Establishment of random survival forest model of the prognosis of anaplastic thyroid cancer and its predictive efficacy analysis
10.3760/cma.j.cn115355-20220920-00594
- VernacularTitle:未分化甲状腺癌预后随机生存森林模型的构建及预测效果分析
- Author:
Feifei QIAO
1
;
Qing HOU
;
Yulei WU
Author Information
1. 山西省肿瘤医院 中国医学科学院肿瘤医院山西医院 山西医科大学附属肿瘤医院头颈外科二病区，太原 030013
- Keywords:
Thyroid neoplasms;
Survival rate analysis;
Random forest;
Prognosis
- From:
Cancer Research and Clinic
2023;35(8):596-604
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the factors influencing the prognosis of anaplastic thyroid cancer (ATC) and to evaluate the application value of established random survival forest (RSF) model in the prognosis prediction of ATC.Methods:A total of 707 ATC patients diagnosed by histopathology in the Surveillance, Epidemiology and End Results (SEER) database of the National Cancer Institute from 2004 to 2015 were selected and randomly divided into the training set (495 cases) and the validation set (212 cases). Univariate Cox regression risk model was used to analyze the related factors affecting overall survival (OS) of patients in the training set. The multivariate Cox proportional risk model based on the minimum Akaike information criterion (AIC) was used to analyze the above variables and then the variables were screened out. The traditional Cox model for predicting OS was constructed based on the screened variables. The RSF algorithm was used to analyze the variables with P < 0.05 in the univariate Cox regression analysis, and 5 important features were selected. Multivariate Cox proportional risk model was selected based on the minimum AIC. Then the RSF-Cox model for predicting OS was constructed by using screened variables. The time-dependent receiver operating characteristic (tROC) curve and the area under the curve (AUC), calibration curve, decision curve and integrated Brier score (IBS) in the training set and the validation set were used to evaluate the prediction performance of the models. Results:Univariate Cox regression analysis showed that age, chemotherapy, lymph node metastasis, radiotherapy, surgical method, tumor infiltration degree, tumor number, tumor diameter and diagnosis time were factors affecting the prognosis of ATC (all P < 0.05). Multivariate Cox regression analysis based on minimal AIC (4 855.8) showed that younger age (61-70 years vs. > 80 years: HR = 0.732, 95% CI 0.56-0.957, P = 0.023; ≤ 50 years vs. > 80 years: HR = 0.561, 95% CI 0.362-0.87, P = 0.010), receiving chemotherapy (receiving or not: HR = 0.623, 95% CI 0.502-0.773, P < 0.001), receiving radiotherapy (receiving or not: HR = 0.695, 95% CI 0.559-0.866, P = 0.001), receiving surgery (lobectomy, no surgery or unknown: HR = 0.712, 95% CI 0.541-0.939, P = 0.016; total resection or subtotal resection vs. no surgery or unknown: HR = 0.535, 95% CI 0.436-0.701, P < 0.001), and tumor diameter (≤ 2 cm vs. > 6 cm: HR = 0.495, 95% CI 0.262-0.938, P = 0.031; > 2 cm and ≤ 4 cm vs. > 6 cm: HR = 0.714, 95% CI 0.520-0.980, P = 0.037; > 4 cm and ≤ 6 cm vs. > 6 cm: HR = 0.699, 95 % CI 0.545-0.897, P = 0.005) were independent protective factors for OS of ATC patients. Lymph node metastasis (N 1 unknown vs. N 0: HR = 1.664, 95% CI 1.158-2.390, P = 0.006; N 1b: HR = 1.312, 95% CI 1.029-1.673, P = 0.028), more aggressive tumor infiltration degree (group 3 vs. group 1: HR = 1.492, 95% CI 1.062-2.096, P = 0.021; group 4 vs. group 1: HR = 1.636, 95% CI 1.194 - 2.241, P = 0.002) were independent risk factors for OS of ATC patients. Although diagnosis time was not statistically significant (2010-2015 vs.2004-2009: HR = 1.166, 95% CI 0.962-1.413, P = 0.118), the inclusion of it could improve the efficacy of the traditional Cox model. RFS algorithm was used to select out 5 important variables: surgical method, tumor diameter, age group, chemotherapy, and tumor number. Multivariate Cox regression analysis based on minimum AIC (4 884.6) showed that chemotherapy (receiving or not: HR = 0.574, 95% CI 0.476-0.693, P < 0.001), surgical method (lobectomy, no surgery or unknown: HR = 0.730, 95% CI 0.567-0.940, P = 0.015; total resection or subtotal resection vs. no surgery or unknown: HR = 0.527, 95% CI 0.423-0.658, P < 0.001), tumor diameter (≤ 2 cm vs. > 6 cm: HR = 0.428, 95% CI 0.231-0.793, P = 0.007; > 2 cm and ≤ 4 cm vs. > 6 cm: HR = 0.701, 95% CI 0.513-0.958, P = 0.026; > 4 cm and ≤ 6 cm vs. > 6 cm: HR = 0.681, 95% CI 0.536-0.866, P = 0.002) were independent factors for OS of ATC patients. RSF-Cox model was constructed based on 3 variables. The tAUC curve analysis showed that RSF-Cox model for predicting the 6-month, 12-month, and 18-month OS rates were 93.56, 92.62, and 90.80, respectively in the training set, and 93.05, 92.47, and 90.20, respectively in the validation set; in the traditional Cox model, the corresponding OS rates were 89.00, 87.76, 85.24, respectively in the training set, and 86.22, 83.68, 82.86, respectively in the validation set. When predicting OS rate at 6-month, 12-month and 18-month, the calibration curve of RSF-Cox model was closer to 45° compared with that of traditional Cox model, and the clinical net benefit of decision curve in RSF-Cox model was higher than that in traditional Cox model. The IBS of RSF-Cox model (0.089) was lower than that of traditional Cox model (0.111). Conclusions:The RSF model based on chemotherapy, surgical method and tumor diameter can effectively predict the OS of ATC patients.
