Study on the prognostic value of serological indicators for nasopharyngeal carcinoma based on nomogram model

doi:10.3760/cma.j.cn371439-20230426-00089

Abstract

Abstract: Objective To explore the prognostic value of serological indicators for nasopharyngeal carcinoma based on nomogram model. Methods The clinal data of 231 patients diagnosed as nasopharyngeal carcinoma who were treated for the first time in the First People's Hospital of Ziyang of Sichuan Province from January 2020 to December 2021 were studied retrospectively. The patients were randomly divided into training group （n=173） and verification group （n=58） according to the ratio of 3∶1 by random number table method. Lasso Cox regression model was used to generate a prognosis model in the training cohort. Consistency index （C index） and decision curve analysis （DCA） were used to compare the prediction accuracy between the new prognosis model and TNM staging and Epstein-Barr virus （EBV） DNA copy number. Subsequently, an overall survival （OS） nomogram including prognosis model, TNM staging and EBV DNA copy number was established to predict the prognosis of nasopharyngeal carcinoma. Results The median follow-up time of the training cohort was 31.4 months, and that of the verification cohort was 30.4 months. In the training cohort, the 12-month, 24-month and 36-month OS rates were 97.4%, 83.8% and 63.3% respectively. In the verification cohort, the 12-month, 24-month and 36-month OS rates were 94.2%, 84.4% and 72.8% respectively. There was no significant difference in the distribution of clinical features and serological indicators between the training cohort and the verification cohort（all P>0.05）. The established prognostic model included the following variables: age, body mass index, platelet, lymphocyte/monocyte ratio, C-reactive protein（CRP）, CRP/albumin ratio, globulin, albumin/globulin ratio and prognostic nutrition index. In the training cohort, the C index of the prognostic model was 0.790 （95%CI: 0.730-0.852）, which was significantly higher than that of EBV DNA copy number （0.693, 95%CI: 0.625-0.760）（χ²=5.74, P=0.028）, but there was no significant difference compared to TNM staging （0.738, 95%CI: 0.688-0.786）（χ²=2.32, P=0.074）. In the validation cohort, the C index of the prognostic model （0.797, 95%CI: 0.742-0.874） was significantly higher than the DNA copy number of EBV （0.636, 95%CI: 0.559-0.713）（χ²=6.03, P=0.024）, but there was no significant difference compared to TNM staging （0.755, 95%CI: 0.705-0.815）（χ²=1.74, P=0.121）. DCA showed that the prognostic model had a better overall net benefit than EBV DNA copy number and was comparable to TNM staging in both the training and validation cohorts. The calibration curve of the training cohort and the verification cohort showed that the 12-month, 24-month and 36-month OS rates predicted by the nomograms were consistent with actual observations. Conclusion The prognosis model established in this study, including serological indicators, shows better accuracy in predicting the prognosis of nasopharyngeal carcinoma than traditional TNM staging and EBV DNA copy number alone. The nomogram based on prognosis model, TNM staging and EBV DNA can enhance the predictive ability of prognosis model.

Key words: Nasopharyngeal carcinoma, Nomograms, Prognosis, Model

Wang Xiao, Li Ying, Luo Yujie, Jin Shu. Study on the prognostic value of serological indicators for nasopharyngeal carcinoma based on nomogram model[J]. Journal of International Oncology, 2023, 50(8): 463-469.

Figures/Tables 7

指标	训练队列（n=173）	验证队列（n=58）	χ²/Z值	P值	指标	训练队列（n=173）	验证队列（n=58）	χ²/Z值	P值
白细胞（×10⁹/L）					TP（g/L）
≤4.3	28（16.2）	10（17.2）	0.04	0.851	≤77.2	136（78.6）	43（74.1）	0.50	0.480
>4.3	145（83.8）	48（82.8）	0.04	0.851	>77.2	37（21.4）	15（25.9）	0.50	0.480
中性粒细胞（×10⁹/L）					ALB（g/L）
≤7.0	153（88.4）	49（84.5）	0.62	0.431	≤42.4	66（38.2）	21（36.2）	0.07	0.792
>7.0	20（11.6）	9（15.5）	0.62	0.431	>42.4	107（61.8）	37（63.8）	0.07	0.792
淋巴细胞（×10⁹/L）					GLOB（g/L）
≤1.41	73（41.9）	25（43.1）	0.02	0.904	≤33.1	137（79.2）	47（81.0）	0.09	0.763
>1.41	100（58.1）	33（56.9）	0.02	0.904	>33.1	36（20.8）	11（19.0）	0.09	0.763
单核细胞（×10⁹/L）					AGR
≤0.4	88（50.8）	27（46.6）	0.32	0.569	≤1.36	54（31.2）	15（26.0）	0.59	0.441
>0.4	85（49.2）	31（53.4）	0.32	0.569	>1.36	119（68.8）	43（74.0）	0.59	0.441
血小板（×10⁹/L）					CRP（mg/L）
≤293.0	149（86.1）	51（87.9）	0.12	0.727	≤5.47	134（77.5）	44（75.9）	0.06	0.803
>293.0	24（13.9）	7（12.1）	0.12	0.727	>5.47	39（22.5）	14（24.1）	0.06	0.803
NLR					CAR
≤3.91	131（75.7）	42（72.4）	0.25	0.615	≤0.16	141（81.5）	47（81.0）	0.01	0.937
>3.91	42（24.3）	16（27.6）	0.25	0.615	>0.16	32（18.5）	11（19.0）	0.01	0.937
dNLR					EBV DNA（拷贝/ml）
≤2.46	127（73.4）	40（69.9）	0.43	0.513	<10³	84（48.5）	23（39.7）	2.03	0.729
>2.46	46（26.6）	18（30.1）	0.43	0.513	10³~9 999	36（20.8）	12（20.7）
LMR					10⁴~99 999	29（16.8）	13（22.4）
≤3.4	70（40.5）	25（43.1）	0.13	0.724	10⁵~999 999	15（8.7）	6（10.3）
>3.4	103（59.5）	33（56.9）	0.13	0.724	≥10⁶	9（5.2）	4（6.9）
PLR					PNI
≤208.89	138（79.8）	47（81.0）	0.04	0.835	≤47.35	32（18.5）	11（19.0）	0.01	0.937
>208.89	35（20.2）	11（19.0）	0.04	0.835	>47.35	141（81.5）	47（81.0）	0.01	0.937
SII
≤1 141.96	147（85.0）	48（82.8）	0.16	0.688
>1 141.96	26（15.0）	10（17.2）	0.16	0.688

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临床特征	训练队列（n=173）	验证队列（n=58）	χ²/Z值	P值
性别
男	132（76.3）	40（69.0）	1.23	0.268
女	41（23.7）	18（31.0）	1.23	0.268
年龄（岁）
≤60	155（89.6）	50（86.2）	0.50	0.480
>60	18（10.4）	8（13.8）	0.50	0.480
家族史
是	45（26.0）	16（27.6）	0.06	0.814
否	128（74.0）	42（72.4）	0.06	0.814
吸烟指数（年支）
≤20.0	113（65.3）	35（60.3）	0.47	0.495
>20.0	60（34.7）	23（39.7）	0.47	0.495
BMI（kg/m²）
≤26.33	149（86.1）	52（89.7）	0.48	0.489
>26.33	24（13.9）	6（10.3）	0.48	0.489
TNM分期
Ⅰ	6（3.5）	2（3.4）	0.81	0.937
Ⅱ	22（12.7）	8（13.8）
Ⅲ	86（49.7）	25（43.1）
Ⅳ	59（34.1）	23（39.7）
治疗方法
放疗	29（16.8）	10（17.2）	0.01	0.933
放化疗	144（83.2）	48（82.8）	0.01	0.933

变量	单变量分析			多变量分析
变量	HR值	95%CI	P值	HR值	95%CI	P值
预后模型	5.48	1.82~9.44	0.002	3.54	1.68~7.14	0.004
TNM分期	3.48	1.54~6.86	0.006	2.56	1.30~4.76	0.011
EBV DNA	2.14	1.24~4.54	0.019	1.82	1.16~3.10	0.031