundglass nodules suspected as lung adenocarcinoma (version 1)[J]. Chinese Journal of Lung Cancer, 2018, 21(3): 147-159.
7	陈思煜, 于振涛, 林俊贤, 等. 早期肺腺癌浸润性对选择微创手术的患者住院经济成本的影响[J]. 现代医院, 2023, 23(4): 607-609, 618.
	CHEN S Y, YU Z T, LIN J X, et al. The impact of early invasive lung adenocarcinoma on the economic cost of hospitalization in patients choosing minimally invasive surgery[J]. Modern Hospitals, 2023, 23(4): 607-609, 618.
8	梁锌, 宋媛媛, 高婷, 等. 围手术期严重并发症和术前基础疾病对肺癌手术患者住院费用的影响分析[J]. 中国医院, 2021, 25(4): 58-61.
	LIANG X, SONG Y Y, GAO T, et al. An analysis of influence of severe perioperative complications and preoperative basic diseases on the hospitalization expenses of surgical lung cancer patients[J]. Chinese Hospitals, 2021, 25(4): 58-61.
9	LORTET-TIEULENT J, SOERJOMATARAM I, FERLAY J, et al. International trends in lung cancer incidence by histological subtype: adenocarcinoma stabilizing in men but still increasing in women[J]. Lung Cancer, 2014, 84(1): 13-22.
10	潘江峰, 邝平定, 应明亮, 等. 肺部纯磨玻璃结节浸润性肺腺癌与浸润前病变的高分辨靶扫描CT鉴别诊断[J]. 浙江医学, 2016, 38(11): 826-828, 832.
	PAN J F, KUANG P D, YING M L, et al. Differential diagnosis of pulmonary invasive adenocarcinoma and preinvasive lesions in pure ground-glass nodules on high resolution targeted CT scan[J]. Zhejiang Medical Journal, 2016, 38(11): 826-828, 832.
11	巴文娟, 许迪, 尹柯, 等. HRCT征象评估纯磨玻璃结节浸润性: 肺结节圆度优于长-短径比值和分叶深度[J]. 放射学实践, 2020, 35(12): 1542-1546.
	BA W J, XU D, YIN K, et al. Application value of pulmonary nodule roundness on HRCT in predicting invasiveness of pure ground glass nodules and its correlation with long-short diameter ratio and depth of lobulation[J]. Radiologic Practice, 2020, 35(12): 1542-1546.
12	MUROFF L R, BERLIN L. Speed versus interpretation accuracy: current thoughts and literature review[J]. AJR Am J Roentgenol, 2019, 213(3): 490-492.
13	LI Y, BAO Q, YANG S X, et al. Bionanoparticles in cancer imaging, diagnosis, and treatment[J]. VIEW, 2022, 3(4): 20200027. DOI: 10.1002/viw.20200027.
14	魏晓玲. 四种肿瘤标记物联合检测在肺癌诊断中的价值[J]. 河北医药, 2017, 39(6): 854-856.
	WEI X L. The value of combined detection of four tumor markers in diagnosis of lung cancer[J]. Hebei Medical Journal, 2017, 39(6): 854-856.
15	吴琪燕, 边革元, 程绘珺, 等. 肺腺癌预后因素及血清肿瘤标记物的诊断效能[J]. 昆明医科大学学报, 2020, 41(9): 32-37.
	WU Q Y, BIAN G Y, CHENG H J, et al. Analysis on prognostic factors of lung adenocarcinoma and diagnostic efficacy of serum tumor markers[J]. Journal of Kunming Medical University, 2020, 41(9): 32-37.
16	朱婷樱, 鲍杨漪. 肺癌患者CEA、CA125、SCC及纤维蛋白原的表达水平及意义[J]. 中国医药指南, 2019, 17(6): 141-142.
	ZHU T Y, BAO Y Y. Expression levels and significance of CEA, CA125, SCC, and fibrinogen in lung cancer patients[J]. Guide of China Medicine, 2019, 17(6): 141-142.
17	徐红萍, 薛冰, 徐笛. 肿瘤标志物CEA、NSE、CYFRA21-1联合检测在肺癌诊断中的应用[J]. 实用医学杂志, 2010, 26(16): 2943-2944.
	XU H P, XUE B, XU D. Application of combined detection of tumor markers CEA, NSE, and CYFRA21-1 in the diagnosis of lung cancer[J]. The Journal of Practical Medicine, 2010, 26(16): 2943-2944.
18	杜军华, 乔洪源, 尹宜发. 血清CEA、CA125及Cyfra21-1水平对中晚期非小细胞肺癌患者预后的影响[J]. 肿瘤防治研究, 2016, 43(2): 137-140.
	DU J H, QIAO H Y, YIN Y F. Prognostic value of serum CEA, CA125 and Cyfra21-1 inpatients with advanced nonsmall cell lung cancer[J]. Cancer Research on Prevention and Treatment, 2016, 43(2): 137-140.
19	张桐硕, 任鹤菲, 曹瑾, 等. 基于集成机器学习的卵巢癌多检验指标联合诊断模型[J]. 临床检验杂志, 2018, 36(12): 908-913.
	ZHANG T S, REN H F, CAO J, et al. A diagnostic model combined with multiple laboratory indexes for ovarian cancer based on integrated machine learning[J]. Chinese Journal of Clinical Laboratory Science, 2018, 36(12): 908-913.
20	章维维, 邹红, 杨蓉, 等. 常规临床检验指标与结直肠癌临床病理参数的关联分析及诊断预测价值[J]. 临床检验杂志, 2021, 39(3): 172-177.
	ZHANG W W, ZOU H, YANG R, et al. Predictive value of routine clinical test indicators in the diagnosis of colorectal cancer (CRC) and their correlation with clinicopathological parameters of CRC[J]. Chinese Journal of Clinical Laboratory Science, 2021, 39(3): 172-177.
21	王浩, 乌永嘎, 郭玉婷, 等. 基于常规临床检验指标构建子痫前期风险预测模型[J]. 临床检验杂志, 2022, 40(10): 731-736.
	WANG H, WU Y G, GUO Y T, et al. Construction of risk prediction model for preeclampsia based on routine clinical examination indicators[J]. Chinese Journal of Clinical Laboratory Science, 2022, 40(10): 731-736.
22	WINTER M C, POTTER V A, WOLL P J. Raised serum urea predicts for early death in small cell lung cancer[J]. Clin Oncol (R Coll Radiol), 2008, 20(10): 745-750.
23	刘秀巧, 王淑娟, 吴振茹, 等. 恶性肿瘤与高纤维蛋白原血症[J]. 中华肿瘤杂志, 2002, 24(1): 51-52.
	LIU X Q, WANG S J, WU Z R, ET AL. Malignant tumor and hyperfibrinogenemia[J]. Chinese Journal of Oncology, 2002, 24(1): 51-52.
24	左震华, 燕霞, 蔡少华. 癌胚抗原对肺腺癌的诊断价值分析[J]. 人民军医, 2015, 58(5): 553-554.
	ZUO Z H, YAN X, CAI S H. Analysis of the diagnostic value of carcinoembryonic antigen in lung adenocarcinoma[J]. People′s Military Surgeon, 2015, 58(5): 553-554.
25	QU T, ZHANG J W, XU N, et al. Diagnostic value analysis of combined detection of Trx, CYFRA21-1 and SCCA in lung cancer[J]. Oncol Lett, 2019, 17(5): 4293-4298.
26	刘磊, 张薇, 刘彬, 等. 肺鳞癌患者血清肿瘤标记物Scc、CYFRA21-1的临床意义[J]. 现代生物医学进展, 2010, 10(20): 3862-3865.
	LIU L, ZHANG W, LIU B, et al. Clinical significance of Scc and CYFRA21-1 (serum tumor markers) in diagnosis of lung squamous cell carcinoma[J]. Progress in Modern Biomedicine, 2010, 10(20): 3862-3865.
27	彭彦, 王燕, 李峻岭, 等. 血清NSE、ProGRP和LDH在小细胞肺癌诊断治疗中的作用[J]. 中国肺癌杂志, 2016, 19(9): 590-594.
	PENG Y, WANG Y, LI J L, et al. Utility of NSE, ProGRP and LDH in diagnosis and treatment in patients with small cell lung cancer[J]. Chinese Journal of Lung Cancer, 2016, 19(9): 590-594.
28	付晓红, 陈碧君, 马萍, 等. 血清肿瘤标记物联合检测诊断肺癌的价值[J]. 广东医学, 2013, 34(3): 401-404.
	FU X H, CHEN B J, MA P, et al. The value of combined detection of serum tumor markers in the diagnosis of lung cancer[J]. Guangdong Medical Journal, 2013, 34(3): 401-404.
29	LIU L J, TENG J L, ZHANG L J, et al. The combination of the tumor markers suggests the histological diagnosis of lung cancer[J]. Biomed Res Int, 2017, 2017: 2013989.
30	ZHONG M H, ZHANG Y, PAN Z G, et al. Clinical utility of circulating tumor cells in the early detection of lung cancer in patients with a solitary pulmonary nodule[J]. Technol Cancer Res Treat, 2021, 20: 15330338211041465.
31	张剑, 聂晓红, 何瀚夫. 非小细胞肺癌患者血浆D-二聚体、纤维蛋白原与癌胚抗原、细胞角蛋白19片段的相关性研究[J]. 华西医学, 2022, 37(2): 218-223.
	ZHANG J, NIE X H, HE H F. Correlation study of plasma D-dimer, fibrinogen, carcinoembryonic antigen, and cytokeratin 19 fragment in non-small cell lung cancer patients[J]. West China Medical Journal, 2022, 37(2): 218-223.
32	YI W W, QIAO T T, YANG Z Y, et al. The regulation role and diagnostic value of fibrinogen-like protein 1 revealed by pan-cancer analysis[J]. Mater Today Bio, 2022, 17: 100470.
33	TANG X Y, XIONG Y L, SHI A P, et al. The downregulation of fibrinogen-like protein 1 inhibits the proliferation of lung adenocarcinoma via regulating MYC-target genes[J]. Transl Lung Cancer Res, 2022, 11(3): 404-419.
34	李亚伦, 李镭, 张立, 等. 血清白蛋白及尿素氮水平与肺癌不同临床病理特征和预后的关系[J]. 中国肺癌杂志, 2017, 20(3): 175-186.
	LI Y L, LI L, ZHANG L, et al. Relationships between serum albumin and urea level andthe clinical pathological characteristics and survival time in patients with lung cancer[J]. Chinese Journal of Lung Cancer, 2017, 20(3): 175-186.
35	KUIKEL S, PATHAK N, POUDEL S, et al. Neutrophil-lymphocyte ratio as a predictor of adverse outcome in patients with community-acquired pneumonia: a systematic review[J]. Health Sci Rep, 2022, 5(3): e630.
36	刘畅, 赵晓珍, 白月琴, 等. 肺腺癌患者临床参数与血清标志物的相关性分析[J]. 同济大学学报(医学版), 2019, 40(1): 87-90, 97.
	LIU C, ZHAO X Z, BAI Y Q, et al. Correlation of clinical parameters with serum markers in patients with lung adenocarcinoma[J]. Journal of Tongji University (Medical Science) , 2019, 40(1): 87-90, 97.
37	翁绳和, 孙祎繁, 徐雪, 等. ¹⁸F-FDG PET联合血清肿瘤标志物对肺腺癌分期的应用价值[J]. 临床放射学杂志, 2020, 39(4): 800-804.
	WENG S H, SUN Y F, XU X, et al. ¹⁸F-FDG PET combined with serum tumor markers in the staging of lung adenocarcinoma[J]. Journal of Clinical Radiology, 2020, 39(4): 800-804.
38	黄国, 蒋蓓蓓, 解学乾, 等. CT和检验指标对浸润倾向肺腺癌的诊断模型建立[J]. CT理论与应用研究, 2021, 30(1): 81-90.
	HUANG G, JIANG B B, XIE X Q, et al. Establishment of a diagnostic model for lung adenocarcinoma with invasive tendency by CT and laboratory indexes[J]. Computerized Tomography Theory and Applications, 2021, 30(1): 81-90.
39	REN X L, ZHANG Y X, LYU Y, et al. Lactate dehydrogenase and serum tumor markers for predicting metastatic status in geriatric patients with lung adenocarcinoma[J]. Cancer Biomark, 2019, 26(2): 139-150.
40	郑慧, 李建玉, 王珊, 等. 基于肺磨玻璃结节CT征象的诊断模型列线图评估肺癌浸润性[J]. 放射学实践, 2021, 36(4): 470-474.
	ZHENG H, LI J Y, WANG S, et al. Evaluation on the invasion of lung cancer by diagnostic model nomogram based on the CT characteristics of pulmonary ground glass nodules[J]. Radiologic Practice, 2021, 36(4): 470-474.
41	ZHANG T Q, LI X L, LIU J H. Prediction of the invasiveness of ground-glass nodules in lung adenocarcinoma by radiomics analysis using high-resolution computed tomography imaging[J]. Cancer Control, 2022, 29: 10732748221089408.
42	YANG S C, LAI W W, SU W C, et al. Estimating the lifelong health impact and financial burdens of different types of lung cancer[J]. BMC Cancer, 2013, 13: 579.

Item	Pre-invasive lesion (n=59)	Invasive lesion (n=143)	Ｚ/χ²/ t value	P value
Age/year	50 (41, 61)	58 (48, 67)	-2.901	0.004
Gender/[n(%)]			1.376	0.241
Male	16 (27.1)	51 (35.7)	N/A	N/A
Female	43 (72.9)	92 (64.3)	N/A	N/A
TNM stage/[n(%)]			N/A	N/A
0	59 (100.0)		N/A	N/A
Ⅰ		130 (90.9)	N/A	N/A
Ⅱ		12 (8.4)	N/A	N/A
Ⅲ		1 (0.7)	N/A	N/A

Item	Pre-invasive lesion (n=59)	Invasive lesion (n=143)	Ｚ/χ²/ t value	P value
Age/year	50 (41, 61)	58 (48, 67)	-2.901	0.004
Gender/[n(%)]			1.376	0.241
Male	16 (27.1)	51 (35.7)	N/A	N/A
Female	43 (72.9)	92 (64.3)	N/A	N/A
TNM stage/[n(%)]			N/A	N/A
0	59 (100.0)		N/A	N/A
Ⅰ		130 (90.9)	N/A	N/A
Ⅱ		12 (8.4)	N/A	N/A
Ⅲ		1 (0.7)	N/A	N/A

Item	Pre-invasive lesion	Invasive lesion	Ｚ/χ²/ t value	P value
Tumor marker
Count/n	59	143	N/A	N/A
CEA/(ng·mL^-1)	1.45 (1.11, 1.86)	2.32 (1.78, 3.34)	-3.09	0.002
CYFRA21-1/(ng·mL^-1)	2.01 (1.56, 2.58)	2.09 (1.62, 2.87)	-1.027	0.304
SCC/(ng·mL^-1)	0.41 (0.29, 0.52)	0.43 (0.32, 0.62)	-1.344	0.179
NSE/(ng·mL^-1)	16.32±4.43	15.84±5.15	0.626	0.532
CA125/(U·mL^-1)	10.9 (7.09, 13.07)	10.66 (7.36, 15.4)	-0.754	0.451
proGRP/(pg·mL^-1)	47.52 (37.93)	47.52 (38.71, 55.85)	-0.046	0.963
Coagulation function indicator
Count/n	49	119	N/A	N/A
FBG/(g·L^-1)	2.58 (2.35, 3.14)	2.98 (2.58, 3.53)	-2.725	0.006
Blood routine indicator
Count/n	58	138	N/A	N/A
MCV/fL	90.05 (87, 92.38)	91.35 (89.2, 93.83)	-2.108	0.035
MCH/(g·L^-1)	30.55 (29.35, 31.33)	31.1 (30, 31.8)	-2.218	0.027
BASO%/%	0.25 (0.1, 0.43)	0.3 (0.2, 0.5)	-2.12	0.034
EO%/%	0.7 (0.1, 1.43)	1.2 (0.48, 2.03)	-2.256	0.024
EO#/(10⁹·L^-1)	0 (0, 0.1)	0.1 (0, 0.1)	-2.047	0.041
Biochemical indicator
Count/n	58	142	N/A	N/A
Urea/(mmol·L^-1)	4.6 (4.18, 5.93)	5.2 (4.3, 6.3)	-2.242	0.025
PAB/(g·L^-1)	0.28 (0.24, 0.31)	0.26 (0.24, 0.29)	-2.415	0.016
RBP/(mg·L^-1)	44.5 (39.75, 51.25)	42 (38, 46)	-2.274	0.023
ALB/(g·dL^-1)	4.62 (4.36, 4.82)	4.48 (4.32, 4.68)	-2.316	0.021
TBA/(μmol·L^-1)	3.5 (2, 5)	4 (2, 6)	-2.061	0.039

Item	Pre-invasive lesion	Invasive lesion	Ｚ/χ²/ t value	P value
Tumor marker
Count/n	59	143	N/A	N/A
CEA/(ng·mL^-1)	1.45 (1.11, 1.86)	2.32 (1.78, 3.34)	-3.09	0.002
CYFRA21-1/(ng·mL^-1)	2.01 (1.56, 2.58)	2.09 (1.62, 2.87)	-1.027	0.304
SCC/(ng·mL^-1)	0.41 (0.29, 0.52)	0.43 (0.32, 0.62)	-1.344	0.179
NSE/(ng·mL^-1)	16.32±4.43	15.84±5.15	0.626	0.532
CA125/(U·mL^-1)	10.9 (7.09, 13.07)	10.66 (7.36, 15.4)	-0.754	0.451
proGRP/(pg·mL^-1)	47.52 (37.93)	47.52 (38.71, 55.85)	-0.046	0.963
Coagulation function indicator
Count/n	49	119	N/A	N/A
FBG/(g·L^-1)	2.58 (2.35, 3.14)	2.98 (2.58, 3.53)	-2.725	0.006
Blood routine indicator
Count/n	58	138	N/A	N/A
MCV/fL	90.05 (87, 92.38)	91.35 (89.2, 93.83)	-2.108	0.035
MCH/(g·L^-1)	30.55 (29.35, 31.33)	31.1 (30, 31.8)	-2.218	0.027
BASO%/%	0.25 (0.1, 0.43)	0.3 (0.2, 0.5)	-2.12	0.034
EO%/%	0.7 (0.1, 1.43)	1.2 (0.48, 2.03)	-2.256	0.024
EO#/(10⁹·L^-1)	0 (0, 0.1)	0.1 (0, 0.1)	-2.047	0.041
Biochemical indicator
Count/n	58	142	N/A	N/A
Urea/(mmol·L^-1)	4.6 (4.18, 5.93)	5.2 (4.3, 6.3)	-2.242	0.025
PAB/(g·L^-1)	0.28 (0.24, 0.31)	0.26 (0.24, 0.29)	-2.415	0.016
RBP/(mg·L^-1)	44.5 (39.75, 51.25)	42 (38, 46)	-2.274	0.023
ALB/(g·dL^-1)	4.62 (4.36, 4.82)	4.48 (4.32, 4.68)	-2.316	0.021
TBA/(μmol·L^-1)	3.5 (2, 5)	4 (2, 6)	-2.061	0.039

Item	B	P	OR	95%CI
BASO%/%	1.527	0.045	4.603	1.033‒20.506
Urea/(mmol·L^-1)	0.534	0.002	1.706	1.219‒2.386
ALB/(g·dL^-1)	-1.916	0.003	0.147	0.041‒0.527
Constant	6.373	0.032	585.568	N/A

基于临床检验指标建立肺腺癌患者浸润程度判别模型

Establishment of discriminative models for predicting the infiltration degree of patients with lung adenocarcinoma based on clinical laboratory indicators

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参考文献 42

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Metrics

Algorithm	AUC	Sensitivity	Specificity	P_{DeLong test}
RR	0.744	0.708	0.702	N/A
LASSO	0.723	0.796	0.574	0.196
NN	0.679	0.558	0.830	0.008
RF	0.663	0.717	0.638	0.030
kNN	0.644	0.363	0.851	0.002
SVM	0.629	0.531	0.723	0.004
Decision Tree	0.552	0.664	0.468	0.000
AdaBoost	0.532	0.681	0.383	0.000

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