Prediction Model of Treatment Effect of Anlotinib on Extensive-stage Small Cell Lung Cancer Based on Combination of Disease and Syndrome Information
10.3971/j.issn.1000-8578.2023.22.1373
- VernacularTitle:基于病证结合信息构建广泛期小细胞肺癌安罗替尼疗效预测模型
- Author:
Qiong WU
1
;
Junyan MA
;
Liang DONG
;
Chunyang LI
;
Zhiwu WANG
Author Information
1. The Second Department of Radiochemistry, Tangshan People's Hospital, Tangshan 063000, China
- Publication Type:Research Article
- Keywords:
Anlotinib;
Small cell lung cancer;
Traditional Chinese medicine syndrome elements;
Predictive model
- From:
Cancer Research on Prevention and Treatment
2023;50(5):483-489
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a nomogram prediction model for the treatment effect of anlotinib with the participation of traditional Chinese medicine syndrome elements on the patients with extensive-stage small cell lung cancer (ES-SCLC) who previously received multiple lines of chemotherapy. Methods The clinical data of 127 patients with ES-SCLC who received at least two cycles of anlotinib treatment were retrospectively studied. Kaplan-Meier method was used to analyze the relationship between each factor and the overall survival time. Cox regression analysis was applied to screen the independent influencing factors of the prognosis of patients with ES-SCLC. R language was employed to build a nomogram prediction model, C-index was used to evaluate the model, and calibration curve was adopted to verify the accuracy of the model. Results Age, PS score, brain metastases, qi deficiency syndrome, yin deficiency syndrome, and blood stasis syndrome were related risk factors for ES-SCLC treated with anlotinib. PS score, brain metastasis, and blood stasis syndrome were independent prognostic factors. On the basis of these three independent influencing factors, a nomogram model was established to predict the prognosis of patients with ES-SCLC treated with anlotinib. The predicted risk was close to the actual risk, showing a high degree of coincidence. Conclusion The nomogram model established with PS score, blood stasis syndrome elements, and brain metastasis as independent factors can predict the prognosis of patients with ES-SCLC receiving second- and third-line treatment of anlotinib.
