基于炎症和营养指标的非小细胞肺癌患者的影响因素构建列线图

彭思敏; 于娜; 车俊; 任锦锦; 李大鹏; 张玉松; 徐加英; 秦立强

doi:10.16462/j.cnki.zhjbkz.2021.12.019

基于炎症和营养指标的非小细胞肺癌患者的影响因素构建列线图

doi: 10.16462/j.cnki.zhjbkz.2021.12.019

1.
215123 苏州，苏州大学公共卫生学院营养与食品卫生教研室
2.
215004 苏州，苏州大学附属第二医院肿瘤科
3.
214062 无锡，江南大学附属医院肿瘤放疗科
4.
215006 苏州，苏州大学附属第一医院肿瘤科
5.
215123 苏州，苏州大学放射医学与防护学院

基金项目:

国家自然科学基金 82073482

详细信息

通讯作者:
秦立强，E-mail: qinliqiang@suda.edu.cn

中图分类号: R734.2
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- 被引次数: 0
出版历程
- 收稿日期: 2020-10-28
- 修回日期: 2020-12-14
- 网络出版日期: 2021-12-27
- 刊出日期: 2021-12-10

Prognostic factors and nomogram construction of non-small cell lung cancer patients based on inflammatory and nutritional indicators

1.
Department of Nutrition and Food Hygiene, School of Public Health, Soochow University, Suzhou 215123, China
2.
Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
3.
Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi 214062, China
4.
Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou 215006, China
5.
School of Radiation Medicine and Protection, Soochow University, Suzhou 215123, China

Funds:

National Natural Science Foundation of China 82073482

More Information

Corresponding author: QIN Li-qiang, E-mail: qinliqiang@suda.edu.cn

摘要

摘要: 目的基于炎症和营养指标探讨非小细胞肺癌(non-small-cell lung cancer, NSCLC)患者预后的影响因素建立预测总生存期(overall survival, OS)的列线图。方法选取苏州肺癌生存队列2016年1月-2020年1月NSCLC患者371例，随机分为训练队列(n=278)和验证队列(n=93)。在训练队列中，通过Cox比例风险模型确定独立危险因素，构建列线图。通过Bootstrap法和十折交叉验证对模型的性能进行评价。结果训练队列和验证队列的1年生存率分别为83.8%和83.9%，2年生存率分别为65.1%和66.7%。最后纳入模型的变量为吸烟状况、分期、治疗方式、BMI、红细胞分布宽度(red cell distribution width, RDW)、中性粒细胞与淋巴细胞比值(neutrophil-to-lymphocyte ratio, NLR)和白蛋白与球蛋白比值(albumin-to-globulin ratio, AGR)(均有P < 0.05)。训练队列和验证队列的预测OS的C-index分别为0.824和0.762，模型十折交叉验证的平均准确率为77.3%，ROC曲线下面积(area under the curve, AUC)为0.885(95% CI: 0.751~1.000)。结论基于炎症和营养指标构建的列线图可以有效地预测NSCLC患者的总生存期。
- 炎症 /
- 营养 /
- 非小细胞肺癌 /
- 列线图
Abstract: Objective To explore the prognostic factors of non-small-cell lung cancer (NSCLC) patients based on inflammation and nutritional indicators, and to establish a nomogram for predicting overall survival (OS). Methods A total of 371 NSCLC patients in Suzhou lung cancer survival cohort from January 2016 to January 2020 were randomly divided into a training cohort (n=278) and a validation cohort (n=93). In the training cohort, independent risk factors were determined by Cox proportional hazard model and a nomogram was constructed. The bootstrap and ten-fold cross-validation were used to evaluate the performance of the model. Results The 1-year survival rate was 83.8% in the training cohort and 83.9% in the validation cohort, and the 2-year survival rate was 65.1% and 66.7%, respectively. Finally, the variables included in the model were smoking, stage, treatment, body mass index (BMI), red blood cell distribution width (RDW), neutrophil-to-lymphocyte ratio (NLR), and albumin-to-globulin ratio (AGR) as risk factors for OS of NSCLC patients (P < 0.05). The C-index of the training cohort and validation cohort for predicting OS were 0.824 and 0.762, respectively. After ten-fold cross-validation of the model, its average accuracy rate was 77.3%, and the area under the curve (AUC) was 0.885 (95% CI: 0.751-1.000). Conclusions The nomogram based on inflammation and nutrition indicators can effectively predict OS of NSCLC patients.
- Inflammation /
- Nutrition /
- Non-small cell lung cancer /
- Nomogram

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图 1 NSCLC患者总生存期预测列线图

Figure 1. The nomogram for predicting OS in NSCLC patients

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图 2 训练队列(A, B)和验证队列(C, D)1年和2年生存率的校准曲线

Figure 2. The calibration curve for the 1-year OS and 2-year OS in training cohort and validation cohort

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图 3 十折交叉验证后的ROC曲线

Figure 3. ROC curve after ten-fold cross-validation

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表 1 训练队列与验证队列中患者的临床特征比较[n(%)]

Table 1. Comparison of clinical characteristics of patients in training cohort and validation cohort [n(%)]

变量	训练队列 (n=278)	验证队列 (n=93)	χ²/Z值	P值	变量	训练队列 (n=278)	验证队列 (n=93)	χ²/Z值	P值
年龄(岁)			0.666	0.466	中性粒细胞(10⁹/L)			2.689	0.101
≤65	160	58			≤6.3	228(82.0)	83(89.2)
>65	118	35			>6.3	50(18.0)	10(10.8)
性别			0.335	0.622	淋巴细胞(10⁹/L)			0.022	0.882
男	170(61.2)	60(64.5)			≤1.1	89(32.0)	29(31.2)
女	108(38.8)	33(35.5)			>1.1	189(68.0)	64(68.8)
BMI(kg/m²)			1.097	0.272	血小板(10⁹/L)			0.238	0.625
< 18.5	27(9.7)	9(9.7)			≤350	246(88.5)	84(90.3)
18.5~ < 24.0	183(65.8)	54(58)			>350	32(11.5)	9(9.7)
24.0~ < 28.0	45(16.2)	22(23.7)			C-反应蛋白(g/L)			0.166	0.683
≥28.0	23(8.3)	8(8.6)			≤3	50(18.0)	15(16.1)
慢性阻塞性肺病			0.900	0.383	>3	228(82.0)	78(83.9)
有	12(4.3)	2(2.2)			球蛋白(g/L)			1.175	0.278
无	266(95.7)	91(97.8)			≤30	174(62.6)	64(68.8)
肿瘤家族史			0.179	0.728	>30	104(37.4)	29(31.2)
有	37(13.3)	14(15.1)			白蛋白(g/L)			0.742	0.389
无	241(86.7)	79(84.9)			≤35	43(15.5)	11(11.8)
吸烟状况			0.564	0.756	>35	235(84.5)	82(88.2)
非吸烟者	139(50.0)	43(46.2)			血红蛋白(g/L)			0.999	0.318
戒烟者	71(25.5)	27(29.1)			≤120	118(42.4)	34(36.6)
吸烟者	68(24.5)	23(24.7)			>120	160(57.6)	59(63.4)
饮酒状况			0.726	0.449	RDW(%)			0.356	0.837
有	61(21.9)	24(25.8)			≤16	237(85.3)	79(84.9)
无	217(78.1)	69(74.2)			>16	40(14.4)	14(15.1)
肿瘤分型			1.179	0.588	PLR			1.250	0.263
腺癌	183(65.8)	63(67.7)			≤147	119(42.8)	46(49.5)
鳞癌	74(26.6)	26(28.0)			>147	159(57.2)	47(50.5)
其他	21(7.6)	4(4.3)			NLR			1.993	0.158
TNM分期			0.470	0.638	≤2.94	147(52.9)	57(61.3)
Ⅰ	37(13.3)	13(14)			>2.94	131(47.1)	36(38.4)
Ⅱ	22(7.9)	9(9.7)			SII			1.512	0.219
Ⅲ	63(22.7)	17(18.3)			≤867	181(65.1)	67(72.0)
Ⅳ	156(56.1)	54(58.0)			>867	97(34.9)	26(28.0)
治疗方式			3.007	0.556	PNI			0.074	0.786
未治疗	16(5.8)	5(5.4)			≤48.5	148(53.2)	48(51.6)
手术+ ^a	115(41.4)	48(51.6)			>48.5	130(46.8)	45(48.4)
化疗或放疗	112(40.2)	30(32.3)			AGR			0.120	0.729
化疗+放疗	27(9.7)	8(8.5)			≤1.38	105(37.8)	37(39.8)
其他	8(2.9)	2(2.2)			>1.38	173(62.2)	56(60.2)
白细胞(10⁹/L)			0.101	0.751
≤10	251(90.3)	85(91.4)
>10	27(9.7)	8(8.6%)
注：^a手术+：仅手术或手术与辅助治疗。

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表 2 NSCLC患者总生存期的单因素和多因素分析

Table 2. Univariate and multivariate analysis of OS for NSCLC patients

变量	单因素分析		多因素分析
变量	HR(95% CI)值	P值	HR(95% CI)值	P值
年龄(≤65为对照)	1.048(0.701~1.565)	0.821		0.428
性别(男为对照)	0.348(0.215~0.565)	< 0.001		0.098
BMI(< 18.5 kg/m²为对照)		0.041		0.017
18.5~＜24.0	0.806(0.437~1.486)	0.489	0.775(0.399~1.504)	0.451
24.0~＜28.0	0.352(0.148~0.836)	0.018	0.279(0.110~0.704)	0.007
≥28.0	0.413(0.145~1.172)	0.096	0.389(0.127~1.195)	0.099
慢性阻塞性肺病(有为对照)	1.011(0.372~2.752)	0.982		0.936
肿瘤家族史(有为对照)	1.580(0.796~3.138)	0.191		0.135
吸烟状况(非吸烟者为对照)		< 0.001		< 0.001
戒烟者	1.864(1.080~3.215)	0.025	1.946(1.101~3.438)	0.022
吸烟者	5.192(3.229~8.350)	< 0.001	5.528(3.325~9.193)	< 0.001
饮酒(有为对照)	0.656(0.420~1.023)	0.063		0.517
肿瘤分型(腺癌为对照)		0.039		0.919
鳞癌	1.745(1.134~2.685)	0.011		0.929
其他	1.338(0.638~2.807)	0.441		0.681
TNM分期(Ⅰ为对照)		< 0.001		0.019
Ⅱ	5.607(0.583~53.911)	0.135	5.013(0.488~51.514)	0.175
Ⅲ	11.715(1.559~88.035)	0.017	10.387(1.299~83.035)	0.027
Ⅳ	23.580(3.278~169.606)	0.002	14.244(1.873~108.303)	0.010
治疗方式(未治疗为对照)		< 0.001		< 0.001
手术+^a	0.119(0.056~0.249)	< 0.001	0.151(0.066~0.345)	< 0.001
化疗或放疗	0.473(0.252~0.886)	0.019	0.445(0.222~0.890)	0.022
化疗+放疗	0.308(0.130~0.733)	0.008	0.195(0.075~0.506)	0.001
其他	0.875(0.328~2.332)	0.789	1.178(0.409~3.394)	0.762
白细胞(≤10×10⁹/L为对照)	2.372(1.367~4.116)	0.002		0.100
中性粒细胞(≤6.3×10⁹/L为对照)	1.930(1.224~3.044)	0.005		0.475
淋巴细胞(≤1.1×10⁹/L为对照)	0.884(0.581~1.345)	0.564		0.281
血小板(≤350×10⁹/L为对照)	3.820(2.397~6.088)	< 0.001		0.096
C-反应蛋白(≤3 mg/L为对照)	2.817(1.366~5.809)	0.005		0.195
球蛋白(≤30 g/L为对照)	1.844(1.238~2.747)	0.003		0.492
白蛋白(≤35 g/L为对照	0.455(0.285~0.727)	0.001		0.404
血红蛋白(≤120 g/L为对照)	0.449(0.301~0.670)	< 0.001		0.495
RDW(≤16%为对照)	2.174(1.339~3.528)	0.002	2.155(1.273~3.648)	0.004
PLR(≤147为对照)	2.624(1.673~4.116)	< 0.001		0.728
NLR(≤2.94为对照)	2.067(1.377~3.104)	< 0.001	1.812(1.141~2.878)	0.012
SII(≤867为对照)	2.622(1.758~3.909)	< 0.001		0.473
PNI(≤48.5为对照)	0.399(0.259~0.615)	< 0.001		0.310
AGR(≤1.38为对照)	0.370(0.248~0.553)	< 0.001	0.495(0.320~0.763)	0.001
注：^a手术+：仅手术或手术与辅助治疗。

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表 3 腺癌和鳞癌患者的多因素分析

Table 3. Multivariate analysis of patients with adenocarcinoma and squamous cell carcinoma

变量	HR(95% CI)值	P值	变量	HR(95% CI)值	P值
腺癌			鳞癌
吸烟状况(非吸烟者为对照)		< 0.001	BMI(< 18.5 kg/m²为对照)		0.022
戒烟者	2.201(1.026~4.720)	0.043	18.5~＜24.0	0.146(0.040~0.531)	0.003
吸烟者	7.013(3.238~15.187)	< 0.001	24.0~＜28.0	0.073(0.007~0.740)	0.027
治疗方式(未治疗为对照)		0.010	≥28.0	0.050(0.000~6.907)	0.970
手术+^a	0.238(0.082~0.688)	0.008	TNM分期(Ⅰ为对照)		0.032
化疗或放疗	0.770(0.323~1.836)	0.556	Ⅱ	44.340(1.342~126.037)	0.034
化疗+放疗	0.414(0.112~1.523)	0.184	Ⅲ	8.899(0.509~155.493)	0.134
其他	2.099(0.532~8.282)	0.290	Ⅳ	20.551(1.249~338.122)	0.034
RDW(≤16%为对照)	2.942(1.472~5.879)	0.002	AGR(≤1.38为对照)	0.399(0.165~0.969)	0.042
NLR(≤2.94为对照)	2.045(1.156~3.616)	0.014	PNI(≤48.5为对照)	0.170(0.046~0.620)	0.007
血小板(≤350×10⁹/L为对照)	2.492(1.184~5.246)	0.016
注：^a手术+：仅手术或手术与辅助治疗。

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参考文献(14)

[1]	Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012[J]. Int J Cancer, 2015, 136(5): E359-E386. DOI: 10.1002/ijc.29210.
[2]	Xie D, Allen MS, Marks R, et al. Nomogram prediction of overall survival for patients with non-small-cell lung cancer incorporating pretreatment peripheral blood markers [J]. Eur J Cardio- Thorac Surg, 2018, 53(6): 1214-1222. DOI: 10.1093/ejcts/ezx462.
[3]	Cagini L, Monacelli M, Giustozzi G, et al. Biological prognostic factors for early stage completely resected non-small cell lung cancer [J]. J Surg Oncol, 2000, 74(1): 53-60. DOI:10.1002/1096-9098(200005)74:1<53::aid-jso13>3.0.co;2-d.
[4]	Hoang T, Xu R, Schiller JH, et al. Clinical model to predict survival in chemonaive patients with advanced non-small-cell lung cancer treated with third-generation chemotherapy regimens based on eastern cooperative oncology group data [J]. J Clin Oncol, 2005, 23(1): 175-183. DOI: 10.1200/JCO.2005.04.177.
[5]	Guo M, Li W, Li B, et al. Prognostic value of delta inflammatory biomarker-based nomograms in patients with inoperable locally advanced NSCLC [J]. Int Immunopharmacol, 2019, 72(440): 395-401. DOI: 10.1016/j.intimp.2019.04.032.
[6]	Guo M, Li B, Yu Y, et al. Delineating the pattern of treatment for elderly locally advanced NSCLC and predicting outcomes by a validated model: A SEER based analysis [J]. Cancer Med, 2019, 8(5): 2587-2598. DOI: 10.1002/cam4.2127.
[7]	Kang J, Ning MS, Feng H, et al. Predicting 5-year progression and survival outcomes for early stage non-small cell lung cancer treated with stereotactic ablative radiation therapy: development and validation of robust prognostic nomograms [J]. Int J Radiat Oncol Biol Phys, 2020, 106(1): 90-99. DOI: 10.1016/j.ijrobp.2019.09.037.
[8]	Kusumanto YH, Dam WA, Hospers GA, et al. Platelets and granulocytes, in particular the neutrophils, form important compartments for circulating vascular endothelial growth factor [J]. Angiogenesis, 2003, 6(4): 283-287. DOI: 10.1023/B:AGEN.0000029415.62384.ba.
[9]	Wang Y, Qu X, Kam NW, et al. An inflammation-related nomogram for predicting the survival of patients with non-small cell lung cancer after pulmonary lobectomy [J]. BMC Cancer, 2018, 18(1): 692. DOI: 10.1186/s12885-018-4513-4.
[10]	Shepshelovich D, Xu W, Lu L, et al. Body Mass Index (BMI), BMI change, and overall survival in patients with SCLC and NSCLC: a pooled analysis of the international lung cancer consortium [J]. J Thorac Oncol, 2019, 14(9): 1594-1607. DOI: 10.1016/j.jtho.2019.05.031.
[11]	Laky B, Janda M, Bauer J, et al. Malnutrition among gynaecological cancer patients [J]. Eur J Clin Nutr, 2007, 61(5): 642-646. DOI: 10.1038/sj.ejcn.1602540.
[12]	He J, Pan H, Liang W, et al. Prognostic effect of albumin-to-globulin ratio in patients with solid tumors: a systematic review and meta-analysis [J]. J Cancer, 2017, 8(19): 4002-4010. DOI: 10.7150/jca.21141.
[13]	Zhang H, Zhang B, Zhu K, et al. Preoperative albumin-to-globulin ratio predicts survival in patients with non-small-cell lung cancer after surgery [J]. J Cell Physiol, 2019, 234(3): 2471-2479. DOI: 10.1002/jcp.26766.
[14]	Koma Y, Onishi A, Matsuoka H, et al. Increased red blood cell distribution width associates with cancer stage and prognosis in patients with lung cancer [J]. PLoS One, 2013, 8(11): e80240. DOI: 10.1371/journal.pone.0080240.